Patients had to remain in the intensive care unit for at least 24 hours. The investigators analysed each marker using the first value obtained after ICU admission, recorded at a median of 0.6 hours with an interquartile range of 0.5 to 4.5 hours, and, in parallel, the worst value within the first 24 hours. They compared discrimination using area under the receiver-operating-characteristic curve (AUC) and the DeLong test, overall and within severity strata and by admission source. The septic-shock approximation was based on vasopressor use and a first post-ICU venous lactate above 2 mmol/L.

With first measurements, CRP had an AUC of 0.504 (95% confidence interval, 0.439 to 0.567) and PCT had an AUC of 0.549 (0.486 to 0.614), both at or near chance-level discrimination. The NLR point estimate was below chance at 0.438 (0.371 to 0.501), although its confidence interval included 0.50. IL-6 had an AUC of 0.694 (0.631 to 0.756), and venous lactate had an AUC of 0.682 (0.622 to 0.742). Results were similar when the worst values within 24 hours were used, with all paired DeLong p values of at least 0.19.

Adding first IL-6 and lactate values to a partially window-aligned clinical model increased AUC from 0.782 to 0.808. The likelihood-ratio p value was 0.0004, while the DeLong comparison yielded p = 0.057. Performance was weaker among the 92 patients who did not meet the septic-shock approximation: AUC was 0.578 for IL-6 and 0.502 for lactate. The authors said this attenuation meant that restriction to a narrow, high-severity case mix could not be excluded as an explanation. By contrast, assay censoring appeared insufficient to explain the limited prognostic information associated with CRP and neutrophil-based indices: CRP and NLR were not significantly correlated with reconstructed SOFA, and only 1.3% of first CRP values reached the assay ceiling. These findings come from a retrospective analysis summarized in the abstract; they do not establish prospective performance beyond the included high-acuity ICU population, and the authors called for prospective evaluation.

Subject of Research: Retrospective comparison of inflammatory markers, IL-6 and lactate for predicting 28-day mortality in high-acuity ICU sepsis

Article Title: Head-to-head comparison of routinely ordered inflammatory markers, interleukin-6 and lactate for 28-day mortality in high-acuity ICU sepsis: a predominantly vasopressor-treated cohort

Article References: Feng, L., Xie, Q., Wang, Y., & Li, K. (2026). Head-to-head comparison of routinely ordered inflammatory markers, interleukin-6 and lactate for 28-day mortality in high-acuity ICU sepsis: a predominantly vasopressor-treated cohort. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14472-3

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14472-3

Keywords: sepsis, intensive care, 28-day mortality, interleukin-6, venous lactate, C-reactive protein, procalcitonin, neutrophil-to-lymphocyte ratio, vasopressors, retrospective cohort, Head-to-head, comparison

The clinical problem is especially important in emergency and acute-care settings, where respiratory symptoms can arise from several causes. A patient may have a viral infection, a bacterial infection, or both, yet symptoms such as fever, cough, shortness of breath and abnormal vital signs may overlap. Misidentifying a viral illness as bacterial can expose patients to unnecessary antibiotics, contribute to adverse reactions and accelerate antimicrobial resistance. Conversely, overlooking bacterial infection can delay appropriate treatment. Laboratory evidence is therefore central to the initial assessment, but the available markers do not always produce an unambiguous classification.

MxA is an antiviral protein that is produced in response to type I interferon signaling. This signaling pathway is activated by viral infections, making MxA biologically different from many general markers of illness. C-reactive protein, for example, is a broad indicator of inflammation and can rise in response to both viral and bacterial disease. Procalcitonin tends to increase more strongly in bacterial infection and is used in some clinical settings, but its performance depends on the population, the infection site and the timing of measurement. MxA offers another route to viral identification because it reflects a host response specifically associated with viral replication rather than inflammation alone.

For the study, 869 consecutive adult patients were allocated to groups according to whether they had an infection and what caused it. The researchers evaluated the diagnostic performance of MxA and MxA-based ratios, then compared their results with an optimal statistical model produced through multiple linear regression. They also examined factors that might influence MxA production and developed an algorithm intended to guide antibiotic prescribing in a subgroup of patients with respiratory tract infections. The design reflects a practical question: whether a measurable biological signal can improve classification in routine adult acute care, not merely whether MxA differs between laboratory-defined groups.

MxA was significantly elevated in patients with viral infection and in those with mixed viral and bacterial infections. At an optimal cutoff of 21 micrograms per liter, the study reported a sensitivity of 79.8% and a specificity of 87.1%. The area under the receiver operating characteristic curve was 0.87, indicating that MxA separated viral from bacterial cases with substantial diagnostic accuracy across possible decision thresholds. These findings mean that many patients with viral disease could be identified by a relatively simple concentration measurement, while most patients without the marker had a condition other than a viral infection.

Sensitivity and specificity describe different sides of the same diagnostic problem. A test with high sensitivity is more likely to detect most cases of the target condition, while high specificity means that a positive result is less likely to occur in people without it. The reported values therefore support MxA as a potentially useful rule-out aid for viral infection, although they do not mean that every value can be interpreted independently of the clinical presentation. A false-negative result could occur in a patient with viral infection whose MxA concentration remains below the selected threshold, while a false-positive result could arise when another condition raises MxA. The optimal cutoff is therefore best understood as a value derived from this study population rather than a universal threshold.

The strongest performance emerged when MxA was combined with C-reactive protein and procalcitonin. In this analysis, the combined approach performed comparably to the optimal statistical model, indicating that MxA contributed information that was not fully captured by the more familiar inflammatory markers. Combining tests can be especially useful in ambiguous cases: MxA supplies evidence for viral activity, while CRP and PCT provide complementary information about the host response and the likelihood of bacterial involvement. Rather than replacing clinical assessment, the result supports an integrated interpretation in which viral biology and systemic inflammation are considered together.

The amount of MxA also differed according to viral etiology, showing that the host response is not identical across viral infections. That variability has implications for interpretation. A highly detectable marker may be especially useful for some pathogens and less informative for others, and clinicians should therefore avoid assuming that a single concentration represents every type of viral disease. The study did not report a consistent change in MxA expression associated with comorbidities, suggesting that common accompanying illnesses did not systematically distort the signal in the cohort. Nevertheless, the observational design and the composition of the enrolled population mean that broader studies would be needed to establish how the marker performs in other hospitals, age groups and disease settings.

The respiratory subgroup was especially relevant to the question of antibiotic stewardship. The researchers developed an algorithm to guide prescribing in patients with respiratory tract infections, where the presence of a bacterial pathogen can be difficult to determine from symptoms alone. The findings do not show that MxA alone can replace cultures, molecular testing or clinical judgment, nor do they establish a universal rule for withholding antibiotics. Instead, the proposed algorithm indicates how biomarker results might be integrated into a structured decision process. In that context, a viral MxA signal could contribute to the evidence used to reconsider unnecessary antibiotic therapy, while bacterial indicators could support continued assessment and treatment when warranted.

Antibiotic stewardship is not simply a matter of reducing antibiotic use. It requires ensuring that patients who need treatment receive it at the appropriate time and that unnecessary exposure is minimized. A biomarker that adds specific information about viral infection could help make that balance more precise, particularly when standard markers are discordant or the clinical picture is uncertain. MxA would not settle every diagnostic question, because the reported sensitivity leaves a meaningful proportion of viral infections below the selected cutoff and because bacterial and viral infections can coexist. Its strongest role may therefore be as one component of a broader strategy that combines laboratory findings, pathogen detection, examination, imaging when appropriate and repeated clinical assessment.

The study was supported through the Cooperatio Program for intensive care medicine, infection and immunity at Charles University, with financial support for biochemical and statistical analyses provided by Biovendor. The sponsor was reported to have had no role in study design, data acquisition, analysis, interpretation, manuscript preparation or the decision to submit the paper, and the authors declared no competing interests. The findings are now reported in BMC Infectious Diseases as a prospective observational evaluation of MxA in adult acute care. The 869-patient cohort, the reported area under the curve of 0.87 and the improvement achieved with CRP and PCT together point toward a promising addition to viral diagnostics, while the need for prospective validation and clinical implementation studies keeps the conclusions appropriately bounded.

Subject of Research: Evaluation of MxA as a diagnostic biomarker for distinguishing viral from bacterial infection in acutely ill adults

Article Title: Clinical utility of Myxovirus resistance protein A (MxA) for differentiation between viral and bacterial infection in adult acute care: a prospective observational study

Article References: Muller, J., Rajdl, D., Vimmerova, H., & Matejovic, M. (2026). Clinical utility of Myxovirus resistance protein A (MxA) for differentiation between viral and bacterial infection in adult acute care: a prospective observational study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14385-1

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14385-1

Keywords: MxA, viral infection, bacterial infection, diagnostic biomarker, procalcitonin, C-reactive protein, acute care, antibiotic stewardship, respiratory infection, interferon response, Clinical, utility

The study analyzed 192 pediatric patients with sepsis, aged one month to 18 years, admitted to the Pediatric Intensive Care Unit of Dr. Hasan Sadikin General Hospital beginning in August 2023. The researchers collected data on patient characteristics, Phoenix sepsis scores, and procalcitonin levels, then compared how well each tool discriminated between children who survived and those who died within 28 days. The mortality rate in the cohort was a sobering 54.7 percent, a figure that underscores the severity of sepsis presentations reaching this tertiary referral center and the high stakes of accurate mortality prediction.

The Phoenix Sepsis Criteria, introduced in 2024 as part of a global redefinition of sepsis and septic shock, evaluate four organ systems using routinely collected clinical data: the respiratory system, the cardiovascular system, the coagulation system, and the neurological system. Each system is scored from zero upward based on the degree of dysfunction, and the total score places children on a spectrum from no organ dysfunction to septic shock. Because the criteria depend entirely on bedside measurements such as oxygen saturation, blood pressure, platelet count, and consciousness level, they require no laboratory tests beyond those already performed in any seriously ill child, making them essentially free to apply.

Procalcitonin, by contrast, is a peptide precursor of the hormone calcitonin that rises dramatically during bacterial infections and has been widely promoted as a biomarker of sepsis severity. Its blood levels correlate with the intensity of the inflammatory response, and many clinicians use it to gauge infection severity and guide antibiotic decisions. But the new study found that while procalcitonin levels were indeed significantly higher in children who died than in those who survived, with both the Phoenix score and procalcitonin showing p values below 0.001 for this difference, the biomarker’s overall discriminatory power was considerably weaker than the clinical criteria.

The technical comparison centered on the area under the receiver operating characteristic curve, or AUC, a statistical measure of how well a test separates those who experience an outcome from those who do not. An AUC of 0.5 indicates performance no better than chance, while 1.0 indicates perfect discrimination. The Phoenix Sepsis Criteria achieved an AUC of 0.848, placing them in the range of good discriminatory performance, whereas procalcitonin managed only 0.689, a value that falls into the weak-to-moderate range and implies substantial overlap in biomarker levels between survivors and nonsurvivors. In practical terms, the clinical criteria misclassified far fewer children at any given threshold than the blood test did.

The researchers also calculated odds ratios at specific cutoff values to quantify how strongly each tool predicted death. A Phoenix sepsis score greater than 4 was associated with an odds ratio of 9.30, meaning children above this threshold had more than nine times the odds of dying compared with those below it. Procalcitonin levels above 13.42 micrograms per liter carried an odds ratio of only 3.00, a threefold increase in odds that, while statistically meaningful, pales next to the clinical criteria. The cutoff of 13.42 micrograms per liter itself emerged from the study’s statistical analysis of the cohort, illustrating how biomarker thresholds often require local calibration rather than universal application.

Perhaps the most intriguing technical finding came from the linear regression analysis, which revealed a multiplicative relationship between the two measures: for every one-point increase in the Phoenix sepsis score, procalcitonin levels increased approximately threefold. This exponential relationship suggests that the clinical score and the biomarker are tracking related but distinct dimensions of the septic process, with the score capturing the cumulative degree of organ dysfunction and the biomarker reflecting the intensity of the underlying inflammatory response. The fact that the score outperformed the biomarker despite this coupling indicates that organ dysfunction, rather than inflammation alone, is the dominant driver of mortality in pediatric sepsis.

The implications for low- and middle-income countries, where the vast majority of childhood sepsis deaths occur, are considerable. Procalcitonin assays are costly, require laboratory infrastructure, and often take hours to result, whereas the Phoenix criteria can be calculated immediately from data already available at the bedside. In settings like the Bandung PICU, where families may face severe financial constraints and laboratory capacity is limited, a free scoring system that identifies high-risk children more accurately than an expensive blood test could redirect scarce resources toward timely intervention for those who need it most. The authors argue that implementing the Phoenix criteria in clinical practice may improve timely and effective identification of high-risk patients.

The study does carry limitations that temper its conclusions. As a retrospective analysis of a single-center cohort of 192 patients, its findings require validation in larger, prospective, multicenter studies before the specific cutoff values can be generalized. The extremely high mortality rate of 54.7 percent reflects the case mix of a tertiary referral hospital in Indonesia and may not represent the mortality seen in community hospitals or in high-income settings, where earlier presentation and different standards of care could shift both mortality and predictive performance. The authors themselves note that the Phoenix criteria showed stronger statistical performance for predicting mortality in their cohort, framing the result as evidence for implementation rather than a final verdict.

Nevertheless, the study adds to a growing body of evidence that the Phoenix Sepsis Criteria, developed through a Delphi consensus process involving hundreds of global experts, are living up to their promise as a practical, accurate tool for pediatric sepsis. For the world’s pediatric intensive care units, particularly those serving the populations where sepsis kills the most children, the message is clear: the most powerful predictor of death may already be written in the clinical chart, waiting to be summed, at no cost, before any blood test returns from the laboratory.

Subject of Research: Comparative performance of the phoenix sepsis criteria and procalcitonin for mortality prediction in pediatric sepsis

Article Title: Comparative performance of the phoenix sepsis criteria and procalcitonin for mortality prediction in pediatric sepsis

Article References: Sirait, A. E., Hakim, D. D. L., Rachmadi, D., Peryoga, S. U., Adrizain, R., & Rahayuningsih, S. E. (2026). Comparative performance of the phoenix sepsis criteria and procalcitonin for mortality prediction in pediatric sepsis. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07409-x

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07409-x

Keywords: Comparative, performance, phoenix, sepsis, criteria, procalcitonin, mortality, prediction, pediatric, scientific research

The research team, led by Guangqi Li and colleagues at the General Hospital of Ningxia Medical University, conducted a three-part investigation. First, they performed a one-year retrospective epidemiological analysis of 16,970 blood culture specimens processed between January and December 2024. Second, they assembled a retrospective clinical cohort of 295 patients with culture-confirmed BSI, extending the study period from January 2022 to December 2024 to capture enough polymicrobial cases, which are relatively rare. Third, they carried out a prospective molecular comparison in 20 patients with suspected BSI, running three detection methods in parallel: conventional blood culture, 16S rDNA PCR library construction, and direct 16S rDNA amplicon high-throughput sequencing.

The epidemiological survey revealed that 1,321 of the 16,970 blood culture specimens—7.8 percent—were positive for microbial growth. The overwhelming majority, 96.6 percent, were classified as monomicrobial infections, while polymicrobial BSIs accounted for just 3.4 percent. Escherichia coli emerged as the most frequently identified pathogen, followed by coagulase-negative staphylococci and Brucella species. The prominence of Brucella is notable: the Ningxia Hui Autonomous Region, where the study was conducted, is an area where human brucellosis remains a significant public health concern linked to extensive livestock farming and pastoral communities, with Brucella melitensis the predominant circulating species. The authors caution that this regional epidemiological background shapes the pathogen spectrum and may not generalize to other healthcare settings.

To understand what distinguishes polymicrobial infections clinically, the researchers compared inflammatory markers between the monomicrobial and polymicrobial groups in the 295-patient cohort. The differences were striking. Polymicrobial BSI was associated with significantly higher white blood cell (WBC) counts, neutrophil percentage, absolute neutrophil count, neutrophil-to-lymphocyte ratio, neutrophil-to-platelet ratio, systemic immune-inflammation index, systemic inflammatory response index, and procalcitonin (PCT) levels—all with P values below 0.05. Median WBC counts reached 15.18 × 10⁹/L in the polymicrobial group versus 10.13 × 10⁹/L in the monomicrobial group, while median PCT levels were 14.00 ng/mL compared with 1.40 ng/mL. Moreover, within polymicrobial infections, those involving mixed Gram types—both Gram-positive and Gram-negative organisms—showed even higher inflammatory burdens than single Gram-type infections, with median PCT soaring to 31.00 ng/mL in mixed cases.

Multivariable logistic regression confirmed that WBC count and PCT were independent predictors of polymicrobial BSI. Elevated WBC carried an odds ratio of 1.723 (95 percent confidence interval 1.472–2.017, P < 0.001), while PCT carried an odds ratio of 1.028 (95 percent confidence interval 1.003–1.054, P = 0.031). For mixed Gram-type infections specifically, WBC (OR = 1.486) and PCT (OR = 1.042) were again independently associated. Receiver operating characteristic (ROC) analysis quantified the discriminatory power of these markers. WBC alone achieved an area under the curve (AUC) of 0.890, with 77.1 percent sensitivity and 90.0 percent specificity at a cutoff of 12.54 × 10⁹/L. PCT alone achieved an AUC of 0.910, with 88.0 percent sensitivity and 80.0 percent specificity at 3.20 ng/mL. Combining the two markers improved performance further, yielding an AUC of 0.927 with balanced sensitivity of 84.0 percent and specificity of 91.8 percent. For identifying mixed Gram-type infections, the combined markers performed even better, reaching an AUC of 0.950 with 94.3 percent sensitivity and 89.8 percent specificity.

These biomarker findings carry practical significance. Because polymicrobial BSIs are associated with higher mortality, increased treatment failure, and greater clinical complexity than monomicrobial infections, identifying patients at elevated risk early in their clinical course could guide decisions about escalating empiric antimicrobial coverage and pursuing additional molecular testing. The authors suggest that when WBC and PCT monitoring suggests a possible polymicrobial infection, 16S rDNA molecular diagnosis can be deployed as a supplementary method to achieve more rapid identification of the full pathogen spectrum.

The molecular comparison cohort of 20 patients provided the study’s most technically revealing results. For the clone-based workflow, cell-free DNA (cfDNA) extracted from plasma was subjected to broad-range PCR amplification of bacterial 16S rDNA using universal primers 16F and 1492R, producing amplicons of approximately 1,500 base pairs—consistent with the near-full-length 16S rRNA gene. The purified amplicons were ligated into T5 cloning vectors and transformed into Escherichia coli DH5α competent cells. Positive clones were screened by colony PCR with M13 primers and then subjected to amplified ribosomal DNA restriction analysis (ARDRA) using the restriction enzymes HhaI and RsaI. Clones displaying identical restriction fragment patterns were clustered into operational taxonomic units (OTUs), and representative clones from each OTU were sequenced by Sanger sequencing, with taxonomic classification determined by BLAST alignment against the NCBI GenBank database. This cloning step physically separates individual rDNA sequences before sequencing, which is the method’s defining feature.

The three methods produced distinctly different detection profiles. Blood culture detected 0 to 1 microbial taxon per patient, identifying one pathogen in 17 patients and returning negative results in 3 patients. The clone-based 16S rDNA PCR library method detected a broader spectrum, identifying 0 to 5 taxa per patient, and uncovered 16 bacterial species overall—including Escherichia coli, Klebsiella pneumoniae, and Chryseobacterium chrysogenum as the most frequently detected taxa—compared with only 10 species detected by culture. Direct 16S rDNA amplicon high-throughput sequencing showed the broadest detection range of all, with more than five taxa detected in 9 of the 20 patients. At the genus level, the most abundant taxa in the sequencing data included Escherichia (23.3 percent), Streptococcus (18.6 percent), and Staphylococcus (14.0 percent). Mixed bacterial signals—defined as detection of two bacterial species in a single sample by the 16S method—were observed in all 20 samples analyzed by the library approach.

When blood culture was used as the reference standard, the clone-based method demonstrated an overall percent agreement of 80.0 percent with a sensitivity of 88.2 percent, whereas high-throughput sequencing showed a concordance rate of 70.0 percent and a sensitivity of 82.4 percent. Concordance analysis between blood culture and the library method classified results as completely consistent in 6 cases (30 percent), partially consistent in 10 cases (50 percent), and inconsistent in 4 cases (20 percent). The researchers interpret the higher agreement of the clone-based approach cautiously, noting that it may partly reflect the narrower taxonomic yield compared with high-throughput sequencing. However, because the library method detected more taxa than culture, its higher concordance cannot be explained simply by an inability to detect additional organisms. Instead, the cloning step appears to provide an additional layer of signal separation, reducing redundant sequencing signals from highly abundant organisms and allowing individual sequence reads to be traced back to representative clones—physical material that is retained for downstream confirmatory testing, an advantage high-throughput sequencing lacks.

The study’s authors position the 16S rDNA PCR library approach not as a replacement for modern next-generation sequencing platforms but as a practical, comparatively lower-cost adjunctive strategy for settings where broad molecular detection is needed but standardized NGS interpretation remains challenging. High-throughput sequencing, despite its superior depth, suffers from the absence of clinically validated diagnostic thresholds, making it difficult to distinguish true pathogens from commensal microbiota, environmental contaminants, and low-abundance background signals. A multilaboratory assessment involving 90 metagenomic NGS laboratories previously documented substantial interlaboratory variability in microbial identification, particularly for low-abundance organisms. The study also illustrates the library method’s real-world value through case examples: in an immunocompromised patient with lung adenocarcinoma whose blood cultures were negative, the 16S library successfully detected Streptococcus constellatus, cross-validated by a parallel wound secretion culture; in a case of hollow viscus perforation where empirical antibiotics rendered routine cultures sterile, the library identified Klebsiella pneumoniae and Escherichia coli, matching the anatomical source of gastrointestinal leakage and validating the clinician’s escalated antibiotic strategy.

Limitations remain. The single-center design and modest sample size constrain generalizability, the exclusion of viral and fungal co-infections leaves the true polymicrobial burden incompletely assessed, and the 30-to-36-hour turnaround time for library construction—although optimized with automated instruments and simultaneous multi-sample processing—remains impractical for acute clinical decisions. Universal primers also introduce amplification bias against highly divergent 16S sequences such as Mycoplasma, and fungal pathogens, which are critical in polymicrobial BSI among neutropenic patients, are not captured by the current bacterial workflow. Cost is another barrier, with per-sample molecular testing expenses often an order of magnitude higher than traditional culture. Nevertheless, the study demonstrates that WBC and PCT can serve as accessible biomarkers to triage patients for molecular testing, and that the cloning-based 16S rDNA PCR library offers a balanced middle ground—broader than culture, more focused than direct amplicon sequencing, and traceable at the sequence level—for unraveling the microbial complexity of polymicrobial bloodstream infections.

Subject of Research: Molecular detection of polymicrobial bloodstream infections using 16S rDNA PCR library amplicons compared with blood culture and high-throughput sequencing

Article Title: Enhanced detection of polymicrobial bloodstream infections with 16S rDNA PCR library amplicons and clinical relevance

Article References: Li, G., Wu, G., Yin, H., McGraw, K. A., Hou, J., & Zhao, Z. (2026). Enhanced detection of polymicrobial bloodstream infections with 16S rDNA PCR library amplicons and clinical relevance. Frontiers in Microbiology, 17, Article 1916658. https://doi.org/10.3389/fmicb.2026.1916658

Image Credits: AI Generated

DOI: 10.3389/fmicb.2026.1916658

Keywords: bloodstream infection, polymicrobial infection, 16S rDNA, PCR library, blood culture, procalcitonin, white blood cell count, high-throughput sequencing, sepsis, molecular diagnostics, Brucella, cell-free DNA

The review, authored by Erika P. Plata-Menchaca of the Universitat Autònoma de Barcelona and Hamad Medical Corporation in Doha, and Enrique Hernández-Jiménez of Loop Diagnostics and the University of Barcelona, takes an unusual angle. Instead of ranking biomarkers by diagnostic accuracy or area under the receiver operating characteristic curve, the authors organize candidate technologies by their regulatory status, intended clinical use, sample type, turnaround time and workflow compatibility. The framing follows the 2026 Surviving Sepsis Campaign guidelines, which emphasize early clinical recognition with validated scores, sensible diagnostic strategies and responsible antimicrobial use and de-escalation. A biomarker, they argue, should be judged by whether it can support a concrete decision, whether to admit, escalate, start or stop antibiotics, or monitor more intensively, within the sepsis pathway.

The starting point is the familiar panel of conventional soluble markers. Procalcitonin, C-reactive protein, interleukin-6 and lactate are widely available, analytically standardized and compatible with routine laboratory workflows, and procalcitonin in particular has accumulated regulatory credentials, with multiple CE-marked assays in Europe and several FDA-cleared platforms in the United States supporting antibiotic initiation or discontinuation claims in specific populations. Lactate, although not specific to sepsis, remains central to severity assessment and shock recognition. The weakness of this entire class is biological non-specificity: CRP and interleukin-6 reflect inflammation in general, and procalcitonin concentrations are confounded by surgery, trauma, renal dysfunction, sampling time and non-infectious inflammation. These markers are useful components of assessment, the authors conclude, but they cannot capture the host-response complexity embedded in the Sepsis-3 definition of the syndrome.

A second generation of soluble and hematological markers aims to sharpen that picture. Presepsin, a soluble fragment of the innate immune receptor CD14, and mid-regional proadrenomedullin, a marker of endothelial dysfunction and vascular stress, both performed well in a meta-analysis of adult sepsis diagnostics, with proadrenomedullin slightly ahead. In practice, presepsin is framed as an infection and sepsis support marker available for near-patient testing in Europe on a dedicated chemiluminescent platform, while proadrenomedullin is more compelling for severity, deterioration risk and level-of-care decisions. Pancreatic stone protein has gone further down the regulatory road: the FDA cleared the IVD CAPSULE PSP assay on the abioSCOPE nanofluidic system in 2024 as an aid in early detection of sepsis in high-risk adult ICU patients, and it is also certified under the EU In Vitro Diagnostic Regulation. Monocyte distribution width, a measure of monocyte shape variability generated automatically by Beckman Coulter hematology analyzers, illustrates yet another implementation model, embedding a sepsis screening signal directly into the routine complete blood count for adult emergency department patients.

The most conceptually significant shift, however, is toward molecular and cellular host-response diagnostics that classify the patient’s biological state rather than detecting a pathogen or a single inflammatory protein. SeptiCyte RAPID, an FDA-cleared RT-qPCR assay running on the Biocartis Idylla cartridge platform, measures a two-gene expression signature involving PLAC8 and PLA2G7 to help distinguish infection-positive sepsis from infection-negative systemic inflammation in ICU patients on their first day of admission. TriVerity, from Inflammatix, extends the logic with a 29-transcript host-response signature read by automated RT-LAMP on PAXgene whole blood, designed to differentiate bacterial from viral from non-infectious illness and to estimate the likelihood of mechanical ventilation, vasopressors or renal replacement therapy within seven days in adult emergency patients. IntelliSep, from Cytovale, takes a biophysical route, using deformability cytometry to measure the mechanical properties of activated leukocytes and stratify emergency department patients by risk of sepsis with organ dysfunction within three days.

A further layer comes from functional immune assays, which measure what immune cells can actually do after stimulation rather than merely how much of a protein they are producing at baseline. Endotoxin tolerance, the blunting of leukocyte responsiveness to a lipopolysaccharide challenge after prior inflammatory stress, is a well-established model of immune reprogramming in sepsis, and studies of monocyte HLA-DR expression and ex vivo tumor necrosis factor-alpha responses support its clinical relevance. In a 2024 study, patients with positive blood cultures produced significantly less TNF-alpha after LPS stimulation than culture-negative patients, linking bacteremia-associated sepsis to functional innate immune suppression. That biology now underpins SeptiLoop, a CE-marked Class C near-patient test under the EU IVDR in which heparinized whole blood is stimulated with LPS and TNF-alpha is detected by lateral flow, identifying patients at risk of induced immunosuppression during suspected bacterial bloodstream infection. The review notes transparently that one of its co-authors is the founder of the company developing this test, and that it was assessed under the same descriptive criteria as every competing technology.

Artificial intelligence adds a complementary strategy: rather than relying on any single analyte, machine-learning models integrate many weakly informative signals, from vital signs and demographics to routine laboratory values and sepsis biomarkers, into risk scores. The regulated exemplar is the Sepsis ImmunoScore, an FDA-authorized software-as-a-medical-device that combines up to 22 electronic health record inputs, including procalcitonin, CRP and lactate, to assess the presence of or progression to sepsis within 24 hours in adults being evaluated for suspected sepsis. The authors caution, however, that AI is not a substitute for validated biological measurement. Its value depends on representative training data, transportability between institutions, calibration, handling of missing data, explainability, avoidance of alert fatigue, and, crucially, prospective evidence that model-guided actions improve outcomes. The most plausible future, they suggest, pairs regulated biomarkers supplying reproducible biological signals with AI integrating them into clinical context.

Underlying the entire review is a persistent translational gap. A molecule can be biologically plausible and statistically associated with sepsis yet still lack a regulated product, a defined intended-use population, validated decision thresholds, workflow compatibility, or any evidence that acting on the result improves outcomes. The regulatory landscape is also tightening and fragmenting. The EU IVDR has raised the evidentiary and quality bar for in vitro diagnostics, particularly higher-risk and near-patient assays, and the first four modules of the EUDAMED database became mandatory on 28 May 2026, increasing transparency across Europe. Regulatory status, the authors stress, must always be reported by jurisdiction and intended use: CE-IVD certification, FDA 510(k) clearance and other national approvals do not imply identical claims, populations or workflows, and the same biological signal may serve very different decisions, from early recognition and prognosis to antimicrobial de-escalation and immune phenotyping.

The authors are candid about their own limitations. The review involved no formal risk-of-bias assessment, evidence grading or quantitative meta-analysis, and comparisons across technologies are complicated by heterogeneous populations, sepsis definitions, reference standards and endpoints. Regulatory status is jurisdiction- and product-specific and changes over time, and many emerging diagnostics and AI tools still lack prospective studies demonstrating an impact on patient outcomes. Even so, the direction of travel is clear. Sepsis diagnostics are moving beyond isolated inflammatory analytes toward integrated, multi-layered host-response testing, combining soluble severity markers, hematology-derived parameters, transcriptomic classifiers, leukocyte biophysical assays and functional immune measurements. The decisive question for each new test is no longer whether its target is associated with sepsis, but whether a certified product, deployed where clinicians actually work, reduces diagnostic uncertainty at the moments when that uncertainty determines treatment, resource use and survival.

Subject of Research: Translation of sepsis biomarkers from discovery into regulated in vitro diagnostic tests

Article Title: From sepsis biomarker discovery to regulated in vitro diagnostics: evidence, certification status and implementation gaps

Article References: Plata-Menchaca, E. P., & Hernández-Jiménez, E. (2026). From sepsis biomarker discovery to regulated in vitro diagnostics: evidence, certification status and implementation gaps. Frontiers in Medical Technology, 8, Article 1916860. https://doi.org/10.3389/fmedt.2026.1916860

Image Credits: AI Generated

DOI: 10.3389/fmedt.2026.1916860

Keywords: sepsis, biomarkers, in vitro diagnostics, procalcitonin, host response, FDA clearance, EU IVDR, transcriptomics, immune monitoring, artificial intelligence, antimicrobial stewardship, point-of-care testing

The study’s primary objective was to evaluate how well admission procalcitonin levels could distinguish patients with confirmed bacterial infections from those without. The results were sobering. The area under the receiver operating characteristic curve, a standard measure of diagnostic discrimination where 1.0 represents perfect accuracy and 0.5 represents no better than chance, was just 0.56 with a 95 percent confidence interval of 0.45 to 0.68. At the optimal cutoff of 0.465 nanograms per milliliter, the test achieved a sensitivity of only 57 percent and a specificity of 60 percent. In practical terms, this means that procalcitonin measured at admission missed more than four in ten true bacterial infections while simultaneously misclassifying a substantial proportion of patients without bacterial infection as positive.

This poor diagnostic performance stands in stark contrast to the biomarker’s prognostic capabilities. When the researchers assessed procalcitonin’s ability to identify patients meeting the study-defined quick Sequential Organ Failure Assessment, or qSOFA, criteria for sepsis, the results were markedly different. The area under the curve rose to 0.83 with a confidence interval of 0.738 to 0.928, indicating good discrimination. The optimal cutoff for identifying sepsis was 2.65 nanograms per milliliter, yielding a sensitivity of 82 percent and a specificity of 85 percent. These figures suggest that while procalcitonin may struggle to confirm the presence of bacterial infection itself, it performs considerably better at flagging patients whose infections have progressed to a more dangerous systemic state.

Procalcitonin is a peptide precursor of the hormone calcitonin, produced primarily by thyroid C cells under normal conditions. However, during systemic bacterial infections, its expression is upregulated in extra-thyroidal tissues, particularly the liver, in response to pro-inflammatory cytokines such as interleukin-1 beta, tumor necrosis factor-alpha, and interleukin-6. Blood levels typically begin rising within two to four hours of a bacterial insult and peak at six to twelve hours, making it theoretically attractive as an early marker of bacterial rather than viral or non-infectious inflammation. Clinical guidelines in Europe and North America have increasingly incorporated procalcitonin into antibiotic stewardship protocols, using falling levels to justify discontinuing antimicrobial therapy in patients whose condition is improving.

The Ethiopian study also examined whether serial procalcitonin measurements could guide antibiotic discontinuation decisions. Among the 50 participants who underwent repeat procalcitonin testing on day three of hospitalization, a decline in the biomarker was significantly associated with clinician-directed antibiotic discontinuation. Day-three procalcitonin demonstrated excellent discriminatory performance for this outcome, with an area under the curve of 0.926 and both sensitivity and specificity of 90 percent. This finding, while promising, comes with important caveats. The researchers emphasize that the serial procalcitonin analysis was exploratory in nature and limited to a selected subgroup of patients, meaning the results should be interpreted cautiously and require validation in larger, more diverse cohorts.

The overall in-hospital mortality rate among the study participants was 6.7 percent, with 14 of the 210 enrolled patients dying during their hospital stay. In an exploratory multivariable analysis that controlled for potential confounding variables, meeting the qSOFA-based sepsis classification was associated with higher odds of mortality, although the researchers note that this estimate was imprecise due to the small number of deaths in the cohort. This limitation underscores the challenges of conducting prognostic research in settings where patient volumes and follow-up capacity may constrain statistical power.

The study employed a hospital-based longitudinal design, enrolling admitted patients at Jimma Medical Center between May and November 2025. Participants were selected consecutively until the expected sample size was achieved, a method that reduces selection bias compared to convenience sampling. Data collection used structured questionnaires covering sociodemographic characteristics, clinical information, and laboratory parameters. The mean admission procalcitonin level across the cohort was 1.65 nanograms per milliliter with a standard deviation of 2.1, indicating considerable variability in baseline biomarker levels among patients presenting with suspected bacterial infection. Statistical analysis was performed using SPSS version 27, with receiver operating characteristic curves used to determine optimal cutoff values and multivariate regression models employed to adjust for confounders.

The findings carry significant implications for clinical practice in low- and middle-income countries, where bacterial infections remain a leading cause of hospitalization and death. Ethiopia, like many sub-Saharan African nations, faces a high burden of infectious diseases alongside rising rates of antimicrobial resistance. If procalcitonin cannot reliably distinguish bacterial from non-bacterial illness in this population, clinicians may need to rely on other diagnostic strategies, including microbiological cultures, clinical scoring systems, and imaging, to guide initial antibiotic decisions. At the same time, the strong performance of procalcitonin for sepsis identification and antibiotic stewardship suggests the biomarker retains value as a prognostic and monitoring tool rather than a standalone diagnostic test.

The disconnect between diagnostic and prognostic performance observed in this study aligns with a growing body of international literature suggesting that procalcitonin’s utility depends heavily on the clinical question being asked. Previous meta-analyses have reported variable diagnostic accuracy for procalcitonin across different patient populations, infection types, and healthcare settings, with performance generally declining in patients with localized infections, early disease, or conditions that elevate procalcitonin through non-bacterial mechanisms such as trauma, surgery, or severe inflammation. The Ethiopian data add an important geographic dimension to this literature, as most existing evidence derives from European and North American cohorts with different epidemiological profiles, comorbidity burdens, and access to pre-hospital care.

The researchers acknowledge several limitations of their work. The single-center design at Jimma Medical Center may limit generalizability to other healthcare settings in Ethiopia and beyond. The modest sample size of 210 patients, while adequate for the primary diagnostic analysis, constrained the precision of mortality-related estimates and limited the serial procalcitonin subgroup to 50 participants. The study also relied on clinician-directed antibiotic discontinuation as an outcome, which reflects real-world practice patterns but may be influenced by factors beyond biomarker values, including clinical judgment, radiological findings, and microbiological results. Despite these constraints, the study represents one of the most detailed prospective evaluations of procalcitonin performance in a sub-Saharan African hospital population and provides a foundation for larger multi-center validation studies that could inform evidence-based antimicrobial stewardship guidelines tailored to resource-limited settings.

Subject of Research: Diagnostic and prognostic performance of the biomarker procalcitonin in hospitalized adults with suspected bacterial infection in Ethiopia

Article Title: Diagnostic and prognostic performance of procalcitonin among hospitalized adults with suspected bacterial infection in Southwest Ethiopia: a prospective observational study

Article References: Abadiko, E. A., Ashine, A. H., Ahimed, M. D., Herego, N. S., Shifa, A. B., Mengistu, F. G., Woyimo, T. G., & Gudina, E. K. (2026). Diagnostic and prognostic performance of procalcitonin among hospitalized adults with suspected bacterial infection in Southwest Ethiopia: a prospective observational study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14655-y

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14655-y

Keywords: procalcitonin, bacterial infection, sepsis, biomarker, antimicrobial stewardship, diagnostic accuracy, Ethiopia, qSOFA, antibiotic discontinuation, hospitalized patients, ROC analysis, infectious diseases

The study, published in BMC Medicine, enrolled 1,202 adults with suspected infection and systematically compared the LIP score against the existing arsenal of bedside and laboratory-based assessments, including SIRS criteria, qSOFA, SOFA, NEWS, and APACHE-II. The results were striking. At a cutoff of 3 points or more, the LIP score achieved an area under the receiver operating characteristic curve (AUROC) of 0.932, with a 95 percent confidence interval of 0.916 to 0.945. In practical terms, that means the score distinguished septic from non-septic patients with near-excellent discrimination. Its sensitivity reached 85.37 percent—meaning it caught the vast majority of true sepsis cases—while its specificity climbed to 94.83 percent, meaning it rarely raised false alarms. Statistically, it significantly outperformed every comparator score tested, with all comparisons yielding P values below 0.001.

What makes this performance notable is not just the numbers but the philosophy behind them. Rather than being mined from a database by machine learning and reverse-engineered afterward, the LIP score was constructed from pathophysiology—each component chosen because it captures a distinct biological axis of sepsis. The lymphocyte count reflects the immune system’s state: in sepsis, lymphocytes become depleted, a phenomenon linked to immunosuppression and the expansion of myeloid-derived suppressor cells that blunt the body’s defenses. The international normalized ratio, or INR, a standard measure of blood clotting tendency, captures the coagulation disturbances that accompany sepsis, from sepsis-induced coagulopathy to the life-threatening cascade of disseminated intravascular coagulation. Procalcitonin, a peptide precursor of the hormone calcitonin, rises sharply in bacterial infection and has long served as one of the most trusted biomarkers for distinguishing bacterial sepsis from non-infectious inflammation.

By combining these three markers, the score essentially triangulates sepsis from three directions at once: immune collapse, clotting dysfunction, and bacterial burden. Each component contributes points, and the total—ranging from 0 upward—signals the probability that a patient meets the Sepsis-3 definition, the current international consensus standard. This design choice matters clinically because all three tests are already ordered routinely in any hospital ward evaluating a febrile patient. No new assay, no specialized equipment, and no additional blood draw is required; the score can be computed from data that already exists in the patient’s chart, making it extraordinarily cheap and fast to deploy.

The study’s analytical rigor went well beyond a simple head-to-head comparison. The researchers used restricted cubic splines, a flexible statistical technique, to model the relationship between LIP scores and intensive care unit length of stay, uncovering a biphasic association—patients at both low and high ends of the score spectrum showed distinct patterns of ICU utilization. They also deployed advanced visualization methods, including Sankey and Chord diagrams, to map how patients flowed between risk strata, and SHAP (SHapley Additive exPlanations) analysis, a machine learning interpretability technique, to quantify how much each of the three components contributed to the score’s predictions. This blend of classical epidemiology and modern interpretability tools reflects a growing trend in critical care research: scores must not only work, but clinicians must understand why they work before trusting them with lives.

Prognostically, the picture was more nuanced. In predicting mortality, the LIP score surpassed the old SIRS criteria decisively (P < 0.001), confirming that the outdated systemic inflammatory response syndrome criteria—which many experts have long argued are too sensitive and too nonspecific—are inferior for risk stratification. However, LIP was outperformed by the National Early Warning Score (P = 0.031) and by SOFA (P < 0.001), while performing comparably to qSOFA (P = 0.28). This is an honest and important caveat: SOFA, which requires a fuller panel of physiological measurements, remains the gold standard for assessing organ dysfunction and predicting death. The LIP score is not positioned to replace it, but rather to serve as a rapid, early screening gate—flagging patients who then warrant full SOFA evaluation and aggressive intervention.

The study also explored the score’s role as an early-warning tool, and here the researchers offered a clever refinement. Using a simple threshold of 2 points or more, the LIP score achieved very high sensitivity of 94.58 percent—catching nearly every patient who would go on to develop sepsis—but specificity dropped to a limited 62.72 percent, meaning many flagged patients would be false positives. To solve this, the team tested a modified rule: instead of summing the total score, clinicians flag a patient if any two of the three components score at least 1 point each. This modification pushed specificity up to 83.14 percent, dramatically reducing false alarms while preserving the score’s ability to catch early cases. This kind of dual-threshold architecture—high sensitivity for initial screening, higher specificity for confirmation—mirrors how successful screening programs operate in other areas of medicine.

Beyond diagnosis and mortality, the researchers probed whether the LIP score could predict microbiological confirmation—whether blood cultures and other tests would actually identify a pathogen. Here the score performed modestly, on par with qSOFA, APACHE-II, and NEWS. That is a realistic limitation worth acknowledging: sepsis is a clinical syndrome, not a microbiological diagnosis, and many septic patients never yield a positive culture. A score that predicts clinical deterioration is arguably more valuable at the bedside than one that predicts culture results, but the modesty of this finding underscores that LIP is a clinical screening tool, not a substitute for microbiological workup.

The multicenter, prospective design strengthens the findings considerably. Prospective studies, in which data are collected according to a pre-specified protocol as patients are enrolled, are far less prone to the biases that plague retrospective analyses of electronic health records. Spanning ten tertiary hospitals across China, the study captured a geographically and demographically diverse patient population, and the authors report that all participants or their legally authorized representatives provided written informed consent, with ethics approval from the Medical Ethics Committee of Chongqing Emergency Medical Center. The research was supported by funding from the National Natural Science Foundation of China and several Chongqing regional science and health programs, and the authors declare no competing interests—an important transparency note given that sepsis biomarkers often intersect with commercial diagnostic interests.

Where does this leave the field? The authors conclude that the LIP score is a simple and efficient screening tool for sepsis in hospitalized patients, with good diagnostic performance at a cutoff of 3 or more and a close association with clinical outcomes. They also note preliminary evidence suggesting promise for emergency department use, though they are careful to state that further validation is needed before extending the score beyond hospitalized wards. Sepsis kills millions worldwide each year, and the Surviving Sepsis Campaign has long emphasized that early recognition is the single most impactful intervention. A tool that requires nothing more than three numbers already sitting in a patient’s chart—and that outperforms the scores clinicians have relied on for decades—could, if validated in broader and more diverse populations, become a genuinely transformative addition to the sepsis detection toolkit. For now, the LIP score stands as a compelling example of how pathophysiology-driven design, tested with modern statistical rigor, can yield clinical tools that are both scientifically elegant and immediately practical.

Subject of Research: Validation of the LIP score, a three-component blood-test tool for sepsis screening and prognosis

Article Title: The LIP score: a pathophysiology-driven tool for sepsis screening and prognosis—a multicenter prospective study

Article References: Liu, B., Zhang, F., Yan, Y., Jiang, J., Wang, J., Wen, K., Wang, Z., Zhou, R., Li, T., Qin, J., Cheng, P., Chen, M., Fu, X., Han, J., Yan, B., Zhang, M., Hu, Q., Gong, J., Zhang, A., … Niu, B. (2026). The LIP score: a pathophysiology-driven tool for sepsis screening and prognosis—a multicenter prospective study. BMC Medicine. https://doi.org/10.1186/s12916-026-05312-x

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05312-x

Keywords: sepsis, LIP score, procalcitonin, INR, lymphocyte count, diagnostic markers, screening, prognosis, SOFA, early warning, critical care, BMC Medicine

The research team, led by Tong Wu and Yu Fang of the hospital’s Department of Critical Care Medicine, together with Shengquan Wang of the Affiliated Children’s Hospital of Jiangnan University, set out to construct and internally validate a prognostic risk score that integrates four dimensions of a patient’s condition: chronological age, the severity of the lung infection itself, the intensity of the systemic bacterial response, and the body’s physiological reserve as reflected in nutritional status. Their starting point was a cohort of 270 consecutive patients hospitalized with bacterial pneumonia between January 2021 and December 2023. Of these, 214 survived their admission and 56 did not, giving the investigators a meaningful contrast group for statistical modeling.

The methodological backbone of the study is a technique known as LASSO Cox regression, short for least absolute shrinkage and selection operator. In classical regression, adding more variables tends to inflate a model’s apparent accuracy while degrading its ability to generalize to new patients, a problem statisticians call overfitting. LASSO combats this by applying a penalty that shrinks the coefficients of weak predictors toward zero, effectively forcing the model to discard noise and retain only the variables that genuinely carry prognostic information. The researchers fed twenty-one candidate baseline predictors into this machinery, spanning demographics, comorbidities, laboratory markers of inflammation and infection, and oxygenation indices measured at admission.

Because any single run of variable selection can be sensitive to the quirks of one dataset, the team repeated the LASSO analysis in 1,000 bootstrap resamples, drawing patients at random with replacement and recording how often each candidate predictor was retained. This bootstrap selection frequency acts as a stability filter: variables that survive the penalty across hundreds of resampled datasets are far more likely to be real signals than artifacts. The results were strikingly lopsided. Age was selected in 92.0 percent of resamples, the Clinical Pulmonary Infection Score in 86.8 percent, procalcitonin in 86.5 percent, and albumin in 66.8 percent. The next strongest contender, C-reactive protein, managed only 28.4 percent, a gap that effectively ended the competition.

Each of the four winning variables tells a distinct physiological story. Age is the blunt but unavoidable measure of declining reserve capacity across immune, cardiovascular, and respiratory systems. The Clinical Pulmonary Infection Score, or CPIS, is a composite index that grades the extent and severity of the lung infection itself using clinical findings, laboratory data, radiographic changes, and microbiological results; higher scores indicate a heavier bacterial burden in the pulmonary parenchyma. Procalcitonin, a peptide precursor of the hormone calcitonin, rises sharply and specifically in response to systemic bacterial infection, making it one of the most widely used laboratory discriminators between bacterial and viral illness and a proxy for the intensity of the septic response. Albumin, the most abundant protein in blood plasma, reflects nutritional status, hepatic synthetic function, and vascular integrity; low albumin signals a depleted physiological reserve and is a well-established marker of frailty in acute illness.

The raw differences between survivors and non-survivors in the cohort were consistent with this four-part narrative. Patients who died were significantly older on average, at 69.61 years compared with 62.33 years among survivors, a difference that was highly statistically significant. They were also more likely to carry a diagnosis of chronic obstructive pulmonary disease, at 44.64 percent versus 27.10 percent, and more likely to require intensive care unit admission, at 39.29 percent versus 21.50 percent. Their CPIS values were higher, their procalcitonin and neutrophil-to-lymphocyte ratios elevated, and their albumin concentrations and PaO₂/FiO₂ ratios, the standard measure of how efficiently oxygen passes from the lungs into the blood, markedly reduced. Notably, although the neutrophil-to-lymphocyte ratio and the oxygenation index distinguished the groups descriptively, neither survived the LASSO stability filter with sufficient frequency to enter the final model.

When age, CPIS, procalcitonin, and albumin were entered into a multivariable Cox proportional hazards model, each emerged as independently associated with in-hospital mortality, meaning that each contributed predictive information that the others could not supply. The resulting composite score was then evaluated with time-dependent receiver operating characteristic analysis, which asks how well the model separates patients who die from those who survive within a defined window after admission. The score achieved an area under the curve of 0.728 at fifteen days, with a 95 percent confidence interval of 0.624 to 0.832, and 0.773 at twenty days, with a confidence interval of 0.627 to 0.919. By comparison, the venerable CURB-65 score managed 0.706 and 0.674 over the same horizons, and its twenty-day confidence interval dipped as low as 0.511, barely better than a coin toss.

Discrimination is only half the story; a risk model must also be calibrated, meaning its predicted probabilities should match observed event rates across the risk spectrum, and it must be clinically useful in the specific sense that acting on it benefits patients more than acting on everyone or no one. The team addressed both questions. Calibration analysis confirmed that predicted and observed mortality tracked each other acceptably. Decision curve analysis, a technique borrowed from health economics that quantifies net benefit across a range of threshold probabilities, showed a positive net benefit for the new score at clinically relevant thresholds over the fifteen-day horizon. The final model achieved an apparent concordance index, or C-index, of 0.779, and after bootstrap internal validation the optimism-corrected C-index settled at 0.762, indicating that the model’s performance held up reasonably well when tested against resampled versions of the same population.

The elegance of the score lies in its practicality. Every one of its four components is measured, or in the case of CPIS assembled, within hours of admission using equipment and assays available in virtually any hospital in the world. There is no reliance on expensive molecular diagnostics, imaging beyond a standard chest radiograph, or proprietary algorithms. A clinician can, in principle, compute a patient’s risk category at the bedside while the admission bloodwork is still being processed, and use that estimate to guide decisions about the level of monitoring, the timing of escalation to intensive care, and the candor of conversations with families about prognosis.

Yet the authors are careful, and rightly so, to frame the findings as preliminary. The study is retrospective and single-center, drawing on records from one hospital in eastern China over a three-year window, and it lacks independent external validation in a different patient population. Retrospective designs are vulnerable to missing data and to the possibility that unmeasured factors, such as the timing and adequacy of antibiotic therapy, influenced outcomes in ways the model cannot see. The moderate discrimination, while better than CURB-65 in this cohort, still leaves substantial uncertainty for any individual patient. The researchers explicitly call for prospective, multicenter studies and external validation before the score earns a place in routine clinical practice. Even so, the study offers a compelling demonstration of a principle that is reshaping modern prognostication: that disciplined statistical selection, applied to ordinary bedside measurements, can sometimes distill the complexity of a life-threatening infection into a handful of numbers that meaningfully forecast its course.

Subject of Research: Development of a composite prognostic risk score for predicting adverse outcomes in hospitalized patients with bacterial pneumonia

Article Title: A simplified composite risk score integrating inflammation, infection, nutrition, and oxygenation to predict adverse outcomes in bacterial pneumonia: a retrospective study

Article References: Wu, T., Wang, S., & Fang, Y. (2026). A simplified composite risk score integrating inflammation, infection, nutrition, and oxygenation to predict adverse outcomes in bacterial pneumonia: a retrospective study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14588-6

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14588-6

Keywords: bacterial pneumonia, prognostic risk score, procalcitonin, albumin, Clinical Pulmonary Infection Score, CURB-65, LASSO Cox regression, in-hospital mortality, risk stratification, retrospective study, bootstrap validation, BMC Infectious Diseases

The study focused on a problem that intensive care clinicians know well: enteral nutrition intolerance. When critically ill patients cannot tolerate feeding delivered directly into the gastrointestinal tract, whether because of delayed gastric emptying, abdominal distension, vomiting, or other complications, clinicians face a painful dilemma. Interrupting nutrition starves the gut lining and may worsen intestinal permeability at precisely the moment the body needs metabolic support, while continuing to feed an intolerant patient risks aspiration, worsening distension, and further physiological stress. The reported incidence of enteral nutrition intolerance varies widely across intensive care units, and the new study set out to test whether the people administering the feeding, rather than the patients receiving it, might account for part of that variation.

To capture nursing competency in a quantifiable form, the researchers used the knowledge-attitudes-practices framework, a long-standing model in health services research that evaluates what clinicians know, how they feel about the importance of that knowledge, and how consistently they translate both into daily practice. Each enrolled patient was assigned to a high-KAP or low-KAP group based on the total nutrition KAP score of the nurse primarily responsible for their care. The design is elegant in its simplicity but also carries inherent limitations: patients were not randomized to nurses, and sicker patients could plausibly have been assigned differently. The investigators addressed this concern statistically by adjusting their analyses for age, sex, and admission APACHE II score, a widely used severity-of-illness index that quantifies physiological derangement on intensive care admission.

The primary endpoint of the study was the longitudinal trajectory of serum procalcitonin, a peptide precursor of the hormone calcitonin that has become one of the most closely watched biomarkers in critical care. Procalcitonin rises sharply in response to bacterial infection and systemic inflammation, and its dynamics over days can signal whether a patient’s inflammatory burden is resolving or escalating. Using a linear mixed-effects model, a statistical approach well suited to repeated measurements nested within individual patients, the researchers found that patients cared for by high-KAP nurses showed a more favorable declining trajectory after enteral nutrition was initiated. At day three of feeding, the estimated marginal mean procalcitonin level was 1.12 plus or minus 0.53 micrograms per liter in the high-KAP group, compared with 1.65 plus or minus 0.62 micrograms per liter in the low-KAP group. By day seven, the gap persisted: 0.76 versus 1.24 micrograms per liter.

This biomarker finding is the most intriguing element of the study because it hints at a biological mechanism rather than a mere process-of-care difference. The authors frame the result in terms of the systemic inflammatory response: better nursing management of enteral nutrition may protect the intestinal barrier, reducing bacterial translocation and the downstream inflammatory cascade that procalcitonin reflects. The gut in critical illness is not a passive conduit for calories but an immunologically active organ whose integrity influences the entire body. When feeding is delivered skillfully, with appropriate rate titration, head-of-bed positioning, monitoring of gastric residual volumes, and early recognition of intolerance, the intestinal mucosa may be better preserved, and the inflammatory signals that flood the bloodstream when the barrier fails may be blunted. The procalcitonin trajectories in this study are consistent with that physiological story, though the observational design cannot prove causation.

The clinical outcomes reinforced the biomarker signal. The crude incidence of enteral nutrition intolerance was 21 percent among patients cared for by high-KAP nurses, less than half the 45 percent observed in the low-KAP group. After multivariable adjustment for age, sex, and APACHE II score, higher nurse KAP remained independently associated with lower risk of intolerance, with an adjusted odds ratio of 0.57 and a 95 percent confidence interval of 0.32 to 0.98. The confidence interval is narrow enough to exclude no effect, though it also indicates appreciable statistical uncertainty, a reminder that a single cohort of 200 patients cannot settle the question definitively.

Length of stay and ventilation duration told a similar story. Patients in the high-KAP group spent fewer days in the intensive care unit, with an adjusted beta coefficient of minus 1.38 days and a 95 percent confidence interval spanning minus 2.67 to minus 0.09, and spent fewer days on mechanical ventilation, with an adjusted beta of minus 1.12 days and a confidence interval of minus 2.21 to minus 0.03. Both confidence intervals exclude zero, but only barely at their upper bounds, and the clinical meaning of a day or so of difference must be weighed against the possibility of residual confounding. Still, the direction of every measured association pointed the same way, which lends the overall pattern credibility even where any single estimate is modest.

The starkest contrast appeared in survival. Crude 28-day all-cause mortality was 9.0 percent in the high-KAP group compared with 21.0 percent in the low-KAP group, a difference of twelve percentage points in a population where every death is a tragedy and every avoided death a triumph. The authors did not report a fully adjusted mortality estimate in the abstract, and with only 200 patients the number of deaths is small enough that chance cannot be excluded as an explanation. Nevertheless, the finding aligns with a broader literature linking nutrition quality in critical care to survival, and it provides a compelling rationale for the interventional studies that the authors argue should follow.

What makes this study resonate beyond its statistical tables is what it implies about the invisible architecture of hospital care. Intensive care medicine celebrates its ventilators, dialysis machines, and precision pharmacology, yet the daily execution of something as mundane as feeding a patient depends on the accumulated knowledge of the nurse at the bedside. The knowledge-attitudes-practices framework captures a chain that runs from education through motivation to action, and the Shandong findings suggest that strengthening the first link may ripple all the way through to inflammatory biology and survival. The authors observed six participating intensive care units in Shandong Province and concluded that targeted training focused on nurses’ nutrition-related KAP merits further evaluation, a deliberately cautious recommendation that acknowledges the limits of an observational design.

Those limits deserve emphasis for readers weighing the findings. Patients were enrolled prospectively, but the exposure, the competency of their nurse, was not randomized, and unmeasured differences between the units, shifts, or patient populations could contribute to the observed associations. The study was approved by the Ethics Committee of Qilu Hospital of Shandong University under approval number KYLL-2025SL-350 and conducted according to the Declaration of Helsinki, with informed consent obtained from participating nurses and a waiver of written patient consent for the use of de-identified routine clinical data. The authors report no external funding and no competing interests. Within those constraints, the study offers a provocative and clinically actionable hypothesis: that investing in the nutrition education of intensive care nurses is not a soft measure but a potential determinant of inflammatory resolution, feeding tolerance, and survival. If randomized training interventions confirm these associations, the humble task of feeding the sickest patients in the hospital may take its rightful place among the measurable levers of critical care medicine.

Subject of Research: Associations between ICU nurses' nutrition knowledge-attitudes-practices and clinical outcomes in critically ill patients receiving enteral nutrition

Article Title: Associations between nurses’ nutrition knowledge, attitudes, and practices and systemic inflammatory response, feeding tolerance, and clinical prognosis in critically Ill patients– a prospective observational cohort study in intensive care units

Article References: Feng, H., Bi, R., & Meng, Z. (2026). Associations between nurses’ nutrition knowledge, attitudes, and practices and systemic inflammatory response, feeding tolerance, and clinical prognosis in critically Ill patients– a prospective observational cohort study in intensive care units. BMC Nursing. https://doi.org/10.1186/s12912-026-05487-x

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05487-x

Keywords: intensive care, enteral nutrition, nursing, procalcitonin, feeding intolerance, critical illness, nutrition knowledge-attitudes-practices, observational cohort, inflammatory response, mechanical ventilation, ICU outcomes, nurse training

The research team, led by Chaeryoung Park, Dongryul Ko, biostatistician Yun Ho Roh, and corresponding author Jin-Ho Beom, conducted a retrospective analysis of adult patients with solid tumors who presented with fever to the emergency department of a tertiary academic hospital between January and December 2024. From this cohort, 466 febrile presentations were included in the final analysis. Blood cultures confirmed bacteremia in 82 of these cases, a prevalence of 17.6 percent. That figure alone underscores the scale of the clinical problem: roughly one in six febrile cancer patients arriving at the emergency department was carrying bacteria in their bloodstream, yet their early symptoms were frequently indistinguishable from those of patients with far less dangerous conditions.

The methodological heart of the study lies in how the team distilled a large field of candidate predictors into a parsimonious model. The researchers began with a broad panel of potential markers, including white blood cell count, absolute neutrophil count, C-reactive protein, erythrocyte sedimentation rate, the delta neutrophil index, red cell distribution width, procalcitonin, blood pressure measurements, and numerous other laboratory and clinical variables. To avoid the pitfalls of overfitting that plague many small clinical prediction studies, they applied least absolute shrinkage and selection operator regression, commonly known as LASSO, a statistical technique that penalizes model complexity and drives the coefficients of uninformative variables to zero. This was followed by conventional multivariable logistic regression to refine the final set of predictors and estimate their independent contributions.

What emerged from this process was a model of elegant economy. Just four variables carried essentially all of the predictive signal: serum procalcitonin, pulse rate, the neutrophil-to-lymphocyte ratio, and serum albumin. Each of these markers tells a different part of the story of invasive bacterial infection. Procalcitonin, a peptide precursor of the hormone calcitonin, rises sharply and specifically in response to systemic bacterial challenge, making it one of the most widely studied biomarkers of sepsis. Pulse rate reflects the hemodynamic stress that circulating bacterial toxins and inflammatory cytokines impose on the cardiovascular system. The neutrophil-to-lymphocyte ratio, calculated simply by dividing two counts from a standard complete blood count, captures the relative balance of the innate immune response, which expands neutrophils during bacterial invasion, against the lymphocyte compartment, which is often depleted in acutely infected and physiologically stressed patients. Albumin, meanwhile, falls as vascular permeability increases and the liver shifts its synthetic output toward acute-phase proteins, serving as a quiet barometer of systemic inflammation and nutritional reserve.

The performance statistics reported for this four-marker combination are, on their face, exceptional. The model achieved an area under the receiver operating characteristic curve, or AUC, of 0.948, a value that implies near-perfect separation between bacteremic and non-bacteremic patients when the model’s probability threshold is swept across its full range. Critically, the researchers did not rely on a single optimistic estimate. They subjected the model to ten-fold cross-validation, a resampling procedure in which the dataset is repeatedly partitioned into training and testing subsets, and the cross-validated AUC remained essentially unchanged at 0.947. This stability between apparent and cross-validated performance is a reassuring sign that the model has not merely memorized the quirks of this particular cohort but has captured a genuine biological signal.

Perhaps most telling is the comparison with procalcitonin alone. Procalcitonin is already the best-established single biomarker for bacterial infection in febrile patients, and in this cohort it achieved an AUC of 0.922 on its own, a strong result by any conventional standard. Yet the four-variable model still improved meaningfully upon it. The addition of pulse rate, neutrophil-to-lymphocyte ratio, and albumin contributed information that procalcitonin cannot supply, likely because these markers reflect complementary dimensions of host physiology, including cardiac response, immune cell dynamics, and protein metabolism, that a single inflammatory peptide cannot capture. At the optimal cutoff, the combined model achieved a sensitivity of 0.819 and a specificity of 0.923, meaning it correctly identified roughly 82 percent of true bacteremia cases while correctly clearing more than 92 percent of patients without bloodstream infection.

Discrimination, however, is only half of what a prediction model must demonstrate. A model can rank patients correctly yet produce probability estimates that are systematically too high or too low, which would mislead clinicians who rely on the absolute numbers. To address this, the team performed calibration assessment using the cross-validated data. The calibration intercept was −0.144 and the slope was 0.913, values close to the ideal of zero and one respectively. In practical terms, this means the predicted probabilities tracked the observed frequencies of bacteremia closely across the risk spectrum, neither inflating risk for low-probability patients nor understating it for those in danger. The researchers also conducted decision curve analysis, a technique that evaluates the net clinical benefit of a model across a range of decision thresholds, weighing the harms of unnecessary antibiotics and admissions against the dangers of missed infection.

The clinical implications of such a tool, if its performance holds up, are considerable. Emergency physicians currently operate under guidelines that push most febrile cancer patients toward broad-spectrum empiric antibiotics and hospital admission, a strategy that is safe but expensive, contributes to antimicrobial resistance, and consumes scarce inpatient capacity. A validated risk score could help triage the substantial minority of patients whose fever stems from non-bacterial causes, allowing earlier discharge or targeted oral therapy, while concentrating rapid diagnostic workup and empiric treatment on those at genuinely elevated risk. Because all four components of the model are already measured in virtually every febrile patient, the score could be computed automatically within the electronic medical record, requiring no additional blood draws, no specialized assays beyond standard procalcitonin, and no waiting period.

The authors are appropriately measured about the limits of their work. This was a single-center, retrospective study conducted at one tertiary academic hospital in Seoul, and the model has undergone internal validation only. Retrospective designs are vulnerable to selection bias and missing data, and a model trained on one institution’s patient mix, antibiotic practices, and laboratory platforms may not transfer cleanly to other settings. The researchers explicitly state that independent external validation and prospective evaluation of clinical utility are required before the model can be recommended for routine clinical implementation. History offers many examples of prediction models that performed brilliantly in derivation cohorts and faded under external scrutiny, which is why the field has converged on staged validation as a prerequisite for clinical adoption.

Nevertheless, the study represents a compelling step toward precision triage in oncologic emergencies. Its strength lies not in exotic artificial intelligence or proprietary algorithms but in disciplined statistical methodology applied to information that emergency departments already generate every day. If external validation in diverse patient populations confirms the Seoul team’s findings, the result could be a bedside rule that separates, within minutes of arrival, the febrile cancer patient who needs immediate broad-spectrum antibiotics and intensive monitoring from the one whose fever can be pursued more leisurely. For a patient population in which every hour of delayed treatment compounds mortality risk, and for health systems straining under the weight of unnecessary admissions, that would be a genuinely transformative advance in the emergency care of people living with cancer.

Subject of Research: Prediction of bacteremia in febrile patients with solid tumors in the emergency department

Article Title: A simplified and clinically applicable prediction model for early identification of bacteremia in febrile patients with solid tumors in the emergency department

Article References: Park, C., Ko, D., Roh, Y. H., & Beom, J.-H. (2026). A simplified and clinically applicable prediction model for early identification of bacteremia in febrile patients with solid tumors in the emergency department. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14584-w

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14584-w

Keywords: bacteremia, solid tumors, fever, emergency department, procalcitonin, neutrophil-to-lymphocyte ratio, albumin, prediction model, LASSO regression, sepsis, biomarkers, risk stratification