New nomogram predicts multidrug-resistant infections at county-level hospital

Antimicrobial resistance is quietly rewriting the rules of hospital medicine, and one of the hardest challenges facing clinicians is knowing, early and reliably, which patients are carrying infections caused by multidrug-resistant organisms. A new study from Taihe County People’s Hospital in China offers a practical step forward. Researchers there have developed and internally validated a nomogram—a simple graphical scoring tool—that estimates the probability that a patient’s infection is caused by bacteria resistant to at least three antibiotic families. The tool is built from six variables that clinicians already have in hand within roughly 48 to 72 hours of a positive culture, filling a critical decision gap when preliminary microbiological results are available but full susceptibility testing is still pending.

The retrospective study analyzed specimens from inpatients admitted between January and December 2023. From 3,151 clinical specimens, the laboratory recovered 1,860 non-repetitive bacterial strains. After applying rigorous deduplication criteria—retaining only the first isolate per patient per infection site within seven days, unless the antibiogram changed significantly—the team assembled a patient-level dataset of 1,045 unique individuals. Six bacterial species dominated the sample: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Enterococcus faecalis, together accounting for 70.1 percent of all non-repetitive isolates. Of the 1,045 patients, 253—24.21 percent—had infections caused by multidrug-resistant organisms, while 792 did not. Bacterial identification and antimicrobial susceptibility testing were performed using the VITEK2 compact automated system, with results interpreted according to the Clinical Laboratory Standards Institute M100-S31 breakpoints.

The baseline comparisons revealed telling differences between the two groups. Patients with multidrug-resistant infections were more often aged 60 or older (64.03 percent versus 56.82 percent), more frequently ESBL-positive (45.85 percent versus 20.96 percent), and more likely to be infected with S. aureus (29.25 percent versus 8.08 percent) or E. faecium (9.49 percent versus 1.01 percent). Interestingly, the multidrug-resistant group included fewer intensive care unit patients (17.00 percent versus 29.04 percent) and fewer respiratory tract infections, but more urinary tract infections (17.00 percent versus 8.84 percent), more patients from general surgical departments, and more from burn surgery. Sex distribution was similar between groups, and E. coli showed no significant difference. These patterns underscore that resistance risk is not confined to intensive care—it threads through surgical wards, burn units, and urology services alike.

Multivariable logistic regression distilled six independent predictors of multidrug-resistant infection: age of 60 years or older, urinary tract infection, ESBL production, P. aeruginosa, S. aureus, and E. faecium. The effect sizes varied dramatically. E. faecium carried the strongest association, with an odds ratio of 55.48 (95 percent confidence interval, 21.66 to 142.15), followed by S. aureus at 29.23 (16.41 to 52.07) and ESBL production at 15.18 (9.04 to 25.51). P. aeruginosa tripled the risk (odds ratio, 3.66), while urinary tract infection (odds ratio, 1.79) and older age (odds ratio, 1.47) contributed more modestly. Each predictor was assigned a score based on its regression coefficient, and the summed total projects onto a probability scale—turning a statistical model into a bedside-usable chart.

Performance metrics were encouraging. Internal validation with 1,000 bootstrap resamples yielded a corrected area under the receiver operating characteristic curve of 0.82 (95 percent confidence interval, 0.79 to 0.85), indicating good discrimination. The model achieved a sensitivity of 85 percent and a specificity of 70 percent, with a negative predictive value of 94 percent—a particularly valuable property, because a low score can help clinicians reasonably rule out multidrug resistance and avoid unnecessary broad-spectrum therapy. Calibration was excellent: the bootstrap-corrected calibration slope was 1.000 with an intercept of 0.000, the Hosmer-Lemeshow test returned a P value of 0.926, and the Brier score of 0.136 fell well below the 0.25 threshold for acceptable predictive accuracy. Decision curve analysis showed positive net benefit across threshold probabilities of roughly 20 to 45 percent, the range where antimicrobial decisions are most consequential.

The authors were notably careful about a subtle statistical pitfall: incorporation bias. Because ESBL positivity was included as a predictor while the outcome—resistance to at least three antibiotic families—is related to ESBL status, the team ran a sensitivity analysis excluding ESBL from the model. The area under the curve dropped from 0.82 to 0.70, confirming that ESBL contributes substantially to discrimination. Intriguingly, P. aeruginosa lost significance without ESBL adjustment (odds ratio falling to 0.74), suggesting its apparent effect was partially mediated by ESBL status, whereas S. aureus and E. faecium remained strongly significant, demonstrating that their predictive power is largely independent of the ESBL variable. The authors also emphasize that ESBL positivity does not equal multidrug resistance—many ESBL-producing isolates remain susceptible to aminoglycosides, fluoroquinolones, and carbapenems—which is precisely why a continuous probability estimate adds value beyond a binary ESBL result.

Robustness checks extended further. Firth penalized logistic regression, a technique that reduces small-sample bias for rare events, was applied because E. faecium isolates were sparse (only 47 isolates, of which 38 were multidrug-resistant). The penalized odds ratio for E. faecium, 51.40 (95 percent confidence interval, 21.24 to 135.52), closely matched the primary estimate. A patient-level sensitivity analysis confirmed that all six risk factors remained directionally and statistically consistent after deduplication. Notably, age failed to reach significance in the isolate-level data (P = 0.07) but became significant once repeated specimens were removed (P = 0.027), suggesting that multiple cultures from the same patient can dilute true risk signals—a methodological lesson with implications well beyond this single study.

The clinical logic of the tool is grounded in established biology. ESBLs are enzymes that inactivate most penicillins, cephalosporins, and related agents, and their encoding genes frequently travel with additional resistance mutations. Elderly patients face elevated risk through immunosenescence, frailty, and multimorbidity. Urinary tract infections are among the most common infections associated with resistant organisms, particularly when broad-spectrum antibiotics are prescribed empirically without urine culture. S. aureus in this cohort was predominantly recovered from burn wound secretions, while E. faecium—a gastrointestinal commensal turned opportunistic pathogen—appeared most often in urine and bile cultures from hepatobiliary surgery and urology patients. P. aeruginosa, a Gram-negative aerobe notorious for hospital-acquired pneumonia, was mostly isolated from sputum of intensive care patients, and its association with high mortality makes accurate early risk assessment especially consequential.

Important caveats temper the enthusiasm. This was a retrospective, single-center study at a county-level hospital, so the findings may reflect regional epidemiology that does not generalize elsewhere. Several potential confounders—prior antibiotic exposure, invasive devices, comorbidities, and immunosuppression—could not be fully captured, although an E-value analysis (E-value of 110 for E. faecium) suggests unmeasured confounding is unlikely to explain the strongest associations. More than 500 rare bacterial species were excluded for statistical stability, so extrapolation to uncommon pathogens should be cautious. The model’s prediction time point is also specific: it is designed for the window after species identification and ESBL phenotype are known but before full susceptibility results return, not for purely empirical decisions made before cultures are drawn.

For now, the authors position the nomogram as a supplementary reference within comprehensive clinical judgment, not a replacement for it. They call for prospective, multicenter external validation before widespread clinical implementation, and future versions may incorporate richer confounder data. Still, the study demonstrates that meaningful resistance prediction does not require academic medical centers or machine learning black boxes—it can emerge from careful, well-deduplicated patient-level data in a county hospital, using variables any microbiology laboratory already reports. As antimicrobial resistance continues to climb globally, tools that convert routine microbiology into early, individualized risk estimates could become a quiet but powerful ally in antibiotic stewardship, helping clinicians reserve last-line drugs for the patients who genuinely need them.

Subject of Research: Development and internal validation of a nomogram for predicting multidrug-resistant organism infections in a county-level hospital

Article Title: A practical nomogram for predicting multidrug-resistant organism infection in a tertiary county-level hospital to guide antimicrobial therapy

Article References: Han, L., Zhao, H., Cheng, J., & Gao, Y. (2026). A practical nomogram for predicting multidrug-resistant organism infection in a tertiary county-level hospital to guide antimicrobial therapy. New Microbes and New Infections, 74, Article 101853. https://doi.org/10.1016/j.nmni.2026.101853

Image Credits: AI Generated

DOI: 10.1016/j.nmni.2026.101853

Keywords: multidrug-resistant organisms, nomogram, antimicrobial resistance, ESBL, risk prediction model, antibiotic stewardship, county-level hospital, Enterococcus faecium, Staphylococcus aureus, urinary tract infection, logistic regression, infection control