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

Recognizing this alarming trend, the Centers for Medicare and Medicaid Services (CMS) has taken an unprecedented step by introducing the first specialty-specific add-on payment code within the Medicare Physician Fee Schedule. Effective in 2025, this new provision, designated G0545, supplements reimbursement for inpatient infectious disease consultations by approximately 20%, reflecting not only the complexity of ID services but also CMS’s acknowledgment of the urgency to reverse workforce decline. This move marks a pivotal shift in federal health policy, signaling a nuanced understanding that adequate compensation must align with clinical expertise and public health value.

Historically, federal reimbursement models have largely applied across-the-board fee adjustments or broadly targeted specialties with widespread shortages without tailoring incentives to specific disciplines. This new CMS add-on code departs from such conventions by directly tying additional payments to the unique responsibilities handled by ID physicians—ranging from intricate risk mitigation for disease transmission, comprehensive public health investigations, advanced laboratory analysis, to nuanced antimicrobial counseling and treatment strategies. This degree of specificity in reimbursement design could set a transformative precedent for physician payment policy.

Researchers from the Harvard Pilgrim Health Care Institute conducted a rigorous analysis of this novel payment model, published in the June 2025 edition of JAMA. Their work underscores both the promise and the challenges inherent in this pioneering federal initiative. According to the study’s lead investigators, while the increase in reimbursement is substantial relative to historic compensation benchmarks, it remains to be seen whether these add-on payments will effectively translate into enhanced salaries for ID physicians rather than merely bolstering hospital revenue streams. The latter scenario risks failing to address the core disincentive that has deterred entry into the specialty.

Moreover, infectious disease specialists often shoulder responsibilities that extend well beyond direct patient care, including critical roles in antimicrobial stewardship programs that combat antibiotic resistance—a global health threat that births strains of bacteria impervious to existing drugs. They also play an indispensable role in pandemic preparedness and response, their expertise often guiding policy decisions and clinical protocols during outbreaks. Current compensation mechanisms, however, have inadequately reflected these multilayered contributions, fostering a disconnect between the demands of the specialty and the financial rewards it yields.

An additional complicating factor highlighted by the researchers is the geographic maldistribution of infectious disease physicians. Rural and underserved regions suffer disproportionately from shortages, contributing to stark disparities in health outcomes related to infectious diseases. The CMS add-on code, while a critical initial step, does not yet integrate location-sensitive adjustments that could incentivize deployment of ID specialists to these high-need areas. Recognizing this gap, the Harvard Pilgrim team advocates for augmenting the add-on payments with differential rates based on geographic and demographic criteria to strategically steer workforce allocation.

The study also calls for a framework of accountability and transparency to ensure that increased reimbursements meaningfully impact physician income. Hospitals and health systems are currently positioned to bill using the add-on code, yet without explicit mechanisms for verifying that these funds are passed through to the specialists themselves, the intended incentive effect could be diluted. The researchers propose that CMS incorporate auditing and reporting requirements tied to physician compensation, analogous to the practices employed in monitoring New Technology Add-on Payments, fostering trust that financial incentives are properly channeled.

Beyond immediate pay adjustments, the policy architects and researchers alike emphasize the importance of viewing this add-on code as a pilot program—a deliberate experiment subject to careful monitoring, data collection, and outcome evaluation. This iterative approach would help CMS and other stakeholders gauge the efficacy of such targeted reimbursement strategies in mitigating specialty shortages before scaling or adapting the model to other clinician groups facing similar challenges. Metrics such as changes in filled fellowship positions, physician retention, patient access, and health outcomes will be pivotal in this evaluative phase.

The implications of this initiative extend beyond the infectious disease specialty itself. Because Medicare reimbursement rates frequently anchor commercial payer fee setting, an upward adjustment in ID compensation under Medicare has the potential to recalibrate nationwide payment standards. This could signal a broader recalibration of how physician services are valued, particularly in specialties characterized by complex, high-risk clinical work that is not adequately captured by conventional billing codes. The researchers view this as an opportunity to realign financial incentives with contemporary healthcare needs and public health priorities.

While the new add-on code is certainly a promising policy innovation, the Harvard Pilgrim analysis underscores that remedying physician shortages demands a multi-faceted strategy encompassing compensation, workforce distribution, educational support, and systemic recognition of specialty-specific contributions. Only through coordinated policy action, transparency measures, and ongoing assessment can the longstanding undervaluation of infectious disease expertise be effectively reversed, ensuring robust access to specialist care in a landscape defined by emerging infectious threats and evolving healthcare complexities.

As the United States faces an uncertain infectious disease horizon marked by emerging pathogens and persistent antimicrobial resistance, strengthening the infectious disease physician workforce becomes not only a matter of clinical necessity but one of public health and national security. CMS’s add-on payment initiative represents a foundational step in this direction, yet its ultimate success will hinge upon sustained commitment to equitable, targeted reimbursement policy, careful performance evaluation, and responsive adaptation to observed outcomes.

In their concluding remarks, the Harvard Pilgrim team imparts a clear message to policymakers and healthcare leaders: Infectious disease specialists possess unique and indispensable expertise that justifies higher compensation commensurate with their impact on patient care and population health. However, translating reimbursement policy changes into tangible improvements in workforce stability and patient health outcomes requires vigilant implementation and accountability. Without this, the ongoing shortage will persist, undermining the healthcare system’s capacity to respond adeptly to infectious disease challenges.

This novel federal reimbursement strategy thus stands at a crossroads, with the potential to reconfigure how specialized medical expertise is recognized and rewarded within the U.S. health system. Its trajectory will be closely watched by clinicians, hospital administrators, and policymakers alike, as it may herald a new era in tailored physician payment reforms designed to sustain critical specialties integral to the health security of the nation.

Subject of Research: Infectious Disease Physician Workforce and Medicare Reimbursement Policy

Article Title: Raising Reimbursement Rates to Combat Specialty Physician Shortages: A New Federal Initiative

News Publication Date: 25-Jun-2025

Web References: Harvard Pilgrim Health Care Institute Department of Population Medicine

References: Yu, H., Ramesh, T., et al. (2025). Raising Reimbursement Rates to Combat Specialty Physician Shortages: A New Federal Initiative. JAMA, June 25, 2025.

Keywords: Health care policy, Health care costs, Health care delivery, Insurance, Public finance, Clinical medicine, Infectious diseases, Health and medicine