Assessment of Bedside-Adaptable Models to Predict Molecular Sepsis Subtypes in a Resource-Limited Setting: A Multicenter Analysis from Uganda

Abstract Objective Biologically defined sepsis subtypes have been identified in low- and middle-income countries (LMICs), but limited access to molecular diagnostics challenges broader evaluation and implementation in resource-limited settings. We assessed whether models including bedside clinical and rapid microbiologic data could accurately stratify Ugandan adults with sepsis by molecular subtype. Design Secondary analysis of two prospective observational sepsis cohorts, testing bedside-adaptable classifier models against transcriptomic and proteomic subtype assignments. Setting Entebbe Regional Referral Hospital (urban) and Tororo General Hospital (rural), Uganda. Patients Adults (≥18 years) hospitalized with sepsis, with available transcriptomic (N=355) and/or proteomic (N=495) profiling enabling subtype assignment. Interventions None. Measurements and Main Results Using data from two prospective cohorts (RESERVE-U-2-TOR and RESERVE-U-1-EBB), we evaluated bedside-adaptable models against Uganda-derived molecular sepsis subtypes, and, secondarily, against molecular subtypes and axes derived in high-income countries. In RESERVE-U-2-TOR, clinical models including demographics and bedside physiological variables demonstrated moderate discrimination for transcriptomic and proteomic subtype assignment (AUROC 0.75 [95% CI, 0.69–0.81] and 0.73 [0.66–0.80], respectively) with strong calibration (Integrated Calibration Index [E avg ] ≤0.015 for both models). Adding rapid diagnostic results for HIV, malaria, and tuberculosis produced similar performance (AUROC 0.76 and 0.74; E avg ≤0.016). In RESERVE-U-1-EBB, discrimination for clinical and clinico-microbiological models was more variable (AUROC range 0.63–0.75) while calibration remained acceptable (E avg ≤0.053). Performance was similar when models were evaluated against molecular sepsis frameworks derived in high-income countries, with acceptable calibration and moderate discrimination. Conclusions Bedside-adaptable clinical models, with or without rapid microbiologic testing, demonstrated acceptable calibration but only modest discrimination for molecular sepsis subtype assignment in Uganda. Expanding laboratory capacity and access to scalable, low-cost molecular biomarker assays will be necessary to advance precision sepsis care in LMIC settings. Key Points Question Among adults hospitalized with sepsis in a resource-limited setting, can bedside clinical variables, alone or combined with rapid pathogen diagnostics, accurately stratify molecular sepsis subtype assignments? Findings In two prospective Ugandan sepsis cohorts, bedside clinical and clinico-microbiologic models showed robust calibration but only modest discrimination for classifying Uganda-derived transcriptomic and proteomic subtypes. Models also achieved moderate performance for stratifying high-income-country-derived transcriptomic subtypes and immune dysfunction axes, suggesting bedside variables reflect illness severity but incompletely capture underlying molecular signatures. Meaning Bedside-adaptable models can support reasonably calibrated risk estimation for molecular sepsis stratification in resource-limited settings but lack sufficient discriminatory power to serve as stand-alone tools. These findings support efforts to improve acute-care laboratory capacity and access to scalable molecular biomarker panels, with the goal of enabling precision sepsis care in low- and middle-income countries.