Sepsis remains a major cause of hospital mortality and prolonged admission, placing sustained pressure on clinical teams and hospital resources. The timing of sepsis onset during hospitalisation influences both detection and outcomes. Community-onset sepsis (COS), identified early in admission, and hospital-onset sepsis (HOS), developing later in a hospital stay, reflect different clinical pathways. Analysis of adult sepsis hospitalisations recorded between January 2019 and August 2023 at a large academic medical centre examined how onset timing relates to mortality, patient characteristics and use of hospital resources. Machine learning methods were also used to identify which clinical variables most strongly predicted mortality, length of stay and the need for intensive therapies.
Hospital-Onset Sepsis Shows Higher Mortality
The analysis included 2,589 adult sepsis hospitalisations drawn from linked Adult Sepsis Events and Vizient datasets. Of these, 1,605 were classified as COS and 984 as HOS. Patient characteristics were generally similar across both groups. Mean age differed only slightly, and sex distribution showed a higher proportion of male patients in HOS. Race and ethnicity distributions were comparable.
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Outcomes differed considerably depending on when sepsis developed. In-hospital mortality was higher in HOS than COS, reaching 38.2% compared with 28.5%. Hospital stays were also much longer when sepsis developed later during admission. Median length of stay was 24 days for HOS and 11 days for COS. Intensive care unit (ICU) stays followed the same pattern, with medians of 7 days in HOS and 2 days in COS. Vasopressor therapy occurred more often in HOS, while mechanical ventilation rates were similar between groups after adjustment. These findings indicate that HOS is associated with greater clinical severity and increased demand for hospital resources compared with COS.
Clinical Differences Between Early and Late Sepsis
Clinical measurements differed between COS and HOS in several ways. COS admissions showed higher maximum and minimum white blood cell counts and higher minimum respiratory rates. Minimum pulse and maximum temperature were lower in COS than HOS. Initial lactate values were higher in COS compared with HOS.
Indicators of underlying illness burden were more pronounced in HOS. The Elixhauser Comorbidity Index was higher among HOS admissions, showing a greater level of comorbidity. Creatinine doubling occurred more often in HOS than COS, suggesting more frequent renal injury, while bilirubin doubling did not differ significantly. HOS admissions also experienced multiple sepsis events during a single hospital stay more often than COS admissions.
Antimicrobial timing differed clearly between the two groups. HOS admissions were much more likely to have received antibiotics more than 24 hours before meeting sepsis criteria. COS admissions were nearly twice as likely to receive antibiotics within 24 hours after meeting sepsis criteria. These patterns reflect differences in hospital exposure before sepsis develops and may influence recognition during admission. Overall, HOS showed less pronounced changes in some commonly monitored vital signs and laboratory measures, which may make early detection more difficult.
Predictive Models Highlight Onset Timing
Random forest models were developed to predict mortality, hospital length of stay, vasopressor use and mechanical ventilation. Clinical Classification Software diagnostic groupings were used to organise diagnostic information. Models were trained on earlier admissions and tested on more recent hospitalisations.
Hospital length of stay was the most important variable for predicting mortality. Other influential predictors included respiratory failure, ICU length of stay, vasopressor use, elevated initial lactate, hepatic failure, dysphagia, malaise and fatigue and mechanical ventilation. Sepsis onset timing ranked among the more important predictors of mortality and was more influential than some individual clinical factors such as age and leukaemia.
For prediction of hospital length of stay, onset timing was the third most important variable. Only the number of sepsis events during a hospital encounter and the proportion of time spent in ICU contributed more strongly. Onset timing did not significantly predict mechanical ventilation or vasopressor use on its own. Model performance was strong for mortality and other binary outcomes and moderate for predicting length of stay. Calibration was less accurate at extreme probability levels, indicating limits to prediction accuracy while still identifying meaningful predictors.
Sepsis onset timing during hospitalisation is closely linked to patient outcomes and hospital resource use. Hospital-onset sepsis is associated with higher mortality, longer hospital and ICU stays, greater comorbidity burden and more frequent indicators of renal injury. It also presents with less pronounced abnormalities in some vital signs and laboratory measures and shows different patterns of antibiotic timing. Machine learning analysis confirms that onset timing is an important predictor of mortality and hospital length of stay. Greater awareness of these differences may support improved monitoring and earlier recognition of sepsis developing later during hospital care.
Source: BMC Medical Informatics and Decision Making
Image Credit: iStock
References:
Verma R, Elhance A, Marsh TJ et al. (2026) Timing matters: a machine learning–driven comparison of community and hospital-onset sepsis. BMC Med Inform Decis Mak: In Press.
