Modified frailty index effectively predicts adverse outcomes in sepsis patients in the intensive care unit

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BackgroundFrailty and sepsis have a significant impact on patient prognosis. However, research into the relationship between frailty and sepsis in the general adult population remains inadequate. This paper aims to investigate the association between frailty and adverse outcomes in this population. MethodThis retrospective analysis investigated sepsis patients who were initially admitted to the intensive care unit (ICU). The Modified Frailty Index (MFI) was derived by tracking patients’ International Classification of Diseases (ICD) codes during their hospitalization. Patients were classified into two groups based on their MFI scores: a frail group (MFI ≥ 3) and a non-frail group (MFI = 0–2). The key outcomes were mortality rates at 90 and 180 days, with secondary outcomes including the incidence of delirium and pressure injury. ResultOf the 21,338 patients who were recruited for this study (median age about 68 years, 41.8 % female), 5,507 were classified as frail and 15,831 were classified as non-frail. Frail patients were significantly more likely to have delirium (48.9 % vs. 36.1 %, p < 0.001) and pressure injury (60.5 % vs. 51.4 %, p < 0.001). After controlling for confounding variables, the multifactorial Cox proportional hazard regression analyses revealed a significantly elevated mortality rate at 90 days (adjusted HR: 1.58, 95 % CI: 1.24–2.02, p < 0.001) and 180 days (adjusted HR: 1.47, 95 % CI: 1.18, 1.83, p < 0.001) in the frail group compared to their non-frail counterparts. ConclusionsFrailty independently predisposes adult sepsis patients in the ICU to adverse outcomes. Future investigations should concentrate on evaluating frailty and developing targeted interventions to improve patient prognosis. Implication for clinical practiceThe MFI provides a simple clinical assessment tool that can be integrated into electronic medical records for immediate calculation. This simplifies the assessment process and plays a key role in predicting patient outcomes.