Factors predicting discharge outcomes of sepsis patients admitted to intensive care unit in a major tertiary care hospital: A retrospective study from Palestine

预测大型三级医院重症监护病房收治的脓毒症患者出院结局的因素:一项来自巴勒斯坦的回顾性研究

阅读:1

Abstract

Sepsis remains a leading cause of morbidity and mortality among critically ill patients, particularly in resource-limited settings where diagnostic capacity and therapeutic options are constrained. In this study, we aimed to identify clinical, laboratory, and treatment factors that independently predict in-hospital mortality among adult sepsis patients admitted to a tertiary care intensive care unit (ICU) in the West Bank of Palestine. We conducted a retrospective cohort study of 326 adult patients (aged 18-80 years) admitted with sepsis to the medical ICU of a major tertiary referral hospital between January 2018 and December 2023. In-hospital mortality was 41.4% (n = 135). Predictors of mortality were assessed using a multivariable logistic regression model. Multivariable logistic regression identified advancing age (OR 1.03 per year; 95% CI: 1.01-1.06; p = 0.010), cardiovascular disease (OR 2.66; 95% CI: 1.17-6.04; p = 0.020), elevated heart rate (OR 1.03 per beat/min; 95% CI: 1.01-1.04; p < 0.001), reduced urine output (OR 1.00 per mL; 95% CI: 1.00-1.00; p = 0.035), elevated serum lactate (OR 1.15 per mmol/L; 95% CI: 1.01-1.30; p = 0.037), prolonged ventilator days (OR 1.15 per day; 95% CI: 1.09-1.21; p < 0.001), lower PaO2/FiO2 ratio (OR 1.00 per unit; 95% CI: 1.00-1.00; p = 0.006), and shorter ICU length of stay (OR 0.91 per day; 95% CI: 0.87-0.96; p < 0.001) as independent predictors of in‑hospital mortality. These findings highlight the prognostic importance of simple bedside measures, core laboratory indices, and markers of illness trajectory. Together, they form a pragmatic panel of universally available variables that reliably stratify mortality risk among septic ICU patients in Palestine. Embedding these predictors into admission checklists and electronic health record alerts could strengthen early risk recognition, guide triage decisions, and optimize allocation of scarce resources in resource‑limited settings.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。