Predicting End-of-Life in a Hospital Setting

Background: The ability to predict the prognosis of a disease and anticipate death is valuable for patients and families especially in an acute care setting for chronically ill patient. Multiple scoring systems are used to measure disease progression and predict hospital mortality in patients with life-threatening illnesses, taking into account acute conditions, catastrophic events, and slow decline.

Aim: Our primary aim is to assess palliative performance score (PPS), early warning score (EWS) and local rumah sakit Dr Hasan Sadikin (RSHS) score to predict 14 days in-hospital mortality.

Methods: This was a single-center prospective cohort study from November 2022 to April 2023. Patients with World Health Organization category of terminal illnesses were scored using PPS, EWS and RSHS score and were followed up for 14 days in hospital. Multivariate analysis were conducted and The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used.

Results: A multivariable model was calculated using Cox regression. The final model results indicate that EWS (HR: 2.9, 95% CI: 1.1-7.7) and a decrease in consciousness (HR: 3.6, 95% CI: 1.2-10.22) were statistically significant in predicting mortality.

Discussions: Most patient in the cohort that died had cancer and were admitted in the previous 6 months. Relying solely on single prediction may not provide enough accuracy, within a 14-day timeframe. Therefore, the results of multivariate analysis were not statistically significant due to Several factors contribute to the complexity of this prediction challenge. As a multifaceted disease with varying stages, treatments, and outcomes, cancer presents a diverse landscape of patient experiences.

Conclusion: EWS and decreased consciousness are significant predictors of in-hospital mortality. It is crucial in clinical setting to use multiple indicators to predict death and improve patient care.