ArticleJournal of multidisciplinary healthcare2024
Predicting End-of-Life in a Hospital Setting.
Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Understanding Code Blue Activations: Insights From Early Warning and Palliative Scores in a Tertiary Hospital.Open access emergency medicine : OAEM · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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