Prognosis Prediction in Adult Intensive Care—A Critical Analysis
- 1Department of Anesthesiology, Intensive Therapy and Perioperative Medicine, Department of Acute Medicine, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, 41-800 Zabrze, Poland
Abstract
Background: Prognosis prediction in adult intensive care units (ICUs) guides clinical decision-making, realistic goal-setting with families, and resource allocation, but is challenged by the marked heterogeneity of critically ill patients in multi-speciality wards. Methods: This narrative review critically analyses commonly used prognostic scoring systems by clinical application timeline. Results: Admission prognostic models, including the Acute Physiology and Chronic Health Evaluation (APACHE II/III/IV), Simplified Acute Physiology Score (SAPS II/SAPS 3) and Mortality Probability Model (MPM), estimate in-hospital mortality from acute physiological derangement and comorbidity; the Clinical Frailty Scale (CFS) adds prognostic value. During the ICU stay, organ dysfunction is tracked with the Sequential Organ Failure Assessment (SOFA/SOFA-2), designed for repeated reassessment, and the Multiple Organ Dysfunction Score (MODS), while the Therapeutic Intervention Scoring System (TISS-28) quantifies nursing workload. At discharge, the Barthel Index, Glasgow Outcome Scale (GOS) and Cerebral Performance Category (CPC) measure functional independence and neurological outcome. Admission-based mortality models use an initial assessment window and may require local recalibration, and discrimination alone does not establish clinical utility. Conclusions: Future directions include trajectory-based models, phenotyping, biomarkers, time-limited trials and machine learning. Prognostic estimates should inform multidisciplinary evaluation and shared decision-making, rather than independently determine treatment intensity or withdrawal.
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BibTeX
@article{liberski2026prognosis,
title = {{Prognosis Prediction in Adult Intensive Care—A Critical Analysis}},
author = {Piotr S. Liberski and Szymon Czajka and Krzysztof Żerdziński and Łukasz J. Krzych},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15208073},
url = {https://www.mdpi.com/2077-0383/15/20/8073},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Prognosis Prediction in Adult Intensive Care—A Critical Analysis
AU - Piotr S. Liberski
AU - Szymon Czajka
AU - Krzysztof Żerdziński
AU - Łukasz J. Krzych
PY - 2026
DA - 2026/10/17
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15208073
UR - https://www.mdpi.com/2077-0383/15/20/8073
AB - Background: Prognosis prediction in adult intensive care units (ICUs) guides clinical decision-making, realistic goal-setting with families, and resource allocation, but is challenged by the marked heterogeneity of critically ill patients in multi-speciality wards. Methods: This narrative review critically analyses commonly used prognostic scoring systems by clinical application timeline. Results: Admission prognostic models, including the Acute Physiology and Chronic Health Evaluation (APACHE II/III/IV), Simplified Acute Physiology Score (SAPS II/SAPS 3) and Mortality Probability Model (MPM), estimate in-hospital mortality from acute physiological derangement and comorbidity; the Clinical Frailty Scale (CFS) adds prognostic value. During the ICU stay, organ dysfunction is tracked with the Sequential Organ Failure Assessment (SOFA/SOFA-2), designed for repeated reassessment, and the Multiple Organ Dysfunction Score (MODS), while the Therapeutic Intervention Scoring System (TISS-28) quantifies nursing workload. At discharge, the Barthel Index, Glasgow Outcome Scale (GOS) and Cerebral Performance Category (CPC) measure functional independence and neurological outcome. Admission-based mortality models use an initial assessment window and may require local recalibration, and discrimination alone does not establish clinical utility. Conclusions: Future directions include trajectory-based models, phenotyping, biomarkers, time-limited trials and machine learning. Prognostic estimates should inform multidisciplinary evaluation and shared decision-making, rather than independently determine treatment intensity or withdrawal.
SN - 2077-0383
ER -