Cabrera Losada A, Correa Oviedo MA, Herrera Villazón VC, Gil-Tamayo S, Molina CF, Gimenez-Esparza Vich C, Nieto Estrada VH. Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review.
Med Intensiva 2024:S2173-5727(24)00199-1. [PMID:
39095268 DOI:
10.1016/j.medine.2024.07.009]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/21/2024] [Indexed: 08/04/2024]
Abstract
OBJECTIVE
To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs).
DESIGN
A systematic review of the literature was conducted using a search algorithm in October 2022. The following databases were searched: PubMed, Scopus, Virtual Health Library (BVS), and Medrxiv. The risk of bias was assessed using the QUADAS-2 scale.
SETTING
ICUs admitting cancer patients.
PARTICIPANTS
Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.
INTERVENTIONS
Integrative study without interventions.
MAIN VARIABLES OF INTEREST
Mortality prediction, standardized mortality, discrimination, and calibration.
RESULTS
Seven mortality risk prediction models were analyzed in cancer patients in the ICU. Most models (APACHE II, APACHE IV, SOFA, SAPS-II, SAPS-III, and MPM II) underestimated mortality, while the ICMM overestimated it. The APACHE II had the SMR (Standardized Mortality Ratio) value closest to 1, suggesting a better prognostic ability compared to the other models.
CONCLUSIONS
Predicting mortality in ICU cancer patients remains an intricate challenge due to the lack of a definitive superior model and the inherent limitations of available prediction tools. For evidence-based informed clinical decision-making, it is crucial to consider the healthcare team's familiarity with each tool and its inherent limitations. Developing novel instruments or conducting large-scale validation studies is essential to enhance prediction accuracy and optimize patient care in this population.
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