为更好地预测重症监护室癌症患者的死亡率:预后量表的比较分析:系统性文献综述。

Andrea Cabrera Losada , Maria Alejandra Correa Oviedo , Vanessa Carolina Herrera Villazón , Sebastián Gil-Tamayo , Carlos Federico Molina , Carola Gimenez-Esparza Vich , Víctor Hugo Nieto Estrada
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引用次数: 0

摘要

目的:评估重症监护病房癌症患者死亡率预测量表的预测能力:评估重症监护病房(ICU)癌症患者死亡率预测量表的预测能力:设计:2022 年 10 月,使用搜索算法对文献进行了系统性回顾。检索了以下数据库:PubMed、Scopus、虚拟健康图书馆(BVS)和Medrxiv。采用QUADAS-2量表评估偏倚风险:环境:收治癌症患者的重症监护病房:干预措施:无干预措施的综合研究:干预:无干预的综合研究:死亡率预测、标准化死亡率、区分度和校准:对重症监护室癌症患者的七种死亡率风险预测模型进行了分析。大多数模型(APACHE II、APACHE IV、SOFA、SAPS-II、SAPS-III 和 MPM II)低估了死亡率,而 ICMM 高估了死亡率。APACHE II的SMR(标准化死亡率)值最接近于1,表明其预后能力优于其他模型:结论:由于缺乏明确的优越模型以及现有预测工具的固有局限性,预测 ICU 癌症患者的死亡率仍然是一项复杂的挑战。要做出基于证据的知情临床决策,关键是要考虑医疗团队对每种工具的熟悉程度及其固有的局限性。开发新型工具或进行大规模验证研究对于提高预测准确性和优化该人群的患者护理至关重要。
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Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review

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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