Abstract：
OBJECTIVE: To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs).
METHODS: 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.
METHODS: ICUs admitting cancer patients.
METHODS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.
METHODS: Integrative study without interventions.
METHODS: 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.
METHODS: 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.
METHODS: ICUs admitting cancer patients.
METHODS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.
METHODS: Integrative study without interventions.
METHODS: 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.
摘要：
目的：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。
方法：在2022年10月使用搜索算法对文献进行了系统回顾。搜索了以下数据库：PubMed，Scopus,虚拟健康图书馆(BVS)还有Medrxiv.使用QUADAS-2量表评估偏倚风险。
方法：ICU接纳癌症患者。
方法：研究包括患有活动性癌症的成年患者，并进入ICU。
方法：无干预的综合研究。
方法：死亡率预测，标准化死亡率，歧视,和校准。
结果：分析了ICU中癌症患者的7种死亡风险预测模型。大多数型号(APACHEII，阿帕奇四世,SOFA,SAPS-II,SAPS-III,和MPMII)低估了死亡率，ICMM高估了它。APACHEII的SMR（标准化死亡率）值最接近1，表明与其他模型相比具有更好的预后能力。
结论：由于缺乏明确的优越模型和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个复杂的挑战。对于基于证据的知情临床决策，重要的是要考虑医疗团队对每个工具的熟悉程度及其固有的局限性。开发新的仪器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。
方法：在2022年10月使用搜索算法对文献进行了系统回顾。搜索了以下数据库：PubMed，Scopus,虚拟健康图书馆(BVS)还有Medrxiv.使用QUADAS-2量表评估偏倚风险。
方法：ICU接纳癌症患者。
方法：研究包括患有活动性癌症的成年患者，并进入ICU。
方法：无干预的综合研究。
方法：死亡率预测，标准化死亡率，歧视,和校准。
结果：分析了ICU中癌症患者的7种死亡风险预测模型。大多数型号(APACHEII，阿帕奇四世,SOFA,SAPS-II,SAPS-III,和MPMII)低估了死亡率，ICMM高估了它。APACHEII的SMR（标准化死亡率）值最接近1，表明与其他模型相比具有更好的预后能力。
结论：由于缺乏明确的优越模型和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个复杂的挑战。对于基于证据的知情临床决策，重要的是要考虑医疗团队对每个工具的熟悉程度及其固有的局限性。开发新的仪器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。
