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
{"title":"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。","authors":"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","doi":"10.1016/j.medine.2024.07.009","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs).</div></div><div><h3>Design</h3><div>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.</div></div><div><h3>Setting</h3><div>ICUs admitting cancer patients.</div></div><div><h3>Participants</h3><div>Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.</div></div><div><h3>Interventions</h3><div>Integrative study without interventions.</div></div><div><h3>Main variables of interest</h3><div>Mortality prediction, standardized mortality, discrimination, and calibration.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":94139,"journal":{"name":"Medicina intensiva","volume":"48 12","pages":"Pages e30-e40"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review\",\"authors\":\"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\",\"doi\":\"10.1016/j.medine.2024.07.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs).</div></div><div><h3>Design</h3><div>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.</div></div><div><h3>Setting</h3><div>ICUs admitting cancer patients.</div></div><div><h3>Participants</h3><div>Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.</div></div><div><h3>Interventions</h3><div>Integrative study without interventions.</div></div><div><h3>Main variables of interest</h3><div>Mortality prediction, standardized mortality, discrimination, and calibration.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>\",\"PeriodicalId\":94139,\"journal\":{\"name\":\"Medicina intensiva\",\"volume\":\"48 12\",\"pages\":\"Pages e30-e40\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medicina intensiva\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2173572724001991\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicina intensiva","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2173572724001991","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.