OBJECTIVES: To assess the long-term survival, health-related quality of life, and quality-adjusted life years of cancer patients admitted to ICUs. DESIGN: Prospective cohort. SETTING: Two cancer specialized ICUs in Brazil. PATIENTS: A total of 792 participants. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The health-related quality of life before ICU admission; at 15 days; and at 3, 6, 12, and 18 months was assessed with the EQ-5D-3L. In addition, the vital status was assessed at 24 months. The mean age of the subjects was 61.6 ± 14.3 years, 42.5% were female subjects and half were admitted after elective surgery. The mean Simplified Acute Physiology Score 3 was 47.4 ± 15.6. Survival at 12 and 18 months was 42.4% and 38.1%, respectively. The mean EQ-5D-3L utility measure before admission to the ICU was 0.47 ± 0.43, at 15 days it was 0.41 ± 0.44, at 90 days 0.56 ± 0.42, at 6 months 0.60 ± 0.41, at 12 months 0.67 ± 0.35, and at 18 months 0.67 ± 0.35. The probabilities for attaining 12 and 18 months of quality-adjusted survival were 30.1% and 19.1%, respectively. There were statistically significant differences in survival time and quality-adjusted life years according to all assessed baseline characteristics (ICU admission after elective surgery, emergency surgery, or medical admission; Simplified Acute Physiology Score 3; cancer extension; cancer status; previous surgery; previous chemotherapy; previous radiotherapy; performance status; and previous health-related quality of life). Only the previous health-related quality of life and performance status were associated with the health-related quality of life during the 18-month follow-up. CONCLUSIONS: Long-term survival, health-related quality of life, and quality-adjusted life year expectancy of cancer patients admitted to the ICU are limited. Nevertheless, these clinical outcomes exhibit wide variability among patients and are associated with simple characteristics present at the time of ICU admission, which may help healthcare professionals estimate patients' prognoses.
OBJECTIVES: To assess the long-term survival, health-related quality of life, and quality-adjusted life years of cancerpatients admitted to ICUs. DESIGN: Prospective cohort. SETTING: Two cancer specialized ICUs in Brazil. PATIENTS: A total of 792 participants. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The health-related quality of life before ICU admission; at 15 days; and at 3, 6, 12, and 18 months was assessed with the EQ-5D-3L. In addition, the vital status was assessed at 24 months. The mean age of the subjects was 61.6 ± 14.3 years, 42.5% were female subjects and half were admitted after elective surgery. The mean Simplified Acute Physiology Score 3 was 47.4 ± 15.6. Survival at 12 and 18 months was 42.4% and 38.1%, respectively. The mean EQ-5D-3L utility measure before admission to the ICU was 0.47 ± 0.43, at 15 days it was 0.41 ± 0.44, at 90 days 0.56 ± 0.42, at 6 months 0.60 ± 0.41, at 12 months 0.67 ± 0.35, and at 18 months 0.67 ± 0.35. The probabilities for attaining 12 and 18 months of quality-adjusted survival were 30.1% and 19.1%, respectively. There were statistically significant differences in survival time and quality-adjusted life years according to all assessed baseline characteristics (ICU admission after elective surgery, emergency surgery, or medical admission; Simplified Acute Physiology Score 3; cancer extension; cancer status; previous surgery; previous chemotherapy; previous radiotherapy; performance status; and previous health-related quality of life). Only the previous health-related quality of life and performance status were associated with the health-related quality of life during the 18-month follow-up. CONCLUSIONS: Long-term survival, health-related quality of life, and quality-adjusted life year expectancy of cancerpatients admitted to the ICU are limited. Nevertheless, these clinical outcomes exhibit wide variability among patients and are associated with simple characteristics present at the time of ICU admission, which may help healthcare professionals estimate patients' prognoses.
Authors: Vanessa Chaves Barreto Ferreira de Lima; Ana Luiza Bierrenbach; Gizelton Pereira Alencar; Ana Lucia Andrade; Luciano Cesar Pontes Azevedo Journal: Intensive Care Med Date: 2018-07-12 Impact factor: 17.440
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Authors: Nerea Fernández Ros; Félix Alegre; Javier Rodríguez Rodriguez; Manuel F Landecho; Patricia Sunsundegui; Alfonso Gúrpide; Ramón Lecumberri; Eva Sanz; Nicolás García; Jorge Quiroga; Juan Felipe Lucena Journal: J Clin Med Date: 2022-06-16 Impact factor: 4.964
Authors: Elie Azoulay; Peter Schellongowski; Michael Darmon; Philippe R Bauer; Dominique Benoit; Pieter Depuydt; Jigeeshu V Divatia; Virginie Lemiale; Maarten van Vliet; Anne-Pascale Meert; Djamel Mokart; Stephen M Pastores; Anders Perner; Frédéric Pène; Peter Pickkers; Kathryn A Puxty; Francois Vincent; Jorge Salluh; Ayman O Soubani; Massimo Antonelli; Thomas Staudinger; Michael von Bergwelt-Baildon; Marcio Soares Journal: Intensive Care Med Date: 2017-07-19 Impact factor: 17.440