Survival predictors in elderly patients with acute respiratory distress syndrome: a prospective observational cohort study.
Kuo-Chin KaoMeng-Jer HsiehShih-Wei LinLi-Pang ChuangChih-Hao ChangHan-Chung HuChiu-Hua WangLi-Fu LiChung-Chi HuangHuang-Ping WuPublished in: Scientific reports (2018)
Acute respiratory distress syndrome (ARDS) has a high mortality rate in intensive care units (ICU). The elderly patients remain to be increased of ICU patients. The aim is to investigate the survival predictors of elderly patients with ARDS. We reported a prospective observational cohort research, including the patients with ARDS between October 2012 and May 2015. Demographic, comorbidities, severity, lung mechanics, laboratory data and survival outcomes were analyzed. A total of 463 patients with ARDS were ≥65 years old were enrolled and analyzed. Multivariate logistic regression analysis identified Charlson comorbidity index (CCI) [odds ratio (OR) 1.111, 95% CI 1.010-1.222, p = 0.031], Sequential Organ Failure Assessment (SOFA) score (OR 1.127, 95% CI 1.054-1.206, p < 0.001) and peak inspiratory pressure (PIP) (OR 1.061, 95% CI 1.024-1.099, p = 0.001) which were independently associated with hospital mortality. Regarding the subgroups patients as 65-74 years old, 75-84 years old and ≥85 years old, the baseline characteristics were not significant difference and the hospital mortality rates were also not significant difference. In conclusion, CCI, SOFA score and PIP were identified as survival predictors in elderly patient with ARDS. Assessing comorbidities with CCI is essential in predicting the survival for elderly patients with ARDS.
Keyphrases
- acute respiratory distress syndrome
- mechanical ventilation
- extracorporeal membrane oxygenation
- intensive care unit
- end stage renal disease
- neuropathic pain
- newly diagnosed
- ejection fraction
- chronic kidney disease
- cardiovascular events
- healthcare
- peritoneal dialysis
- prognostic factors
- free survival
- patient reported outcomes
- coronary artery disease
- emergency department
- cardiovascular disease
- artificial intelligence
- machine learning
- community dwelling
- cross sectional