Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort

Journal article

IntroductionThe causes of thrombocytopenia (TP) in critically ill patients are numerous and heterogeneous. Currently, subphenotype identification is a popular approach to address this problem. Therefore, this study aimed to identify subphenotypes that respond differently to therapeutic interventions in patients with TP using routine clinical data and to improve individualized management of TP.MethodsThis retrospective study included patients with TP admitted to the intensive care unit (ICU) of Dongyang People’s Hospital during 2010–2020. Subphenotypes were identified using latent profile analysis of 15 clinical variables. The Kaplan–Meier method was used to assess the risk of 30-day mortality for different subphenotypes. Multifactorial Cox regression analysis was used to analyze the relationship between therapeutic interventions and in-hospital mortality for different subphenotypes.ResultsThis study included a total of 1,666 participants. Four subphenotypes were identified by latent profile analysis, with subphenotype 1 being the most abundant and having a low mortality rate. Subphenotype 2 was characterized by respiratory dysfunction, subphenotype 3 by renal insufficiency, and subphenotype 4 by shock-like features. Kaplan–Meier analysis revealed that the four subphenotypes had different in-30-day mortality rates. The multivariate Cox regression analysis indicated a significant interaction between platelet transfusion and subphenotype, with more platelet transfusion associated with a decreased risk of in-hospital mortality in subphenotype 3 [hazard ratio (HR): 0.66, 95% confidence interval (CI): 0.46–0.94]. In addition, there was a significant interaction between fluid intake and subphenotype, with a higher fluid intake being associated with a decreased risk of in-hospital mortality for subphenotype 3 (HR: 0.94, 95% CI: 0.89–0.99 per 1 l increase in fluid intake) and an increased risk of in-hospital mortality for high fluid intake in subphenotypes 1 (HR: 1.10, 95% CI: 1.03–1.18 per 1 l increase in fluid intake) and 2 (HR: 1.19, 95% CI: 1.08–1.32 per 1 l increase in fluid intake).ConclusionFour subphenotypes of TP in critically ill patients with different clinical characteristics and outcomes and differential responses to therapeutic interventions were identified using routine clinical data. These findings can help improve the identification of different subphenotypes in patients with TP for better individualized treatment of patients in the ICU

Authors: Soussi, S; Sharma, D; Jüni, P; Lebovic, G; Brochard, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Critical Care
• Volume: 26
• Issue: 1
• Pages: 114-114
• Article number: 114
• Publication date: 2022-04-21
• DOI: 10.1186/s13054-022-03972-8
• EISSN: 1364-8535
• ISSN: 1364-8535
• Language: English
• Keywords: Medicine; Intensive care medicine; Sepsis; Latent class model; Cohort; Population; Intensive care unit; Clinical trial; Observational study; Confidence interval; Renal replacement therapy; Cohort study; Internal medicine; Emergency medicine; Hazard ratio; Clinical endpoint; Prospective cohort study