Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database

Zhang, Zhongheng; Yao, Min; Ho, Kwok M.; Hong, Yucai

doi:10.1038/s41598-019-50178-0

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…In the current study, three distinct profiles of critically ill patients with CS after AMI were determined by using 33 clinical variables obtained from electronic health record (EHR) and the LPA, a kind of unsupervised machine learning technique. The main difference between LPA and other clustering algorithms (such as latent variable mixture modeling, K-means clustering, and LCA) is that LPA uses a “model-based clustering” method to derive clusters from a probabilistic model of the data’s distribution ( 28 , 35 – 37 ). Therefore, LPA fits a model that describes the distribution of the data, and you evaluate probabilities of particular patients being members of particular latent profiles based on this model rather than searching for clusters with some arbitrarily chosen distance measure.…”

Section: Discussionmentioning

confidence: 99%

Phenotyping cardiogenic shock that showed different clinical outcomes and responses to vasopressor use: a latent profile analysis from MIMIC-IV database

Yu,

Rao,

et al. 2023

Front. Med.

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BackgroundCardiogenic shock (CS) is increasingly recognized as heterogeneous in its severity and response to therapies. This study aimed to identify CS phenotypes and their responses to the use of vasopressors.MethodThe current study included patients with CS complicating acute myocardial infarction (AMI) at the time of admission from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Laboratory and clinical variables were collected and used to conduct latent profile (LPA) analysis. Furthermore, we used a multivariable logistic regression (LR) model to explore the independent association between the use of vasopressors and endpoints.ResultA total of 630 eligible patients with CS after AMI were enrolled in the study. The LPA identified three profiles of CS: profile 1 (n = 259, 37.5%) was considered as the baseline group; profile 2 (n = 261, 37.8%) was characterized by advanced age, more comorbidities, and worse renal function; and profile 3 (n = 170, 24.6%) was characterized by systemic inflammatory response syndrome (SIRS)-related indexes and acid–base balance disturbance. Profile 3 showed the highest all-cause in-hospital mortality rate (45.9%), followed by profile 2 (43.3%), and profile 1 (16.6%). The LR analyses showed that the phenotype of CS was an independent prognostic factor for outcomes, and profiles 2 and 3 were significantly associated with a higher risk of in-hospital mortality (profile 2: odds ratio [OR] 3.95, 95% confidence interval [CI] 2.61–5.97, p < 0.001; profile 3: OR 3.90, 95%CI 2.48–6.13, p < 0.001) compared with profile 1. Vasopressor use was associated with an improved risk of in-hospital mortality for profile 2 (OR: 2.03, 95% CI: 1.15–3.60, p = 0.015) and profile 3 (OR: 2.91, 95% CI: 1.02–8.32, p = 0.047), respectively. The results of vasopressor use showed no significance for profile 1.ConclusionThree phenotypes of CS were identified, which showed different outcomes and responses to vasopressor use.

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Section: Discussionmentioning

confidence: 99%

Phenotyping cardiogenic shock that showed different clinical outcomes and responses to vasopressor use: a latent profile analysis from MIMIC-IV database

Yu,

Rao,

et al. 2023

Front. Med.

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“…Latent profile analysis (LPA), an unsupervised machine learning algorithm, is a modeling approach for classifying latent variables that focuses on identifying potential subgroups within a population, based on a specific set of variables, using an expectation–maximization algorithm to estimate the parameters of the latent class model ( 18 ). The variables included in LPA modeling are clinical and are incorporated from domain expertise and from the relevant literature ( 16 , 19 , 20 ). Pearson’s correlation analysis was used to determine the correlations among characteristic variables, and variables with correlation coefficients >0.7 were removed.…”

Section: Methodsmentioning

confidence: 99%

Identification of subphenotypes in critically ill thrombocytopenic patients with different responses to therapeutic interventions: a retrospective study

et al. 2023

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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.

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“…Continuous variables were expressed as median (interquartile range [IQR]) or the mean (standard deviation) as appropriate. Differences between groups were compared using analysis of variance (ANOVA) [ 21 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, CA patients in the ICU also exhibit significant heterogeneity [ 19 , 20 ], and subphenotypes exploration may help to identify patients who might benefit most from certain therapeutics. Our previous work has identified subphenotypes of CA using cross-sectional data on the first day of ICU entry [ 21 ]. However, it is largely unknown whether the subphenotypes are stable or subject to transitions and how could this transition inform clinical decisions.…”

Section: Introductionmentioning

confidence: 99%

Latent transition analysis of cardiac arrest patients treated in the intensive care unit

et al. 2021

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Background and objective Post-cardiac arrest (CA) syndrome is heterogenous in their clinical presentations and outcomes. This study aimed to explore the transition and stability of subphenotypes (profiles) of CA treated in the intensive care unit (ICU). Patients and methods Clinical features of CA patients on day 1 and 3 after ICU admission were modeled by latent transition analysis (LTA) to explore the transition between subphenotypes over time. The association between different transition patterns and mortality outcome was explored using multivariable logistic regression. Results We identified 848 eligible patients from the database. The LPA identified three distinct subphenotypes: Profile 1 accounted for the largest proportion (73%) and was considered as the baseline subphenotype. Profile 2 (13%) was characterized by brain injury and profile 3 (14%) was characterized by multiple organ dysfunctions. The same three subphenotypes were identified on day 3. The LTA showed consistent subphenotypes. A majority of patients in profile 2 (72%) and 3 (82%) on day 1 switched to profile 1 on day 3. In the logistic regression model, patients in profile 1 on day 1 transitioned to profile 3 had worse survival outcome than those continue to remain in profile 1 (OR: 20.64; 95% CI: 6.01 to 70.94; p < 0.001) and transitioned to profile 2 (OR: 8.42; 95% CI: 2.22 to 31.97; p = 0.002) on day 3. Conclusion The study identified three subphenotypes of CA, which was consistent on day 1 and 3 after ICU admission. Patients who transitioned to profile 3 on day 3 had significantly worse survival outcome than those remained in profile 1 or 2.

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Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database

Cited by 5 publications

References 32 publications

Phenotyping cardiogenic shock that showed different clinical outcomes and responses to vasopressor use: a latent profile analysis from MIMIC-IV database

Phenotyping cardiogenic shock that showed different clinical outcomes and responses to vasopressor use: a latent profile analysis from MIMIC-IV database

Identification of subphenotypes in critically ill thrombocytopenic patients with different responses to therapeutic interventions: a retrospective study

Latent transition analysis of cardiac arrest patients treated in the intensive care unit

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