Association of latent class analysis-derived subphenotypes of acute kidney injury with mortality in critically ill patients with cardiovascular disease: a retrospective cohort study

Huang, Yongqing; Xiao, Zhifeng; Xie, Yong; Zheng, Shuguo; Yu, Taihui; Guo, Ziyue; Su, Dan; Song, Anqi; Chen, Yangxin; Songyang, Zhou; Guo, Qi; Wang, Jingfeng

doi:10.1186/s12872-022-02587-9

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Also, a series of novel biomarkers, including urinary hemojuvelin (uHJV), kidney injury molecule-1 (uKIM-1), and neutrophil gelatinase-associated lipocalin (uNGAL), were also studied to better predict the occurrence of AKI [12,13]. However, these markers are rarely used in the In our earlier study, we had explored the potential heterogeneity of AKI and evaluated the prognostic differences among AKI subphenotypes in critically ill patients with cardiovascular diseases [14]. In this study, we further built several nomograms of prediction models for different populations of AKI patients.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Risk Factors of Acute Kidney Injury in the First 7 Days after Admission: Analysis of a Group of Critically Ill Patients

Wen

Huang

Guo

et al. 2022

Cardiovascular Therapeutics

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Background. Acute kidney injury (AKI) is a common complication in critically ill patients. Some predictive models have been reported, but the conclusions are controversial. The aim of this study was the formation of nomograms to predict risk factors for AKI in critically ill patients within the first 7 days after admission to the intensive care unit (ICU). Methods. Data were extracted from the Medical Information Mart for Intensive Care- (MIMIC-) III database. The random forest method was used to fill in the missing values, and least absolute shrinkage and selection operator (Lasso) regression analysis was performed to screen for possible risk factors. Results. A total of 561 patients were enrolled. Complication with AKI is significantly associated with a longer length of stay (LOS). For all patients, the predictors contained in the prediction nomogram included hypertension, coronary artery disease (CAD), cardiopulmonary bypass (CPB), coronary artery bypass grafting (CABG), Simplified Acute Physiology Score II (SAPS II), central venous pressure (CVP) measured for the first time after admission, and maximum and minimum mean artery pressure (MAP). The model showed good discrimination ( C − index = 0.818 , 95% CI: 0.779-0.857). In the subgroup of patients with well-controlled blood glucose levels, the significant predictors included hypertension, CABG, CPB, SAPS II, and maximum and minimum MAP. Good discrimination was also present before ( C − index = 0.785 , 95% CI: 0.736–0.834) and after adjustment (adjusted C − index = 0.770 ). Conclusion. Hypertension, CAD, CPB, CABG, SAPS II, CVP measured for the first time after admission, and maximum and minimum MAP were independent risk factors for AKI in critically ill patients.

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Section: Introductionmentioning

confidence: 99%

“…In our earlier study, we had explored the potential heterogeneity of AKI and evaluated the prognostic differences among AKI subphenotypes in critically ill patients with cardiovascular diseases [ 14 ]. In this study, we further built several nomograms of prediction models for different populations of AKI patients.…”

Section: Introductionmentioning

confidence: 99%

Predicting Risk Factors of Acute Kidney Injury in the First 7 Days after Admission: Analysis of a Group of Critically Ill Patients

Wen

Huang

Guo

et al. 2022

Cardiovascular Therapeutics

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Methods for phenotyping adult patients with acute kidney injury: a systematic review

Shah,

Snead,

Daga

et al. 2024

J Nephrol

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Machine Learning Derivation of Two Cardiac Arrest Subphenotypes with Distinct Responses to Treatment

Zhang,

Wu,

et al. 2024

Preprint

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INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making. METHODS: For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.0 database. We excluded patients under 16 years old, those not initially admitted to the intensive care unit (ICU), or treated in the ICU for less than 72 hours. A total of 52 clinical parameters relevant to CA patients were selected for analysis. These included demographic data, vital signs, and laboratory parameters. After an extensive literature review and expert consultations, key factors such as temperature (T), sodium (Na), creatinine (CR), glucose (GLU), heart rate (HR), PaO2/FiO2 ratio (P/F), hemoglobin (HB), mean arterial pressure (MAP), platelets (PLT), and white blood cell count (WBC) were identified as the most significant for cluster analysis. Consensus cluster analysis was utilized to examine the mean values of these routine clinical parameters within the first 24 hours post-ICU admission to categorize patient classes. Furthermore, in-hospital and 28-day mortality rates of patients across different CA subphenotypes were assessed using multivariate logistic and Cox regression analysis. RESULTS: After applying exclusion criteria, 719 CA patients were included in the study, with a median age of 67.22 years (IQR: 55.50-79.34), of whom 63.28% were male. The analysis delineated two distinct subphenotypes: Subphenotype 1 (SP1) and Subphenotype 2 (SP2). Compared to SP1, patients in SP2 exhibited significantly higher levels of P/F, HB, MAP, PLT, and Na, but lower levels of T, HR, GLU, WBC, and CR. SP2 patients had a notably higher in-hospital mortality rate compared to SP1 (53.01% for SP2 vs. 39.36% for SP1, P < 0.001). 28-day mortality decreased continuously for both subphenotypes, with a more rapid decline in SP2. These differences remained significant after adjusting for potential covariates (adjusted OR = 1.82, 95% CI: 1.26–2.64, P = 0.002; HR = 1.84, 95% CI: 1.40–2.41, P < 0.001). CONCLUSIONS: The study successfully identified two distinct clinical subphenotypes of CA by analyzing routine clinical data from the first 24 hours following ICU admission. SP1 was characterized by a lower rate of in-hospital and 28-day mortality when compared to SP2. This differentiation could play a crucial role in tailoring patient care, assessing prognosis, and guiding more targeted treatment strategies for CA patients.

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Association of latent class analysis-derived subphenotypes of acute kidney injury with mortality in critically ill patients with cardiovascular disease: a retrospective cohort study

Cited by 3 publications

References 25 publications

Predicting Risk Factors of Acute Kidney Injury in the First 7 Days after Admission: Analysis of a Group of Critically Ill Patients

Predicting Risk Factors of Acute Kidney Injury in the First 7 Days after Admission: Analysis of a Group of Critically Ill Patients

Methods for phenotyping adult patients with acute kidney injury: a systematic review

Machine Learning Derivation of Two Cardiac Arrest Subphenotypes with Distinct Responses to Treatment

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