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

BACKGROUND: To explore the potential heterogeneity of acute kidney injury (AKI) and evaluate the prognostic differences among AKI subphenotypes in critically ill patients with cardiovascular diseases. METHODS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database. Latent class analysis (LCA) was used to explore the potential subphenotypes of AKI in critically ill patients with cardiovascular diseases. The number of classes was identified by the Bayesian information criterion and entropy. The differences in prognostic ability among the AKI subphenotypes were evaluated by logistic regression analysis. RESULT: A total of 7738 AKI patients were enrolled in this study. Using LCA, AKI patients were divided into 4 heterogeneous subphenotypes, which were obviously different from the Kidney Disease: Improving Global Outcomes (KDIGO) stages. Interestingly, class 3 classified by LCA was dominated by stage 2, while the mortality rate in class 3 was significantly different from that in class 1 (15.2% vs. 1.6%, p < 0.05). After further adjustment, the mortality rate in class 3 remained higher than that in class 1, with an odds ratio of 12.31 (95% confidence interval, 8.96-16.89). CONCLUSIONS: LCA was feasible for AKI classification in critically ill patients with cardiovascular disease, and 4 distinct subphenotypes of AKI patients with different prognoses were identified. Our results highlighted the potential heterogeneity of AKI patients, which is worthy of further investigation.

Heart rate fluctuation predicts mortality in critically ill patients in the intensive care unit: a retrospective cohort study.

BACKGROUND: To evaluate the association between heart rate (HR) fluctuation and mortality in critically ill patients in the intensive care unit (ICU). METHODS: A total of 27,814 patients were enrolled from the Medical Information Mart for Intensive Care database and were divided into 3 groups: low HR fluctuation [<25 beats per minute (bpm)], control (25-34 bpm), and high HR fluctuation (≥35 bpm), based on the initial 24-hour HR fluctuation (calculated as the maximum HR minus minimum HR). Multivariate Cox regression and restricted cubic spline models were used. RESULTS: Compared to the control group, higher risk of 28-day and 1-year mortality remained significant in an adjusted model, with hazard ratios of 1.210 [95% confidence interval (CI), 1.103-1.327] and 1.150 (95% CI, 1.078-1.227), respectively, in the high HR fluctuation group, as well as hazard ratios of 1.130 (95% CI, 1.035-1.232) and 1.087 (95% CI, 1.022-1.157), respectively, in the low HR fluctuation group. Restricted cubic splines showed a U-type curve, with the lowest risk of mortality at an HR fluctuation of 30 bpm. CONCLUSIONS: This retrospective cohort study revealed that both high and low HR fluctuation correlated with increased mortality in critically ill ICU patients, providing new insights for optimizing HR control strategies.