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. 2024 Dec 9:11:1513212.
doi: 10.3389/fcvm.2024.1513212. eCollection 2024.

Association of cardiovascular-kidney-metabolic index with all-cause mortality during hospitalization in critically ill patients: a retrospective cohort study from MIMIC IV2.2

Xiaolong Qu #  1 , Yuping Liu #  2 , Peng Nie  1 , Lei Huang  3
Affiliations

Association of cardiovascular-kidney-metabolic index with all-cause mortality during hospitalization in critically ill patients: a retrospective cohort study from MIMIC IV2.2

Xiaolong Qu et al. Front Cardiovasc Med. .

Abstract

Background: The cardiovascular-kidney-metabolic index (CKMI), a novel functional indicator proposed in this study, aims to accurately reflect the functional status of the heart, kidneys, and metabolism. However, its ability to predict mortality risk in critically ill patients during their stay in the intensive care unit (ICU) remains uncertain. Therefore, this study aims to validate the correlation between the CKMI during hospitalization and all-cause mortality.

Methods: The study utilized the Medical Information Mart for Intensive Care IV 2.2 (MIMIC-IV) dataset for a retrospective analysis of cohorts. The cohorts were divided into quartiles based on CKMI index levels. The primary endpoint was all-cause mortality during ICU and hospital stay, while secondary endpoints included the duration of ICU stay and overall hospitalization period. We established Cox proportional hazards models and employed multivariable Cox regression analysis and restricted cubic spline (RCS) regression analysis to explore the relationship between CKMI index and all-cause mortality during hospitalization in critically ill patients. Additionally, subgroup analyses were conducted based on different subgroups.

Results: The study enrolled 1,576 patients (male 60.79%). In-patient and ICU mortality was 11.55% and 6.73%. Multivariate COX regression analysis demonstrated a significant negative correlation between CKMI index and the risk of hospital death [HR, 0.26 (95% CI 0.07-0.93), P = 0.038] and ICU mortality [HR, 0.13 (95% CI 0.03-0.67), P = 0.014].RCS regression model revealed that in-hospital mortality (P-value =0.015, P-Nonlinear =0.459) and ICU mortality (P-value =0.029, P-Nonlinear =0.432) increased linearly with increasing CKMI index. Subgroup analysis confirmed consistent effect size and direction across different subgroups, ensuring stable results.

Conclusion: Our research findings suggest that a higher CKMI index is associated with a significant reduction in both in-hospital and ICU mortality among critically ill patients. Therefore, CKMI index emerges as a highly valuable prognostic indicator for predicting the risk of in-hospital death in this population. However, to strengthen the validity of these results, further validation through larger-scale prospective studies is imperative.

Keywords: MIMIC-IV database; cardiovascular-kidney-metabolic index; in-hospital mortality; intensive care unit; retrospective cohort study.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of study participants.
Figure 2
Figure 2
Histogram of CKMI.
Figure 3
Figure 3
The Kaplan-Meier survival analysis curves depict the cumulative probability of all-cause mortality in quartile groups within the hospital (a), and ICU (b).
Figure 4
Figure 4
RCS analysis was conducted to examine the association between CKMI and all-cause mortality in both hospital and ICU settings. (a) The RCS curve illustrates the relationship between CKMI and all-cause hospital mortality. (b) The RCS curve demonstrates the correlation between CKMI and ICU mortality.
Figure 5
Figure 5
The CKMI correlation ROC curve for predicting in-hospital (a) and ICU mortality (b).
Figure 6
Figure 6
Subgroup analysis for the correlation between the CKMI index and the risk of ICU (a) and in-hospital (b) mortality in critically ill patients.
See this image and copyright information in PMC

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