International journal of medical informatics · 2025
Predictive value of dynamic creatinine cumulative exposure and trajectory classification onmortality in severe acute pancreatitis: a multicenter retrospective cohort study.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Wan Jianhua, Zou Yaoyu, Kuang Maobin, Xiong Shixuan, He Wenhua, Zhu Yin, Lu Nonghua, Xia Liang
Аннотация
AIMS: This study aims to evaluate the predictive value of cumulative creatinine exposure (CumCr) and dynamic creatinine trajectories for severe acute pancreatitis (SAP) prognosis and construct a machine learning-based risk stratification model.
METHODS: An international multicenter retrospective cohort study included SAP patients from the Nanchang cohort (n = 1,545) and the MIMIC-IV database (n = 530). CumCr during the first 7 days of hospitalization was calculated. Latent class growth modeling (LCGM) identified creatinine trajectory patterns, and restricted cubic spline (RCS) analysis explored non-linear relationships between CumCr and mortality. The Boruta algorithm screened variables, and LASSO regression construct a nomogram model.
RESULTS: A non-linear association between CumCr and mortality was observed, with a distinct threshold (K = 964.029 in the Nanchang cohort). Below this threshold, each standard deviation increases in CumCr elevated mortality risk 12-fold (OR = 12.135). LCGM classified four creatinine trajectory patterns. The persistently high-level group (PHL-T4) exhibited the highest mortality (43 % vs. 6.8 % in persistently low-level group (PLL-T1), P < 0.001). The nomogram integrated age, heart rate, calcium, and creatinine trajectories, achieving an AUC of 0.79, outperforming APACHE II (0.68) and SIRS (0.58). External validation yielded an AUC of 0.71.
CONCLUSIONS: This study combining CumCr and trajectory modeling demonstrates that dynamic creatinine monitoring enhances early identification of high-risk SAP patients.
METHODS: An international multicenter retrospective cohort study included SAP patients from the Nanchang cohort (n = 1,545) and the MIMIC-IV database (n = 530). CumCr during the first 7 days of hospitalization was calculated. Latent class growth modeling (LCGM) identified creatinine trajectory patterns, and restricted cubic spline (RCS) analysis explored non-linear relationships between CumCr and mortality. The Boruta algorithm screened variables, and LASSO regression construct a nomogram model.
RESULTS: A non-linear association between CumCr and mortality was observed, with a distinct threshold (K = 964.029 in the Nanchang cohort). Below this threshold, each standard deviation increases in CumCr elevated mortality risk 12-fold (OR = 12.135). LCGM classified four creatinine trajectory patterns. The persistently high-level group (PHL-T4) exhibited the highest mortality (43 % vs. 6.8 % in persistently low-level group (PLL-T1), P < 0.001). The nomogram integrated age, heart rate, calcium, and creatinine trajectories, achieving an AUC of 0.79, outperforming APACHE II (0.68) and SIRS (0.58). External validation yielded an AUC of 0.71.
CONCLUSIONS: This study combining CumCr and trajectory modeling demonstrates that dynamic creatinine monitoring enhances early identification of high-risk SAP patients.