Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

Abstract

BackgroundIn sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.MethodsA total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.ResultsIn sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).ConclusionThe nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.

Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care

“…In this study, we utilized the SHAP method to enhance the interpretability of the XGBoost model and identified several key factors that contribute to the risk of MAKE 30 in elderly patients in the ICU. Our analysis identified low platelet count, high baseline estimated glomerular filtration rate, and SOFA score as high-risk factors for MAKE30, which have been previously implicated in the construction of prognostic models for adverse renal outcomes in other populations [ 3 , 8 , 16 , 17 ]. Our findings revealed that various demographic and physiological factors, including the APACHE II score, serum creatinine and BUN levels, and SOFA score, are positively associated with the risk of MAKE30.…”

“…Medical advancements have significantly prolonged human life expectancy [ 1 ], leading to a growth of critically ill patients over the age of 65 [ 2 ]. Such patients often experience a high rate of renal adverse events, and to better reflect overall renal outcomes, the use of a composite and objective clinical outcome indicator called major adverse kidney events (MAKE30) is recommended [ 3 ]. Studies have shown that the incidence of MAKE30 in intensive care unit (ICU) patients ranges from 13.6% to 38.9% [ 4–10 ].…”

Hubungan Kadar Gula Darah Puasa Dengan Kejadian Sepsis Pada Pasien Diabetes Melitus Tipe 2 Di Rsud Dr. H. Abdul Moeloek Provinsi Lampung

“…Telah dilaporkan terdapat 17-20% pasien dengan sepsis mengalami diabetes melitus. Pada pasien DM tipe 2 kekebalan tubuh tidak berfungsi dengan baik, oleh karena itu pasien DM tipe 2 memiliki peluang tinggi untuk mengembangkan infeksi sehingga beresiko terkena sepsis (Xin et al, 2022). Dari uraian tersebut, peneliti tertarik untuk melakukan penelitian tentang hubungan kadar gula darah puasa dengan kejadian sepsis pada pasien diabetes melitus tipe 2 di RSUD DR. H. Abdul Moeloek Provinsi Lampung.…”