The Independent Impact of Peripheral Arterial Disease on Mortality in Nonagenarians and Centenarians Who Were Treated in an Intensive Care Unit: A Consecutive Cohort of 1 108 Patients

Roedl, Kevin; Daniels, Rikus; Theile, Pauline; Kluge, Stefan; Müller, J; Behrendt, Christian‐Alexander

doi:10.1016/j.ejvs.2023.01.026

Cited by 1 publication

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“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Major adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted to the Second Xiangya Hospital of Central South University between January 2020 and December 2021. Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. Out of the 2366 patients, 1656 were used for model derivation and 710 for testing. The incidence of MAKE30 was 13.8% in the derivation cohort and 13.2% in the test cohort. The average area under the receiver operating characteristic curve of the XGBoost model was 0.930 (95% CI: 0.912–0.946) in the training set and 0.851 (95% CI: 0.810–0.890) in the test set. The top 8 predictors of MAKE30 tentatively identified by the Shapley additive explanations method were Acute Physiology and Chronic Health Evaluation II score, serum creatinine, blood urea nitrogen, Simplified Acute Physiology Score II score, Sequential Organ Failure Assessment score, aspartate aminotransferase, arterial blood bicarbonate, and albumin. The XGBoost model accurately predicted the occurrence of MAKE30 in elderly ICU patients, and the findings of this study provide valuable information to clinicians for making informed clinical decisions.

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Section: Introductionmentioning

confidence: 99%