Prediction and Clinically Important Factors of Acute Kidney Injury Non-recovery

Chien-Liang Liu; You-Lin Tain; Yun-Chun Lin; Chien-Ning Hsu

doi:10.3389/fmed.2021.789874

Prediction and Clinically Important Factors of Acute Kidney Injury Non-recovery

Front Med (Lausanne). 2022 Jan 17:8:789874. doi: 10.3389/fmed.2021.789874. eCollection 2021.

Authors

Chien-Liang Liu¹, You-Lin Tain², Yun-Chun Lin¹, Chien-Ning Hsu^{3

4}

Affiliations

¹ Department of Industrial Engineering and Management, National Yang Min Chiao Tung University, Hsinchu, Taiwan.
² Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and College of Medicine, Chang Gung University, Kaohsiung, Taiwan.
³ Department of Pharmacy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
⁴ School of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan.

Abstract

Objective: This study aimed to identify phenotypic clinical features associated with acute kidney injury (AKI) to predict non-recovery from AKI at hospital discharge using electronic health record data.

Methods: Data for hospitalized patients in the AKI Recovery Evaluation Study were derived from a large healthcare delivery system in Taiwan between January 2011 and December 2017. Living patients with AKI non-recovery were used to derive and validate multiple predictive models. In total, 64 candidates variables, such as demographic characteristics, comorbidities, healthcare services utilization, laboratory values, and nephrotoxic medication use, were measured within 1 year before the index admission and during hospitalization for AKI.

Results: Among the top 20 important features in the predictive model, 8 features had a positive effect on AKI non-recovery prediction: AKI during hospitalization, serum creatinine (SCr) level at admission, receipt of dialysis during hospitalization, baseline comorbidity of cancer, AKI at admission, baseline lymphocyte count, baseline potassium, and low-density lipoprotein cholesterol levels. The predicted AKI non-recovery risk model using the eXtreme Gradient Boosting (XGBoost) algorithm achieved an area under the receiver operating characteristic (AUROC) curve statistic of 0.807, discrimination with a sensitivity of 0.724, and a specificity of 0.738 in the temporal validation cohort.

Conclusion: The machine learning model approach can accurately predict AKI non-recovery using routinely collected health data in clinical practice. These results suggest that multifactorial risk factors are involved in AKI non-recovery, requiring patient-centered risk assessments and promotion of post-discharge AKI care to prevent AKI complications.

Keywords: acute kidney injury; electronic health records—EHR; interpretability; kidney function recovery; machine learning; risk prediction.