Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients

Abstract

Objective There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients’ early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n=799) and test (20%;n=201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results In predicting AKI, nine ML algorithms posted AUC of 0.656–1.000 in the training cohort, with the randomforest standing out and AUC of 0.674–0.821 in the test cohort, with the logistic regression model standing out. Thus, we applied the logistic regression model to establish nomogram. The nomogram was comprised of ten variables: age, body mass index, American Society of Anesthesiologists, hypoproteinemia, hypertension, diabetes, anemia, duration of low mean arterial pressure, mean arterial pressure, transfusion.The calibration curves showed good agreement between prediction and observation in both the training and test sets. Conclusion By including intraoperative and preoperative risk factors, ML algorithm can predict AKI and logistic regression model performing the best. Our prediction model and nomogram that are based on this ML algorithm can help lead decision-making for strategies to inhibit AKI over the perioperative duration.

Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Hip Fracture Surgery

“…11,12 Chen et al studied patients following orthopedic surgery and proposed that age, hypertension, DM, hypoproteinemia, receipt of transfusion, and duration of low mean arterial pressure were independent risk factors for AKI. 17 We analyzed the and many other factors, and our multivariate logistic regression results indicated that advanced age, high ASA score, preexisting CKD, receipt of cemented surgery, and large decrease of hemoglobin on the first day after surgery were significant and independent risk factors for AKI.…”

“…Several studies have demonstrated a relationship between high ASA score and AKI after surgery. [17][18][19] Bell et al 19 and Chen et al 17 reported the ASA score was an independent risk factor for AKI in patients undergoing orthopedic surgery, and they established prediction models. Our results are consistent with the results of these previous studies, in that a higher ASA score indicated a higher risk of AKI after surgery.…”

Potential Predictors for Deterioration of Renal Function After Transfusion

“…23 Despite these efforts, several studies have shown that transfusion of RBC concentrates is associated with relevant adverse effects 24 that are challenging to predict. 25 One could increase patient safety if it were possible to find a procedure in which the characteristics of a pRBC could be related to the features of the recipient in such a way that one could foresee complications beyond pure immunological transfusion incidents.…”

“…23 Despite these efforts, several studies have shown that transfusion of RBC concentrates is associated with relevant adverse effects 24 that are challenging to predict. 25…”

Predicting hypoproteinemia among patients undergoing maintenance hemodialysis: A development and validation study based on machine learning algorithms

“…The RF algorithm also has many applications in other disease prediction models. For example, Chen et al [24] veri ed nine machine learning algorithms to predict the occurrence of acute kidney injury (AKI) in elderly orthopedic patients, and the results showed that the RF algorithm could better predict AKI. In the study of Kala et al [25] predicting the survival rate of 4902 breast cancer patients, the results showed that the RF algorithm had a higher accuracy of 83.3%, while the SVM algorithm had a lower accuracy of 80.5%.…”