Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning

Abstract

Background: Acute kidney injury (AKI) is a common complication associated with significant morbidity and mortality in high-energy trauma patients. Given the poor efficacy of interventions after AKI development, it is important to predict AKI before its diagnosis. Therefore, this study aimed to develop models using machine learning algorithms to predict the risk of AKI in patients with femoral neck fractures.

Methods: We developed machine-learning models using the Medical Information Mart from Intensive Care (MIMIC)-IV database. AKI was predicted using 10 predictive models in three-time windows, 24, 48, and 72 h. Three optimal models were selected according to the accuracy and area under the receiver operating characteristic curve (AUROC), and the hyperparameters were adjusted using a random search algorithm. The Shapley additive explanation (SHAP) analysis was used to determine the impact and importance of each feature on the prediction. Compact models were developed using important features chosen based on their SHAP values and clinical availability. Finally, we evaluated the models using metrics such as accuracy, precision, AUROC, recall, F1 scores, and kappa values on the test set after hyperparameter tuning.

Results: A total of 1,596 patients in MIMIC-IV were included in the final cohort, and 402 (25%) patients developed AKI after surgery. The light gradient boosting machine (LightGBM) model showed the best overall performance for predicting AKI before 24, 48, and 72 h. AUROCs were 0.929, 0.862, and 0.904. The SHAP value was used to interpret the prediction models. Renal function markers and perioperative blood transfusions are the most critical features for predicting AKI. In compact models, LightGBM still performs the best. AUROCs were 0.930, 0.859, and 0.901.

Conclusions: In our analysis, we discovered that LightGBM had the best metrics among all algorithms used. Our study identified the LightGBM as a solid first-choice algorithm for early AKI prediction in patients after femoral neck fracture surgery.

Keywords: MIMIC-IV database; acute kidney injury; femoral neck fracture; machine learning; model interpretation; postoperative.

Figure 1

Flow chart of patient selection.

Figure 2

Data collection windows.

Figure 3

ROC curves of four prediction models using all features (A,C,E) and important features (B,D,F).

Figure 4

Distribution of the impact that each feature has on the full 24 h prediction model output estimated using the SHapley Additive exPlanations (SHAP) values. The plot sorts features by the sum of SHAP value magnitudes over all samples. The color represents the feature value (red high, blue low). The x axis measures the impact on the model output (right positive, left negative). BUN, blood urea nitrogen; ICU, intensive care unit; RBC, red blood cell; RDW, red blood cell distribution width; SCr, serum creatinine, WBC, white blood cell count; AG, anion gap.

Figure 5

Explanation of the prediction results for specific instances. The base value (−3.865) is the average value of the predictive model; the output values are the predicted AKI risks. The bars in red and blue represent risk factors and protective factors, respectively; longer bars mean greater feature importance. Here, these values are the model outputs before the SoftMax layer, and therefore, they are not equal to the final predicted probabilities. This figure shows the explanation for a high-risk instance (A) and a low-risk instance (B). BUN, blood urea nitrogen; RBC, red blood cell count; WBC, white blood cell count; RDW, red cell distribution width; CCI, charlson comorbidity index; ICU, intensive care unit.