Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

Fig 1. Clinical workflow of the Intraoperative Data Embedded Analytics (IDEA) algorithm for postoperative acute kidney injury prediction. Phase I, all available health care data from the electronic health record and other public datasets is fed into the preoperative model. The preoperative model calculates a risk score for postoperative acute kidney injury (AKI) and shares the risk score with the clinical team before the surgery. Phase II, the preoperative data is combined with intraoperative data and fed into the IDEA algorithm which calculates a risk score for postoperative acute kidney injury and shares the risk score with the clinical team after surgery.

Fig 2. Conceptual diagram of the Intraoperative Data Embedded Analytics (IDEA) AKI prediction model. This diagram shows the aggregation of the data transformer, data engineering, and data analytics modules in the preoperative and intraoperative layers. The two layers can be integrated by either (1) stacking the preoperative prediction scores with the cleaned and feature engineered intraoperative data (blue arrow) or (2) obtaining the full perioperative dataset by merging all the clean features from both layers (orange arrow).

Fig 3. Intraoperative physiological time series variations stratified by the occurrence of acute kidney injury. Intraoperative physiological time series variations for 100 randomly selected patients during the first 200 minutes of surgery stratified by the occurrence of acute kidney injury (AKI) within the first seven days after surgery. Mean and 95% Confidence Interval (CI) stratified by the occurrence of AKI are shown for (A) intraoperative mean arterial blood pressure, MAP (mmHg), (B) intraoperative heart rate, HR (beats/minute), and (C) intraoperative mean alveolar concentration of anesthetics, MAC.

Fig 4. Association between risk of postoperative acute kidney injury and magnitude and duration of intraoperative physiological variations. The risk for the development of postoperative acute kidney injury (AKI) within the first seven days after surgery, as predicted by the postoperative stacked model, was aggregated across the entire cohort. The color represents the risk of developing postoperative AKI, where red is high-risk, and green is low-risk. The y-axis is time (minutes) and the x-axes are (A) diastolic blood pressure (mmHg), (B) systolic blood pressure (mmHg), (C) Mean arterial blood pressure (mmHg), and (D) Heart rate (beats/minute).

Fig 5. Nonlinear association between risk of postoperative acute kidney injury and intraoperative features. Each blue dot represents a patient that developed postoperative AKI. The y-axis represents the risk probability for postoperative acute kidney injury, which ranges from 0 to 1. The red line represents the median acute kidney injury risk score given the values of x. The x-axes represent (A) maximum intraoperative percentage of methemoglobin in arterial blood gasses, (B) intraoperative variance of bicarbonate (mmol/L)2 in arterial blood gasses, (C) intraoperative mean of lactic acid (mmol/L), (D) minimum intraoperative platelet count (thou/mm3), (E) intraoperative variance of mean corpuscular hemoglobin, MCH (pg/cell)2, (F) intraoperative mean of red cell distribution (%), (G) total of blood products administered during surgery (mL), and (H) duration of surgery (minutes).

Fig 6. Receiver operating characteristic curves and other performance metrics for prediction of postoperative acute kidney injury. The graph at the top of the figure is the receiver operating characteristic curves for prediction of postoperative acute kidney injury (AKI) within the first seven days after surgery for the preoperative model (pink), the postoperative stacked model (green), and the postoperative full model (blue). The diamonds on each curve represent the cutoffs calculated by maximizing the Youden Index. The y-axis is sensitivity, which ranges from zero to one. The x-axis is one minus specificity, which ranges from zero to one. The table in the bottom of the figure contains the following performance metrics for the preoperative and both postoperative models: (A) accuracy (percentage of patients correctly classified), (B) sensitivity (percentage of patients that developed AKI that were classified as high-risk), (C) positive predictive value (percentage of high-risk patients that developed AKI), (D) negative predictive value (percentage of low-risk patients that did not develop AKI), (E) net reclassification improvement for patients that developed postoperative AKI (F) net…

Fig 7. Reclassification performance of the postoperative stacked and full models for predicting postoperative acute kidney injury. The postoperative risk scores in the first column are from the stacked model, while the postoperative risk scores for the second column are from the full model. The patients in the first row developed postoperative AKI within seven days of surgery, whereas the patients in the second row did not develop postoperative AKI within seven days of surgery. The y-axis on each plot is the postoperative model acute kidney injury risk score, which ranges from zero to one. The x-axis on each plot is the preoperative risk group. The red dots are patients at high-risk for AKI according to the postoperative model, whereas the green dots are patients at low-risk for AKI according to the postoperative model. (A & B) The proposed postoperative stacked and full models effectively reclassified false negative patients from the preoperative model as high AKI risk patients.

Fig 8. Association between intraoperative variables (mean arterial pressure and administered blood products) and postoperative risk group reclassification. The first column represents patients that developed postoperative acute kidney injury (AKI), yet were classified as low-risk by the preoperative model. The second column represents patients that did not develop postoperative AKI, but were classified as high-risk by the preoperative model. The boxes represent 95% confidence intervals for the given variables. (A) Patients correctly classified as having high-risk for AKI by the postoperative stacked model tended to have lower mean arterial pressure. (B) Patients correctly classified as having low-risk for AKI by the postoperative stacked model tended to have higher mean arterial pressure. (C) Patients correctly classified as having high-risk for AKI by the postoperative stacked model tended to have greater volumes of administered intraoperative blood products. (D) Patients correctly classified as having low-risk for AKI by the postoperative stacked model tended to have lesser volumes of administered intraoperative blood products.