Background
Acute kidney injury (AKI) is a serious complication after cardiac surgery with an incidence of 5–30% depending upon procedure type and definitions used [
1‐
5]. It is associated with an increased rate of mortality, hospital length of stay, and healthcare cost [
6,
7]. As the incidence of AKI is higher after cardiac surgery as compared to medical and noncardiac surgical populations [
8], much research has been dedicated to the identification of modifiable risk factors and/or derivation of AKI risk prediction models in this group [
9‐
12].
Recent research demonstrates that there is no standard approach to AKI prediction for patients undergoing cardiac surgery. Existing predictive models are based on different combinations of risk factors and rely heavily on intra- and post-operative events to achieve predictive accuracy [
12,
13], while
preoperative risk stratification is most important and remains challenging. In addition, most existing predictive models were developed to identify patient at risk of severe AKI requiring renal replacement therapy [
5,
12], despite mild AKI being associated with up to a threefold increase in the risk of short- and long-term mortality after cardiac surgery [
3,
14].
Renal function has long been held as a surrogate for systemic perfusion, and accurate
preoperative prediction can help to identify patients who may benefit most from intensive monitoring and personalized management strategies throughout the perioperative period. In the advent of artificial intelligence (AI) in medicine, Machine learning (ML) methods such as Random Forests have successfully been applied to create accurate and reliable predictive models in several fields of study [
15,
16]. Moreover, hybrid ML algorithms offer improved performance, [
17] interpretability and ease of use, making the AI “explainable” to clinicians.
We performed a case study to: (1) derive and internally validate a preoperative model to predict AKI of any severity after cardiac surgery, using a hybrid ML approach, consisting of Random Forests, followed by high-performance logistic regression, and (2) compare the performance of this ML model with traditional and enhanced regression models. We hypothesized that the ML model will outperform traditional models, both in terms of performance and parsimony.
Discussion
To our knowledge, this study is the first to date that uses a hybrid ML approach to derive and validate a model to predict cardiac surgery-associated AKI of any severity, using only preoperative variables. Our findings suggest that a hybrid ML algorithm predicts better, and is computationally more efficient, than traditional and enhanced techniques for risk modeling.
Previous research has shown that the use of automated variable selection methods could result in the selection of non-reproducible sets of independent variables, thus biasing the estimated regression coefficients [
40]. Because of this, the use of backward variable selection in repeated bootstrap samples would likely result in improved estimation of regression coefficients with narrower confidence intervals [
30]. Our hybrid ML approach benefits form its ability to accommodate inter-correlation between multiple explanatory variables and providing protection from over-fitting the data [
15], and thus, outperforms both traditional and enhanced regression models.
Several cardiac surgery-associated AKI risk models have been proposed to date, with the models predicting renal replacement therapy being most robust [
9‐
11]. Despite the clinical importance of renal replacement therapy, its low incidence rate (2–3%), late occurrence [
41], and end stage physiology limit the practical benefit of these risk models. In contrast, mild AKI is very common (pooled incidence rate of 22.3%) [
42] and contributes to considerable perioperative and long-term morbidity and mortality [
14]. The kidneys are sensitive to unfavorable physiologic processes in the setting of cardiac surgery, which include hypotension, low cardiac output syndrome, systemic inflammation resulting from the mechanical trauma of extracorporeal red blood cell in contact with artificial surfaces [
43,
44], as well as the catecholamine surge, decreased vasomotor reactivity and the mismatch of medullary blood flow and renal oxygen consumption that occur during the post-bypass period. Taken together, accurate
preoperative prediction of AKI of any severity, prior to exposure to intra- and post-operative stresses, affords clinicians the greatest window of opportunity to proactively intensify physiologic monitoring, personalizing fluid management and hemodynamic goals to optimize systemic and renal perfusion in at-risk patients [
18].
We used KDIGO to define AKI [
19], which enables standardization of reporting and compatibility with similar studies. Our high quality, comprehensive clinical databases provided a large number of standardized candidate variables for ML and statistical modeling. Our ML risk model contains 11 variables that are etiologically associated with AKI after cardiac surgery [
12]. We found that our ML model was more accurate than the traditional and enhanced statistical models (AUC = 0.75 vs. 0.70 and 0.73, respectively).
In addition, the ML and enhanced statistical models were well calibrated, while the traditional statistical model was not. From a practical perspective, the ML model was more computationally efficient than the enhanced backward selection algorithm using 500 bootstrap samples. Our findings are consistent with the literature, where recent medical applications of ML have shown a high degree of accuracy in predicting various outcomes across a spectrum of clinical settings and diseases [
45,
46].
Few published studies to date predicted cardiac surgery-associated AKI of any severity. Our ML risk model had a higher predictive ability and was more parsimonious (AUC = 0.75, H–L
p = 0.804) than a recent preoperative model for cardiac surgery-associated AKI of any severity (AUC = 0.73, H–L
p = 0.490) [
20], which was derived using a traditional statistical approach and consisted of 15 risk factors. This model was developed using prospectively collected data from over 30,000 subjects undergoing cardiac surgery at three hospitals in the UK and was externally validated. Our ML model also had similar predictive accuracy and better calibration compared to another contemporary preoperative risk score [
22] for any-stage AKI consisted of 10 risk factors (AUC = 0.77, H–L
p = 0.06), that was derived using bootstrapping methods and was validated internally. It is to be noted that in the latter model, AKI was defined as that occurring within 30 days of cardiac surgery. This definition likely captures events occurring during surgical readmissions or during complicated and prolonged postoperative stays. These events may be unrelated to the index surgery and may thus be impractical for informing preventative therapy in the intraoperative setting.
Two other published risk models for predicting AKI of any severity after cardiac surgery combined various pre-, intra- and postoperative factors [
13,
47]. These studies demonstrate that the addition of perioperative factors could improve model performance (AUC = 0.84, and AUC = 0.81, respectively). Further research could be aimed to investigate the additive predictive value of key perioperative variables such as hypotension and low cardiac output, to produce “staged models”. Such models would inform preoperative AKI risk stratification for the planning and personalization of pre- and intraoperative management, as well as to enhance prognostication based on intra- and post-operative events.
Clinical prediction models and associated risk-scoring systems are popular statistical methods as they permit a rapid assessment of patient risk without the use of computers or other electronic devices [
48]. The additive point score assigned to each predictor in the developed models to predict AKI of any severity was derived from well-fit logistic regression models, and can readily be applied at the bedside. These validated scores to predict AKI of any severity following cardiac surgery will aid in clinical decision-making, patient counseling and informed decision-making, resource utilization, and preoperative medical optimization [
12]. Future research is recommended to prospectively assess the efficacy of these models to enhance personalized fluid and hemodynamic management, as well as minimizing exposure to nephrotoxins, in preventing perioperative AKI.
Our findings should be interpreted in light of several limitations.
First, our study was conducted in the setting of a single tertiary care hospital. Therefore, our ML model needs to be externally validated before it can confidently be used at other institutions and geographic regions.
Second, a relatively small number of covariates was included in this study. The performance of the Random Forests approach may be improved in the presence of a larger distribution of covariates [
49].
Third, our risk model is tailored to patients undergoing procedures involving cardiopulmonary bypass and may not be applicable in the setting of off-pump CABG [
50].
Forth, we did not incorporate urine output criteria in identifying patients with AKI, because this information was not available in our databases.
Finally, unmeasured confounding characteristics are an important consideration in any retrospective analysis.
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