Date Published: April 4, 2019
Publisher: Public Library of Science
Author(s): Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Matthew Ruppert, R. W. M. A. Madushani, Srajan Paliwal, Haleh Hashemighouchani, Feng Zheng, Ming Tao, Juliano M. Lopes, Xiaolin Li, Parisa Rashidi, Azra Bihorac, Gaetano Valenza.
Acute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.
A retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI).
The predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%).
Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.
Acute kidney injury (AKI) is one of the most common, yet underdiagnosed, postoperative complications with lasting consequences [1, 2]. It is associated with an increase in mortality, short- and long-term morbidity, chronic kidney disease, and cardiovascular disease [3–7]. An episode of postoperative AKI imposes an average hospital cost increase of $9000, even after adjusting for all other complications [8, 9]. The implementation of existing clinical guidelines for prevention and treatment of AKI is often hindered by the inability to accurately and timely assess the risk for AKI while accounting for the dynamic nature of the pathophysiological events during surgery.
This study was approved by the University of Florida Institutional Review Board and Privacy Office as an exempt study with a waiver of informed consent. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) recommendations were followed under the Type 2a analysis category (random split sample development and validation) (S1 Table) .
Using a large single-center cohort of surgical patients, we developed and validated a dynamic machine-learning algorithm that readjusts the preoperative risk for postoperative AKI using physiological time series data and other data collected during surgery to provide a personalized risk panel for acute kidney injury with both preoperative and immediate postoperative risk assessments. This work expands on our previously validated MySurgeryRisk algorithm which predicts preoperative risk for major postoperative complications, including AKI , to leverage temporal enrichment of the preoperative model with the new information related to patients’ changes in physiological status during surgery. The advantages of the algorithm include a) prediction entirely based on routinely available preoperative and intraoperative data, b) universal applicability to any surgical context, c) exportability to other EHR systems, and d) the ability to handle any data type in EHR (including time series and sparse data). Most importantly, the dynamic reassessment of the risk for postoperative AKI using temporal enrichment with intraoperative data allows for more precise reclassification of AKI risk based on how patients’ clinical trajectory progresses. While preoperative models mainly asses risk based on patients’ pre-existing health conditions and general risks associated with the type of planned procedures, the addition of intraoperative time series data reflects acute physiological responses to the stresses of surgery, which provides a better risk assessment for postoperative AKI to a physician who would want to use the model. For example, a patient without significant comorbidities who undergoes a moderate risk surgery would have low-risk for postoperative AKI based on a preoperative model. If that patient was to develop one or more complications during surgery, such as severe bleeding, adverse reaction to anesthetics, or treatment with nephrotoxic drugs, his/her physiological responses captured in the intraoperative data would reclassify him/her to the high-risk group. A change in classification such as this would be extremely valuable for a physician. The IDEA algorithm demonstrated the ability to integrate intraoperative data that not only resulted in an improved AUC compared to the preoperative model, but resulted in effectively reclassifying up to 40% of patients from the preoperative model into a new risk category based on intraoperative events.
In a large single-center cohort of surgical patients, our proposed Intraoperative Data Embedded Analytics (IDEA) algorithm employed a machine learning approach based on a random forest classifier to improve patients’ postoperative acute kidney injury (AKI) risk prediction with high sensitivity and specificity by utilizing intraoperative data. The IDEA algorithm was able to correctly reclassify approximately 40% of patients who were considered low-risk for postoperative AKI by preoperative model to high-risk. This illustrates the importance of intraoperative data in AKI risk stratification. Given the association between AKI and increased morbidity, mortality, and cost, it is important for clinicians to have dynamic AKI risk prediction algorithms capable of adjusting AKI risk as new information becomes available. Further research can address other post-surgical complications as well as validation of the proposed algorithm on external datasets.