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

ABSTRACT: BACKGROUND: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. METHODS: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). RESULTS: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%). CONCLUSIONS:Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.

SUBMITTER: Adhikari L

PROVIDER: S-EPMC6448850 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Adhikari Lasith L Ozrazgat-Baslanti Tezcan T Ruppert Matthew M Madushani R W M A RWMA Paliwal Srajan S Hashemighouchani Haleh H Zheng Feng F Tao Ming M Lopes Juliano M JM Li Xiaolin X Rashidi Parisa P Bihorac Azra A

PloS one 20190404 4

<h4>Background</h4>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.<h4>Metho ...[more]

PMID: 30947282

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Project description:BACKGROUND AND OBJECTIVES:High BP variability may cause AKI because of inappropriate kidney perfusion. This study aimed to investigate the association between intraoperative BP variability and postoperative AKI in patients who underwent noncardiac surgery. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:We performed a cohort study of adults undergoing noncardiac surgery in hospitals in South Korea. We studied three cohorts using the following recording windows for intraoperative BP: discovery cohort, 1-minute intervals; first validation cohort, 5-minute intervals; and second validation cohort, 2-second intervals. We calculated four variability parameters (SD, coefficient of variation, variation independent of mean, and average real variability) based on the measured mean arterial pressure values. The primary outcomes were postoperative AKI (defined by the Kidney Disease Improving Global Outcomes serum creatinine cutoffs) and critical AKI (consisting of stage 2 or higher AKI and post-AKI death or dialysis within 90 days). RESULTS:In the three cohorts, 45,520, 29,704, and 7435 patients were analyzed, each with 2230 (443 critical), 1552 (444 critical), and 300 (91 critical) postoperative AKI events, respectively. In the discovery cohort, all variability parameters were significantly associated with risk of AKI, even after adjusting for intraoperative hypotension. For example, average real variability was associated with higher risks of postoperative AKI (adjusted odds ratio, 1.13 per 1 SD increment; 95% CI, 1.07 to 1.19) and critical AKI (adjusted odds ratio, 1.13 per 1 SD increment; 95% CI, 1.02 to 1.26). Associations were evident predominantly among patients who also experienced intraoperative hypotension. In the validation analysis with 5-minute-interval BP records, all four variability parameters were associated with the risk of postoperative AKI or critical AKI. In the validation cohort with 2-second-interval BP records, average real variability was the only significant variability parameter. CONCLUSIONS:Higher intraoperative BP variability is associated with higher risks of postoperative AKI after noncardiac surgery, independent of hypotension and other clinical characteristics.

Project description:BackgroundOnline pharmacies have grown significantly in recent years, from US $29.35 billion in 2014 to an expected US $128 billion in 2023 worldwide. Although legitimate online pharmacies (LOPs) provide a channel of convenience and potentially lower costs for patients, illicit online pharmacies (IOPs) open the doors to unfettered access to prescription drugs, controlled substances (eg, opioids), and potentially counterfeits, posing a dramatic risk to the drug supply chain and the health of the patient. Unfortunately, we know little about IOPs, and even identifying and monitoring IOPs is challenging because of the large number of online pharmacies (at least 30,000-35,000) and the dynamic nature of the online channel (online pharmacies open and shut down easily).ObjectiveThis study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs.MethodsWe first collected web traffic and engagement data to study and compare how consumers access and engage with LOPs and IOPs. We then proposed a simple but novel framework for predicting the status of online pharmacies (legitimate or illicit) through the referral links between websites. Under this framework, we developed 2 prediction models, the reference rating prediction method (RRPM) and the reference-based K-nearest neighbor.ResultsWe found that direct (typing URL), search, and referral are the 3 major traffic sources, representing more than 95% traffic to both LOPs and IOPs. It is alarming to see that direct represents the second-highest traffic source (34.32%) to IOPs. When tested on a data set with 763 online pharmacies, both RRPM and R2NN performed well, achieving an accuracy above 95% in their predictions of the status for the online pharmacies. R2NN outperformed RRPM in full performance metrics (accuracy, kappa, specificity, and sensitivity). On implementing the 2 models on Google search results for popular drugs (Xanax [alprazolam], OxyContin, and opioids), they produced an error rate of only 7.96% (R2NN) and 6.20% (RRPM).ConclusionsOur prediction models use what we know (referral links) to tackle the many unknown aspects of IOPs. They have many potential applications for patients, search engines, social media, payment companies, policy makers or government agencies, and drug manufacturers to help fight IOPs. With scarce work in this area, we hope to help address the current opioid crisis from this perspective and inspire future research in the critical area of drug safety.

Project description:BackgroundPostoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery.MethodsA single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass procedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used electronic health record data to forecast patient-level probabilistic risk scores for postoperative acute kidney injury using random forest models with preoperative data alone and perioperative data (preoperative plus intraoperative). The MySurgeryRisk predictions were compared with each other as well as with the American Society of Anesthesiologists physical status classification.ResultsMachine learning models using perioperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification (accuracy: 0.70 vs 0.64 vs 0.62, area under the receiver operating characteristics curve: 0.77 vs 0.68 vs 0.61, area under the precision-recall curve: 0.70 vs 0.58 vs 0.48).ConclusionIn predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.

Dataset Information

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

Publications

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

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