Dataset Information

Identifying surgical site infections in electronic health data using predictive models.

ABSTRACT: Objective:The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery. Methods:We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV). Results:We identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity?=?0.9, PPV?=?0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity?=?0.84, PPV?=?0.28). Conclusions:EHR data can facilitate SSI surveillance with adequate sensitivity and PPV.

SUBMITTER: Grundmeier RW

PROVIDER: S-EPMC7646895 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Identifying surgical site infections in electronic health data using predictive models.

Grundmeier Robert W RW Xiao Rui R Ross Rachael K RK Ramos Mark J MJ Karavite Dean J DJ Michel Jeremy J JJ Gerber Jeffrey S JS Coffin Susan E SE

Journal of the American Medical Informatics Association : JAMIA 20180901 9

<h4>Objective</h4>The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery.<h4>Methods</h4>We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated perfo ...[more]

PMID: 29982511

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Project description:BackgroundHigh-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs).MethodsDeep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart notes recorded within 30 days of a surgical procedure. The two language models were subsequently finetuned on labeled data for the classification of SSSIs. Validation and testing were performed on surgical cases performed during the month of November 2021. We propose two different use cases: a stand-alone machine learning (SAM) pipeline and a human-in-the-loop (HITL) pipeline. Performances of both pipelines were compared to administrative data and to manual curation.ResultsThe models were trained on 3,983,864 unlabeled chart notes and finetuned on 1,231,656 labeled notes. Models had a test area under the receiver operating characteristic curves (ROC AUC) of 0.989 on individual chart notes and 0.980 on an aggregated case level. The SAM pipeline had a sensitivity of 0.604, a specificity of 0.996, a positive predictive value (PPV) of 0.763, and a negative predictive value (NPV) of 0.991. Prior to human review, the HITL pipeline had a sensitivity of 0.854, a specificity of 0.987, a PPV of 0.603, and a NPV of 0.997.ConclusionThe performance of the SAM pipeline was superior to administrative data, and significantly outperformed previously published results. The performance of the HITL pipeline approached that of manual curation.

Project description:ObjectiveTo demonstrate that electronic health record (EHR) data can be used in an automated approach to evaluate cataract surgery outcomes.DesignRetrospective analysis.SubjectsResident and faculty surgeons.MethodsElectronic health record data were collected from cataract surgeries performed at the Johns Hopkins Wilmer Eye Institute, and cases were categorized into resident or attending as primary surgeon. Preoperative and postoperative visual acuity (VA) and unplanned return to operating room were extracted from the EHR.Main outcome measuresPostoperative VA and reoperation rate within 90 days.ResultsThis study analyzed 14 537 cataract surgery cases over 32 months. Data were extracted from the EHR using an automated approach to assess surgical outcomes for resident and attending surgeons. Of 337 resident surgeries with both preoperative and postoperative VA data, 248 cases (74%) had better postoperative VA, and 170 cases (51%) had more than 2 lines improvement. There was no statistical difference in the proportion of cases with better postoperative VA or more than 2 lines improvement between resident and attending cases. Attending surgeons had a statistically greater proportion of cases with postoperative VA better than 20/40, but this finding has to be considered in the context that, on average, resident cases started out with poorer baseline VA.A multivariable regression model of VA outcomes vs. resident/attending status that controlled for preoperative VA, patient age, American Society of Anesthesiologists (ASA) score, and estimated income found that resident status, preoperative VA, patient age, ASA score, and estimated income were all significant predictors of VA. The rate of unplanned return to the operating room within 90 days of cataract surgery was not statistically different between resident (1.8%) and attending (1.2%) surgeons.ConclusionsThis study demonstrates that EHR data can be used to evaluate and monitor surgical outcomes in an ongoing way. Analysis of EHR-extracted cataract outcome data showed that preoperative VA, ASA classification, and attending/resident status were important in predicting postoperative VA outcomes. These findings suggest that the utilization of EHR data could enable continuous assessment of surgical outcomes and inform interventions to improve resident training. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

Project description:BackgroundPythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk.Methods and findingsA curated data repository of surgical outcomes was created using automated SQL and R code that extracted and processed patient clinical and surgical data across 37 million clinical encounters from the EHRs. A total of 194 clinical features including patient demographics (e.g., age, sex, race), smoking status, medications, comorbidities, procedure information, and proxies for surgical complexity were constructed and aggregated. A cohort of 66,370 patients that had undergone 99,755 invasive procedural encounters between January 1, 2014, and January 31, 2017, was studied further for the purpose of predicting postoperative complications. The average complication and 30-day postoperative mortality rates of this cohort were 16.0% and 0.51%, respectively. Least absolute shrinkage and selection operator (lasso) penalized logistic regression, random forest models, and extreme gradient boosted decision trees were trained on this surgical cohort with cross-validation on 14 specific postoperative outcome groupings. Resulting models had area under the receiver operator characteristic curve (AUC) values ranging between 0.747 and 0.924, calculated on an out-of-sample test set from the last 5 months of data. Lasso penalized regression was identified as a high-performing model, providing clinically interpretable actionable insights. Highest and lowest performing lasso models predicted postoperative shock and genitourinary outcomes with AUCs of 0.924 (95% CI: 0.901, 0.946) and 0.780 (95% CI: 0.752, 0.810), respectively. A calculator requiring input of 9 data fields was created to produce a risk assessment for the 14 groupings of postoperative outcomes. A high-risk threshold (15% risk of any complication) was determined to identify high-risk surgical patients. The model sensitivity was 76%, with a specificity of 76%. Compared to heuristics that identify high-risk patients developed by clinical experts and the ACS NSQIP calculator, this tool performed superiorly, providing an improved approach for clinicians to estimate postoperative risk for patients. Limitations of this study include the missingness of data that were removed for analysis.ConclusionsExtracting and curating a large, local institution's EHR data for machine learning purposes resulted in models with strong predictive performance. These models can be used in clinical settings as decision support tools for identification of high-risk patients as well as patient evaluation and care management. Further work is necessary to evaluate the impact of the Pythia risk calculator within the clinical workflow on postoperative outcomes and to optimize this data flow for future machine learning efforts.

Project description:BackgroundThe main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks.MethodsWe compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We performed the study first on simulated data that mimics real world EHR data and then on actual EHR data from eight Advocate hospitals.ResultsOn simulated data with incorrect categories and missing data, the precision for autoencoder is 24.16% when fixing recall at 0.7, which is higher than Random Forest (23.61%) and lower than LASSO (25.32%). The precision is 20.92% in Simple Reg and improves to 24.89% in Enhanced Reg. When using real EHR data to predict the 30-day readmission rate, the precision of autoencoder is 19.04%, which again is higher than Random Forest (18.48%) and lower than LASSO (19.70%). The precisions for Simple Reg and Enhanced Reg are 18.70 and 19.69% respectively. That is, Enhanced Reg can have competitive prediction performance compared to LASSO. In addition, results show that Enhanced Reg usually relies on fewer features under the setting of simulations of this paper.ConclusionsWe conclude that autoencoder can create useful features representing the entire space of EHR data and which are applicable to a wide array of predictive tasks. Together with important response-specific predictors, we can derive efficient and robust predictive models with less labor in data extraction and model training.

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Identifying surgical site infections in electronic health data using predictive models.

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Identifying surgical site infections in electronic health data using predictive models.

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