scholarly journals Postoperative delirium prediction using machine learning models and preoperative electronic health record data

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Andrew Bishara ◽  
Catherine Chiu ◽  
Elizabeth L. Whitlock ◽  
Vanja C. Douglas ◽  
Sei Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Risk Stratification ◽  
Electronic Health Record ◽  
Regression Models ◽  
Postoperative Delirium ◽  
Anesthesia Care ◽  
Health Record ◽  
Neural Net ◽  
Preoperative Risk

Abstract Background Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression. Methods This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded. Results POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816–0.863] and for XGBoost was 0.851 [95% CI 0.827–0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734–0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800–0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713–0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk. Conclusion Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.

scholarly journals Medication Management of Anxiety and Depression by Primary Care Pediatrics Providers: A Retrospective Electronic Health Record Study

2021 ◽  
Author(s):  
Talia Roshini Lester ◽  
Yair Bannett ◽  
Rebecca M. Gardner ◽  
Heidi M. Feldman ◽  
Lynne C. Huffman
Keyword(s):  
Primary Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Regression Models ◽  
Medication Management ◽  
Anxiety And Depression ◽  
Health Record ◽  
Logistic Regression Models ◽  
Medication Prescription ◽  
Anxiety Depression

Objectives: To describe medication management of children diagnosed with anxiety and depression by primary care providers. Study Design/Methods: We performed a retrospective cross-sectional analysis of electronic health record (EHR) structured data. All visits for pediatric patients seen at least twice during a four-year period within a network of primary care clinics in Northern California were included. Descriptive statistics summarized patient variables and most commonly prescribed medications. For each subcohort (anxiety, depression, and both (anxiety+depression)), logistic regression models examined the variables associated with medication prescription. Results: Of all patients (N=93,025), 2.8% (n=2635) had a diagnosis of anxiety only, 1.5% (n=1433) depression only, and 0.79% (n=737) both anxiety and depression (anxiety+depression); 18% of children with anxiety and/or depression had comorbid ADHD. A total of 14.0% with anxiety (n=370), 20.3% with depression (n=291), and 47.5% with anxiety+depression (n=350) received a psychoactive non-stimulant medication. For anxiety only and depression only, sertraline, citalopram, and fluoxetine were most commonly prescribed. For anxiety+depression, citalopram, sertraline, and escitalopram were most commonly prescribed. The top prescribed medications also included benzodiazepines. Logistic regression models showed that older age and having developmental or mental health comorbidities were independently associated with increased likelihood of medication prescription for children with anxiety, depression, and anxiety+depression. Insurance type and sex were not associated with medication prescription. Conclusions: PCPs prescribe medications more frequently for patients with anxiety+depression than for patients with either diagnosis alone. Medication choices generally align with current recommendations. Future research should focus on the use of benzodiazepines due to safety concerns in children.

Abstract 15999: Machine Learning to Identify Patients at High Risk for Peripheral Arterial Disease From Electronic Health Record Data

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mark Sonderman ◽  
Eric Farber-Eger ◽  
Aaron W Aday ◽  
Matthew S Freiberg ◽  
Joshua A Beckman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
High Risk ◽  
Peripheral Arterial Disease ◽  
Electronic Health Record ◽  
Arterial Disease ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Peripheral Arterial

Introduction: Peripheral arterial disease (PAD) is a common and underdiagnosed disease associated with significant morbidity and increased risk of major adverse cardiovascular events. Targeted screening of individuals at high risk for PAD could facilitate early diagnosis and allow for prompt initiation of interventions aimed at reducing cardiovascular and limb events. However, no widely accepted PAD risk stratification tools exist. Hypothesis: We hypothesized that machine learning algorithms can identify patients at high risk for PAD, defined by ankle-brachial index (ABI) <0.9, from electronic health record (EHR) data. Methods: Using data from the Vanderbilt University Medical Center EHR, ABIs were extracted for 8,093 patients not previously diagnosed with PAD at the time of initial testing. A total of 76 patient characteristics, including demographics, vital signs, lab values, diagnoses, and medications were analyzed using both a random forest and least absolute shrinkage and selection operator (LASSO) regression to identify features most predictive of ABI <0.9. The most significant features were used to build a logistic regression based predictor that was validated in a separate group of individuals with ABI data. Results: The machine learning models identified several features independently correlated with PAD (age, BMI, SBP, DBP, pulse pressure, anti-hypertensive medication, diabetes medication, smoking, and statin use). The test statistic produced by the logistic regression model was correlated with PAD status in our validation set. At a chosen threshold, the specificity was 0.92 and the positive predictive value was 0.73 in this high-risk population. Conclusions: Machine learning can be applied to build unbiased models that identify individuals at risk for PAD using easily accessible information from the EHR. This model can be implemented either through a high-risk flag within the medical record or an online calculator available to clinicians.

scholarly journals Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

2021 ◽  
Author(s):  
Jeffrey G Klann ◽  
Griffin M Weber ◽  
Hossein Estiri ◽  
Bertrand Moal ◽  
Paul Avillach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Chart Review ◽  
Learning Approach ◽  
Health Record ◽  
Learning Approaches ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Machine Learning Approach ◽  
Electronic Health

Abstract Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

scholarly journals Electronic Health Record Acceptance by Physicians: A Single Hospital Experience in Daily Practice

2021 ◽  
Vol 1 (1) ◽  
pp. 6-17
Author(s):  
Andrija Pavlovic ◽  
Nina Rajovic ◽  
Jasmina Pavlovic Stojanovic ◽  
Debora Akinyombo ◽  
Milica Ugljesic ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Daily Practice ◽  
Ease Of Use ◽  
Future Research ◽  
Health Record ◽  
Cross Sectional ◽  
Electronic Health

Introduction: Potential benefits of implementing an electronic health record (EHR) to increase the efficiency of health services and improve the quality of health care are often obstructed by the unwillingness of the users themselves to accept and use the available systems. Aim: The aim of this study was to identify factors that influence the acceptance of the use of an EHR by physicians in the daily practice of hospital health care. Material and Methods: The cross-sectional study was conducted among physicians in the General Hospital Pancevo, Serbia. An anonymous questionnaire, developed according to the technology acceptance model (TAM), was used for the assessment of EHR acceptance. The response rate was 91%. Internal consistency was assessed by Cronbach’s alpha coefficient. A logistic regression analysis was used to identify the factors influencing the acceptance of the use of EHR. Results: The study population included 156 physicians. The mean age was 46.4 ± 10.4 years, 58.8% participants were female. Half of the respondents (50.1%) supported the use of EHR in comparison to paper patient records. In multivariate logistic regression modeling of social and technical factors, ease of use, usefulness, and attitudes towards use of EHR as determinants of the EHR acceptance, the following predictors were identified: use of a computer outside of the office for reading daily newspapers (p = 0.005), EHR providing a greater amount of valuable information (p = 0.007), improvement in the productivity by EHR use (p < 0.001), and a statement that using EHR is a good idea (p = 0.014). Overall the percentage of correct classifications in the model was 83.9%. Conclusion: In this research, determinants of the EHR acceptance were assessed in accordance with the TAM, providing an overall good model fit. Future research should attempt to add other constructs to the TAM in order to fully identify all determinants of physician acceptance of EHR in the complex environment of different health systems.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

Concordance Between Electronic Health Record and Tumor Registry Documentation of Smoking Status Among Patients With Cancer

2021 ◽  
pp. 518-526
Author(s):  
Jennifer H. LeLaurin ◽  
Matthew J. Gurka ◽  
Xiaofei Chi ◽  
Ji-Hyun Lee ◽  
Jaclyn Hall ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Electronic Health Record ◽  
Smoking Status ◽  
Registry Data ◽  
Health Record ◽  
Predictive Values ◽  
Tumor Registry ◽  
Patients With Cancer ◽  
Moderate Agreement ◽  
Status Data

PURPOSE Patients with cancer who use tobacco experience reduced treatment effectiveness, increased risk of recurrence and mortality, and diminished quality of life. Accurate tobacco use documentation for patients with cancer is necessary for appropriate clinical decision making and cancer outcomes research. Our aim was to assess agreement between electronic health record (EHR) smoking status data and cancer registry data. MATERIALS AND METHODS We identified all patients with cancer seen at University of Florida Health from 2015 to 2018. Structured EHR smoking status was compared with the tumor registry smoking status for each patient. Sensitivity, specificity, positive predictive values, negative predictive values, and Kappa statistics were calculated. We used logistic regression to determine if patient characteristics were associated with odds of agreement in smoking status between EHR and registry data. RESULTS We analyzed 11,110 patient records. EHR smoking status was documented for nearly all (98%) patients. Overall kappa (0.78; 95% CI, 0.77 to 0.79) indicated moderate agreement between the registry and EHR. The sensitivity was 0.82 (95% CI, 0.81 to 0.84), and the specificity was 0.97 (95% CI, 0.96 to 0.97). The logistic regression results indicated that agreement was more likely among patients who were older and female and if the EHR documentation occurred closer to the date of cancer diagnosis. CONCLUSION Although documentation of smoking status for patients with cancer is standard practice, we only found moderate agreement between EHR and tumor registry data. Interventions and research using EHR data should prioritize ensuring the validity of smoking status data. Multilevel strategies are needed to achieve consistent and accurate documentation of smoking status in cancer care.

Identification of patients at high risk for preventable emergency department visits and inpatient admissions after starting chemotherapy: Machine learning applied to comprehensive electronic health record data.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1511-1511
Author(s):  
Dylan J. Peterson ◽  
Nicolai P. Ostberg ◽  
Douglas W. Blayney ◽  
James D. Brooks ◽  
Tina Hernandez-Boussard
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Electronic Health Record ◽  
Acute Care ◽  
Cancer Care ◽  
Model Performance ◽  
Emergency Department Visits ◽  
Health Record ◽  
Patients At Risk ◽  
Ed Visits

1511 Background: Acute care use is one of the largest drivers of cancer care costs. OP-35: Admissions and Emergency Department Visits for Patients Receiving Outpatient Chemotherapy is a CMS quality measure that will affect reimbursement based on unplanned inpatient admissions (IP) and emergency department (ED) visits. Targeted measures can reduce preventable acute care use but identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data available in the Electronic Health Record (EHR). We hypothesized dense, structured EHR data could be used to train machine learning algorithms to predict risk of preventable ED and IP visits. Methods: Patients treated at Stanford Health Care and affiliated community care sites between 2013 and 2015 who met inclusion criteria for OP-35 were selected from our EHR. Preventable ED or IP visits were identified using OP-35 criteria. Demographic, diagnosis, procedure, medication, laboratory, vital sign, and healthcare utilization data generated prior to chemotherapy treatment were obtained. A random split of 80% of the cohort was used to train a logistic regression with least absolute shrinkage and selection operator regularization (LASSO) model to predict risk for acute care events within the first 180 days of chemotherapy. The remaining 20% were used to measure model performance by the Area Under the Receiver Operator Curve (AUROC). Results: 8,439 patients were included, of whom 35% had one or more preventable event within 180 days of starting chemotherapy. Our LASSO model classified patients at risk for preventable ED or IP visits with an AUROC of 0.783 (95% CI: 0.761-0.806). Model performance was better for identifying risk for IP visits than ED visits. LASSO selected 125 of 760 possible features to use when classifying patients. These included prior acute care visits, cancer stage, race, laboratory values, and a diagnosis of depression. Key features for the model are shown in the table. Conclusions: Machine learning models trained on a large number of routinely collected clinical variables can identify patients at risk for acute care events with promising accuracy. These models have the potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted preventative interventions. Future work will include prospective and external validation in other healthcare systems.[Table: see text]

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