Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.
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ABSTRACT:
SUBMITTER: Ross EG
PROVIDER: S-EPMC6415677 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
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Project description:Clinicians who are using the Framingham Risk Score (FRS) or the American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) to estimate risk for their patients based on electronic health data (EHD) face 4 questions. (1) Do published risk scores applied to EHD yield accurate estimates of cardiovascular risk? (2) Are FRS risk estimates, which are based on data that are up to 45 years old, valid for a contemporary patient population seeking routine care? (3) Do the PCE make the FRS obsolete? (4) Does refitting the risk score using EHD improve the accuracy of risk estimates? Data were extracted from the EHD of 84 116 adults aged 40 to 79 years who received care at a large healthcare delivery and insurance organization between 2001 and 2011. We assessed calibration and discrimination for 4 risk scores: published versions of FRS and PCE and versions obtained by refitting models using a subset of the available EHD. The published FRS was well calibrated (calibration statistic K=9.1, miscalibration ranging from 0% to 17% across risk groups), but the PCE displayed modest evidence of miscalibration (calibration statistic K=43.7, miscalibration from 9% to 31%). Discrimination was similar in both models (C-index=0.740 for FRS, 0.747 for PCE). Refitting the published models using EHD did not substantially improve calibration or discrimination. We conclude that published cardiovascular risk models can be successfully applied to EHD to estimate cardiovascular risk; the FRS remains valid and is not obsolete; and model refitting does not meaningfully improve the accuracy of risk estimates.
| S-EPMC5532984 | biostudies-literature
Project description:In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine-learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient's discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty-five percent of medications ordered by physicians were ranked in the sequence model's top-10 predictions (logistic model: 49%) and 75% ranked in the top-25 (logistic model: 69%). Ninety-three percent of the sequence model's top-10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.
| S-EPMC7325318 | biostudies-literature
Project description:BACKGROUND:Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. OBJECTIVE:To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). DESIGN:Observational cohort study using stepwise-backward selection and cross-validation. SUBJECTS:Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES:All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals. RESULTS:Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18). CONCLUSIONS:EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.
| S-EPMC6296251 | biostudies-literature
Project description:ImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main outcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and relevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
| S-EPMC10863024 | biostudies-literature
Project description:Predictive analytics with electronic health record (EHR) data holds promise for improving outcomes of psychiatric care. This study evaluated models for predicting outcomes of psychotherapy for depression in a clinical practice setting. EHR data from two large integrated health systems (Kaiser Permanente Colorado and Washington) included 5,554 new psychotherapy episodes with a baseline Patient Health Questionnaire (PHQ-9) score ≥ 10 and a follow-up PHQ-9 14-180 days after treatment initiation. Baseline predictors included demographics and diagnostic, medication, and encounter history. Prediction models for two outcomes-follow-up PHQ-9 score and treatment response (≥ 50% PHQ-9 reduction)-were trained in a random sample of 70% of episodes and validated in the remaining 30%. Two methods were used for modeling: generalized linear regression models with variable selection and random forests. Sensitivity analyses considered alternate predictor, outcome, and model specifications. Predictions of follow-up PHQ-9 scores poorly estimated observed outcomes (mean squared error = 31 for linear regression, 40 for random forest). Predictions of treatment response had low discrimination (AUC = 0.57 for logistic regression, 0.61 for random forest), low classification accuracy, and poor calibration. Sensitivity analyses showed similar results. We note that prediction model performance may vary for settings with different care or EHR documentation practices. In conclusion, prediction models did not accurately predict depression treatment outcomes despite using rich EHR data and advanced analytic techniques. Health systems should proceed cautiously when considering prediction models for psychiatric outcomes using baseline intake information. Transparent research should be conducted to evaluate performance of any model intended for clinical use.
| S-EPMC8448296 | biostudies-literature
Project description:BackgroundPeripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes.ObjectiveThe aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system.MethodsAn initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance.ResultsThe initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862.ConclusionsThe algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.
| S-EPMC7468640 | biostudies-literature
Project description:Prediction of major adverse kidney events in critically ill patients may help target therapy, allow risk adjustment, and facilitate the conduct of clinical trials. In a cohort comprised of all critically ill adults admitted to five intensive care units at a single tertiary care center over one year, we developed a logistic regression model for the outcome of Major Adverse Kidney Events within 30 days (MAKE30), the composite of persistent renal dysfunction, new renal replacement therapy (RRT), and in-hospital mortality. Proposed risk factors for the MAKE30 outcome were selected a priori and included age, race, gender, University Health System Consortium (UHC) expected mortality, baseline creatinine, volume of isotonic crystalloid fluid received in the prior 24 h, admission service, intensive care unit (ICU), source of admission, mechanical ventilation or receipt of vasopressors within 24 h of ICU admission, renal replacement therapy prior to ICU admission, acute kidney injury, chronic kidney disease as defined by baseline creatinine value, and renal failure as defined by the Elixhauser index. Among 10,983 patients in the study population, 1489 patients (13.6%) met the MAKE30 endpoint. The strongest independent predictors of MAKE30 were UHC expected mortality (OR 2.32 [95%CI 2.06-2.61]) and presence of acute kidney injury at ICU admission (OR 4.98 [95%CI 4.12-6.03]). The model had strong predictive properties including excellent discrimination with a bootstrap-corrected area-under-the-curve (AUC) of 0.903, and high precision of calibration with a mean absolute error prediction of 1.7%. The MAKE30 composite outcome can be reliably predicted from factors present within 24 h of ICU admission using data derived from the electronic health record.
| S-EPMC5821255 | biostudies-literature
Project description:Use of the electronic health record (EHR) is expected to increase rapidly in the near future, yet little research exists on whether analyzing internal EHR data using flexible, adaptive statistical methods could improve clinical risk prediction. Extensive implementation of EHR in the Veterans Health Administration provides an opportunity for exploration.To compare the performance of various approaches for predicting risk of cerebrovascular and cardiovascular (CCV) death, using traditional risk predictors versus more comprehensive EHR data.Retrospective cohort study. We identified all Veterans Health Administration patients without recent CCV events treated at 12 facilities from 2003 to 2007, and predicted risk using the Framingham risk score, logistic regression, generalized additive modeling, and gradient tree boosting.The outcome was CCV-related death within 5 years. We assessed each method's predictive performance with the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, plots of estimated risk, and reclassification tables, using cross-validation to penalize overfitting.Regression methods outperformed the Framingham risk score, even with the same predictors (AUC increased from 71% to 73% and calibration also improved). Even better performance was attained in models using additional EHR-derived predictor variables (AUC increased to 78% and net reclassification improvement was as large as 0.29). Nonparametric regression further improved calibration and discrimination compared with logistic regression.Despite the EHR lacking some risk factors and its imperfect data quality, health care systems may be able to substantially improve risk prediction for their patients by using internally developed EHR-derived models and flexible statistical methodology.
| S-EPMC4081533 | biostudies-literature
Project description:ObjectiveTo propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events.Materials and methodsThis retrospective study explores the efficacy of machine learning algorithms in classifying clinical deterioration events among patients in intensive care units using sequences of timestamps of vital sign measurements, flowsheets comments, order entries, and nursing notes. We design a data pipeline to partition events into discrete, regular time bins that we refer to as timesteps. Logistic regressions, random forest classifiers, and recurrent neural networks are trained on datasets of different length of timesteps, respectively, against a composite outcome of death, cardiac arrest, and Rapid Response Team calls. Then these models are validated on a holdout dataset.ResultsA total of 6720 intensive care unit encounters meet the criteria and the final dataset includes 830 578 timestamps. The gated recurrent unit model utilizes timestamps of vital signs, order entries, flowsheet comments, and nursing notes to achieve the best performance on the time-to-outcome dataset, with an area under the precision-recall curve of 0.101 (0.06, 0.137), a sensitivity of 0.443, and a positive predictive value of 0. 092 at the threshold of 0.6.Discussion and conclusionThis study demonstrates that our recurrent neural network models using only timestamps of longitudinal electronic health record data that reflect healthcare processes achieve well-performing discriminative power.
| S-EPMC8363809 | biostudies-literature
Project description:RationalePatients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.ObjectivesWe aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.MethodsThis observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).ResultsEleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.ConclusionsA machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.
| S-EPMC6207111 | biostudies-literature