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Yan S, Melnick K, He X, Lyu T, Moor RSF, Still MEH, Mitchell DA, Shenkman EA, Wang H, Guo Y, Bian J, Ghiaseddin AP. Developing a computable phenotype for glioblastoma. Neuro Oncol 2023:noad249. [PMID: 38141226 DOI: 10.1093/neuonc/noad249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Indexed: 12/25/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are non-specific. The aim of this study was to create a computable phenotype (CP) for GBM from structured and unstructured data to identify patients with this condition in a large electronic health record (EHR). METHODS We used the UF Health Integrated Data Repository, a centralized clinical data warehouse that stores clinical and research data from various sources within the UF Health system, including the EHR system. We performed multiple iterations to refine the GBM-relevant diagnosis codes, procedure codes, medication codes, and keywords through manual chart review of patient data. We then evaluated the performances of various possible proposed CPs constructed from the relevant codes and keywords. RESULTS We underwent six rounds of manual chart reviews to refine the CP elements. The final CP algorithm for identifying GBM patients was selected based on the best F1-score. Overall, the CP rule "if the patient had at least 1 relevant diagnosis code and at least 1 relevant keyword" demonstrated the highest F1-score using both structured and unstructured data. Thus, it was selected as the best-performing CP rule. CONCLUSIONS We developed a CP algorithm for identifying patients with GBM using both structured and unstructured EHR data from a large tertiary care center. The final algorithm achieved an F1-score of 0.817, indicating a high performance which minimizes possible biases from misclassification errors.
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Affiliation(s)
- Sandra Yan
- Department of Neurosurgery, College of Medicine, University of Florida, FL, USA
| | - Kaitlyn Melnick
- Department of Neurosurgery, College of Medicine, University of Florida, FL, USA
| | - Xing He
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, FL, USA
| | - Tianchen Lyu
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, FL, USA
| | - Rachel S F Moor
- Department of Neurosurgery, College of Medicine, University of Florida, FL, USA
| | - Megan E H Still
- Department of Neurosurgery, College of Medicine, University of Florida, FL, USA
| | - Duane A Mitchell
- Department of Neurosurgery, College of Medicine, University of Florida, FL, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, FL, USA
| | - Han Wang
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, FL, USA
| | - Yi Guo
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, FL, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, FL, USA
| | - Ashley P Ghiaseddin
- Department of Neurosurgery, College of Medicine, University of Florida, FL, USA
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He T, Belouali A, Patricoski J, Lehmann H, Ball R, Anagnostou V, Kreimeyer K, Botsis T. Trends and opportunities in computable clinical phenotyping: A scoping review. J Biomed Inform 2023; 140:104335. [PMID: 36933631 DOI: 10.1016/j.jbi.2023.104335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023]
Abstract
Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.
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Affiliation(s)
- Ting He
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Anas Belouali
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jessica Patricoski
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harold Lehmann
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Ball
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Valsamo Anagnostou
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kory Kreimeyer
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Taxiarchis Botsis
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Jun I, Rich SN, Chen Z, Bian J, Prosperi M. Challenges in replicating secondary analysis of electronic health records data with multiple computable phenotypes: A case study on methicillin-resistant Staphylococcus aureus bacteremia infections. Int J Med Inform 2021; 153:104531. [PMID: 34332468 DOI: 10.1016/j.ijmedinf.2021.104531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 06/03/2021] [Accepted: 06/24/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND Replication of prediction modeling using electronic health records (EHR) is challenging because of the necessity to compute phenotypes including study cohort, outcomes, and covariates. However, some phenotypes may not be easily replicated across EHR data sources due to a variety of reasons such as the lack of gold standard definitions and documentation variations across systems, which may lead to measurement error and potential bias. Methicillin-resistant Staphylococcus aureus (MRSA) infections are responsible for high mortality worldwide. With limited treatment options for the infection, the ability to predict MRSA outcome is of interest. However, replicating these MRSA outcome prediction models using EHR data is problematic due to the lack of well-defined computable phenotypes for many of the predictors as well as study inclusion and outcome criteria. OBJECTIVE In this study, we aimed to evaluate a prediction model for 30-day mortality after MRSA bacteremia infection diagnosis with reduced vancomycin susceptibility (MRSA-RVS) considering multiple computable phenotypes using EHR data. METHODS We used EHR data from a large academic health center in the United States to replicate the original study conducted in Taiwan. We derived multiple computable phenotypes of risk factors and predictors used in the original study, reported stratified descriptive statistics, and assessed the performance of the prediction model. RESULTS In our replication study, it was possible to (re)compute most of the original variables. Nevertheless, for certain variables, their computable phenotypes can only be approximated by proxy with structured EHR data items, especially the composite clinical indices such as the Pitt bacteremia score. Even computable phenotype for the outcome variable was subject to variation on the basis of the admission/discharge windows. The replicated prediction model exhibited only a mild discriminatory ability. CONCLUSION Despite the rich information in EHR data, replication of prediction models involving complex predictors is still challenging, often due to the limited availability of validated computable phenotypes. On the other hand, it is often possible to derive proxy computable phenotypes that can be further validated and calibrated.
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Affiliation(s)
- Inyoung Jun
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Shannan N Rich
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Zhaoyi Chen
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
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Afshar M, Sharma B, Bhalla S, Thompson HM, Dligach D, Boley RA, Kishen E, Simmons A, Perticone K, Karnik NS. External validation of an opioid misuse machine learning classifier in hospitalized adult patients. Addict Sci Clin Pract 2021; 16:19. [PMID: 33731210 PMCID: PMC7967783 DOI: 10.1186/s13722-021-00229-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/10/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. METHODS An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. RESULTS Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99-0.99) across the encounter and 0.98 (95% CI 0.98-0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77-0.84) and 0.72 (95% CI 0.68-0.75). For the first 24 h, they were 0.75 (95% CI 0.71-0.78) and 0.61 (95% CI 0.57-0.64). CONCLUSIONS Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.
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Affiliation(s)
- Majid Afshar
- Division of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL, USA.
- Department of Medicine, University of Wisconsin, 1685 Highland Avenue, Madison, WI, 53705, USA.
| | - Brihat Sharma
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Sameer Bhalla
- Rush Medical College, Rush University, Chicago, IL, USA
| | - Hale M Thompson
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Dmitriy Dligach
- Department of Computer Science, Loyola University Chicago, Chicago, IL, USA
| | - Randy A Boley
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Ekta Kishen
- Clinical Research Analytics, Research Core, Rush University Medical Center, Chicago, IL, USA
| | - Alan Simmons
- Clinical Research Analytics, Research Core, Rush University Medical Center, Chicago, IL, USA
| | - Kathryn Perticone
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Niranjan S Karnik
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
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Sharma B, Dligach D, Swope K, Salisbury-Afshar E, Karnik NS, Joyce C, Afshar M. Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients. BMC Med Inform Decis Mak 2020; 20:79. [PMID: 32349766 PMCID: PMC7191715 DOI: 10.1186/s12911-020-1099-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/22/2020] [Indexed: 12/15/2022] Open
Abstract
Background Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier. Methods An observational cohort sampled from adult hospital inpatient encounters at a health system between 2007 and 2017. A case-control stratified sampling (n = 1000) was performed to build an annotated dataset for a reference standard of cases and non-cases of opioid misuse. Models for training and testing included CUI codes, character-based, and n-gram features. Models applied were machine learning with neural network and logistic regression as well as expert consensus with a rule-based model for opioid misuse. The area under the receiver operating characteristic curves (AUROC) were compared between models for discrimination. The Hosmer-Lemeshow test and visual plots measured model fit and calibration. Results Machine learning models with CUI codes performed similarly to n-gram models with PHI. The top performing models with AUROCs > 0.90 included CUI codes as inputs to a convolutional neural network, max pooling network, and logistic regression model. The top calibrated models with the best model fit were the CUI-based convolutional neural network and max pooling network. The top weighted CUI codes in logistic regression has the related terms ‘Heroin’ and ‘Victim of abuse’. Conclusions We demonstrate good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI. Herein we share a PHI-free, trained opioid misuse classifier for other researchers and health systems to use and benchmark to overcome privacy and security concerns.
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Affiliation(s)
- Brihat Sharma
- Department of Computer Science, Loyola University Chicago, Chicago, IL, USA
| | - Dmitriy Dligach
- Department of Computer Science, Loyola University Chicago, Chicago, IL, USA.,Center for Health Outcomes and Informatics Research, Loyola University Chicago, 2160 S. First Avenue, Maywood, IL, 60156, USA
| | - Kristin Swope
- Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Elizabeth Salisbury-Afshar
- Center for Multi-System Solutions to the Opioid Epidemic, American Institute for Research, Chicago, IL, USA
| | - Niranjan S Karnik
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA
| | - Cara Joyce
- Center for Health Outcomes and Informatics Research, Loyola University Chicago, 2160 S. First Avenue, Maywood, IL, 60156, USA.,Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Majid Afshar
- Center for Health Outcomes and Informatics Research, Loyola University Chicago, 2160 S. First Avenue, Maywood, IL, 60156, USA. .,Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL, USA. .,Department of Medicine, Loyola University Medical Center, Maywood, IL, USA.
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Dhungana P, Serafim LP, Ruiz AL, Bruns D, Weister TJ, Smischney NJ, Kashyap R. Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit. World J Crit Care Med 2019; 8:120-126. [PMID: 31853447 PMCID: PMC6918045 DOI: 10.5492/wjccm.v8.i7.120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 08/21/2019] [Accepted: 10/29/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND With the recent change in the definition (Sepsis-3 Definition) of sepsis and septic shock, an electronic search algorithm was required to identify the cases for data automation. This supervised machine learning method would help screen a large amount of electronic medical records (EMR) for efficient research purposes.
AIM To develop and validate a computable phenotype via supervised machine learning method for retrospectively identifying sepsis and septic shock in critical care patients.
METHODS A supervised machine learning method was developed based on culture orders, Sequential Organ Failure Assessment (SOFA) scores, serum lactate levels and vasopressor use in the intensive care units (ICUs). The computable phenotype was derived from a retrospective analysis of a random cohort of 100 patients admitted to the medical ICU. This was then validated in an independent cohort of 100 patients. We compared the results from computable phenotype to a gold standard by manual review of EMR by 2 blinded reviewers. Disagreement was resolved by a critical care clinician. A SOFA score ≥ 2 during the ICU stay with a culture 72 h before or after the time of admission was identified. Sepsis versions as V1 was defined as blood cultures with SOFA ≥ 2 and Sepsis V2 was defined as any culture with SOFA score ≥ 2. A serum lactate level ≥ 2 mmol/L from 24 h before admission till their stay in the ICU and vasopressor use with Sepsis-1 and-2 were identified as Septic Shock-V1 and-V2 respectively.
RESULTS In the derivation subset of 100 random patients, the final machine learning strategy achieved a sensitivity-specificity of 100% and 84% for Sepsis-1, 100% and 95% for Sepsis-2, 78% and 80% for Septic Shock-1, and 80% and 90% for Septic Shock-2. An overall percent of agreement between two blinded reviewers had a k = 0.86 and 0.90 for Sepsis 2 and Septic shock 2 respectively. In validation of the algorithm through a separate 100 random patient subset, the reported sensitivity and specificity for all 4 diagnoses were 100%-100% each.
CONCLUSION Supervised machine learning for identification of sepsis and septic shock is reliable and an efficient alternative to manual chart review.
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Affiliation(s)
- Prabij Dhungana
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Laura Piccolo Serafim
- Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Arnaldo Lopez Ruiz
- Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Danette Bruns
- Anesthesia Clinical Research Unit, Mayo Clinic, MN 55905, United States
| | - Timothy J Weister
- Anesthesia Clinical Research Unit, Mayo Clinic, MN 55905, United States
| | - Nathan Jerome Smischney
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Rahul Kashyap
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
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Fishman E, Barron J, Dinh J, Jones WS, Marshall A, Merkh R, Robertson H, Haynes K. Validation of a claims-based algorithm identifying eligible study subjects in the ADAPTABLE pragmatic clinical trial. Contemp Clin Trials Commun 2018; 12:154-160. [PMID: 30480162 PMCID: PMC6240793 DOI: 10.1016/j.conctc.2018.11.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/29/2018] [Accepted: 11/05/2018] [Indexed: 11/17/2022] Open
Abstract
Objective Validate an algorithm that uses administrative claims data to identify eligible study subjects for the ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness) pragmatic clinical trial (PCT). Materials and methods This study used medical records from a random sample of patients identified as eligible for the ADAPTABLE trial. The inclusion criteria for ADAPTABLE were a history of acute myocardial infarction (AMI) or percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), or other coronary artery disease (CAD), plus at least one of several risk-enrichment factors. Exclusion criteria included a history of bleeding disorders or aspirin allergy. Using a claims-based algorithm, based on International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and 10th Edition (ICD-10) codes and Current Procedural Terminology (CPT) codes, we identified patients eligible for the PCT. The primary outcome was the positive predictive value (PPV) of the identification algorithm: the proportion of sampled patients whose medical records confirmed their ADAPTABLE study eligibility. Exact 95% confidence limits for binomial random variables were calculated for the PPV estimates. Results Of the 185 patients whose medical records were reviewed, 168 (90.8%; 95% Confidence Interval: 85.7%, 94.6%) were confirmed study eligible. This proportion did not differ between patients identified with codes for AMI and patients identified with codes for PCI or CABG. Conclusion The estimated PPV was similar to those in claims-based identification of drug safety surveillance events, indicating that administrative claims data can accurately identify study-eligible subjects for pragmatic clinical trials.
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Affiliation(s)
- Ezra Fishman
- HealthCore, Inc., Wilmington, DE, USA
- Corresponding author. HealthCore, Inc. 123 Justison Street, Suite 200, Wilmington, DE 19801, USA.
| | | | - Jade Dinh
- HealthCore, Inc., Wilmington, DE, USA
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Kukhareva P, Staes C, Noonan KW, Mueller HL, Warner P, Shields DE, Weeks H, Kawamoto K. Single-reviewer electronic phenotyping validation in operational settings: Comparison of strategies and recommendations. J Biomed Inform 2016; 66:1-10. [PMID: 27956265 DOI: 10.1016/j.jbi.2016.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 12/05/2016] [Accepted: 12/08/2016] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Develop evidence-based recommendations for single-reviewer validation of electronic phenotyping results in operational settings. MATERIAL AND METHODS We conducted a randomized controlled study to evaluate whether electronic phenotyping results should be used to support manual chart review during single-reviewer electronic phenotyping validation (N=3104). We evaluated the accuracy, duration and cost of manual chart review with and without the availability of electronic phenotyping results, including relevant patient-specific details. The cost of identification of an erroneous electronic phenotyping result was calculated based on the personnel time required for the initial chart review and subsequent adjudication of discrepancies between manual chart review results and electronic phenotype determinations. RESULTS Providing electronic phenotyping results (vs not providing those results) was associated with improved overall accuracy of manual chart review (98.90% vs 92.46%, p<0.001), decreased review duration per test case (62.43 vs 76.78s, p<0.001), and insignificantly reduced estimated marginal costs of identification of an erroneous electronic phenotyping result ($48.54 vs $63.56, p=0.16). The agreement between chart review and electronic phenotyping results was higher when the phenotyping results were provided (Cohen's kappa 0.98 vs 0.88, p<0.001). As a result, while accuracy improved when initial electronic phenotyping results were correct (99.74% vs 92.67%, N=3049, p<0.001), there was a trend towards decreased accuracy when initial electronic phenotyping results were erroneous (56.67% vs 80.00%, N=55, p=0.07). Electronic phenotyping results provided the greatest benefit for the accurate identification of rare exclusion criteria. DISCUSSION Single-reviewer chart review of electronic phenotyping can be conducted more accurately, quickly, and at lower cost when supported by electronic phenotyping results. However, human reviewers tend to agree with electronic phenotyping results even when those results are wrong. Thus, the value of providing electronic phenotyping results depends on the accuracy of the underlying electronic phenotyping algorithm. CONCLUSION We recommend using a mix of phenotyping validation strategies, with the balance of strategies based on the anticipated electronic phenotyping error rate, the tolerance for missed electronic phenotyping errors, as well as the expertise, cost, and availability of personnel involved in chart review and discrepancy adjudication.
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Affiliation(s)
- Polina Kukhareva
- Department of Biomedical Informatics and Knowledge Management and Mobilization, University of Utah, 421 Wakara Way, Suite #140, Salt Lake City, UT 84108, United States.
| | - Catherine Staes
- Department of Biomedical Informatics and Knowledge Management and Mobilization, University of Utah, 421 Wakara Way, Suite #140, Salt Lake City, UT 84108, United States.
| | - Kevin W Noonan
- University of Utah Medical Group, 127 S. 500 E., Suite #660, Salt Lake City, UT 84102, United States.
| | - Heather L Mueller
- University of Utah Medical Group, 127 S. 500 E., Suite #660, Salt Lake City, UT 84102, United States.
| | - Phillip Warner
- Department of Biomedical Informatics and Knowledge Management and Mobilization, University of Utah, 421 Wakara Way, Suite #140, Salt Lake City, UT 84108, United States.
| | - David E Shields
- Department of Biomedical Informatics and Knowledge Management and Mobilization, University of Utah, 421 Wakara Way, Suite #140, Salt Lake City, UT 84108, United States.
| | - Howard Weeks
- University of Utah Medical Group, 127 S. 500 E., Suite #660, Salt Lake City, UT 84102, United States; Department of Psychiatry, University of Utah, 501 Chipeta Way, Salt Lake City, UT 84108, United States.
| | - Kensaku Kawamoto
- Department of Biomedical Informatics and Knowledge Management and Mobilization, University of Utah, 421 Wakara Way, Suite #140, Salt Lake City, UT 84108, United States.
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