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Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application.

ABSTRACT: BACKGROUND:Systematic identification of patients allows researchers and clinicians to test new models of care delivery. EHR phenotypes-structured algorithms based on clinical indicators from EHRs-can aid in such identification. OBJECTIVE:To develop EHR phenotypes to identify decedents with stage 4 solid-tumor cancer or stage 4-5 chronic kidney disease (CKD). DESIGN:We developed two EHR phenotypes. Each phenotype included International Classification of Diseases (ICD)-9 and ICD-10 codes. We used natural language processing (NLP) to further specify stage 4 cancer, and lab values for CKD. SUBJECTS:Decedents with cancer or CKD who had been admitted to an academic medical center in the last 6 months of life and died August 26, 2017-December 31, 2017. MAIN MEASURE:We calculated positive predictive values (PPV), false discovery rates (FDR), false negative rates (FNR), and sensitivity. Phenotypes were validated by a comparison with manual chart review. We also compared the EHR phenotype results to those admitted to the oncology and nephrology inpatient services. KEY RESULTS:The EHR phenotypes identified 271 decedents with cancer, of whom 186 had stage 4 disease; of 192 decedents with CKD, 89 had stage 4-5 disease. The EHR phenotype for stage 4 cancer had a PPV of 68.6%, FDR of 31.4%, FNR of 0.5%, and 99.5% sensitivity. The EHR phenotype for stage 4-5 CKD had a PPV of 46.4%, FDR of 53.7%, FNR of 0.0%, and 100% sensitivity. CONCLUSIONS:EHR phenotypes efficiently identified patients who died with late-stage cancer or CKD. Future EHR phenotypes can prioritize specificity over sensitivity, and incorporate stratification of high- and low-palliative care need. EHR phenotypes are a promising method for identifying patients for research and clinical purposes, including equitable distribution of specialty palliative care.

SUBMITTER: Ernecoff NC

PROVIDER: S-EPMC6854193 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application.

Ernecoff Natalie C NC Wessell Kathryn L KL Hanson Laura C LC Lee Adam M AM Shea Christopher M CM Dusetzina Stacie B SB Weinberger Morris M Bennett Antonia V AV

Journal of general internal medicine 20190808 12

<h4>Background</h4>Systematic identification of patients allows researchers and clinicians to test new models of care delivery. EHR phenotypes-structured algorithms based on clinical indicators from EHRs-can aid in such identification.<h4>Objective</h4>To develop EHR phenotypes to identify decedents with stage 4 solid-tumor cancer or stage 4-5 chronic kidney disease (CKD).<h4>Design</h4>We developed two EHR phenotypes. Each phenotype included International Classification of Diseases (ICD)-9 and ...[more]

PMID: 31396813

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Project description:OBJECTIVE:Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. DESIGN:We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. SETTING:These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. PARTICIPANTS:The codelist was used to estimate the frequency of shortness of breath in a cohort of 28?216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). RESULTS:Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13?091 episodes of shortness of breath were identified in the cohort of 28?216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. CONCLUSIONS:Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

Dataset Information

Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application.

Publications

Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application.

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