Dataset Information

Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records.

ABSTRACT:

Objective

To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities.

Patients and methods

The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities.

Results

In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS.

Conclusion

Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.

SUBMITTER: Singh B

PROVIDER: S-EPMC3538495 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records.

Singh Balwinder B Singh Amandeep A Ahmed Adil A Wilson Gregory A GA Pickering Brian W BW Herasevich Vitaly V Gajic Ognjen O Li Guangxi G

Mayo Clinic proceedings 20120901 9

<h4>Objective</h4>To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities.<h4>Patients and methods</h4>The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through D ...[more]

PMID: 22958988

Similar Datasets

Project description:BackgroundElectronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.ObjectiveThis review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.MethodsA scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.ResultsA total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule-based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.ConclusionsRecognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Dataset Information

Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records.

Objective

Patients and methods

Results

Conclusion

Publications

Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records.

Similar Datasets

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