TY - JOUR
T1 - Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records
AU - Singh, Balwinder
AU - Singh, Amandeep
AU - Ahmed, Adil
AU - Wilson, Gregory A.
AU - Pickering, Brian W.
AU - Herasevich, Vitaly
AU - Gajic, Ognjen
AU - Li, Guangxi
N1 - Funding Information:
Grant Support: This work was supported by the National Institutes of Health grant RC1 LM10468Z-01 .
PY - 2012/9
Y1 - 2012/9
N2 - 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.
AB - 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.
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U2 - 10.1016/j.mayocp.2012.04.015
DO - 10.1016/j.mayocp.2012.04.015
M3 - Article
C2 - 22958988
AN - SCOPUS:84866398337
VL - 87
SP - 817
EP - 824
JO - Mayo Clinic Proceedings
JF - Mayo Clinic Proceedings
SN - 0025-6196
IS - 9
ER -