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

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.

[1]  Ann Kutney-Lee,et al.  The Effect of Hospital Electronic Health Record Adoption on Nurse-Assessed Quality of Care and Patient Safety , 2011, The Journal of nursing administration.

[2]  D. Pittet,et al.  Importance of pre-existing co-morbidities for prognosis of septicemia in critically ill patients , 1993, Intensive Care Medicine.

[3]  Jonathan A. Zlabek,et al.  Early cost and safety benefits of an inpatient electronic health record , 2011, J. Am. Medical Informatics Assoc..

[4]  A S Kosinski,et al.  A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. , 1999, Journal of clinical epidemiology.

[5]  J. Jollis,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. , 1993, Journal of clinical epidemiology.

[6]  V. Herasevich,et al.  Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury. , 2011, Mayo Clinic proceedings.

[7]  Shiao-Chi Wu,et al.  Open Access Research Article Comparison of Different Comorbidity Measures for Use with Administrative Data in Predicting Short- and Long-term Mortality , 2022 .

[8]  Robert A. Weinstein,et al.  Application of Information Technology: Development of a Clinical Data Warehouse for Hospital Infection Control , 2003, J. Am. Medical Informatics Assoc..

[9]  E. Hing,et al.  Electronic health record systems and intent to apply for meaningful use incentives among office-based physician practices: United States, 2001-2011. , 2011, NCHS data brief.

[10]  L I Iezzoni,et al.  Does clinical evidence support ICD-9-CM diagnosis coding of complications? , 2000, Medical care.

[11]  D. Blumenthal,et al.  The "meaningful use" regulation for electronic health records. , 2010, The New England journal of medicine.

[12]  R. Heller,et al.  Accuracy of administrative data to assess comorbidity in patients with heart disease. an Australian perspective. , 2001, Journal of clinical epidemiology.

[13]  L. Iezzoni.  Risk adjustment for medical effectiveness research: an overview of conceptual and methodological considerations. , 1995, Journal of investigative medicine : the official publication of the American Federation for Clinical Research.

[14]  B. Doebbeling,et al.  Prognostic factors for mortality following interhospital transfers to the medical intensive care unit of a tertiary referral center , 2003, Critical care medicine.

[15]  L. Bouter,et al.  How to measure comorbidity. a critical review of available methods. , 2003, Journal of clinical epidemiology.

[16]  James J. Cimino,et al.  Reliability of SNOMED-CT Coding by Three Physicians using Two Terminology Browsers , 2006, AMIA.

[17]  D McLerran,et al.  Using administrative data to describe casemix: a comparison with the medical record. , 1994, Journal of clinical epidemiology.

[18]  Vitaly Herasevich,et al.  Epidemiology of critical care syndromes, organ failures, and life-support interventions in a suburban US community. , 2011, Chest.

[19]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.

[20]  David Aron,et al.  Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes. , 2006, Health services research.

[21]  M. Cleves,et al.  Evaluation of two competing methods for calculating Charlson's comorbidity index when analyzing short-term mortality using administrative data. , 1997, Journal of clinical epidemiology.

[22]  Hude Quan,et al.  Cross-National Comparative Performance of Three Versions of the ICD-10 Charlson Index , 2007, Medical care.

[23]  E. Fisher,et al.  Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? , 1992, JAMA.

[24]  R. Bays,et al.  Electronic medical records for the office. , 2010, Journal of vascular surgery.

[25]  J. Jollis,et al.  A comparison of administrative versus clinical data: coronary artery bypass surgery as an example. Ischemic Heart Disease Patient Outcomes Research Team. , 1994, Journal of clinical epidemiology.

[26]  H. Quan,et al.  Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data , 2005, Medical care.

[27]  A R Feinstein,et al.  THE PRE-THERAPEUTIC CLASSIFICATION OF CO-MORBIDITY IN CHRONIC DISEASE. , 1970, Journal of chronic diseases.

[28]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[29]  V. Herasevich,et al.  The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance* , 2011, Critical care medicine.

[30]  Peter J Pronovost,et al.  A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk adjustment in critical care research. , 2005, Journal of critical care.

[31]  S. Keenan,et al.  Intensive care unit admission has minimal impact on long-term mortality. , 2002, Critical care medicine.

[32]  A Bouckaert,et al.  Practical considerations on the use of the Charlson comorbidity index with administrative data bases. , 1996, Journal of clinical epidemiology.

[33]  Steven H. Brown,et al.  Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists. , 2006, Mayo Clinic proceedings.

[34]  C. Bombardier,et al.  Accuracy of administrative data for assessing outcomes after knee replacement surgery. , 1997, Journal of clinical epidemiology.

[35]  S. Trent Rosenbloom,et al.  Direct Comparison of MEDCIN® and SNOMED CT® for Representation of a General Medical Evaluation Template , 2007, AMIA.

[36]  Matthew Knuiman,et al.  Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model , 2008, PloS one.

[37]  D. Wagner,et al.  Impact of Different Measures of Comorbid Disease on Predicted Mortality of Intensive Care Unit Patients , 2002, Medical care.

[38]  R M Poses,et al.  Prediction of survival of critically ill patients by admission comorbidity. , 1996, Journal of clinical epidemiology.

[39]  Hude Quan,et al.  Validity of Information on Comorbidity Derived From ICD-9-CCM Administrative Data , 2002, Medical care.

[40]  Bing Li,et al.  Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. , 2011, American journal of epidemiology.

[41]  C. Mullins,et al.  Resources and use of the intensive care unit in patients who undergo surgery for ovarian carcinoma , 2002, Cancer.

[42]  S. Lemeshow,et al.  Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care , 2011, Clinical epidemiology.

[43]  D. C. Brewster,et al.  Guidelines for the treatment of abdominal aortic aneurysms. Report of a subcommittee of the Joint Council of the American Association for Vascular Surgery and Society for Vascular Surgery. , 2003, Journal of vascular surgery.