Accuracy of ICD‐9‐CM Codes by Hospital Characteristics and Stroke Severity: Paul Coverdell National Acute Stroke Program

Background Epidemiological and health services research often use International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes to identify patients with clinical conditions in administrative databases. We determined whether there are systematic variations between stroke patient clinical diagnoses and ICD‐9‐CM codes, stratified by hospital characteristics and stroke severity. Methods and Results We used the records of patients discharged from hospitals participating in the Paul Coverdell National Acute Stroke Program in 2013. Within this stroke‐enriched cohort, we compared agreement between the attending physician's clinical diagnosis and principal ICD‐9‐CM code and determined whether disagreements varied by hospital characteristics (presence of a stroke unit, stroke team, number of hospital beds, and hospital location). For patients with a documented National Institutes of Health Stroke Scale score at admission, we assessed whether diagnostic agreement varied by stroke severity. Agreement was generally high (>89%); differences between the physician diagnosis and ICD‐9‐CM codes were primarily attributed to discordance between ischemic stroke and transient ischemic attack (TIA), and subarachnoid and intracerebral hemorrhage. Agreement was higher for patients in metropolitan hospitals with stroke units, stroke teams, and >200 beds (all P<0.001). Agreement was lowest (60.3%) for rural hospitals with ≤200 beds and without stroke units or teams. Agreement was also lower for milder (94.9%) versus more‐severe (96.4%) ischemic strokes (P<0.001). Conclusions We identified disagreements in stroke/TIA coding by hospital characteristics and stroke severity, particularly for milder ischemic strokes. Such systematic variations in ICD‐9‐CM coding practices can affect stroke case identification in epidemiological studies and may have implications for hospital‐level quality metrics.

[1]  Eric E. Smith,et al.  Data quality in the American Heart Association Get With The Guidelines-Stroke (GWTG-Stroke): results from a national data validation audit. , 2012, American heart journal.

[2]  Louette R. Johnson Lutjens.  Research , 2006 .

[3]  Use of a registry to improve acute stroke care--seven states, 2005-2009. , 2011, MMWR. Morbidity and mortality weekly report.

[4]  J. Whisnant,et al.  Accuracy of Hospital Discharge Abstracts for Identifying Stroke , 1994, Stroke.

[5]  Mark D. Huffman,et al.  AHA Statistical Update Heart Disease and Stroke Statistics — 2012 Update A Report From the American Heart Association WRITING GROUP MEMBERS , 2010 .

[6]  Mary G. George,et al.  An Updated Definition of Stroke for the 21st Century: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2013, Stroke.

[7]  Sebastian Schneeweiss,et al.  Validation of claims‐based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially‐insured population , 2010, Pharmacoepidemiology and drug safety.

[8]  J. Broderick,et al.  The Greater Cincinnati/Northern Kentucky Stroke Study: preliminary first-ever and total incidence rates of stroke among blacks. , 1998, Stroke.

[9]  Eric E. Smith,et al.  Risk Adjustment of Ischemic Stroke Outcomes for Comparing Hospital Performance: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2014, Stroke.

[10]  W. Longstreth,et al.  Validating Administrative Data in Stroke Research , 2002, Stroke.

[11]  Joseph P. Broderick,et al.  Relationship of National Institutes of Health Stroke Scale to 30-Day Mortality in Medicare Beneficiaries With Acute Ischemic Stroke , 2012, Journal of the American Heart Association.

[12]  Stephen Prudhomme,et al.  Revised and Updated Recommendations for the Establishment of Primary Stroke Centers: A Summary Statement From the Brain Attack Coalition , 2011, Stroke.

[13]  W. Rosamond,et al.  Validity of Hospital Discharge Diagnosis Codes for Stroke: The Atherosclerosis Risk in Communities Study , 2014, Stroke.

[14]  Nimesh Patel,et al.  Surveillance for violent deaths--national violent death reporting system, 16 States, 2006. , 2009, Morbidity and mortality weekly report. Surveillance summaries.

[15]  W. Winkelmayer,et al.  Comparison of Medicare Claims Versus Physician Adjudication for Identifying Stroke Outcomes in the Women’s Health Initiative , 2014, Stroke.

[16]  P. Duncan,et al.  Inaccuracy of the International Classification of Diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease , 1997, Neurology.

[17]  B. Kissela,et al.  Validity of Claims-Based Stroke Algorithms in Contemporary Medicare Data: Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study Linked With Medicare Claims , 2014, Circulation. Cardiovascular quality and outcomes.

[18]  C. Kooperberg,et al.  Comparison of self-report, hospital discharge codes, and adjudication of cardiovascular events in the Women's Health Initiative. , 2004, American journal of epidemiology.

[19]  Jordi Castellsague,et al.  Validation of ICD‐9 codes with a high positive predictive value for incident strokes resulting in hospitalization using Medicaid health data , 2008, Pharmacoepidemiology and drug safety.

[20]  Xin Tong,et al.  Paul Coverdell National Acute Stroke Registry Surveillance - four states, 2005-2007. , 2009, Morbidity and mortality weekly report. Surveillance summaries.

[21]  H. Ellekjær,et al.  Identification of incident stroke in Norway: hospital discharge data compared with a population-based stroke register. , 1999, Stroke.

[22]  Ugo Fedeli,et al.  Measuring Accuracy of Discharge Diagnoses for a Region-Wide Surveillance of Hospitalized Strokes , 2005, Stroke.

[23]  E. Fisher,et al.  The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. , 1992, American journal of public health.

[24]  Areej Al-Wabil,et al.  Comparison of active and passive surveillance for cerebrovascular disease: The Brain Attack Surveillance in Corpus Christi (BASIC) Project. , 2002, American journal of epidemiology.

[25]  J. Stevens,et al.  The Atherosclerosis Risk in Communities Study , 2013 .

[26]  B. Gage,et al.  Accuracy of ICD-9-CM Codes for Identifying Cardiovascular and Stroke Risk Factors , 2005, Medical care.

[27]  J. Spinelli,et al.  Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? , 2000, Journal of clinical epidemiology.

[28]  L. Goldstein.  Accuracy of ICD-9-CM coding for the identification of patients with acute ischemic stroke: effect of modifier codes. , 1998, Stroke.