Validation of ICD‐9 codes with a high positive predictive value for incident strokes resulting in hospitalization using Medicaid health data

Roumie, Christianne L.; Mitchel, Edward F.; Gideon, Patricia; Varas‐Lorenzo, Cristina; Castellsagué, Jordi; Griffin, Marie R.

doi:10.1002/pds.1518

Cited by 176 publications

(163 citation statements)

References 21 publications

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“…The secondary outcomes were hospitalization for AMI, hospitalization for stroke, hospitalization for unstable angina, coronary revascularization, and a composite of all outcomes together, including hHF, during the follow-up period (8)(9)(10). The secondary outcomes were separately measured and analyzed as time-to-event variables, with times to each event being measured independently from one another, regardless of the occurrence of other events.…”

Section: Measurement Of Study Outcomesmentioning

confidence: 99%

Association Between Hospitalization for Heart Failure and Dipeptidyl Peptidase 4 Inhibitors in Patients With Type 2 Diabetes: An Observational Study

Johnston

Ghannam

et al. 2016

Diabetes Care

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OBJECTIVETo examine, among patients with type 2 diabetes, the association between hospitalization for heart failure (hHF) and treatment with dipeptidyl peptidase 4 inhibitors (DPP-4is) versus sulfonylureas (SUs), and treatment with saxagliptin versus sitagliptin. RESEARCH DESIGN AND METHODSThis was a retrospective, observational study using a U.S. insurance claims database. Patients initiated treatment between 1 August 2010 and 30 August 2013, and had no use of the comparator treatments in the prior 12 months (baseline). Each comparison consisted of patients matched 1:1 on a propensity score. Time to each outcome was compared between matched groups using Cox models. Analyses were stratified by the presence of baseline cardiovascular disease (CVD). Secondary analyses examined associations between comparator treatments and other selected cardiovascular events. RESULTSAfter matching, the study included 218,556 patients in comparisons of DPP-4i and SU, and 112,888 in comparisons of saxagliptin and sitagliptin. The hazard ratios (HRs) of hHF were as follows: DPP-4i versus SU (reference): HR 0.95 (95% CI 0.78-1.15), P = 0.580 for patients with baseline CVD; HR 0.59 (95% CI 0.38-0.89), P = 0.013 for patients without baseline CVD; saxagliptin versus sitagliptin (reference): HR 0.95 (95% CI 0.70-1.28), P = 0.712 for patients with baseline CVD; HR 0.99 (95% CI 0.56-1.75), P = 0.972 for patients without baseline CVD. Comparisons of the individual secondary and composite cardiovascular outcomes followed a similar pattern. CONCLUSIONSIn patients with type 2 diabetes, there was no association between hHF, or other selected cardiovascular outcomes, and treatment with a DPP-4i relative to SU or treatment with saxagliptin relative to sitagliptin.

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Section: Measurement Of Study Outcomesmentioning

confidence: 99%

Association Between Hospitalization for Heart Failure and Dipeptidyl Peptidase 4 Inhibitors in Patients With Type 2 Diabetes: An Observational Study

Johnston

Ghannam

et al. 2016

Diabetes Care

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“…Disease surveillance and quality‐of‐care studies frequently use information from administrative claims databases, such as Medicare, by identifying patients with specific conditions based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes. Studies comparing the accuracy of ICD‐9‐CM codes with medical record review find variations in agreement depending on code selection,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 as well as patient characteristics, such as age, race/ethnicity,10, 11 sex,11 and length of hospitalization 3. Aside from teaching hospital status10 and hospital departments,16 potential variations in accuracy based on other hospital characteristics are largely unknown.…”

Section: Introductionmentioning

confidence: 99%

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

Chang

Lichtman

Goldstein

et al. 2016

JAHA

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BackgroundEpidemiological 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 ResultsWe 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).ConclusionsWe 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.

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“…Subjects hospitalized for ischemic stroke were identified using valid ICD-9 codes (ICD-9-CM codes 433.x1, 434.xx, 436.xx as the primary diagnosis). 21,22 For those with multiple hospitalizations for ischemic stroke, the first hospitalization was used. To capture incident stroke events, individuals who had a diagnosis of ischemic stroke during a medical visit/stay before the index hospitalization were excluded (n 5 19).…”

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confidence: 99%

Acute infection contributes to racial disparities in stroke mortality

2014

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Objective: It is unknown whether racial differences in exposure to acute precipitants of stroke, specifically infection, contribute to racial disparities in stroke mortality.Methods: Among participants in the nationally representative Health and Retirement Study with linked Medicare data (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007), we conducted a case-crossover study employing withinperson comparisons to study racial/ethnic differences in the risks of death and hospitalization from ischemic stroke following acute infection.Results: There were 964 adults hospitalized for ischemic stroke. Acute infection increased the 30-day risks of ischemic stroke death (5.82-fold) and ischemic stroke hospitalization (1.87-fold). Acute infection was a more potent trigger of acute ischemic stroke death in non-Hispanic blacks (odds ratio [OR] 39.21; 95% confidence interval [CI] 9.26-166.00) than in nonHispanic whites (OR 4.50; 95% CI 3.14-6.44) or Hispanics (OR 5.18; 95% CI 1.34-19.95) (race-by-stroke interaction, p 5 0.005). When adjusted for atrial fibrillation, infection remained more strongly associated with stroke mortality in blacks (OR 34.85) than in whites (OR 3.58) and Hispanics (OR 3.53). Acute infection increased the short-term risk of incident stroke similarly across racial/ethnic groups. Infection occurred often before stroke death in non-Hispanic blacks, with 70% experiencing an infection in the 30 days before stroke death compared to a background frequency of 15%.Conclusions: Acute infection disproportionately increases the risk of stroke death for nonHispanic blacks, independently of atrial fibrillation. Stroke incidence did not explain this finding. Acute infection appears to be one factor that contributes to the black-white disparity in stroke mortality. Significant racial disparities in stroke mortality persist in the United States and are widening. [1][2][3] Non-Hispanic blacks have a greater risk of dying from stroke than non-Hispanic whites, and the excess risk of stroke death increased 55% for men and 26% for women from 1979 to 2006. 2While much attention has focused on the role of differences in vascular risk factors or health behaviors in these racial disparities, 2,4 less attention has focused on whether racial differences in exposure to acute precipitants of stroke, so-called triggers, might contribute to racial differences in stroke mortality.Acute infection may precipitate stroke. 5 However, confounding by vascular risk factors or social class may bias estimates, leading to false-positive associations between infection and stroke.6 Given that randomized controlled trials of infection precipitating stroke are unfeasible, observational study designs that minimize confounding, such as a case-crossover study in which each person serves as his or her own control, can be used.7-9 Two case-crossover studies 10,11 suggest that acute infection is a trigger of incident stroke, yet another does not. 12From the

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Validation of ICD‐9 codes with a high positive predictive value for incident strokes resulting in hospitalization using Medicaid health data

Cited by 176 publications

References 21 publications

Association Between Hospitalization for Heart Failure and Dipeptidyl Peptidase 4 Inhibitors in Patients With Type 2 Diabetes: An Observational Study

Association Between Hospitalization for Heart Failure and Dipeptidyl Peptidase 4 Inhibitors in Patients With Type 2 Diabetes: An Observational Study

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

Acute infection contributes to racial disparities in stroke mortality

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