To conduct a systematic review of studies reporting on the validity of International Classification of Diseases (ICD) codes for identifying stroke in administrative data. MEDLINE and EMBASE were searched (inception to February 2015) for studies: (a) Using administrative data to identify stroke; or (b) Evaluating the validity of stroke codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or Kappa scores) for stroke, or data sufficient for their calculation. Additional articles were located by hand search (up to February 2015) of original papers. Studies solely evaluating codes for transient ischaemic attack were excluded. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Seventy-seven studies published from 1976–2015 were included. The sensitivity of ICD-9 430-438/ICD-10 I60-I69 for any cerebrovascular disease was ≥ 82% in most [≥ 50%] studies, and specificity and NPV were both ≥ 95%. The PPV of these codes for any cerebrovascular disease was ≥ 81% in most studies, while the PPV specifically for acute stroke was ≤ 68%. In at least 50% of studies, PPVs were ≥ 93% for subarachnoid haemorrhage (ICD-9 430/ICD-10 I60), 89% for intracerebral haemorrhage (ICD-9 431/ICD-10 I61), and 82% for ischaemic stroke (ICD-9 434/ICD-10 I63 or ICD-9 434&436). For in-hospital deaths, sensitivity was 55%. For cerebrovascular disease or acute stroke as a cause-of-death on death certificates, sensitivity was ≤ 71% in most studies while PPV was ≥ 87%. While most cases of prevalent cerebrovascular disease can be detected using 430-438/I60-I69 collectively, acute stroke must be defined using more specific codes. Most in-hospital deaths and death certificates with stroke as a cause-of-death correspond to true stroke deaths. Linking vital statistics and hospitalization data may improve the ascertainment of fatal stroke.

Though administrative databases are increasingly being used for research related to myocardial infarction (MI), the validity of MI diagnoses in these databases has never been synthesized on a large scale. To conduct the first systematic review of studies reporting on the validity of diagnostic codes for identifying MI in administrative data. MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify MI; or (b) Evaluating the validity of MI codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value (PPV), negative predictive value, or Kappa scores) for MI, or data sufficient for their calculation. Additonal articles were located by handsearch (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Thirty studies published from 1984–2010 were included; most assessed codes from the International Classification of Diseases (ICD)-9^th revision. Sensitivity and specificity of hospitalization data for identifying MI in most [≥50%] studies was ≥86%, and PPV in most studies was ≥93%. The PPV was higher in the more-recent studies, and lower when criteria that do not incorporate cardiac troponin levels (such as the MONICA) were employed as the gold standard. MI as a cause-of-death on death certificates also demonstrated lower accuracy, with maximum PPV of 60% (for definite MI). Hospitalization data has higher validity and hence can be used to identify MI, but the accuracy of MI as a cause-of-death on death certificates is suboptimal, and more studies are needed on the validity of ICD-10 codes. When using administrative data for research purposes, authors should recognize these factors and avoid using vital statistics data if hospitalization data is not available to confirm deaths from MI.

Heart failure (HF) is an important covariate and outcome in studies of elderly populations and cardiovascular disease cohorts, among others. Administrative data is increasingly being used for long-term clinical research in these populations. We aimed to conduct the first systematic review and meta-analysis of studies reporting on the validity of diagnostic codes for identifying HF in administrative data. MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify HF; or (b) Evaluating the validity of HF codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value [PPV], negative predictive value, or Kappa scores) for HF, or data sufficient for their calculation. Additional articles were located by hand search (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Using a random-effects model, pooled sensitivity and specificity values were produced, along with estimates of the positive (LR+) and negative (LR−) likelihood ratios, and diagnostic odds ratios (DOR = LR+/LR−) of HF codes. Nineteen studies published from1999–2009 were included in the qualitative review. Specificity was ≥95% in all studies and PPV was ≥87% in the majority, but sensitivity was lower (≥69% in ≥50% of studies). In a meta-analysis of the 11 studies reporting sensitivity and specificity values, the pooled sensitivity was 75.3% (95% CI: 74.7–75.9) and specificity was 96.8% (95% CI: 96.8–96.9). The pooled LR+ was 51.9 (20.5–131.6), the LR− was 0.27 (0.20–0.37), and the DOR was 186.5 (96.8–359.2). While most HF diagnoses in administrative databases do correspond to true HF cases, about one-quarter of HF cases are not captured. The use of broader search parameters, along with laboratory and prescription medication data, may help identify more cases.

Objective: Patients with systemic autoimmune rheumatic diseases (ARDs) continue to be concerned about risks of severe coronavirus disease 2019 (COVID‐19) outcomes. This study was undertaken to evaluate the risks of severe outcomes in COVID‐19 patients with systemic ARDs compared to COVID‐19 patients without systemic ARDs.Methods: Using a large multicenter electronic health record network, we conducted a comparative cohort study of patients with systemic ARDs diagnosed as having COVID‐19 (identified by diagnostic code or positive molecular test result) compared to patients with COVID‐19 who did not have systemic ARDs, matched for age, sex, race/ethnicity, and body mass index (primary matched model) and additionally matched for comorbidities and health care utilization (extended matched model). Thirty‐day outcomes were assessed, including hospitalization, intensive care unit (ICU) admission, mechanical ventilation, acute renal failure requiring renal replacement therapy, ischemic stroke, venous thromboembolism, and death.Results: We initially identified 2,379 COVID‐19 patients with systemic ARDs (mean age 58 years; 79% female) and 142,750 comparators (mean age 47 years; 54% female). In the primary matched model (2,379 patients with systemic ARDs and 2,379 matched comparators with COVID‐19 without systemic ARDs), patients with systemic ARDs had a significantly higher risk of hospitalization (relative risk [RR] 1.14 [95% confidence interval (95% CI) 1.03–1.26]), ICU admission (RR 1.32 [95% CI 1.03–1.68]), acute renal failure (RR 1.81 [95% CI 1.07–3.07]), and venous thromboembolism (RR 1.74 [95% CI 1.23–2.45]) versus comparators but did not have a significantly higher risk of mechanical ventilation or death. In the extended model, all risks were largely attenuated, except for the risk of venous thromboembolism (RR 1.60 [95% CI 1.14–2.25]).Conclusion: Our findings indicate that COVID‐19 patients with systemic ARDs may be at a higher risk of hospitalization, ICU admission, acute renal failure, and venous thromboembolism when compared to COVID‐19 patients without systemic ARDs. These risks may be largely mediated by comorbidities, except for the risk of venous thromboembolism.