Abstract
BackgroundAdministrative healthcare databases are useful and inexpensive tools that can provide a comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. However, a crucial issue is the reliability of information gathered. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research.MethodsData from the centralised administrative database of the Umbria Region (890,000 residents, located in Central Italy) were considered. Patients with a first hospital discharge for AMI, AF/flutter, and HF, between 2012 and 2014, were identified using ICD-9-CM codes in primary position. A sample of cases and non-cases was randomly selected, and the corresponding medical charts reviewed by specifically trained investigators. For each disease, case ascertainment was based on all clinical, laboratory, and instrumental examinations available in medical charts. Sensitivity, specificity, and predictive values with 95% confidence intervals (CIs), were calculated.ResultsWe reviewed 458 medical charts, 128 for AMI, 127 for AF/flutter, 127 for HF, and 76 of non-cases for each condition. Diagnostic accuracy measures of the original discharge diagnosis were as follows. AMI: sensitivity 98% (95% CI, 94–100%), specificity 91% (95% CI, 83–97%), positive predictive value (PPV) 95% (95% CI, 89–98%), negative predictive value (NPV) 97% (95% CI, 91–100%). AF/flutter: sensitivity 95% (95% CI, 90–98%), specificity 95% (95% CI, 87–99%), PPV 97% (95% CI, 92–99%), NPV 92% (95% CI, 84–97%). HF: sensitivity 96% (95% CI, 91–99%), specificity 90% (95% CI, 81–96%), PPV 94% (95% CI, 88–97%), NPV 93% (95% CI, 85–98%).ConclusionThe case ascertainment for AMI, AF and flutter, and HF, showed a high level of accuracy (≥ 90%). The healthcare administrative database of the Umbria Region can be confidently used for epidemiological, outcome, and health services research.
Highlights
Administrative databases are considerable data repositories that are increasingly being used within healthcare systems[1, 2]
From the entire discharge abstract database of Umbria we identified three cohorts of “cases”, that is patients having the International Classification of Diseases (ICD)-9 codes located in primary position of acute myocardial infarction (ICD-9 codes 410.x), atrial fibrillation and flutter, and heart failure, between 2012 and 2014
We identified a cohort of “non-cases”, that is patients who had been discharged in the same period of time, with a diagnosis of cardiovascular disease (ICD-9 codes 390– 459), but other than acute myocardial infarction (AMI), atrial fibrillation/flutter (AF) and flutter, and heart failure (HF)
Summary
Administrative databases are considerable data repositories that are increasingly being used within healthcare systems[1, 2]. In addition to maintaining a rigorous anonymity of patient’s demographic, the most relevant data that makes these healthcare databases interesting for research purposes is the diagnosis provided to the patient at hospital discharge This diagnosis is coded according to the International Classification of Diseases (ICD) which is a standardized diagnostic tool planned to map health conditions. When individual patient data are linked with other data (prescription and laboratory data) it is possible to explore a wide range of clinical issues, research questions as well as quality performance evaluations To reach this target, administrative databases need to be validated, which means the diagnoses that correspond to the ICD-9 code need to be ascertained according to a defined disease criteria by consulting a reference standard which is usually the medical chart[1, 3, 4]. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research
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