![]() ![]() This sample of non-cases was used as control for each of the three conditions. From this cohort of non-cases, we extracted a random sample of 80 patients. At the same time, we identified a cohort of “non-cases”, that is patients who had been discharged in the same period of time, also with a diagnosis of cardiovascular disease (ICD-9 codes 390–459), but other than AMI, AF and flutter, and HF. From each cohort, we extracted a random sample of 130 cases (see Statistical Methods for details). Repeated hospital admissions were also excluded. patients discharged from hospitals with the same diagnosis in the five years before. We excluded estimated prevalent cases, i.e. According to Italian legislation, the primary diagnosis constitutes the main cause of the need for treatment and/or diagnostic tests, and is mainly responsible for the use of resources. In addition, they can contribute to identifying the risk factors in the development of cardiovascular diseases as well as the outcomes, including mortality, that they can determine.Īccording to our published protocol, the objective of the present study was to evaluate the accuracy of the ICD-9-CM codes related to AMI, AF and flutter, and HF in the administrative database of the Regional Health Authority of Umbria.įrom the entire discharge abstract database of Umbria we identified three cohorts of “cases”, that is patients having the ICD-9 codes located in primary position of acute myocardial infarction (ICD-9 codes 410.x), atrial fibrillation (code 427.31) and flutter (code 427.32), and heart failure (codes 428.x), between 20. Administrative databases are significant tools that can provide the best assessment of the incidence, prevalence and general prognosis of cardiovascular diseases. 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.Īcute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) are the most common cardiovascular diseases in developed countries and they are the leading cause of morbidity and mortality, thereby representing a major social and economic problem. 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. This diagnosis is coded according to the International Classification of Diseases (ICD) which is a standardized diagnostic tool planned to map health conditions. 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. The continuous collection of demographic data together with diagnosis, therapeutic interventions as well as prescription information makes these databases attractive for comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. These databases are organized and maintained at different administrative levels including hospitals, local health units, and at regional level. ![]() Francesco Cozzolino, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, 1, 2 Alessandro Montedori, Conceptualization, Funding acquisition, Project administration, Resources, Software, Supervision, Validation, Writing – review & editing, 1 Iosief Abraha, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing, 1, 3, * Paolo Eusebi, Formal analysis, Writing – review & editing, 1 Chiara Grisci, Resources, Writing – review & editing, 4 Anna Julia Heymann, Resources, Visualization, Writing – review & editing, 5 Guido Lombardo, Writing – review & editing, 6 Anna Mengoni, Investigation, Writing – review & editing, 2 Massimiliano Orso, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, 1, 2 and Giuseppe Ambrosio, Conceptualization, Funding acquisition, Project administration, Software, Supervision, Validation, Writing – original draft, Writing – review & editing 2Īdministrative databases are considerable data repositories that are increasingly being used within healthcare systems. ![]()
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