Monique F Kilkenny, Lachlan L Dalli, Ailie Sanders, Muideen T Olaiya, Joosup Kim, David Ung, Nadine E Andrew
{"title":"比较行政数据、调查、临床试验和队列研究中收集的中风合并症。","authors":"Monique F Kilkenny, Lachlan L Dalli, Ailie Sanders, Muideen T Olaiya, Joosup Kim, David Ung, Nadine E Andrew","doi":"10.1177/18333583221124371","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Administrative data are used extensively for research purposes, but there remains limited information on the quality of these data for identifying comorbidities related to stroke.</p><p><strong>Objective: </strong>To compare the prevalence of comorbidities of stroke identified using International Classification Diseases, Australian Modification (ICD-10-AM) or Anatomical Therapeutic Chemical codes, with those from (i) self-reported data and (ii) published studies.</p><p><strong>Method: </strong>The cohort included patients with stroke or transient ischaemic attack admitted to hospitals (2012-2016; Victoria and Queensland) in the Australian Stroke Clinical Registry <i>(N</i> = 26,111). Data were linked with hospital and pharmaceutical datasets to ascertain comorbidities using published algorithms. The sensitivity, specificity, and positive predictive value of these comorbidities were compared with survey responses from 623 patients (reference standard). An indirect comparison was also performed with clinical data from published stroke studies.</p><p><strong>Results: </strong>The sensitivity of hospital ICD-10-AM data was poor for most comorbidities, except for diabetes (93.0%). Specificity was excellent for all comorbidities (87-96%), except for hypertension (70.5%). Compared to published stroke studies (3 clinical trials and 1 incidence study), the prevalence of diabetes and atrial fibrillation in our cohort was similar using ICD-10-AM codes, but lower for dyslipidaemia and anxiety/depression. Whereas in the pharmaceutical dispensing data, the sensitivity was excellent for dyslipidaemia (94%) and modest for anxiety/depression (77%). In the pharmaceutical data, specificity was modest for hypertension (78%) and anxiety or depression (76%), but specificity was poor for dyslipidaemia (19%) and heart disease (46%).</p><p><strong>Conclusion: </strong>Variation was observed in the reporting of comorbidities of stroke in administrative data, and consideration of multiple sources of data may be necessary for research. Further work is needed to improve coding and clinical documentation for reporting of comorbidities in administrative data.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":" ","pages":"104-111"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of comorbidities of stroke collected in administrative data, surveys, clinical trials and cohort studies.\",\"authors\":\"Monique F Kilkenny, Lachlan L Dalli, Ailie Sanders, Muideen T Olaiya, Joosup Kim, David Ung, Nadine E Andrew\",\"doi\":\"10.1177/18333583221124371\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Administrative data are used extensively for research purposes, but there remains limited information on the quality of these data for identifying comorbidities related to stroke.</p><p><strong>Objective: </strong>To compare the prevalence of comorbidities of stroke identified using International Classification Diseases, Australian Modification (ICD-10-AM) or Anatomical Therapeutic Chemical codes, with those from (i) self-reported data and (ii) published studies.</p><p><strong>Method: </strong>The cohort included patients with stroke or transient ischaemic attack admitted to hospitals (2012-2016; Victoria and Queensland) in the Australian Stroke Clinical Registry <i>(N</i> = 26,111). Data were linked with hospital and pharmaceutical datasets to ascertain comorbidities using published algorithms. The sensitivity, specificity, and positive predictive value of these comorbidities were compared with survey responses from 623 patients (reference standard). An indirect comparison was also performed with clinical data from published stroke studies.</p><p><strong>Results: </strong>The sensitivity of hospital ICD-10-AM data was poor for most comorbidities, except for diabetes (93.0%). Specificity was excellent for all comorbidities (87-96%), except for hypertension (70.5%). Compared to published stroke studies (3 clinical trials and 1 incidence study), the prevalence of diabetes and atrial fibrillation in our cohort was similar using ICD-10-AM codes, but lower for dyslipidaemia and anxiety/depression. Whereas in the pharmaceutical dispensing data, the sensitivity was excellent for dyslipidaemia (94%) and modest for anxiety/depression (77%). In the pharmaceutical data, specificity was modest for hypertension (78%) and anxiety or depression (76%), but specificity was poor for dyslipidaemia (19%) and heart disease (46%).</p><p><strong>Conclusion: </strong>Variation was observed in the reporting of comorbidities of stroke in administrative data, and consideration of multiple sources of data may be necessary for research. Further work is needed to improve coding and clinical documentation for reporting of comorbidities in administrative data.</p>\",\"PeriodicalId\":73210,\"journal\":{\"name\":\"Health information management : journal of the Health Information Management Association of Australia\",\"volume\":\" \",\"pages\":\"104-111\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health information management : journal of the Health Information Management Association of Australia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/18333583221124371\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/11/15 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health information management : journal of the Health Information Management Association of Australia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/18333583221124371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/11/15 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of comorbidities of stroke collected in administrative data, surveys, clinical trials and cohort studies.
Background: Administrative data are used extensively for research purposes, but there remains limited information on the quality of these data for identifying comorbidities related to stroke.
Objective: To compare the prevalence of comorbidities of stroke identified using International Classification Diseases, Australian Modification (ICD-10-AM) or Anatomical Therapeutic Chemical codes, with those from (i) self-reported data and (ii) published studies.
Method: The cohort included patients with stroke or transient ischaemic attack admitted to hospitals (2012-2016; Victoria and Queensland) in the Australian Stroke Clinical Registry (N = 26,111). Data were linked with hospital and pharmaceutical datasets to ascertain comorbidities using published algorithms. The sensitivity, specificity, and positive predictive value of these comorbidities were compared with survey responses from 623 patients (reference standard). An indirect comparison was also performed with clinical data from published stroke studies.
Results: The sensitivity of hospital ICD-10-AM data was poor for most comorbidities, except for diabetes (93.0%). Specificity was excellent for all comorbidities (87-96%), except for hypertension (70.5%). Compared to published stroke studies (3 clinical trials and 1 incidence study), the prevalence of diabetes and atrial fibrillation in our cohort was similar using ICD-10-AM codes, but lower for dyslipidaemia and anxiety/depression. Whereas in the pharmaceutical dispensing data, the sensitivity was excellent for dyslipidaemia (94%) and modest for anxiety/depression (77%). In the pharmaceutical data, specificity was modest for hypertension (78%) and anxiety or depression (76%), but specificity was poor for dyslipidaemia (19%) and heart disease (46%).
Conclusion: Variation was observed in the reporting of comorbidities of stroke in administrative data, and consideration of multiple sources of data may be necessary for research. Further work is needed to improve coding and clinical documentation for reporting of comorbidities in administrative data.