The accuracy of administrative health data for identifying patients with rheumatoid arthritis: a retrospective validation study using medical records in Western Australia

2021 ◽  
Vol 41 (4) ◽  
pp. 741-750
Author(s):  
Khalid Almutairi ◽  
Charles Inderjeeth ◽  
David B. Preen ◽  
Helen Keen ◽  
Katrina Rogers ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Western Australia ◽  
Validation Study ◽  
Medical Records ◽  
Health Data ◽  
Administrative Health Data

scholarly journals SUN-154 Identifying Type 1 Diabetes in Electronic Medical Records and Administrative Health Data in Ontario: A Validation Study

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Alanna Weisman ◽  
Jacqueline Young ◽  
Karen Tu ◽  
Liisa Jaakimainen ◽  
Lorraine Lipscombe ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Electronic Medical Records ◽  
Validation Study ◽  
Medical Records ◽  
Health Data ◽  
Administrative Health Data

scholarly journals AB0103 THE ACCURACY OF ADMINISTRATIVE HEALTH DATA FOR IDENTIFYING PATIENTS WITH RHEUMATOID ARTHRITIS: A VALIDATION STUDY USING MEDICAL RECORDS IN WESTERN AUSTRALIA

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1079.3-1080
Author(s):  
K. Almutairi ◽  
J. Nossent ◽  
D. Preen ◽  
H. Keen ◽  
K. Roger ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Positive Predictive Value ◽  
Hospital Discharge ◽  
Western Australia ◽  
Predictive Value ◽  
Medical Records ◽  
Health Data ◽  
Classification Criteria ◽  
Diagnostic Code ◽  
Diagnostic Codes

Background:The use of large administrative health datasets is increasingly important in Rheumatology for disease trends and outcome research (1). We established the West Australian Rheumatic Disease Epidemiological Registry containing longitudinal health data for over 10000 patients with Rheumatoid Arthritis (RA) in Western Australia (WA). Accuracy of coding for RA is essential to validity of the datasets.Objectives:Investigate the diagnostic accuracy of International Classification of Diseases (ICD) based discharge codes for RA at WA’s largest tertiary hospital.Methods:Medical records for RA patients randomly selected from the hospital discharge database with ICD 10 codes (M05.00–M06.99) from 2008–2020 were retrospectively reviewed. Rheumatologist reported diagnosis and ACR/EULAR classification were used as gold standards to determine positive predictive value (PPV) with 95% Confidence Interval (CI) for RA primary diagnostic codes.Results:Medical chart review was completed for 87 patients (mean age 64.7 years, 67% female). Total of 80 (92%) patients had specialist confirmed RA diagnoses, while seven patients (8%) had alternate clinical diagnoses providing a PPV of 93.5% (95%CI: 89.9 to 95.86). Overall, 69 out 87 patients (79.3%) fulfilled ACR/EULAR classification criteria based on RA primary diagnostic codes with a PPV of 80.5% (95%CI: 76.81 to 83.7). A combination of a diagnostic RA code with biologic infusion codes in two or more codes increased the PPV to 97.9%.Conclusion:Hospital discharge diagnostic codes in WA identify RA patients with a high degree of accuracy. Combining a primary diagnostic code for RA with biological infusion codes can further increase the PPV.References:[1]Hanly et al. The use of administrative health care databases to identify patients with rheumatoid arthritis. Open Access Rheumatol 2015; 7: 69–75.Table 1.Accuracy measures of different algorithms for random sample of rheumatoid arthritis (RA) patients with one or more RA codes.Rheumatologist-reported diagnosisACR/EULAR classification criteriaAdministrative dataSNSPPPVNPVSNSPPPVNPVOne or more RA primary codes90%28.5%93.5%7.6%89.8%16.6%80.5%30%One or more RA biological infusion codes25%71.4%90.9%7.7%20.3%55.5%63.6%15.3%Two or more RA codes including biological codes60%85.7%97.9%15.8%56.5%44.4%79.6%21%RA=Rheumatoid Arthritis, SN=Sensitivity, SP=Specificity, PPV= Positive predictive value, NPV= Negative predictive value.Acknowledgements:Khalid Almutairi was supported by an Australian Government research training Program PhD Scholarship at the University of Western Australia.Disclosure of Interests:Khalid Almutairi: None declared, Johannes Nossent Speakers bureau: Janssen, David Preen: None declared, Helen Keen Speakers bureau: Pfizer Australia, Abbvie Australia, Katrina Roger: None declared, Charles Inderjeeth Speakers bureau: Eli Lilly

scholarly journals Determination of Rheumatoid Arthritis Incidence and Prevalence in Alberta Using Administrative Health Data

2020 ◽  
Vol 2 (7) ◽  
pp. 424-429
Author(s):  
Deborah A. Marshall ◽  
Tram Pham ◽  
Peter Faris ◽  
Guanmin Chen ◽  
Siobhan O’Donnell ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Health Data ◽  
Administrative Health Data

scholarly journals Validation study of German inpatient administrative health data for epidemiological surveillance and measurement of quality of care for sepsis: the OPTIMISE study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e035763
Author(s):  
Daniel Schwarzkopf ◽  
Carolin Fleischmann-Struzek ◽  
Peter Schlattmann ◽  
Heike Dorow ◽  
Dominique Ouart ◽  
...  
Keyword(s):  
Risk Factors ◽  
Quality Of Care ◽  
Gold Standard ◽  
Medical Records ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Health Data ◽  
Epidemiological Surveillance ◽  
Administrative Health Data

IntroductionSepsis is a major cause of preventable deaths in hospitals. This study aims to investigate if sepsis incidence and quality of care can be assessed using inpatient administrative health data (IAHD).Methods and analysisDesign: Retrospective observational validation study using routine data to assess the diagnostic accuracy of sepsis coding in IAHD regarding sepsis diagnosis based on medical record review. Procedure: A stratified sample of 10 000 patients with an age ≥15 years treated in between 2015 and 2017 in 10 German hospitals is investigated. All available information of medical records is screened by trained physicians to identify true sepsis cases (‘gold standard’) both according to current (‘sepsis-1’) definitions and new (‘sepsis-3’) definitions. Data from medical records are linked to IAHD on patient level using a pseudonym. Analyses: Proportions of cases with sepsis according to sepsis-1 and sepsis-3 definitions are calculated and compared with estimates from coding of sepsis in IAHD. Predictive accuracy (sensitivity, specificity) of different coding abstraction strategies regarding the gold standard is estimated. Predictive accuracy of mortality risk factors obtained from IAHD regarding the respective risk factors obtained from medical records is calculated. An IAHD-based risk model for hospital mortality is compared with a record-based risk model regarding model-fit and predicted risk of death. Analyses adjust for sampling weights. The obtained estimates of sensitivity and specificity for sepsis coding in IAHD are used to estimate adjusted incidence proportions of sepsis based on German national IAHD.Ethics and disseminationThe study has been approved by the ethics commission of the Jena University Hospital (No. 2018-1065-Daten). The results of the study will be discussed in an expert panel to write a memorandum on improving the utility of IAHD for epidemiological surveillance and quality management of sepsis care.Trial registration numberDRKS00017775; Pre-results.

scholarly journals Using administrative health data to describe colorectal and lung cancer care in New South Wales, Australia: a validation study

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
David E Goldsbury ◽  
Katie Armstrong ◽  
Leonardo Simonella ◽  
Bruce K Armstrong ◽  
Dianne L O’Connell
Keyword(s):  
Lung Cancer ◽  
Validation Study ◽  
Cancer Care ◽  
New South ◽  
New South Wales ◽  
Health Data ◽  
Administrative Health Data ◽  
South Wales

Identification of incident pancreatic cancer in Ontario administrative health data: A validation study

2018 ◽  
Vol 29 (S1) ◽  
pp. 78-85 ◽  
Author(s):  
Jennifer W. Wu ◽  
Laurent Azoulay ◽  
Anjie Huang ◽  
Michael Paterson ◽  
Fangyun Wu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Validation Study ◽  
Health Data ◽  
Administrative Health Data

scholarly journals Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis

BMJ Open ◽  
2017 ◽  
Vol 7 (6) ◽  
pp. e016173 ◽  
Author(s):  
Kristine Kroeker ◽  
Jessica Widdifield ◽  
Saman Muthukumarana ◽  
Depeng Jiang ◽  
Lisa M Lix
Keyword(s):  
Rheumatoid Arthritis ◽  
Health Data ◽  
Case Definitions ◽  
Administrative Health Data ◽  
Model Based ◽  
Data Application
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