Case-Ascertainment Models to Identify Adults with Obstructive Sleep Apnea Using Health Administrative Data: Internal and External Validation

Autor: William Reisman, Carl van Walraven, Tetyana Kendzerska, Sunita Mulpuru, Shawn D. Aaron, Marcus Povitz, Daniel I. McIsaac, Andrea S. Gershon, Robert Talarico, Isac Lima
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Clinical Epidemiology
ISSN: 1179-1349
Popis: Tetyana Kendzerska,1– 3 Carl van Walraven,1– 3 Daniel I McIsaac,1,3,4 Marcus Povitz,5,6 Sunita Mulpuru,1,2 Isac Lima,1,3 Robert Talarico,1,3 Shawn D Aaron,1,2 William Reisman,5,7 Andrea S Gershon3,8,9 1Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada; 2Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada; 3ICES, Ottawa, Toronto, Ontario, Canada; 4Departments of Anesthesiology & Pain Medicine, University of Ottawa and Ottawa Hospital, Ottawa, Ontario, Canada; 5Department of Medicine at Schulich School of Medicine and Dentistry at Western University, London, Ontario, Canada; 6Cumming School of Medicine, Department of Medicine, University of Calgary, Calgary, Alberta, Canada; 7Department of Medicine, London Health Sciences Centre, London, Ontario, Canada; 8Faculty of Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; 9Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, CanadaCorrespondence: Tetyana KendzerskaOttawa Hospital Research Institute, Division of Respirology, University of Ottawa, The Ottawa Hospital, Civic Campus, 1053 Carling Ave, Ottawa, ON, K1Y 4E9, CanadaEmail tkendzerska@toh.caBackground: There is limited evidence on whether obstructive sleep apnea (OSA) can be accurately identified using health administrative data.Study Design and Methods: We derived and validated a case-ascertainment model to identify OSA using linked provincial health administrative and clinical data from all consecutive adults who underwent a diagnostic sleep study (index date) at two large academic centers (Ontario, Canada) from 2007 to 2017. The presence of moderate/severe OSA (an apnea–hypopnea index≥ 15) was defined using clinical data. Of 39 candidate health administrative variables considered, 32 were tested. We used classification and regression tree (CART) methods to identify the most parsimonious models via cost-complexity pruning. Identified variables were also used to create parsimonious logistic regression models. All individuals with an estimated probability of 0.5 or greater using the predictive models were classified as having OSA.Results: The case-ascertainment models were derived and validated internally through bootstrapping on 5099 individuals from one center (33% moderate/severe OSA) and validated externally on 13,486 adults from the other (45% moderate/severe OSA). On the external cohort, parsimonious models demonstrated c-statistics of 0.75– 0.81, sensitivities of 59– 60%, specificities of 87– 88%, positive predictive values of 79%, negative predictive values of 73%, positive likelihood ratios (+LRs) of 4.5– 5.0 and –LRs of 0.5. Logistic models performed better than CART models (mean integrated calibration indices of 0.02– 0.03 and 0.06– 0.12, respectively). The best model included: sex, age, and hypertension at the index date, as well as an outpatient specialty physician visit for OSA, a repeated sleep study, and a positive airway pressure treatment claim within 1 year since the index date.Interpretation: Among adults who underwent a sleep study, case-ascertainment models for identifying moderate/severe OSA using health administrative data had relatively low sensitivity but high specificity and good discriminative ability. These findings could help study trends and outcomes of OSA individuals using routinely collected health care data.Keywords: obstructive sleep apnea, case-ascertainment modelling, health administrative data
Databáze: OpenAIRE
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