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Clinical Epidemiology Volume 13, 2021 - Issue
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Original Research

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

Tetyana Kendzerska1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;2 Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada;3 ICES, Ottawa, Toronto, Ontario, CanadaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-5301-1796View further author information
ORCID Icon,
Carl van Walraven1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;2 Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada;3 ICES, Ottawa, Toronto, Ontario, CanadaView further author information
,
Daniel I McIsaac1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;3 ICES, Ottawa, Toronto, Ontario, Canada;4 Departments of Anesthesiology & Pain Medicine, University of Ottawa and Ottawa Hospital, Ottawa, Ontario, CanadaView further author information
,
Marcus Povitz5 Department of Medicine at Schulich School of Medicine and Dentistry at Western University, London, Ontario, Canada;6 Cumming School of Medicine, Department of Medicine, University of Calgary, Calgary, Alberta, CanadaView further author information
,
Sunita Mulpuru1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;2 Department of Medicine, University of Ottawa, Ottawa, Ontario, CanadaORCID Iconhttps://orcid.org/0000-0001-6214-0613View further author information
ORCID Icon,
Isac Lima1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;3 ICES, Ottawa, Toronto, Ontario, CanadaView further author information
,
Robert Talarico1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;3 ICES, Ottawa, Toronto, Ontario, CanadaView further author information
,
Shawn D Aaron1 Department of Medicine, The Ottawa Hospital Research Institute/The Ottawa Hospital, Ottawa, Ontario, Canada;2 Department of Medicine, University of Ottawa, Ottawa, Ontario, CanadaView further author information
,
William Reisman5 Department of Medicine at Schulich School of Medicine and Dentistry at Western University, London, Ontario, Canada;7 Department of Medicine, London Health Sciences Centre, London, Ontario, CanadaView further author information
&
Andrea S Gershon3 ICES, Ottawa, Toronto, Ontario, Canada;8 Faculty of Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada;9 Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, CanadaORCID Iconhttps://orcid.org/0000-0002-0246-594XView further author information
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Pages 453-467 | Published online: 17 Jun 2021

Figures & data

Table 1 Characteristics of Adults Who Underwent a Diagnostic Sleep Study at the Ottawa Hospital (TOH) Sleep Center (Internal Validation Cohort) and at the London Health Sciences Centre (External Validation Cohort)

Table 2 Sensitivities and Specificities, %, (with 95% Confidence Intervals) for Individual Variables to Be Considered in a Case-Ascertainment Model for Moderate to Severe Obstructive Sleep Apnea (OSA) (AHI ≥ 15)

Table 3 Sensitivities and Specificities, %, (with 95% Confidence Intervals) for Individual Variables to Be Considered in a Case-Ascertainment Model for Severe OSA (AHI >30)

Table 4 Internal and External Validation Measures of CARTs and Logistic Regression Models Developed to Identify Individuals with Moderate to Severe or Severe Obstructive Sleep Apnea (OSA)

Figure 1 External validation: Calibration plots to classify individuals with moderate to severe (apnea-hypopnea index [AHI] ≥15) or severe (AHI >30) obstructive sleep apnea (OSA) for the parsimonious classification and regression tree (CART) and logistic regression models (same variables), and full logistic regression models. Perfect predictions should be on the ideal diagonal line, described with an intercept of 0 and slope of 1. Imperfect calibration can be characterized by deviations from these ideal values.

Figure 1 External validation: Calibration plots to classify individuals with moderate to severe (apnea-hypopnea index [AHI] ≥15) or severe (AHI >30) obstructive sleep apnea (OSA) for the parsimonious classification and regression tree (CART) and logistic regression models (same variables), and full logistic regression models. Perfect predictions should be on the ideal diagonal line, described with an intercept of 0 and slope of 1. Imperfect calibration can be characterized by deviations from these ideal values.

Figure 2 External validation: Area under the curve to classify individuals with moderate to severe obstructive sleep apnea (OSA) (apnea-hypopnea index [AHI] ≥15) utilizing parsimonious classification and regression tree (CART) and logistic regression models (same variables).

Figure 2 External validation: Area under the curve to classify individuals with moderate to severe obstructive sleep apnea (OSA) (apnea-hypopnea index [AHI] ≥15) utilizing parsimonious classification and regression tree (CART) and logistic regression models (same variables).

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