Validation of algorithms identifying diagnosed obstructive sleep apnoea and narcolepsy in coded primary care and linked hospital activity data in England

• Primary care records of OSA accurately represent hospital specialist diagnoses. • Primary care records of narcolepsy are mostly accurate but diagnosis dates are not. • More stringent definitions improve accuracy but identify fewer cases. • Epidemiologists should use the best algorithm for their question and state limitations. To assist sleep epidemiology research, we created and tested the accuracy of five algorithms identifying diagnosed Obstructive Sleep Apnoea (OSA) and narcolepsy in routinely collected data from England (01/01/1998–29/03/2021). The primary algorithm identified the first coded record in Clinical Practice Research Datalink (CPRD) primary care or linked hospital admissions data as an incident diagnosis of OSA ( n = 92,222) or narcolepsy ( n = 1072). Alternative algorithms required codes in CPRD, both datasets, or an additional proximate possible-sleep-related outpatient visit or excessive daytime sleepiness drug prescription (narcolepsy only). Staff in 73/1574 CPRD practices completed online questionnaires for a convenience sample of 144 OSA and 101 narcolepsy cases. We estimated Positive Predictive Values (PPVs) describing the proportion of cases confirmed by a gold standard hospital specialist diagnosis, the percentage of gold standard cases from the primary algorithm retained with alternative algorithms, and time between specialist and recorded diagnosis dates. Using the primary algorithm, the PPV (95 % CI) was 75.3 % (69.2–81.3) and 65.2 % (57.0–73.4) for OSA and narcolepsy, respectively: 80.6 % and 62.7 % of confirmed cases were recorded within 6 months of the specialist diagnosis. The CPRD-only algorithm increased the PPV to 85.3 (77.3–91.4, OSA) and 71.0 (58.8–81.3, narcolepsy) and retained high proportions of gold standard cases. Requiring additional outpatient or prescribing data increased PPVs, and for OSA improved diagnostic date accuracy, but omitted a high proportion of gold standard cases. Highly accurate OSA diagnoses can be identified in routinely collected data. Recorded cases of narcolepsy are moderately accurate, but diagnosis dates are not.

Authors

Publication Details

Journal
Sleep Epidemiology
Published
2025-07-20
DOI
https://doi.org/10.1016/j.sleepe.2025.100110
Citations
1
Primary Topic
Obstructive Sleep Apnea Research
Type
article
Field-Weighted Citation Impact
1.16

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article

Validation of algorithms identifying diagnosed obstructive sleep apnoea and narcolepsy in coded primary care and linked hospital activity data in England

Charlotte Warren‐Gash, Krishnan Bhaskaran, Sofia Eriksson, Kristin Veighey et al.
1 citations
Sleep Epidemiology
Obstructive Sleep Apnea Research
1.16
article

Validation of algorithms identifying diagnosed obstructive sleep apnoea and narcolepsy in coded primary care and linked hospital activity data in England

Charlotte Warren‐Gash, Krishnan Bhaskaran, Sofia Eriksson, Kristin Veighey, Michelle A. Miller, Helen Strongman, Kwabena Asare, Hema Mistry, Martina Sýkorová
article en
1 citations

Abstract

• Primary care records of OSA accurately represent hospital specialist diagnoses. • Primary care records of narcolepsy are mostly accurate but diagnosis dates are not. • More stringent definitions improve accuracy but identify fewer cases. • Epidemiologists should use the best algorithm for their question and state limitations. To assist sleep epidemiology research, we created and tested the accuracy of five algorithms identifying diagnosed Obstructive Sleep Apnoea (OSA) and narcolepsy in routinely collected data from England (01/01/1998–29/03/2021). The primary algorithm identified the first coded record in Clinical Practice Research Datalink (CPRD) primary care or linked hospital admissions data as an incident diagnosis of OSA ( n = 92,222) or narcolepsy ( n = 1072). Alternative algorithms required codes in CPRD, both datasets, or an additional proximate possible-sleep-related outpatient visit or excessive daytime sleepiness drug prescription (narcolepsy only). Staff in 73/1574 CPRD practices completed online questionnaires for a convenience sample of 144 OSA and 101 narcolepsy cases. We estimated Positive Predictive Values (PPVs) describing the proportion of cases confirmed by a gold standard hospital specialist diagnosis, the percentage of gold standard cases from the primary algorithm retained with alternative algorithms, and time between specialist and recorded diagnosis dates. Using the primary algorithm, the PPV (95 % CI) was 75.3 % (69.2–81.3) and 65.2 % (57.0–73.4) for OSA and narcolepsy, respectively: 80.6 % and 62.7 % of confirmed cases were recorded within 6 months of the specialist diagnosis. The CPRD-only algorithm increased the PPV to 85.3 (77.3–91.4, OSA) and 71.0 (58.8–81.3, narcolepsy) and retained high proportions of gold standard cases. Requiring additional outpatient or prescribing data increased PPVs, and for OSA improved diagnostic date accuracy, but omitted a high proportion of gold standard cases. Highly accurate OSA diagnoses can be identified in routinely collected data. Recorded cases of narcolepsy are moderately accurate, but diagnosis dates are not.

Sleep EpidemiologyVol. 5
Wellcome, National Institute for Health and Care Research, Department of Health and Social Care, University College London
Good health and well-being
Openalex Percentile: Top 20%
Obstructive Sleep Apnea Research
1.16
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