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Published on: December 6, 2016
Sleep-disordered breathing in chronic heart failure: development and validation of a clinical screening score
Juliette Parisot1, Thibaud Damy2, Barnabas Gellen3
1Public Health Department, APHP, Henri-Mondor Teaching Hospital, Créteil, F-94000, France; UPEC, Laboratoire d'Investigation Clinique (LIC), EA 4393, Créteil F-94000, France; INSERM Clinical Investigation Centre 006, Paris F-75000, France; Department of Clinical research and Public Health, Clinical Investigation Centre 006, Paris F-75000, France.
Aims:
Sleep-disordered breathing (SDB) is highly prevalent and of adverse prognostic significance in patients with chronic heart failure (CHF). Polygraphy is used for diagnosing SDB but polygraphy resources fall short of needs. Here, our aim was to develop a score for SDB screening in patients with CHF.
Methods And Results:
Consecutive patients with stable chronic CHF referred to our CHF clinic for a scheduled follow-up evaluation were included prospectively between 2000 and 2012. SDB was defined as an apnoea-hypopnoea index ≥ 5/h as assessed by routine polygraphy. A screening score was developed as a linear combination of factors independently associated with SDB by multivariate logistic regression. Calibration and discrimination were evaluated using the Hosmer-Lemeshow (HL) test and area under the receiver-operating characteristics curve (AUC), respectively. Bootstrapping was performed to assess internal validity. Of 450 included patients (mean age, 59.5 ± 13.7 years), 397 (88%) had SDB. An easy-to-use score was based on age (2 points if ≥65 years), body mass index (2 points if ≥25 kg/m(2)), New York Heart Association (NYHA) class (2 points if ≥3 or 4) and male gender (3 points). A score cut-off of 5 was 78.9% sensitive and 61.5% specific for SDB. The final model exhibited adequate calibration (pHL ≥ 0.3) and discrimination (AUC, 0.737; 95% confidence interval, 0.663; 0.810).
Conclusion:
An easy-to-use clinical score combining age, body mass index, NYHA class, and gender may help to identify those CHF patients most likely to have SDB, thereby improving the allocation of scarce polygraphy resources and early diagnosis of SDB.
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