A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea

被引:34
作者
Rodsutti, J
Hensley, M
Thakkinstian, A
D'Este, C
Attia, J
机构
[1] Bhumibol Adulyadej Hosp, Dept Oto Rhino Laryngol Head & Neck Surg, Bangkok 10220, Thailand
[2] Univ Newcastle, Sleep Disorders Ctr, Newcastle, NSW, Australia
[3] Mahidol Univ, Fac Med, Clin Epidemiol Unit, Bangkok, Thailand
[4] Univ Newcastle, Fac Hlth, Ctr Clin Epidemiol & Biostat, Newcastle, NSW, Australia
关键词
clinical decision rule; sleep apnea; clinical feature; polysomnography;
D O I
10.1093/sleep/27.4.694
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Study Objectives: To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography. Design: Prospective data collection on consecutive patients referred to a sleep center. Setting: The Newcastle Sleep Disorders Centre, University of Newcastle, NSW, Australia. Patients: Consecutive adult patients who had been scheduled for initial diagnostic polysomnography. Measurements and Results: Eight hundred and thirty-seven patients were used for derivation of the decision rule. An apnea-hypopnoea index of at least 5 was used as the cutoff point to diagnose sleep apnea. Fifteen clinical features were included in the analyses using logistic regression to construct a model from the derivation data set. Only 5 variables-age, sex, body mass index, snoring, and stopping breathing during sleep-were significantly associated with sleep apnea. A scoring scheme based on regression coefficients was developed, and the total score was tri-chotornized into low-, moderate-, and high-risk groups with prevalence of sleep apnea of 8%, 51%, and 82%, respectively. Color-coded tables were developed for ease of use. The clinical decision rule was validated on a separate set of 243 patients. Receiver operating characteristic analysis confirmed that the decision rule performed well, with the area under the curve being similar for both the derivation and validation sets: 0.81 and 0.79, P = .612. Conclusion: We conclude that this decision rule was able to accurately classify the risk of sleep apnea and will be useful for prioritizing patients with suspected sleep apnea who are on waiting lists for polysomnography.
引用
收藏
页码:694 / 699
页数:6
相关论文
共 30 条
[11]   An algorithm to stratify sleep apnea risk in a sleep disorders clinic population [J].
Gurubhagavatula, I ;
Maislin, G ;
Pack, AI .
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2001, 164 (10) :1904-1909
[12]   Likelihood ratios: getting diagnostic testing into perspective [J].
Halkin, A ;
Reichman, J ;
Schwaber, M ;
Paltiel, O ;
Brezis, M .
QJM-AN INTERNATIONAL JOURNAL OF MEDICINE, 1998, 91 (04) :247-258
[13]  
HOFFSTEIN V, 1993, SLEEP, V16, P118
[14]  
Hosmer W., 2000, Applied Logistic Regression, VSecond
[15]   THE PREDICTIVE ACCURACY OF HOME OXIMETRY IN PATIENTS WITH SUSPECTED OBSTRUCTIVE SLEEP-APNEA [J].
KEENAN, SP ;
ANDERSON, B ;
WIGGS, B ;
RYAN, CF ;
FLEETHAM, JA .
SLEEP, 1993, 16 (08) :S133-S134
[16]   Prediction of the risk of bleeding during anticoagulant treatment for venous thromboembolism [J].
Kuijer, PMM ;
Hutten, BA ;
Prins, MH ;
Büller, HR .
ARCHIVES OF INTERNAL MEDICINE, 1999, 159 (05) :457-460
[17]   A predictive morphometric model for the obstructive sleep apnea syndrome [J].
Kushida, CA ;
Efron, B ;
Guilleminault, C .
ANNALS OF INTERNAL MEDICINE, 1997, 127 (08) :581-+
[18]   A SURVEY SCREEN FOR PREDICTION OF APNEA [J].
MAISLIN, G ;
PACK, AI ;
KRIBBS, NB ;
SMITH, PL ;
SCHWARTZ, AR ;
KLINE, LR ;
SCHWAB, RJ ;
DINGES, DF .
SLEEP, 1995, 18 (03) :158-166
[19]   Should scoring rules be based on odds ratios or regression coefficients? [J].
Moons, KGM ;
Harrell, FE ;
Steyerberg, EW .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2002, 55 (10) :1054-1055
[20]  
*NAT HLTH MED RES, 1993, TREATM OBSTR SLEEP A