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Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea.

ABSTRACT: STUDY OBJECTIVES:We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States. METHODS:Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ? 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated. RESULTS:The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ? 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ? 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases. CONCLUSIONS:A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.

SUBMITTER: Keenan BT

PROVIDER: S-EPMC7053021 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea.

Keenan Brendan T BT Kirchner H Lester HL Veatch Olivia J OJ Borthwick Kenneth M KM Davenport Vicki A VA Feemster John C JC Gendy Maged M Gossard Thomas R TR Pack Frances M FM Sirikulvadhana Laura L Teigen Luke N LN Timm Paul C PC Malow Beth A BA Morgenthaler Timothy I TI Zee Phyllis C PC Pack Allan I AI Robishaw Janet D JD Derose Stephen F SF

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 20200113 2

<h4>Study objectives</h4>We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States.<h4>Methods</h4>Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ≥ 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780. ...[more]

PMID: 31992429

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Project description:IntroductionDiagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists.MethodWe used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT.ResultsFrom the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm.ConclusionTonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist.

Project description:Rationale: Obstructive sleep apnea (OSA) is common in pregnancy and associated with maternal and fetal complications. Early detection of OSA may have important implications for maternal-fetal well-being. A screening tool combining several methods of assessment may better predict OSA among pregnant women compared with tools that rely solely on self-reported information.Objectives: To develop a screening tool combining subjective and objective measures to predict OSA in pregnant women.Methods: This study is a secondary analysis using data collected from a completed cohort of pregnant women (n = 121 during the first and n = 87 during the third trimester). Participants underwent full polysomnography and completed the Multivariable Apnea Prediction Questionnaire. The Obstructive Sleep Apnea/Hypopnea Syndrome Score and Facco apnea predictive model were obtained. Logistic regression analysis and area under the curve (AUC) were used to identify models predicting OSA risk.Results: Participants' mean age was 27.4 ± 7.0 years. The prevalence of OSA during the first and third trimester was 10.7% and 24.1%, respectively. The final model predicting OSA risk consisted of body mass index, age, and presence of tongue enlargement. During the first trimester, the AUC was 0.86 (95% confidence interval [CI], 0.76-0.96). During the third trimester, the AUC was 0.87 (95% CI, 0.77-0.96). When the first-trimester data were used to predict third-trimester OSA risk, the AUC was 0.87 (95% CI, 0.77-0.97). This model had high sensitivity and specificity when used during both trimesters. The negative posttest probabilities (probability of OSA given a negative test result) ranged from 0.03 to 0.07.Conclusions: A new model consisting of body mass index, age, and presence of tongue enlargement provided accurate screening of OSA in pregnant women, particularly African-Americans. This tool can be easily and rapidly administered in busy clinical practices without depending on patients' awareness of experiencing apnea symptoms.

Project description:Rationale: Sleep-disordered breathing (SDB) is associated with increased risk of adverse pregnancy outcomes, including gestational diabetes mellitus (GDM). GDM is a significant cause of maternal and infant morbidities. Assessing these risk factors concurrently may facilitate both the identification of women at GDM risk and the initiation of GDM prevention strategies.Objectives: To investigate whether SDB events, including SDB in rapid eye movement (REM) sleep and other sleep parameters, are associated with increased risk of GDM and to evaluate the performance of the models investigating associations between breathing and sleep parameters and GDM risk.Methods: In this case-control study, 46 women with newly diagnosed GDM and 46 healthy control subjects, who were individually matched for age, gestational age, body mass index, race, and parity, completed overnight polysomnographic studies and sleep questionnaires after being screened for GDM during the late-second to mid-third trimesters. Conditional logistic regression analysis was used to identify models investigating associations between risk factors and GDM risk. The Bayesian information criterion (BIC) was employed to compare models; the model with the lowest BIC is preferred.Results: Obstructive sleep apnea (OSA; defined as an apnea-hypopnea index [AHI] >5 events/h) was present in 22% of subjects with GDM and 9% of control subjects (P < 0.001). Women with OSA had a higher GDM risk (odds ratio [OR], 4.71; 95% confidence interval [CI], 1.05-21.04). In individual models, GDM risk was also significantly higher among women with higher overall AHI (events/h OR, 1.81; 95% CI, 1.01-3.27), higher AHI in REM (events/h OR, 2.09; 95% CI, 1.02-4.31), higher oxygen desaturation index greater than or equal to 4% (ODI4; events/h OR, 2.21; 95% CI, 1.03-4.73), and higher Sleep Apnea Symptom Score (OR, 2.72; 95% CI, 1.11-6.69). The percentage of non-REM sleep was significantly associated with decreased risk of GDM (percentage of non-REM sleep OR, 0.88; 95% CI, 0.78-0.99). The BIC supports the conclusion that there is a strong association between AHI in REM and GDM risk compared with the other significant models.Conclusions: SDB events, including REM-related OSA, are linked to increased GDM risk. GDM risk is also influenced by intercorrelated sleep variables.

Project description:BackgroundEpidemiological studies demonstrate associations between obstructive sleep apnea (OSA) and cancer incidence and mortality. The aim of this study was to measure OSA in women with breast (BC) or endometrial cancer (EC) and associations with clinico-pathological tumor variables.Methods and findingsIn a cross sectional study, women with BC (12 months) or EC (3 months) post-diagnosis were recruited from cancer clinics. We collected demographic, anthropometric data, cancer stage, grade, histopathology and history of cancer treatment and all subjects had in-laboratory polysomnography. Sleepiness was assessed with the Epworth Sleepiness Scale (ESS). We compared anthropometric and polysomnographic data between cancer groups (unpaired t-tests), and assessed relationships between cancer characteristics and OSA variables (Fishers exact test). There were no significant differences between average age (BC:59.6±8.7 years(n = 50); EC:60.3±7.7 years(n = 37)), or ESS score (BC:6.4±4.4; EC 6.8±4.7; mean±SD; all p>0.2), however, BMI was higher in EC (BC: 29.7±7.9kgm-2; EC: 34.2±8.0 kgm-2; p<0.05). BC had longer sleep latency (BC:31.8±32minutes; EC:19.3±17.9 minutes), less Stage 3 sleep (BC:20.0±5.2%; EC:23.6±8.2%) and more REM sleep (BC:21.1±6.9%; EC: 16.6±5.7%), all p<0.05. EC had lower average awake and asleep oxygen saturation levels (BC: 95.6±1.3%; EC: 94.6±1.9% [awake]: BC: 94.8±2.1%; EC: 93.3±2.4% [asleep]; both p<0.05). Apnea-Hypopnea Index (AHI) (BC: 21.2(7.3-36.9) events/hr; EC: 15.7 (10-33.5) events/hour (median (interquartile range)) was not different p = 0.7), however, 58% and 57% of women with BC and EC respectively, had an AHI>15 events/hour. In this small sample size group, no significant associations (all p>0.1) were detected between OSA metrics and clinico-pathological tumor variables.ConclusionIn postmenopausal women with breast or endometrial cancer there is high prevalence of OSA, with no association with specific tumor characteristics detected. Recognition of the high prevalence of OSA in women with cancer is important to recognise as it may impact on surgical risk and quality of life.

Project description:BackgroundThe American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.ObjectiveThis study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.MethodsA total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).ResultsA total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).ConclusionsDefined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Dataset Information

Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea.

Publications

Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea.

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