Sleep Apnea Screening Research Articles

Validation of NoSAS score for the screening of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder (SBD) characterized by the repetitive collapse of the upper airway during sleep. The aim of the present study was to validate the Neck circumference, Obesity, Snoring, Age, Sex (NoSAS) score in a sample population and to compare its validity for OSA screening, with that of the Berlin questionnaire, STOP-BANG questionnaire and Epworth Sleepiness Scale (ESS). A retrospective analysis was conducted on individuals, aged 18 to 80 years, who reported symptoms indicating SBD and were examined with full-night polysomnography (PSG) at a sleep center. Demographics, anthropometric parameters, comorbidities, ESS, STOP-BANG questionnaire, Berlin questionnaire and PSG data were obtained from the recorded data of the patients. The NoSAS score was determined using the recorded data. A total of 347 participants were enrolled in the study. The NoSAS scores identified individuals with OSA, with an area under the curve (AUC) of 0.774. The NoSAS score performed significantly better than the Berlin questionnaire (AUC 0.617) and the ESS (AUC 0.642), and similarly to STOP-BANG (AUC 0.777) for OSA screening. Using a NoSAS score >7 to predict OSA, the sensitivity and specificity were 85.6 and 50%, respectively; using the STOP-BANG questionnaire, for a score >2, the values were 98.32 and 22% respectively; using the Berlin questionnaire for >1 positive categories, the values were 93.6 and 20%, and using the ESS, for a score >10, the values were 30.3% and 72%, respectively. On the whole, the present study demonstrates that the NoSAS score is a simple, efficient and easy method for screening OSA in the clinical setting. The NoSAS score performs significantly more efficiently than the Berlin questionnaire and ESS, and similarly to STOP-BANG questionnaire for OSA screening.

Whom to Screen and How to Screen for Obstructive Sleep Apnea in the Cardiology Clinic.

HomeCirculationVol. 147, No. 8Whom to Screen and How to Screen for Obstructive Sleep Apnea in the Cardiology Clinic No AccessArticle CommentaryRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessArticle CommentaryRequest AccessFull TextWhom to Screen and How to Screen for Obstructive Sleep Apnea in the Cardiology Clinic Meghna P. Mansukhani, MD and Virend K. Somers, MD, PhD Meghna P. MansukhaniMeghna P. Mansukhani https://orcid.org/0000-0003-2351-5640 Center for Sleep Medicine (M.P.M.), Mayo Clinic, Rochester, MN. Search for more papers by this author and Virend K. SomersVirend K. Somers Correspondence to: Virend K. Somers, MD, PhD, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. Email E-mail Address: [email protected] https://orcid.org/0000-0003-4045-4341 Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN. Search for more papers by this author Originally published20 Feb 2023https://doi.org/10.1161/CIRCULATIONAHA.122.060899Circulation. 2023;147:621–623FootnotesFor Sources of Funding and Disclosures, see page 623.Circulation is available at www.ahajournals.org/journal/circThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to: Virend K. Somers, MD, PhD, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. Email somers.[email protected]edu

Development and validation of moderate to severe obstructive sleep apnea screening test (ColTon) in a pediatric population

ObjectiveDevelopment and validation of a machine learning algorithm to predict moderate to severe obstructive sleep apnea syndrome (OSAS) in otherwise healthy children. DesignMultivariable logistic regression and cforest algorithm of a large cross-sectional data set of children with sleep-disordered breathing. SettingAn university pediatric sleep centre. ParticipantsChildren underwent clinical examination, acoustic rhinometry and pharyngometry, and surveying through parental sleep questionnaires, allowing the recording of 14 predictors that have been associated with OSAS. The dataset was nonrandomly split into a training (development) versus test (external validation) set (2:1 ratio) based on the time of the polysomnography. We followed the TRIPOD checklist. ResultsWe included 336 children in the analysis: 220 in the training set (median age [25th–75th percentile]: 10.6 years [7.4; 13.5], z-score of BMI: 1.96 [0.73; 2.50], 89 girls) and 116 in the test set (10.3 years [7.8; 13.0], z-score of BMI: 1.89 [0.61; 2.46], 51 girls). The prevalence of moderate to severe OSAS was 106/336 (32%). A machine learning algorithm using the cforest with pharyngeal collapsibility (pharyngeal volume reduction from sitting to supine position measured by pharyngometry) and tonsillar hypertrophy (Brodsky scale), constituting the ColTon index, as predictors yielded an area under the curve of 0.89, 95% confidence interval [0.85–0.93]. The ColTon index had an accuracy of 76%, sensitivity of 63%, specificity of 81%, negative predictive value of 84%, and positive predictive value of 59% on the validation set. ConclusionA cforest classifier allows valid predictions for moderate to severe OSAS in mostly obese, otherwise healthy children.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy.

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis.

We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification. Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians. The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively. In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening. Yue H, Li P, Li Y, etal. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.

Evaluation of patients with suspected obstructive sleep apnea in a low-middle income country: Lagos experience

Obstructive sleep apnea (OSA) is a common chronic disorder that decreases the quality of life of patients. It is an underdiagnosed medical condition in Nigeria. This study aimed to describe the clinical presentations and validate the sleep apnea screening questionnaires with a home sleep study for the diagnosis of patients with suspected OSA seen in Lagos. This was a descriptive cross sectional study carried out on adult patients with suspicion of OSA referred to the Respiratory Clinic of Lagos State University Teaching Hospital. A proforma was used to obtain information and also data from home polysomnography was obtained for each patient. We selected 22 patients. The commonest presentations include snoring (20 or 90.9%), daytime somnolence (16 or 72.7%) and choking while sleeping (12 or 54.5%). The commonest comorbidities were hypertension (16 or 72.7%) and obesity (6 or 27.3%). The STOP-Bang score identified more patients with a high clinical probability for OSA than the Epworth score (20 and 12 patients respectively). Polysomnography showed evidence of sleep apnea in most suspected patients with severity ranging from mild, to moderate to severe disease (3 or 13.6%, 3 or 13.6%, and 10 or 45.5% respectively). The use of combined Epworth and STOP-Bang questionnaires combination is great tool in identifying patients with suspected cases of OSA based on clinical presentations that will eventually benefit in a resource-limited environment like Lagos. There should be increased awareness of the use of this readily available and cheap questionnaire among physicians in Lagos for ease of OSAS diagnosis for many patients.

Obstructive Sleep Apnea and Risk of Miscarriage

The purpose of this project was to evaluate whether screening positive on obstructive sleep apnea questionnaires in the first trimester of pregnancy was associated with miscarriage. This was a secondary analysis of a prospective observational cohort study of participants who were screened for sleep apnea during pregnancy with the Epworth Sleepiness Scale, Berlin Questionnaire, and novel items related to sleep and napping. This secondary analysis was IRB exempt. Our primary outcome was miscarriage in the index pregnancy. An association between responses to the sleep apnea screening questions with miscarriage of the index pregnancy was queried via Poisson regression. We found that gravidae who had elevated scores on both the Epworth Sleepiness Scale and the Berlin Questionnaire were more likely to experience miscarriage than those who had elevated scores on only one questionnaire or neither (p = 0.018). Gravidae who reported snoring (p = 0.042) or hypertension (p = 0.013) in the first trimester were more likely to experience miscarriage than gravidae who did not. Gravidae who reported napping in the first trimester were less likely to experience miscarriage (p = 0.045), even after adjusting for confounding variables (p = 0.007). In conclusion, we found that screening positive on both the Berlin Questionnaire and Epworth Sleepiness Scale was statistically significantly associated with miscarriage prior to adjustment for confounding variables, as did snoring and hypertension. After adjusting for confounding variables, only not napping was associated with miscarriage. Given the small sample size, further investigation into this topic is warranted.

Clinical application of a previously validated pregnancy-specific screening tool for sleep apnea in a cohort with a high prevalence of obesity

ObjectiveThe purpose of this project was to determine the positive predictive value of existing obstructive sleep apnea (OSA) screening tools in clinical use, in a real-world clinical population of gravidae, and to explore the development of a new questionnaire for screening for OSA during pregnancy. MethodsPregnant people were administered sleep screening questionnaires as part of routine clinical care. These included Facco's four variable OSA screening tool, the STOP-BANG, and the Epworth Sleepiness Scale. Those who screened positive were referred for diagnostic sleep testing, typically with a type III home monitoring device. Here we analyzed the screening responses used by those who completed diagnostic testing to determine the positive predictive value of the existing tools. Results159 pregnant people completed diagnostic OSA testing and were included in this analysis. The positive predictive value of Facco's four variable sleep screening tool was 74.3%, STOP-BANG was 75.3%, and the Epworth Sleepiness Scale was 69.8%. Our sample size was insufficient to create a new screening tool. ConclusionsHere we calculated the positive predictive value of Facco's 4 variable screening tool for screening for OSA in pregnancy in a real-world pregnant population. While we were not able to generate a new screening tool for screening for OSA during pregnancy, both STOP-BANG and Facco's four variable tool had positive predictive values over 70% in our population which was characterized by high BMI and advanced maternal age. Increased clinical use of the pregnancy-specific tool may be warranted.

Screening for obstructive sleep apnea in patients with cancer - a machine learning approach.

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in patients with cancer. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk. This is a retrospective study of patients with cancer at a single tertiary cancer institution who underwent a home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross-validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT. From a training dataset of 249 patients, kernel principal component analysis (PCA) extracted eight components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA + RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA + RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set. The PCA + RF ML model had the highest accuracy in screening for OSA in patients with cancer. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.

Role of Telemedicine in Screening, Diagnosis, and Management of Obstructive Sleep Apnea

Role of Telemedicine in Screening, Diagnosis, and Management of Obstructive Sleep Apnea

Screening for obstructive sleep apnea in a sample of Egyptian idiopathic intracranial hypertension female patients using the Berlin questionnaire

Background: Obstructive sleep apnea (OSA) is one of the risk factors of idiopathic intracranial hypertension (IIH). The Berlin questionnaire (BQ) is a validated reliable method for identification of increased likelihood of OSA. We aimed to screen a sample of female IIH patients for OSA using the BQ. Methods: This study included 40 females; 20 newly diagnosed IIH patients and 20 healthy control subjects. The BQ was obtained from all of the study participants and it was divided into high or low risk for OSA. Results: The patient and control cohorts were age matched. The BMI was significantly higher in the IIH group (median 36, range 24-42) than the control group (median 28, range 20-41) (p=0.005). 45% of the IIH patients group patients had a high risk BQ score, while only 15 % of the healthy subject control group had high risk score (P=0.038) Conclusion: Stratifying the risk of OSA in IIH may be feasible using the BQ. High likelihood for OSA in BQ is more common in patients of IIH. Polysomnography may be warranted in IIH patients with high-risk BQ scores to reduce the morbidities associated with OSA, especially in obese patients.

Knowledge, attitude, and practice regarding different domains of pediatric obstructive sleep apnea among pediatric dentists from Kerala: A cross-sectional study.

Pediatric dentists should play an essential role in obstructive sleep apnea (OSA) screening, referral, and management of their child patients. However, few studies have investigated pediatric dentists' knowledge, attitude, and practice aspects regarding different domains of pediatric OSA. This cross-sectional survey aimed to assess the knowledge, attitude, and practice aspects of different domains of pediatric OSA among pediatric dentists from Kerala. This cross-sectional study was conducted in Kerala. The sample frame consisted of 163 dentists, and the minimum required was 115. Ethical approval was obtained before starting the study. An invitation E-mail explaining the purpose of the study with a survey link was sent to the pediatric dentists from Kerala (n = 163). Nonrespondents were contacted at timely intervals. Categorical and quantitative variables were expressed as frequency (percentage) and mean ± standard deviation, respectively. Chi-square test was used to find an association between categorical variables. Statistical analysis was performed using a statistical software package, SPSS, version 20.0. This cross-sectional study showed that pediatric dentists in Kerala have a moderate-to-good level of knowledge, along with a positive attitude toward different domains of pediatric OSA, but lag behind in the practice aspects. Hence, they are required to direct themselves toward continuing dental education programs in order to boost their routine practice. For this, steps must be taken to ensure proper networking of the practitioners and amendments in the dental billing system similar to the medical counterpart.

Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening

Objective Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea–hypopnea index cutoff thresholds: [Formula: see text] 5 and [Formula: see text] 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database ( N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results For cutoff [Formula: see text] 5/hours, logistic regression model achieved the highest Matthew’s correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff [Formula: see text] 30/hours, support vector machine model achieved the highest Matthew’s correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.

Benefits in quality of life following an obstructive sleep apnea screening and treatment program in patients with acute ischemic stroke.

Obstructive sleep apnea (OSA) has been proposed as a factor that worsens stroke prognosis. Our aim was to determine if an OSA intervention could improve quality of life (QOL, first objective) and modified Rankin score (mRS, second objective). The intervention group of this quasi-experimental study included patients with acute ischemic stroke <72 hours who underwent polygraphy and Continuous Positive Airway Pressure (CPAP) and hygienic-dietary measures if required. The control group followed routine clinical practice. The Short Form 36 Health Survey (SF-36) and mRS were applied at the sixth month after stroke in both groups. Fifty-five vs. sixty-two patients were included in the intervention and control group respectively. In the intervention group, 64.71% of patients accepted the proposed CPAP (16 cases with a good adherence). An improvement in SF-36 items was detected in the intervention group: physical functioning (p = 0.008), role physical (p = 0.002), bodily pain (p = 0.008), general health (p <0.001), vitality (p = 0.001) and role emotional (p = 0.015). In a per-protocol analysis, all these improvements were verified in the group of patients treated with good CPAP adherence (p < 0.05 in all the same SF-36 items). The percentage of patients with physical component summatory = 50 was higher in the intervention group (p = 0.003). There were no differences in the median of mRS (p = 0.262). Although more evidence is needed, a significant improvement in QOL was suggested after our OSA intervention, particularly in patients with good CPAP adherence.

Detection and screening of obstructive sleep apnea in primary care

The most prevalent sleep-related breathing condition, obstructive sleep apnea (OSA), is linked to greater morbidity and poor cardiovascular outcomes. Even though sleep disorders are frequently present, primary care physicians hardly see or treat them. Better OSA symptom and intensity identification and classification to support OSA evaluation and diagnosis were the first outcomes of a task group of the American Academy of Sleep Medicine that produced quality metrics for the treatment of adult patients with the condition in 2015. The OSA screening tools (Berlin questionnaire, Epworth sleepiness scale, STOP Bang) have proven to be effective instruments for screening of sleep disorders. The present primary care practice approach for OSA screening and evaluation is disorganized and inadequate. Patients with OSA symptomatology are seen by primary care physicians, but they are not regularly screened, assessed, or referred to a sleep specialist. For the OSA screening assessments in general practice, more psychometric investigations are required. The findings from these investigations can be applied in real-world settings to improve OSA identification.

Utility of Screening for Obstructive Sleep Apnea with the Pediatric Sleep Questionnaire (PSQ) in Children with Craniofacial Anomalies.

To determine the accuracy of the Pediatric Sleep Questionnaire (PSQ) as a screening tool for obstructive sleep apnea in children with craniofacial anomalies. Retrospective cohort study. Multidisciplinary cleft and craniofacial clinic at a tertiary care center. Children with craniofacial anomalies 2 to ≤18 years of age who both completed a PSQ screen and underwent polysomnography (PSG) without interval surgery. Sensitivity and specificity of the PSQ in detecting an obstructive apnea-hypopnea index (AHI) ≥ 5 events/hour. Fifty children met study criteria, with 66% (n = 33) having an associated syndrome. Mean patient age at time of PSQ was 9.6 + 4.0 years. Overall, 33 (64%) screened positive on the PSQ, while 20 (40%) had an AHI ≥ 5. The sensitivity and specificity for identifying AHI ≥ 5 was 70% and 40%, respectively. With subgroup analysis, the sensitivity and specificity were higher (100% and 50%) in children with non-syndromic palatal clefting but lower (65% and 31%) in children with a syndrome or chromosomal anomaly. There was no correlation detected between PSQ score and AHI severity (p = 0.25). The mean obstructive AHI in the study population was 10.1 ± 22.7 despite 44% (n = 22) undergoing prior adenotonsillectomy. The PSQ was less sensitive and specific in detecting an AHI ≥ 5 in children with craniofacial anomalies than in a general population, and particularly poor in for children with syndrome-associated craniofacial conditions. Given the high prevalence of OSA in this patient population, a craniofacial-specific validated screening tool would be beneficial.

Screening for obstructive sleep apnea in a sample of Egyptian idiopathic intracranial hypertension female patients using the Berlin questionnaire

Screening for obstructive sleep apnea in a sample of Egyptian idiopathic intracranial hypertension female patients using the Berlin questionnaire

Screening for obstructive sleep apnoea in post-treatment cancer patients.

For cancer patients, comorbid obstructive sleep apnea (OSA) poses additional risk to their surgical/anaesthetic outcomes, quality of life, and survival. However, OSA screening is not well-established in oncology settings. We tested two screening tools (STOP-Bang questionnaire [SBQ] and the at-home monitoring device, ApneaLink™Air), for predicting polysomnography (PSG) confirmed OSA in post-treatment cancer patients. Breast (n=56), endometrial (n=37) and melanoma patients (n=50) were recruited from follow-up clinics at Westmead Hospital (Sydney, Australia). All underwent overnight PSG, 137 completed SBQ, and 99 completed ApneaLink™Air. Positive (PPV) and negative (NPV) predictive values for PSG-determined moderate-to-severe OSA and severe OSA, were calculated using an SBQ threshold ≥3 au and ApneaLink™Air apnoea-hypopnea index thresholds of ≥10, ≥15 and ≥30 events/h. Both SBQ and ApneaLink™Air had high NPVs (92.7% and 85.2%-95.6% respectively) for severe OSA, but NPVs were lower for moderate-to-severe OSA (69.1% and 59.1%-75.5%, respectively). PPV for both tools were relatively low (all <73%). Combining both tools did not improve screening performance. These screening tools may help identify cancer patients without severe OSA, but both are limited in identifying those with moderate-to-severe or severe OSA. PSG remains optimal for adequately identifying and managing comorbid OSA in cancer patients.

BS P03 Safety of Laparoscopic Cholecystectomy in patients with BMI &amp;gt; 35 kg/m2 without preoperative screening for Obstructive Sleep Apnoea

Abstract Background Preoperative Obstructive Sleep Apnoea (OSA) screening, followed by sleep studies as indicated, is commonly performed for patients undergoing bariatric surgery in our hospital but not for patients with similar weight undergoing other surgeries such as laparoscopic cholecystectomy. The purpose of this audit was to understand if laparoscopic cholecystectomy can be performed safely in patients with a Body Mass Index (BMI) ≥35 kg/m2 without such screening. Methods Patients with a BMI ≥ 35 kg/m2 undergoing elective laparoscopic cholecystectomy between 2nd May 2014 and 4th April 2022 were included. Patients with known OSA were excluded and no patients were referred for sleep studies. Data on patient demographics, co-morbidities, and any intraoperative or postoperative surgical complications were extracted and analysed. Univariate associations between different preoperative and operative variables and 30-day morbidity were studied using the Fisher's exact test. Results A total of 434 patients were included in the audit (64 male, 370 female). The mean age was 49 +/- 15 years. The mean weight and BMI were 110 +/- 20 kg (range 70–79kg) and 37 +/- 6 kg/m2 respectively. The mean operating time was 70 +/- 31 minutes (median=62). Approximately, 92% (398/434) had a day case procedure. The readmission rate at 30-day was 1% (5/434). There were no intraoperative complications. 17 patients had post-operative complications including deep vein thrombosis (n=2), leak in transverse colon secondary to cholecysto-colic fistula (n=1), wound infection (n=9), bile leak and post-op ERCP (n=3), common bile duct stone requiring ERCP (n=1) and pneumonia (n=1). No patients required ITU or HDU admission for further organ support. The 30-day mortality rate was 0% (0/434) while the 30-day morbidity rate was 3.9% (17/434). ASA grade, previous abdominal surgery, hypertension, diabetes, dyslipidemia, smoking history, chronic obstructive pulmonary disease, ischemic heart disease, fatty liver disease, preoperative liver reducing diet, number of ports, and conversion to laparotomy were not associated with increased 30-day morbidity rate (p&amp;lt;0.05) on univariate analysis. Higher BMI values (≥40 and ≥50 kg/m2) were also not associated with increased 30-day morbidity rate (p&amp;lt;0.05). Conclusions This audit demonstrated that laparoscopic cholecystectomy can be performed safely without the need for preoperative sleep studies in patients with BMI ≥35 kg/m2.

Screening for Obstructive Sleep Apnea using Epworth Sleepiness Score and Berlin Questionnaire: Which is Better?

Background: Obstructive sleep apnea (OSA) is a highly prevalent, but underdiagnosed and undertreated disorder. There is a need for simple but accurate tool for early detection of patients based on their clinical symptoms and physical findings into high- or low-risk group and give information for urgent polysomnography (PSG) and further treatment to prevent serious consequences. Epworth Sleepiness Score (ESS) and Berlin Questionnaire (BQ) are the most popular and widely acceptable instruments for identification of high-risk patients of OSA. The aim of our study was to compare these two established and well-known sleep questionnaires regarding their ability to find out the probable cases of OSA in Indian scenario. Methods: This cross-sectional study was conducted at a tertiary care center on 72 adult patients with symptoms of sleep-related breathing disorders. All patients were asked to fill both ESS and BQ questionnaires. Subsequently, all patients were subjected to level-1 PSG. For each patient, the Apnea–hypopnoea index was calculated to assess the diagnosis and severity of OSA and to further compare the ESS and BQ questionnaires for their sensitivity (SN) in OSA patients. Results: A total of 14 (19.4%) patients were diagnosed as OSA by ESS while by BQ, 32 (44.4%) patients were diagnosed as having OSA with SN of 31% and 71.11%, respectively (P = 0.00004). The positive predictive value was 82.3% and 88.8% for ESS and BQ, respectively, with a negative predictive value of 43.6% by ESS and 63.8% by BQ questionnaires. Conclusion: ESS is a less sensitive diagnostic tool for early detection of high-risk patients of OSA in general population. BQ is a valid, reliable, and more sensitive parameter to screen patients for OSA and may help in improving the quality of life in such patients with proper OSA management.