Trastornos respiratorios del sueño en Insuficiencia Cardíaca con fracción de eyección reducida

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Introduction: Sleep-disordered breathing (SDB) are highly prevalent in patients with heart failure (HF). The presence of obstructive sleep apnea syndrome (OSA) determines a worse prognosis in these patients. There are questionnaires aimed at evaluating the probability of OSA, although none have been validated in patients with HF. The primary objective of this study was to establish the prevalence of SDB in a cohort of patients with HF and reduced ejection fraction (HFrEF) from the Multidisciplinary HF Unit (UMIC). As a secondary objective, to evaluate the usefulness of the Stop-Bang, Berlin, and 2ABN3M questionnaires for TRS screening in these patients. Methodology: Cross-sectional, observational study, including the active cohort of the UMIC, over 18 years with HFrEF, clinically stable and informed consent. Patients with cognitive, neurological or hearing impairment with limitations when conducting the interview were excluded. Patients with other limiting or uncontrolled sleep disorders, continuous home oxygen therapy requirements, did not enter the study. Berlin, Stop-Bang, and 2ABN3M questionnaires were administered, classifying the population into high-risk, intermediate-risk, and low-risk groups of presenting SDB. All patients underwent outpatient respiratory polygraphy (RP). Descriptive statistics were used to characterize demographic variables, measures of central tendency and dispersion. SPSS statistical software was used. Results: 387 patients were included, 248 men (64.1%), mean age was 63.5 ± 0.6 years. The etiology of HF was ischemic in 41.6% of patients. The body mass index was 29.3 ± 0.3 kg/m2. LVEF was 34.2 ± 0.5, pro-BNP 1233.8 ± 137.6 pg/ml. The results of the questionnaires showed that 52.1% (198) presented a high risk of SDB according to the Berlin questionnaire. With Stop-Bang, 35.9% (139) were high risk, 42.1% (163) intermediate risk, and the remaining 22% (85) low risk. With the 2ABN3M score, 62% (240) were high risk. A total of 156 respiratory polygraphs (40.3% of the population) were performed. The cut-off point to define the presence of sleep apnea was considered to be an AHI >15. 58.3% (91) of the patients presented TRS. Of these, 95% presented obstructive apnea and 5% central apnea with periodic Cheyne-Stokes breathing. A high percentage (26%) presented AHI greater than 30. The sensitivity of the Berlin and Stop-Bang questionnaires was 75.8% and 91.2%, respectively, with a specificity of 53.8% and 24.6%. Regarding the 2ABN3M score, a sensitivity of 71.4% and a specificity of 44.6% were observed. Conclusions: The prevalence of sleep-disordered breathing in patients with HFrEF was high in our cohort and obstructive apnea predominated. Given the high sensitivity (91.2%) of the Stop-Bang questionnaire found in our study, it could be useful as a screening tool for TRS in this type of patient. The importance of investigating this pathology whose clinical presentation can be non-specific and remain underdiagnosed is highlighted.

Introdução: Os distúrbios respiratórios do sono (DRS) são altamente prevalentes em pacientes com insuficiência cardíaca (IC). A presença da síndrome da apneia obstrutiva do sono (SAOS) determina pior prognóstico nesses pacientes. Existem questionários destinados a avaliar a probabilidade de AOS, porém nenhum foi validado em pacientes com IC. O objetivo primário deste estudo foi estabelecer a prevalência de DRS em uma coorte de pacientes com IC e fração de ejeção reduzida (ICFEr) da Unidade Multidisciplinar de IC (UMIC). Como objetivo secundário, avaliar a utilidade dos questionários Stop-Bang, Berlin e 2ABN3M para triagem de SRT nesses pacientes. Metodologia: Estudo transversal, observacional, inclui a coorte ativa da UMIC, maiores de 18 anos com ICFEr, clinicamente estável e consentimento informado. Foram excluídos pacientes com deficiência cognitiva, neurológica ou auditiva com limitações na realização da entrevista. Pacientes com outros distúrbios do sono limitantes ou descontrolados, requisitos de oxigenoterapia domiciliar contínua, não entraram no estudo. Os questionários Berlin, Stop-Bang e 2ABN3M foram aplicados, classificando a população em grupos de alto risco, risco intermediário e baixo risco de apresentar DRS. Todos os pacientes foram submetidos à poligrafia respiratória (PR) ambulatorial. A estatística descritiva foi utilizada para caracterizar as variáveis ​​demográficas, medidas de tendência central e dispersão. Foi utilizado o software estatístico SPSS. Resultados: foram incluídos 387 pacientes, 248 homens (64,1%), com idade média de 63,5 ± 0,6 anos. A etiologia da IC foi isquêmica em 41,6% dos pacientes. O índice de massa corporal foi de 29,3 ± 0,3 kg/m2. FEVE foi de 34,2 ± 0,5, pro-BNP 1233,8 ± 137,6 pg/ml. Os resultados dos questionários mostraram que 52,1% (198) apresentaram alto risco de DRS de acordo com o questionário de Berlim. Com Stop-Bang, 35,9% (139) eram de alto risco, 42,1% (163) de risco intermediário e os restantes 22% (85) de baixo risco. Com a pontuação 2ABN3M, 62% (240) eram de alto risco. Foram realizados 156 polígrafos respiratórios (40,3% da população). O ponto de corte para definir a presença de apneia do sono foi considerado um IAH >15. 58,3% (91) dos pacientes apresentaram SRT. Destes, 95% apresentavam apnéia obstrutiva e 5% apnéia central com respiração Cheyne-Stokes periódica. Uma alta porcentagem (26%) apresentou IAH maior que 30. A sensibilidade dos questionários Berlin e Stop-Bang foi de 75,8% e 91,2%, respectivamente, com especificidade de 53,8% e 24,6%. Em relação ao escore 2ABN3M, observou-se sensibilidade de 71,4% e especificidade de 44,6%. Conclusões: A prevalência de distúrbios respiratórios do sono em pacientes com ICFEr foi alta em nossa coorte, com predominância de apneias obstrutivas. Dada a alta sensibilidade (91,2%) do questionário Stop-Bang encontrado em nosso estudo, ele pode ser útil como uma ferramenta de triagem para ERT nesse tipo de paciente. Ressalta-se a importância da investigação dessa patologia cuja apresentação clínica pode ser inespecífica e permanecer subdiagnosticada.

Obstructive sleep apnea in surgical patients and its relationship with difficult intubation: two years of experience from a single center.

BACKGROUND AND OBJECTIVES: In this study, we aimed to determine the risk of obstructive sleep apnea (OSA) in patients undergoing elective surgery and its relationship with difficult intubation (DI). METHODS: This prospective, descriptive, cross-sectional study was conducted between December 2018 and February 2020 in the anesthesiology and reanimation service of a training and research hospital. The study included patients who were ASA I...II, 18 years of age, and older who underwent elective surgery under general anesthesia. A form regarding the baseline characteristics of the participants as well as STOP-Bang score, Mallampati, and Cormack-Lehane classification was used to collect the data. RESULTS: The study included 307 patients. It was determined that 64.2% of patients had a high risk of OSA. The presence of DI (determined by repeated attempts at intubation) was 28.6% in the high-risk OSA group, while there was no DI in the low-risk OSA group. A statistically significant difference was found between the groups in terms of OSA risk according to the presence of DI according to repeated attempts, Cormack-Lehane classification, and Mallampati classification (p...<...0.001). CONCLUSION: Due to the high rate of DI in patients with a high risk of OSA, the security of the airway in these patients is endangered. Early clinical recognition of OSA can help in designing a safer care plan.

Obstructive sleep apnea in surgical patients and its relationship with difficult intubation: two years of experience from a single center

Abstract Background and objectives: In this study, we aimed to determine the risk of obstructive sleep apnea (OSA) in patients undergoing elective surgery and its relationship with difficult intubation (DI). Methods: This prospective, descriptive, cross-sectional study was conducted between December 2018 and February 2020 in the anesthesiology and reanimation service of a training and research hospital. The study included patients who were ASA I-II, 18 years of age, and older who underwent elective surgery under general anesthesia. A form regarding the baseline characteristics of the participants as well as STOP-Bang score, Mallampati, and Cormack-Lehane classification was used to collect the data. Results: The study included 307 patients. It was determined that 64.2% of patients had a high risk of OSA. The presence of DI (determined by repeated attempts at intubation) was 28.6% in the high-risk OSA group, while there was no DI in the low-risk OSA group. A statistically significant difference was found between the groups in terms of OSA risk according to the presence of DI according to repeated attempts, Cormack-Lehane classification, and Mallampati classification (p < 0.001). Conclusion: Due to the high rate of DI in patients with a high risk of OSA, the security of the airway in these patients is endangered. Early clinical recognition of OSA can help in designing a safer care plan.

Evaluación de riesgo de apnea obstructiva del sueño y de la adherencia al tratamiento farmacológico en pacientes con hipertensión arterial / Risk assessment of obstructive sleep apnea and adherence to pharmacological treatment in patients with arterial hypertension

Introducción:existe una sospecha sobre la relación bidireccional entre la apnea obstructiva del sueño (AOS) y la hipertensión arterial (HTA). Ambas ejercen una acción sinérgica sobre desenlaces cardiovasculares porlo quees trascendente ponderar la prevalencia de riesgo para AOS en los hipertensos. En este último grupo también hemos investigado la tasa de adherencia a los fármacos prescritos. Metodología:mediante un estudio de casos y controles y con la aplicación del cuestionario STOP-BANG se han discriminado las categorías de riesgo para apnea de sueño en las dos cohortes. Para el análisis de la adherencia a fármacos antihipertensivos se utilizó el cuestionario abreviado de Morisky. Resultados:se incluyeron a 590 individuos (295 casos y 295 controles. Se observó alto riesgo para AOS en el grupo de hipertensos (36,6%) comparado con el 14,2% del grupo control. Por otro lado, el sexo masculino OR 7,77 (IC95% 4,33-13,84), la obesidad OR 5,03 (IC95% 3,11-8,13) y la HTA OR 4,31 (IC95% 2,64-7,03) se ponderan significativos en un modelo de ajuste logístico aquí estudiado. El 61,69% de los hipertensos refería adherencia al tratamiento farmacológico prescrito. Discusión:el tamizaje de AOS es factible con un cuestionario aplicable en la práctica clínica diaria. De la probabilidad clínica pre-test hay que partir hacia métodos diagnósticos específicos para el diagnóstico de AOS, enfatizando casos de HTA resistente, HTA nocturna y HTA enmascarada. Se deberían realizar estudios locales que nos ayuden a comprender las causas de la falta de adherencia a fármacos antihipertensivos en una fracción importante de los individuos con HTA

Introduction:there is a suspicion about the bidirectional relationship between obstructive sleep apnea (OSA) and arterial hypertension (AHT). Both have a synergistic action on cardiovascular outcomes, so it is important to assess the prevalence of risk for OSA in hypertensive patients. In this last group we have also investigated the rate of adherence to prescribed drugs.Metodology:through a case-control study and with the application of the STOP-BANG questionnaire, the risk categories for sleep apnea in the two cohorts have been discriminated. For the analysis of adherence to antihypertensive drugs, the abbreviated Morisky questionnaire was used. Results:590 individuals were included (295 cases and 295 controls. A high risk for OSA was observed in the hypertensive group (36.6%) compared to 14.2% in the control group. On the other hand, the male sex OR 7.77 (95%CI 4.33-13.84), obesity OR 5.03 (95%CI 3.11-8.13) and hypertensionOR4.31(95%CI 2.64-7.03) they areweighted significant in a logistic adjustment model studied here.61.69% of hypertensive patients reported adherence to the prescribed pharmacological treatment.Discussion:OSA screening is feasible with a questionnaire applicable in daily clinical practice. From the pre-test clinical probability, specific diagnostic methods for the diagnosis of OSA must be started, emphasizing cases of resistant AHT, nocturnal AHT, andmasked AHT. Local studies should be carried out to help us understand the causes of non-adherence to antihypertensive drugs in a significant fraction of individuals with AHT

Biochemical Markers and Obstructive Sleep Apnea Risk in Individuals After Long-Term Bariatric Surgery.

BACKGROUND: Individuals who have undergone long-term bariatric surgery may be at increased obstructive sleep apnea (OSA) risk. The purpose of this study was to estimate the frequency of OSA risk and its associations, via biochemical markers, in patients who have undergone long-term bariatric surgery. METHODS: This cross-sectional study evaluated patients after 5 years or more post Roux-en-Y gastric bypass. Biochemical markers, anthropometrics, and OSA risk, via the STOP-Bang score screening tool, were evaluated. Independent Student t, Pearson's chi-squared, or correlation tests were applied, according to total OSA risk score groups or its isolated components. RESULTS: Among the 77 patients evaluated (88.3% female; body mass index = 32.7 ± 5.8 kg/ m2; postoperative time = 9.9 ± 3.1 years), 36 were at risk for OSA. OSA risk score was positively correlated to high-sensitivity C-reactive protein levels (r2 = 0.270; p = 0.025), triglycerides (r2 = 0.338, p = 0.004), total cholesterol (r2 = 0,262; p = 0,028), and HbA1c (r2 = 0.332; p = 0.005). Compared to each counterpart, basal insulin and triglycerides were higher among those who self-reported witnessed apnea (12.8 ± 6.5 vs 8.1 ± 3.8, p = 0.013; 136.4 ± 41.1 vs 88.5 ± 34.8, p = 0.001, respectively), while levels of total cholesterol and LDL-C were higher in participants who reported tiredness (183.9 ± 27.0 vs 164.8 ± 33.4, p = 0.005; 105.9 ± 24.4 vs 92.0 ± 26.6, p = 0.018). Participants with snoring also had higher levels of triglycerides (107 ± 41.1 vs 83.7 ± 33.9, p = 0.010). CONCLUSIONS: OSA risk was highly prevalent among patients who had undergone long-term bariatric surgery, as noted via increased STOP-Bang scores, as were isolated components related to inflammatory markers and lipid and glycemic profile.

Uso de psicofármacos y riesgo de apnea del sueño en pacientes internados en hospital general / Psychotropic drugs and obstructive sleep apnea risk use in hospitalized medical patients

RESUMEN: Se realizó un estudio descriptivo observacional, de corte transversal, con el objetivo de identificar la asociación del consumo de psicofármacos y el aumento del riesgo de padecer apnea obstructiva del sueño (A.O.S.), en pacientes internados y bajo tratamiento con psicofármacos en Hospital General (Hospital Pasteur, Montevideo, Uruguay) durante julio-septiembre de 2019. Se aplicó el cuestionario STOP BANG, hallándose riesgo elevado de A.O.S en el 59,4% de la muestra, del cual 75,6% corresponde al sexo masculino y el 24,4% corresponde al sexo femenino. El riesgo elevado para A.O.S fue: 54,3% para pacientes en tratamiento con un solo psicofármaco y 71,4% con dos. El grupo de antipsicóticos fue el que se asoció con mayor frecuencia al riesgo elevado de A.O.S.

SUMMARY A cross-sectional study was conducted with the objective of identifying the link between psychotropic medications and an increased risk of suffering from obstructive sleep apnea (OSA) in patients under treatment with psychotropic medication who were hospitalized in General Hospital (Hospital Pasteur, Montevideo, Uruguay) during the July-September 2019 period. The STOP BANG questionnaire was applied, elevated risk of OSA was found in 59.4% of the sample, of which 75.6% were male, while 24.4% were female. The elevated risk of OSA was: 54.4% for patients under treatment with a single psychotropic medication and 71.4% for patients under treatment with two psychotropic medications. Antipsychotics were the most frequently group of psychotropic drugs linked to an elevated OSA risk.

Risk of obstructive sleep apnea and traffic accidents among male bus drivers in Ecuador: Is there a significant relationship?

BACKGROUND: Obstructive sleep apnea (OSA) represents an important occupational health concern in the transportation industry, affecting a substantial percentage of transportation operators. Our study aims to determine the frequency of individuals at high risk of obstructive sleep apnea, and excessive daytime sleepiness, as well as any potential association between these conditions and traffic accidents among a sample of Ecuadorian bus drivers. METHODS: We conducted a cross-sectional study involving 340 commercial bus drivers from Ecuador. Descriptive statistics were used to determine frequency and proportions for demographic and clinical variables. A Kendall's tau-b was performed to ascertain the relationship between the STOP-Bang score towards the Epworth Sleepiness Scale (ESS) score and the number of accidents and near accidents. RESULTS: In general, 18.5% (n = 63) of participants were found to be at high-risk for OSA. There was a weak positive correlation between STOP-Bang score and ESS score (τb = 0.244, p = .000). We also found a statistically significant, although negligible, correlation between the STOP-Bang score and the number of accidents (τb = 0.096, p = .039) and near accidents (τb = 0.120, p = .008). CONCLUSION: Our results suggest that a considerable proportion of Ecuadorian bus drivers were at high-risk for obstructive sleep apnea. Higher STOP-Bang scores were correlated with an increased number of accidents and near accidents. Additional studies are needed to determine whether additional interventions could increase road safety by taking care of undiagnosed and untreated OSA cases in a timely manner.

Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study.

BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION: Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI? STUDY DESIGN AND METHODS: This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently. RESULTS: There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P < .01). INTERPRETATION: The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event.

Prevalence of sleep apnea and cardiovascular risk factors in patients with hypertension in a day hospital model.

INTRODUCTION: To identify patients at risk for obstructive sleep apnea (OSA) syndrome at a specialized hypertension center, we administered questionnaires and used respiratory polygraphy (RP). RESULTS: We studied 168 patients (64.8% men and 35.2% women). Patients' body mass index (BMI) was 34.7 ± 6.79 and Epworth Sleepiness Scale (ESS) scores were 8.01 for male and 8.92 for women (p = 0.69). RP recordings revealed AHI (Apnea-Hypopnea Index) of 18.03 ± 15.7, an ODI (Oxygen Desaturation Index) of 18.6 ± 15.2, and a time oxygen saturation <90% (%) of 20.8 ± 24.3. Around 44% of patients had an AHI of >15 events/h, and continuous positive airway pressure (CPAP) was recommended to 69 patients (41.07%). Pulse wave velocity (PWV) showed high values in AHI > 15/h (p = 0.050), and carotid intima-media thickness (IMT) did not correlate with AHI > 15; right IMT: 0.83 ± 1.3 versus 0.78 ± 0.13 mm (p = 0.41) and 0.82 ± 0.16 versus 0.78 ± 0.19 mm (p = 0.40). However, we find correlation with carotid plaque (p = 0.046). The ACC/AHA calculator revealed a gradual increase in the risk of cardiovascular events: 8.7% with AHI < 5/h, and 30.3% in severe OSA. CONCLUSIONS: In hypertension (HT) patients, RP revealed a high prevalence of OSA associated with carotid artery disease, high PWV, and increased cardiovascular risk.

High Rate of Undiagnosed Obstructive Sleep Apnea in Patients Undergoing Total Joint Arthroplasty.

This study described the incidence and severity of obstructive sleep apnea (OSA) and determined the sensitivity and specificity of the STOP-BANG Questionnaire in patients undergoing total joint arthroplasty (TJA) at a military academic medical center. All subjects completed the questionnaire and an unattended sleep study preoperatively. Incidence and severity of OSA (apnea-hypopnea index [AHI] ≥ 5) was calculated. Sensitivity and specificity for STOP-BANG cut scores greater than or equal to 3 and 5 for AHI of 5, 15, and 30 or more were determined. The rate of OSA was 51.2% (42/82), moderate to severe OSA was 29.3% (n = 29), and severe OSA was 7.3% (n = 6). Sensitivity and specificity for a STOP-BANG score of 3 or greater were 85.7% and 43.6% for OSA, 91.7% and 36.8% for moderate OSA, and 100% and 30.7% for severe OSA. A STOPBANG score of 5 or greater increased specificity for mild, moderate, and severe OSA to 84.6%, and 78.9%, and 72%. Patients undergoing TJA have a high rate of undiagnosed OSA. It is recommended to screen these patients using the STOP-BANG, implement OSA risk reduction strategies, and refer patients postoperatively for a sleep study if their STOP-BANG score is at least 3.

Escala de Epwoth y cuestionario de Stop Bang como predictores del síndrome de apneas hipopneas obstructivas del sueño / Epworth Sleepiness Scale and Stop Bang questionnaire as predictors for the obstructive sleep apnea syndrome / Escala de Epworth e questionário PARAR BANG como preditores de hipopnéias apneia obstrutiva do sono. Síndrome do sono

Introducción: El SAHOS (Síndrome de Apneas e Hipopneas Obstructivas del Sueño) surge de apneas e hipopneas que generan una hipoxia intermitente. La polisomnografía es el gold standard para su diagnóstico. La Escala de Somnolencia de Epworth (ESS) identifica pacientes con somnolencia diurna. El cuestionario Stop Bang reconoce pacientes con riesgo de SAHOS. El objetivo es describir la sensibilidad y especificidad de la ESS y Stop Bang para el diagnóstico de SAHOS realizado con polisomnografía. Métodos: 125 pacientes completaron la ESS, Stop Bang y realizaron una polisomnografía de noche completa. Se confeccionaron dos grupos: pacientes con IAH < 15, y pacientes con IAH ≥ 15. Se calcularon sensibilidad, especificidad, razón de probabilidades (OR) y curvas ROC para el diagnóstico de SAHOS de la ESS y el Stop Bang. Resultados: La prevalencia del grupo IAH ‹ 15 fue de 36%, y del grupo IAH ≥ 15 fue de 64%. Para la ESS, 71 pacientes presentaron somnolencia diurna, 49,3% con un IAH < 15 y 50,7% con un IAH ≥ 15. Especificidad 77,78%, sensibilidad 55%, área bajo la curva ROC 0,6553. Para el cuestionario Stop Bang, 110 pacientes presentaron alto riesgo para SAHOS, 30% con un IAH < 15 y 70% con IAH ≥ 15. Especificidad 26,67%, sensibilidad 96,25%, área bajo la curva ROC 0,7671. Se enfrentaron ambos cuestionarios y calcularon sus OR: ESS, OR=1,1014 (p=0,038); Stop Bang, OR=8,099 (p=0,002). Conclusiones: La sensibilidad de ESS es baja y su área bajo la curva ROC poco significativa. La gran sensibilidad del cuestionario Stop Bang junto con su área bajo la curva ROC, lo convierten en una herramienta de importancia para realizar screening de SAHOS.

Introduction: osa (obstructive sleep apnea) arises from apneas and hypopneas that cause intermittent hypoxia. Polysomnography is the gold standard for its diagnosis. The Epworth Sleepiness Scale (ESS) measures daytime sleepiness. The Stop Bang Questionnaire (SBQ) recognizes patients at risk of OSA. Objectives: describe the sensitivity and specificity of the ESS and SBQ for the diagnosis of OSA accomplished by polysomnography. Methods: 125 adult patients completed the ESS, SBQ and a full night polysomnography. Patients were grouped into two: those with AHI < 15 and those with AHI ≥ 15. Sensibility, specificity, odds ratio (OR) and ROC curves were determined for the ESS and SBQ. Results: The group with AHI ≥ 15 prevailed (64%). 71 patients (56.8%) showed an abnormal ESS´s score; 49.3% showed an AHI < 15 and 50.7% AHI ≥ 15. The specificity was 77.78% and sensitivity 55%. The area under the ROC curve was 0.6553. Regarding the SBQ, 110 patients were within the high risk group; 30% corresponded to an AHI < 15 and 70% AHI ≥ 15. The specificity was 26.67% and sensitivity 96.25%. The area under the ROC curve was 0.7671. The OR for the ESS was 1.1014 (p=0.038) and SBQ, OR = 8.099 (p=0.002). Conclusion: The sensitivity of the ESS is low and the area under the ROC curve insubstantial. The SBQ shows high sensitivity and a remarkable area under the ROC curve, which turn it into an important tool for screening OSA.

Introdução: sahos (síndrome da apneia e hipopneia obstrutiva do sono) surge de apnéias e hipopnéias que geram hipóxia intermitente. A polissonografia (PSG) é o gold standard para o diagnóstico. A Escala de Sonolência de Epworth (ESS) identifica pacientes com sonolência diurna. O questionário Stop bang reconhece pacientes em risco de doenca de SAHOS. O objetivo de este trabalho é descrever a sensibilidade e especificidade da ESS e do questionario Stop Bang para diagnóstico de SAHOS feito coma PSG. Métodos: 125 pacientes completaram a ess, o stop bang efisseram uma psg con oximetria de noite completa. Dividiram-se os pacientes em dois grupos: com IAH < 15 e 50,7% com um IAH ≥ 15. A especificidade foi de 77,78%, a sensibilidade de 55%, e a área abaixo da curva ROC 0,6553. Enquanto ao questionário stop bang, 110 pacientes apresentaram alto risco de SAHOS, 30% com um IAH < 15 e 70% com IAH ≥ 15. Especificidade de 26,67%, 96,25% de sensibilidade, e 0,7671 da área abaixo da curva. Se comparam ambos questionários e foi calculada sua OR: ESS, OR = 1,1014 (p = 0,038); Stop Bang, OR = 8,099 (p = 0,002). Conclusões: a sensibilidade ess é baixa e a área baixo da curva roc insignificante. A alta sensibilidade do questionário Stop Bang junto com a área baixo da curva ROC o tornam uma ferramenta muito importante para o sreening de esta doença.

Utility of the STOP-BANG components to identify sleep apnea using home respiratory polygraphy.

BACKGROUND: Utility of questionnaires to estimate the probability of obstructive sleep apneas (OSA) is varying, and it is challenging to know the performance of STOP (Snore, Tired, Observed apnea, and Pressure)-BANG (BMI, Age, Neck and Gender) with simplified methods. To assess the performance of STOP-BANG and its ability to predict sleep apnea in patients with high pre-test like-hood to present OSA referred for a home respiratory polygraphy (RP) were studied. METHOD: A cross-sectional study of patients recruited over 26 months was performed. They were asked to complete the STOP-BANG questionnaire during evaluation prior to RP and were evaluated according to different apnea-hypopnea index (AHI) cut-offs. Areas under receiver operating characteristic (ROC) curves and multiple logistic regression models were calculated. RESULTS: Eight hundred sixty-nine patients were studied; 557 were male (64.1 %) with a median age of 52.82 ± 14.43 years, a body mass index (BMI) of 32.88 ± 8.51, and Epworth Sleepiness Scale (ESS) score of 7.95 ± 5.17. The performance for AHI ≥5/h (ROC area) was: STOP 0.62, BANG 0.66, and STOP-BANG 0.69. The best sensitivity (S)-specificity (Sp) relationship for AHI ≥5/h was found with 5 components in any combination (S 56.02 %; Sp 70 %). For AHI ≥30/h, STOP was 0.68, BANG 0.66 and STOP-BANG 0.73 and the best S-Sp relationship was obtained with 5 components (S 68 %; Sp 63.6 %). Six variables (snoring, observed apneas, high blood pressure (HBP), BMI >35, neck perimeter >40 cm, and male gender) showed the best performance for AHI >30/h; ROC area 0.76. CONCLUSION: STOP-BANG shows different discrimination power for AHI >5 and ≥30/h using RP. Five components in any combination have acceptable diagnostic S to identify patients with severe OSA. STOP-BANG performed best to identify AHI ≥30/h.

Utilidad de los componentes del cuestionario Stop-Bang para identificar pacientes con apneas del sueño / Usefulness of the components of the Stop-Bang questionnaire to identify patients with sleep apnea

Introducción: Los cuestionarios para calcular la probabilidad de padecer apneas del sueño (SAHOS) tienen utilidad variable, por lo que resultaría interesante conocer el desempeño del cuestionario STOP-BANG en nuestra población de alto riesgo usando métodos simplificados de diagnóstico. Objetivo: Evaluar el desempeño de STOP-BANG y su capacidad de predicción para identificar un índice de apneas e hipopneas por hora de registro (IAH) elevado en pacientes con sospecha clínica de apneas del sueño derivados para la realización de una poligrafía respiratoria domiciliaria auto-administrada (PR) de nivel III. Métodos: Estudio longitudinal en pacientes referidos para PR (nivel III) durante catorce meses. Las habilidades de STOP-BANG para discriminar pacientes con SAHOS para cada grado de severidad se validaron contra los resultados de la PR usando el IAH. Se evaluaron la combinación de síntomas (STOP), los parámetros antropométricos (BANG) y STOP-BANG para cada punto de corte propuesto en el IAH manual (>5 y ≥30/hora) y se construyeron modelos de regresión logística múltiple expresando Odds Ratio (OR) con sus intervalos de Confianza (IC) para el 95% para cada uno de los componentes. Se evaluaron en cada modelo el poder de discriminación, calculando el área bajo la curva ROC y la bondad de ajuste mediante la prueba de Hosmer-Lemershow. Resultados: Se estudiaron 299 pacientes. 194 fueron hombres (64.9%), media de 52.77 años (SD: 14.67) e IMC de 32.49 (SD: 7.67). 161 casos (53.8%) presentaron un índice de masa corporal (IMC) >30 (obesos). El desempeño para IAH >5/hora (área bajo la curva ROC) para cada combinación del número de componentes presentes fue; STOP: 0.58, BANG: 0.66 y STOP-BANG: 0.66. La mejor relación sensibilidad (S) y especificidad (E) para la identificación de IAH >5/h se obtuvo con tres componentes de STOP en cualquier combinación posible (S: 52.97%; E: 60%) y con dos componentes de BANG (S: 79%; E: 43.75%). Para un IAH ≥ 30/h el área bajo la curva ROC para cada combinación fue; STOP: 0.67, BANG: 0.67, y STOP-BANG: 0.73 y la mejor relación S-E se obtuvo con dos componentes de STOP (S: 79% - E: 43.75%). De manera similar, 3 componentes de BANG alcanzaron una S de 61.7% y E de 65.48%. Cinco componentes de STOP-BANG (cualquier combinación) alcanzaron una S de 60.73% y E de 65.00% (RV+: 1.73- RV-: 0.60). Finalmente, utilizando selector automático de variables para los ocho componentes de STOP-BANG hallamos un modelo para predecir IAH ≥30/hora formado por; apneas observadas (O): OR: 3.62 (CI 95%: 1.69-7.77) p= 0.001, IMC >30 (B): OR: 2.51 (CI95%: 1.19-5.28) p= 0.015 y sexo masculino (G): OR: 6.63 (CI95%: 2.39-18.3) p= 0.0001 (Área bajo la curva; 0.75. Bondad de ajuste: 0.722). Conclusiones: STOP-BANG muestra un comportamiento diferente para IAH >5 y ≥ 30/ hora cuando se utiliza PR. La combinación STOP muestra escasa capacidad de discriminación para IAH >5/hora y este comportamiento difiere de los resultados publicados con polisomnografía en el laboratorio de sueño. Las variables antropométricas (BANG) muestran buena capacidad de discriminación evaluada por el área bajo la curva del modelo para ambos puntos de corte en el IAH analizados. Cinco componentes de STOP-BANG en cualquier combinación tienen una S diagnóstica elevada para identificar pacientes con alteraciones respiratorias del sueño de grado severo. Mostraron buen desempeño como predictores tres variables antropométricas (IMC, edad y sexo masculino) siendo esta última la de mayor peso para identificar IAH patológico (>5/hora) o elevado severo (≥30/h). En nuestra población el modelo de predicción O-G-B obtuvo el mejor desempeño.

Purpose: The questionnaires used to estimate the probability of suffering from obstructive sleep apnea (OSA) have variable utility. The ability of the STOP-BANG questionnaire has not been evaluated in our high risk population. Aims: The aim of this study was to evaluate the ability of the STOP-BANG assessment tool to predict sleep hourly apnea-hypopnea index (AHI) in patients with high clinical suspicion compared to a self-administered home level III respiratory polygraphy (RP). Methods: We conducted a longitudinal study in patients referred to RP (level III) over fourteen months. The ability of STOP-BANG questionnaire to identify patients with OSA for each severity grade was validated against the results of RP using AHI. The relationships between symptoms (STOP), anthropometrics parameters (BANG) and the combination (STOP-BANG) and AHI (>5 and ≥ 30/hour) were evaluated using multiple logistic regression linear models expressing Odds Ratio (OR) with 95% confidence intervals (CI) for each of the components. For each model, we studied the discrimination power by calculating the area under ROC curve and the fitness using the Hosmer-Lemershow test. Results: 299 patients were studied. 194 were male (64.9%), average age was 52.77 years (SD: 14.67) and body mass index (BMI) was 32.49 (SD: 7.67). 161 cases (53.8%) showed BMI > 30 (obesity). The frequency of identifying AHI >5/hour (area under ROC curve) for each measured component were; STOP: 0.58, BANG: 0.66, and STOP-BANG: 0.66. The best relationship between sensitivity (S) and specificity (Sp) for identifying AHI > 5/h was found by using three STOP components in any possible combination (S: 52.97%; Sp: 60%) with two BANG components (S: 79%; Sp: 43.75%). For an AHI ≥ 30/h the area under ROC curve for each combination were; STOP: 0.67, BANG: 0.67 and STOP-BANG: 0.73. The best relation including S-Sp has been obtained with two STOP components (S: 79%-Sp: 43.75%). Similarly, 3 BANG components reached S of 61% and Sp of 65.48%. Five components of STOP-BANG (in each combination) reached S of 60.73% and Sp of 65.00% (RV+: 1.73 - RV-: 0.60). Finally, we used an automatic selector of variables for the eight STOP-BANG components and we found a model to predict AHI ≥ 30/hour formed by; observed apneas (O): OR: 3.62 (CI 95%: 1.69-7.77); p = 0.001, IMC > 30 (B): OR: 2.51 (CI 95%: 1.19 - 5.28); p = 0.015 and male sex (G): OR: 6.63 (CI 95%: 2.39 -18.3); p = 0.0001 (Area under the curve; 0.75. Goodness of fit). Conclusions: The STOP-BANG questionnaire shows different results for AHI >5 and AHI ≥ 30/hour when RP has been used. The STOP combination shows low capacity to discriminate for AHI > 5/hour and this result differs from the results reported with polisomnography in the sleep laboratory. The anthropometric variables (BANG) show good discriminating capacity evaluated by the area under curve of the model for both cutoff in the analyzed AHI. Five STOP-BANG components in any combination have a high diagnostic sensitivity to identify patients with sleep respiratory disturbance in severe grade. Three anthropometric variables showed good performance as predictors (BMI, age and male sex); the last one was the most important to identify pathologic AHI (> 5/hour) or severe high AHI (≥30/h). In our population the prediction model O-G-B had the best performance.