Is the time below 90% of SpO2 during sleep (T90%) a metric of good health? A longitudinal analysis of two cohorts.

BACKGROUND: Novel wireless-based technologies can easily record pulse oximetry at home. One of the main parameters that are recorded in sleep studies is the time under 90% of SpO2 (T90%) and the oxygen desaturation index 3% (ODI-3%). We assessed the association of T90% and/or ODI-3% in two different scenarios (a community-based study and a clinical setting) with all-cause mortality (primary outcome). METHODS: We included all individuals from the Sleep Heart Health Study (SHHS, community-based cohort) and Santiago Obstructive Sleep Apnea (SantOSA, clinical cohort) with complete data at baseline and follow-up. Two measures of hypoxemia (T90% and ODI-3%) were our primary exposures. The adjusted hazard ratios (HRs) per standard deviation (pSD) between T90% and incident all-cause mortality (primary outcome) were determined by adjusted Cox regression models. In the secondary analysis, to assess whether T90% varies across clinical factors, anthropometrics, abdominal obesity, metabolic rate, and SpO2, we conducted linear regression models. Incremental changes in R2 were conducted to test the hypothesis. RESULTS: A total of 4323 (56% male, median 64 years old, follow-up: 12 years, 23% events) and 1345 (77% male, median 55 years old, follow-up: 6 years, 11.6% events) patients were included in SHHS and SantOSA, respectively. Every 1 SD increase in T90% was associated with an adjusted HR of 1.18 [95% CI: 1.10-1.26] (p value < 0.001) in SHHS and HR 1.34 [95% CI: 1.04-1.71] (p value = 0.021) for all-cause mortality in SantOSA. Conversely, ODI-3% was not associated with worse outcomes. R2 explains 62% of the variability in T90%. The main contributors were baseline-mean change in SpO2, baseline SpO2, respiratory events, and age. CONCLUSION: The findings suggest that T90% may be an important marker of wellness in clinical and community-based scenarios. Although this nonspecific metric varies across the populations, ventilatory changes during sleep rather than other physiological or comorbidity variables explain their variability.

The U-Shaped Association between Sleep Duration, All-Cause Mortality and Cardiovascular Risk in a Hispanic/Latino Clinically Based Cohort.

Sleep is essential for life, and inappropriate sleep duration patterns may lead to chronic consequences regarding human health. Several studies have confirmed the presence of a U-shaped association between sleep duration and mortality. Moreover, many consequences related to cardiometabolic aspects have been suggested in patients with abnormal sleep durations. In this study, we analyzed the associations between sleep duration, total sleep time (TST), the risk of all-cause mortality, and 10-year cardiovascular risk in a cohort of patients at a sleep medicine center in Santiago, Chile. We conducted a prospective cohort study of patients (SantOSA). A short TST was defined as ≤6 h, a normal TST as 6 to 9 h, and a long TST as ≥9 h. Adjusted hazard ratios (aHRs) for all-cause mortality were calculated. A cross-sectional analysis between TST and 10-year cardiovascular risk (calculated using the Framingham 2008 formula) was determined using logistic regression models. A total of 1385 subjects were included in the results (78% male; median age: 53, interquartile range (IQR): 42-64 years; median BMI: 29.5, IQR: 16.7-33.1). A total of 333 subjects (24%) reported short TSTs, 938 (67.7%) reported normal TSTs, and 114 (8.3%) reported long TSTs. In the fully adjusted model, the association remained significant for short (aHR: 2.51 (1.48-4.25); p-value = 0.01) and long TSTs (aHR: 3.97 (1.53-10.29); p-value = 0.04). Finally, a U-shaped association was found between short and long TSTs, with an increase in cardiovascular risk at 10 years. Compared with normal TSTs, short (≤6 h) and long (≥9 h) TSTs were significantly associated with all-cause mortality and increased 10-year cardiovascular risk.

Clinical Application of the Multicomponent Grading System for Sleep Apnea Classification and Incident Cardiovascular Mortality.

Objective To evaluate the clinical utility of the Baveno classification in predicting incident cardiovascular mortality after five years of follow-up in a clinic-based cohort of patients with obstructive sleep apnea (OSA). Materials and Methods We evaluated the reproducibility of the Baveno classification using data from the Santiago Obstructive Sleep Apnea (SantOSA) study. The groups were labeled Baveno A (minor symptoms and comorbidities), B (severe symptoms and minor comorbidities), C (minor symptoms and severe comorbidities), and D (severe symptoms and comorbidities). Within-group comparisons were performed using analysis of variance (ANOVA) and post hoc tests. The associations between groups and incident cardiovascular mortality were determined through the Mantel-Cox and Cox proportional hazard ratios (HRs) adjusted by covariables. Results A total of 1,300 OSA patients were included (Baveno A: 27.7%; B: 28%; C: 16.8%; and D: 27.5%). The follow-up was of 5.4 years. Compared to Baveno A, the fully-adjusted risk of cardiovascular mortality with Baveno B presented an HR of 1.38 (95% confidence interval [95%CI]: 0.14-13.5; p = 0.78); with Baveno C, it was of 1.71 (95%CI: 0.18-16.2; p = 0.63); and, with Baveno D, of 1.04 (95%CI: 0.12-9.2; p = 0.98). We found no interactions involving Baveno group, sex and OSA severity. Discussion Among OSA patients, the Baveno classification can describe different subgroups. However, its utility in identifying incident cardiovascular mortality is unclear. Long-term follow-up studies and the inclusion of demographic variables in the classification could improve its ability to detect a high-risk phenotype associated with cardiovascular mortality. Conclusion The Baveno classification serves as a valuable method for categorizing varying groups of patients afflicted with OSA. Nevertheless, its precision in identifying occurrence of cardiovascular mortality is still unclear.

A Validation Study of Four Different Cluster Analyses of OSA and the Incidence of Cardiovascular Mortality in a Hispanic Population.

BACKGROUND: Previous studies reported a strong association between sleepiness-related symptoms and comorbidities with poor cardiovascular outcomes among patients with moderate to severe OSA (msOSA). However, the validation of these associations in the Hispanic population from South America and the ability to predict incident cardiovascular disease remain unclear. RESEARCH QUESTION: In Hispanic patients with msOSA, are four different cluster analyses reproducible and able to predict incident cardiovascular mortality? STUDY DESIGN AND METHODS: Using the SantOSA cohort, we reproduced four cluster analyses (Sleep Heart Health Study [SHHS], Icelandic Sleep Apnea Cohort [ISAC], Sleep Apnea Cardiovascular Endpoints [SAVE], and The Institute de Recherche en Sante Respiratoire des Pays de la Loire [IRSR] cohorts) following a cluster analysis similar to each training dataset. The incidence of cardiovascular mortality was constructed using a Kaplan-Meier (log-rank) model, and Cox proportional hazards models were adjusted by confounders. RESULTS: Among 780 patients with msOSA in our cohort, two previous cluster analyses (SHHS and ISAC) were reproducible. The SAVE and IRSR cluster analyses were not reproducible in our sample. We identified the following subtypes for SHHS: "minimally symptomatic," "disturbed sleep," "moderate sleepiness," and "severe sleepiness." For ISAC, three different subtypes ("minimally symptomatic," "disturbed sleep," and "excessive sleepiness") were similar to the original dataset. Compared with "minimally symptomatic," we found a significant association between "excessive sleepiness" and cardiovascular mortality after 5 years of follow-up in SantOSA, hazard ratio (HR), 5.47; 95% CI, 1.74-8.29; P < .01; and HR, 3.23; 95% CI, 1.21-8.63; P = .02, using the SHHS and ISAC cluster analyses, respectively. INTERPRETATION: Among patients with msOSA, a symptom-based approach can validate different OSA patient subtypes, and those with excessive sleepiness have an increased risk of incident cardiovascular mortality in the Hispanic population from South America.

Risk of mortality among patients with moderate to severe obstructive sleep apnea and diabetes mellitus: results from the SantOSA cohort.

INTRODUCTION: Patients with obstructive sleep apnea (OSA) and comorbid diabetes mellitus (DM) are reported to have an increased risk of cardiovascular (CV) outcomes; however, data on CV mortality are scant. AIM: This study aimed to evaluate if patients with comorbid OSA and DM have an increased risk of CV mortality that is higher than the two diseases in isolation. METHODS: In this prospective cohort study, we included patients referred for a sleep study with and without DM at baseline. We developed four study groups as follows: group 1 (reference group), OSA (-) DM (-); group 2, OSA (-) DM (+); group 3, OSA (+) DM (-); group 4, OSA (+) DM (+). Intergroup differences were evaluated using the t test and χ2 test, and multivariate analysis was performed using logistic regression. The incidence rates of CV mortality were calculated using the Kaplan-Meier (log-rank) model, and adjusted HRs were calculated using the Cox regression model. RESULTS: A total of 1447 patients were included in the analysis-group 1: 441 participants; group 2: 141 participants; group 3: 736 participants; group 4: 151 participants. The mean follow-up was 5 years. The association between OSA + DM showed an independent risk of incident CV mortality (HR 2.37, CI 1.16-4.82, p = 0.02) and an increased prevalence of coronary heart disease (OR 3.44, CI 1.73-5.59, p < 0.01). In addition, T90% was also associated with CV mortality. CONCLUSION: The coexistence of OSA + DM was associated with an independent risk of CV mortality. In addition, T90% was also associated with CV mortality.

Hypoxemic features of obstructive sleep apnea and the risk of mortality: a cluster analysis.

INTRODUCTION: Patients with moderate to severe obstructive sleep apnea (OSA) have an increased risk of cardiovascular comorbidities and mortality. Although different subtypes of OSA have been described, data about oximetric parameters and their suitability to identify a different phenotype are scant. In this study, we evaluate the association between moderate to severe OSA and oximetric parameters included in the home sleep apnea test (HSAT) and the risks of all-cause mortality, cardiovascular mortality, and cancer mortality. METHODS: Adult patients with moderate to severe OSA from a clinical cohort in Chile were included (SantOSA study). We developed a latent class analysis (LCA) incorporating oximetric measures commonly reported on HSAT. Differences between the groups were evaluated using ANOVA and the chi-squared test. Survival curves were constructed using a Kaplan-Meier (log-rank) model, and adjusted hazard ratios of mortality were calculated using a Cox regression model following a confounder analysis of cardiovascular comorbidities. RESULTS: A total of 889 patients were included in the analysis. LCA identified three different clusters: Cluster 1, "nonhypoxemic" (n = 591); cluster 2, "moderately hypoxemic" (n = 297); and cluster 3, "severely hypoxemic" (n = 115). The mean follow-up was 4.7 years. The hypoxemic groups showed an increased risk of cardiometabolic comorbidities and an independent risk of all-cause mortality (adjusted HR 1.67 (CI 1.0-2.64) p value = 0.027). The moderately hypoxemic group had an adjusted HR of 2.92 (CI 1.00-8.58), p value = 0.05, while the severely hypoxemic group had an adjusted HR of 2.55 (CI 1.08-6.02), p value = 0.031. For cardiovascular mortality, we found an HR of 2.03 (CI 0.50-8.136), p value = 0.31, and for cancer mortality, we found an HR of 5.75 (CI 1.03-32.17), p value = 0.042. CONCLUSION: Oximetric parameters are useful for describing a different phenotype with a high risk of mortality among patients with moderate to severe OSA, beyond the apnea-hypopnea index.

A clinic-based cluster analysis in patients with moderate-severe obstructive sleep apnea (OSA) in Chile.

RATIONALE: Patients commonly report differences in either clinical or symptomatic profiles, despite having the same severity of obstructive sleep apnea (OSA). OBJECTIVE: To identify clinical and symptomatic phenotypes and to evaluate cardiovascular mortality in each phenotype. METHODS: Data from 1370 participants (788 with moderate-severe OSA and 582 controls as a reference group) were extracted using the SantOSA database. Sixteen variables were analyzed using latent class analysis to define clinical subtypes. The association between subtypes and cardiovascular mortality was evaluated using Kaplan-Meier survival analysis and the Cox proportional hazards model. Adjusted hazard ratios (HRs) with confidence intervals (CIs) were modified by cardiovascular confounders. RESULTS: The median observation period was 5.2 years. We found four clusters: cluster #1: symptomatic men with major comorbidities (n = 252); cluster #2: symptomatic women with comorbidities (n = 154); cluster #3: asymptomatic men with comorbidities (n = 143); and cluster #4: symptomatic young men without major comorbidities (n = 239). In cluster #1, mortality was 4.76% and was independently associated with age (HR 1.12; CI 1.07-1.17), type 2 diabetes mellitus (HR 3.37; CI 1.29-8.78) and coronary heart disease (HR 3.85; CI 1.27-11.56); in cluster #2, mortality was 3.89% and was independently associated with age (HR 1.12; CI 1.06-1.19) and the oxygen desaturation index (ODI, HR 1.02; CI 1.01-1.04); and in cluster #3, mortality was 3.49% (HR 3.50; CI 1.03-11.90) and was independently associated with age (HR 1.19; CI 1.10-1.29). In cluster #4, mortality was 1.25% and showed nonsignificant associations. CONCLUSION: In patients with moderate-severe OSA, we described four phenotypes of patients according to clinical features with different risks of cardiovascular mortality. STUDY REGISTER: ISRCTN62293645.

Clinical utility of oximetric parameters to identify a high-risk phenotype of moderate-severe Obstructive Sleep Apnea (OSA).

It is unclear if oximetric parameters, such as total time of SpO2  < 90%, (T90), oxygen desaturation index-3% (ODI), minimum SpO2 , are able to describe a high-risk subtype of cardiovascular (CV) comorbidities in patients with Obstructive sleep apnea (OSA) beyond the apnea-hypopnea index. OBJECTIVE: To analyzed oximetric variables in patients with moderate-severe OSA to assess their predictive value regarding as hypertension, type 2 diabetes mellitus (T2DM), coronary heart disease (CHD) and CV mortality. METHODS: Using data from SantOSA cohort, we develop receiver operating characteristic curve and area under the curve (AUC) for each parameter, defining the proposed cutoff point in a training set. Then, in a validation set with a 5 years follow-up, we evaluate the clinical differences between groups using the proposed cutoff. We also calculated adjusted Hazard Ratios (HR) of mortality using a Cox regression model. RESULTS: About 965 patients with moderate-severe OSA (525 in training and 440 in validation group) were included. The best AUC was achieved with T90 (AUC = 0.66) and ODI (AUC = 0.61). Proposed cutoffs of T90 were hypertension: 10%, T2DM: 20%, CHD: 15%, meanwhile, proposed cutoff of ODI was ≥ 30 ev for hypertension and T2DM. Regarding CV mortality, T90 ≥ 20% was independently associated with an adjusted HR 2.44 (CI, 1.21-4.94), P-value = 0.01, meanwhile, ODI ≥ 30 ev. reported and adjusted HR 1.59 (CI, 0.75-3.39), P-value = 0.22. CONCLUSION: In patients with moderate-severe OSA, oximetric parameters, especially T90 ≥ 20% remained a predictor of mortality after adjusting for a range of demographic and disease predictors.

Evaluación del Error Dimensional en la Reproducción Tridimensional de una Mandíbula Humana Disecada Mediante Prototipado Rápido de Modelado por Deposición Fundida / Evaluation of the Dimensional Error in the Three-Dimensional Reproduction of a Dissected Human Jaw by Rapid Prototyping of Molten Deposition Modeling

RESUMEN: Las técnicas de prototipado rápido se están utilizando de forma masiva en distintas áreas médicas, siendo de gran utilidad para el manejo preoperatorio y para el ahorro de tiempo clínico en diversos procedimientos quirúrgicos. El propósito de este trabajo fue fabricar un biomodelo mediante impresión 3D utilizando la técnica de modelado por deposición fundida, corroborando que fuese una réplica fiel de una mandíbula humana disecada. Se generó un modelo 3D a partir de la adquisición volumétrica con CBCT de una mandíbula con marcadores craneométricos de referencia y se imprimió utilizando técnica de modelado por deposición fundida. La comparación de ambas mandíbulas se realizó con la medición de las distancias entre los distintos puntos craneométricos, de forma homóloga. No hubo diferencia estadística para los resultados de la comparación entre ambas estructuras, presentando un error dimensional promedio de 0,16 ± 0,11. El modelo obtenido corresponde a una réplica fidedigna de la mandíbula original, permitiendo validar la técnica para posteriores fines clínicos.

ABSTRACT: Rapid prototyping techniques are widely used in different medical areas, being useful for preoperative management and saving clinical time in various surgical procedures. The purpose of this work was the manufacture of a biomodel by the means of 3D printing using the fused deposition modeling technique, proving that it was a faithful replica of a dissected human jaw. A 3D model was generated from the volumetric acquisition with CBCT of a mandible with craniometric reference markers and was printed using fused deposition modeling technique. The comparison of both jaws was made with the measurement of the distances between the different craniometric points, in a homologous way. There was no statistical difference for the results of the comparison between both structures, obtaining an average dimensional error of 0.16 ± 0.11. The manufactured model corresponds to a reliable replica of the original jaw, allowing validation of the technique for later clinical purposes.

Differences in patients derived from otolaryngology and other specialties with sleep apnea.

BACKGROUND: Snoring is a main concern in patients who consult an otolaryngologist (ENT physicians) and patients who have cardiovascular comorbidities or excessive daytime sleepiness who usually consult with other specialists. The aim of this study was to describe the clinical differences in patients with obstructive sleep apnea (OSA) referred from ENT or other specialists. METHODS: A prospective study was carried out between June 2015 and July 2018 in a tertiary center. We included patients with suspected OSA referred by the Home Sleep Apnea Test (HSAT) from different specialties such as ENT or other specialties. The main outcome measures of our study were demographic characteristics, clinical characteristics, sleep questionnaire results and HSAT results between OSA patients referred from ENT or other specialists. We used a t-test and chi-squared test for analysis. The diagnostic accuracy of the sleep questionnaires was achieved using receiver operating characteristic (ROC) curve and the area under the curve (AUC). RESULTS: A total of 481 patients were included. OSA was occurred in 82.4% of the subjects (90 in ENT and 306 in other specialties). Patients with OSA referred from other specialists were older than ENT patients (55 ± 13 vs 44 ± 12; p < 0.001), there was more obesity (IMC 31 ± 5.0 vs 28.7 ± 3.8; p < 0,001), a larger neck circumference (42.2 cm ± 3.7 vs 40.6 cm ± 3.0; p < 0.001) and more reported comorbidities (p < 0.001). ENT patients reported mild OSA (46% vs 31%, p = 0.015) and more positional apnea (62% vs 39%, p = 0.002). In this group, the STOP-BANG questionnaire showed an AUC 0.695 vs AUC 0.804, and for sensitivity, the best cutoff was 4 points. Patients referred from otorhinolaryngology are different from those referred from other specialties. Clinical evaluation and screening of OSA should be patient-centered according to these clinical findings.

Differences between manual and automatic analysis in determining the severity of obstructive sleep apnea using home sleep apnea testing.

INTRODUCTION: Home sleep apnea testing (HSAT) is a diagnostic measure for obstructive sleep apnea hypopnea syndrome (OSAHS) in moderate/high risk patients. Some HSAT companies contain automatic analysis (AA). However, guidelines recommend manual analysis (MA) despite the weak evidence for this recommendation. OBJECTIVE: Evaluate the concordance between AA and MA of HSAT to make either a diagnosis and severity classification. METHODS: We evaluated AA and MA of HSAT between 2015 and 2016. The study was a blind analysis reviewed by two physicians using currents recommendations. The differences between AA and MA were compared with single variable T analysis, inter-scorer agreement for diagnosis was evaluated with Cohen Kappa coefficient, correlation was examined using Tau-b Kendall, and Bland-Altman plot was constructed to analyze differences between AA and MA. RESULTS: One hundred and ninety-eight patients were included. In our study, the mean age was 50 ± 15 years, 83% male, BMI 30 ± 5 and neck circumference 41 ± 4 cm. Eighty-two percent of subjects showed an apnea-hypopnea index (AHI) > 5 ev/h. Thirty-five percent of patients with OSAHS were mild (AHI: 5-15 ev/h), 34% moderate and 31% severe (>30 ev/h). The kappa coefficient between physicians was 1.0 (high), between AA and MA was 0.58 (moderate) for the diagnosis of OSAHS and 0.33 (weak) for severity with 0.70 Tau-b. The AA underestimates the IAH -8 ev/h, (95% CI -9 to -7 ev/h, p < 0.001) and delivers a misclassification of severity by 47%. CONCLUSIONS: AA underestimates the rate of respiratory events and alters the classification of the severity of the disease and may modify the therapeutic approach.