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.

Mortalidad a 30 días en neumonías adquiridas en la comunidad graves por SARS-COV-2 y otros virus respiratorios en Chile / Risk of mortality of severe SARS-CoV-2 infection and other causes of viral pneumonia in Chile

INTRODUCCIÓN: En Chile, los virus respiratorios son una causa frecuente de neumonía adquirida en la comunidad (NAC), con admisión a unidades de paciente crítico en adultos. Los agentes etiológicos asociados son influenza A y B, virus respiratorio sincicial (VRS) y Hantavirus, sumándose el SARS-CoV-2 desde 2020. OBJETIVO: Identificar variables clínicas y de laboratorio asociadas a mortalidad a 30 días en NAC virales graves en un centro del sur de Chile. Metodología: Estudio observacional, se agruparon dos cohortes de pacientes con NAC grave según criterio IDSA/ATS (años 2013-2018, "No COVID-19") y año 2020 ("COVID-19"). Se recolectaron datos sociodemográficos, clínica, laboratorio y mortalidad a 30 días. Se utilizaron pruebas de Chi-cuadrado y Prueba t- student, para variables categóricas y continuas respectivamente. La mortalidad se evaluó mediante regresión logística binaria, con resultados reportados como Odd ratios (ORs). RESULTADOS: La mortalidad a 30 días fue: Hanta virus 54.5%, H1N1 36,8%, 30,4% otras influenza, 25% VRS y 23,6% para COVID-19. Sin diferencia significativa entre el tipo de virus (COVID-19 o NO COVID-19). La mortalidad se asoció con edad > 65 años (OR: 4,6; p 65 años, inmunosupresión, cianosis y uremia al ingreso se asociaron con mayor mortalidad a 30 días en los ingresos por NAC viral grave.

INTRODUCTION: Severe community-acquired pneumonia (CAP) due to respiratory viruses is highly prevalent in Chile. Common etiologies include Influenza A and B, respiratory syncytial virus (RSV), Hantavirus, and SARS-CoV-2 since 2020. OBJECTIVE: To identify clinical and laboratory features associated with 20-day mortality in severe viral CAP in a high complexity health care center in southern Chile. METHODS: The observational study included two cohorts of patients with severe CAP according to IDSA/ATS criteria: the years 2013-2018 (No COVID-19) and the year 2020 (COVID-19). Sociodemographic, clinical, laboratory, and 30-day mortality data were collected. We used Chi-square and Student's T for categorical and continuous variables. We used a binary logistic regression model for mortality analysis, reporting the results as Odd ratios (ORs). RESULTS: Mortality at 30 days was: Hantavirus 54.4%, Influenza H1N1 36.8%, other influenza 30.4%, RSV 25%, and COVID-19 23.6%. We found no significant difference regarding type of virus (COVID-19 or NO COVID-19). Mortality was associated with older age (OR: 4.6; p-value < 0.01), immunosuppression (OR: 5.8; p-value 0.01), and cyanosis (OR: 3.8, p-value 0.02). Conclusion: COVID-19 was not associated with an increased risk of 30-day mortality compared to other common respiratory viruses in our study. Older age, immunosuppression, and cyanosis were associated with higher risk among patients with severe viral CAP.

[Risk of mortality of severe SARS-CoV-2 infection and other causes of viral pneumonia in Chile]. / Mortalidad a 30 días en neumonías adquiridas en la comunidad graves por SARS-COV-2 y otros virus respiratorios en Chile.

INTRODUCTION: Severe community-acquired pneumonia (CAP) due to respiratory viruses is highly prevalent in Chile. Common etiologies include Influenza A and B, respiratory syncytial virus (RSV), Hantavirus, and SARS-CoV-2 since 2020. OBJECTIVE: To identify clinical and laboratory features associated with 20-day mortality in severe viral CAP in a high complexity health care center in southern Chile. METHODS: The observational study included two cohorts of patients with severe CAP according to IDSA/ATS criteria: the years 2013-2018 (No COVID-19) and the year 2020 (COVID-19). Sociodemographic, clinical, laboratory, and 30-day mortality data were collected. We used Chi-square and Student's T for categorical and continuous variables. We used a binary logistic regression model for mortality analysis, reporting the results as Odd ratios (ORs). RESULTS: Mortality at 30 days was: Hantavirus 54.4%, Influenza H1N1 36.8%, other influenza 30.4%, RSV 25%, and COVID-19 23.6%. We found no significant difference regarding type of virus (COVID-19 or NO COVID-19). Mortality was associated with older age (OR: 4.6; p-value < 0.01), immunosuppression (OR: 5.8; p-value 0.01), and cyanosis (OR: 3.8, p-value 0.02). CONCLUSION: COVID-19 was not associated with an increased risk of 30-day mortality compared to other common respiratory viruses in our study. Older age, immunosuppression, and cyanosis were associated with higher risk among patients with severe viral CAP.

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.