Citations

AbstractNocturnal hypoxemia is a prevalent feature of various respiratory diseases, significantly impacting patient outcomes and therapeutic strategies. Oximetry, a noninvasive and widely accessible tool, enables the measurement of nocturnal hypoxemia through oxyhemoglobin saturation (SpO2)-derived metrics such as the oxygen desaturation index, percentage of sleep time with SpO2 below 90%, mean SpO2, and measures of the area under the desaturation curve (e.g., sleep apnea-specific hypoxic burden). While these metrics are well established in obstructive sleep apnea (OSA), their application in other respiratory conditions, including chronic obstructive pulmonary disease, pulmonary hypertension, obesity hypoventilation syndrome, heart failure, neuromuscular disorders, pregnancy, and high-altitude residents, remains an area of active investigation. This review explores the pathophysiology of hypoxemia in these conditions and evaluates the role of SpO2-derived metrics in risk stratification beyond OSA. We also discuss the challenges of interpreting SpO2 data, particularly the difficulty differentiating disease-related hypoxemia from comorbid OSA. Additionally, we examine the limitations of oximetry, including sensor inaccuracies, motion artifacts, and skin pigmentation. Finally, we emphasize the need for further research to standardize these metrics across diverse conditions and advocate for their integration into clinical practice to enhance patient management and outcomes.

Abstract The objective of this study was to estimate the average hospitalization cost (AHC) for deliveries affected by severe maternal morbidity (SMM) and analyze trends from 2014 to 2019. The study also aimed to explore cost stratification based on patient, delivery, and hospital characteristics. Using the National Inpatient Sample dataset, all delivery hospitalizations from 2014 to 2019 were identified. Deliveries affected by SMM were determined based on the Centers for Disease Control definition. Deliveries were categorized into three groups: no SMM (nSMM), any SMM (aSMM), and SMM excluding cases with blood transfusion as the only indicator (SMMeBTo). A regression model accounting for survey design and adjusting for variables including age, race/ethnicity, primary payer, income, delivery method, hospital location/teaching status, and hospital region was used to test the trends in incidence. Hospital charges were adjusted using cost-to-charge ratios and presented in 2022 U.S. dollars ($). A regression model adjusting for the same variables was used to assess costs. From 2014 to 2019, 4,444,957 deliveries were identified, with a weighted estimate of 22,224,775. The rates of aSMM and SMMeBTo were 1.9 and 0.7%, respectively. AHC was $5,218 (95% confidence intervals [CI]: $5,200–5,235) for nSMM, $11,101 (95% CI: $11,038–11,165) for aSMM, and $11,541 (95% CI: $114,330–11,650) for SMMeBTo. Hospitalization costs across all SMM categories rose annually from 2014 to 2017, decreased in 2018, and peaked in 2019. All races had significantly higher costs than non-Hispanic Whites across all SMM categories. SMM costs were higher for cesarean deliveries. The highest cost was in deliveries involving a temporary tracheostomy. Urban teaching hospitals and those in the Northeast had the highest SMM costs. Deliveries affected by SMM incur significantly higher costs, with these costs increasing over time. Understanding disparities across patient factors, delivery methods, and hospital characteristics can inform interventions aimed at addressing inequities.

Objectives: New methods are needed to detect pancreatic ductal adenocarcinoma (PDAC) earlier to improve outcomes. We previously reported that a panel of protein N-glycosylation traits (NGTs) discriminated PDAC from healthy controls with an area under the curve (AUC) of 0.81–0.88. However, it remained unclear whether this panel accurately differentiates PDAC from other benign pancreatic disorders. Our study aims to evaluate the performance of the NGT panel in combination with CA19-9 in a diverse cohort, including PDAC cases, healthy controls, and controls with benign pancreatic disorders. Methods: Protein N-glycosylation profiles were determined in plasma samples using an in-house developed mass spectrometry assay. CA19-9 levels were measured using a routine immunoassay test. Results of total plasma NGTs and CA19-9 were evaluated separately as well as in combination. Logistic regression was performed to calculate odds ratios (ORs), AUC, sensitivity, and specificity to determine the performance of NGTs and CA19-9 in distinguishing PDAC from controls. Results: In total, 221 individuals were included: 45 (20.4%) with PDAC, and 176 (79.6%) controls (53 healthy and 123 with benign pancreatic disease). The AUC for differentiating PDAC from the total control cohort based on the combination of the NGT panel and CA19-9 was 0.94 (95% CI, 0.90–0.97), with a sensitivity of 0.89 (95% CI, 0.78–0.98) and specificity of 0.86 (95% CI, 0.81–0.91). Comparison of PDAC cases with healthy controls only resulted in an AUC of 0.96 (95% CI, 0.93–0.99), with a sensitivity of 0.84 (95% CI, 0.73–0.93) and specificity of 0.98 (95% CI, 0.94–1.00). Conclusions: Both plasma NGTs and CA19-9 distinguish PDAC from a diverse control cohort. The accuracy further improves when these readouts are combined, showing promise for future early detection methods.