Vaishnavi Kundel works in General / Preventive / Lipids affiliated with Icahn School of Medicine at Mount Sinai.
Vaishnavi Kundel, MD, MS is an Associate Professor of Medicine in the Division of Pulmonary, Critical Care and Sleep Medicine at the Icahn School of Medicine at Mount Sinai and Program Director of the Sleep Medicine Fellowship. She is board certified in Pulmonary, Critical Care, Sleep, and Internal Medicine. She received her MD from the Medical College of Georgia and completed Internal Medicine residency at Emory University before fellowships at Mount Sinai.
57 pubs · 385 cites · h-index 11
MD
Medical College of Georgia
MS
Icahn School of Medicine at Mount Sinai
Associate Professor of Medicine
Icahn School of Medicine at Mount Sinai · 2019 - Present
Program Director, Sleep Medicine Fellowship
Mount Sinai
Pulmonary, Critical Care and Sleep Medicine Fellow
Icahn School of Medicine at Mount Sinai
Internal Medicine Resident
Emory University
General / Preventive / Lipids · Icahn School of Medicine at Mount Sinai
If approved to treat obstructive sleep apnea, orforglipron could be a promising oral pharmacologic option for people with overweight or obesity and this condition. A convenient daily pill may help expand access to treatment for sleep apnea, and it could potentially be used with CPAP or other therapies to better meet individual patient's needs.
JAMAMalhotra et al.
BMC Pulmonary MedicineAl-Taie et al.Meta-Analysis
The Framingham Risk Score (FRS) is a widely used clinical tool for estimating 10-year cardiovascular (CV) risk based on traditional risk factors. In patients with obstructive sleep apnea (OSA) and established cardiovascular disease, where disease-specific CV risk tools are lacking, clinicians often use general primary-prevention risk tools such as the FRS or the ACC/AHA ASCVD Risk Score, even though these tools are not formally validated for secondary-prevention risk estimation. The purpose of this study is to evaluate the predictive performance of the FRS and ACC/AHA ASCVD Risk Score for CV events and mortality in patients with OSA enrolled in secondary-prevention CPAP trials, and to determine whether the FRS can guide personalized treatment strategies in OSA. We conducted an individual participant data meta-analysis of three randomized controlled trials (SAVE, ISAACC, RICCADSA) including 3,676 adults with OSA and established CV disease. Associations between risk scores and outcomes were assessed using logistic regression and Cox proportional hazards models with mixed effects to account for between-study variability. Predictive performance was evaluated using area under the receiver operating characteristic curve (AUC). Interaction analyses were performed to determine whether baseline CV risk modified CPAP treatment effects. Higher FRS was significantly associated with increased risk of CV events across all trials (meta-analysis OR = 1.03 per point, p < 0.001). However, predictive discrimination was modest (AUC range: 0.56–0.62). The ASCVD score showed similar patterns. The apnea-hypopnea index (AHI) showed no consistent association with outcomes and did not improve predictive performance when added to the risk scores. Addition of the oxygen desaturation index (ODI) failed to produce consistent gains in CV prediction. The FRS did not identify subgroups with differential benefit from CPAP (interaction p = 0.56 for CV events and p = 0.15 for CV death). General CV risk scores demonstrate limited predictive accuracy in patients with OSA and do not inform CPAP treatment decisions. These findings underscore the need for OSA-specific, mechanism-based risk stratification approaches to enable precise prediction of CV risk and individualized treatment strategies. Not applicable. This study is a secondary analysis of previously registered trials: SAVE (NCT00738179), ISAACC (NCT01335087), and RICCADSA (NCT00519597).
Malik et al.Original Research
Rationale: Obstructive sleep apnea (OSA) is associated with major adverse cardiac events (MACE); continuous positive airway pressure (CPAP) has not reduced MACE in randomized trials. We hypothesized that multi-domain machine learning would identify OSA subgroups differing in cardiovascular risk and CPAP response. Methods: We conducted a secondary analysis of SAVE trial data. After preprocessing, Gaussian mixture models were fitted; Bayesian information criterion and C-index selected five prototypes. Cox models estimated MACE risk by prototype. Baseline quality of life (EQoL), Epworth Sleepiness Scale (ESS), and Framingham Risk Score (FRS) were compared; an existing causal survival forest model estimated prototype-specific CPAP effects on cardiovascular risk. Measurements and Main Results: After preprocessing, five OSA prototypes were identified among 2,676 participants using 88 baseline variables. Prototypes differed in selected demographic, cardiometabolic, medication, and sleep variables, including smoking status, diabetes, prior myocardial infarction/stroke, antiplatelet/lipid-lowering therapy, BMI, oxygenation metrics, apnea-hypopnea index, and ESS (overall p<0.001). They also differed in baseline EQoL, FRS, MACE rate, and predicted CPAP response. Prototype 1 had the highest ESS, FRS, and MACE rate, whereas Prototype 5 had the lowest FRS and MACE rate. Using the causal survival forest model to assess prototype-specific CPAP effects on cardiovascular risk, Prototype 5 was predicted to benefit from CPAP (p<0.05), whereas Prototypes 1, 3, and 4 showed no benefit or possible harm. Conclusion: These findings demonstrate heterogeneity in cardiovascular risk and predicted CPAP response, potentially explaining neutral trial results. After external validation, these prototypes could guide trial enrichment and targeted CPAP use to improve cardiovascular outcomes.
Annals of the American Thoracic SocietyGürkan et al.Original Research
Evaluates adaptive servo-ventilation effectiveness in central sleep apnea, suggesting improved patient outcomes.
Contemporary Clinical Trials CommunicationsMalhotra et al.RCT
ATTAIN-OSA will test oral GLP-1 agonism in OSA; extends obesity pharmacotherapy trials to sleep apnea outcomes.
Journal of the American Heart AssociationTreacy et al.cohort
MRI scarring/edema may aid noninvasive EPS risk stratification in cardiac sarcoidosis with LVEF ≥40%; leaves open prospective validation for ICD decisions.
Communications MedicineCohen et al.Original Research
Findings demonstrate individualized effects of CPAP on cardiovascular outcomes in obstructive sleep apnea, suggesting precision medicine can guide treatment decisions.
Future Science OAHall et al.Original Research
Identifies soluble cytokine receptors as indicators of therapeutic response in obstructive sleep apnea, suggesting their potential use in monitoring treatment efficacy.
Annals of the American Thoracic SocietyKundel et al.cohort
CPAP shows no uniform carotid inflammation benefit in OSA; leaves open identification of responders and need for RCTs before practice change.
DiagnosticsFauveau et al.Original Research
May aid OSA cardiometabolic research via automated VAT/SAT segmentation; leaves open clinical adoption pending outcome studies.
Schmickl et al.Original Research
Survey reveals differing attitudes towards CPAP and tirzepatide in treating comorbid obesity and sleep apnea, suggesting the need for tailored strategies.
Advances in TherapyXu et al.Review
Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder characterized by repetitive collapse of the upper airway during sleep, resulting in intermittent hypoxemia and sleep fragmentation. OSA is a highly prevalent condition, strongly associated with obesity and linked to an increased risk of cardiovascular disease. While traditional therapies such as positive airway pressure (PAP) devices are effective in treating OSA and alleviating daytime symptoms, they are often not well tolerated and have limited impact on long-term cardiovascular outcomes. As our understanding of the heterogenous factors driving OSA advances, novel therapies targeting specific physiological traits-such as arousal threshold, ventilatory instability, and upper airway muscle responsiveness-are being actively explored, with a promise to expand our therapeutic arsenal. Notably, the recent approval of tirzepatide for the treatment of obesity-related OSA marks a significant milestone in this evolving therapeutic landscape. This incretin-based drug, previously approved for diabetes and obesity management, offers dual benefits, promoting weight loss and improving OSA severity and symptoms. Given this therapeutic evolution in the OSA landscape over the last decade, this review provides a comprehensive overview of the current and emerging treatment strategies for personalized OSA management, with a particular emphasis on the growing role and promise of incretin-based therapies.
Nature and Science of SleepKundel et al.cohort
No association with infarct size should not change acute MI practice; hypothesis-generating for moderate sleep apnea benefits.
Annals of the American Thoracic SocietyCohen et al.RCT
The prevalence of obstructive sleep apnea (OSA) is on the rise, driven by various factors, including more sensitive diagnostic criteria, increased awareness, enhanced technology through at-home testing enabling easy and cost-effective diagnosis, and a growing incidence of comorbid conditions such as obesity. Treating symptomatic patients with OSA syndrome to enhance quality of life remains a cornerstone approach. However, there is a lack of consensus regarding treatment to improve cardiovascular disease (CVD) outcomes, particularly in light of overall negative results from several randomized controlled trials indicating no benefit of positive airway pressure therapy on primary and secondary CVD events. These randomized controlled trials were limited by suboptimal positive airway pressure adherence, use of composite CVD outcomes, and limited diversity and generalizability to sleep clinic patients. As such, this workshop assembled clinical experts, as well as researchers in basic and translational science, epidemiology, clinical trials, and population health, to discuss the current state and future research directions to guide personalized therapeutic strategies and future research directions in OSA. There was overall consensus among workshop participants that OSA represents a heterogeneous disease with variable endotypes and phenotypes and heterogeneous responses to treatment. Future research should prioritize using multimodal therapeutic approaches within innovative and adaptive trial designs, focusing on specific subgroups of patients with OSA hypothesized to benefit from a CVD perspective. Future work should also be inclusive of diverse populations and consider the life course of OSA to better comprehend treatment strategies that can address the disproportionate impact of OSA on racially minoritized groups. Furthermore, a more holistic approach to sleep must be adopted to include broader assessments of symptoms, sleep duration, and comorbid sleep and circadian disorders. Finally, it is imperative to establish a sleep research consortium dedicated to collecting raw data and biospecimens categorized by OSA subtypes. This will facilitate mechanistic determinations, foster collaborative research, and help bolster the pipeline of early-career researchers.
Journal of Clinical MedicineCohen et al.Literature Review
Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.
Annals of the American Thoracic SocietyCohen et al.Original Research
Rationale: Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with obstructive sleep apnea (OSA) have been largely neutral. However, given OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects. Objectives: Apply a novel data-drive approach to identify non-sleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the ISAACC study. Methods: Participants were randomly partitioned into two datasets. One for training (70%) our machine learning model and a second (30%) for validation of significant findings. Model-based recursive partitioning was applied to identify subgroups with heterogeneous treatment effects. Survival analysis was conducted to compare treatment (CPAP versus usual care [UC]) outcomes within subgroups. Results: A total of 1,224 non-sleepy OSA participants were included. Of fifty-five features entered into our model only two appeared in the final model (i.e., average OSA event duration and hypercholesterolemia). Among participants at or below the model-derived average event duration threshold (19.5 seconds), CPAP was protective for a composite of CVD events (training Hazard Ratio [HR] 0.46, p=0.002). For those with longer event duration (>19.5 seconds), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared to UC (training HR 2.24, p=0.011). The point estimate for this harmful signal was also replicated in the testing dataset (HR 1.83, p=0.118). Conclusions: We discovered subgroups of non-sleepy OSA participants within the ISAACC study with heterogeneous effects of CPAP. Among the training dataset, those with longer OSA event duration and hypercholesterolemia had nearly 2.5-times more CVD events with CPAP compared to UC, while those with shorter OSA event duration had roughly half the rate of CVD events if randomized to CPAP.
SLEEP AdvancesHealy et al.Original Research
An accumulating body of evidence suggests a bidirectional relationship between sleep and cardiovascular (CV) health. A high level of evidence has linked obstructive sleep apnea (OSA) with cardiovascular disease (CVD). Accordingly, clinical sleep medicine emphasizes the diagnosis and treatment of OSA in the context of promoting CV health. While continuous positive airway pressure (CPAP), the mainstay treatment for OSA, is effective in improving several sleep-related quality-of-life outcomes and leads to modest reductions in blood pressure, there is currently insufficient evidence to justify using CPAP alone for improving CVD outcomes in OSA. Sleep physicians are uniquely positioned to expand their focus beyond the evaluation of OSA and administering CPAP, in efforts to enhance the CV health of sleep patients. Herein, we suggest the role of sleep physicians as CV preventionists. Key focus areas for managing CV risk beyond CPAP therapy in OSA include identifying comorbid disorders that are vital for optimizing CV health. This involves risk-stratifying patients and providing appropriate counseling, referrals, and treatment as appropriate for comorbid sleep conditions such as insomnia and insufficient sleep, comorbid CV risk factors including hypertension, dyslipidemia, metabolic dysfunction-associated steatohepatitis, as well as counseling for weight management programs, smoking, and alcohol cessation. We urge sleep clinicians to play an active and integral role in optimizing the CV health of patients with sleep disorders.
Annals of the American Thoracic SocietyKundel et al.Original Research
Rationale Studies have shown elevated inflammatory biomarkers in obstructive sleep apnea (OSA), but data after continuous positive airway pressure (CPAP) treatment are inconsistent. Objectives We used the Olink proteomics panel to identify unique OSA clusters on the basis of inflammatory protein expression and assess the impact of CPAP therapy. Methods Adults with newly diagnosed OSA had blood drawn at baseline and three to four months after CPAP. Samples were analyzed using the Olink proteomics platform, which measures 92 prespecified inflammatory proteins using proximity extension assay. Linear mixed-effects models were used to model changes in protein expression during the period of CPAP use, adjusting for batch, age, and sex. Unsupervised hierarchical clustering was performed to identify unique inflammatory OSA clusters on the basis of inflammatory biomarkers. Within-cluster impact of CPAP on inflammatory protein expression was assessed. Results Among 46 patients, the mean age was 46 ± 12 years (22% women), mean body mass index was 31 ± 5 kg/m2, and mean respiratory disturbance index was 33 ± 17 events/hour. Unsupervised cluster and heatmap analysis revealed three unique proteomic clusters, with low (n = 21), intermediate (n = 19), and high (n = 6) inflammatory protein expression. After CPAP, there were significant within-cluster differences in protein expression. The low inflammatory cluster had a significant increase in protein expression (16%; P = 0.02), and the high inflammatory cluster had a significant decrease in protein expression (−20%; P = 0.003), more significant among those compliant with CPAP in the low (25%; P = 0.04) and high (−22%; P = 0.01) clusters. Conclusions We identified three unique inflammatory clusters in patients with OSA using plasma proteomics, with a differential response to CPAP by cluster. Our results are hypothesis generating and require further investigation in larger longitudinal studies for enhanced cardiovascular risk profiling in OSA.
CirculationMehra et al.Guideline
Sleep-disordered breathing (SDB), characterized by specific underlying physiological mechanisms, comprises obstructive and central pathophysiology, affects nearly 1 billion individuals worldwide, and is associated with excessive cardiopulmonary morbidity. Strong evidence implicates SDB in cardiac arrhythmogenesis. Immediate consequences of SDB include autonomic nervous system fluctuations, recurrent hypoxia, alterations in carbon dioxide/acid-base status, disrupted sleep architecture, and accompanying increases in negative intrathoracic pressures directly affecting cardiac function. Day-night patterning and circadian biology of SDB-induced pathophysiological sequelae collectively influence the structural and electrophysiological cardiac substrate, thereby creating an ideal milieu for arrhythmogenic propensity. Cohort studies support strong associations of SDB and cardiac arrhythmia, with evidence that discrete respiratory events trigger atrial and ventricular arrhythmic events. Observational studies suggest that SDB treatment reduces atrial fibrillation recurrence after rhythm control interventions. However, high-level evidence from clinical trials that supports a role for SDB intervention on rhythm control is not available. The goals of this scientific statement are to increase knowledge and awareness of the existing science relating SDB to cardiac arrhythmias (atrial fibrillation, ventricular tachyarrhythmias, sudden cardiac death, and bradyarrhythmias), synthesizing data relevant for clinical practice and identifying current knowledge gaps, presenting best practice consensus statements, and prioritizing future scientific directions. Key opportunities identified that are specific to cardiac arrhythmia include optimizing SDB screening, characterizing SDB predictive metrics and underlying pathophysiology, elucidating sex-specific and background-related influences in SDB, assessing the role of mobile health innovations, and prioritizing the conduct of rigorous and adequately powered clinical trials.
SLEEPKundel et al.RCT
STUDY OBJECTIVES: Several studies have examined sleep patterns in rural/indigenous communities, however little is known about sleep characteristics in women of reproductive age, and children within these populations. We investigate sleep-wake patterns in mothers and children (ages 3-5 years) leveraging data from the Ghana Randomized Air Pollution and Health Study (GRAPHS). METHODS: The GRAPHS cohort comprises of rural/agrarian communities in Ghana and collected multiday actigraphy in a subset of women and children to assess objective sleep-wake patterns. Data were scored using the Cole-Kripke and Sadeh algorithms for mothers/children. We report descriptive, baseline characteristics and objective sleep measures, compared by access to electricity/poverty status. RESULTS: We analyzed data for 58 mothers (mean age 33 ± 6.6) and 64 children (mean age 4 ± 0.4). For mothers, mean bedtime was 9:40 pm ± 56 min, risetime 5:46 am ± 40 min, and total sleep time (TST) was 6.3 h ± 46 min. For children, median bedtime was 8:07 pm (interquartile range [IQR]: 7:50,8:43), risetime 6:09 am (IQR: 5:50,6:37), and mean 24-h TST 10.44 h ± 78 min. Children with access to electricity had a reduced TST compared to those without electricity (p = 0.02). Mean bedtime was later for both mothers (p = 0.05) and children (p = 0.08) classified as poor. CONCLUSIONS: Mothers in our cohort demonstrated a shorter TST, and earlier bed/risetimes compared to adults in postindustrialized nations. In contrast, children had a higher TST compared to children in postindustrialized nations, also with earlier sleep-onset and offset times. Investigating objective sleep-wake patterns in rural/indigenous communities can highlight important differences in sleep health related to sex, race/ethnicity, and socioeconomic status, and help estimate the impact of industrialization on sleep in developed countries.
