Latha Palaniappan
Stanford University · Rheumatology
Active 2000–2024
About
Latha Palaniappan is a Professor of Medicine in the field of Cardiovascular Medicine and, by courtesy, of Epidemiology and Population Health at Stanford University. She is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI) at Stanford. Her work focuses on the intersection of medicine, epidemiology, and artificial intelligence, contributing to advancements in healthcare through research and education. As a faculty member, she is involved in initiatives that leverage AI to improve medical imaging and patient outcomes, supporting the center's mission to innovate in artificial intelligence applications in medicine.
Research topics
- Medicine
- Internal medicine
- Intensive care medicine
- Pathology
- Environmental health
- Computer Science
- Machine Learning
- Political Science
- Cardiology
- Physical therapy
- Nursing
- Gerontology
Selected publications
Circulation · 2024 · 2335 citations
Senior authorCorresponding- Medicine
- Intensive care medicine
- Physical therapy
BACKGROUND: The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association
Circulation · 2023 · 1498 citations
- Medicine
- Intensive care medicine
- Internal medicine
Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.
2022 ACC/AHA/HFSA Guideline for the Management of Heart Failure
Journal of Cardiac Failure · 2022 · 390 citations
- Medicine
- Intensive care medicine
- Cardiology
Annals of Internal Medicine · 2022 · 114 citations
- Political Science
- Medicine
- Gerontology
Asian Americans (AsA), Native Hawaiians, and Pacific Islanders (NHPI) comprise 7.7% of the U.S. population, and AsA have had the fastest growth rate since 2010. Yet the National Institutes of Health (NIH) has invested only 0.17% of its budget on AsA and NHPI research between 1992 and 2018. More than 40 ethnic subgroups are included within AsA and NHPI (with no majority subpopulation), which are highly diverse culturally, demographically, linguistically, and socioeconomically. However, data for these groups are often aggregated, masking critical health disparities and their drivers. To address these issues, in March 2021, the National Heart, Lung, and Blood Institute, in partnership with 8 other NIH institutes, convened a multidisciplinary workshop to review current research, knowledge gaps, opportunities, barriers, and approaches for prevention research for AsA and NHPI populations. The workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. Two recurring themes emerged: Very limited data on the epidemiology, risk factors, and outcomes for most conditions are available, and most existing data are not disaggregated by subgroup, masking variation in risk factors, disease occurrence, and trajectories. Leveraging the vast phenotypic differences among AsA and NHPI groups was identified as a key opportunity to yield novel clues into etiologic and prognostic factors to inform prevention efforts and intervention strategies. Promising approaches for future research include developing collaborations with community partners, investing in infrastructure support for cohort studies, enhancing existing data sources to enable data disaggregation, and incorporating novel technology for objective measurement. Research on AsA and NHPI subgroups is urgently needed to eliminate disparities and promote health equity in these populations.
npj Digital Medicine · 2020 · 105 citations
- Machine Learning
- Medicine
- Computer Science
lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825-0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755-0.794). Among patients aged 40-79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759-0.808) and after (AUC 0.790, 95% CI: 0.765-0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.
Recent grants
Cardiovascular Disease among Asians and Pacific Islanders (CASPER)
NIH · $3.0M · 2015–2020
META2 - Mentor, Educate, Train, Advocate: Patient Oriented Researchers inCardiometabolic Disease
NIH · $846k · 2019–2029
Strength Training Regimen for Normal Weight Diabetics (STRONG-D)
NIH · $5.4M · 2008–2019
NIH · $153k · 2003
Initiate and Maintain Physical Activity in Clinics: The IMPACT Diabetes Study
NIH · $2.2M · 2013–2018
Frequent coauthors
- 81 shared
Mercedes R. Carnethon
Northwestern University
- 60 shared
Salim S. Virani
Michael E. DeBakey VA Medical Center
- 60 shared
Sukyung Chung
- 55 shared
Eldrin F. Lewis
Stanford Health Care
- 54 shared
Sadiya S. Khan
- 54 shared
Michael Pencina
Duke University Health System
- 53 shared
Janani Rangaswami
GW Medical Faculty Associates
- 53 shared
Mitchell S.V. Elkind
Education
- 1998
M.D., Medicine
Stanford University
- 1993
M.S., Epidemiology and Biostatistics
University of California, San Francisco
- 1989
B.S., Microbiology
University of Madras
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