Faculty Bibliography
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214
Racial Disparities in Hospitalization Rates During Long-Term Follow-Up After Deceased-Donor Kidney Transplantation
Objective: To compare hospitalization rates between African American (AA) and European American (EA) deceased-donor (DD) kidney transplant (KT) recipients during over a10-year period. Method: Data from the Scientific Registry of Transplant Recipients and social determinants of health (SDoH), measured by the Social Deprivation Index, were used. Hospitalization rates were estimated for kidney recipients from AA and EA DDs who had one kidney transplanted into an AA and one into an EA, leading to four donor/recipient pairs (DRPs): AA/AA, AA/EA, EA/AA, and EA/EA. Poisson-Gamma models were fitted to assess post-transplant hospitalizations. Result: Unadjusted hospitalization rates (95% confidence interval) were higher among all DRP involving AA, 131.1 (122.5, 140.3), 134.8 (126.3, 143.8), and 102.4 (98.9, 106.0) for AA/AA, AA/EA, and EA/AA, respectively, compared to 97.1 (93.7, 100.6) per 1000 post-transplant person-years for EA/EA pairs. Multivariable analysis showed u-shaped relationships across SDoH levels within each DRP, but findings varied depending on recipients"™ race, i.e., AA recipients in areas with the worst SDoH had higher hospitalization rates. However, EA recipients in areas with the best SDoH had higher hospitalization rates than their counterparts. Conclusions: Relationship between healthcare utilization and SDoH depends on DRP, with higher hospitalization rates among AA recipients living in areas with the worst SDoH and among EA recipients in areas with the best SDoH profiles. SDoH plays an important role in driving disparities in hospitalizations after kidney transplantation.
SCOPUS:85175811652
ISSN: 2197-3792
CID: 5616342
Initiation of Antihypertensive Medication from Midlife on Incident Dementia: The Health and Retirement Study
BACKGROUND:Hypertension has been identified as a risk factor of dementia, but most randomized trials did not show efficacy in reducing the risk of dementia. Midlife hypertension may be a target for intervention, but it is infeasible to conduct a trial initiating antihypertensive medication from midlife till dementia occurs late life. OBJECTIVE:We aimed to emulate a target trial to estimate the effectiveness of initiating antihypertensive medication from midlife on reducing incident dementia using observational data. METHODS:The Health and Retirement Study from 1996 to 2018 was used to emulate a target trial among non-institutional dementia-free subjects aged 45 to 65 years. Dementia status was determined using algorithm based on cognitive tests. Individuals were assigned to initiating antihypertensive medication or not, based on the self-reported use of antihypertensive medication at baseline in 1996. Observational analog of intention-to-treat and per-protocol effects were conducted. Pooled logistic regression models with inverse-probability of treatment and censoring weighting using logistic regression models were applied, and risk ratios (RRs) were calculated, with 200 bootstrapping conducted for the 95% confidence intervals (CIs). RESULTS:A total of 2,375 subjects were included in the analysis. After 22 years of follow-up, initiating antihypertensive medication reduced incident dementia by 22% (RR = 0.78, 95% CI: 0.63, 0.99). No significant reduction of incident dementia was observed with sustained use of antihypertensive medication. CONCLUSION/CONCLUSIONS:Initiating antihypertensive medication from midlife may be beneficial for reducing incident dementia in late life. Future studies are warranted to estimate the effectiveness using large samples with improved clinical measurements.
PMID: 37424471
ISSN: 1875-8908
CID: 5537352
Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
Background Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in-hospital mortality in patients hospitalized for PAD based on a national database. Methods and Results Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD-related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in-hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital-related factors. In-hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision-making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in-hospital mortality. Conclusions This study demonstrates the feasibility of ML in predicting in-hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high-risk patients for poor outcomes and guiding personalized intervention.
PMID: 36216437
ISSN: 2047-9980
CID: 5351942
Long-Term Effects of Cognitive-Behavioral Therapy and Yoga for Worried Older Adults
OBJECTIVES/OBJECTIVE:Cognitive-behavioral therapy (CBT) and yoga decrease worry and anxiety. There are no long-term data comparing CBT and yoga for worry, anxiety, and sleep in older adults. The impact of preference and selection on these outcomes is unknown. In this secondary data analysis, we compared long-term effects of CBT by telephone and yoga on worry, anxiety, sleep, depressive symptoms, fatigue, physical function, social participation, and pain; and examined preference and selection effects. DESIGN/METHODS:In this randomized preference trial, participants (N = 500) were randomized to a: 1) randomized controlled trial (RCT) of CBT or yoga (n = 250); or 2) preference trial (selected CBT or yoga; n = 250). Outcomes were measured at baseline and Week 37. SETTING/METHODS:Community. PARTICIPANTS/METHODS:Community-dwelling older adults (age 60+ years). INTERVENTIONS/METHODS:CBT (by telephone) and yoga (in-person group classes). MEASUREMENTS/METHODS: CONCLUSIONS:CBT and yoga both demonstrated maintained improvements from baseline on multiple outcomes six months after intervention completion in a large sample of older adults. TRIAL REGISTRATION/BACKGROUND:www. CLINICALTRIALS/RESULTS:gov Identifier NCT02968238.
PMID: 35260292
ISSN: 1545-7214
CID: 5220922
Coronavirus Disease 2019 and the Injured Patient: A Multicenter Review
INTRODUCTION/BACKGROUND:Coronavirus disease 2019 (COVID-19) has been shown to affect outcomes among surgical patients. We hypothesized that COVID-19 would be linked to higher mortality and longer length of stay of trauma patients regardless of the injury severity score (ISS). METHODS:We performed a retrospective analysis of trauma registries from two level 1 trauma centers (suburban and urban) from March 1, 2019, to June 30, 2019, and March 1, 2020, to June 30, 2020, comparing baseline characteristics and cumulative adverse events. Data collected included ISS, demographics, and comorbidities. The primary outcome was time from hospitalization to in-hospital death. Outcomes during the height of the first New York COVID-19 wave were also compared with the same time frame in the prior year. Kaplan-Meier method with log-rank test and Cox proportional hazard models were used to compare outcomes. RESULTS:There were 1180 trauma patients admitted during the study period from March 2020 to June 2020. Of these, 596 were never tested for COVID-19 and were excluded from the analysis. A total of 148 COVID+ patients and 436 COVID- patients composed the 2020 cohort for analysis. Compared with the 2019 cohort, the 2020 cohort was older with more associated comorbidities, more adverse events, but lower ISS. Higher rates of historical hypertension, diabetes, neurologic events, and coagulopathy were found among COVID+ patients compared with COVID- patients. D-dimer and ferritin were unreliable indicators of COVID-19 severity; however, C-reactive protein levels were higher in COVID+ relative to COVID- patients. Patients who were COVID+ had a lower median ISS compared with COVID- patients, and COVID+ patients had higher rates of mortality and longer length of stay. CONCLUSIONS:COVID+ trauma patients admitted to our two level 1 trauma centers had increased morbidity and mortality compared with admitted COVID- trauma patients despite age and lower ISS. C-reactive protein may play a role in monitoring COVID-19 activity in trauma patients. A better understanding of the physiological impact of COVID-19 on injured patients warrants further investigation.
PMCID:9263818
PMID: 36084394
ISSN: 1095-8673
CID: 5337332
Clinical journal of the American Society of Nephrology : CJASN. 2022:17(7):1017-1025.DOI: 10.2215/CJN.16481221
Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis
BACKGROUND AND OBJECTIVES/OBJECTIVE:Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS/METHODS:) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve. RESULTS:=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59). CONCLUSION/CONCLUSIONS:A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period. PODCAST/UNASSIGNED:This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.
PMID: 35667835
ISSN: 1555-905x
CID: 5248242
Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals
Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.
PMCID:9192715
PMID: 35697829
ISSN: 2399-3642
CID: 5290962
"Impact of the COVID pandemic on the incidence of prematurity: Critical role of gestational age and environment." [Letter]
PMID: 35218696
ISSN: 1097-6868
CID: 5172662
Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth
OBJECTIVE:Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S. RESEARCH DESIGN AND METHODS/METHODS:We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/-) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other). RESULTS:T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA-/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA-/IS individuals had a greater probability of T1D than T2D. In DAA- individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency. CONCLUSIONS:Diabetes type-specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA- children.
PMID: 35312757
ISSN: 1935-5548
CID: 5220322
Journal of occupational & environmental medicine. 2022:64(3):e136-e144.DOI: 10.1097/JOM.0000000000002467
The Effect of Abuse and Mistreatment on Healthcare Providers (TEAM): A Survey Assessing the Prevalence of Aggression From Patients and Their Families and Its Impact
OBJECTIVE:Aggression from patients and families on health care providers (HCP) is common yet understudied. We measured its prevalence and impact on HCPs in inpatient and outpatient settings. METHODS:Four thousand six hundred seven HCPs employed by a community teaching hospital received an anonymous survey with results analyzed. RESULTS:Of 1609 HCPs (35%) completing the survey, 88% of inpatient staff reported experiencing different types of aggression compared to 82% in outpatient setting. Almost half did not report it to their supervisor. Younger staff were more likely to report abuse. Negative impacts on productivity and patient care were reported. A third of all responders' indicated negative effects on mental health. CONCLUSIONS:Despite negative impacts on staff wellbeing and productivity, patient/family aggression toward HCPs is highly prevalent and underreported. Our healthcare system needs measures to address staff security and wellness.
PMID: 34935679
ISSN: 1536-5948
CID: 5203382
