Faculty Bibliography
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department:Medicine. General Internal Medicine
recentyears:2
school:SOM
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14488
The case for simplifying and using absolute targets for viral hepatitis elimination goals
The 69th World Health Assembly endorsed the Global Health Sector Strategy for Viral Hepatitis, embracing a goal to eliminate hepatitis infection as a public health threat by 2030. This was followed by the World Health Organization's (WHO) global targets for the care and management of hepatitis B virus (HBV) and hepatitis C virus (HCV) infections. These announcements and targets were important in raising awareness and calling for action; however, tracking countries' progress towards these elimination goals has provided insights to the limitations of these targets. The existing targets compare a country's progress relative to its 2015 values, penalizing countries who started their programmes prior to 2015, countries with a young population, or countries with a low prevalence. We recommend that (1) WHO simplify the hepatitis elimination targets, (2) change to absolute targets and (3) allow countries to achieve these disease targets with their own service coverage initiatives that will have the maximum impact. The recommended targets are as follows: reduce HCV new chronic cases to ≤5 per 100 000, reduce HBV prevalence among 1-year-olds to ≤0.1%, reduce HBV and HCV mortality to ≤5 per 100 000, and demonstrate HBV and HCV year-to-year decrease in new HCV- and HBV-related HCC cases. The objective of our recommendations is not to lower expectations or diminish the hepatitis elimination standards, but to provide clearer targets that recognize the past and current elimination efforts by countries, help measure progress towards true elimination, and motivate other countries to follow suit.
PMID: 32979881
ISSN: 1365-2893
CID: 4679282
Prognostic value of coronary artery calcium score, area, and density among individuals on statin therapy vs. non-users: The coronary artery calcium consortium
BACKGROUND AND AIMS:Statins do not decrease coronary artery calcium (CAC) and may increase existing calcification or its density. Therefore, we examined the prognostic significance of CAC among statin users at the time of CAC scanning. METHODS:We included 28,025 patients (6151 statin-users) aged 40-75 years from the CAC Consortium. Cox regression models were used to assess the association of CAC with coronary heart disease (CHD) and cardiovascular disease (CVD) mortality. Models were adjusted for traditional CVD risk factors. Additionally, we examined the predictive performance of CAC components including CAC area, volume, and density using an age- and sex-adjusted Cox regression model. RESULTS:Participants (mean age 53.9 ± 10.3 years, 65.0% male) were followed for median 11.2 years. There were 395 CVD and 182 CHD deaths. One unit increase in log CAC score was associated with increased risk of CVD mortality (hazard ratio (HR), 1.2; 95% CI = 1.1-1.3) and CHD mortality (HR, 1.2; 95% CI = 1.1-1.4)) among statin users. There was a small but significant negative interaction between CAC score and statin use for the prediction of CHD (p-value = 0.036) and CVD mortality (p-value = 0.025). The volume score and CAC area were similarly associated with outcomes in statin users and non-users. Density was associated with CVD and CHD mortality in statin naïve patients, but with neither in statin users. CONCLUSION:CAC scoring retains robust risk prediction in statin users, and the changing relationship of CAC density with outcomes may explain the slightly weaker relationship of CAC with outcomes in statin users.
PMID: 33121743
ISSN: 1879-1484
CID: 4961742
Practice of Psycho-oncology with Latino Patients: An International Study
Cancer is the second leading cause of death globally, approximately 70% of deaths from cancer occur in low- and middle-income countries.1 About 1.3 million new cancer cases and 666,000 cancer deaths were estimated to have occurred in 2018 in Latin America and the Caribbean. Cancer is also the leading cause of death of Latinos in the U.S., accounting for approximately 42,700 deaths/year in the U.S. Latino population.2 This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
PMID: 32914912
ISSN: 1099-1611
CID: 4589592
COVID-19 in New Orleans: A Nephrology Clinical and Education Perspective and Lessons Learned
New Orleans' first case of COVID-19 was reported on March 9, 2020 with a subsequent rapid rise in the number of cases throughout the state of Louisiana. Traditional educational efforts were no longer viable with social distancing and stay-at-home orders, therefore virtual didactics were integrated into our curriculum. Due to an exponential increase in the number of patients with acute kidney injury requiring kidney replacement therapy, the nephrology sections at Louisiana State University School of Medicine and Tulane University School of Medicine adapted their clinical workflows to accommodate these increased clinical volumes by utilizing prolonged intermittent kidney replacement therapies and acute peritoneal dialysis as well as other strategies to mitigate nursing burnout and decrease scarce resource utilization. Telehealth was implemented in outpatient clinics and dialysis units to protect vulnerable patients with kidney disease while maintaining access to care. Lessons learned from this pandemic and subsequent response may be utilized for future responses in similar situations.
PMCID:7708233
PMID: 33283183
ISSN: 2590-0595
CID: 4708532
Respiratory impedance measured using impulse oscillometry in a healthy urban population
This study derives normative prediction equations for respiratory impedance in a healthy asymptomatic urban population using an impulse oscillation system (IOS). In addition, this study uses body mass index (BMI) in the equations to describe the effect of obesity on respiratory impedance. Data from an urban population comprising 472 healthy asymptomatic subjects that resided or worked in lower Manhattan, New York City were retrospectively analysed. This population was the control group from a previously completed case-control study of the health effects of exposure to World Trade Center dust. Since all subjects underwent spirometry and oscillometry, these previously collected data allowed a unique opportunity to derive normative prediction equations for oscillometry in an urban, lifetime non-smoking, asymptomatic population without underlying respiratory disease. Normative prediction equations for men and women were successfully developed for a broad range of respiratory oscillometry variables with narrow confidence bands. Models that used BMI as an independent predictor of oscillometry variables (in addition to age and height) demonstrated equivalent or better fit when compared with models that used weight. With increasing BMI, resistance and reactance increased compatible with lung and airway compression from mass loading. This study represents the largest cohort of healthy urban subjects assessed with an IOS device. Normative prediction equations were derived that should facilitate application of IOS in the clinical setting. In addition, the data suggest that modelling of lung function may be best performed using height and BMI as independent variables rather than the traditional approach of using height and weight.
PMCID:8005688
PMID: 33816605
ISSN: 2312-0541
CID: 4838882
Kairos care in a Chronos world: Midwifery care as model of resistance and accountability in public health settings
ISI:000662147200001
ISSN: 0730-7659
CID: 4962212
Constructing a Nurse-led Cardiovascular Disease Intervention in Rural Ghana: A Qualitative Analysis
Background/UNASSIGNED:Cardiovascular disease (CVD) is a growing burden in low- and middle-income countries. Ghana seeks to address this problem by task-shifting CVD diagnosis and management to nurses. The Community-Based Health Planning and Services (CHPS) initiative offers maternal and pediatric health care throughout Ghana but faces barriers to providing CVD care. We employed in-depth interviews to identify solutions to constraints in CVD care to develop a nurse-led CVD intervention in two districts of Ghana's Upper East Region. Objective/UNASSIGNED:This study sought to identify non-physician-led interventions for the screening and treatment of cardiovascular disease to incorporate into Ghana's current primary health care structure. Methods/UNASSIGNED:Using a qualitative descriptive design, we conducted 31 semistructured interviews of community health officers (CHOs) and supervising subdistrict officers (SDOs) at CHPS community facilities. Summative content analysis revealed the most common intervention ideas and endorsements by the participants. Findings/UNASSIGNED:Providers endorsed three interventions: increasing community CVD knowledge and engagement, increasing nonphysician prescribing abilities, and ensuring provider access to medical and transportation equipment. Providers suggested community leaders and volunteers should convey CVD knowledge, marshaling established gathering practices to educate communities and formulate action plans. Providers requested lectures paired with experiential learning to improve their prescribing confidence. Providers recommended revising reimbursement and equipment procurement processes for expediting access to necessary supplies. Conclusions/UNASSIGNED:Frontline CHPS primary care providers believe CVD care is feasible. They recommended a three-pronged intervention that combines community outreach, provider training, and logistical support, thereby expanding task-shifting beyond hypertension to include other CVD risk factors. This model could be replicable elsewhere.
PMCID:8641531
PMID: 34900621
ISSN: 2214-9996
CID: 5079812
Journal of general internal medicine. 2021:Conference:(2021):S114-S115.DOI: 10.1007/s11606-021-06830-5
Validation of the comprehensive clinical skills exam (CCSE) measurement model [Meeting Abstract]
BACKGROUND: Performance-based assessment & feedback during medical training is essential for a successful transition before moving onto residency and independent clinical practice. Learners at New York University's School of Medicine (NYUSOM) participate in a routine comprehensive clinical skills examination (CCSE) that takes place at the tail end of medical school. During this exam, learners interact with standardized patients (SPs) and are rated on specific skills using a standardized checklist, measuring important clinical skills domains. NYUSOM has utilized the same assessment tool since 2005. To date, there is limited evidence on the tool's validity and ability to differentiate among students. We sought to provide evidence for it's reliability, validity, and generalizability.
METHOD(S): 1157 learners participated in the CCSE from 2011-2019 and were included in the analysis. Communication domain items assessed included patient education (3 items), relationship development (4 items), information gathering (6 items) and organization/ time management (3 items). Items were scored using a 3-point behaviorally-anchored scale (not, partly, or well done). In order to determine the degree to which the data mapped onto our theoretically-informed communication domains, we conducted a four-factor confirmatory factor analysis (CFA) allowing for factors to correlate (oblique rotation) and using means and variance adjusted weighted-least squares estimation (WLSMV) in order to account for the ordered categorical nature of the communication items. Model fit was assessed using root mean square of approximation (RMSEA) < 0.08, comparative fit index (CFI) > 0.95, and standardized root mean square error (SRMR) <0.08.
RESULT(S): The model fit the data using RMSEA (0.04), CFI (0.98), and SRMR (0.05). All factors were significantly correlated with one another (p < 0.05), with the largest correlation between patient education and organization/ time management (0.86), and information gathering (0.77). The smallest correlation was between organization/ time management and information gathering (0.66). All items (factor loadings) significantly loaded on the factors they measured. Only one item had an insignificant threshold loading between partly and well done, suggesting this part of the response scale may be hard for SPs to differentiate between students with varying ability on this item. Each factor had at least one item that had a factor loading less than 0.7.
CONCLUSION(S): The analysis suggests each item on the communication checklist significantly measures domains they were designed to measure, and that items can be summated to compute overall scores. Domains had one item with a lower loading than the rest, suggesting these items may be measuring something different. Follow up measurement modeling and profile analysis is the next logical step in determining if there is an important sub-domain that identifies a student group operating differentially. LEARNING OBJECTIVE #1: Understand clinical communication LEARNING OBJECTIVE #2: Describe communication measures
METHOD(S): 1157 learners participated in the CCSE from 2011-2019 and were included in the analysis. Communication domain items assessed included patient education (3 items), relationship development (4 items), information gathering (6 items) and organization/ time management (3 items). Items were scored using a 3-point behaviorally-anchored scale (not, partly, or well done). In order to determine the degree to which the data mapped onto our theoretically-informed communication domains, we conducted a four-factor confirmatory factor analysis (CFA) allowing for factors to correlate (oblique rotation) and using means and variance adjusted weighted-least squares estimation (WLSMV) in order to account for the ordered categorical nature of the communication items. Model fit was assessed using root mean square of approximation (RMSEA) < 0.08, comparative fit index (CFI) > 0.95, and standardized root mean square error (SRMR) <0.08.
RESULT(S): The model fit the data using RMSEA (0.04), CFI (0.98), and SRMR (0.05). All factors were significantly correlated with one another (p < 0.05), with the largest correlation between patient education and organization/ time management (0.86), and information gathering (0.77). The smallest correlation was between organization/ time management and information gathering (0.66). All items (factor loadings) significantly loaded on the factors they measured. Only one item had an insignificant threshold loading between partly and well done, suggesting this part of the response scale may be hard for SPs to differentiate between students with varying ability on this item. Each factor had at least one item that had a factor loading less than 0.7.
CONCLUSION(S): The analysis suggests each item on the communication checklist significantly measures domains they were designed to measure, and that items can be summated to compute overall scores. Domains had one item with a lower loading than the rest, suggesting these items may be measuring something different. Follow up measurement modeling and profile analysis is the next logical step in determining if there is an important sub-domain that identifies a student group operating differentially. LEARNING OBJECTIVE #1: Understand clinical communication LEARNING OBJECTIVE #2: Describe communication measures
EMBASE:635796583
ISSN: 1525-1497
CID: 4986652
LLDAS (LOW LUPUS DISEASE ACTIVITY STATE), LOW DISEASE ACTIVITY (LDA) AND REMISSION (ON- OR OFF-TREATMENT) PREVENT DAMAGE ACCRUAL IN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) PATIENTS IN A MULTINATIONAL MULTICENTER COHORT [Meeting Abstract]
ISI:000692629300286
ISSN: 0003-4967
CID: 5017572
Notesense: development of a machine learning algorithm for feedback on clinical reasoning documentation [Meeting Abstract]
BACKGROUND: Clinical reasoning (CR) is a core component of medical training, yet residents often receive little feedback on their CR documentation. Here we describe the process of developing a machine learning (ML) algorithm for feedback on CR documentation to increase the frequency and quality of feedback in this domain.
METHOD(S): To create this algorithm, note quality first had to be rated by gold standard human rating. We selected the IDEA Assessment Tool-a note rating instrument across four domains (I=Interpretive summary, D=Differential diagnosis, E=Explanation of reasoning, A=Alternative diagnoses explained) that uses a 3-point Likert scale without descriptive anchors. To develop descriptive anchors we conducted an iterative process reviewing notes from the EHR written by medicine residents and validated the Revised-IDEA Assessment Tool using Messick's framework- content validity, response process, relation to other variables, internal structure, and consequences. Using the Hofstee standard setting method, cutoffs for high quality clinical reasoning for the IDEA and DEA scores were set. We then created a dataset of expertrated notes to create the ML algorithm. First, a natural language processing software was applied to the set of notes that enabled recognition and automatic encoding of clinical information as a diagnosis or disease (D's), a sign or symptom (E or A), or semantic qualifier (e.g. most likely). Input variables to the ML algorithm included counts of D's, E/A's, semantic qualifiers, and proximity of semantic qualifiers to disease/ diagnosis. ML output focused on DEA quality and was binarized to low or high quality CR. Finally, 200 notes were randomly selected for human validation review comparing ML output to human rated DEA score.
RESULT(S): The IDEA and DEA scores ranged from 0-10 and 0-6, respectively. IDEA score of >= 6.5 and a DEA score of >= 3 was deemed high quality. 252 notes were rated to create the dataset and 20% were rated by 3 raters with high intraclass correlation 0.84 (95% CI 0.74-0.90). 120 of these notes comprised the testing set for ML model development. The logistic regression model was the best performing model with an AUC 0.87 and a positive predictive value (PPV) of 0.65. 48 (40%) of the notes were high quality. There was substantial interrater reliability between ML output and human rating on the 200 note validation set with a Cohen's Kappa 0.64.
CONCLUSION(S): We have developed a ML algorithm for feedback on CR documentation that we hypothesize will increase the frequency and quality of feedback in this domain. We have subsequently developed a dashboard that will display the output of the ML model. Next steps will be to provide internal medicine residents' feedback on their CR documentation using this dashboard and assess the impact this has on their documentation quality. LEARNING OBJECTIVE #1: Describe the importance of high quality documentation of clinical reasoning. LEARNING OBJECTIVE #2: Identify machine learning as a novel assessment tool for feedback on clinical reasoning documentation
METHOD(S): To create this algorithm, note quality first had to be rated by gold standard human rating. We selected the IDEA Assessment Tool-a note rating instrument across four domains (I=Interpretive summary, D=Differential diagnosis, E=Explanation of reasoning, A=Alternative diagnoses explained) that uses a 3-point Likert scale without descriptive anchors. To develop descriptive anchors we conducted an iterative process reviewing notes from the EHR written by medicine residents and validated the Revised-IDEA Assessment Tool using Messick's framework- content validity, response process, relation to other variables, internal structure, and consequences. Using the Hofstee standard setting method, cutoffs for high quality clinical reasoning for the IDEA and DEA scores were set. We then created a dataset of expertrated notes to create the ML algorithm. First, a natural language processing software was applied to the set of notes that enabled recognition and automatic encoding of clinical information as a diagnosis or disease (D's), a sign or symptom (E or A), or semantic qualifier (e.g. most likely). Input variables to the ML algorithm included counts of D's, E/A's, semantic qualifiers, and proximity of semantic qualifiers to disease/ diagnosis. ML output focused on DEA quality and was binarized to low or high quality CR. Finally, 200 notes were randomly selected for human validation review comparing ML output to human rated DEA score.
RESULT(S): The IDEA and DEA scores ranged from 0-10 and 0-6, respectively. IDEA score of >= 6.5 and a DEA score of >= 3 was deemed high quality. 252 notes were rated to create the dataset and 20% were rated by 3 raters with high intraclass correlation 0.84 (95% CI 0.74-0.90). 120 of these notes comprised the testing set for ML model development. The logistic regression model was the best performing model with an AUC 0.87 and a positive predictive value (PPV) of 0.65. 48 (40%) of the notes were high quality. There was substantial interrater reliability between ML output and human rating on the 200 note validation set with a Cohen's Kappa 0.64.
CONCLUSION(S): We have developed a ML algorithm for feedback on CR documentation that we hypothesize will increase the frequency and quality of feedback in this domain. We have subsequently developed a dashboard that will display the output of the ML model. Next steps will be to provide internal medicine residents' feedback on their CR documentation using this dashboard and assess the impact this has on their documentation quality. LEARNING OBJECTIVE #1: Describe the importance of high quality documentation of clinical reasoning. LEARNING OBJECTIVE #2: Identify machine learning as a novel assessment tool for feedback on clinical reasoning documentation
EMBASE:635796491
ISSN: 1525-1497
CID: 4985012
