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1. Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease

   https://doi.org/10.1093/eurheartj/ehae048  

   Li, Ling ; Pang, Shichao ; Starnecker, Fabian ; Mueller-Myhsok, Bertram ; Schunkert, Heribert ( March 2024 , European Heart Journal)

   It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2.

   A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)—referred to as PRS-factor—for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model ‘SCORE2 × PRS-factor’ was tested by risk reclassification.

   In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC.

   This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.

    

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2. Association of Cardiovascular Disease Risk Factors with Exclusive Smokeless Tobacco Use among US Males: Cross-Sectional Analysis of NHANES Data 2003-2018

   https://doi.org/10.1177/08901171221141980  

   Mushtaq, Nasir ; Sarwar, Zoona ; Kouplen, Kate ; Ahmed, Rizwan ; Beebe, Laura A ( June 2023 , American Journal of Health Promotion)

   To evaluate cardiovascular disease (CVD) risk factors among smokeless tobacco (ST) users. Exclusive ST users were compared to exclusive cigarette smokers and non-tobacco users.

   Cross-sectional study

   Data were used from 16,336 adult males who participated in one of the National Health and Nutrition Examination Surveys (NHANES) from 2003 to 2018.

   Biochemically verified tobacco use, CVD risk factors (hypertension, cholesterol levels, BMI categories), physical activity, cotinine concentration, and sociodemographic variables.

   Weighted analysis of the aggregate data was performed. ST users were compared with cigarette smokers and nontobacco users for their association with CVD risk factors. Associations were examined using univariate and multiple logistic regression with odds ratios (OR) and 95% confidence intervals (CI) reported.

   Prevalence of exclusive ST use was 4.4% whereas, exclusive smoking was 22.2%. Among ST users, 36.2% were hypertensive, 24.5% had high cholesterol levels, and most of them were overweight (31.1%) or obese (52.6%). ST users were more likely to have hypertension compared to smokers (aOR = 1.48, 95%CI: 1.12, 1.95) and nontobacco users (aOR = 1.41, 95%CI: 1.09, 1.83) adjusted for other covariates. ST users were twice more likely to be obese than nontobacco users (aOR = 2.18, 95%CI: 1.52, 3.11). ST users had significantly higher cotinine concentration than smokers.

   Study findings indicate substantial association of ST use among males with hypertension and obesity which are independent risk factors of CVD.

    

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3. Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States

   https://doi.org/10.1001/jamanetworkopen.2023.34945  

   Perlis, Roy H. ; Lunz Trujillo, Kristin ; Safarpour, Alauna ; Quintana, Alexi ; Simonson, Matthew D. ; Perlis, Jasper ; Santillana, Mauricio ; Ognyanova, Katherine ; Baum, Matthew A. ; Druckman, James N. ; et al ( September 2023 , JAMA Network Open)

   Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters.

   To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors.

   This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC.

   Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index.

   The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity.

   In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed.

    

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4. Large Language Model–Based Responses to Patients’ In-Basket Messages

   https://doi.org/10.1001/jamanetworkopen.2024.22399  

   Small, William R ; Wiesenfeld, Batia ; Brandfield-Harvey, Beatrix ; Jonassen, Zoe ; Mandal, Soumik ; Stevens, Elizabeth R ; Major, Vincent J ; Lostraglio, Erin ; Szerencsy, Adam ; Jones, Simon ; et al ( July 2024 , JAMA Network Open)

   Virtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful.

   To assess PCPs’ perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy.

   This cross-sectional quality improvement study tested the hypothesis that PCPs’ ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI.

   Randomly assigned patient messages coupled with either an HCP message or the draft GenAI response.

   PCPs rated responses’ information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy.

   A total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20];P = .01,U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27];P = .37;U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47],P = .49,t = −0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23];P < .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25];P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8];P = .002; difference, 31.2%).

   In this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs’, a significant concern for patients with low health or English literacy.

    

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5. Prevalence and Correlates of Long COVID Symptoms Among US Adults

   https://doi.org/10.1001/jamanetworkopen.2022.38804  

   Perlis, Roy H. ; Santillana, Mauricio ; Ognyanova, Katherine ; Safarpour, Alauna ; Lunz Trujillo, Kristin ; Simonson, Matthew D. ; Green, Jon ; Quintana, Alexi ; Druckman, James ; Baum, Matthew A. ; et al ( October 2022 , JAMA Network Open)

   Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection.

   To estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk.

   This cross-sectional study comprised 8 waves of a nonprobability internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older, inclusive of all 50 states and the District of Columbia.

   Long COVID, defined as reporting continued COVID-19 symptoms beyond 2 months after the initial month of symptoms, among individuals with self-reported positive results of a polymerase chain reaction test or antigen test.

   The 16 091 survey respondents reporting test-confirmed COVID-19 illness at least 2 months prior had a mean age of 40.5 (15.2) years; 10 075 (62.6%) were women, and 6016 (37.4%) were men; 817 (5.1%) were Asian, 1826 (11.3%) were Black, 1546 (9.6%) were Hispanic, and 11 425 (71.0%) were White. From this cohort, 2359 individuals (14.7%) reported continued COVID-19 symptoms more than 2 months after acute illness. Reweighted to reflect national sociodemographic distributions, these individuals represented 13.9% of those who had tested positive for COVID-19, or 1.7% of US adults. In logistic regression models, older age per decade above 40 years (adjusted odds ratio [OR], 1.15; 95% CI, 1.12-1.19) and female gender (adjusted OR, 1.91; 95% CI, 1.73-2.13) were associated with greater risk of persistence of long COVID; individuals with a graduate education vs high school or less (adjusted OR, 0.67; 95% CI, 0.56-0.79) and urban vs rural residence (adjusted OR, 0.74; 95% CI, 0.64-0.86) were less likely to report persistence of long COVID. Compared with ancestral COVID-19, infection during periods when the Epsilon variant (OR, 0.81; 95% CI, 0.69-0.95) or the Omicron variant (OR, 0.77; 95% CI, 0.64-0.92) predominated in the US was associated with diminished likelihood of long COVID. Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95% CI, 0.60-0.86).

   This study suggests that long COVID is prevalent and associated with female gender and older age, while risk may be diminished by completion of primary vaccination series prior to infection.

    

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