#MonganScienceMinute featuring @JeffAshburner Yuchiao Chang, PhD, Daniel Singer, MD, and Steven Atlas, MD, MPH @MGH_DGIM https://t.co/r1FdlsppgG

Our model to predict 5 year risk of post-stroke cognitive impairment using routinely collected EHR data. Important tool for risk stratification / targeted #BrainHealth preventive interventions. @CDAndersonMD @JRosand_MD

Afib can increase risk of stroke, morbidity & hospitalization. Lifestyle modification programs may reduce symptoms, progression & improve QOL. Dr. @JeffAshburner et al. examine a behavioral program for pts w/Afib. https://t.co/GVa82WHHXP @pnatarajanmd, @anne_thorndike, @RomitB_MD

Really important work. Afib should be covered for Cardiac rehab #afib #cardiacrehab

Atrial fibrillation is not a covered indication for cardiac rehab. Our 12k cardiac rehab-like program for pts with AF in single-arm @MGHHeartHealth pilot showed improvement in AF burden, symptoms, and quality of life https://t.co/gk4DVhJLDD @JCRPjrnl @JeffAshburner @RomitB_MD

Interesting work lead by @JeffAshburner in #VITALAF trial ➡️ among pts w/ known #afib, no diff in new OAC initiation w/ vs. w/o 1L ECG screening, but 𝟏𝐋 𝐄𝐂𝐆 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐚𝐟𝐟𝐞𝐜𝐭𝐞𝐝 𝐎𝐀𝐂 𝐢𝐧𝐢𝐭𝐢𝐚𝐭𝐢𝐨𝐧 (⬆️ if possible AF result, ⬇️ if normal)! @MGHHeartHealth

Among 17k controls in the VITAL-AF trial, 13% had prevalent #afib; new #afib incidence 2.3%/yr. Even more #afib in older patients (29% in age ≥85) and patients with #HeartFailure (43%). @shaan_khurshid @JeffAshburner @steven_lubitz @MGHHeartHealth

How common is #afib in today's primary care? Prevalence 13%, incidence 2.4%/yr among pts ≥65 yrs in VITAL-AF trial, adjudicated outcomes #afib prevalence 29% in ≥85 yrs and 43% in #heartfailure Paper @JAMANetworkOpen ➡️ https://t.co/iB3GqtJRy6 @MGHHeartHealth @MGHMedicine

Proud to get this work out into the world! Thanks to everyone who made this possible: @shaan_khurshid @xxinxwang @RVpacing @DPipilasMD @JeffAshburner @patrick_ellinor Daniel Singer @satlas60 @steven_lubitz

#OriginalResearch: Screening for AF using a single-lead ECG at primary care visits did not affect new AF diagnoses among individuals aged >= 65 years compared to usual care #VITAL_AF @patrick_ellinor @shaan_khurshid @JeffAshburner @steven_lubitz https://t.co/rL45J9yS3w

Study by #MGH investigators finds handheld devices for A-fib screening may be most effective in the oldest adults during primary care visits

We need a Massachusetts special edition with a chapter titled “Do I Have to Remove the Sheets of Ice Off the Roof of My Range Rover before Driving on the Highway and Potentially Killing Someone? I Mean…It May Take Me 10 Whole Minutes.” @KenTremendous

2021 in a playlist. @TheWeatherStn and @laurastevenson are standouts with stunning albums. https://t.co/HlBIYgqc7K

Accurate post #stroke #AFib risk estimation achieved w/ established risk factors and full model refitting: Re-CHARGE-AF. https://t.co/C8iUgr823T @JeffAshburner @CDAndersonMD @steven_lubitz @shaan_khurshid @l_trinquart

As more patients gain access to consumer devices capable of monitoring heart rhythm - what is the future of point of care screening for atrial fibrillation? Our new piece in @CVDH_journal - with @shaan_khurshid @satlas60 @steven_lubitz and Danny Singer. https://t.co/7re2unoAIY

Thanks to collaborators @CDAndersonMD, @steven_lubitz, @shaan_khurshid, @l_trinquart, @EmeliaBenjamin, @satlas60, @darae_ko, @patrick_ellinor, and others as well as @American_Heart for funding.

When incorporating risk estimates from prognostic models into clinical tools to aid decision making, it is critical to evaluate model performance, calibration + discrimination, to ensure we provide the most useful information in the context of the population being treated.

Our findings suggest that accurate #AFib risk estimation in the post-stroke setting can be achieved using covariates from an established risk model, but only after adjustment to account for varying risk and relative influence of covariates.

The fully refitted Re-CHARGE-AF model demonstrated substantially greater discrimination of #AFib and achieved good calibration between predicted and observed #AFib incidence.

CHARGE-AF was poorly calibrated and substantially underestimated the risk of #AFib. Calibration remained poor after recalibrating to the baseline risk of the post-stroke sample.