*Caution non-peer reviewed preprint* There have been many anecdotes about prolonged #COVID19 symptoms but little systematic data collection. Here, results from prospective study of 152 patients @nyulangone hospitalized with #COVID19. /1 medrxiv.org/content/10.110…
tl;dr results: 113/152 (74%) reported persistent shortness of breath 30-40 days after discharge. 13.5% still needed oxygen. Overall physical health was rated 44/100 after vs 54 before - full standard deviation drop vs national norms. Mental health score dropped from 54 to 47.
Details: We enrolled 152 (38% of eligible) patients; all had lab confirmed #COVID19 & needed at least 6L oxygen during hospitalization; each completed the PROMIS 10 global health questionnaire and the PROMIS dyspnea scale, answering for current and pre-COVID state.
Patients were median age 62, 44% white, 77% English speaking, 83% with at least one chronic condition. Median length of stay 18 days: so, interviewed at ~7-10 weeks after symptom onset. Here's the dyspnea scale.
Note that we included only people who were quite sick during hospitalization, BUT, we also excluded those too frail to answer, those rehospitalized, those with cognitive impairment etc, all of whom likely had even worse symptoms.
A recent report from Italy of discharged patients at a similar time frame in disease course shows similar results, through focused on somatic symptoms not function. jamanetwork.com/journals/jama/…
By contrast this preprint of non-hospitalized patients in Atlanta shows lower symptom severity 30 days after symptom onset medrxiv.org/content/10.110…
And there have been non-systematic assessments of persistent symptoms like this preprint, but of course skewed towards those who choose to self report. medrxiv.org/content/10.110…
Out today in @jama_current, the 2024 update to the #NIHRECOVER 2023 #LongCOVID index. Explainer follows. 🧵 1/njamanetwork.com/journals/jama/…
We use the same methodology as in 2023 to update the index, but now include data on >3,800 more people. See this 2023 explainer for details of the methodology, not repeated here. 2/n
What’s new? Two symptoms newly contribute points: shortness of breath (2 points) and snoring/sleep apnea (1 point). (Sleep disturbance was in the original but rounded to 0 points.) 3/n
#NIHRECOVER Adult is a cohort study of ~15k adults with/without #COVID, followed prospectively. They answer symptom surveys every 3 months and do additional tests yearly. 93% of cohort has been enrolled; this paper includes 9,764 participants. recovercovid.org
Main goal of this paper is to establish an expanded, working symptom-based definition of #LongCOVID for research purposes. Please note we do not propose this as a clinical definition right now pending further validation & refinement.
Have been too busy enjoying seeing people in person and seeing so much great research at #SGIM22 to have been tweeting, but what a great meeting. @nyugrossman was out in force /1
Med student Kyle Smith had a wonderful oral presentation on how we have developed a method of finding people on oral anti psychotics who haven’t had a1c testing (no pics cause I was so busy watching!) with @SaulBlecker /2
T32 trainee Rachel Engelberg had a great poster on incarceration and health outcomes /3
Fascinating article about how research into types and efficacy of traffic stops in multiple CT communities led to changes that both reduced disparities in stops and better targeted actual public safety issues. Some examples follow:
In Newington, 40% (1,608) traffic stops were for defective lights but found only 1 DUI. Dept switched focus to moving violations (defective lights ⬇️67%, moving violations ⬆️60%). Stops with DUI arrest ⬆️250%, from 18 to 63, and disparities substantially reduced: safer & fairer!
Hamden tried increasing stops for admin issues (lights, registration) to reduce crime in Black neighborhood but rarely found contraband (7%), no effect on crime, caused huge disparities. Switched to stops for hazardous driving: crime ⬇️5%, accidents ⬇️10%, found more contraband.
A lot of chatter about hospitalization "with" versus "for" COVID, implying current hospitalization wave isn't "real." NY state is going to start trying to report the distinction; UK already does. Some thoughts, with exemplar data. /1
1st, not so easy to tell. Our health system calls "for" COVID: patients with problem list or clinical impression of respiratory failure with hypoxia (various codes), or x "due to COVID" or COVID positive is the only problem. Specific, but likely not very sensitive. /2
That is, people who meet those criteria are very likely being admitted for COVID, but others will be missed (e.g. diagnosis pneumonia, sepsis, COVID-related stroke/heart attack/PE). So, likely an underestimate. Still, if used consistently, may be useful approximation. /3
Phenomenal preprint from South Africa on #omicron severity. Insanely fast analysis with multiple linked national datasets. Kudos to the authors. Results? You'll see headlines about reduced severity, but full story more complicated. My thoughts. medrxiv.org/content/10.110…
First off, methods. They link lab tests, case data, genome data and hospital data from across all of South Africa. (Wow!) They use a proxy for omicron (SFTF) and require Ct <=30 ("real" infection).
Then they run two comparisons: omicron vs not omicron Oct-Nov, and omicron Oct-Nov vs delta Apr-Nov, and compre frequency of hospitalization and of severe disease (=hospitalised + any of ICU/O2/ventilated/ECMO/ARDS/death). Outcomes assessed on 21 Dec (day preprint posted?!).