Evidence map›Paper›PMID 35350202›Full record

ArticlemedRxiv : the preprint server for health sciences2022

Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National EHR Research Consortium Study.

Jeffrey G Klann, Zachary H Strasser, Meghan R Hutch, Chris J Kennedy, Jayson S Marwaha, Michele Morris, Malarkodi Jebathilagam Samayamuthu, Ashley C Pfaff, Hossein Estiri, Andrew M South and 12 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors at 8 institutions in 1 country.

Zachary H StrasserORCID 0000-0002-4846-6059
Malarkodi Jebathilagam SamayamuthuORCID 0000-0003-0061-8196
Kavishwar B WagholikarORCID 0000-0002-6219-861X
Consortium for Clinical Characterization of COVID-19 by EHR (4CE)
Shyam VisweswaranORCID 0000-0002-2079-8684
Massachusetts General Hospital · USHarvard University · USUniversity of Pittsburgh · USBeth Israel Deaconess Medical Center · USNorthwestern University · USBrenner Children's Hospital · USUniversity of Michigan · USUniversity of Pennsylvania · US

Funding

Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STUART A BATTERMAN · 2011 to 2026
$21.3M
RESEARCH TRAINING IN PEDIATRIC EMERGENCY MEDICINET32HD040128 · NICHD · CHILDREN'S HOSPITAL BOSTON · PI Kenneth D. Mandl · 2001 to 2026
$5.0M
Leveraging electronic health records to optimize treatment selection and response in multiple sclerosisR01NS098023 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Zongqi Xia · 2016 to 2026
$4.6M
Biases introduced by filtering electronic health records for patients with "complete data"R01LM013345 · NLM · HARVARD MEDICAL SCHOOL · PI WEBER, GRIFFIN M · 2020 to 2023
$1.4M
Real-world impact of the COVID-19 pandemic in people with multiple sclerosisR01NS124882 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI XIA, ZONGQI · 2022 to 2024
$1.2M
The role of angiotensin-(1-7) in hypertension and hypertension-induced heart and kidney damageK23HL148394 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SOUTH, ANDREW MICHAEL · 2020 to 2024
$783k
NCATS NIH HHS UL1 TR001420NHLBI NIH HHS K23 HL148394NHLBI NIH HHS L40 HL148910NICHD NIH HHS T32 HD040128NIEHS NIH HHS P30 ES017885NINDS NIH HHS R01 NS098023NINDS NIH HHS R01 NS124882NLM NIH HHS R01 LM013345
6 · The paper itself

Abstract

Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. EHR-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. From a retrospective EHR-based cohort in four US healthcare systems, a random sample of 1,123 SARS-CoV-2 PCR-positive patients hospitalized between 3/2020â€"8/2021 was manually chart-reviewed and classified as admitted-with-COVID-19 (incidental) vs. specifically admitted for COVID-19 (for-COVID-19). EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in 26%. The top site-specific feature sets had 79-99% specificity with 62-75% sensitivity, while the best performing across-site feature set had 71-94% specificity with 69-81% sensitivity. A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Identifiers

PMID35350202
PMCPMC8963684
OpenAlexW4213424541

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.