Evidence map›Paper›PMID 32511520›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Clinical predictors of COVID-19 mortality.

Arjun S Yadaw, Yan-Chak Li, Sonali Bose, Ravi Iyengar, Supinda Bunyavanich, Gaurav Pandey

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. 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.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Arjun S YadawDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA.
Yan-Chak LiDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA.
Sonali BoseDepartment of Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029, USA.
Ravi IyengarDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA.
Supinda BunyavanichDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA.
Gaurav PandeyDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA.

Funding

System Biology Center in New YorkP50GM071558 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI IYENGAR, SRINIVAS RAVI V · 2007 to 2017
$22.8M
Mount Sinai ECHO site for Perinatal Environment and Development StudiesUG3OD023337 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CARROLL, KECIA NICOLE, WRIGHT, ROBERT O · 2016 to 2024
$20.6M
Drug Combination Signatures for Prediction and Mitigation of ToxicityU54HG008098 · NHGRI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BIRTWISTLE, MARC R., BIRTWISTLE, MARC R. · 2014 to 2019
$11.8M
Threshold, Severity, and Immunotherapy of Peanut AllergyU19AI136053 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BUNYAVANICH, SUPINDA · 2018 to 2023
$9.1M
Influence of prenatal air pollutant and stress exposures on sleep outcomes in urban preschool-aged childrenR01HL147328 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BOSE, SONALI, ROSS, KRISTIE R · 2019 to 2023
$4.2M
Nasal biomarkers of asthmaR01AI118833 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BUNYAVANICH, SUPINDA · 2015 to 2019
$3.8M
Gut Microbiome Dynamics in Peanut AllergyR01AI147028 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BUNYAVANICH, SUPINDA · 2019 to 2022
$3.3M
NHGRI NIH HHS U54 HG008098NHLBI NIH HHS R01 HL147328NIAID NIH HHS R01 AI118833NIAID NIH HHS R01 AI147028NIAID NIH HHS U19 AI136053NIGMS NIH HHS P50 GM071558NIH HHS UG3 OD023337
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) pandemic has affected over millions of individuals and caused hundreds of thousands of deaths worldwide. It can be difficult to accurately predict mortality among COVID-19 patients presenting with a spectrum of complications, hindering the prognostication and management of the disease.

methodsWe applied machine learning techniques to clinical data from a large cohort of 5,051 COVID-19 patients treated at the Mount Sinai Health System in New York City, the global COVID-19 epicenter, to predict mortality. Predictors were designed to classify patients into Deceased or Alive mortality classes and were evaluated in terms of the area under the receiver operating characteristic (ROC) curve (AUC score).

findingsUsing a development cohort (n=3,841) and a systematic machine learning framework, we identified a COVID-19 mortality predictor that demonstrated high accuracy (AUC=0.91) when applied to test sets of retrospective (n= 961) and prospective (n=249) patients. This mortality predictor was based on five clinical features: age, minimum O2 saturation during encounter, type of patient encounter (inpatient vs. various types of outpatient and telehealth encounters), hydroxychloroquine use, and maximum body temperature.

interpretationAn accurate and parsimonious COVID-19 mortality predictor based on five features may have utility in clinical settings to guide the management and prognostication of patients affected by this disease.

Identifiers

PMID32511520
PMCPMC7273288

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.