Evidence map›Paper›PMID 34863394›Full record

ArticleMayo Clinic proceedings2021

Development and Validation of a Multivariable Risk Prediction Model for COVID-19 Mortality in the Southern United States.

Aashish Gupta, Sergey M Kachur, Jose D Tafur, Harsh K Patel, Divina O Timme, Farnoosh Shariati, Kristen D Rogers, Daniel P Morin, Carl J Lavie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Mayo Clinic proceedings, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Aashish GuptaJohn Ochsner Heart and Vascular Institute, Ochsner Clinic Foundation, New Orleans, LA; University of Queensland School of Medicine, Brisbane, Australia. Electronic address: aashish.gupta@ochsner.org.
Sergey M KachurJohn Ochsner Heart and Vascular Institute, Ochsner Clinic Foundation, New Orleans, LA; University of Queensland School of Medicine, Brisbane, Australia; Department of Medicine, University of Central Florida School of Medicine, Orlando, FL.
Jose D TafurJohn Ochsner Heart and Vascular Institute, Ochsner Clinic Foundation, New Orleans, LA; University of Queensland School of Medicine, Brisbane, Australia.
Harsh K PatelDepartment of Medicine, Ochsner Clinic Foundation, New Orleans, LA.
Divina O TimmeDepartment of Medicine, Ochsner Clinic Foundation, New Orleans, LA.
Farnoosh ShariatiDepartment of Medicine, Ochsner Clinic Foundation, New Orleans, LA.
Kristen D RogersDepartment of Medicine, Ochsner Clinic Foundation, New Orleans, LA.
Daniel P MorinJohn Ochsner Heart and Vascular Institute, Ochsner Clinic Foundation, New Orleans, LA; University of Queensland School of Medicine, Brisbane, Australia.
Carl J LavieJohn Ochsner Heart and Vascular Institute, Ochsner Clinic Foundation, New Orleans, LA; University of Queensland School of Medicine, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate clinical characteristics of patients admitted to the hospital with coronavirus disease 2019 (COVID-19) in Southern United States and development as well as validation of a mortality risk prediction model. PATIENTS AND

methodsSouthern Louisiana was an early hotspot during the pandemic, which provided a large collection of clinical data on inpatients with COVID-19. We designed a risk stratification model to assess the mortality risk for patients admitted to the hospital with COVID-19. Data from 1673 consecutive patients diagnosed with COVID-19 infection and hospitalized between March 1, 2020, and April 30, 2020, was used to create an 11-factor mortality risk model based on baseline comorbidity, organ injury, and laboratory results. The risk model was validated using a subsequent cohort of 2067 consecutive hospitalized patients admitted between June 1, 2020, and December 31, 2020.

resultsThe resultant model has an area under the curve of 0.783 (95% CI, 0.76 to 0.81), with an optimal sensitivity of 0.74 and specificity of 0.69 for predicting mortality. Validation of this model in a subsequent cohort of 2067 consecutively hospitalized patients yielded comparable prognostic performance.

conclusionWe have developed an easy-to-use, robust model for systematically evaluating patients presenting to acute care settings with COVID-19 infection.

Indexed as

COVID-19Proportional Hazards ModelsClinical Laboratory TechniquesComorbidityEpidemiological ModelsFemaleHospitalizationHospital MortalityHumansLouisianaMaleMiddle AgedOrgan Dysfunction ScoresPrognosisReproducibility of ResultsRisk Assessment

Identifiers

PMID34863394
PMCPMC8445799

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Registered trials

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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.