Evidence map›Paper›PMID 33296132›Full record

ArticleInternational journal of clinical practice2021

Validation of pneumonia prognostic scores in a statewide cohort of hospitalised patients with COVID-19.

Yiyun Shi, Aakriti Pandita, Anna Hardesty, Meghan McCarthy, Jad Aridi, Zoe F Weiss, Curt G Beckwith, Dimitrios Farmakiotis

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Article in International journal of clinical practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Yiyun ShiDepartment of Medicine, The Warren Alpert Medical School of Brown University, Providence, RI, USA.ORCID https://orcid.org/0000-0002-7175-2125
Aakriti PanditaDivision of Infectious Diseases, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Anna HardestyDepartment of Medicine, The Warren Alpert Medical School of Brown University, Providence, RI, USA.ORCID https://orcid.org/0000-0002-5519-4477
Meghan McCarthyDivision of Infectious Diseases, The Warren Alpert Medical School of Brown University, Providence, RI, USA.ORCID https://orcid.org/0000-0002-0384-030X
Jad AridiDivision of Infectious Diseases, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Zoe F WeissDivision of Infectious Diseases, Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID https://orcid.org/0000-0003-2931-3660
Curt G BeckwithDivision of Infectious Diseases, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Dimitrios FarmakiotisDivision of Infectious Diseases, The Warren Alpert Medical School of Brown University, Providence, RI, USA.ORCID https://orcid.org/0000-0001-8489-108X

Funding

Brown Physicians Inc. Academic Assessment Grant
6 · The paper itself

Abstract

objectiveWe aimed to externally validate the predictive performance of two recently developed COVID-19-specific prognostic tools, the COVID-GRAM and CALL scores, and prior prognostic scores for community-acquired pneumonia (CURB-65), viral pneumonia (MuBLSTA) and H1N1 influenza pneumonia (Influenza risk score) in a contemporary US cohort.

methodsWe included 257 hospitalised patients with laboratory-confirmed COVID-19 pneumonia from three teaching hospitals in Rhode Island. We extracted data from within the first 24 hours of admission. Variables were excluded if values were missing in >20% of cases, otherwise, missing values were imputed. One hundred and fifteen patients with complete data after imputation were used for the primary analysis. Sensitivity analysis was performed after the exclusion of one variable (LDH) in the complete dataset (n = 257). Primary and secondary outcomes were in-hospital mortality and critical illness (mechanical ventilation or death), respectively.

resultsOnly the areas under the receiver-operating characteristic curves (RO-AUC) of COVID-GRAM (RO-AUC = 0.775, 95% CI 0.525-0.915) for in-hospital death, and CURB65 for in-hospital death (RO-AUC = 0.842, 95% CI 0.674-0.932) or critical illness (RO-AUC = 0.766, 95% CI 0.584-0.884) were significantly better than random. Sensitivity analysis yielded similar trends. Calibration plots showed better agreement between the estimated and observed probability of in-hospital death for CURB65, compared with COVID-GRAM. The negative predictive value (NPV) of CURB65 ≥2 was 97.2% for in-hospital death and 88.1% for critical illness.

conclusionsThe COVID-GRAM score demonstrated acceptable predictive performance for in-hospital death. The CURB65 score had better prognostic utility for in-hospital death and critical illness. The high NPV of CURB65 values ≥2 may be useful in triaging and allocation of resources.

Indexed as

Community-Acquired InfectionsCOVID-19Influenza A Virus, H1N1 SubtypePneumoniaHospital MortalityHumansPrognosisSARS-CoV-2Severity of Illness Index

Identifiers

PMID33296132
PMCPMC7883205

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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.