Evidence map›Paper›PMID 38094088›Full record

ArticleCritical care explorations2023

Impact of Surge Strain and Pandemic Progression on Prognostication by an Established COVID-19-Specific Severity Score.

Christina Yek, Jing Wang, Jonathan Fintzi, Alex G Mancera, Michael B Keller, Sarah Warner, Sameer S Kadri

Abstract read
In one paragraph

Article in Critical care explorations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. [Performance of prognostic scores during the first and second waves of COVID-19 pneumonia]Revista de la Facultad de Ciencias Medicas (Cordoba, Argentina) · 2026
    Article
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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

7 authors.

Christina YekCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD.
Jing WangClinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD.
Jonathan FintziBiostatistics Research Branch, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, Bethesda, MD.
Alex G ManceraCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD.
Michael B KellerCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD.
Sarah WarnerCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD.
Sameer S KadriCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

importanceMany U.S. State crisis standards of care (CSC) guidelines incorporated Sequential Organ Failure Assessment (SOFA), a sepsis-related severity score, in pandemic triage algorithms. However, SOFA performed poorly in COVID-19. Although disease-specific scores may perform better, their prognostic utility over time and in overcrowded care settings remains unclear.

objectivesWe evaluated prognostication by the modified 4C (m4C) score, a COVID-19-specific prognosticator that demonstrated good predictive capacity early in the pandemic, as a potential tool to standardize triage across time and hospital-surge environments.

designRetrospective observational cohort study.

settingTwo hundred eighty-one U.S. hospitals in an administrative healthcare dataset.

participantsA total of 298,379 hospitalized adults with COVID-19 were identified from March 1, 2020, to January 31, 2022. m4C scores were calculated from admission diagnosis codes, vital signs, and laboratory values. MAIN OUTCOMES AND MEASURES: Hospital-surge index, a severity-weighted measure of COVID-19 caseload, was calculated for each hospital-month. Discrimination of in-hospital mortality by m4C and surge index-adjusted models was measured by area under the receiver operating characteristic curves (AUC). Calibration was assessed by training models on early pandemic waves and measuring fit (deviation from bisector) in subsequent waves.

resultsFrom March 2020 to January 2022, 298,379 adults with COVID-19 were admitted across 281 U.S. hospitals. m4C adequately discriminated mortality in wave 1 (AUC 0.779 [95% CI, 0.769-0.789]); discrimination was lower in subsequent waves (wave 2: 0.772 [95% CI, 0.765-0.779]; wave 3: 0.746 [95% CI, 0.743-0.750]; delta: 0.707 [95% CI, 0.702-0.712]; omicron: 0.729 [95% CI, 0.721-0.738]). m4C demonstrated reduced calibration in contemporaneous waves that persisted despite periodic recalibration. Performance characteristics were similar with and without adjustment for surge. CONCLUSIONS AND RELEVANCE: Mortality prediction by the m4C score remained robust to surge strain, making it attractive for when triage is most needed. However, score performance has deteriorated in recent waves. CSC guidelines relying on defined prognosticators, especially for dynamic disease processes like COVID-19, warrant frequent reappraisal to ensure appropriate resource allocation.

Indexed as

COVID-19healthcare rationingmortalitysurge capacitytriage

Identifiers

PMID38094088
PMCPMC10718382

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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.