Evidence map›Paper›PMID 39762090›Full record

Observational studyBMJ open2025

Early prediction of long COVID-19 syndrome persistence at 12 months after hospitalisation: a prospective observational study from Ukraine.

Oleksii Honchar, Tetiana Ashcheulova, Tetyana Chumachenko, Dmytro Chumachenko

Abstract readObservational Study
In one paragraph

Observational study in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Review
  6. Article
  7. 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

4 authors.

Oleksii HoncharDepartment of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine oleksiygonchar@gmail.com.ORCID http://orcid.org/0000-0001-6834-8845
Tetiana AshcheulovaDepartment of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine.
Tetyana ChumachenkoDepartment of Epidemiology, Kharkiv National Medical University, Kharkiv, Ukraine.
Dmytro ChumachenkoDepartment of Mathematical Modelling and Artificial Intelligence, National Aerospace University Kharkiv Aviation Institute, Kharkiv, Ukraine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify the early predictors of a self-reported persistence of long COVID syndrome (LCS) at 12 months after hospitalisation and to propose the prognostic model of its development.

designA combined cross-sectional and prospective observational study.

settingA tertiary care hospital.

participants221 patients hospitalised for COVID-19 who have undergone comprehensive clinical, sonographic and survey-based evaluation predischarge and at 1 month with subsequent 12-month follow-up. The final cohort included 166 patients who had completed the final visit at 12 months.

main outcome measureA self-reported persistence of LCS at 12 months after discharge.

resultsSelf-reported LCS was detected in 76% of participants at 3 months and in 43% at 12 months after discharge. Patients who reported incomplete recovery at 1 year were characterised by a higher burden of comorbidities (Charlson index of 0.69±0.96 vs 0.31±0.51, p=0.001) and residual pulmonary consolidations (1.56±1.78 vs 0.98±1.56, p=0.034), worse blood pressure (BP) control (systolic BP of 138.1±16.2 vs 132.2±15.8 mm Hg, p=0.041), renal (estimated glomerular filtration rate of 59.5±14.7 vs 69.8±20.7 mL/min/1.73 m

conclusionsCompared with post-COVID-19 patients who have completely recovered by 12 months after hospital discharge, those who have subsequently developed 'very long' COVID were characterised by a variety of more pronounced residual predischarge abnormalities that had mostly subsided by 1 month, except for steady differences in the physical symptoms levels. A simple artificial neural networks-based binary classification model using peak ESR, creatinine, ALT and weight loss during the acute phase, predischarge 6-minute walk distance and complex survey-based symptoms assessment as inputs has shown a 92% accuracy with an area under receiver-operator characteristic curve 0.931 in prediction of LCS symptoms persistence at 12 months.

Indexed as

COVID-19HospitalizationPost-Acute COVID-19 SyndromeAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedPrognosisProspective StudiesSARS-CoV-2Self ReportUkraineFollow-Up StudiesPost-Acute COVID-19 SyndromePrognosis

Identifiers

PMID39762090
PMCPMC11748775

What OpenQuestion holds

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LicenceCC BY-NC
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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.