Evidence map›Paper›PMID 40642241›Full record

Observational studyFrontiers in public health2025

Deep learning analysis of long COVID and vaccine impact in low- and middle-income countries (LMICs): development of a risk calculator in a multicentric study.

Ahmed Shaheen, Nour Shaheen, Long COVID Collaboration Study Group in the LMICs, Sheikh Shoib, Fahimeh Saeed, Mudathiru Buhari, Vishal Bharmauria, Oliver Flouty

Registry-linked trialAbstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05059184 (Long-term Sequelae of COVID-19), which is not on this map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

NCT05059184 completednot on this map

Long-term Sequelae of COVID-19 (Myalgic Encephalomyelitis): An International Cross-Sectional Study

TypeobservationalSponsorAlexandria UniversityRan2021 to 2022Enrolled2,450ConditionsMyalgic EncephalomyelitisArmsThis is an observational cross-sectional study, there is no need for intervention
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Ahmed ShaheenAlexandria Faculty of Medicine, Alexandria, Egypt.
Nour ShaheenAlexandria Faculty of Medicine, Alexandria, Egypt.
Long COVID Collaboration Study Group in the LMICs
Sheikh ShoibPsychosis Research Centre, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Fahimeh SaeedPsychosis Research Centre, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Mudathiru BuhariDivision of Infectious Disease, University of South Florida, Tampa, FL, United States.
Vishal BharmauriaThe Tampa Human Neurophysiology Lab and Department of Neurosurgery, Brain and Spine, University of South Florida, Morsani College of Medicine, Tampa, FL, United States.
Oliver FloutyThe Tampa Human Neurophysiology Lab and Department of Neurosurgery, Brain and Spine, University of South Florida, Morsani College of Medicine, Tampa, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic affecting millions worldwide. This study aims to bridge the knowledge gap between acute and chronic symptoms, vaccination impact, and associated factors in patients across different low- and middle-income countries (LMICs). Materials and methods: The study included 2,445 participants aged 18 years and older, testing positive for COVID-19. Data collection involved screening for medical histories, testing records, symptomatology, and persistent symptoms. Validated instruments, including the DePaul Symptom Questionnaire (DSQ-2) and the Patient Health Questionnaire-9 (PHQ-9), were used. We applied a self-supervised and unsupervised deep neural network to extract features from the questionnaire. Gradient boosted machines (GBM) model was used to build a risk calculator for chronic fatigue syndrome (CFS), depression, and prolonged COVID-19 symptoms. Results: Out of the study cohort, 68.1% of the patients had symptoms lasting longer than 2 weeks. The most frequent symptoms were loss of smell (46.8%), dry cough (40.1%), loss of taste (37.8%), headaches (37.2%), and sore throat (28.9%). The patients also reported high rates of depression (47.7%), chronic fatigue (6.5%), and infection after vaccination (23.7%). Factors associated with CFS included sex, age, and smoking. Vaccinated individuals demonstrated lower odds of experiencing prolonged COVID-19 symptoms, CFS, and depression. The predictive models achieved a high area under the curve (AUC) scores of 0.87, 0.82, and 0.74, respectively. Conclusion: The findings underscore the significant burden of long-term symptoms such as chronic fatigue and depression, affecting a considerable proportion of individuals post-infection. Moreover, the study reveals promising insights into the potential benefits of vaccination in mitigating the risk of prolonged COVID-19 symptoms, CFS, and depression. Overall, this research contributes valuable knowledge towards comprehensive management and prevention efforts amidst the ongoing global pandemic. Clinical trial registration: Clinical trials.gov, NCT05059184.

Indexed as

COVID-19COVID-19 VaccinesDeep LearningDeveloping CountriesAdultAgedDepressionFemaleHumansMaleMiddle AgedRisk AssessmentSARS-CoV-2Surveys and QuestionnairesVaccinationYoung AdultCOVID-19 Vaccineschronic fatigue syndromeCOVID-19depressionhospitalizationLMICslong COVIDpost-acute sequelaevaccination

Identifiers

PMID40642241
PMCPMC12240947

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.