ArticleBMC infectious diseases2023
Risk factors for disease severity among children with Covid-19: a clinical prediction model.
Article in BMC infectious diseases, 2023. 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.
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.
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.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Comparison of febrile seizures associated with SARS-CoV-2 infection in pre-Omicron and Omicron-predominant periods: a systematic review and meta-analysis.European journal of pediatrics · 2026Pooled it
- Pathogen and Patient Characteristics and the Severity of Viral Respiratory Infections in Children.JAMA network open · 2026Article
- Clinical outcomes and healthcare utilization of hospitalized children with influenza versus COVID-19.Clinical and experimental pediatrics · 2025Article
- Clinical Burden of Pediatric Influenza A and B in Malaysia: Outcomes and Resource Utilization in A Tropical Setting.Infection & chemotherapy · 2025Article
- Clinical characteristics and burden of COVID-19 in children and adolescents in Colombia: a retrospective database analysis.BMJ paediatrics open · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- Clinical, biochemical, and genetic study of TACE/TNF-α/ACE signaling pathway in pediatric COVID-19 infection.Clinical and experimental pediatrics · 2024Article
- Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry.Respiratory research · 2024Article
- Predictive factors for COVID-19 severity and mortality in hospitalized children.BMC infectious diseases · 2024Article
- Review
- Clinical comparison of HMPV and RSV infections in hospitalised Malaysian children: A propensity score matched study.The clinical respiratory journal · 2024Observational
- Hematocrit and Albumin Levels at Admission Predict in-Hospital Mortality in Pediatric COVID-19 Omicron Variant Patients.Infection and drug resistance · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChildren account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19.
methodsWe identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.
resultsA total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.
conclusionOur nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.
Indexed as
Identifiers
What OpenQuestion holds
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.