Evidence map›Paper›PMID 38146473›Full record

ArticleFrontiers in public health2023

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

Saeed Shakibfar, Jing Zhao, Huiqi Li, Hedvig Nordeng, Angela Lupattelli, Milena Pavlovic, Geir Kjetil Sandve, Fredrik Nyberg, Björn Wettermark, Mohammadhossein Hajiebrahimi and 2 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

12 authors.

Saeed ShakibfarDepartment of Drug Design and Pharmacology, Pharmacovigilance Research Center, University of Copenhagen, Copenhagen, Denmark.
Jing ZhaoDepartment of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
Huiqi LiSchool of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Hedvig NordengDepartment of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
Angela LupattelliDepartment of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
Milena PavlovicUiO: RealArt Convergence Environment, University of Oslo, Oslo, Norway.
Geir Kjetil SandveUiO: RealArt Convergence Environment, University of Oslo, Oslo, Norway.
Fredrik NybergSchool of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Björn WettermarkDepartment of Pharmacy, Pharmacoepidemiology and Social Pharmacy, Uppsala University, Uppsala, Sweden.
Mohammadhossein HajiebrahimiDepartment of Pharmacy, Pharmacoepidemiology and Social Pharmacy, Uppsala University, Uppsala, Sweden.
Morten AndersenDepartment of Drug Design and Pharmacology, Pharmacovigilance Research Center, University of Copenhagen, Copenhagen, Denmark.
Maurizio SessaDepartment of Drug Design and Pharmacology, Drug Safety Group, University of Copenhagen, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway. Method: We employed linked data from the national health registries of Sweden and Norway to conduct our study. We focused on individuals in Sweden with confirmed SARS-CoV-2 infection through RT-PCR testing up to August 2022 as our study cohort. Within this group, we identified hospitalized cases as those who were admitted to the hospital within 14 days of testing positive for SARS-CoV-2 and matched them with five controls from the same cohort who were not hospitalized due to SARS-CoV-2. Additionally, we identified individuals who died within 30 days after being hospitalized for COVID-19. To develop our disease risk scores, we considered various factors, including demographics, infectious, somatic, and mental health conditions, recorded diagnoses, and pharmacological treatments. We also conducted age-specific analyses and assessed model performance through 5-fold cross-validation. Finally, we performed external validation using data from the Norwegian population with COVID-19 up to December 2021. Results: During the study period, a total of 124,560 individuals in Sweden were hospitalized, and 15,877 individuals died within 30 days following COVID-19 hospitalization. Disease risk scores for both hospitalization and mortality demonstrated predictive capabilities with ROC-AUC values of 0.70 and 0.72, respectively, across the entire study period. Notably, these scores exhibited a positive correlation with the likelihood of hospitalization or death. In the external validation using data from the Norwegian COVID-19 population (consisting of 53,744 individuals), the disease risk score predicted hospitalization with an AUC of 0.47 and death with an AUC of 0.74. Conclusion: The disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

Indexed as

COVID-19HospitalizationHumansMachine LearningRisk FactorsSARS-CoV-2Swedenartificial intelligenceCOVID-19disease risk scoremachine learningprediction modeling

Identifiers

PMID38146473
PMCPMC10749372

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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.