Evidence map›Paper›PMID 38789939›Full record

ArticleBMC geriatrics2024

Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination.

Zorian Radomyslsky, Sara Kivity, Yaniv Alon, Mor Saban

Abstract read
In one paragraph

Article in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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.

Zorian RadomyslskyMaccabi Healthcare Services, 6812509, Tel Aviv-Jaffa, Israel. radomysl_z@mac.org.il.
Sara KivityMaccabi Healthcare Services, 6812509, Tel Aviv-Jaffa, Israel. dejav_s@mac.org.il.
Yaniv AlonNursing Department, School of Health Professions, Faculty of Medical & Health Sciences, Tel Aviv University, Ramat Aviv, Tel Aviv, 69978, Israel.
Mor SabanNursing Department, School of Health Professions, Faculty of Medical & Health Sciences, Tel Aviv University, Ramat Aviv, Tel Aviv, 69978, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment.

methodElectronic health records from Israel from March 2020-February 2022 were analyzed for a large cohort (N = 85,288) aged 65 + . Machine learning constructed models to predict mortality risk from patient factors. Outcomes examined were COVID-19 mortality and hospitalization post-vaccination.

resultsOur study highlights the significant reduction in mortality risk among older adults with cognitive disorders following COVID-19 vaccination, showcasing a survival rate improvement to 93%. Utilizing machine learning for mortality prediction, we found the XGBoost model, enhanced with inverse probability of treatment weighting, to be the most effective, achieving an AUC-PR value of 0.89. This underscores the importance of predictive analytics in identifying high-risk individuals, emphasizing the critical role of vaccination in mitigating mortality and supporting targeted healthcare interventions.

conclusionsCOVID-19 vaccination strongly reduced poor outcomes in older adults with cognitive impairment. Predictive analytics can help identify highest-risk cases requiring targeted interventions.

Indexed as

COVID-19COVID-19 VaccinesDementiaMachine LearningAgedAged, 80 and overCognitive DysfunctionCohort StudiesFemaleHospitalizationHumansIsraelMaleVaccinationCOVID-19 VaccinesCognitive impairmentCOVID-19 vaccinationMortalityOlder adultsPredictive analytics

Identifiers

PMID38789939
PMCPMC11127399

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.