TY - JOUR
T1 - Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination
AU - Radomyslsky, Zorian
AU - Kivity, Sara
AU - Alon, Yaniv
AU - Saban, Mor
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/5/24
Y1 - 2024/5/24
N2 - Objective: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment. Method: Electronic 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. Results: Our 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. Conclusions: COVID-19 vaccination strongly reduced poor outcomes in older adults with cognitive impairment. Predictive analytics can help identify highest-risk cases requiring targeted interventions.
AB - Objective: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment. Method: Electronic 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. Results: Our 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. Conclusions: COVID-19 vaccination strongly reduced poor outcomes in older adults with cognitive impairment. Predictive analytics can help identify highest-risk cases requiring targeted interventions.
KW - COVID-19 vaccination
KW - Cognitive impairment
KW - Mortality
KW - Older adults
KW - Predictive analytics
UR - http://www.scopus.com/inward/record.url?scp=85194219212&partnerID=8YFLogxK
U2 - 10.1186/s12877-024-04982-7
DO - 10.1186/s12877-024-04982-7
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 38789939
AN - SCOPUS:85194219212
SN - 1471-2318
VL - 24
JO - BMC Geriatrics
JF - BMC Geriatrics
IS - 1
M1 - 454
ER -