Predictors for survival in patients with Alzheimer's disease: a large comprehensive meta-analysis.
Xiaoting ZhengShichan WangJingxuan HuangChunyu LiHui Fang ShangPublished in: Translational psychiatry (2024)
The prevalence of Alzheimer's disease (AD) is increasing as the population ages, and patients with AD have a poor prognosis. However, knowledge on factors for predicting the survival of AD remains sparse. Here, we aimed to systematically explore predictors of AD survival. We searched the PubMed, Embase and Cochrane databases for relevant literature from inception to December 2022. Cohort and case-control studies were selected, and multivariable adjusted relative risks (RRs) were pooled by random-effects models. A total of 40,784 reports were identified, among which 64 studies involving 297,279 AD patients were included in the meta-analysis after filtering based on predetermined criteria. Four aspects, including demographic features (n = 7), clinical features or comorbidities (n = 13), rating scales (n = 3) and biomarkers (n = 3), were explored and 26 probable prognostic factors were finally investigated for AD survival. We observed that AD patients who had hyperlipidaemia (RR: 0.69) were at a lower risk of death. In contrast, male sex (RR: 1.53), movement disorders (including extrapyramidal signs) (RR: 1.60) and cancer (RR: 2.07) were detrimental to AD patient survival. However, our results did not support the involvement of education, hypertension, APOE genotype, Aβ 42 and t-tau in AD survival. Our study comprehensively summarized risk factors affecting survival in patients with AD, provided a better understanding on the role of different factors in the survival of AD from four dimensions, and paved the way for further research.
Keyphrases
- case control
- systematic review
- prognostic factors
- poor prognosis
- free survival
- healthcare
- end stage renal disease
- cognitive decline
- type diabetes
- chronic kidney disease
- emergency department
- ejection fraction
- squamous cell carcinoma
- newly diagnosed
- machine learning
- high fat diet
- meta analyses
- young adults
- patient reported outcomes
- electronic health record
- climate change
- drug induced
- quality improvement
- lymph node metastasis