Frontiers in public health · 2026
Risk prediction models for cognitive impairment in patients with chronic kidney disease: a systematic review.
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Dong Lei, Chen Yun, Lin Xiaohong, Shi Jiyuan, Liu Hongxia, Zhong Yinqin
Аннотация
INTRODUCTION: Prediction models for cognitive impairment offer significant advantages in public health preventive strategies in high-risk chronic kidney disease populations, but their methodological quality and applicability remain unclear. The aims of this study were to systematically review and critically appraise the existing prediction models for cognitive impairment in chronic kidney disease.
METHODS: PubMed, Web of Science, Embase, CINAHL, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, SinoMed, and China Science and Technology Journal Database were searched from inception to 12 October 2025. The prediction model risk of bias assessment tool was used for quality assessment, and the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies was used for data extraction. A narrative synthesis was performed to summarize the characteristics, performance, and risk of bias of existing models. Subgroup analyses were performed stratified by outcome definitions, CKD population, and cognitive assessment tools.
RESULTS: A total of 5,972 studies were identified, ultimately including 17 models for three outcomes: cognitive impairment (52.9%), mild cognitive impairment (11.8%), and cognitive frailty (35.3%). The reported prevalence ranged from 12.8 to 77.9% for cognitive impairment, around 50% for mild cognitive impairment, and 14.2-25.8% for cognitive frailty. Most models (58.8%) were developed in hemodialysis populations. Fifteen studies were internally validated, and 7 were externally validated. For internal validation, the AUC of cognitive impairment models ranged from 0.745 to 0.918, mild cognitive impairment models from 0.926 to 0.928, and cognitive frailty models from 0.842 to 0.945. For external validation, the AUC of cognitive impairment models ranged from 0.752 to 0.899, the mild cognitive impairment model was 0.897, and cognitive frailty models ranged from 0.832 to 0.904. All studies were rated as high risk of bias, mainly due to methodological problems in the analysis domain, with great concerns regarding applicability in 13 studies.
CONCLUSION: Current research on prediction models for cognitive impairment in chronic kidney disease remains in the developing stage, with heterogeneous outcome definitions and all studies at high risk of bias. Most studies are limited by methodological shortcomings.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251007577, Identifier CRD420251007577.
METHODS: PubMed, Web of Science, Embase, CINAHL, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, SinoMed, and China Science and Technology Journal Database were searched from inception to 12 October 2025. The prediction model risk of bias assessment tool was used for quality assessment, and the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies was used for data extraction. A narrative synthesis was performed to summarize the characteristics, performance, and risk of bias of existing models. Subgroup analyses were performed stratified by outcome definitions, CKD population, and cognitive assessment tools.
RESULTS: A total of 5,972 studies were identified, ultimately including 17 models for three outcomes: cognitive impairment (52.9%), mild cognitive impairment (11.8%), and cognitive frailty (35.3%). The reported prevalence ranged from 12.8 to 77.9% for cognitive impairment, around 50% for mild cognitive impairment, and 14.2-25.8% for cognitive frailty. Most models (58.8%) were developed in hemodialysis populations. Fifteen studies were internally validated, and 7 were externally validated. For internal validation, the AUC of cognitive impairment models ranged from 0.745 to 0.918, mild cognitive impairment models from 0.926 to 0.928, and cognitive frailty models from 0.842 to 0.945. For external validation, the AUC of cognitive impairment models ranged from 0.752 to 0.899, the mild cognitive impairment model was 0.897, and cognitive frailty models ranged from 0.832 to 0.904. All studies were rated as high risk of bias, mainly due to methodological problems in the analysis domain, with great concerns regarding applicability in 13 studies.
CONCLUSION: Current research on prediction models for cognitive impairment in chronic kidney disease remains in the developing stage, with heterogeneous outcome definitions and all studies at high risk of bias. Most studies are limited by methodological shortcomings.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251007577, Identifier CRD420251007577.