JMIR Publications

ABSTRACT

Postoperative delirium of cardiovascular surgery (PODOCVS) is an acute brain dysfunction characterised by inattention, impaired consciousness, and cognitive disorders, and the severity and presence of these symptoms fluctuate over time. PODOCVS occurs during the early postoperative period and is associated with adverse outcomes, including prolonged mechanical ventilation, extended hospital stays, increased requirements for long-term care, exacerbation of preexisting cognitive impairments, new-onset dementia, heightened anxiety and depression, and premature mortality. Advances in its early diagnosis and treatment have mitigated some of the initial adverse effects of PODOCVS, but models for predicting risk in patients who have already developed PODOCVS remain inadequate for effective secondary prevention. Developing multivariable prediction models for stratifying PODOCVS risk would enable early, personalised interventions. To this end, this protocol aims to systematically review and critically evaluate the development, performance, and applicability of existing prediction models for PODOCVS. We will search various databases including Embase, PubMed, the Web of Science Core Collection, Wan Fang, Wei Pu, and the China National Knowledge Infrastructure (CNKI) for studies on multivariate prediction models for PODOCVS. A manual search of the included studies’ reference lists will also be conducted to identify any additional relevant publications. No restrictions will be placed on publication status or language. The studies will undergo that meet the following criteria: studies with subject populations comprising adult cardiovascular surgery patients aged ≥ 18 years; studies involving the development and internal or external validation of predictive models for PODOCVS via multivariate analysis; studies with outcome measures focused on postoperative delirium. Two researchers will independently extract the data and assess the included studies’ model quality using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Predictive Model Bias Risk Assessment Tool (PROBAST). This protocol ensures methodological transparency, stimulates discourse, and paves the way for informed interventions to improve overall health outcomes. PROSPERO registry: CRD42024578957.