Doctors currently have no reliable way to tell whether a patient with cancer, organ failure, or a neurodegenerative disease has six months or less to live. This review will gather and assess all existing evidence on multicomponent prognostic models—tools that combine multiple clinical factors, such as age, symptoms, and lab results—to predict six-month mortality. The problem matters because accurate six-month prognoses are essential for care planning, accessing financial support, and informing proposed UK legislation on assisted dying. Without reliable models, clinicians and patients make these high-stakes decisions based on guesswork. If this review identifies models with strong predictive accuracy, it could give doctors a validated, evidence-based tool to support conversations about end-of-life care. That would help patients and families plan ahead, ensure fair access to financial and palliative support, and provide a clearer evidence base for policy decisions around assisted dying. If the evidence is weak, the review will highlight where future research is needed, preventing premature adoption of inaccurate tools.
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Prognostic accuracy of multicomponent prognostic models for predicting last six months of life – an overview of reviews Background: Prognostication for individuals living with life-limiting conditions is important for the individuals themselves, the clinicians who care for them, and for those who support them. An understand of how a condition may change over time is important to enable care planning, and to determine eligibility for financial support/enhanced care in certain jurisdictions. Understanding six-month prognosis has become topical in the UK context due to proposed legislative changes around assisted dying. Internationally, cancer, organ failure and neurodegenerative diseases account are the commonest diagnoses among those seeking assisted dying, where such data are recorded. Review question: What is the prognostic accuracy of multicomponent models for predicting last six months of life among people living with cancer, organ failure or neurodegenerative disease? Methods: We will undertake a review of systematic reviews, identifying multicomponent prognostic models for our conditions of interest. Multicomponent models are those containing more than two independent variables. We will exclude reviews of prognostic factors which are not combined to develop a prognostic model. The search will include the following databases: Cochrane Database of Systematic Reviews, Embase, MEDLINE and CINAHL. We will limit to studies of adults (aged 18 years and over), systematic reviews published in peer-reviewed journals, and publications from 2010 onwards. Our primary outcome is accuracy in predicting all-cause six-month mortality. Analysis: Data will be presented using a mix of visualisation, tabulation and narrative description. This will include review level summaries of model performance. Meta-analysis will be performed at prognostic model level, where possible. Risk of bias will be assessed at review level using the AMSTAR-2 tool. We will use the risk of bias assessment undertaken by study authors to appraise risk of bias in the included studies. We will use the GRADE approach to rate confidence in summary estimates around the strength of evidence to support use of prognostic models to inform decisions around six-month mortality. Involvement: Our review is supported by involvement of two public and patient involvement representatives from our review PPI group and engagement with clinical interest-holders, representative groups and other PPI collaborators connected with palliative and end-of-life care.
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