Bonares Michael, Fisher Stacey, Quinn Kieran, Wentlandt Kirsten, Tanuseputro Peter
Department of Medicine, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Department of Medicine, University of Toronto, Toronto, ON, Canada.
Diagn Progn Res. 2024 Mar 19;8(1):5. doi: 10.1186/s41512-024-00168-2.
Patients with dementia and their caregivers could benefit from advance care planning though may not be having these discussions in a timely manner or at all. A prognostic tool could serve as a prompt to healthcare providers to initiate advance care planning among patients and their caregivers, which could increase the receipt of care that is concordant with their goals. Existing prognostic tools have limitations. We seek to develop and validate a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia.
The derivation cohort will include approximately 235,000 patients with dementia, who were admitted to hospital in Ontario from April 1st, 2009, to December 31st, 2017. Predictor variables will be fully prespecified based on a literature review of etiological studies and existing prognostic tools, and on subject-matter expertise; they will be categorized as follows: sociodemographic factors, comorbidities, previous interventions, functional status, nutritional status, admission information, previous health care utilization. Data-driven selection of predictors will be avoided. Continuous predictors will be modelled as restricted cubic splines. The outcome variable will be mortality within 1 year of admission, which will be modelled as a binary variable, such that a logistic regression model will be estimated. Predictor and outcome variables will be derived from linked population-level healthcare administrative databases. The validation cohort will comprise about 63,000 dementia patients, who were admitted to hospital in Ontario from January 1st, 2018, to March 31st, 2019. Model performance, measured by predictive accuracy, discrimination, and calibration, will be assessed using internal (temporal) validation. Calibration will be evaluated in the total validation cohort and in subgroups of importance to clinicians and policymakers. The final model will be based on the full cohort.
We seek to develop and validate a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia. The model would be integrated into the electronic medical records of hospitals to automatically output 1-year mortality risk upon hospitalization. The tool could serve as a trigger for advance care planning and inform access to specialist palliative care services with prognosis-based eligibility criteria. Before implementation, the tool will require external validation and study of its potential impact on clinical decision-making and patient outcomes.
NCT05371782.
痴呆患者及其照料者可从预先护理计划中获益，但可能未及时进行此类讨论，甚至根本未进行讨论。一种预后工具可促使医疗服务提供者在患者及其照料者中启动预先护理计划，这可能会增加符合其目标的护理的接受度。现有的预后工具有局限性。我们旨在开发并验证一种临床预测工具，以估计住院痴呆患者1年内的死亡风险。
推导队列将包括约235,000名痴呆患者，他们于2009年4月1日至2017年12月31日在安大略省住院。预测变量将根据病因学研究和现有预后工具的文献综述以及专业知识进行完全预先指定；它们将分类如下：社会人口统计学因素、合并症、既往干预措施、功能状态、营养状况、入院信息、既往医疗保健利用情况。将避免数据驱动的预测变量选择。连续预测变量将建模为受限立方样条。结局变量将是入院后1年内的死亡率，将其建模为二元变量，以便估计逻辑回归模型。预测变量和结局变量将从关联的人群层面医疗管理数据库中得出。验证队列将包括约63,000名痴呆患者，他们于2018年1月1日至2019年3月31日在安大略省住院。将使用内部（时间）验证评估以预测准确性、区分度和校准度衡量的模型性能。将在整个验证队列以及对临床医生和政策制定者重要的亚组中评估校准度。最终模型将基于完整队列。
我们旨在开发并验证一种临床预测工具，以估计住院痴呆患者1年内的死亡风险。该模型将集成到医院的电子病历中，以便在住院时自动输出1年内的死亡风险。该工具可作为预先护理计划的触发因素，并根据基于预后的资格标准为获得专科姑息治疗服务提供信息。在实施之前，该工具需要进行外部验证并研究其对临床决策和患者结局的潜在影响。
NCT05371782。
