Study protocol for the development and validation of a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia.

Michael Bonares, Stacey Fisher, Kieran Quinn, Kirsten Wentlandt, Peter Tanuseputro
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Abstract

Background: 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.

Methods: 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.

Discussion: 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.

Trial registration: NCT05371782.

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开发和验证临床预测工具以估算痴呆症住院患者 1 年死亡风险的研究方案。
背景:痴呆症患者及其护理者可以从预先护理计划中获益,但他们可能没有及时或根本没有讨论过这些问题。预后工具可以作为一种提示,促使医疗服务提供者在患者及其护理者中启动预先护理计划,从而提高患者接受符合其目标的护理的机会。现有的预后工具存在局限性。我们试图开发并验证一种临床预测工具,用于估算住院痴呆症患者的 1 年死亡风险:推导队列将包括 2009 年 4 月 1 日至 2017 年 12 月 31 日期间在安大略省住院的约 23.5 万名痴呆症患者。预测变量将根据对病因学研究和现有预后工具的文献综述以及相关专业知识进行充分预设;它们将分为以下几类:社会人口学因素、合并症、既往干预、功能状态、营养状况、入院信息、既往医疗保健使用情况。将避免根据数据选择预测因子。连续预测因子将以受限立方样条进行建模。结果变量为入院 1 年内的死亡率,将以二元变量建模,从而估算出逻辑回归模型。预测变量和结果变量将来自相关联的人口级医疗保健管理数据库。验证队列将包括约 6.3 万名痴呆症患者,他们于 2018 年 1 月 1 日至 2019 年 3 月 31 日在安大略省入院治疗。将通过内部(时间)验证来评估模型的性能,包括预测准确性、区分度和校准。校准将在全部验证队列以及对临床医生和政策制定者具有重要意义的亚组中进行评估。最终模型将以整个验证队列为基础:我们试图开发并验证一种临床预测工具,用于估算住院痴呆症患者的 1 年死亡风险。该模型将被整合到医院的电子病历中,在住院时自动输出 1 年死亡风险。该工具可作为预先护理计划的触发器,并为获得基于预后的资格标准的姑息关怀专科服务提供信息。在实施之前,该工具将需要外部验证,并研究其对临床决策和患者预后的潜在影响:试验注册:NCT05371782。
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