Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Statistics Denmark, Denmark.
PLoS One. 2023 Aug 7;18(8):e0289632. doi: 10.1371/journal.pone.0289632. eCollection 2023.
The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.
This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013-2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis).
The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models.
Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals.
准确预测老年人的生存率至关重要，因为它指导着临床决策。使用医疗保健使用情况来预测死亡率的附加价值尚未得到探索。本研究的目的是调查在配偶丧亲的老年人中，医疗支出的时间模式是否可以改善死亡率的预测性能，除了其他广泛使用的社会人口统计学变量之外。
这是一项基于人群的队列研究，涉及 2013-2016 年内丧偶的 48944 名丹麦 65 岁及以上的公民。从配偶去世之日起，对每个个体进行随访，直至因任何原因死亡或 2016 年 12 月 31 日，以先到者为准。在随访期间，每个人每周的医疗支出情况均可获得，并用于极端梯度增强模型中死亡率风险的预测因子。通过比较模型的区分度（AUC）、总体预测误差（Brier 评分）、校准和临床获益（决策曲线分析），评估医疗支出轨迹对死亡率预测的改善程度。
配偶去世后一年内死亡的年龄和性别预测的 AUC 为 70.8%[95%CI 68.8,72.8]。添加社会人口统计学变量可使 AUC 增加 0.9%至 3.1%，但并未显著降低总体预测误差。结合上述变量和医疗支出使用情况的模型的 AUC 为 80.8%[79.3,82.4]，其 Brier 评分和校准也更好。总体而言，医疗支出模式对死亡率的预测改善最大，在其余模型中也表现出最高的临床获益。
医疗支出的时间模式有可能显著提高我们对配偶丧亲后谁有高死亡风险的评估能力。所提出的方法可以帮助更有效地对丧亲个体进行风险分析和预后评估。
