Development and Validation of 10-Year Life Expectancy Calculators to Individualize Veterans’ Prevention Decisions

忽视预期寿命会导致糟糕的临床决策。健康的老年人可以受益于 筛查率低。患有痴呆症或转移性癌症的老年人接受慢- 生长中的癌症不太可能引起症状，但可能导致假阳性结果的困扰， 侵入性检查和治疗。预期寿命计算器提供了超越武断的可能性， 以年龄为基础的截止值，以更合理的预期寿命为基础的决策，包括年龄以及其他 已知可预测死亡率的因素，如合并症、人口统计学、实验室结果和药房数据。 这项研究的目标是：（a）开发和验证一个10年预期寿命计算器， 2005年VA电子数据（人口统计学、合并症、实验室结果和药房记录）;（B）制定 并验证添加了2009年功能评估的增强预期寿命计算器（活动 日常生活或ADL和工具性日常生活活动或IADL评估）与其他风险因素 并确定ADL和IADL数据的添加是否导致改善的辨别力;）;（c）开发和 验证VA医疗保险预期寿命计算器，该计算器将2009年医疗保险数据添加到其他风险因素中， 确定医疗保险数据的增加是否会导致歧视的改善;（d）适用预期寿命 计算器到国家VA队列，以确定发生结肠直肠癌（CRC）筛查的比例 在预期寿命有限的退伍军人（<25%的预期寿命可能性为10年以上）和延长寿命的退伍军人中， 在推荐年龄内，两名退伍军人的预期寿命（10年以上预期寿命的可能性>75%） （50-75）以及年龄较大的退伍军人（76-85），当筛查不是常规时， 推荐 通过开发和验证VA电子数据驱动的预期寿命计算器，该项目将 确定年龄合适的退伍军人（50-75岁）中潜在不适当的CRC过度筛查率 预期寿命有限（预期寿命10年以上的可能性<25%）。此外，该项目还将 在超过推荐筛查年龄的退伍军人中识别潜在不适当的CRC筛查不足 (age 76-85岁），预期寿命延长（10年以上预期寿命的可能性>75%）。这项工作将 为干预措施提供关键基础，以提高CRC筛查的针对性， 个体退伍军人的预期寿命1）抑制CRC筛查临床提示患者寿命有限 预期（年龄50-75）或2）触发CRC筛查临床提醒延长寿命的患者 预期寿命（76-85岁）。此外，还可以利用准确的预期寿命估计来个性化地 其他预防决策，有很长的时间受益，如乳腺癌筛查和密集 血糖控制因此，这项工作将是另一种方式，退伍军人管理局将成为一个学习医疗保健 使用临床数据的系统，以个性化退伍军人的预防决策，并告知全系统的决策 关于预防临床警报。

Ignoring life expectancy can lead to poor clinical decisions. Healthy older adults who could benefit from screening have low screening rates. Older adults with dementia or metastatic cancer are screened for slow- growing cancers that are unlikely to cause symptoms but may lead to distress from false-positive results, invasive work-ups and treatments. Life expectancy calculators offer the possibility of moving beyond arbitrary age-based cutoffs to more rational life expectancy based decision-making, incorporating age as well as other factors known to predict mortality such as comorbidities, demographics, laboratory results and pharmacy data. The objectives of the study are to (a) develop and validate a 10-year life expectancy calculator using 2005 VA electronic data (demographics, comorbidities, laboratory results and pharmacy records); (b) develop and validate an Augmented life expectancy calculator that adds 2009 functional assessments (Activities of Daily Living or ADL and Instrumental Activities of Daily Living or IADL assessments) to the other risk factors and determine whether the addition of ADL and IADL data lead to improved discrimination; ); (c) develop and validate a VA-Medicare life expectancy calculator that adds 2009 Medicare data to the other risk factors and determine whether the addition of Medicare data lead to improved discrimination; (d) apply the life expectancy calculator to a national VA cohort to determine the proportion of colorectal cancer (CRC) screening that occurs in veterans with limited life expectancy (<25% likelihood of life expectancy of 10+ years) and extended life expectancy (>75% likelihood of life expectancy of 10+ years) in both veterans within the recommended age (50-75) for CRC screening as well as veterans at more advanced age (76-85) when screening is not routinely recommended. By developing and validating a VA electronic data driven life expectancy calculator, this project will determine the rates of potentially inappropriate CRC overscreening in age-appropriate veterans (age 50-75) with a limited life expectancy (<25% likelihood of life expectancy 10+ years). Conversely, this project will also identify potentially inappropriate CRC underscreening in veterans beyond the recommend age for screening (age 76-85) with an extended life expectancy (>75% likelihood of life expectancy of 10+ years). This work will provide the critical foundation for an intervention to improve the targeting of CRC screening will estimate an individual veterans' life expectancy to 1) suppress CRC screening clinical reminders for patients with limited life expectancy (age 50-75) or 2) trigger CRC screening clinical reminders for patients with extended life expectancy (age 76-85). Further, accurate life expectancy estimates could also be utilized to individualize other prevention decisions that have a long time to benefit, such as breast cancer screening and intensive glycemic control. Thus, this work would be another way that the VA would become a Learning Healthcare System that uses clinical data to individualize veterans' prevention decisions and inform system-wide decisions regarding prevention clinical alerts.