JMIR出版物评估全州全因未来1年死亡率:卡塔尔世界杯8强波胆分析对生活质量、资源利用和医疗无果的前瞻性研究%A郭，%A郑彦婷，%A傅刚，%A郝天云，%A叶士颖，%A郑成银，% Le %A Liu,Modi %A Xia,Minjie %A Jin， % Bo %A Zhu， %春青%A Wang,Oliver %A Wu,Qian %A Culver,Devore S %A Alfreds,Shaun T %A Stearns,Frank %A Kanov,Laura %A Bhatia,Ajay %A Sylvester,Karl G %A Widen,Eric %A McElhinney,Doff B %A Ling, Bruce雪峰%+斯坦福大学外科学系，S370 Grant Building斯坦福，CA，美国，1 650 427 9198,bxling@stanford.edu %K 1年死亡风险预测%K电子病历%K生活质量%K医疗资源利用%K社会决定因素%D 2018 %7 04.06.2018 %9原始论文%J J医学互联网研究%G英文%X对于许多老年患者来说，尽管许多积极的医疗方法会带来不适，并降低生活质量，但在生命的最后一年，医疗保健资源和支出的比例不成比例。然而，很少有预后工具专注于在全州范围内预测老年患者的全因1年死亡率，这一问题对改善生活质量和公平分配稀缺资源具有重要意义。目的:使用来自全州老年人口(年龄≥65岁)的数据，我们试图前瞻性验证一种算法，以识别明年有死亡风险的患者，以最大限度地减少决策不确定性，提高生活质量，减少无效治疗。方法:使用来自缅因州健康信息交换的电子病历进行分析，该病历涵盖了全州近95%的人口。该模型是在2013年9月5日至2015年9月4日期间从健康信息交换网络的任何护理机构出院的125,896名年龄在65岁以上的患者中开发的。2014年9月5日至2016年9月4日，采用相同纳入和排除标准的153199例患者进行验证。患者被分为不同的危险组。 The association between all-cause 1-year mortality and risk factors was screened by chi-squared test and manually reviewed by 2 clinicians. We calculated risk scores for individual patients using a gradient tree-based boost algorithm, which measured the probability of mortality within the next year based on the preceding 1-year clinical profile. Results: The development sample included 125,896 patients (72,572 women, 57.64%; mean 74.2 [SD 7.7] years). The final validation cohort included 153,199 patients (88,177 women, 57.56%; mean 74.3 [SD 7.8] years). The c-statistic for discrimination was 0.96 (95% CI 0.93-0.98) in the development group and 0.91 (95% CI 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12% in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95% CI). Age was on the top of list (1.41; 1.06-1.48); congestive heart failure (20.90; 15.41-28.08) and different tumor sites were also recognized as driving risk factors, such as cancer of the ovaries (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), and stomach (13.64; 3.26-86.57). Disparities were also found in patients’ social determinants like respiratory hazard index (1.24; 0.92-1.40) and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. Conclusions: Our study prospectively validated an accurate 1-year risk prediction model and stratification for the elderly population (≥65 years) at risk of mortality with statewide electronic medical record datasets. It should be a valuable adjunct for helping patients to make better quality-of-life choices and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment. %M 29866643 %R 10.2196/10311 %U //www.mybigtv.com/2018/6/e10311/ %U https://doi.org/10.2196/10311 %U http://www.ncbi.nlm.nih.gov/pubmed/29866643