@Article{信息:doi/10.2196/10311，作者=“郭，燕婷和郑，刚和傅，天云和郝，诗英和叶，承印和郑，乐和刘，Modi和夏，敏杰和金，波和朱，春青和王，奥利弗和吴，钱和卡尔弗，德沃尔S和阿尔弗，肖恩T和斯特恩，弗兰克和卡诺，劳拉和巴蒂亚，阿贾伊和西尔vester，卡尔G和盖德，埃里克和麦克尔辛尼，多夫B和凌，布鲁斯雪峰”，标题=“评估全州全因未来一年的死亡率:对生活质量、资源利用和医疗无效影响的前瞻性研究”，期刊=“J Med Internet Res”，年=“2018”，月=“6”，日=“04”，卷=“20”，号=“6”，页=“e10311”，关键词=“1年死亡率风险预测;电子病历;生活质量;医疗资源利用;社会决定因素"，摘要="背景:对许多老年患者来说，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，他们花费了不成比例的医疗资源和支出。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。 Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014, to September 4, 2016. Patients were stratified into risk groups. 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. ", issn="1438-8871", doi="10.2196/10311", url="//www.mybigtv.com/2018/6/e10311/", url="https://doi.org/10.2196/10311", url="http://www.ncbi.nlm.nih.gov/pubmed/29866643" }