Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.
Am J Hosp Palliat Care. 2023 Jun;40(6):652-657. doi: 10.1177/10499091221129602. Epub 2022 Sep 26.
Serious Illness Conversations (SICs) explore patients' prognostic awareness, hopes, and worries, and can help establish priorities for their care during and after hospitalization. While identifying patients who benefit from an SIC remains a challenge, this task may be facilitated by use of validated prediction scores available in most commercial electronic health records (EHRs), such as Epic's Readmission Risk Score (RRS). We identified the RRS on admission for all hospital encounters from October 2018 to August 2019 and measured the area under the receiver operating characteristic (AUROC) curve to determine whether RRS could accurately discriminate post discharge 6-month mortality. For encounters with standardized SIC documentation matched in a 1:3 ratio to controls by sex and age (±5 years), we constructed a multivariable, paired logistic regression model and measured the odds of SIC documentation per every 10% absolute increase in RRS. RRS was predictive of 6-month mortality with acceptable discrimination (AUROC .71) and was significantly associated with SIC documentation (adjusted OR 1.42, 95% CI 1.24-1.63). An RRS >28% used to identify patients with post discharge 6-month mortality had a high specificity (89.0%) and negative predictive value (NPV) (97.0%), but low sensitivity (25.2%) and positive predictive value (PPV) (7.9%). RRS may serve as a practical EHR-based screen to exclude patients not requiring an SIC, thereby leaving a smaller cohort to be further evaluated for SIC needs using other validated tools and clinical assessment.
严重疾病对话 (SIC) 探讨了患者的预后意识、希望和担忧，并可以帮助确定患者在住院期间和出院后的护理重点。虽然确定哪些患者受益于 SIC 仍然是一个挑战，但可以通过使用大多数商业电子健康记录 (EHR) 中提供的经过验证的预测评分来促进这项任务，例如 Epic 的再入院风险评分 (RRS)。我们确定了 2018 年 10 月至 2019 年 8 月期间所有住院就诊的入院 RRS，并测量了接收器操作特征 (ROC) 曲线下的面积，以确定 RRS 是否可以准确区分出院后 6 个月的死亡率。对于通过性别和年龄（±5 岁）以 1:3 比例与对照相匹配的具有标准化 SIC 文档记录的就诊，我们构建了多变量、配对逻辑回归模型，并测量了每增加 10%的 RRS 时 SIC 文档记录的几率。RRS 对 6 个月死亡率具有可接受的预测能力（AUROC.71），与 SIC 文档记录显著相关（调整后的 OR 1.42，95%CI 1.24-1.63）。用于识别出院后 6 个月死亡率患者的 RRS >28%具有高特异性 (89.0%) 和阴性预测值 (NPV) (97.0%)，但低灵敏度 (25.2%) 和阳性预测值 (PPV) (7.9%)。RRS 可以作为一种基于 EHR 的实用筛选工具，排除不需要 SIC 的患者，从而留下一个较小的队列，使用其他经过验证的工具和临床评估进一步评估 SIC 的需求。
