Seven-Day Mortality Can Be Predicted in Medical Patients by Blood Pressure, Age, Respiratory Rate, Loss of Independence, and Peripheral Oxygen Saturation (the PARIS Score): A Prospective Cohort Study with External Validation

BackgroundMost existing risk stratification systems predicting mortality in emergency departments or admission units are complex in clinical use or have not been validated to a level where use is considered appropriate. We aimed to develop and validate a simple system that predicts seven-day mortality of acutely admitted medical patients using routinely collected variables obtained within the first minutes after arrival.Methods and FindingsThis observational prospective cohort study used three independent cohorts at the medical admission units at a regional teaching hospital and a tertiary university hospital and included all adult (≥15 years) patients. Multivariable logistic regression analysis was used to identify the clinical variables that best predicted the endpoint. From this, we developed a simplified model that can be calculated without specialized tools or loss of predictive ability. The outcome was defined as seven-day all-cause mortality. 76 patients (2.5%) met the endpoint in the development cohort, 57 (2.0%) in the first validation cohort, and 111 (4.3%) in the second. Systolic blood Pressure, Age, Respiratory rate, loss of Independence, and peripheral oxygen Saturation were associated with the endpoint (full model). Based on this, we developed a simple score (range 0–5), ie, the PARIS score, by dichotomizing the variables. The ability to identify patients at increased risk (discriminatory power and calibration) was excellent for all three cohorts using both models. For patients with a PARIS score ≥3, sensitivity was 62.5–74.0%, specificity 85.9–91.1%, positive predictive value 11.2–17.5%, and negative predictive value 98.3–99.3%. Patients with a score ≤1 had a low mortality (≤1%); with 2, intermediate mortality (2–5%); and ≥3, high mortality (≥10%).ConclusionsSeven-day mortality can be predicted upon admission with high sensitivity and specificity and excellent negative predictive values.

背景：现有绝大多数用于预测急诊或住院病房患者死亡风险的分层系统，要么临床操作复杂，要么未达到可临床应用的验证标准。本研究旨在开发并验证一款简易系统，基于急性收住内科患者入院初期数分钟内即可获取的常规采集变量，预测其7天死亡率。方法与结果：本项观察性前瞻性队列研究纳入两家医院内科住院病房的三个独立队列，研究对象为所有成年（≥15岁）患者，两家医院分别为区域教学医院与三级大学附属医院。采用多变量logistic回归分析筛选出对研究终点预测效果最优的临床变量，据此构建了一款无需专业辅助工具即可计算，且预测性能无衰减的简化模型。本研究的结局定义为7天全因死亡率。开发队列中共有76例患者（2.5%）达到研究终点，第一验证队列与第二验证队列分别为57例（2.0%）与111例（4.3%）。收缩压（Systolic blood pressure）、年龄、呼吸频率、自理能力丧失情况与外周血氧饱和度（peripheral oxygen saturation）均与研究终点相关（全变量模型）。基于上述结果，通过对各变量进行二分类赋值，我们构建了一款评分范围为0~5的简易评分工具，即PARIS评分。两款模型在三个队列中均展现出优异的高危患者识别能力（区分度与校准度）。当PARIS评分≥3分时，患者的灵敏度为62.5%~74.0%，特异度为85.9%~91.1%，阳性预测值为11.2%~17.5%，阴性预测值为98.3%~99.3%。评分≤1分的患者死亡率较低（≤1%），评分为2分者死亡率处于中等水平（2%~5%），而评分≥3分者则为高死亡风险人群（≥10%）。结论：入院时即可对患者7天死亡率进行预测，该模型具备较高的灵敏度、特异度以及优异的阴性预测价值。