Khurana Hargobind S, Groves Robert H, Simons Michael P, Martin Mary, Stoffer Brenda, Kou Sherri, Gerkin Richard, Reiman Eric, Parthasarathy Sairam
Banner TeleHealth, Mesa, Ariz; Care Management, Banner Health, Phoenix, Ariz; Health Management, Banner Health, Phoenix, Ariz.
Banner TeleHealth, Mesa, Ariz; Care Management, Banner Health, Phoenix, Ariz; Health Management, Banner Health, Phoenix, Ariz.
Am J Med. 2016 Jul;129(7):688-698.e2. doi: 10.1016/j.amjmed.2016.02.037. Epub 2016 Mar 24.
Real-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality.
An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The systemic inflammatory response syndrome and organ dysfunction alert was applied in real time to 312,214 patients in 24 hospitals and analyzed in 2 phases: training and validation datasets.
In the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died, whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval, 26.1-34.5; P < .0001). In the validation phase, the sensitivity, specificity, area under the curve, and positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted hazards ratio 4.0; 95% confidence interval, 3.3-4.9; P < .0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P < .0001).
An automated alert system that continuously samples electronic medical record data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.
对电子病历数据进行实时自动连续采样可迅速识别有死亡风险的患者，并能及时进行挽救生命的干预措施。我们推测基于电子病历的实时警报能够识别住院患者的死亡风险。
开发并实施了一种自动警报，以持续采样电子病历数据，并在检测到4项全身炎症反应综合征标准中的至少2项以及14项急性器官功能障碍参数中的至少1项时触发警报。全身炎症反应综合征和器官功能障碍警报被实时应用于24家医院的312214名患者，并分两个阶段进行分析：训练数据集和验证数据集。
在训练阶段，29317名（18.8%）患者触发了警报，此类患者中有5.2%死亡，而未触发警报的患者中只有0.2%死亡（未调整优势比为30.1；95%置信区间为26.1 - 34.5；P <.0001）。在验证阶段，预测死亡率的敏感性、特异性、曲线下面积、阳性和阴性似然比分别为0.86、0.82、0.84、4.9和0.16。多变量Cox比例风险回归模型显示，触发警报时医院死亡率更高（调整后风险比为4.0；95%置信区间为3.3 - 4.9；P <.0001）。触发警报与额外的住院天数（增加3.0天）和呼吸机使用天数（增加1.6天；P <.0001）相关。
一种对电子病历数据进行连续采样的自动警报系统可以实施，具有出色的测试特征，并且能够协助实时识别有死亡风险的住院患者。
