Department of Medicine, NorthShore University HealthSystem, Evanston, IL.
Department of Medicine, University of Chicago, Chicago, IL.
Crit Care Med. 2022 Sep 1;50(9):1339-1347. doi: 10.1097/CCM.0000000000005492. Epub 2022 Aug 15.
To determine the impact of a machine learning early warning risk score, electronic Cardiac Arrest Risk Triage (eCART), on mortality for elevated-risk adult inpatients.
A pragmatic pre- and post-intervention study conducted over the same 10-month period in 2 consecutive years.
Four-hospital community-academic health system.
All adult patients admitted to a medical-surgical ward.
During the baseline period, clinicians were blinded to eCART scores. During the intervention period, scores were presented to providers. Scores greater than or equal to 95th percentile were designated high risk prompting a physician assessment for ICU admission. Scores between the 89th and 95th percentiles were designated intermediate risk, triggering a nurse-directed workflow that included measuring vital signs every 2 hours and contacting a physician to review the treatment plan.
The primary outcome was all-cause inhospital mortality. Secondary measures included vital sign assessment within 2 hours, ICU transfer rate, and time to ICU transfer. A total of 60,261 patients were admitted during the study period, of which 6,681 (11.1%) met inclusion criteria (baseline period n = 3,191, intervention period n = 3,490). The intervention period was associated with a significant decrease in hospital mortality for the main cohort (8.8% vs 13.9%; p < 0.0001; adjusted odds ratio [OR], 0.60 [95% CI, 0.52-0.71]). A significant decrease in mortality was also seen for the average-risk cohort not subject to the intervention (0.49% vs 0.26%; p < 0.05; adjusted OR, 0.53 [95% CI, 0.41-0.74]). In subgroup analysis, the benefit was seen in both high- (17.9% vs 23.9%; p = 0.001) and intermediate-risk (2.0% vs 4.0 %; p = 0.005) patients. The intervention period was also associated with a significant increase in ICU transfers, decrease in time to ICU transfer, and increase in vital sign reassessment within 2 hours.
Implementation of a machine learning early warning score-driven protocol was associated with reduced inhospital mortality, likely driven by earlier and more frequent ICU transfer.
确定机器学习预警风险评分（eCART）对高危成年住院患者死亡率的影响。
在连续两年的同一 10 个月期间进行的务实的干预前后研究。
四所医院的社区学术医疗系统。
入住内科-外科病房的所有成年患者。
在基线期，临床医生对 eCART 评分不知情。在干预期间，向提供者提供评分。评分大于或等于第 95 百分位被指定为高风险，促使医生评估 ICU 入院。评分在第 89 百分位至第 95 百分位之间被指定为中危，触发护士主导的工作流程，包括每 2 小时测量生命体征并联系医生审查治疗计划。
主要结局是全因院内死亡率。次要测量指标包括 2 小时内的生命体征评估、ICU 转率和 ICU 转时间。在研究期间共收治了 60261 名患者，其中 6681 名（11.1%）符合纳入标准（基线期 n=3191，干预期 n=3490）。干预期与主要队列的医院死亡率显著降低相关（8.8%比 13.9%；p<0.0001；调整后的优势比[OR]，0.60[95%可信区间，0.52-0.71]）。未接受干预的低危队列的死亡率也显著降低（0.49%比 0.26%；p<0.05；调整后的 OR，0.53[95%可信区间，0.41-0.74]）。亚组分析显示，高危（17.9%比 23.9%；p=0.001）和中危（2.0%比 4.0%；p=0.005）患者均有获益。干预期还与 ICU 转率显著增加、ICU 转时间缩短以及 2 小时内生命体征重新评估增加相关。
实施机器学习预警评分驱动的方案与院内死亡率降低相关，这可能是由于更早、更频繁的 ICU 转。
