自動預測模型識別高風險住院患者可顯著降低死亡率
作者：
小柯機器人發布時間：2020/11/15 0:50:08
美國凱撒健康計劃和醫療集團Gabriel J. Escobar團隊研究了醫院使用自動識別系統對成人住院期間臨床惡化風險的影響。2020年11月11日，該研究發表在《新英格蘭醫學雜誌》上。
在病房（除ICU外）病情惡化的住院成人有相當高的發病率和死亡率。早期識別臨床惡化風險的患者依賴於人工計算的分數。自動檢測即將發生的臨床惡化的效果尚未可知。
研究組基於一個驗證過的模型，利用電子病歷中的信息來識別臨床惡化高危的非ICU住院患者（允許自動、實時風險評分計算），制定了一個幹預方案，由護士遠程監測，護士審查被認定為高危患者的記錄，然後將監測結果傳達給醫院的快速反應小組。研究組比較了系統運行的醫院和尚未部署系統的醫院中住院患者的結局。
該系統在2016年8月1日至2019年2月28日交錯部署在19家醫院，研究組共確認了548838次非ICU住院，涉及326816例患者。共有43949次住院（涉及35669例患者）病情達到警戒閾值；15487例住院患者納入幹預隊列，28462例住院患者納入對照隊列。幹預組患者在發出警報後30天內的死亡率顯著低於對照組，校正後的相對風險比為0.84。
總之，使用自動預測模型來識別高風險患者，快速反應小組對其進行幹預，可顯著降低死亡率。
附：英文原文
Title: Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration
Author: Gabriel J. Escobar, M.D.,, Vincent X. Liu, M.D.,, Alejandro Schuler, Ph.D.,, Brian Lawson, Ph.D.,, John D. Greene, M.A.,, and Patricia Kipnis, Ph.D.
Issue&Volume: 2020-11-11
Abstract:
Background
Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported.
Methods
On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient’s condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions.
Results
The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001).
Conclusions
The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality.
DOI: 10.1056/NEJMsa2001090
Source: https://www.nejm.org/doi/full/10.1056/NEJMsa2001090