Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital.
Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital.
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DOI：
10.1186/s13054-018-2194-7
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发表时间：
2018-10-30
期刊：
影响因子：
--
通讯作者：
Gong MN
中科院分区：
文献类型：
--
作者：
Dziadzko MA;Novotny PJ;Sloan J;Gajic O;Herasevich V;Mirhaji P;Wu Y;Gong MN
急性呼吸衰竭经常发生在住院患者中，通常在ICU入院前就开始了。预测机械通气(MV)的死亡率和风险的风险分层工具可能允许更早的评估和干预。我们开发并验证了一种基于自动电子健康记录的模型--长时间机械通气的准确预测(Approval)--以识别有死亡或呼吸衰竭风险的患者，需要>；= 48小时的MV。这是一项观察性研究，对象是2013年住进四家医院或2017年上半年住进第五家医院的成年人。临床数据摘录自电子病历。2013名患者被随机分成50：50的派生/验证队列。符合条件的事件为死亡或插管导致MV>；= 48h。模型推导采用随机森林方法。Approval是在2013年有数据时追溯计算的，预计在2017年入院后每4小时计算一次。同时计算修正预警评分(MEWS)和国家预警评分(NEWS)。除了2017年的队列批准外，临床医生没有得到任何警告。2013年录取人数为68775人，2017年为2258人。Approve在2013年和2017年的接收器操作员曲线下面积分别为0.87(95%CI 0.85-0.88)和0.90(95%CI 0.84-0.95)，明显好于2013年的MEWS和NEWS，但与2017年的MEWS和NEWS相似。在阈值>； 0.25时，Approve具有类似的敏感度和阳性预测值(2013年敏感度分别为63%和21%，2017年分别为和16%)。与2013年批准的达到可比购买力平价的门槛相比(MEWS>； 4的敏感度为19%，News>； 6的敏感度为22%)，MEWS和新闻的敏感度较低(MEWS和新闻的敏感度为16%)。同样，在2017年，在类似的敏感度阈值(Approve&Gt； 0.25%，MEWS和NEWS>； 4的67%)下，更多触发警报的患者使用Approve(PPV值为16%)发生事件，而假阳性率(FPR值为5%)低于MEWS(PPV值为7%，FPR值为14%)和NEWS(PPVPPV值为4%，FPR值为25%)。一个自动化的EHR模型用于识别MV或死亡的高风险患者，经过回顾性和前瞻性的验证，并被确定为可用于实时风险识别。临床试验.gov，NCT02488174。注册日期为2015年3月18日。本文的在线版本(10.1186/s13054-0182194-7)包含补充材料，可供授权用户使用。
Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model—Accurate Prediction of Prolonged Ventilation (APPROVE)—to identify patients at risk of death or respiratory failure requiring >= 48 h of MV. This was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort. There were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85–0.88) in 2013 and 0.90 (95% CI 0.84–0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%). An automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification. ClinicalTrials.gov, NCT02488174. Registered on 18 March 2015. The online version of this article (10.1186/s13054-018-2194-7) contains supplementary material, which is available to authorized users.
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影响因子：
2.6
作者：
Finlay, G. Duncan;Rothman, Michael J.;Smith, Robert A.
通讯作者：
Smith, Robert A.
DOI：
10.1164/rccm.201203-0522oc
发表时间：
2012-10-15
影响因子：
24.7
作者：
Shehabi, Yahya;Bellomo, Rinaldo;Weisbrodt, Leonie
通讯作者：
Weisbrodt, Leonie
影响因子：
9.6
作者：
SCHEIN, RMH;HAZDAY, N;SPRUNG, CL
通讯作者：
SPRUNG, CL
影响因子：
8
作者：
Brabrand, Mikkel;Hallas, Peter;Posth, Stefan
通讯作者：
Posth, Stefan
影响因子：
3
作者：
Escobar, Gabriel J.;Gardner, Marla N.;Kipnis, Patricia
通讯作者：
Kipnis, Patricia