SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care.
SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care.
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DOI：
10.1371/journal.pone.0246669
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Hall DE
中科院分区：
文献类型：
--
作者：
Altieri Dunn SC;Bellon JE;Bilderback A;Borrebach JD;Hodges JC;Wisniewski MK;Harinstein ME;Minnier TE;Nelson JB;Hall DE
将患者转移到危重度更高的机构的过程缺乏标准化的预测方法，增加了低价值护理的风险，给患者及其家人带来了沉重的负担，而且收益不明确。我们试图开发一种快速且可行的工具，利用医院转移时容易获得的变量来预测死亡率。所有工作都是在一个大型、多医院的综合医疗系统中进行的。我们使用回顾性队列进行模型开发，其中包括从另一家医院、临终关怀医院、熟练护理人员或其他医疗机构转入医疗保健系统的 18 岁或以上患者，其入院优先级为直接紧急入院。该队列被随机分为训练集和测试集，首先开发 54 个变量，然后开发 14 个变量的梯度增强模型，以预测全因院内死亡率的主要结果。次要结局包括 30 天和 90 天死亡率以及过渡到仅舒适措施或临终关怀。为了进行模型验证，我们使用了一个前瞻性队列，其中包括从 3 家转诊医院之一转入一家三级护理医院的所有患者，不包括因心肌梗塞或产妇临产转院的患者。通过使用基于网络的工具来计算转移时的死亡风险来进行前瞻性验证。将观察到的结果与预测结果进行比较，以评估模型性能。开发队列包括 20,985 名患者，其中院内死亡率为 1,937 例（9.2%），30 天死亡率为 2,884 例（13.7%），90 天死亡率为 3,899 例（18.6%）。 14 变量梯度增强模型有效预测了院内 30 天和 90 天死亡率（c = 0.903 [95% CI:0.891–0.916]）、c = 0.877 [95% CI:0.864–0.890]）和 c = 0.869 [95% CI:0.857–0.881]，分别）。该工具被证明对于 679 名顺序转移患者的前瞻性队列床边实施是可行和有效的，床边护士在转移时计算了 SafeNET 评分，每位患者仅花费 4-5 分钟，并与院内、30 天和 90 天死亡率的开发样本一致（c = 0.836 [95% CI：0.751 - 0.921]、0.815 [95% CI：分别为 0.730–0.900] 和 0.794 [95% CI：0.725–0.864]）。 SafeNET 算法对于转院时的实时床边死亡风险预测是可行且有效的。目前正在努力建立由该分数触发的途径，将所需的资源引导至最有不良结果风险的患者。
Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively). The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.
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DOI：
10.1001/jama.2016.0288
发表时间：
2016-02-23
期刊：
JAMA
影响因子：
--
作者：
Seymour CW;Liu VX;Iwashyna TJ;Brunkhorst FM;Rea TD;Scherag A;Rubenfeld G;Kahn JM;Shankar-Hari M;Singer M;Deutschman CS;Escobar GJ;Angus DC
通讯作者：
Angus DC
影响因子：
2.7
作者：
Ogrinc, Greg;Davies, Louise;Stevens, David
通讯作者：
Stevens, David
影响因子：
11.1
作者：
Olsson, T;Terent, A;Lind, L
通讯作者：
Lind, L
影响因子：
3.4
作者：
Cook, Allyson C.;Joseph, Bellal;Phelan, Herb A.
通讯作者：
Phelan, Herb A.
影响因子：
9.8
作者：
Duckitt, R. W.;Buxton-Thomas, R.;Forni, L. G.
通讯作者：
Forni, L. G.