Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach.
Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach.
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DOI：
10.1136/bmjopen-2017-017199
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发表时间：
2017-09-15
期刊：
影响因子：
2.9
通讯作者：
Ercole A
中科院分区：
文献类型：
--
作者：
Desautels T;Das R;Calvert J;Trivedi M;Summers C;Wales DJ;Ercole A
计划外再入院重症监护室（ICU）是非常不可取的，增加了护理的差异，使资源规划困难，并可能增加住院时间和死亡率在某些情况下。识别可能遭受计划外ICU再入院的患者可以减少这种不良事件的频率。一个单一的学术，三级护理医院在英国。2014年10月至2016年8月期间收集的一组3326次ICU发作。所有记录都是在住院期间某个时候去过ICU的患者。我们排除了以下患者：≤16岁;访问了除普通和神经科学ICU之外的ICU;缺失关键电子病历测量值;或ICU出院结局不确定或出院时间非常早或非常晚。排除后，仍保留2018年结局标签事件。受试者工作特征曲线下面积（AUROC），用于预测首次ICU出院后48小时内计划外ICU再入院或院内死亡。在10倍交叉验证中，对来自目标医院和重症监护医学信息市场（MIMIC-III）数据库的数据进行了集成预测，并对目标医院的数据进行了测试。该预测因子区分了有计划外ICU再入院或死亡结局的患者和没有这种结局的患者，平均AUROC为0.7095（SE 0.0260），上级于专门建立的稳定性和转移指数（SWIFT）评分（AUROC=0.6082，SE 0.0249; p=0.014，成对t检验）。尽管存在固有的困难，但我们证明了基于迁移学习的新型机器学习算法可以实现良好的区分，超过治疗临床医生或SWIFT评分增加的价值。对计划外再入院的准确预测可用于更有效地瞄准资源。
Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. A single academic, tertiary care hospital in the UK. A set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ≤16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained. Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge. In 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test). Despite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.
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影响因子：
3.2
作者：
Desautels T;Calvert J;Hoffman J;Jay M;Kerem Y;Shieh L;Shimabukuro D;Chettipally U;Feldman MD;Barton C;Wales DJ;Das R
通讯作者：
Das R
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Renton, J.;Pilcher, D. V.;Duke, G.
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Duke, G.
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通讯作者：
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通讯作者：
Pittard, AJ
影响因子：
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作者：
Santamaria, John D.;Duke, Graeme J.;Bellomo, Rinaldo
通讯作者：
Bellomo, Rinaldo