Department of Acute and Specialty Care, University of Virginia School of Nursing, P.O. Box 800782, Charlottesville, VA, 22908, USA.
Department of Pediatrics, School of Medicine, Charlottesville, VA, USA.
J Clin Monit Comput. 2020 Aug;34(4):797-804. doi: 10.1007/s10877-019-00361-5. Epub 2019 Jul 20.
Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.
在急症病房病情恶化并紧急转至重症监护病房（ICU）的患者死亡率较高。迄今为止，应用于急症病房的临床恶化风险评分依赖于生命体征和评估参数的静态或间歇性输入。我们建议使用连续预测分析监测，或依赖于从心电图、记录的生命体征、实验室结果和其他临床评估中捕获的实时生理监测数据的数据，以预测临床恶化。将警报阈值应用于经过训练以检测临床恶化的连续预测分析的实际步骤是了解其性能。本研究的目的是评估“风险飙升”的阳性预测值，或预测临床恶化的风险统计模型输出的大幅突然增加。我们研究了连续 8111 例心血管内科和外科病房患者的连续心电图数据。我们首先在测试集中训练了用于紧急 ICU 转移的多变量逻辑回归模型，并在 4059 例患者入院的验证集中测试了模型的特征。然后，在嵌套分析中，我们确定了风险的大幅、突然飙升（与前 6 小时相比增加了三个单位；一个单位是 ICU 转移的风险增加 24 小时），并审查了 91 例患者的住院记录，以了解紧急 ICU 转移等临床事件。我们将结果与 59 名患者进行了比较，这些患者在基线风险包括国家预警评分（NEWS）相匹配的情况下处于同一时间。风险飙升患者的事件发生率高出 3.4 倍（阳性预测值为 24%，而 7%，p=0.006）。如果我们将风险飙升用作警报，那么在 73 张病床的急症病房中，每天大约会发出一次警报。主要由呼吸变化（心电图衍生呼吸（EDR）或记录的呼吸频率）驱动的风险飙升具有最高的阳性预测值（30-35%），而主要由心率驱动的风险飙升具有最低的阳性预测值（7%）。源自连续预测分析监测的警报阈值可以作为从个人自身基线的变化程度来操作，而不是任意的阈值切点，这可能更好地考虑到个人自身的固有敏锐度水平。急性病房环境中的床边临床医生需要量身定制的警报策略，以在识别临床恶化和评估警报方法的效用之间取得平衡。
