Itelman Edward, Shlomai Gadi, Leibowitz Avshalom, Weinstein Shiri, Yakir Maya, Tamir Idan, Sagiv Michal, Muhsen Aia, Perelman Maxim, Kant Daniella, Zilber Eyal, Segal Gad
Chaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.
JMIR Form Res. 2022 Jun 9;6(6):e36066. doi: 10.2196/36066.
Patients admitted to general wards are inherently at risk of deterioration. Thus, tools that can provide early detection of deterioration may be lifesaving. Frequent remote patient monitoring (RPM) has the potential to allow such early detection, leading to a timely intervention by health care providers.
This study aimed to assess the potential of a novel wearable RPM device to provide timely alerts in patients at high risk for deterioration.
This prospective observational study was conducted in two general wards of a large tertiary medical center. Patients determined to be at high risk to deteriorate upon admission and assigned to a telemetry bed were included. On top of the standard monitoring equipment, a wearable monitor was attached to each patient, and monitoring was conducted in parallel. The data gathered by the wearable monitors were analyzed retrospectively, with the medical staff being blinded to them in real time. Several early warning scores of the risk for deterioration were used, all calculated from frequent data collected by the wearable RPM device: these included (1) the National Early Warning Score (NEWS), (2) Airway, Breathing, Circulation, Neurology, and Other (ABCNO) score, and (3) deterioration criteria defined by the clinical team as a "wish list" score. In all three systems, the risk scores were calculated every 5 minutes using the data frequently collected by the wearable RPM device. Data generated by the early warning scores were compared with those obtained from the clinical records of actual deterioration among these patients.
In total, 410 patients were recruited and 217 were included in the final analysis. The median age was 71 (IQR 62-78) years and 130 (59.9%) of them were male. Actual clinical deterioration occurred in 24 patients. The NEWS indicated high alert in 16 of these 24 (67%) patients, preceding actual clinical deterioration by 29 hours on average. The ABCNO score indicated high alert in 18 (75%) of these patients, preceding actual clinical deterioration by 38 hours on average. Early warning based on wish list scoring criteria was observed for all 24 patients 40 hours on average before clinical deterioration was detected by the medical staff. Importantly, early warning based on the wish list scoring criteria was also observed among all other patients who did not deteriorate.
Frequent remote patient monitoring has the potential for early detection of a high risk to deteriorate among hospitalized patients, using both grouped signal-based scores and algorithm-based prediction. In this study, we show the ability to formulate scores for early warning by using RPM. Nevertheless, early warning scores compiled on the basis of these data failed to deliver reasonable specificity. Further efforts should be directed at improving the specificity and sensitivity of such tools.
ClinicalTrials.gov NCT04220359; https://clinicaltrials.gov/ct2/show/NCT04220359.
入住普通病房的患者本身就有病情恶化的风险。因此,能够早期发现病情恶化的工具可能会挽救生命。频繁的远程患者监测(RPM)有潜力实现这种早期发现,从而使医护人员能够及时进行干预。
本研究旨在评估一种新型可穿戴RPM设备在高危患者中提供及时警报的潜力。
这项前瞻性观察性研究在一家大型三级医疗中心的两个普通病房进行。纳入入院时被确定为病情恶化高危且被分配到遥测病床的患者。除了标准监测设备外,为每位患者佩戴一个可穿戴监测器,并并行进行监测。对可穿戴监测器收集的数据进行回顾性分析,医护人员在实时监测时对这些数据不知情。使用了几种病情恶化风险的早期预警评分,均根据可穿戴RPM设备频繁收集的数据计算得出:这些评分包括(1)国家早期预警评分(NEWS)、(2)气道、呼吸、循环、神经及其他(ABCNO)评分,以及(3)临床团队定义为“愿望清单”评分的恶化标准。在所有这三个系统中,使用可穿戴RPM设备频繁收集的数据每5分钟计算一次风险评分。将早期预警评分生成的数据与这些患者实际病情恶化的临床记录中获得的数据进行比较。
总共招募了410名患者,最终分析纳入217名。中位年龄为71岁(四分位间距62 - 78岁),其中130名(59.9%)为男性。24名患者出现了实际临床恶化。NEWS在这24名患者中的16名(67%)显示高警报,平均比实际临床恶化提前29小时。ABCNO评分在其中18名(75%)患者中显示高警报,平均比实际临床恶化提前38小时。基于愿望清单评分标准的早期预警在所有24名患者中均有观察到,平均在医护人员检测到临床恶化前40小时。重要的是,在所有其他未恶化的患者中也观察到了基于愿望清单评分标准的早期预警。
频繁的远程患者监测有潜力通过基于分组信号的评分和基于算法的预测来早期发现住院患者病情恶化的高风险。在本研究中,我们展示了使用RPM制定早期预警评分的能力。然而,基于这些数据编制的早期预警评分未能提供合理的特异性。应进一步努力提高此类工具的特异性和敏感性。
ClinicalTrials.gov NCT04220359;https://clinicaltrials.gov/ct2/show/NCT04220359