• J Clin Monit Comput · Oct 2017

    Implementation of a novel postoperative monitoring system using automated Modified Early Warning Scores (MEWS) incorporating end-tidal capnography.

    • Joseph M Blankush, Robbie Freeman, Joy McIlvaine, Trung Tran, Stephen Nassani, and I Michael Leitman.
    • Department of Surgery, Mount Sinai Beth Israel - Icahn School of Medicine at Mount Sinai, 10 Union Square East, Suite 2M, New York, NY, 10003, USA.
    • J Clin Monit Comput. 2017 Oct 1; 31 (5): 1081-1092.

    AbstractModified Early Warning Scores (MEWS) provide real-time vital sign (VS) trending and reduce ICU admissions in post-operative patients. These early warning calculations classically incorporate oxygen saturation, heart rate, respiratory rate, systolic blood pressure, and temperature but have not previously included end-tidal CO2 (EtCO2), more recently identified as an independent predictor of critical illness. These systems may be subject to failure when physiologic data is incorrectly measured, leading to false alarms and increased workload. This study investigates whether the implementation of automated devices that utilize ongoing vital signs monitoring and MEWS calculations, inclusive of a score for end-tidal CO2 (EtCO2), can be feasibly implemented on the general care hospital floor and effectively identify derangements in a post-operative patient's condition while limiting the amount of false alarms that would serve to increase provider workload. From July to November 2014, post-operative patients meeting the inclusion criteria (BMI > 30 kg/m2, history of obstructive sleep apnea, or the use of patient-controlled analgesia (PCA) or epidural narcotics) were monitored using automated devices that record minute-by-minute VS included in classic MEWS calculations as well as EtCO2. Automated messages via pagers were sent to providers for instances when the device measured elevated MEWS, abnormal EtCO2, and oxygen desaturations below 85 %. Data, including alarm and message details from the first 133 patients, were recorded and analyzed. Overall, 3.3 alarms and pages sounded per hour of monitoring. Device-only alarms sounded 2.7 times per hour-21 % were technical alarms. The remaining device-only alarms for concerning VS sounded 2.0/h, 70 % for falsely recorded VS. Pages for abnormal EtCO2 sounded 0.4/h (82 % false recordings) while pages for low blood oxygen saturation sounded 0.1/h (55 % false alarms). 143 times (0.1 pages/h) the devices calculated a MEWS warranting a page (rise in MEWS by 2 or 5 or greater)-62 % were false scores inclusive of falsely recorded VS. An abnormal EtCO2 value resulted in or added to an elevated MEWS score in 29 % of notifications, but 50 % of these included a falsely abnormal EtCO2 value. To date, no adverse events have occurred. There were no statistically significant demographic, post-operative condition, or pre-existing comorbidity differences between patients who had a majority of true alarms from those who had mostly false-positive alarms. Although not statistically significant, the group of patients in whom automated MEWS suggested greater utility included those with a history of hypertension (p = 0.072) and renal disease (p = 0.084). EtCO2 monitoring was more likely to be useful in patients with a history of type 2 diabetes, coronary artery disease, and obstructive sleep apnea (p < 0.05). These patients were also more likely to have been on a PCA post-operatively (p < 0.05). Overall, non-invasive physiologic monitoring incorporating an automated MEWS system, modified to include end-tidal CO2 can be feasibly implemented in a hospital ward. Further study is needed to evaluate its clinical utility, including an end-tidal CO2 score, is feasibly implemented and can be useful in monitoring select post-operative patients for derangements in physiologic metrics. Like any other monitoring system, false alarms may occur at high rates. While further study is needed to determine the additive utility of EtCO2 in MEWS calculations, this study suggests utility of EtCO2 in select post-operative patients.

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