Early Warning Scores With and Without Artificial Intelligence

Importance: Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance. Objective: To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores. Design, Setting, and Participants: This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System. All consecutive adult medical-surgical ward hospital encounters between March 9, 2019, and November 9, 2023, were included. Exposures: Simultaneous Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2 scores. Main Outcomes and Measures: Clinical deterioration, defined as a transfer from ward to intensive care unit or death within 24 hours of an observation. Results: Of the 362 926 patient encounters (median patient age, 64 [IQR, 47-77] years; 200 642 [55.3%] female), 16 693 (4.6%) experienced a clinical deterioration event. eCART had the highest area under the receiver operating characteristic curve at 0.895 (95% CI, 0.891-0.900), followed by NEWS2 at 0.831 (95% CI, 0.826-0.836), NEWS at 0.829 (95% CI, 0.824-0.835), RI at 0.828 (95% CI, 0.823-0.834), EDI at 0.808 (95% CI, 0.802-0.812), and MEWS at 0.757 (95% CI, 0.750-0.764). After matching scores at the moderate-risk sensitivity level for a NEWS score of 5, overall positive predictive values (PPVs) ranged from a low of 6.3% (95% CI, 6.1%-6.4%) for an EDI score of 41 to a high of 17.3% (95% CI, 16.9%-17.8%) for an eCART score of 94. Matching scores at the high-risk specificity of a NEWS score of 7 yielded overall PPVs ranging from a low of 14.5% (95% CI, 14.0%-15.2%) for an EDI score of 54 to a high of 23.3% (95% CI, 22.7%-24.2%) for an eCART score of 97. The moderate-risk thresholds provided a median of at least 20 hours of lead time for all the scores. Median lead time at the high-risk threshold was 11 (IQR, 0-69) hours for eCART, 8 (IQR, 0-63) hours for NEWS, 6 (IQR, 0-62) hours for NEWS2, 5 (IQR, 0-56) hours for MEWS, 1 (IQR, 0-39) hour for EDI, and 0 (IQR, 0-42) hours for RI. Conclusions and Relevance: In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. Given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.</p

Quality metrics for the evaluation of Rapid Response Systems: Proceedings from the third international consensus conference on Rapid Response Systems.

BACKGROUND: Clinically significant deterioration of patients admitted to general wards is a recognized complication of hospital care. Rapid Response Systems (RRS) aim to reduce the number of avoidable adverse events. The authors aimed to develop a core quality metric for the evaluation of RRS. METHODS: We conducted an international consensus process. Participants included patients, carers, clinicians, research scientists, and members of the International Society for Rapid Response Systems with representatives from Europe, Australia, Africa, Asia and the US. Scoping reviews of the literature identified potential metrics. We used a modified Delphi methodology to arrive at a list of candidate indicators that were reviewed for feasibility and applicability across a broad range of healthcare systems including low and middle-income countries. The writing group refined recommendations and further characterized measurement tools. RESULTS: Consensus emerged that core outcomes for reporting for quality improvement should include ten metrics related to structure, process and outcome for RRS with outcomes following the domains of the quadruple aim. The conference recommended that hospitals should collect data on cardiac arrests and their potential predictability, timeliness of escalation, critical care interventions and presence of written treatment goals for patients remaining on general wards. Unit level reporting should include the presence of patient activated rapid response and metrics of organizational culture. We suggest two exploratory cost metrics to underpin urgently needed research in this area. CONCLUSION: A consensus process was used to develop ten metrics for better understanding the course and care of deteriorating ward patients. Others are proposed for further development

Mechanical chest compressions improve rate of return of spontaneous circulation and allow for initiation of percutaneous circulatory support during cardiac arrest in the cardiac catheterization laboratory.

BACKGROUND: Performing advanced cardiac life support (ACLS) in the cardiac catheterization laboratory (CCL) is challenging. Mechanical chest compression (MCC) devices deliver compressions in a small space, allowing for simultaneous percutaneous coronary intervention and reduced radiation exposure to rescuers. In refractory cases, MCC devices allow rescuers to initiate percutaneous mechanical circulatory support (MCS) and extracorporeal life support (ECLS) during resuscitation. This study sought to assess the efficacy and safety of MCC when compared to manual compressions in the CCL. METHODS: We performed a retrospective analysis of patients who received ACLS in the CCL at our institution between May 2011 and February 2016. Baseline characteristics, resuscitation details, and outcomes were compared between patients who received manual and mechanical compressions. RESULTS: Forty-three patients (67% male, mean age 58 years) required chest compressions for cardiac arrest while in the CCL (12 manual and 31 MCC). Patients receiving MCC were more likely to achieve return of spontaneous circulation (ROSC) (74% vs. 42%, p=0.05). Of those receiving MCC, twenty-two patients (71%) were treated with MCS. Patients receiving percutaneous ECLS were more likely to achieve ROSC (100% vs. 53%, p=0.003) and suffered no episodes of limb loss or TIMI major bleeding. There were no significant differences in 30-day survival or survival to hospital discharge between groups. CONCLUSIONS: Use of MCC during resuscitation of cardiac arrest in the CCL increases the rate of ROSC. Simultaneous implantation of MCS, including percutaneous ECLS, is feasible and safe during MCC-assisted resuscitation in the CCL