Prehospital system delay in patients with ST-segment elevation myocardial infarction in Singapore

BACKGROUND: Timely reperfusion in ST-segment elevation myocardial infarction(STEMI)improves outcomes. System delay is that between first medical contact and reperfusion therapy,comprising prehospital and hospital components. This study aimed to characterize prehospital system delay in Singapore.METHODS: A retrospective chart review was performed for 462 consecutive STEMI patients presenting to a tertiary hospital from December 2006 to April 2008. Patients with cardiac arrest secondarily presented were excluded. For those who received emergency medical services(EMS),ambulance records were reviewed. Time intervals in the hospital were collected prospectively. The patients were divided into two equal groups of high/low prehospital system delay using visual binning technique.RESULTS: Of 462 patients, 76 received EMS and 52 of the 76 patients were analyzed. The median system delay was 125.5 minutes and the median prehospital system delay was 33.5minutes(interquartile range [IQR]=27.0, 42.0). Delay between call-received-by-ambulance and ambulance-dispatched was 2.48 minutes(IQR=1.47, 16.55); between ambulance-dispatch and arrival-at-patient-location was 8.07 minutes(IQR=1.30, 22.13); between arrival-at- and departurefrom-patient-location was 13.12 minutes(IQR=3.12, 32.2); and between leaving-patient-location to ED-registration was 9.90 minutes(IQR=1.62, 32.92). Comparing patients with prehospital system delay of less than 35.5 minutes versus more showed that the median delay between ambulancedispatch and arrival-at-patient-location was shorter(5.75 vs. 9.37 minutes, P<0.01). The median delay between arrival-at-patient-location and leaving-patient-location was also shorter(10.78 vs.14.37 minutes, P<0.01).CONCLUSION: Prehospital system delay in our patients was suboptimal. This is the first attempt at characterizing prehospital system delay in Singapore and forms the basis for improving efficiency of STEMI care.

Building a Machine Learning-based Ambulance Dispatch Triage Model for Emergency Medical Services

Background:In charge of dispatching the ambulances,Emergency Medical Services(EMS)call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time.Although there are protocols to guide their decision-making,observed performance can still lack sensitivity and specificity.Machine learning models have been known to capture complex relationships that are subtle,and well-trained data models can yield accurate predictions in a split of a second.Methods:In this study,we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases.We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020.Features were created using call records,and multiple machine learning models were trained.Results:A Random Forest model achieved the best performance,reducing the over-triage rate by an absolute margin of 15%compared to the call center specialists while maintaining a similar level of under-triage rate.Conclusions:The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.