Abstract Background A prompt diagnosis to initiate the appropriate reperfusion therapy is crucial to improve clinical outcomes in acute ST-elevation myocardial infarction (STEMI) patients. Socio-economic status (SES) refers to parameters like income, educational status and occupation. A low SES negatively interferes with the prognosis of STEMI patients. However, the impact of SES on delay time in acute STEMI remains matter of debate. Methods We used databases from two French multicentric and prospective registries: ACIRA (patients undergoing coronary angiography in any catheterization laboratories of Aquitaine) and REANIM (acute STEMI patients supported by emergency medical system (EMS) in Aquitaine). An ecological indicator of social deprivation Fdep09 was calculated to describe geographical inequalities in health based on municipality of residence. The higher the value, the more disadvantaged the population. Low SES was defined as Fdep09 > median value. Results Two-thousand-eight-hundred-and-forty consecutive patients with acute STEMI undergoing coronary angiography from January 2017 to December 2018 in Aquitaine were included. Patients with lower SES were more often initially referred to emergency departments of non-percutaneous coronary intervention capable centers whereas patients with higher SES were more often directly transferred to PCI centers by the mobile emergency care units as recommended by the most recent European guidelines (p<10–4). Patients with low SES had longer delays from symptom onset to first medical contact (FMC) (116 [60–119] vs 98 [55–233] min, p=0.0078) and were more likely to receive fibrinolysis (9.9 vs 5.2%, p<10–4). Linear regression modeling showed that each point of the Fdep09 index was associated with increase in the delay from symptom onset to FMC by a factor 1.1 (95% CI: 1.04–1.17, p<10–3) after adjusting for potential confounders. Conclusion SES inequality has negative influence on the delays in the management of acute STEMI patients. Efforts to raise awareness of suspicious signs of acute MI among individuals in lower SES could be valuable. FDep09 distribution Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): ARS Nouvelle-Aquitaine
Objectives The early identification of patients with Acute Heart Failure Syndrome (AHFS) among patients admitted to the Emergency Department (ED) with dyspnoea can facilitate the introduction of appropriate treatments. The objectives are to identify the predictive factors for AHFS diagnosis in patients with acute dyspnoea (primary objective) and the clinical ‘gestalt’ (secondary objective) in ED. Methods PREDICA is an observational, prospective, multicentre study. The enrolment of patients admitted to the ED for nontraumatic acute dyspnoea and data collection on admission were recorded by the patient’s emergency physician. The AHFS endpoints were assessed following a duplicate expert evaluation by pairs of cardiologists and emergency physicians. Step-by-step logistic regression was used to retain predictive criteria, and the area under the receiver operating characteristic (ROC) curve of the model was constructed to assess the ability of the selected factors to identify real cases. The probability of AHFS was estimated on a scale from 1 to 10 based on the emergency physician’s perception and understanding (gestalt). Results Among 341 patients consecutively enrolled in three centres, 149 (44%) presented AHFS. Eight predictive factors of AHFS were detected with a performance test showing an area under the model ROC curve of 0.86. Gestalt greater than or equal to five showed sensitivity of 78% and specificity of 90% (AUC 0.91) and diagnosed 88% of AHF in our population. Conclusions We identified several independant predictors of final AHFS diagnosis. They should contribute to the development of diagnostic strategies in ED. However, unstructured gestalts seem to perform very well alone.
By focusing on symptoms and not diagnoses, the socalled syndromic surveillance system gains in immediacy what it loses in specificity with respect to other more traditional options for public health surveillance. Reports of calls to emergency medical communication centers (EMCC) supplemented by the data collected by the rescue workers who arrived on the scene constitute a cost-effective and rich source of information. Unfortunately, EMCC data are infrequently used and their utility has not been demonstrated.The aim of this study was to explore the usefulness for public health surveillance of EMCC data when analyzed using text mining and visualization tools. Transformer-based deep learning architectures were used to classify call reports according to the reason for the call. We also extracted indicators that could serve as proxy measures using a keyword-search algorithm. We then developed a dashboard visualization tool to enable dynamic and spatial exploratory analyses. Finally, we explored the potential of this tool for two applications. While the tool proved unable to detect Covid-19 outbreaks, it appeared to be promising for a better understanding of territorial inequalities in healthcare access.
During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic. To determine the performance of keyword-search algorithm in call reports to emergency medical communication centers (EMCC) to describe trends in symptoms during the COVID-19 crisis. We retrospectively retrieved all free text call reports from the EMCC of the Gironde department (SAMU 33), France, between 2005 and 2020 and classified them with a simple keyword-based algorithm to identify symptoms relevant to COVID-19. A validation was performed using a sample of manually coded call reports. The six selected symptoms were fever, cough, muscle soreness, dyspnea, ageusia and anosmia. We retrieved 38,08,243 call reports from January 2005 to October 2020. A total of 8539 reports were manually coded for validation and Cohen's kappa statistics ranged from 75 (keyword anosmia) to 59% (keyword dyspnea). There was an unprecedented peak in the number of daily calls mentioning fever, cough, muscle soreness, anosmia, ageusia, and dyspnea during the COVID-19 epidemic, compared to the past 15 years. Calls mentioning cough, fever and muscle soreness began to increase from February 21, 2020. The number of daily calls reporting cough reached 208 on March 3, 2020, a level higher than any in the previous 15 years, and peaked on March 15, 2020, 2 days before lockdown. Calls referring to dyspnea, anosmia and ageusia peaked 12 days later and were concomitant with the daily number of emergency room admissions. Trends in symptoms cited in calls to EMCC during the COVID-19 crisis provide insights into the natural history of COVID-19. The content of calls to EMCC is an efficient epidemiological surveillance data source and should be integrated into the national surveillance system.
Background Only a few cardiac-arrest victims receive external chest compression (ECC) by a bystander. Objective To test the hypothesis that the general public might start ECC more often if they used an automated device rather than a manual massage. Methods Web-based public opinion survey based on two short videos, one showing manual ECC and the other automated ECC (Autopulse, Zoll, France). Advantages and disadvantages (perceived efficacy, reproducibility, hazard, apprehension and acceptability) of the two techniques were evaluated on a visual analogue scale (VAS). A VAS of 1–3 was considered to indicate preference for manual ECC, 8–10 for automated ECC and 4–7 for no clear preference. The final global score was the difference between advantage and disadvantage scores. Results Overall, 1769 persons answered the questionnaire. The median VAS score for each variable was as follows: 7 (25–75 percentiles, 5–9) for efficacy, 8 (3–10) for reproducibility, 5 (3–8) for hazard, 5 (2–8) for apprehension and 5 (2–8) for acceptability. The overall median score indicated that 1034 persons (58%) preferred use of the device, 618 (35%) preferred manual ECC and 117 (7%) had no preference. There was no significant difference in the preference according to gender, education and training in first aid. However, older persons (0) preferred the use of device. Conclusions The better ‘advantages over disadvantages’ score for the automated ECC device over manual ECC indicated that the general public might envisage use of the device. This could contribute to increase the frequency of resuscitation attempts by bystanders.
