Changing the stroke network during pandemic scenarios does not affect the management of patients with a positive Cincinnati prehospital stroke scale.

INTRODUCTION: Time plays a crucial role in the management of stroke, and changing the prehospital emergency network, altering the HUB and spoke relationship in pandemic scenarios, might have an impact on time to fibrinolysis or thrombectomy. The aim of this study was to evaluate the time-dependent stroke emergency network in Lombardy region (Italy) by comparing 2019 with 2020 and early 2021. Three parameters were investigated: (i) time of arrival of the first vehicle at the scene, (ii) overall duration of missions, and (iii) number of patients transported by emergency vehicles. METHODS: Data analysis process conducted using the SAS-AREU portal (SAS Institute, USA). RESULTS: The number of patients with a positive CPSS was similar among the different pandemic waves. Mission duration increased from a mean time (SD) of 52.9 (16.1) min in 2019 to 64.1 (19.7) in 2020 and 55.0 (16.8) in 2021. Time to first vehicle on scene increased to 15.7 (8.4) min in 2020 and 16.0 (7.0) in 2021 compared to 2019, 13.6 (7.2) (P < 0.05). The number of hospital with available stroke units decreased from 46 in 2019 to 10 during the first pandemic wave. CONCLUSIONS: The pandemic forced changes in the clinical mission of many hospitals by reducing the number of stroke units. Despite this, the organization of the emergency system allowed to identify strategic hospitals and thus avoid excessive transport time. The result was an adequate time for fibrinolysis/thrombectomy, in agreement with the guidelines. Coordinated management in emergency situations makes it possible to maintain service quality standards, despite the unfavorable scenario.

Detection of patients with COVID-19 by the emergency medical services in Lombardy through an operator-based interview and machine learning models.

BACKGROUND: The regional emergency medical service (EMS) in Lombardy (Italy) developed clinical algorithms based on operator-based interviews to detect patients with COVID-19 and refer them to the most appropriate hospitals. Machine learning (ML)-based models using additional clinical and geospatial epidemiological data may improve the identification of infected patients and guide EMS in detecting COVID-19 cases before confirmation with SARS-CoV-2 reverse transcriptase PCR (rtPCR). METHODS: This was an observational, retrospective cohort study using data from October 2020 to July 2021 (training set) and October 2021 to December 2021 (validation set) from patients who underwent a SARS-CoV-2 rtPCR test within 7 days of an EMS call. The performance of an operator-based interview using close contact history and signs/symptoms of COVID-19 was assessed in the training set for its ability to determine which patients had an rtPCR in the 7 days before or after the call. The interview accuracy was compared with four supervised ML models to predict positivity for SARS-CoV-2 within 7 days using readily available prehospital data retrieved from both training and validation sets. RESULTS: The training set includes 264 976 patients, median age 74 (IQR 55-84). Test characteristics for the detection of COVID-19-positive patients of the operator-based interview were: sensitivity 85.5%, specificity 58.7%, positive predictive value (PPV) 37.5% and negative predictive value (NPV) 93.3%. Contact history, fever and cough showed the highest association with SARS-CoV-2 infection. In the validation set (103 336 patients, median age 73 (IQR 50-84)), the best-performing ML model had an AUC of 0.85 (95% CI 0.84 to 0.86), sensitivity 91.4% (95 CI% 0.91 to 0.92), specificity 44.2% (95% CI 0.44 to 0.45) and accuracy 85% (95% CI 0.84 to 0.85). PPV and NPV were 13.3% (95% CI 0.13 to 0.14) and 98.2% (95% CI 0.98 to 0.98), respectively. Contact history, fever, call geographical distribution and cough were the most important variables in determining the outcome. CONCLUSION: ML-based models might help EMS identify patients with SARS-CoV-2 infection, and in guiding EMS allocation of hospital resources based on prespecified criteria.

Effectiveness of Cardiopulmonary Resuscitation at the Workplace.

BACKGROUND: Out-of-Hospital Cardiac Arrest (OHCA) is a medical emergency whose chances of survival can be increased by rapid Cardiopulmonary Resuscitation (CPR) and early use of Public Access Defibrillators (PAD). Basic Life Support (BLS) training became mandatory in Italy to spread knowledge of resuscitation maneuvers in the workplace. Basic Life Support (BLS) training became mandatory according to the DL 81/2008 law. To improve the level of cardioprotection in the workplace, the national law DL 116/2021 increased the number of places required to be provided with PADs. The study highlights the possibility of a Return to spontaneous circulation in OHCA in the workplace. METHODS: A multivariate logistic regression model was fitted to the data to extrapolate associations between ROSC and the dependent variables. The associations' robustness was evaluated through sensitivity analysis. RESULTS: The chance to receive CPR (OR 2.3; 95% CI:1.8-2.9), PAD (OR 7.2; 95% CI:4.9 - 10.7), and achieve Return to spontaneous circulation (ROSC) (crude OR 2.2; 95% CI:1.7-3.0, adjusted OR 1.6; 95% CI:1.2-2.2) is higher in the workplace compared to all other places. CONCLUSION: The workplace could be considered cardioprotective, although further research is necessary to understand the causes of missed CPRs and identify the best places to increase BLS and defibrillation training to help policymakers implement correct programming on the activation of PAD projects.

Out-of-Hospital Cardiac Arrest in the Paediatric Patient: An Observational Study in the Context of National Regulations.

Introduction: Cardiac arrest results in a high death rate if cardiopulmonary resuscitation and early defibrillation are not performed. Mortality is strongly linked to regulations, in terms of prevention and emergency-urgency system organization. In Italy, training of lay rescuers and the presence of defibrillators were recently made mandatory in schools. Our analysis aims to analyze Out-of-Hospital Cardiac Arrest (OHCA) events in pediatric patients (under 18 years old), to understand the epidemiology of this phenomenon and provide helpful evidence for policy-making. Methods: A retrospective observational analysis was conducted on the emergency databases of Lombardy Region, considering all pediatric OHCAs managed between 1 January 2016, and 31 December 2019. The demographics of the patients and the logistics of the events were statistically analyzed. Results: The incidence in pediatric subjects is 4.5 (95% CI 3.6-5.6) per 100,000 of the population. School buildings and sports facilities have relatively few events (1.9% and 4.4%, respectively), while 39.4% of OHCAs are preventable, being due to violent accidents or trauma, mainly occurring on the streets (23.2%). Conclusions: Limiting violent events is necessary to reduce OHCA mortality in children. Raising awareness and giving practical training to citizens is a priority in general but specifically in schools.

Severe Acute Respiratory Syndrome Coronavirus 2 and Medical Evacuation in Lombardy: Lessons Learned from an Unprecedented Pandemic.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerging infectious disease pandemic developed in Lombardy (northern Italy) during the last week of February 2020 with a progressive increase of patients presenting with serious clinical findings. Despite the efforts of the Central Italian Government, regional resources were rapidly at capacity. The solution was to plan the medical evacuation (MEDEVAC) of 119 critically ill patients (median age 61 years) to in-patient intensive care units in other Italian regions (77) and Germany (42). Once surviving patients were deemed suitable, the repatriation process concluded the assignment. The aim of this report is to underline the importance of a rapid organization and coordination process between different nodes of an effective national and international network during an emerging infectious disease outbreak and draw lessons learned from similar published reports.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Changes to the Major Trauma Pre-Hospital Emergency Medical System Network before and during the 2019 COVID-19 Pandemic.

Objectives: During the coronavirus disease 2019 pandemic, emergency medical services (EMSs) were among the most affected; in fact, there were delays in rescue and changes in time-dependent disease networks. The aim of the study is to understand the impact of COVID-19 on the time-dependent trauma network in the Lombardy region. Methods: A retrospective analysis on major trauma was performed by analysing all records saved in the EmMa database from 1 January 2019 to 31 December 2019 and from 1 January 2020 to 31 December 2020. Age, gender, time to first emergency vehicle on scene and mission duration were collected. Results: In 2020, compared to 2019, there was a reduction in major trauma diagnoses in March and April, during the first lockdown, OR 0.59 (95% CI 0.49−0.70; p < 0.0001), and a reduction in road accidents and accidents at work, while injuries related to falls from height and violent events increased. There was no significant increase in the number of deaths in the prehospital setting, OR 1.09 (95% CI 0.73−1.30; p = 0.325). Conclusions: The COVID-19 pandemic has changed the epidemiology of major trauma, but in the Lombardy region there was no significant change in mortality in the out-of-hospital setting.

The Impact of COVID-19 on Lombardy Region ST-Elevation Myocardial Infarction Emergency Medical System Network-A Three-Year Study.

Objectives: The COVID-19 pandemic had a significant impact on emergency medical systems (EMS). Regarding the ST-elevation myocardial infarction (STEMI) dependent time network, however, there is little evidence linked to the post-pandemic phase regarding this issue. Such information could prove to be of pivotal importance regarding STEMI clinical management, especially pre-hospital clinical protocols such as fibrinolysis. Methods: A retrospective observational cohort study of all STEMI rescues recorded in the Lombardy EMS registry from the 1st of January 2019 to the 30th of December 2021. Results: Regarding the number of STEMI diagnoses, March 2020 (first pandemic wave in Italy) saw a reduction compared to March 2019 (OR 0.76 [0.60-0.93], p = 0.011). The average time of the entire mission increased to 63.1 min in 2021, reaching 64.7 min in 2020, compared with 57.7 min in 2019. The number of HUBs for STEMI patients saw a reduction, falling from 52 HUBs in the pre-pandemic phase to 13 HUBs during the first wave. Conclusions: During the pandemic phase, there was an increase in the transportation times of STEMI patients from home to the hospital. Such changes did not alter the clinical approach in the out-of-hospital phase. Indeed, the implementation of fibrinolysis was not required.

Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy).

BACKGROUND: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncertain. AIM: the paper explores the effect of air pollutants in the first spread of COVID-19 in Lombardy, with a novel geomatics approach addressing the possible confounding factors, the reliability of data, the measurement of diffusion speed, and the biasing effect of the lockdown measures. METHODS AND RESULTS: all municipalities were assigned to one of five possible territorial classes (TC) according to land-use and socio-economic status, and they were grouped into districts of 100,000 residents. For each district, the speed of COVID-19 diffusion was estimated from the ambulance dispatches and related to indicators of mean concentration of air pollutants over 1, 6, and 12 months, grouping districts in the same TC. Significant exponential correlations were found for ammonia (NH3) in both prevalently agricultural (R2 = 0.565) and mildly urbanized (R2 = 0.688) areas. CONCLUSIONS: this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH3. As NH3 could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated.

OHCA survey in Lombardy: comparison between years 2014 and 2019 (pre COVID-19).

BACKGROUND AND AIM: The incidence of Out of Hospital Cardiac Arrest (OHCA) is estimated at 1/1000 persons/year. In the pre-Covid-19 era world, OHCA survival rate in Europe was 7-6%. The main objective is to analyze OHCA survival in the Lombardy region by highlighting the factors related to both the victims' characteristics and the chain of survival. METHODS: All OHCAs were grouped into four pre-established periods in 2019 (14-23 January; 15-24 April; 15-24 July; 14-23 October). Following the Utstein method, we selected witnessed OHCAs with presumed cardiac etiology. The outcome of each case was collected in four moments in time: Return of spontaneous circulation (ROSC), Emergency Department (ED), 24 hours and 30 days. The neurological outcome 30 days after OHCA was also investigated and stratified with the Cerebral Performance Category Score (CPC). RESULTS: We selected 456 cases of OHCA with witnessed cardiac etiology. ROSC was achieved in 121 cases (26.5%), survival in the Emergency Departments in 110 patients (24.1%), after 24 hours in 86 (18.86%) and after 30 days in 72 (15.8%). Male sex was shown to improve OHCA survival. A shockable presentation rhythm, Cardiopulmonary Resuscitation (CPR) performed by bystanders and the activation of Public Access Defibrillation (PAD) positively influenced OHCA outcome. CONCLUSIONS: Males are more predisposed to incur an OHCA event than females, but they have greater chances of survival. Factors most related to survival are: shockable rhythm, bystanders CPR and the activation of a PAD. (www.actabiomedica.it).

Out-of-hospital cardiac arrests in a large metropolitan area: synergistic effect of exposure to air particulates and high temperature.

AIMS: Air pollution and climate change are intrinsically linked to emerging hazards for global health. High air particulate matter (PM) levels may trigger out-of-hospital cardiac arrest (OHCA). High temperature could act synergistically with PM in determining OHCA. The aim of the present study was to investigate the effect of PM exposure alone, and in combination with temperature, on the risk of OHCA, in a large European metropolitan area with population >4 million. METHODS: We evaluated the association between short-term PM exposure, temperature, and the risk of OHCA over a two-year study period, allowing us to investigate 5761 events using a time-stratified case-crossover design combined with a distributed lag non-linear model. RESULTS: Higher risk of OHCA was associated with short-term exposure to PM10. The strongest association was experienced three days before the cardiac event where the estimated change in risk was 1.70% (0.48-2.93%) per 10 µg/m3 of PM. The cumulative exposure risk over the lags 0-6 was 8.5% (0.0-17.9%). We observed a joint effect of PM and temperature in triggering cardiac arrests, with a maximum effect of 14.9% (10.0-20.0%) increase, for high levels of PM before the cardiac event, in the presence of high temperature. CONCLUSION: The present study helps to clarify the controversial role of PM as OHCA determinant. It also highlights the role of increased temperature as a key factor in triggering cardiac events. This evidence suggests that tackling both air pollution and climate change might have a relevant impact in terms of public health.

Business Intelligence applied to Emergency Medical Services in the Lombardy region during SARS-CoV-2 epidemic.

BACKGROUND AND AIM OF THE WORK: On the 21st of February, the first patient was tested positive for SARS-CoV-2 at Codogno hospital in the Lombardy region. From that date, the Regional Emergency Medical Services (EMS) Trust (AREU) of the Lombardy region decided to apply Business Intelligence (BI) to the management of EMS during the epidemic. The aim of the study is to assess in this context the impact of BI on EMS management outcomes. METHODS: Since the beginning of the COVID-19 outbreak, AREU is using BI daily to track the number of first aid requests received from 112. BI analyses the number of requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview. Moreover, BI allows identifying the numerical trend of episodes in each municipality (increasing, stable, decreasing). RESULTS: AREU decides to reallocate in the territory the resources based on real-time data recorded and elaborated by BI. Indeed, based on that data, the numbers of vehicles and personnel have been implemented in the municipalities that registered more episodes and where the clusters are supposed to be. BI has been of paramount importance in taking timely decisions on the management of EMS during COVID-19 outbreak.  Conclusions: Even if there is little evidence-based literature focused on BI impact within the health care, this study suggests that BI can be usefully applied to promptly identify clusters and patterns of the SARS-CoV-2 epidemic and, consequently, make informed decisions that can improve the EMS management response to the outbreak.