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.

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.

Heatwave Definition and Impact on Cardiovascular Health: A Systematic Review.

Objectives: We aimed to analyze recent literature on heat effects on cardiovascular morbidity and mortality, focusing on the adopted heat definitions and their eventual impact on the results of the analysis. Methods: The search was performed on PubMed, ScienceDirect, and Scopus databases: 54 articles, published between January 2018 and September 2022, were selected as relevant. Results: In total, 21 different combinations of criteria were found for defining heat, 12 of which were based on air temperature, while the others combined it with other meteorological factors. By a simulation study, we showed how such complex indices could result in different values at reference conditions depending on temperature. Heat thresholds, mostly set using percentile or absolute values of the index, were applied to compare the risk of a cardiovascular health event in heat days with the respective risk in non-heat days. The larger threshold's deviation from the mean annual temperature, as well as higher temperature thresholds within the same study location, led to stronger negative effects. Conclusion: To better analyze trends in the characteristics of heatwaves, and their impact on cardiovascular health, an international harmonization effort to define a common standard is recommendable.

Identifying Trauma Patients in Need for Emergency Surgery in the Prehospital Setting: The Prehospital Prediction of In-Hospital Emergency Treatment (PROPHET) Study.

Prehospital field triage often fails to accurately identify the need for emergent surgical or non-surgical procedures, resulting in inefficient resource utilization and increased costs. This study aimed to analyze prehospital factors associated with the need for emergent procedures (such as surgery or interventional angiography) within 6 h of hospital admission. Additionally, our goal was to develop a prehospital triage tool capable of estimating the likelihood of requiring an emergent procedure following hospital admission. We conducted a retrospective observational study, analyzing both prehospital and in-hospital data obtained from the Lombardy Trauma Registry. We conducted a multivariable logistic regression analysis to identify independent predictors of emergency procedures within the first 6 h from admission. Subsequently, we developed and internally validated a triage score composed of factors associated with the probability of requiring an emergency procedure. The study included a total of 3985 patients, among whom 295 (7.4%) required an emergent procedure within 6 h. Age, penetrating injury, downfall, cardiac arrest, poor neurological status, endotracheal intubation, systolic pressure, diastolic pressure, shock index, respiratory rate and tachycardia were identified as predictors of requiring an emergency procedure. A triage score generated from these predictors showed a good predictive power (AUC of the ROC curve: 0.81) to identify patients requiring an emergent surgical or non-surgical procedure within 6 h from hospital admission. The proposed triage score might contribute to predicting the need for immediate resource availability in trauma patients.

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.

Impact of the COVID-19 outbreak on severe trauma trends and healthcare system reassessment in Lombardia, Italy: an analysis from the regional trauma registry.

BACKGROUNDS: The COVID-19 pandemic drastically strained the health systems worldwide, obligating the reassessment of how healthcare is delivered. In Lombardia, Italy, a Regional Emergency Committee (REC) was established and the regional health system reorganized, with only three hospitals designated as hubs for trauma care. The aim of this study was to evaluate the effects of this reorganization of regional care, comparing the distribution of patients before and during the COVID-19 outbreak and to describe changes in the epidemiology of severe trauma among the two periods. METHODS: A cohort study was conducted using retrospectively collected data from the Regional Trauma Registry of Lombardia (LTR). We compared the data of trauma patients admitted to three hub hospitals before the COVID-19 outbreak (September 1 to November 19, 2019) with those recorded during the pandemic (February 21 to May 10, 2020) in the same hospitals. Demographic data, level of pre-hospital care (Advanced Life Support-ALS, Basic Life Support-BLS), type of transportation, mechanism of injury (MOI), abbreviated injury score (AIS, 1998 version), injury severity score (ISS), revised trauma score (RTS), and ICU admission and survival outcome of all the patients admitted to the three trauma centers designed as hubs, were reviewed. Screening for COVID-19 was performed with nasopharyngeal swabs, chest ultrasound, and/or computed tomography. RESULTS: During the COVID-19 pandemic, trauma patients admitted to the hubs increased (46.4% vs 28.3%, p < 0.001) with an increase in pre-hospital time (71.8 vs 61.3 min, p < 0.01), while observed in hospital mortality was unaffected. TRISS, ISS, AIS, and ICU admission were similar in both periods. During the COVID-19 outbreak, we observed substantial changes in MOI of severe trauma patients admitted to three hubs, with increases of unintentional (31.9% vs 18.5%, p < 0.05) and intentional falls (8.4% vs 1.2%, p < 0.05), whereas the pandemic restrictions reduced road- related injuries (35.6% vs 60%, p < 0.05). Deaths on scene were significantly increased (17.7% vs 6.8%, p < 0.001). CONCLUSIONS: The COVID-19 outbreak affected the epidemiology of severe trauma patients. An increase in trauma patient admissions to a few designated facilities with high level of care obtained satisfactory results, while COVID-19 patients overwhelmed resources of most other hospitals.

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.

Out-of-hospital cardiac arrest (OHCA) Survey in Lombardy: data analysis through prospective short time period assessment.

BACKGROUND AND AIM OF THE WORK: The results of out-of-hospital cardiac arrests (OHCA) are usually reported through data collected collected via "ad hoc"  registries, but in large populations, samples of short time periods can be used to apply the results to the entire population. We would like to describe the situation of Lombardy to provide evidence on successful procedures, which may be carried out in a larger context. METHODS: Observational, prospective, analytical, single cohort study in Lombardy population. Data of OHCA of cardiac aetiology, according to "Utstein Style", with resuscitation attempts started by the Emergency Medical Service (EMS), were collected for 40 days subdivided in 10-day-periods in all seasons 2014-15 via Operating System "Emergency Management" (EmMa). RESULTS: Of 1219 cases, 536 events of witnessed OHCA of presumed cardiac etiology were analyzed. Outcomes were: sustained Return Of Spontaneous Circulation ROSC (25.6%), Survival Event in Emergency Department (22.8%), Survival after 24 hours (21.2%) and Survival after hospital discharge at home 30 days after (11.2%). Statistically significant results were found in age, rhythm of presentation, and resuscitation by bystanders. Sex, seasonality and rescue timing did not differ statistically. CONCLUSIONS: Overall the thirty-day survival rate was similar to studies with larger databases. Our data are consistent with the concept that all emergency service should provide CPR instructions for every citizen who activate the EMS in the suspect of a SCA; further investigation should clarify how long interval could be useful for ROSC and sustained ROSC in patients resuscitated by lay people using CPR instructions.