We report the development, implementation, and results of a sustainable region-wide mass-casualty management prehospital training program implemented by the Regione Lombardia emergency medical services (EMS) agency AREU in Italy.

The educational program learning objectives are: (1) command and control, communications, and resource management; (2) mass casualty triage and the START triage protocol; (3) on-scene management; (4) Regione Lombardia and AREU Mass Casualty standard operating procedures; and (5) inter-agency communications and relations. For each course edition data on participants’ summative assessment, participants’ feedback and costs were collected.

Between June 26, 2013, and December 31, 2020, a total of 84 editions of the provider training event were delivered, training an overall 1329 prehospital providers; 1239 (93%) passed the summative assessment and were qualified as being operationally “ready.” Regarding participant feedback, the overall program was rated 4.4 ± 0.7 out of 5. The overall cost of running the provider program during the study period was €321 510 (circa US $382 000). The average cost per edition was €3828 and €242 per participant.

We have described a simple yet interactive simulation and blended-learning approach, which has yielded good pass rates, good participant satisfaction, and contained costs to systematically train emergency medical service personnel.

Mass-casualty incidents (MCIs) and disasters are characterized by a high heterogeneity of effects and may pose important logistic challenges that could hamper the emergency rescue operations.

The main objective of this study was to establish the most frequent logistic challenges (red flags) observed in a series of Italian disasters with a problem-based approach and to verify if the 80-20 rule of the Pareto principle is respected.

A series of 138 major events from 1944 through 2020 with a Disaster Severity Score (DSS) ≥ four and five or more victims were analyzed for the presence of twelve pre-determined red flags.

A Pareto graph was built considering the most frequently observed red flags, and eventual correlations between the number of red flags and the components of the DSS were investigated.

Eight out of twelve red flags covered 80% of the events, therefore not respecting the 80-20 rule; the number of red flags showed a low positive correlation with most of the components of the DSS score. The Pareto analysis showed that potential hazards, casualty nest area > 2.5km2, number of victims over 50, evacuation noria over 20km, number of nests > five, need for extrication, complex access to victims, and complex nest development were the most frequently observed red flags.

Logistic problems observed in MCIs and disaster scenarios do not follow the 80-20 Pareto rule; this demands for careful and early evaluation of different logistic red flags to appropriately tailor the rescue response.

Anesthesiologists play a pivotal role in mass-casualty incidents management. Disaster medicine is part of the anesthesiologist’s core skills; however, dedicated training is still scarce and, often, it does not follow a standardized program.

We designed and delivered a crash course in disaster medicine for Italian anesthesiology residents participating in the nationwide program, Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI) Academy Critical Emergency Medicine 2019. Residents totaling 145, from 39 programs, participated in a 75-minute workstation on the principles of disaster management. Following this, each participant was involved in a full-scale mass-casualty drill. A plenary debriefing followed to present simulation data, maximize feedback, and highlight all situations needing improvement.

Overall, participant performance was good: Triage accuracy was 85% prehospital and 84% in-hospital. Evacuation flow respected triage priority. During the debriefing, residents were very open to share and reflect on their experiences. A narrative qualitative analysis of the debriefing highlights that many participants felt overwhelmed by events during the exercise. Participants in coordination positions shared how they appreciated the need to switch from a clinical mindset to a managerial role.

This was an invaluable experience for anesthesiology trainees, providing them with the skill set to understand the fundamental principles of a mass-casualty response.

To compare the epidemiology, clinical characteristics, and mortality of patients with bloodstream infections (BSI) caused by extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (ESBL-EC) versus ESBL-producing Klebsiella pneumoniae (ESBL-KP) and to examine the differences in clinical characteristics and outcome between BSIs caused by isolates with CTX-M versus other ESBL genotypes

As part of the INCREMENT project, 33 tertiary hospitals in 12 countries retrospectively collected data on adult patients diagnosed with ESBL-EC BSI or ESBL-KP BSI between 2004 and 2013. Risk factors for ESBL-EC versus ESBL-KP BSI and for 30-day mortality were examined by bivariate analysis followed by multivariable logistic regression.

The study included 909 patients: 687 with ESBL-EC BSI and 222 with ESBL-KP BSI. ESBL genotype by polymerase chain reaction amplification of 286 isolates was available. ESBL-KP BSI was associated with intensive care unit admission, cardiovascular and neurological comorbidities, length of stay to bacteremia >14 days from admission, and a nonurinary source. Overall, 30-day mortality was significantly higher in patients with ESBL-KP BSI than ESBL-EC BSI (33.7% vs 17.4%; odds ratio, 1.64; P=.016). CTX-M was the most prevalent ESBL subtype identified (218 of 286 polymerase chain reaction-tested isolates, 76%). No differences in clinical characteristics or in mortality between CTX-M and non–CTX-M ESBLs were detected.

Clinical characteristics and risk of mortality differ significantly between ESBL-EC and ESBL-KP BSI. Therefore, all ESBL-producing Enterobacteriaceae should not be considered a homogeneous group. No differences in outcomes between genotypes were detected.

ClinicalTrials.gov. Identifier: NCT01764490.

Infect Control Hosp Epidemiol 2018;39:660–667

Babies with CHDs are a particularly vulnerable population with significant mortality in their 1st year. Although most deaths occur in the hospital within the early postoperative period, around one-fifth of postoperative deaths in the 1st year of life may occur after hospital discharge in infants who have undergone apparently successful cardiac surgery.

To systematically review the published literature and identify risk factors for adverse outcomes, specifically deaths and unplanned re-admissions, following hospital discharge after infant surgery for life-threatening CHDs.

A systematic search was conducted in MEDLINE, EMBASE, CINAHL, Cochrane Library, Web of Knowledge, and PsycINFO electronic databases, supplemented by manual searching of conference abstracts.

A total of 15 studies were eligible for inclusion. Almost exclusively, studies were conducted in single US centres and focussed on children with complex single ventricle diagnoses. A wide range of risk factors were evaluated, and those more frequently identified as having a significant association with higher mortality or unplanned re-admission risk were non-Caucasian ethnicity, lower socio-economic status, co-morbid conditions, age at surgery, operative complexity and procedure type, and post-operative feeding difficulties.

Studies investigating risk factors for adverse outcomes post-discharge following diverse congenital heart operations in infants are lacking. Further research is needed to systematically identify higher risk groups, and to develop interventions targeted at supporting the most vulnerable infants within an integrated primary and secondary care pathway.

Surge capacity, or the ability to manage an extraordinary volume of patients, is fundamental for hospital management of mass-casualty incidents. However, quantification of surge capacity is difficult and no universal standard for its measurement has emerged, nor has a standardized statistical method been advocated. As mass-casualty incidents are rare, simulation may represent a viable alternative to measure surge capacity.

The objective of the current study was to develop a statistical method for the quantification of surge capacity using a combination of computer simulation and simple process-control statistical tools. Length-of-stay (LOS) and patient volume (PV) were used as metrics. The use of this method was then demonstrated on a subsequent computer simulation of an emergency department (ED) response to a mass-casualty incident.

In the derivation phase, 357 participants in five countries performed 62 computer simulations of an ED response to a mass-casualty incident. Benchmarks for ED response were derived from these simulations, including LOS and PV metrics for triage, bed assignment, physician assessment, and disposition. In the application phase, 13 students of the European Master in Disaster Medicine (EMDM) program completed the same simulation scenario, and the results were compared to the standards obtained in the derivation phase.

Patient-volume metrics included number of patients to be triaged, assigned to rooms, assessed by a physician, and disposed. Length-of-stay metrics included median time to triage, room assignment, physician assessment, and disposition. Simple graphical methods were used to compare the application phase group to the derived benchmarks using process-control statistical tools. The group in the application phase failed to meet the indicated standard for LOS from admission to disposition decision.

This study demonstrates how simulation software can be used to derive values for objective benchmarks of ED surge capacity using PV and LOS metrics. These objective metrics can then be applied to other simulation groups using simple graphical process-control tools to provide a numeric measure of surge capacity. Repeated use in simulations of actual EDs may represent a potential means of objectively quantifying disaster management surge capacity. It is hoped that the described statistical method, which is simple and reusable, will be useful for investigators in this field to apply to their own research.

FrancJM, IngrassiaPL, VerdeM, ColomboD, Della CorteF. A Simple Graphical Method for Quantification of Disaster Management Surge Capacity Using Computer Simulation and Process-control Tools. Prehosp Disaster Med. 2015;30(1):1-7.