Assessing the heterogeneity of the impact of COVID-19 incidence on all-cause excess mortality among healthcare districts in Lombardy, Italy, to evaluate the local response to the pandemic: an ecological study.

OBJECTIVES: The fragmentation of the response to the COVID-19 pandemic at national, regional and local levels is a possible source of variability in the impact of the pandemic on society. This study aims to assess how much of this variability affected the burden of COVID-19, measured in terms of all-cause 2020 excess mortality. DESIGN: Ecological retrospective study. SETTING: Lombardy region of Italy, 2015-2020. OUTCOME MEASURES: We evaluated the relationship between the intensity of the epidemics and excess mortality, assessing the heterogeneity of this relationship across the 91 districts after adjusting for relevant confounders. RESULTS: The epidemic intensity was quantified as the COVID-19 hospitalisations per 1000 inhabitants. Five confounders were identified through a directed acyclic graph: age distribution, population density, pro-capita gross domestic product, restriction policy and population mobility.Analyses were based on a negative binomial regression model with district-specific random effects. We found a strong, positive association between COVID-19 hospitalisations and 2020 excess mortality (p<0.001), estimating that an increase of one hospitalised COVID-19 patient per 1000 inhabitants resulted in a 15.5% increase in excess mortality. After adjusting for confounders, no district differed in terms of COVID-19-unrelated excess mortality from the average district. Minimal heterogeneity emerged in the district-specific relationships between COVID-19 hospitalisations and excess mortality (6 confidence intervals out of 91 did not cover the null value). CONCLUSIONS: The homogeneous effect of the COVID-19 spread on the excess mortality in the Lombardy districts suggests that, despite the unprecedented conditions, the pandemic reactions did not result in health disparities in the region.

Strategies to convert hospital beds for COVID-19 patients to minimize emergency department overcrowding.

Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability.

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.

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.

Emergency department performances during overcrowding: the experience of the health protection agency of Brianza.

Background: Hospital emergency departments (ED) can contribute to improve health outcomes and reduce costs of health care system. This study evaluated ED admissions during a twelve months period, analyzing characteristics of patients who underwent to emergency care in order to understand factors involved in ED overcrowding and promote adequate management. Methods: This retrospective study analyzed a twelve months window, with in-depth focus on December/January when almost all EDs reported overcrowding. All ED admissions were recorded in electronic schedules including: demographic characteristics, time/date of the access, incoming triage code, diagnosis, performed procedures, discharge, time/date of discharge. A backward multivariable logistic regression model was used to estimate relationships between investigated variables and ED pattern mortality. Results: A total of 416,299 ED admissions were analyzed. During the overcrowded period there was an increase in patients admissions (+32 patients per day, p = 0.0079) with a statistically significant rise of critical patients (+1.7% yellow codes and +0.7% red codes, p < 0.001) and older subjects (+1.4% patients aged 75 or more years, p < 0.001). Moreover, there were statistically significant increases in waiting times and in length of visits, a higher percentage of patients who were hospitalized (13.3% vs. 12.2%, p < 0.001), left ED (4.46% vs. 4.15%, p < 0.001) and died (0.27% vs. 0.17%, p < 0.0001). This latter result maintained a marginal statistical significance (OR = 1.16, 95% CI = 0.98-1.38, p = 0.075) after adjustment for confounding. Conclusion: Our study highlights that ED crowding can determine measurable worsening in ED services and patient outcomes as mortality, waiting times, lengths of stay, percentage of abandonment without being seen and, probably, costs. Thus, address ED crowding has to be considered an important public health priority requiring policymakers involvement.

Amplitude spectrum area to guide defibrillation: a validation on 1617 patients with ventricular fibrillation.

BACKGROUND: This study sought to validate the ability of amplitude spectrum area (AMSA) to predict defibrillation success and long-term survival in a large population of out-of-hospital cardiac arrests. METHODS AND RESULTS: ECGs recorded by automated external defibrillators from different manufacturers were obtained from patients with cardiac arrests occurring in 8 city areas. A database, including 2447 defibrillations from 1050 patients, was used as the derivation group, and an additional database, including 1381 defibrillations from 567 patients, served as validation. A 2-second ECG window before defibrillation was analyzed, and AMSA was calculated. Univariable and multivariable regression analyses and area under the receiver operating characteristic curve were used for associations between AMSA and study end points: defibrillation success, sustained return of spontaneous circulation, and long-term survival. Among the 2447 defibrillations of the derivation database, 26.2% were successful. AMSA was significantly higher before a successful defibrillation than a failing one (13 ± 5 versus 6.8 ± 3.5 mV-Hz) and was an independent predictor of defibrillation success (odds ratio, 1.33; 95% confidence interval, 1.20-1.37) and sustained return of spontaneous circulation (odds ratio, 1.22; 95% confidence interval, 1.17-1.26). Area under the receiver operating characteristic curve for defibrillation success prediction was 0.86 (95% confidence interval, 0.85-0.88). AMSA was also significantly associated with long-term survival. The following AMSA thresholds were identified: 15.5 mV-Hz for defibrillation success and 6.5 mV-Hz for defibrillation failure. In the validation database, AMSA ≥ 15.5 mV-Hz had a positive predictive value of 84%, whereas AMSA ≤ 6.5 mV-Hz had a negative predictive value of 98%. CONCLUSIONS: In this large derivation-validation study, AMSA was validated as an accurate predictor of defibrillation success. AMSA also appeared as a predictor of long-term survival.

[The epidemiologic surveillance of malignant mesothelioma in the Lower Iseo Lake area]. / La sorveglianza epidemiologica del mesotelioma maligno nel basso lago d'Iseo.

Starting from an hospital observation of the mesotheliomas cluster in women living in a lakeside area (Iseo lake, Northern Italy), an epidemiological surveillance of this tumour was performed by the local occupational health service. This cluster wasn't notified, in spite of the relevant number of factories producing asbestos textile materials in this area. From 1977 to august 2006, 45 cases of mesothelioma were detected among the workers of 3 textile industries located in 3 little villages: 14 cases occurred working crocidolite and chrysotile rope and gasket; 20 cases in a textile factory producing cotton garments, that was adjacent to and polluted by the farmer and were asbestos insulation and blankets used for fireproofing are present; 11 cases occurred among women working in silk factories. The mesothelioma cases occurred in the same period in this area, which constituted the recruitment area of the people working in the 3 textile plants (11 villages, about 43,000 inhabitants), are 55.93% of which had been occupationally exposed to asbestos. Out of the dockyard and the asbestos-cement industries, this frequency of occupational exposed workers is the highest never observed in Italy. The majority of the cases (66%) occurred among women working in the textile factories. In a women, producing asbestos textile materials and suffered form peritoneal mesothelioma and pleural plaques, the analysis (by SEM) of asbestos fibre lung burden show 286 million fibres x gr. of dry tissue. Between the 42 mesothelioma cases occurring in the population of the 3 villages where the textile plants was located, we observed only one case with possible environmental exposure to asbestos: a gardener of the village where the manufacturing asbestos ropes and gasket plant is present. In the silk factories, asbestos exposure was probable because of the presence of asbestos insulated pipes. The female pleural mesothelioma standard incidence observed in this area (6.8 x 100,000, 1977-2005) is the highest never estimate in Italy. The epidemiological surveillance of the mesothelioma appear essential to identify cases unreported and allow the collection of information useful to understand clearly the asbestos exposure effects on health's workers and to estimate the tumour incidence in the population.

A novel prognostic index to determine the impact of cardiac conditions and co-morbidities on one-year outcome in patients with heart failure.

Prognostic stratification is relevant in clinical decision making in heart failure (HF). Predictors identified during hospitalization or in clinical trials may be unrepresentative of HF in the community. The aim of this study was to derive and validate, in different clinical settings, a risk stratification model for the prediction of stable HF outcomes. The study included 807 patients, 350 enrolled at discharge from the hospital (44%), 309 in the outpatient clinic (38%), and 148 in the home-care setting (18%). There were 292 patients in the derivation cohort and 515 in the validation cohort. A multivariate logistic analysis was performed to obtain the CardioVascular Medicine Heart Failure (CVM-HF) index. One-year mortality was 20.8% in the derivation cohort and 20.7% in the validation cohort. The CVM-HF index included cardiac conditions and co-morbidities and stratified the 1-year mortality risk as low (death rate 4%), average (32%), high (63%), and very high (96%). The area under the curve of the receiver-operating characteristic curve was 0.844 (95% confidence interval [CI] 0.779 to 0.89) for the derivation cohort and 0.812 (95% CI 0.76 to 0.86) for the validation cohort. Model performance was equally good in the 3 different HF settings. In a subgroup of 409 patients, the CVM-HF index (area under the curve 0.821, 95% CI 0.79 to 0.89) outperformed the most-used prognostic models (the Charlson index and the Heart Failure Risk Scoring System). In conclusion, the CVM-HF index, a novel prognostic model that is easy to derive and applicable to unselected patients, may represent a valuable tool for the prognostication of stable HF outcomes.

[Cardiovascular medicine unit: preliminary results of an in-hospital multidisciplinary intervention in heart failure]. / Unità di medicina cardiovascolare: prime esperienze di un approccio intraospedaliero multidisciplinare allo scompenso cardiaco.

BACKGROUND: In Italy the majority of patients with heart failure are admitted to Internal Medicine Divisions, where diagnostic and therapeutic procedures, suggested by guidelines, are less utilized than in Cardiology Divisions. In order to provide a better assistance to patients with heart failure, a Cardiovascular Medicine Unit was founded with interdepartmental valence (Cardiovascular and Internal Medicine) in the "Ospedali Riuniti" Hospital of Bergamo. The purpose of this study was to evaluate the preliminary results, concerning management of heart failure, in the first 6 months of activities carried out at the Cardiovascular Medicine Unit and to compare them with those of other structures that usually manage this disease. METHODS: Analysis of data concerning 150 admissions to the Cardiovascular Medicine Unit for heart failure in 147 patients during the first 6 months of 2003. Comparisons of admittance data for heart failure in the first semester 2001 deriving from the Internal Medicine Division of the "Ospedali Riuniti" Hospital of Bergamo, the TEMISTOCLE study and the Niguarda Ca' Granda Hospital of Milan. RESULTS: A higher prescription of beta-blockers and spironolactone was observed in the Cardiovascular Medicine Unit than in other structures (p < 0.001). Echocardiography and coronary angiography were performed more frequently (p < 0.001) and the number of rehospitalizations was lower for all cases (p < 0.05) as well as for heart failure (p < 0.001). The extensive use of diagnostic procedures and multidisciplinary evaluation of co-morbidities allowed to identify patients who could be managed successfully with surgical therapy; preliminary data suggest a net benefit in terms of NYHA functional class and reduction in rehospitalization in this subgroup of patients. CONCLUSIONS: These data demonstrate that the Cardiovascular Medicine model, based on integration of knowledge between cardiologists and internists, is effective with respect to organization and assistance and allows to optimize heart failure patient management.