Development and internal validation of a multivariable model for the prediction of the probability of 1-year readmission to the emergency department for acute alcohol intoxication.

To develop and internally validate a multivariable logistic regression model (LRM) for the prediction of the probability of 1-year readmission to the emergency department (ED) in patients with acute alcohol intoxication (AAI). We developed and internally validated the LRM on a previously analyzed retrospective cohort of 3304 patients with AAI admitted to the ED of the Sant'Orsola-Malpighi Hospital (Bologna, Italy). The benchmark LRM employed readmission to the same ED for AAI within 1 year as the binary outcome, age as a continuous predictor, and sex, alcohol use disorder, substance use disorder, at least one previous admission for trauma, mental or behavioral disease, and homelessness as the binary predictors. Optimism correction was performed using the bootstrap on 1000 samples without replacement. The benchmark LRM was gradually simplified to get the most parsimonious LRM with similar optimism-corrected overall fit, discrimination and calibration. The 1-year readmission rate was 15.7% (95% CI 14.4-16.9%). A reduced LRM based on sex, age, at least one previous admission for trauma, mental or behavioral disease, and homelessness, performed nearly as well as the benchmark LRM. The reduced LRM had the following optimism-corrected metrics: scaled Brier score 17.0%, C-statistic 0.799 (95% CI 0.778 to 0.821), calibration in the large 0.000 (95% CI - 0.099 to 0.099), calibration slope 0.985 (95% CI 0.893 to 1.088), and an acceptably accurate calibration plot. An LRM based on sex, age, at least one previous admission for trauma, mental or behavioral disease, and homelessness can be used to estimate the probability of 1-year readmission to ED for AAI. To begin proving its clinical utility, this LRM should be validated in external cohorts.

Exercise-induced modulation of Interferon-signature: a therapeutic route toward management of Systemic Lupus Erythematosus.

Systemic Lupus Erythematosus (SLE) is a multisystemic autoimmune disorder characterized by flares-ups/remissions with a complex clinical picture related to disease severity and organ/tissue injury, which, if left untreated, may result in permanent damage. Enhanced fatigue and pain perception, worsened quality of life (QoL) and outcome are constant, albeit symptoms may differ. An aberrant SLE immunoprofiling, note as "interferon (IFN)α-signature", is acknowledged to break immunotolerance. Recently, a deregulated "IFNγ-signature" is suggested to silently precede/trigger IFNα profile before clinical manifestations. IFNα- and IFNγ-over-signaling merge in cytokine/chemokine overexpression exacerbating autoimmunity. Remission achievement and QoL improvement are the main goals. The current therapy (i.e., corticosteroids, immunosuppressants) aims to downregulate immune over-response. Exercise could be a safe treatment due to its ever-emerging ability to shape and re-balance immune system without harmful side-effects; in addition, it improves cardiorespiratory capacity and musculoskeletal strength/power, usually impaired in SLE. Nevertheless, exercise is not yet included in SLE care plans. Furthermore, due to the fear to worsening pain/fatigue, SLE subjects experience kinesiophobia and sedentary lifestyle, worsening physical health. Training SLE patients to exercise is mandatory to fight inactive behavior and ameliorate health. This review aims to focus the attention on the role of exercise as a non-pharmacological therapy in SLE, considering its ability to mitigate IFN-signature and rebalance (auto)immune response. To this purpose, the significance of IFNα- and IFNγ-signaling in SLE etiopathogenesis will be addressed first and discussed thereafter as biotarget of exercise. Comments are addressed on the need to make aware all SLE care professional figures to promote exercise for health patients.

Mortality and its association with chronic alcohol-related diseases in patients admitted to the emergency department for acute alcoholic intoxication: retrospective cohort study.

We assessed long-term mortality and its association with chronic alcohol-related diseases in patients admitted to the emergency department (ED) because of acute alcoholic intoxication (AAI). A retrospective cohort study was performed at the ED of Sant'Orsola-Malpighi Hospital, Bologna, Italy. 3304 patients, corresponding to 6415 admissions for AAI, who accessed the ED from January 1, 2005, to December 31, 2017, were studied. The ED electronic registry system was used to assess living status on 08 May 2020 and to obtain the prespecified potential predictors, i.e., age at first admission, sex, alcohol use disorder (AUD), substance use disorder (SUD), more than 1 admission to ED for trauma, mental and behavioral disorders, neurological disorders, and cardiovascular disease. The median follow-up time was 9.3 years and the time on risk was 30,053 person years (PY) with a death rate corresponding to 4.42 (95% CI 3.74-5.26) per 1000 PY (n = 133 deaths). The death rate was higher in patients with AUD (17.30) than in those without AUD (1.98) and in those with SUD (13.58) than in those without SUD (3.80). Lastly, there was a clearly higher death rate among AUD+ SUD+ (20.89) compared to AUD-SUD-patients (1.74). At multivariable Cox regression, AUD, SUD, and liver cirrhosis were strong and independent predictors of time-to-death. Using standardized mortality ratios, a clear excess of mortality was evident for all the age bands from (40-45] to (60-65] years. Mortality is higher in AAI than in the general population and chronic alcohol-related diseases are strongly associated with it.

Human-level play in the game of Diplomacy by combining language models with strategic reasoning.

Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.

Cross-sectional analysis of the humoral response after SARS-CoV-2 vaccination in Sardinian multiple sclerosis patients, a follow-up study.

Monitoring immune responses to SARS-CoV-2 vaccination and its clinical efficacy over time in Multiple Sclerosis (MS) patients treated with disease-modifying therapies (DMTs) help to establish the optimal strategies to ensure adequate COVID-19 protection without compromising disease control offered by DMTs. Following our previous observations on the humoral response one month after two doses of BNT162b2 vaccine (T1) in MS patients differently treated, here we present a cross-sectional and longitudinal follow-up analysis six months following vaccination (T2, n=662) and one month following the first booster (T3, n=185). Consistent with results at T1, humoral responses were decreased in MS patients treated with fingolimod and anti-CD20 therapies compared with untreated patients also at the time points considered here (T2 and T3). Interestingly, a strong upregulation one month after the booster was observed in patients under every DMTs analyzed, including those treated with fingolimod and anti-CD20 therapies. Although patients taking these latter therapies had a higher rate of COVID-19 infection five months after the first booster, only mild symptoms that did not require hospitalization were reported for all the DMTs analyzed here. Based on these findings we anticipate that additional vaccine booster shots will likely further improve immune responses and COVID-19 protection in MS patients treated with any DMT.

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson's Disease.

BACKGROUND: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson's disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. METHODS: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. RESULTS: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2-3%. CONCLUSIONS: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.

Are "cases", "waves", "tests" and "modeling" deceiving indicators to describe the COVID-19 pandemic?

This commentary elaborates on different methodological aspects complicating the interpretation of epidemiological data related to the current COVID-19 pandemic, thus preventing reliable within and across-country estimates. Firstly, an inaccuracy of epidemiological data maybe arguably be attributed to passive surveillance, a relatively long incubation period during which infected individuals can still shed high loads of virus into the surrounding environment and the very high proportion of cases not even developing signs and/or symptoms of COVID-19. The latter is also the major reason for the inappropriateness of the abused "wave" wording, which gives the idea that health system starts from scratch to respond between "peaks". Clinical data for case-management on the other hand often requires complex technology in order to merge and clean data from health care facilities. Decision-making is often further derailed by the overuse of epidemiological modeling: precise aspects related to transmissibility, clinical course of COVID-19 and effectiveness of the public health and social measures are heavily influenced by unbeknownst and unpredictable human behaviors and modelers try to overcome missing epidemiological information by relying on poorly precise or questionable assumptions. Therefore the COVID-9 pandemic may provide a valuable opportunity to rethink how we are dealing with the very basic principles of epidemiology as well as risk communication issues related to such an unprecedented emergency situation.

Combination treatment optimization using a pan-cancer pathway model.

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans-that is, optimized sequences of potentially different drug combinations-providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

CovHos, a New Score to Predict the Need of Hospitalization for Coronavirus Disease 2019 (COVID-19) Patients at the Emergency Department.

INTRODUCTION AND AIM: As first receivers of suspected coronavirus disease 2019 (COVID-19) patients, clinicians of the Emergency Department (ED) have to rapidly perform the first clinical assessment evaluating the intensity of care needed. So far, clear management guidelines still lack. We identified variables associated with hospitalization in order to give a quick tool to assist clinicians in stratifying cases based on the severity at their arrival at the ED and in predicting the need for hospital care.  Methods: This is a monocentric observational prospective study enrolling COVID-19 patients. A score for hospitalization prediction (CovHos Score) was created using variables associated with hospitalization at multivariate analysis and then validated on an internal subsequent cohort. RESULTS: A total of 667 patients were included; 465 (69.7%) were hospitalized and 108 (16.2%) died at 30-days follow-up. In a multivariate analysis, male sex, age>65, alveolar-to-arterial oxygen gradient percentage increase compared to that expected for age, neutrophils/lymphocytes ratio and C-reactive protein levels were significantly associated with a higher rate of hospital admission. A CovHos score cut-off of 12 points predicted hospitalization with 85% sensitivity and 82.4 % specificity (area under a receiver operating characteristic curve [AUROC] = 0.909, 95% CI 0.884 - 0.935). Similar results were obtained in the validation court. A cut-off of 22 has 79% sensitivity and 77% specificity in predicting mortality (AUROC = 0.824; 95% CI 0.782-0.866); sensitivity and specificity were respectively 71.4% and 71.3% in the validation group. CONCLUSIONS: Although medical judgment still remains crucial, the CovHos score is an effective tool to assist emergency clinicians in predicting the need for hospitalization or to optimize allocation in a shortage of hospital resources.

Violence Against Healthcare: A Public Health Issue beyond Conflict Settings.

A 3-year analysis released in August 2021 by the WHO indicated that more than 700 healthcare workers and patients have died (2,000 injured) as a result of attacks against health facilities since 2017. The COVID-19 pandemic has made the risks even worse for doctors, nurses, and support staff, unfortunately. According to the latest figures from the International Committee of the Red Cross, 848 COVID-19-related violent incidents were recorded in 2020, and this is likely an underrepresentation of a much more widespread phenomenon. In response to rises in COVID-19-related attacks against healthcare, some countries have taken action. In Algeria, for instance, the penal code was amended to increase protection for healthcare workers against attacks and to punish individuals who damage health facilities. In the United Kingdom, the police, crime, sentencing, and courts bill proposed increased the maximum penalty from 12 months to 2 years in prison for anyone who assaults an emergency worker. Measures taken by countries represent a good practical way to counteract this crisis within COVID-19. However, we stress the importance of primary prevention with the use of communication: social media and other communication channels are fundamentally important to combat violence against health professionals, both to inform the population with quality data and to disseminate campaigns to prevent these acts.

Patterns of Emergency Department visits for acute and chronic diseases during the two pandemic waves in Italy.

BACKGROUND: Evidence is lacking about the impact of subsequent COVID-19 pandemic waves on Emergency Departments (ED). We analyzed the differences in patterns of ED visits in Italy during the two pandemic waves, focusing on changes in accesses for acute and chronic diseases. METHODS: We conducted a retrospective study using data from a metropolitan area in northern Italy that includes twelve ED. We analyzed weekly trends in non-COVID-19 ED visits during the first (FW) and second wave (SW) of the pandemic. Incidence rate ratios (IRRs) of triage codes, patient destination, and cause-specific ED visits in the FW and SW of the year 2020 vs. 2019 were estimated using Poisson regression models. MAIN FINDINGS: We found a significant decrease of ED visits by triage code, which was more marked for low priority codes and during the FW. We found an increased share of hospitalizations compared to home discharges both in the FW and in the SW. ED visits for acute and chronic conditions decreased during the FW, ranging, from -70% for injuries (IRR = 0.2862, p < 0.001) to -50% and - 60% for ischemic heart disease and heart failure. CONCLUSIONS: The two pandemic waves led to a selection of patients with higher and more urgent needs of acute hospital care. These findings should lead to investigate how to improve systems' capacity to manage changes in population needs.

Role of ROX index in the first assessment of COVID-19 patients in the emergency department.

During the first outbreak of Coronavirus disease 2019 (COVID-19) Emergency Departments (EDs) were overcrowded. Hence, the need for a rapid and simple tool to support clinical decisions, such as the ROX index (Respiratory rate - OXygenation), defined as the ratio of peripheral oxygen saturation and fraction of inspired oxygen, to respiratory rate. The aim of the study was to evaluate the accuracy of the ROX index in predicting hospitalization and mortality in patients with a diagnosis of COVID-19 in the ED. The secondary outcomes were to assess the number of readmissions and the variations in the ROX index between the first and the second admission. This was an observational prospective monocentric study, carried out in the ED of Sant'Orsola-Malpighi Hospital in Bologna, Italy. Five hundred and fifty-four consecutive patients with COVID-19 were enrolled and the ROX index was calculated. Patients were followed until hospital discharge or death. A ROX index value < 25.7 was associated with hospitalization (area under the curve [AUC] = 0.737, 95% CI 0.696-0.779, p < 0.001). The ROX index < 22.3 was statistically related to higher 30-day mortality (AUC = 0.764, 95% CI 0.708-0.820, p < 0.001). Eight patients were discharged and returned to the ED within the subsequent 7 days, their mean ROX index was 30.3 (6.2; range 21.9-39.4) at the first assessment and 24.6 (5.5; 14.5-29.5) at the second assessment, (p = 0.012). The ROX index, together with laboratory, imaging and clinical findings, correlated with the need for hospital admission, mechanical ventilation and mortality risk in COVID-19 patients.

Non-COVID-19 patients in times of pandemic: Emergency department visits, hospitalizations and cause-specific mortality in Northern Italy.

The COVID-19 pandemic forced healthcare services organization to adjust to mutating healthcare needs. Not exhaustive data are available on the consequences of this on non-COVID-19 patients. The aim of this study was to assess the impact of the pandemic on non-COVID-19 patients living in a one-million inhabitants' area in Northern Italy (Bologna Metropolitan Area-BMA), analyzing time trends of Emergency Department (ED) visits, hospitalizations and mortality. We conducted a retrospective observational study using data extracted from BMA healthcare informative systems. Weekly trends of ED visits, hospitalizations, in- and out-of-hospital, all-cause and cause-specific mortality between December 1st, 2019 to May 31st, 2020, were compared with those of the same period of the previous year. Non-COVID-19 ED visits and hospitalizations showed a stable trend until the first Italian case of COVID-19 has been recorded, on February 19th, 2020, when they dropped simultaneously. The reduction of ED visits was observed in all age groups and across all severity and diagnosis groups. In the lockdown period a significant increase was found in overall out-of-hospital mortality (43.2%) and cause-specific out-of-hospital mortality related to neoplasms (76.7%), endocrine, nutritional and metabolic (79.5%) as well as cardiovascular (32.7%) diseases. The pandemic caused a sudden drop of ED visits and hospitalizations of non-COVID-19 patients during the lockdown period, and a concurrent increase in out-of-hospital mortality mainly driven by deaths for neoplasms, cardiovascular and endocrine diseases. As recurrencies of the COVID-19 pandemic are underway, the scenario described in this study might be useful to understand both the population reaction and the healthcare system response at the early phases of the pandemic in terms of reduced demand of care and systems capability in intercepting it.