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Background: This paper aims to present open-source computer simulation programs developed to simulate, track, and estimate the COVID-19 outbreak. Methods: The programs included two separate parts, one set of programs built in Simulink with a block diagram display, and another one coded as a script in MATLAB R2020b. The mathematical model used in this package was the suspectable-infected-removed (SIR), suspectable-exposed-infected-removed (SEIR), and susceptible-exposed-infected-recovereddeceased (SEIRD) models represented by a set of differential-algebraic equations. It can be easily modified to develop new models for the problem. A generalized method was adopted to simulate worldwide outbreaks in an efficient, fast, and simple way. Results: To get a good tracking of the virus spread, a sum of sigmoid functions was proposed to capture any dynamic changes in the data. The parameters used for the input (infection and recovery rate functions) were computed using the parameter estimation tool in MATLAB. Several statistical methods were applied for the rate function, including linear, Mean±SD and root mean square (RMS). In addition, an adaptive neuro-fuzzy inference system (ANFIS) was employed and proposed to train the model and predict its output. Conclusion: This procedure is presented in such a way that it can be generalized and applied in other parts and applications of estimating the scenarios of an event, including the potential of several models, including suspectable-infected-removed (SIR), which is sensitive to pollution, etc. This program can be used as an educational tool or for research studies and this article promises some lasting contributions in the field of COVID-19. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Introduction: The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter defibrillator (ICD) algorithm accurately identifies severe sleep apnea (SA). In the present analysis, we tested the hypothesis that RDI could also predict atrial fibrillation (AF) burden. Methods: Patients with ejection fraction ≤35% implanted with an ICD were enrolled and followed up for 24 months. One month after implantation, patients underwent a polysomnographic study. The weekly mean RDI value was considered, as calculated during the entire follow‐up period and over a 1‐week period preceding the sleep study. The endpoints were as follows: daily AF burden of ≥5 min, ≥6 h, ≥23 h. Results: Here, 164 patients had usable RDI values during the entire follow‐up period. Severe SA (RDI ≥ 30 episodes/h) was diagnosed in 92 (56%) patients at the time of the sleep study. During follow‐up, AF burden ≥ 5 min/day was documented in 70 (43%), ≥6 h/day in 48 (29%), and ≥23 h/day in 33 (20%) patients. Device‐detected RDI ≥ 30 episodes/h at the time of the polygraphy, as well as the polygraphy‐measured apnea hypopnea index ≥ 30 episodes/h, were not associated with the occurrence of the endpoints, using a Cox regression model. However, using a time‐dependent model, continuously measured weekly mean RDI ≥ 30 episodes/h was independently associated with AF burden ≥ 5 min/day (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.24–3.65, p =.006), ≥6 h/day (HR: 2.75, 95% CI: 1.37–5.49, p =.004), and ≥23 h/day (HR: 2.26, 95% CI: 1.05–4.86, p =.037). Conclusions: In heart failure patients, ICD‐diagnosed severe SA on follow‐up data review identifies patients who are from two‐ to three‐fold more likely to experience an AF episode, according to various thresholds of daily AF burden. [ABSTRACT FROM AUTHOR]

Background: We quantified, among diabetic adults, the frequency, costs, and factors associated with visits to the emergency department (EDs) and subsequent hospitalizations for acute hypoglycaemic and hyperglycaemic events. Methods: We included adults with diabetes residing in the Milan Agency for Health Protection between 2015 and 2017. From healthcare databases, we identified demographic variables, comorbidities, type of treatment, insulin treatment duration, previous ED attendances for acute glycaemic events, and two indicators of glycaemic monitoring. Using a validated ICD-9-CM coding algorithm, we identified all ED attendances for acute glycaemic events from the ED database and calculated their incidence. We computed the direct costs from health databases and presented them as average annual mean costs for those having had at least an ED attendance. The analysis of the association between the number of ED attendances and potential determinants was performed using zero-inflated negative binomial regression models. These two-part models concomitantly estimate two sets of parameters: the odds-ratios (ORs) of having no attendances and the incidence rate ratios (IRRs) of attendance. Results: The cohort included 168,285 subjects, 70% of subjects were older than 64 years, 56% were males, and 26% were treated with insulin. The incidence of acute glycaemic events for those attending the ED was 7.0 per 1000 patient-years, followed by hospitalization 26.0% of the time. The total annual direct cost for ED attendances due to acute glycaemic events was 174,000 €. Type of antidiabetic treatment had the strongest association with ED attendances for hypoglycaemia. Patients assuming insulin only had a lower probability of having no attendances (OR compared to those who assumed non-insulin antidiabetic drugs =0.01, 95% CI = 0.00–0.02). These patients also had the highest rate of hyperglycaemic episodes (IRR = 7.7, 95% CI = 5.1–11.7 for insulin only vs. non-insulin antidiabetic drugs). Subjects having had a previous episode of the same type leading to an ED visit had a higher rate of subsequent attendances (IRR for hypoglycaemia = 5.3, 95% CI = 3.9–7.3 and IRR for hyperglycaemia = 3.7, 95% CI = 1.3–10.2). Conclusion: Insulin treatment and having had a prior acute glycaemic event leading to an ED visit were major predictors of ED attendance for hyper and hypoglycaemia in a population of adults with diabetes. [ABSTRACT FROM AUTHOR]

Purpose. To describe intravitreal anti-VEGF drug and dexamethasone use in four Italian regions. Methods. Four regional claims databases were used to measure drug prevalence, compare dosing intervals to those recommended in the summary of product characteristics (SPC), and identify switchers. Bilateral treatment and diabetic macular edema (DME) coding algorithms were validated, linking claims with a sample of prospectively collected ophthalmological data. Results. Overall, 41,836 patients received ≥1 study drug in 2010–2016 (4.8 per 10,000 persons). In 2016, anti-VEGF drug use ranged from 0.8 (Basilicata) to 5.7 (Lombardy) per 10,000 persons while intravitreal dexamethasone use ranged from 0.2 (Basilicata) to 1.4 (Lombardy) per 10,000 persons. Overall, 40,815 persons were incident users of study drugs. Among incident users with ≥1 year of follow-up (N = 30,745), 16.0% (N = 4,890) had only one pharmacy claim, especially for ranibizumab (60.9%). Switching occurred in 8.0% of users with ≥2 pharmacy claims (N = 33,637). The algorithms had an accuracy of 83.8 (95% CI: 79.7–87.3) concerning bilateral treatment and 72.3% (95% CI: 67.5–76.8) for DME. Conclusion. Study drug use increased over time in Lombardy, Basilicata, Calabria, and Sicily, despite a large heterogeneity in prevalence of use across regions. Drug treatment appeared to be partly in line with SPC, suggesting that improvement in clinical practice may be needed to maximize drug benefits. [ABSTRACT FROM AUTHOR]

Non‐deferrable medical conditions (NDMC) are clinical entities other than exacerbations of chronic diseases which are too frequently managed by emergency departments. The primary care setting would be indeed the ideal environment to proficiently treat them. Few studies have investigated the epidemiology of NDMC in primary care, especially in Italy. With the aim to better identify NDMC sufferers, we assessed the accuracy of the NDMC definition by means of four algorithms, featured by different specificities and sensitivities. Four algorithms, based on the hierarchical definition of NDMC, were developed to calculate the incidence rate of NDMC in 2014. Each was tested using a Cox univariate model adjusted for gender, comparing patients aged <45 years old with older patients. Algorithms 1 and 3 gave similar results (408.1 vs. 405.5 per 1,000 person‐years); Algorithms 2 and 4 reported sensibly lower rates (84.7 and 84.0 per 1,000 person‐years). Incidence rate of NDMC for female patients was higher than in males and for older age group (i.e. 75–84 age group) than younger patients, for both gender groups. Regarding the regression model, a higher risk of NDMC was estimated in patients aged 45 years or more and in females. This study allowed us to assess the incidence rate of NDMC in Italy which was unexpectedly higher among older patients. Given the crucial role of general practitioners (GPs) in the diagnosis and management of these conditions, as well as the healthcare system reforms imposed by the ongoing financial crisis, our findings may contribute to informing the capacity and strategic workload planning in group of GPs to improve service quality and profitably to reduce the excessive use of emergency departments. [ABSTRACT FROM AUTHOR]

Background: Identifying a single disease as the underlying cause of death (UCOD) is an oversimplification of the clinical-pathological process leading to death. The multiple causes of death (MCOD) approach examines any mention of a disease in death certificates. Taking diabetes as an example, the study investigates: patterns of death certification, differences in mortality figures based on the UCOD and on MCOD, factors associated to the mention of diabetes in death certificates, and potential of MCOD in the analysis of the association between chronic diseases. Methods: The whole mortality archive of the Veneto Region-Italy was extracted from 2008 to 2010. Mortality rates and proportional mortality were computed for diabetes as the UCOD and as MCOD. The position of the death certificate where diabetes was mentioned was analyzed. Conditional logistic regression was applied with chronic liver diseases (CLD) as the outcome and diabetes as the exposure variable. A subset of 19,605 death certificates of known diabetic patients (identified from the archive of exemptions from medical charges) was analyzed, with mention of diabetes as the outcome and characteristics of subjects as well as other diseases reported in the certificate as predictors. Results: In the whole mortality archive, diabetes was mentioned in 12.3 % of death certificates, and selected as the UCOD in 2.9 %. The death rate for diabetes as the UCOD was 26.8 x 105 against 112.6 x 105 for MCOD; the UCOD/ MCOD ratio was higher in males. The major inconsistencies of certification were entering multiple diseases per line and reporting diabetes as a consequence of circulatory diseases. At logistic regression the mention of diabetes was associated with the mention of CLD (mainly non-alcohol non-viral CLD). In the subset of known diabetic subjects, diabetes was reported in 52.1 %, and selected as the UCOD in 13.4 %. The probability of reporting diabetes was higher with coexisting circulatory diseases and renal failure and with long duration of diabetes, whereas it was lower in the presence of a neoplasm. Conclusions: The use of MCOD makes the analysis of mortality data more complex, but conveys more information than usual UCOD analyses. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to develop and validate an algorithm to assist the attribution of neuropsychiatric (NP) events to underlying disease in SLE patients. Methods. Phase 1 identified and categorized candidate items to be included in the algorithm for the attribution of an NP event to SLE and their relative weights through a literature-informed consensus-driven process. Using a retrospective training cohort of SLE, phase 2 validated items selected in phase 1 and refined weights through a data-driven process, fitting items as independent variables and expert evaluation (clinical judgement) as reference standard in logistic models. Phase 3 consisted of a validation process using an external multicentre retrospective SLE cohort. Results. Phase 1 identified four different items: timing of the NP event, type of event, confounding factors and favouring factors. The training and validating cohorts included 228 and 221 patients, respectively. Each patient experienced at least one NP event characterized using the ACR case definition. In these samples, items selected in phase 1 showed good performance in discriminating patients with NPSLE: the area under the receiver operating characteristic curve using dichotomous outcomes was 0.87 in the training set and 0.82 in the validating set. Relevant cut-offs of the validated score identify events with a positive predictive value of 100% (95% CI 93.2, 100) and 86.3% (95% CI 76.2, 93.2) in the training and validating cohorts, respectively. Conclusion. A new algorithm based on a probability score was developed and validated to determine the relationship between NP events and SLE. [ABSTRACT FROM AUTHOR]

Background Administrative databases have become an important tool to monitor diseases. Patients with epilepsy could be traced using disease-specific codes and prescriptions, but formal validation is required to obtain an accurate case definition. The aim of the study was to correlate administrative data on epilepsy with an independent source of patients with epilepsy in a district of Lombardy, Northern Italy, from 2000 to 2008. Methods Data of nearly 320 600 inhabitants in the district of Lecco collected from the Drug Administrative Database of the Lombardy Region were analysed. Among them were included patients who fulfilled the International Classification of Diseases 9 (ICD-9) codes and/or the disease-specific exemption code for epilepsy and those who had at least one EEG record and took antiepileptic drugs (AEDs) as monotherapy or in variable combinations. To ascertain epilepsy cases, 11 general practitioners (GPs) with 15 728 affiliates were contacted. Multiple versions of the diagnostic algorithm were developed using different logistic regression models and all combinations of the four independent variables. Results Among the GP affiliates, 71 (4.5/1000) had a gold standard diagnosis of epilepsy. The best and most conservative algorithm included EEG and selected treatment schedules and identified 61/71 patients with epilepsy (sensitivity 85.9%, CI 76.0% to 92.2%) and 15 623/15 657 patients without epilepsy (specificity 99.8%,CI 99.7% to 99.8%). The positive and negative predictive values were 64.2% and 99.9%. Sensitivity (86.7%) and the positive predictive value (68.4%) increased only slightly when patients with single seizures were included. Conclusions A diagnostic algorithm including EEG and selected treatment schedules is only moderately sensitive for the detection of epilepsy and seizures. These findings apply only to the Northern Italian scenario. [ABSTRACT FROM AUTHOR]

Aims To explore the potentiality of cardiovascular magnetic resonance (CMR) in the quantitative evaluation of mitral valve annulus (MVA) and tricuspid valve annulus (TVA) morphology and dynamics. Methods and results CMR was performed in 13 normal subjects and 9 patients with mitral (n = 7) or tricuspid regurgitation (n = 2), acquiring cine-images in 18 radial long-axis planes passing through the middle of MVA or TVA. A novel algorithm was used to obtain dynamic three-dimensional (3D) reconstruction of MVA and TVA. Analysis was feasible in all cases, allowing accurate 3D annular reconstruction and tracking. The 3D area increased from systole [MVA, median = 10.0 cm2 (first quartile = 8.6, third quartile = 11.4); TVA, 11.2 cm2 (8.8–13.2)] to diastole [MVA, 10.6 cm2 (9.4, 11.7); TVA, 11.9 cm2 (9.2–13.5)], with TVA larger than MVA. While the longest diameter showed similar systolic and diastolic values, the shortest diameter elongated from systole [MVA, 30 mm (29–33); TVA, 33 mm (31–36)] to diastole [MVA, 31 mm (29–32); TVA, 36 mm (33–39)]. Also, TVA became more circular than MVA. TVA showed lower peak systolic excursion in the septal [15.9 mm (13.0–18.5)] and anterior regions [17.9 mm (12.2–20.7)] compared with the posterior [21.9 mm (18.6–24.0)] segment. Values in MVA were smaller than in TVA, slightly higher in anterior [11.2 mm (9.5–13.0)] than in posterior [12.4 mm (10.2–14.6)] segments. Valvular regurgitation was associated with enlarged, flattened, and more circular annuli. Conclusion The applied method was feasible and accurate in normal and regurgitant valves, and may potentially have an impact on diagnosis, improvement of surgical techniques and design of annular prostheses. [ABSTRACT FROM AUTHOR]