4 research outputs found
An artificial neural network approach for modelling the ward atmosphere in a medical unit
Artificial neural networks (ANNs) have been developed, implemented and tested on the basis of a four-year-long experimental data set, with the aim of analyzing the performance and clinical outcome of an existing medical ward, and predicting the effects that possible readjustments and/or interventions on the structure may produce on it. Advantages of the ANN technique over more traditional mathematical models are twofold: on one hand, this approach deals quite naturally with a large number of parameters/variables, and also allows to identify those variables which do not play a crucial role in the system dynamics; on the other hand, the implemented ANN can be more easily used by a staff of non-mathematicians in the unit, as an on-site predictive tool. As such, the ANN model is particularly suitable for the case study. The predictions from the ANN technique are then compared and contrasted with those obtained from a generalized kinetic approach previously proposed and tested by the authors. The comparison on the two case periods shows the ANN predictions to be somewhat closer to the experimental values. However, the mean deviations and the analysis of the statistical coefficients over a span of multiple years suggest the kinetic model to be more reliable in the long run, i.e., its predictions can be considered as acceptable even on periods that are quite far away from the two case periods over which the many parameters of the model had been optimized. The approach under study, referring to paradigms and methods of physical and mathematical models integrated with psychosocial sciences, has good chances of gaining the attention of the scientific community in both areas, and hence of eventually obtaining wider diffusion and generalization.
An artificial neural network approach for modeling the ward atmosphere in a medical unit
Artificial neural networks (ANNs) have been developed, implemented and tested on the basis of a four-year-long experimental data set, with the aim of analyzing the performance and clinical outcome of an existing medical ward, and predicting the effects that possible readjustments and/or interventions on the structure may produce on it. Advantages of the ANN technique over more traditional mathematical models are twofold: on one hand, this approach deals quite naturally with a large number of parameters/variables, and also allows to identify those variables which do not play a crucial role in the system dynamics; on the other hand, the implemented ANN can be more easily used by a staff of non-mathematicians in the unit, as an on-site predictive tool. As such, the ANN model is particularly suitable for the case study. The predictions from the ANN technique are then compared and contrasted with those obtained from a generalized kinetic approach previously proposed and tested by the authors. The comparison on the two case periods shows the ANN predictions to be somewhat closer to the experimental values. However, the mean deviations and the analysis of the statistical coefficients over a span of multiple years suggest the kinetic model to be more reliable in the long run, i.e., its predictions can be considered as acceptable even on periods that are quite far away from the two case periods over which the many parameters of the model had been optimized. The approach under study, referring to paradigms and methods of physical and mathematical models integrated with psychosocial sciences, has good chances of gaining the attention of the scientific community in both areas, and hence of eventually obtaining wider diffusion and generalization
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Metformin and risk of mortality in patients hospitalised with COVID-19: a retrospective cohort analysis
Type 2 diabetes and obesity, as states of chronic inflammation, are risk factors for severe COVID-19. Metformin has cytokine-reducing and sex-specific immunomodulatory effects. Our aim was to identify whether metformin reduced COVID-19-related mortality and whether sex-specific interactions exist.
In this retrospective cohort analysis, we assessed de-identified claims data from UnitedHealth Group (UHG)'s Clinical Discovery Claims Database. Patient data were eligible for inclusion if they were aged 18 years or older; had type 2 diabetes or obesity (defined based on claims); at least 6 months of continuous enrolment in 2019; and admission to hospital for COVID-19 confirmed by PCR, manual chart review by UHG, or reported from the hospital to UHG. The primary outcome was in-hospital mortality from COVID-19. The independent variable of interest was home metformin use, defined as more than 90 days of claims during the year before admission to hospital. Covariates were comorbidities, medications, demographics, and state. Heterogeneity of effect was assessed by sex. For the Cox proportional hazards, censoring was done on the basis of claims made after admission to hospital up to June 7, 2020, with a best outcome approach. Propensity-matched mixed-effects logistic regression was done, stratified by metformin use.
6256 of the 15 380 individuals with pharmacy claims data from Jan 1 to June 7, 2020 were eligible for inclusion. 3302 (52·8%) of 6256 were women. Metformin use was not associated with significantly decreased mortality in the overall sample of men and women by either Cox proportional hazards stratified model (hazard ratio [HR] 0·887 [95% CI 0·782–1·008]) or propensity matching (odds ratio [OR] 0·912 [95% CI 0·777–1·071], p=0·15). Metformin was associated with decreased mortality in women by Cox proportional hazards (HR 0·785, 95% CI 0·650–0·951) and propensity matching (OR 0·759, 95% CI 0·601–0·960, p=0·021). There was no significant reduction in mortality among men (HR 0·957, 95% CI 0·82–1·14; p=0·689 by Cox proportional hazards).
Metformin was significantly associated with reduced mortality in women with obesity or type 2 diabetes who were admitted to hospital for COVID-19. Prospective studies are needed to understand mechanism and causality. If findings are reproducible, metformin could be widely distributed for prevention of COVID-19 mortality, because it is safe and inexpensive.
National Heart, Lung, and Blood Institute; Agency for Healthcare Research and Quality; Patient-Centered Outcomes Research Institute; Minnesota Learning Health System Mentored Training Program, M Health Fairview Institutional Funds; National Center for Advancing Translational Sciences; and National Cancer Institute