Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring-in a very short time-of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010-2020 at the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R2 value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R2 values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann-Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions)

Endocannabinoid-related compounds in gastrointestinal diseases

The endocannabinoid system (ECS) is an endogenous signalling pathway involved in the control of several gastrointestinal (GI) functions at both peripheral and central levels. In recent years, it has become apparent that the ECS is pivotal in the regulation of GI motility, secretion and sensitivity, but endocannabinoids (ECs) are also involved in the regulation of intestinal inflammation and mucosal barrier permeability, suggesting their role in the pathophysiology of both functional and organic GI disorders. Genetic studies in patients with irritable bowel syndrome (IBS) or inflammatory bowel disease have indeed shown significant associations with polymorphisms or mutation in genes encoding for cannabinoid receptor or enzyme responsible for their catabolism, respectively. Furthermore, ongoing clinical trials are testing EC agonists/antagonists in the achievement of symptomatic relief from a number of GI symptoms. Despite this evidence, there is a lack of supportive RCTs and relevant data in human beings, and hence, the possible therapeutic application of these compounds is raising ethical, political and economic concerns. More recently, the identification of several EC-like compounds able to modulate ECS function without the typical central side effects of cannabinomimetics has paved the way for emerging peripherally acting drugs. This review summarizes the possible mechanisms linking the ECS to GI disorders and describes the most recent advances in the manipulation of the ECS in the treatment of GI diseases

Stu­dio e programmazione di un sistema di ac­quisizione e control­lo per gallerie del vento

Il seguente elaborato si colloca all'interno dello studio del moto turbolento, in cui è specializzato il laboratorio CICLoPE (Center for International Cooperation in Long Pipe Experiments). Il principale obiettivo è stato creare un programma in grado di misurare la pressione e di controllarne l'acquisizione tramite l'utilizzo di una particolare strumentazione che consentisse di ottenere risultati affetti da incertezze di entità sempre minore. Questa necessità è dovuta al forte legame che ha la caduta di pressione, lungo un condotto, con le grandezze caratteristiche della turbolenza, quali velocità ed attrito, per esempio. Gli strumenti utilizzati finora presentavano errori variabili per ogni punto lungo il pipe, riconducendo il tutto ad un'alta inesattezza nella misurazione della pressione. E' stato dunque necessario sviluppare un programma da adattare ad un nuovo sistema di acquisizione, che comprendesse anche la movimentazione di un connettore circolare (Scanivalve). In questo modo è possibile ottenere dei valori di pressione viziati da un'imprecisione minore ma, soprattutto, costante. Una volta implementato lo schema base del programma sul software LabVIEW sono state individuate diverse ipotesi per effettuare l'acquisizione e per imprimere la rotazione alla Scanivalve. Sono state, quindi, specificate le modalità che potrebbero essere utilizzate al centro CICLoPE per testare il programma in maniera sperimentale. Altre modalità sono state descritte per casi più generali, in modo da poter adattare lo schema in base alle necessità ed alla strumentazione del singolo caso

The interplay of structural pathway and weathering intensity in forming mass-wasting processes in deeply weathered gneissic rocks (Sila Massif, Calabria, Italy)

This paper presents a detailed map (Main Map) showing geology, tectonics, weathering intensity and spatial distribution of landslides in the San Pietro in Guarano study area (about 7.5 km2), located in the north-western sector of Calabria (southern Italy). In this area, deeply weathered high-grade metamorphic rocks and different types/categories of mass movements are widespread. The Main Map, at 1:5000 scale, results from the combination of information gathered via analysis and interpretation of aerial photographs at different times and scales, multi-temporal geostructural and geomorphological surveys, field investigations and mapping of weathering grade in outcrop – through observation of geologically distinctive characteristics and qualitative and semi-quantitative engineering geological tests – integrated by means of the analysis of both weathering profiles on cutslopes and boreholes logs. The Main Map can represent a useful tool for authorities in charge of land-use planning and can profitably concur to typify landslides and to assess quantitative landslide risk

Full integration of geomorphological, geotechnical, A-DInSAR and damage data for detailed geometric-kinematic features of a slow-moving landslide in urban area

AbstractThe reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value

Weathering grade in granitoid rocks: The San Giovanni in Fiore area (Calabria, Italy)

This paper illustrates the methodology and techniques for the compilation of a thematic (engineering) geological map based on detailed mapping of the weathering grade of crystalline rocks occurring in a portion of the Sila Massif close to the San Giovanni in Fiore Village (Calabria, Italy). The map (1:5000 scale), covering an area of about 20 km2, was compiled combining new geological and structural data with the results of a weathering grade field survey. The methodology, used to distinguish and map the weathering grade classes, was performed using qualitative criteria, semi-quantitative tests, and petrographic analysis of weathered rock samples. The Main Map, presented in this paper, aims to provide a useful tool for land-use planning, for geological hazard assessment and engineering perspectives

In Silico Meta-Analysis of Boundary Conditions for Experimental Tests on the Lumbar Spine

The study of the spine range of motion under given external load has been the object of many studies in literature, finalised to a better understanding of the spine biomechanics, its physiology, eventual pathologic conditions and possible rehabilitation strategies. However, the huge amount of experimental work performed so far cannot be straightforwardly analysed due to significant differences among loading set-ups. This work performs a meta-analysis of various boundary conditions in literature, focusing on the flexion/extension behaviour of the lumbar spine. The comparison among range of motions is performed virtually through a validated multibody model. Results clearly illustrated the effect of various boundary conditions which can be met in literature, so justifying differences of biomechanical behaviours reported by authors implementing different set-up: for example, a higher value of the follower load can indeed result in a stiffer behaviour; the application of force producing spurious moments results in an apparently more deformable behaviour, however the respective effects change at various segments along the spine due to its natural curvature. These outcomes are reported not only in qualitative, but also in quantitative terms. The numerical approach here followed to perform the meta-analysis is original and it proved to be effective thanks to the bypass of the natural variability among specimens which might completely or partially hinder the effect of some boundary conditions. In addition, it can provide very complete information since the behaviour of each functional spinal unit can be recorded. On the whole, the work provided an extensive review of lumbar spine loading in flexion/extension

Modelling the length of hospital stay after knee replacement surgery through Machine Learning and Multiple Linear Regression at San Giovanni di Dio e Ruggi daAragonaa University Hospital

Knee arthroplasty is one of the most commonly performed procedures within a hospital. The progressive aging of the population and the spread of clinical conditions such as obesity will lead to an increasing use of this procedure. Therefore, being able to make the process related to this procedure more effective and efficient becomes strategic within hospitals, subject to increasingly stringent clinical and financial pressures. A useful parameter for this purpose is the length of stay (LOS), whose early prediction allows for better bed management and resource allocation, models patient expectations and facilitates discharge planning. In this work, the data of 124 patients who underwent knee surgery in the two-year period 2019-2020 at the San Giovanni di Dio and Ruggi d’Aragona university hospital were studied using multiple linear regression and machine learning algorithms in order to evaluate and predict how patient data affect LOS