Neurological comorbidity and severity of COVID-19

Objective Neurological symptoms of COVID-19 patients have been recently described. However, no comprehensive data have been reported on pre-existing neurological comorbidities and COVID-19. This study aims at evaluating the prevalence of neurological comorbidities, and their association with COVID-19 severity. Methods We evaluated all consecutive patients admitted to the Emergency Room (ER) of our hospital between the 3rd March and the 14th April 2020, and diagnosed with COVID-19. Data on neurological and non-neurological diseases were extracted, as well as data on demographic characteristics and on severity degree of COVID-19. The prevalence of neurological comorbidities was calculated, and multivariate binary logistic regression analyses were used to estimate the association between neurological diseases and COVID-19 severity. Results We included 344 patients. Neurological comorbidities accounted for 22.4% of cases, with cerebrovascular diseases and cognitive impairment being the most frequent. Neurological comorbidity resulted independently associated with severe COVID-19 (OR 2.305; p = 0.012), as well as male gender (p = 0.001), older age (p = 0.001), neoplastic diseases (p = 0.039), and arterial hypertension (p = 0.045). When neurological comorbidity was associated with non-neurological comorbidities, the OR for severe COVID-19 rose to 7.394 (p = 0.005). Neurological patients, in particular cerebrovascular and cognitively impaired ones, received more respiratory support indication. Conclusion Neurological comorbidities represent a significant determinant of COVID-19 severity, deserving a thorough evaluation since the earliest phases of infection. The vulnerability of patients affected by neurological diseases should suggest a greater attention in targeting this population for proactive viral screening

SPINE SURGERY AFTER THE COVID-19 EMERGENCY: AN ALGORITHM FOR MANAGEMENT OF ELECTIVE SURGICAL CASES.: Spine surgery during Covid-19.

Introduction During the COVID-19 pandemic emergency, all non-urgent surgical procedures including elective spine surgery have not been performed during the emergency period. Now many countries have passed over the epidemic peak and the time to organize re-opening of non-essential activities has come. After the emergency phase of the COVID-19 pandemic, the viral outbreak is supposed to reduce but will not reasonably disappear until a vaccine is available. Resuming elective spine surgery while ensuring safety for patients and healthcare workers has become an issue of critical importance. We propose a simple algorithm with the aim to help worldwide spine surgeons in management of elective spine surgery cases after the COVID-19 emergency ensuring safety for patients and healthcare workers   Methods An expert panel composed by Spine Surgeons, Neurosurgeons, Anesthesiologists and Intensivists with direct experience in COVID-19 management developed an algorithm for management of elective spine surgery based on evidence-based indications. The algorithm has been used for management of hospital admissions of undelayable spine surgery cases during the COVID-19 emergency period. Data regarding COVID-19 nosocomial transmission on patients and healthcare workershave been retrospectively reviewed and reported.   Results Hospital admissions of 159 patients have been managed according to the proposed algorithm. Since the application of the protocol, we have not reported COVID-19 nosocomial transmission in our department.   Conclusions According to our preliminary results, we think that the proposed algorithm may successfully help management of spinal elective surgical patients in the post-COVID-19 emergency era, avoiding unnecessary risks for patients and healthcare workers

ROmA Loop Pairing Criteria for Multivariable Processes

An important aspect in the control of multivariable (MIMO) systems is the pairing of the input and output variables for decentralized control. The relative gain array (RGA) introduced in 1966 by Bristol gives a quantitative measure of static interaction and is a simple technique widely applied by process engineers. Many improved approaches, RGA-like, have been proposed for defining different measures of dynamic loop interactions. However, none of these techniques is generally accepted as a globally satisfactory solution by process engineers. In this paper an alternative approach is presented: a global index, called Relative Omega Array (ROmA), is proposed. In single-input single-output systems the critical frequency remains unchanged in the passage from open loop to closed loop. This property holds also for multi-input multi-output systems, whenever perfect decoupling occurs. The key concept in ROmA index is to capture information from critical frequencies variation in the passage from open loop to closed loop, for measuring interactions in multi-input multi-output systems. This method retains the characteristics of simplicity of RGA but include additional information about dynamic interactions. Examples are given in order to show the effectiveness of the ROmA index in critical cases, where incorrect loop pairings is suggested by RGA. It is also shown how to combine RGA and ROmA in a relative performance array, similar to the effective RGA (ERGA) recently introduced

Intelligent Variable Structure Control in Electrical Drives

The variable structure approach to control synthesis leads to a systematic desugn technique realizing strong stability characteristics and high speed of response by means of simple control laws. Usually sliding modtion occurs so that the closed loop system becomes less sensitive to parameter variations and noise disturbances. These advantages are contrasted by some undesired effects due to high gain subsystems; typically chattering and control discontinuity prevent us from the implementation. In order to overcome these difficulties the variable structure control has to be modified giving rise to intelligent variable structure systems. A successful approach is obtained by replacing ideal relays by high gain elements and by fading. The dynamic performance improvements are pointed out on a high performance speed control system with PI nonlinear controllers for d.c. or brushless electrical drives

Sulla Selezione delle Variabili in un Sistema Multivariabile: da RGA a ROmA

Un problema fondamentale nel controllo di un sistema multivariabile è la scelta preliminare dell’accoppiamento fra le variabili di controllo e di uscita. La tecnica più usata è la RGA, proposta da Bristol nel 1966; tale tecnica presenta notevoli limitazioni, per cui sono tate proposte numerose modifiche. Tuttavia nessuna tecnica tra quelle proposte presenta caratteristiche globalmente soddisfacenti. In questo lavoro viene presentato un approccio alternativo, che consente di considerare la dinamica del sistema in esame con semplici indicatori globali, mantenendo le caratteristiche di semplicità di uso tipiche della RGA. Le informazioni sulla dinamica dell’impianto necessarie per la costruzione del nuovo indice, denominato ROmA, possono essere rilevate con l’uso di procedure di autoscillazione, mediante classica inserzione di relé. Vengono infine presentati alcuni casi tipici, che mostrano i vantaggi dell’approccio proposto

Stable Deadbeat Controllers Design with Constrained Input

This paper presents a design strategy for stable deadbeat controllers. Inequality bounds on the magnitude of the output response and of the controller outputs are considered. The proposed method is based on the application of classic optimization algorithms used for solving the quadratic programming problem with additional constraints. The software tool developed for controller design is based on the selection of only two weight factors for achieving design specifications. Numerical examples are presented to illustrate the effectiveness of the proposed method

Parameter identification of continuous systems with multiple input time delays via modulating functions

The identification of continuous systems with multiple-input delays is discussed. A batch algorithm for parametric identification of both multiple time delays and unknown parameters is proposed, using the modulating function approach. If the unknown delay is expressed as a linear combination of a bank of known delays, it is proven that the distribution of the identified coefficients is a sampling centred on the unknown delays; the result is then extended to the multiple delay case. The modulating function method guarantees a continuous-time approach and robustness for a high value of noise-to-signal ratio. Simulation results are included to illustrate the proposed technique