A Computational Approach in the Diagnostic Process of COVID-19: The Missing Link between the Laboratory and Emergency Department.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the right evaluation of viral infection. According to the Centers for Disease Control and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in respiratory samples is the gold standard for confirming the disease. However, it has practical limitations as time-consuming procedures and a high rate of false-negative results. We aim to assess the accuracy of COVID-19 classifiers based on Arificial Intelligence (AI) and statistical classification methods adapted on blood tests and other information routinely collected at the Emergency Departments (EDs). METHODS: Patients admitted to the ED of Careggi Hospital from April 7th-30th 2020 with pre-specified features of suspected COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19 likely/unlikely case, based on clinical features and bedside imaging support. Considering the limits of each method to identify a case of COVID-19, further evaluation was performed after an independent clinical review of 30-day follow-up data. Using this as a gold standard, several classifiers were implemented: Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor (K-NN), Naive Bayes (NB). RESULTS: Most of the classifiers show a ROC >0.80 on both internal and external validation samples but the best results are obtained applying RF, LR and NN. The performance from the external validation sustains the proof of concept to use such mathematical models fast, robust and efficient for a first identification of COVID-19 positive patients. These tools may constitute both a bedside support while waiting for RT-PCR results, and a tool to point to a deeper investigation, by identifying which patients are more likely to develop into positive cases within 7 days. CONCLUSIONS: Considering the obtained results and with a rapidly changing virus, we believe that data processing automated procedures may provide a valid support to the physicians facing the decision to classify a patient as a COVID-19 case or not.

Diagnostic accuracy of clinical gestalt of doctors with different experiences in COVID-19 suspected patients

This study aims to evaluate the ability of physicians' predictions to predict mortality in COVID-19 patients and compare physician predictions with scores developed for COVID-19 patients in predicting mortality and patient worsening. This study was conducted prospectively in the emergency department. Patient data were collected between 20.03.2021 and 20.06.2021. Patients who applied to our hospital with COVID-19 symptoms and were confirmed to be COVID-19 by rt-PCR results were included in our study. Patients aged 18 years and over who were tr-PCR positive were included in the study. Quick COVID-19 Severity Index (qCSI), Brescia-COVID Respiratory Severity Scale (BCRSS), and CURB-65 scale were calculated and recorded by a researcher. A total of 176 patients were included in our study. There was no significant relationship between physicians' gestalt and 28-day mortality (p=0.121, p=0.282, Mann-Whitney U Test, respectively). Physicians' gestalt was found to be insufficient to predict mortality in COVID-19 patients. There was a significant difference between the CURB-65 short-term mortality group and the survivors. Copyright © 2022 Ondokuz Mayis Universitesi. All rights reserved.

A Single-Case Design Investigation for Measuring the Efficacy of Gestalt Therapy to Treat Depression in Older Adults with Dementia in Italy and in Mexico: A Research Protocol.

Psychotherapy is one of the evidence-based clinical interventions for the treatment of depression in older adults with dementia. Randomised controlled trials are often the first methodological choice to gain evidence, yet they are not applicable to a wide range of humanistic psychotherapies. Amongst all, the efficacy of the Gestalt therapy (GT) is under-investigated. The purpose of this paper is to present a research protocol, aiming to assess the effects of a GT-based intervention on people with dementia (PWD) and indirect influence on their family carers. The study implements the single-case experimental design with time series analysis that will be carried out in Italy and Mexico. Six people in each country, who received a diagnosis of dementia and present depressive symptoms, will be recruited. Eight or more GT sessions will be provided, whose fidelity will be assessed by the GT fidelity scale. Quantitative outcome measures are foreseen for monitoring participants' depression, anxiety, quality of life, loneliness, carers' burden, and the caregiving dyad mutuality at baseline and follow-up. The advantages and limitations of the research design are considered. If GT will effectively result in the treatment of depression in PWD, it could enrich the range of evidence-based interventions provided by healthcare services.

A computational approach in the diagnostic process of COVID-19. Synergy between the laboratory and emergency department

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic.According to the CDC, RT-PCR in respiratory samples is the gold standard for confirming the disease, although it has practical limitations as time-consuming procedures and a high rate of false-negative results. Based on data collected at Careggi Hospital from April 7th-30th 2020,we aim to assess the accuracy of a COVID-19 diagnosis through classification methods based on blood tests and information collected at the ED. 971 pts with pre-specified features of suspected COVID-19 were enrolled;physicians prospectively dichotomized patients in COVID-19 likely/unlikely based on clinical features plus results of bedside imaging.Considering the limits of each method to classify a case COVID-19 positive, further evaluation was performed to form the COVID-19 final diagnosis, established after independent clinical review of 30-day follow-up data. Several classifiers were implemented, both parametric (Logistic Regression, LR;Quadratic Discriminant Analysis, QDA) and non-parametric (Random Forest, RF;Support Vector Machine;Neural Networks;K-nearest neighbour;Naive Bayes). Log transform was applied to some of the covariates and results compared with non transformed data.The dataset was divided in training and validation sets.Results based on validation sample show an AUC>0.8 for all classifiers. Best results are obtained applying RF, LR and QDA to a rebalanced sample using the SMOTE techniques on the log transformed data, showing an AUC of 0.890 (LR),0.896 (QDA) and 0.864 (RF). In parallel, best Sens and Spec are obtained via the above methods, the highest chieved by the LR (Sens 0.696;Spec 0.877). The rather high rate of false negative seems to be a feature inherently characterizing this classification problem.Good discriminatory power was shown for: WBC, Neut, AST, LDH, PCR, Na, IL-6 plus symptoms' information. Parametric models have the additional advantage of allowing a scientific interpretation.The performance of the classifiers with respect to the physician's gestalt and data validation are ongoing. The proposed classifiers show a good level of Sens.To improve Spec, a 3-level classification can be implemented;this tool can help in taking decisions when time and resources are scarce.

Reflections on "Dialogues on psychotherapy in the time of coronavirus": An international webinar hosted by Margherita Spagnuolo Lobb and the Istituto Gestalt HCC Italy

Presents a reflection on "Dialogues on psychotherapy in the time of coronavirus": An international webinar hosted by Margherita Spagnuolo Lobb and the Istituto Gestalt HCC Italy. The webinar was both impressive and daunting: 880 people were able to feel well connected over continents, physical and virtual spaces, time zones, and multiple languages as they listened to, and interacted with, a cadre of stellar psychologists and Gestalt psychotherapists. These specialists talked about their experiences of the coronavirus pandemic and suggested aspects of (Gestalt) therapy that might help discover ways for this unprecedented event of global trauma to be an opportunity for growth. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Responses to "Working with the body in mind": Workshop led by Ann Carr and Archie Roberts, Gestalt International Study Center, April 30, 2020

The author reflects on the experience of adjusting to new routines and ways of practicing three months deep into COVID-19 adaptations. The author also rreflects on the broader cultural context and societal issues such as racial discrimination that continues to affect the Asian community and the Black community. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Challenges in Developing 'Insight Learning' in the Virtual Learning Environment with Special Reference to Gestalt Theory of Perception

A paradigm shift has altered our world in practically every sphere and it can be attributed to the Covid-19 pandemic. Modern science and technologies have been called upon to redesign various processes in order to cope with this outbreak and the changes it has wrought. Even the world of education has been subject to severe disruption. To deal with the new situation, the students and teachers have been forced to change the way they customarily taught, learnt and interacted. The entire educational framework will have to be transformed from the traditional face-to-face or physical system to the virtual Moodle education system. The entire school system and higher education system in Sri Lanka are now called upon to confront these new challenges if they are to continue the learning and teaching processes. Fortunately, the higher education institutions have managed to find a quick remedy, by moving to online learning to overcome the difficult situation. Although many learning theories have been introduced to evaluate the learning processes in the physical classroom, the Gestalt learning theory is slightly different from the other theories. This is because the Gestalt theorists emphasize the need for whole perception, which can only be achieved by focusing on the entire learning process and making sense of things by thinking of them deeply. The process of thinking involved selecting, organizing, interpreting and creating meaning. This processing method is called 'insight learning'. Hence, this study is an attempt to assess the feasibility of developing insight learning in virtual learning. The data was collected through Google forms, and the conclusion is based on the more than 700 responses received from undergraduate students in the Faculty of Arts and Culture, Eastern University, Sri Lanka. The SPSS software was used to record and analyze data using factor analysis and descriptive statistics. The study shows that there are many factors that prove to be obstacles in the way of developing insight learning via an online learning system. Therefore, the study recommends some strategies to overcome these obstacles and adopt the new online learning process in the future. © 2022 Somasundaram Jeganathan and Thanigaivelan Shanmugam.

Diagnostic accuracy of physician's gestalt in suspected COVID-19: Prospective bicentric study.

OBJECTIVES: Physicians' gestalt is central in the diagnostic pipeline of suspected COVID-19, due to the absence of a single tool allowing conclusive rule in or rule out. The aim of this study was to estimate the diagnostic test characteristics of physician's gestalt for COVID-19 in the emergency department (ED), based on clinical findings or on a combination of clinical findings and bedside imaging results. METHODS: From April 1 to April 30, 2020, patients with suspected COVID-19 were prospectively enrolled in two EDs. Physicians prospectively dichotomized patients in COVID-19 likely or unlikely twice: after medical evaluation of clinical features (clinical gestalt [CG]) and after evaluation of clinical features and results of lung ultrasound or chest x-ray (clinical and bedside imaging-integrated gestalt [CBIIG]). The final diagnosis was adjudicated after independent review of 30-day follow-up data. RESULTS: Among 838 ED enrolled patients, 193 (23%) were finally diagnosed with COVID-19. The area under the curve (AUC), sensitivity, and specificity of CG and CBIIG for COVID-19 were 80.8% and 91.6% (p < 0.01), 82.9% and 91.4% (p = 0.01), and 78.6% and 91.8% (p < 0.01), respectively. CBIIG had similar AUC and sensitivity to reverse transcription-polymerase chain reaction (RT-PCR) for SARS-CoV-2 on the first nasopharyngeal swab per se (93.5%, p = 0.24; and 87%, p = 0.17, respectively). CBIIG plus RT-PCR had a sensitivity of 98.4% for COVID-19 (p < 0.01 vs. RT-PCR alone) compared to 95.9% for CG plus RT-PCR (p = 0.05). CONCLUSIONS: In suspected COVID-19, CG and CBIIG have fair diagnostic accuracy, in line with physicians' gestalt for other acute conditions. Negative RT-PCR plus low probability based on CBIIG can rule out COVID-19 with a relatively low number of false-negative cases.