Severity in schizophrenia patients receiving atypical antipsychotic medications

Atypical antipsychotic drugs are nowadays the mainstay of treatment of schizophrenia due to their lesser extrapyramidal symptoms (EPS) as adverse effects. However, these drugs have different profiles of adverse drug reactions (ADRs). Here, the objective of this study was to analyze the probability, occurrences, and more significant involvement of various risk factors. A prospective observational study was carried out on a patient with schizophrenia who has prescribed atypical antipsychotic drugs for their treatment. The probability of the ADR was analyzed by using the Naranjo causality assessment scale. While Glasgow antipsychotic Side effect Scale (GASS) was used to estimate the severity of side effects. Statistical software for social science (SPSS) ver 25;was used for different descriptive statistics and chi-square analysis. A total of 140 patients were included in the study of which the majority (58.57 %) was male. However, atypical antipsychotic drugs were primarily prescribed to the patient as mono therapy (81.43 %). Interestingly, COVID-19 infections were reported as positive in 39.29 % of total patients. Probability assessment of ADRs revealed that most (55 %) were "Probable". Subsequently, the GASS score was evaluated for severity, the majority (55.71 %) were reported as "Mild". The statistically significant association between gender and severity of side effects &duration of illness and severity of side effects were found (P>0.5).The Present study aids in knowing the risk factors and improving the management practices of ADR, thereby improving the guidelines in terms of safe clinical approaches for psychiatric patients.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers.

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.