Introduction The COVID-19 pandemic significantly reshaped involuntary psychiatric hospitalizations, disrupting the balance between patient rights, public safety, and healthcare delivery. This study aims to examine the pandemic’s impact on involuntary admissions (IA) from a major psychiatric hospital in Sibiu Romania. Furthermore, it proposes a prediction model for informed consent refusal rates (ICRR). Materials and methods We conducted a retrospective, observational analysis of 781 involuntary admissions using records by comparing socio-demographic, clinical, and procedural variables across two periods: pre-pandemic (March 2018–February 2020) and during the pandemic (March 2020–March 2022). Variables analyzed included demographics, clinical symptoms, procedural circumstances, and hospitalization duration with Chi-Square, Cochran–Mantel–Haenszel (CMH), Breslow-Day, Cramer’s V tests and logistic regression model applied as appropriate. Results Psychomotor agitation, aggression, and suicidal behavior were leading reasons for involuntary admission. Confirmation rates were significantly higher among non-aggressive patients ( p 0.0001). Schizophrenia spectrum disorders were predominant diagnoses, with significantly higher confirmation rates during the pandemic ( p 0.0001). Police-initiated admissions increased significantly, while family-initiated admissions significantly declined ( p 0.001). Other consistently significant predictors included insurance status, marital status, residence type, psychotic symptoms, psychiatric comorbidities, and the source initiating the involuntary admission request (all CMH tests p ≤ 0.002). Logistic regression modeling demonstrated strong predictive performance (AUC = 0.807, accuracy = 80.7%), identifying education level, alcohol consumption, psychoactive substance use, and police involvement as significant predictors of ICRR. Conclusion The pandemic introduced significant procedural and management challenges to involuntary admissions at a tertiary hospital in Romania. Our predictive modeling highlights key factors influencing hospitalization outcomes, underscoring the critical need for streamlined ethical and procedural frameworks, strengthened multidisciplinary collaboration, and the integration of machine learning methodologies to enhance predictive accuracy and clinical decision-making in future public health crises.
Băcilă et al. (Mon,) studied this question.