Risk factors associated with readmissions of patients with severe mental disorders under treatment with antipsychotics

Predictors of Readmission in Young Adults with First-Episode Psychosis: A Multicentric Retrospective Study with a 12-Month Follow-Up

BACKGROUND

A significant number of young individuals are readmitted one or more times shortly after their first episode of psychosis. Readmission may represent a marker of psychopathological vulnerability. Our primary aim was to evaluate the impact of clinical and socio-demographic variables on readmission at 12-month follow-up. Secondly, our goal was to determine whether the use of Long-Acting Injection (LAI) antipsychotics provides notable benefits compared to oral medications in preventing subsequent readmissions.

SUBJECTS AND METHODS

80 patients hospitalised for the first time with a diagnosis of psychotic disorder (ICD-10 criteria) were retrospectively assessed through clinical records. The mean age was 21.7 years. Patients were predominantly male (n = 62, 77.5%), and 55 subjects had at least 8 years of education. 50% of the sample was "NEET" (not in education, employment, or training).

RESULTS

35 patients (43.8%) were discharged with a LAI antipsychotic, while 45 (56.2%) recieved oral antipsychotic therapy. Substance use (p = 0.04) and oral antipsychotics at discharge (p = 0.003) were significantly associated with readmission at 1 year. We did not find any significant predictors of being discharged with LAI therapy.

CONCLUSION

Our findings underlined the importance of identifying patients at risk of readmission in order to prevent future rehospitalization and promote appropriate prevention strategies. LAIs should be considered as a first-choice treatment for patients hospitalised for FEP since they proved to be effective in preventing relapse.

Comparative effectiveness of clozapine and non-clozapine atypical antipsychotics provided by the Brazilian National Health System in adults with schizophrenia

Introduction

Currently, 21 million people live with the disease, mostly in low to middle-income countries. We aimed to assess the survival of patients with schizophrenia using clozapine compared with non-clozapine atypical antipsychotics provided by the Brazilian National Health System using real-world data.

Materials and methods

This is an open retrospective cohort study of patients diagnosed with schizophrenia to whom atypical antipsychotics were dispensed by the Brazilian National Health System between 2000 and 2015, based on deterministic-probabilistic pairing of administrative data records. The Kaplan-Meier method was used to estimate the cumulative probability of survival and the Cox proportional hazards model was adjusted to assess the risk factors for survival via the hazard ratio (HR).

Result

Participants were 375,352 adults with schizophrenia, with an overall survival rate of 76.0% (95%CI 75.0-76.0) at the end of the cohort. Multivariate analysis indicated a greater risk of death for men (HR=1.30; 95%CI 1.27-1.32), older adults (HR=17.05; 95%CI 16.52-17.60), and in the Southeast region of Brazil (HR=1.20; 95%CI 1.17-1.23). Patients who used non-clozapine atypical antipsychotics had a 21% greater risk of death when compared to those taking clozapine (HR=1.21; 95%CI 1.14-1.29). Additionally, a history of hospitalization for pneumonia (HR=2.17; 95%CI 2.11-2.23) was the main clinical variable associated with increased risk of death, followed by hospitalization for lung cancer (HR=1.82; 95%CI 1.58-2.08), cardiovascular diseases (HR=1.44; 95%CI 1.40-1.49) and any type of neoplasia (HR=1.29; 95%CI 1.19-1.40).

Discussion

This is the first published Brazilian cohort study that evaluated survival in people with schizophrenia, highlighting the impact of atypical antipsychotics. In this real-world analysis, the use of clozapine had a protective effect on survival when compared to olanzapine, risperidone, quetiapine, and ziprasidone.

Enhancing psychiatric rehabilitation outcomes through a multimodal multitask learning model based on BERT and TabNet: An approach for personalized treatment and improved decision-making

Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI. BERT processes unstructured textual data, such as doctor's notes, whereas TabNet manages structured diagnostic information. The model's interpretability function serves to assist healthcare professionals in understanding the model's predictive decisions, improving patient care. Our model exhibited excellent predictive performance for all four tasks, with an accuracy exceeding 0.78 and an area under the curve of 0.70. In addition, a series of tests proved the model's robustness, fairness, and interpretability. This study combines multimodal and multitask learning strategies into a model and applies it to rehabilitation assessment tasks, offering a promising new tool that can be seamlessly integrated with the clinical workflow to support the provision of optimized patient care.

Efficacy of Long-Acting Injectable Antipsychotics Versus Oral Antipsychotics in Preventing Psychiatric Rehospitalizations

PURPOSE/BACKGROUND

Schizophrenia is a chronic, debilitating mental illness that incurs a large economic burden. Decreasing hospital readmissions is a priority in health care to improve patient quality of life and decrease health care costs. Determining ways to prevent readmissions such as improving access to long-acting injectable (LAI) antipsychotics is important to assess.

METHODS/PROCEDURES

A single-center retrospective review was conducted comparing readmission rates of patients diagnosed with schizophrenia or schizoaffective disorder discharged on LAI or oral antipsychotics between August 1, 2019, and June 30, 2022. The primary outcome was the 30-day psychiatric readmission rate. Secondary outcomes included chlorpromazine equivalent doses and use of anticholinergic medications.

FINDINGS/RESULTS

The 30-day readmission rate was 1.9% for the LAI antipsychotic group and 8.3% for the oral antipsychotic group ( P = 0.03; 95% confidence interval, 1.05-20.02). The average chlorpromazine equivalent antipsychotic dose of patients discharged on LAI versus oral antipsychotic medications was 477.3 and 278.6 mg/d, respectively ( P < 0.001). In addition, the prevalence of medications used to treat extrapyramidal symptom was 22.3% (n = 23) for the LAI antipsychotic group and 30.8% (n = 74) for the oral antipsychotic group ( P = 0.12). Sixty-four percent of LAI antipsychotics utilized were obtained from pharmaceutical company hospital inpatient free trial programs.

IMPLICATIONS/CONCLUSIONS

Long-acting injectable antipsychotics showed a statistically significant reduction in 30-day rehospitalizations as compared with oral antipsychotics and hospital inpatient free trial programs aided in LAI antipsychotic acquisition.

Influencing factors of medication adherence in schizophrenic patients: a meta-analysis

Medication adherence of schizophrenic patients is a growing public health problem. We conducted a meta-analysis on the influencing factors of medication compliance in schizophrenic patients. We searched PubMed, Embase, Cochrane Library, and Web Of Science for relevant articles published up to December 22, 2022. Combined odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess influencing factors. Egger's test, funnel plot, the trim and fill method, and meta-regression analysis were used to assess publication bias. A total of 20 articles were included in the analysis. Twenty influencing factors were divided into seven categories: drug factors (OR = 1.96, 95% CI: 1.48-2.59), problem behavior (OR = 1.77, 95% CI: 1.43-2.19), income and quality of life (OR = 1.23, 95% CI: 1.08-1.39), personal characteristics (OR = 1.21, 95% CI: 1.14-1.30), disease factors (OR = 1.14, 95% CI: 1.98-1.21), support level (OR = 0.54, 95% CI: 0.42-0.70), and positive attitude and behavior (OR = 0.52, 95% CI: 0.45-0.62). This meta-analysis found that drug factors, disease factors, problem behavior, low income and quality of life, and factors related to personal characteristics appear to be risk factors for medication adherence in people with schizophrenia. And support level, positive attitude and behavior appear to be protective factors.