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Besana F, Civardi SC, Mazzoni F, Carnevale Miacca G, Arienti V, Rocchetti M, Politi P, Martiadis V, Brondino N, Olivola M. Predictors of Readmission in Young Adults with First-Episode Psychosis: A Multicentric Retrospective Study with a 12-Month Follow-Up. Clin Pract 2024; 14:1234-1244. [PMID: 39051293 PMCID: PMC11270315 DOI: 10.3390/clinpract14040099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 06/10/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
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.
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Affiliation(s)
- Filippo Besana
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
| | - Serena Chiara Civardi
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
| | - Filippo Mazzoni
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
| | - Giovanni Carnevale Miacca
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
| | - Vincenzo Arienti
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
| | - Matteo Rocchetti
- Department of Mental Health and Addiction, ASST Pavia, 27100 Pavia, Italy;
| | - Pierluigi Politi
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
- Department of Mental Health and Addiction, ASST Pavia, 27100 Pavia, Italy;
| | | | - Natascia Brondino
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
- Department of Mental Health and Addiction, ASST Pavia, 27100 Pavia, Italy;
| | - Miriam Olivola
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy; (S.C.C.); (F.M.); (G.C.M.); (V.A.); (P.P.); (N.B.); (M.O.)
- Department of Mental Health and Addiction, ASST Pavia, 27100 Pavia, Italy;
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Vieira JCM, Reis EA, Guerra AA, de Oliveira HN, Ruas CM. Comparative effectiveness of clozapine and non-clozapine atypical antipsychotics provided by the Brazilian National Health System in adults with schizophrenia. Front Psychiatry 2024; 15:1421501. [PMID: 38962057 PMCID: PMC11220267 DOI: 10.3389/fpsyt.2024.1421501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/04/2024] [Indexed: 07/05/2024] Open
Abstract
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.
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Affiliation(s)
- Júlio César Menezes Vieira
- Program in Medications and Pharmaceutical Care – PPGMAF, School of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Edna Afonso Reis
- Department of Statistics, Institute of Exact Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Augusto Afonso Guerra
- Brazilian National Health System’s Collaborating Centre for Technology Assessment and Excellence in Health (CCATES), School of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
- Department of Social Pharmacy, School of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Helian Nunes de Oliveira
- Department of Social and Preventive Medicine, School of Medicine, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Cristina Mariano Ruas
- Brazilian National Health System’s Collaborating Centre for Technology Assessment and Excellence in Health (CCATES), School of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
- Department of Social Pharmacy, School of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
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Yang H, Zhu D, He S, Xu Z, Liu Z, Zhang W, Cai J. Enhancing psychiatric rehabilitation outcomes through a multimodal multitask learning model based on BERT and TabNet: An approach for personalized treatment and improved decision-making. Psychiatry Res 2024; 336:115896. [PMID: 38626625 DOI: 10.1016/j.psychres.2024.115896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/18/2024]
Abstract
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.
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Affiliation(s)
- Hongyi Yang
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Dian Zhu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Siyuan He
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiqi Xu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Zhao Liu
- School of Design, Shanghai Jiao Tong University, Shanghai, China.
| | - Weibo Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
| | - Jun Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
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Thaman P, Kulig CE, Greer D. Efficacy of Long-Acting Injectable Antipsychotics Versus Oral Antipsychotics in Preventing Psychiatric Rehospitalizations. J Clin Psychopharmacol 2024; 44:96-99. [PMID: 38227617 DOI: 10.1097/jcp.0000000000001810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
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.
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Affiliation(s)
| | - Caitlin E Kulig
- Rutgers University Ernest Mario School of Pharmacy, Piscataway, NJ
| | - Daniel Greer
- Rutgers University Ernest Mario School of Pharmacy, Piscataway, NJ
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Guo J, Lv X, Liu Y, Kong L, Qu H, Yue W. Influencing factors of medication adherence in schizophrenic patients: a meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:31. [PMID: 37188714 DOI: 10.1038/s41537-023-00356-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023]
Abstract
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.
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Affiliation(s)
- Jing Guo
- Department of Psychology, Medical Humanities Research Center, Binzhou Medical University, Yantai, 264003, China
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Xue Lv
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
- The First Affiliated Hospital of Xinxiang Medical College, Xinxiang, Henan, 453100, China
| | - Yan Liu
- Department of Psychology, Medical Humanities Research Center, Binzhou Medical University, Yantai, 264003, China
| | - Lingling Kong
- Department of Psychology, Medical Humanities Research Center, Binzhou Medical University, Yantai, 264003, China
| | - Haiying Qu
- Department of Psychology, Medical Humanities Research Center, Binzhou Medical University, Yantai, 264003, China.
| | - Weihua Yue
- Department of Psychology, Medical Humanities Research Center, Binzhou Medical University, Yantai, 264003, China.
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
- The First Affiliated Hospital of Xinxiang Medical College, Xinxiang, Henan, 453100, China.
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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Góngora Alonso S, Herrera Montano I, Ayala JLM, Rodrigues JJPC, Franco-Martín M, de la Torre Díez I. Machine Learning Models to Predict Readmission Risk of Patients with Schizophrenia in a Spanish Region. Int J Ment Health Addict 2023. [DOI: 10.1007/s11469-022-01001-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
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