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Extrapyramidal symptoms (EPS), including movement disorders, tremors, and muscle contractions are common side effects of atypical antipsychotic (AAP) drugs in patients with schizophrenia. This study examined the incidence and burden of EPS in patients with schizophrenia initiating AAPs.
Patients with schizophrenia initiating AAPs with no prior EPS were identified in the MarketScan Multi-state Medicaid database from 1/1/2012-12/31/2018. Incidence of EPS (identified via ICD-9/ICD-10 diagnoses and medications) was assessed during the 12-months following AAP initiation. Cohorts with and without EPS were defined. Demographics, clinical characteristics, and healthcare resource use and costs over 12 months following the first EPS claim (EPS) or randomly assigned index date (Non-EPS) were assessed.
A total of 11,642 patients with schizophrenia were identified; 21.2% developed EPS in the 12-months following AAP initiation. EPS and Non-EPS cohorts included 2,295 (mean age 38, 61% male, CCI 0.6) and 5,607 (mean age 39, 57% male, CCI 0.7) patients, respectively. Over the 12-month post-index period, EPS cohort had significantly higher rates of all-cause (30.2% vs. 24.6%, p<0.001) and schizophrenia-related hospitalizations (22.5% vs. 12.9%, p<0.001) and schizophrenia-related emergency room visits (25.5% vs. 16.7%, p<0.001) compared to Non-EPS cohort. All-cause ($25,911 vs. $21,550, p<0.001) and schizophrenia-related healthcare costs ($12,134 vs. $6,230, p<0.001) were significantly higher in EPS vs. Non-EPS cohort.
In the 12 months following AAP initiation, over 20% of schizophrenia patients developed EPS, which was associated with increased healthcare resource utilization and costs. Treatment options that minimize EPS may reduce the economic burden of schizophrenia.
In the past decade, network analysis (NA) has been applied to psychopathology to quantify complex symptom relationships. This statistical technique has demonstrated much promise, as it provides researchers the ability to identify relationships across many symptoms in one model and can identify central symptoms that may predict important clinical outcomes. However, network models are highly influenced by node selection, which could limit the generalizability of findings. The current study (N = 6850) tests a comprehensive, cognitive–behavioral model of eating-disorder symptoms using items from two, widely used measures (Eating Disorder Examination Questionnaire and Eating Pathology Symptoms Inventory).
We used NA to identify central symptoms and compared networks across the duration of illness (DOI), as chronicity is one of the only known predictors of poor outcome in eating disorders (EDs).
Our results suggest that eating when not hungry and feeling fat were the most central symptoms across groups. There were no significant differences in network structure across DOI, meaning the connections between symptoms remained relatively consistent. However, differences emerged in central symptoms, such that cognitive symptoms related to overvaluation of weight/shape were central in individuals with shorter DOI, and behavioral central symptoms emerged more in medium and long DOI.
Our results have important implications for the treatment of individuals with enduring EDs, as they may have a different core, maintaining symptoms. Additionally, our findings highlight the importance of using comprehensive, theoretically- or empirically-derived models for NA.
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