Real-world predictors of relapse in patients with schizophrenia and schizoaffective disorder in a large health system.

IF 3 Q2 PSYCHIATRY
Anne Rivelli, Veronica Fitzpatrick, Michael Nelson, Kimberly Laubmeier, Courtney Zeni, Srikrishna Mylavarapu
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Abstract

Schizophrenia is often characterized by recurring relapses, which are associated with a substantial clinical and economic burden. Early identification of individuals at the highest risk for relapse in real-world treatment settings could help improve outcomes and reduce healthcare costs. Prior work has identified a few consistent predictors of relapse in schizophrenia, however, studies to date have been limited to insurance claims data or small patient populations. Thus, this study used a large sample of health systems electronic health record (EHR) data to analyze relationships between patient-level factors and relapse and model a set of factors that can be used to identify the increased prevalence of relapse, a severe and preventable reality of schizophrenia. This retrospective, observational cohort study utilized EHR data extracted from the largest Midwestern U.S. non-profit healthcare system to identify predictors of relapse. The study included patients with a diagnosis of schizophrenia (ICD-10 F20) or schizoaffective disorder (ICD-10 F25) who were treated within the system between October 15, 2016, and December 31, 2021, and received care for at least 12 months. A relapse episode was defined as an emergency room or inpatient encounter with a pre-determined behavioral health-related ICD code. Patients' baseline characteristics, comorbidities and healthcare utilization were described. Modified log-Poisson regression (i.e. log Poisson regression with a robust variance estimation) analyses were utilized to estimate the prevalence of relapse across patient characteristics, comorbidities and healthcare utilization and to ultimately identify an adjusted model predicting relapse. Among the 8119 unique patients included in the study, 2478 (30.52%) experienced relapse and 5641 (69.48%) experienced no relapse. Patients were primarily male (54.72%), White Non-Hispanic or Latino (54.23%), with Medicare insurance (51.40%), and had baseline diagnoses of substance use (19.24%), overweight/obesity/weight gain (13.06%), extrapyramidal symptoms (48.00%), lipid metabolism disorder (30.66%), hypertension (26.85%), and diabetes (19.08%). Many differences in patient characteristics, baseline comorbidities, and utilization were revealed between patients who relapsed and patients who did not relapse. Through model building, the final adjusted model with all significant predictors of relapse included the following variables: insurance, age, race/ethnicity, substance use diagnosis, extrapyramidal symptoms, number of emergency room encounters, behavioral health inpatient encounters, prior relapses episodes, and long-acting injectable prescriptions written. Prevention of relapse is a priority in schizophrenia care. Challenges related to historical health record data have limited the knowledge of real-world predictors of relapse. This study offers a set of variables that could conceivably be used to construct algorithms or models to proactively monitor demographic, comorbidity, medication, and healthcare utilization parameters which place patients at risk for relapse and to modify approaches to care to avoid future relapse.

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大型医疗系统中精神分裂症和情感分裂症患者复发的现实预测因素。
精神分裂症通常以反复复发为特征,而复发与巨大的临床和经济负担相关。在实际治疗环境中及早识别复发风险最高的患者,有助于改善治疗效果和降低医疗成本。之前的研究发现了一些精神分裂症复发的一致预测因素,但迄今为止的研究仅限于保险理赔数据或小规模患者群体。因此,本研究利用医疗系统电子病历(EHR)的大样本数据分析了患者层面的因素与复发之间的关系,并建立了一套可用于识别复发率增加的因素模型,复发是精神分裂症的一个严重且可预防的现实问题。这项回顾性观察队列研究利用从美国中西部最大的非营利性医疗保健系统中提取的电子病历数据来确定复发的预测因素。研究对象包括被诊断为精神分裂症(ICD-10 F20)或分裂情感障碍(ICD-10 F25)的患者,这些患者在 2016 年 10 月 15 日至 2021 年 12 月 31 日期间在该系统内接受治疗,并接受了至少 12 个月的护理。复发事件被定义为急诊室或住院病人遇到预先确定的行为健康相关 ICD 代码。对患者的基线特征、合并症和医疗保健使用情况进行了描述。利用修正的对数泊松回归(即具有稳健方差估计的对数泊松回归)分析来估计不同患者特征、合并症和医疗保健使用情况下的复发率,并最终确定预测复发的调整模型。在纳入研究的 8119 名患者中,有 2478 人(30.52%)复发,5641 人(69.48%)未复发。患者主要为男性(54.72%)、非西班牙裔或拉丁裔白人(54.23%)、有医疗保险(51.40%),基线诊断为药物使用(19.24%)、超重/肥胖/体重增加(13.06%)、锥体外系症状(48.00%)、脂代谢紊乱(30.66%)、高血压(26.85%)和糖尿病(19.08%)。复发患者与未复发患者在患者特征、基线合并症和使用情况方面存在许多差异。通过建立模型,最终的调整模型包含了所有重要的复发预测因素,其中包括以下变量:保险、年龄、种族/民族、药物使用诊断、锥体外系症状、急诊就诊次数、行为健康住院就诊次数、之前的复发发作以及开具的长效注射剂处方。预防复发是精神分裂症治疗的首要任务。与历史健康记录数据相关的挑战限制了人们对现实世界中复发预测因素的了解。本研究提供了一组变量,可用于构建算法或模型,以主动监测使患者面临复发风险的人口统计学、合并症、药物治疗和医疗保健使用参数,并修改护理方法以避免未来复发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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