Precursors of first-episode psychosis in a population-based sample

描述（由申请人提供）：精神分裂症估计是15至44岁人群因残疾和过早死亡而损失生命年的第8位原因。减少这种疾病负担是公共卫生的优先事项。在首次出现精神病症状的人群中，延迟接受有效治疗显著导致长期预后不良。此外，警告信号或前驱症状可能在实际精神病症状发作之前被识别。越来越多的证据表明，预防性干预（在实际精神病症状发作之前）或早期临床干预（缩短症状发作和接受有效护理之间的间隔）都可以改善长期预后。现有的早期发现和早期干预模式-无论是用于研究还是用于提供护理-覆盖面和可扩展性有限。我们的初步研究表明，回顾性地应用于电子病历数据的可推广算法可以准确地识别精神病的首次发作，阳性预测值为80%至90%， 敏感度超过80% -与结构化图表审查相比。如果大型卫生系统中的电子记录可以用于有效地识别经历首次精神病发作的人的大规模和代表性样本，这种方法可以大大加快研究并改变护理提供。我们提出了一个以人群为基础的研究计划，以解决有关早期干预计划的直接问题，并制定方法，以支持下一代的早期干预研究。这项研究将借鉴5个大型卫生系统，为超过750万人的多样化和代表性人口提供服务。该计划的具体目标包括：1）使用来自大型综合医疗保健系统的电子记录数据来验证和改进用于识别精神病首次呈现的可推广算法。2)在首次诊断精神病之前检查卫生保健接触模式，以确定早期检测计划和预防干预的最佳护理环境和目标人群。3)检查精神病首次诊断后的治疗模式，以确定导致未治疗精神病持续时间延长的护理差距。4)在首次诊断精神障碍时检查健康保险覆盖范围的来源以及随后的覆盖范围失误，以便为未来干预计划的设计提供信息。5)探索使用文本挖掘方法，以确定潜在的指标前驱症状的门诊就诊记录首次诊断精神病之前，以制定创新的战略，准确的前驱症状的实时识别。6)了解患者，家庭和临床医生对基于人群的研究外展精神病的新诊断后的观点-为未来的研究和护理提供信息。

DESCRIPTION (provided by applicant): Schizophrenia is estimated to be the 8th-ranked cause of life years lost to disability and premature death among people aged 15 to 44. Reducing this disease burden is a public health priority. Among people experiencing first onset of psychotic symptoms, delay in receipt of effective treatment contributes significantly to poor long-term outcomes. Furthermore, warning signs or prodromal symptoms may be identifiable prior to onset of actual psychotic symptoms. Accumulating evidence suggests that preventive interventions (prior to onset of actual psychotic symptoms) or early clinical interventions (to reduce the interval between onset of symptoms and receipt of effective care) can both improve long-term prognosis. Existing models for early detection and early intervention - either for research or care delivery - have limited reach and scalability. Our preliminary studies suggest that a generalizable algorithm retrospectively applied to electronic medical records data can accurately identify first episodes of psychosis with a positive predictive value of 80 to 90% and a sensitivity of over 80% - when compared to structured chart review. If electronic records in large health systems could be used to efficiently identify large and representative samples of people experiencing first- episode psychosis, this method could dramatically accelerate research and transform care delivery. We propose a population-based research program to address immediate questions regarding early intervention programs and to develop methods to support the next generation of early intervention research. This research will draw from 5 large health systems serving a diverse and representative population of over 7.5 million people. Specific aims of this program include: 1) Use electronic records data from large integrated health care systems to validate and refine a generalizable algorithm for identifying first presentations of psychosis. 2) Examine patterns of health care contact prior to first diagnosis of psychosis to identify the optimal care settings and target populations for early detection programs and preventive interventions. 3) Examine patterns of treatment following first diagnosis of psychosis in order to identify the gaps in care leading to prolonged duration of untreated psychosis. 4) Examine sources of health insurance coverage at first diagnosis of psychotic disorder and subsequent lapses in coverage in order to inform the design of future intervention programs. 5) Explore the use of text mining methods to identify potential indicators of prodromal symptoms in notes of outpatient visits prior to first diagnosis of psychosis in order to develop innovative strategies for accurate real-time identification of prodromal symptoms. 6) Understand patient, family, and clinician perspectives regarding population-based research outreach following a new diagnosis of psychosis - to inform future research and care delivery.