| Project Name: Effects of Medical Products on Suicidal Ideation and Behavior |
| Principal Investigator: Gregory Simon, MD, MPH |
| Principal Investigator Contact Information: gregory.e.simon@kp.org |
| Principal Investigator Institution: KP Washington Health Research Institute |
| Funder: Food and Drug Administration (FDA) |
| Funding Period: 9/30/2018 to 9/30/2021 |
| Abstract: We propose a comprehensive program of infrastructure development and methods development to support future generation of real-world evidence addressing these critical gaps. The project team will include health systems and embedded research organizations with deep expertise in stakeholder engagement, medical informatics, data science, clinical epidemiology, biostatistics, pragmatic clinical trial methods, implementation science, and innovations in care delivery. Specific Tasks include: Augment the existing FDA Sentinel Initiative data infrastructure to support study of severe mental illness, suicidal ideation, and suicidal behavior. Evaluate and improve generalizability of models predicting suicidal behavior for use in future observational research and pragmatic trials. This program will be embedded in 4 integrated health systems serving a combined population of approximately 10 million members. This work will be conducted in collaboration with health system and patient/family stakeholders, to assure that methods and evidence developed will actually address real-world questions. This infrastructure and methods development will enable a robust program of research regarding the effects of medical products on suicidal ideation and behavior, including: Scalable and re-usable methods to assess suicidal ideation and behavior as an adverse effect of existing products. Scalable and re-usable methods to assess therapeutic effects of existing products for reducing suicidal ideation and behaviorScalable and re-usable methods to rapidly evaluate possible therapeutic and adverse effects of new medical products on suicidal ideation and behavior. Large pragmatic trials to evaluate therapeutic effects of promising new product(s) on suicidal behavior |
| Grant Number: N/A |
| Participating Sites: Kaiser Permanente Washington Harvard Pilgrim Healthcare Kaiser Permanente Northern California Kaiser Permanente Southern California Henry Ford Health System |
| Investigators: Gregory Simon MD MPH Susan Shortreed PhD Yates Coley PhD Richard Platt MD MS Jeffrey Brown PhD Darren Toh ScD Jessica Young PhD Stacy Sterling PhD Karen Coleman PhD Jean Lawrence ScD Brian Ahmedani PhD |
| Major Goals Augment the existing FDA Sentinel Initiative data infrastructure to support study of severe mental illness, suicidal ideation, and suicidal behavior. Evaluate and improve generalizability of models predicting suicidal behavior for use in future observational research and pragmatic trials. |
| Description of study sample: Various analyses are using data regarding approximately 4.5 million members of participating health systems. |
| Current Status: Completed data infrastructure work includes: – A toolkit to assess re-identification risk when sharing data derived from healthcare records: – More timely updating of mortality data in health system research data warehouses. – Regular reporting of availability and quality of patient-reported outcome data in health system research data warehouses. Analyses are complete regarding: – Value of more detailed data representation and more complex modeling methods for prediction of suicidal behavior. – Accuracy of ICD-10-CM encounter diagnoses for identifying self-harm events. – Value of data typically only available from electronic health records for prediction of suicidal behavior. |
| Study Registration: N/A |
| Publications: Simon GE, Shortreed SM, Boggs JM, Clarke GN, Rossom RC, Richards JE, Beck A, Ahmedani BK, Coleman KJ, Bhakta B, Stewart CC, Sterling S, Schoenbaum M, Coley RY, Stone M, Mosholder AD, Yaseen ZS. Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events. J Am Med Inform Assoc. 2022 Aug 26:ocac144. doi: 10.1093/jamia/ocac144. |
| Resources: N/A |
| Lessons Learned: For prediction of suicidal behavior following outpatient mental health visits, more detailed temporal representation and more complex model development methods (random forest or neural networks vs. penalized logistic regression) do not meaningfully improve prediction accuracy. When using prediction models to account for confounding by indication in observational studies of medication effects on suicidal behavior, random forest models may be slightly – but not meaningfully – superior to penalized logistic regression. When using health records data to predict suicidal behavior, additional data available only from electronic health records (race, ethnicity, patient-reported outcome results) do not significantly improve prediction over data typically available from insurance claims. |
| What’s next? Additional analyses will examine: – Similarities and differences in prediction of opioid vs. other overdoses – Similarities and differences in prediction of self-harm vs. accidental overdoses – Changes in accuracy of suicide risk prediction models with health system implementation of Zero Suicide care improvement programs. |
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