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Davis M, Dysart GC, Doupnik SK, Hamm ME, Schwartz KTG, George-Milford B, Ryan ND, Melhem NM, Stepp SD, Brent DA, Young JF. Adolescent, Parent, and Provider Perceptions of a Predictive Algorithm to Identify Adolescent Suicide Risk in Primary Care. Acad Pediatr 2024; 24:645-653. [PMID: 38190885 PMCID: PMC11056301 DOI: 10.1016/j.acap.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/27/2023] [Accepted: 12/30/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVE To understand adolescent, parent, and provider perceptions of a machine learning algorithm for detecting adolescent suicide risk prior to its implementation primary care. METHODS We conducted semi-structured, qualitative interviews with adolescents (n = 9), parents (n = 12), and providers (n = 10; mixture of behavioral health and primary care providers) across two major health systems. Interviews were audio recorded and transcribed with analyses supported by use of NVivo. A codebook was developed combining codes derived inductively from interview transcripts and deductively from implementation science frameworks for content analysis. RESULTS Reactions to the algorithm were mixed. While many participants expressed privacy concerns, they believed the algorithm could be clinically useful for identifying adolescents at risk for suicide and facilitating follow-up. Parents' past experiences with their adolescents' suicidal thoughts and behaviors contributed to their openness to the algorithm. Results also aligned with several key Consolidated Framework for Implementation Research domains. For example, providers mentioned barriers inherent to the primary care setting such as time and resource constraints likely to impact algorithm implementation. Participants also cited a climate of mistrust of science and health care as potential barriers. CONCLUSIONS Findings shed light on factors that warrant consideration to promote successful implementation of suicide predictive algorithms in pediatric primary care. By attending to perspectives of potential end users prior to the development and testing of the algorithm, we can ensure that the risk prediction methods will be well-suited to the providers who would be interacting with them and the families who could benefit.
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Affiliation(s)
- Molly Davis
- Department of Child and Adolescent Psychiatry and Behavioral Sciences (M Davis, GC Dysart, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; PolicyLab (M Davis, GC Dysart, SK Doupnik, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; Clinical Futures (M Davis and SK Doupnik), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Psychiatry (M Davis and JF Young), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa; Penn Implementation Science Center at the Leonard Davis Institute of Health Economics (PISCE@LDI) (M Davis and SK Doupnik), University of Pennsylvania, Philadelphia, Pa.
| | - Gillian C Dysart
- Department of Child and Adolescent Psychiatry and Behavioral Sciences (M Davis, GC Dysart, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; PolicyLab (M Davis, GC Dysart, SK Doupnik, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa
| | - Stephanie K Doupnik
- PolicyLab (M Davis, GC Dysart, SK Doupnik, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; Clinical Futures (M Davis and SK Doupnik), Children's Hospital of Philadelphia, Philadelphia, Pa; Penn Implementation Science Center at the Leonard Davis Institute of Health Economics (PISCE@LDI) (M Davis and SK Doupnik), University of Pennsylvania, Philadelphia, Pa; Division of General Pediatrics (SK Doupnik), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Pediatrics (SK Doupnik), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa
| | - Megan E Hamm
- Department of Medicine (ME Hamm), University of Pittsburgh, Pittsburgh, Pa
| | - Karen T G Schwartz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences (M Davis, GC Dysart, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; PolicyLab (M Davis, GC Dysart, SK Doupnik, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa
| | - Brandie George-Milford
- University of Pittsburgh Medical Center Western Psychiatric Hospital (B George-Milford and DA Brent), Pittsburgh, Pa
| | - Neal D Ryan
- Department of Psychiatry (ND Ryan, NM Melhem, SD Stepp, and DA Brent), University of Pittsburgh School of Medicine, Pittsburgh, Pa; Clinical and Translational Science Institute (ND Ryan), University of Pittsburgh, Pittsburgh, Pa
| | - Nadine M Melhem
- Department of Psychiatry (ND Ryan, NM Melhem, SD Stepp, and DA Brent), University of Pittsburgh School of Medicine, Pittsburgh, Pa
| | - Stephanie D Stepp
- Department of Psychiatry (ND Ryan, NM Melhem, SD Stepp, and DA Brent), University of Pittsburgh School of Medicine, Pittsburgh, Pa
| | - David A Brent
- University of Pittsburgh Medical Center Western Psychiatric Hospital (B George-Milford and DA Brent), Pittsburgh, Pa; Department of Psychiatry (ND Ryan, NM Melhem, SD Stepp, and DA Brent), University of Pittsburgh School of Medicine, Pittsburgh, Pa
| | - Jami F Young
- Department of Child and Adolescent Psychiatry and Behavioral Sciences (M Davis, GC Dysart, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; PolicyLab (M Davis, GC Dysart, SK Doupnik, KTG Schwartz, and JF Young), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Psychiatry (M Davis and JF Young), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa
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Mortier P, Amigo F, Bhargav M, Conde S, Ferrer M, Flygare O, Kizilaslan B, Latorre Moreno L, Leis A, Mayer MA, Pérez-Sola V, Portillo-Van Diest A, Ramírez-Anguita JM, Sanz F, Vilagut G, Alonso J, Mehlum L, Arensman E, Bjureberg J, Pastor M, Qin P. Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project. BMC Psychiatry 2024; 24:220. [PMID: 38509500 PMCID: PMC10956300 DOI: 10.1186/s12888-024-05659-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. METHODS PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. DISCUSSION Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.
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Grants
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- AC22/00006; AC22/00045 Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia
- ESF+; CP21/00078 ISCIII-FSE Miguel Servet co-funded by the European Social Fund Plus
- PI22/00107 ISCIII and co-funded by the European Union
- PI22/00107 ISCIII and co-funded by the European Union
- PI22/00107 ISCIII and co-funded by the European Union
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- 202220-30-31 Fundación la Marató de TV3
- FI23/00004 PFIS ISCIII
- FI23/00004 PFIS ISCIII
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- SGR 00624 the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- CIBERESP; CB06/02/0046 CIBER of Epidemiology & Public Health
- CIBERESP; CB06/02/0046 CIBER of Epidemiology & Public Health
- CIBERESP; CB06/02/0046 CIBER of Epidemiology & Public Health
- CIBERESP; CB06/02/0046 CIBER of Epidemiology & Public Health
- CIBERESP; CB06/02/0046 CIBER of Epidemiology & Public Health
- CIBERESP; CB06/02/0046 CIBER of Epidemiology & Public Health
- ERAPERMED2022 the Health Research Board Ireland
- ERAPERMED2022 the Health Research Board Ireland
- no. 2022-00549 the Swedish Innovation Agency
- no. 2022-00549 the Swedish Innovation Agency
- project no. 342386 the Research Council of Norway
- project no. 342386 the Research Council of Norway
- project no. 342386 the Research Council of Norway
- the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement of the Generalitat de Catalunya AGAUR 2021
- CIBER of Epidemiology & Public Health
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Affiliation(s)
- Philippe Mortier
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain.
- CIBER of Epidemiology and Public Health, Carlos III Health Institute (CIBERESP, ISCIII), Madrid, Spain.
| | - Franco Amigo
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
- CIBER of Epidemiology and Public Health, Carlos III Health Institute (CIBERESP, ISCIII), Madrid, Spain
| | - Madhav Bhargav
- School of Public Health & National Suicide Research Foundation, University College Cork, Cork, Ireland
| | - Susana Conde
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
| | - Montse Ferrer
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
- CIBER of Epidemiology and Public Health, Carlos III Health Institute (CIBERESP, ISCIII), Madrid, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Oskar Flygare
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden
| | - Busenur Kizilaslan
- National Centre for Suicide Research and Prevention, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Laura Latorre Moreno
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
| | - Angela Leis
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Angel Mayer
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Víctor Pérez-Sola
- Neuropsychiatry and Drug Addiction Institute, Barcelona MAR Health Park Consortium PSMAR, Barcelona, Spain
- CIBER of Mental Health and Carlos III Health Institute (CIBERSAM, ISCIII), Madrid, Spain
- Department of Paediatrics, Obstetrics and Gynaecology and Preventive Medicine and Public Health Department, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Ana Portillo-Van Diest
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
- CIBER of Epidemiology and Public Health, Carlos III Health Institute (CIBERESP, ISCIII), Madrid, Spain
| | - Juan Manuel Ramírez-Anguita
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- National Bioinformatics Institute - ELIXIR-ES (IMPaCT-Data-ISCIII), Barcelona, Spain
| | - Gemma Vilagut
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
- CIBER of Epidemiology and Public Health, Carlos III Health Institute (CIBERESP, ISCIII), Madrid, Spain
| | - Jordi Alonso
- Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, 88, 08003, Barcelona, Spain
- CIBER of Epidemiology and Public Health, Carlos III Health Institute (CIBERESP, ISCIII), Madrid, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Lars Mehlum
- National Centre for Suicide Research and Prevention, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ella Arensman
- School of Public Health & National Suicide Research Foundation, University College Cork, Cork, Ireland
| | - Johan Bjureberg
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ping Qin
- National Centre for Suicide Research and Prevention, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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Hibi A, Cusimano MD, Bilbily A, Krishnan RG, Tyrrell PN. Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study. Can J Neurol Sci 2024:1-9. [PMID: 38438281 DOI: 10.1017/cjn.2024.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
BACKGROUND Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. METHODS Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. RESULTS The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. CONCLUSION Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.
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Affiliation(s)
- Atsuhiro Hibi
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Division of Neurosurgery, St Michael's Hospital, Unity Health Toronto, Toronto, Canada
| | - Michael D Cusimano
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Division of Neurosurgery, St Michael's Hospital, Unity Health Toronto, Toronto, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Pascal N Tyrrell
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
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Giddings R, Joseph A, Callender T, Janes SM, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. Lancet Digit Health 2024; 6:e131-e144. [PMID: 38278615 DOI: 10.1016/s2589-7500(23)00241-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/20/2023] [Accepted: 11/14/2023] [Indexed: 01/28/2024]
Abstract
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.
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Affiliation(s)
- Rebecca Giddings
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
| | - Anabel Joseph
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Thomas Callender
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK
| | - Jessica Sheringham
- Department of Applied Health Research, University College London, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
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Ripperger MA, Kolli J, Wilimitis D, Robinson K, Reale C, Novak LL, Cunningham CA, Kasuske LM, Grover SG, Ribeiro JD, Walsh CG. External Validation and Updating of a Statistical Civilian-Based Suicide Risk Model in US Naval Primary Care. JAMA Netw Open 2023; 6:e2342750. [PMID: 37938841 PMCID: PMC10632956 DOI: 10.1001/jamanetworkopen.2023.42750] [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] [Received: 07/13/2023] [Accepted: 09/29/2023] [Indexed: 11/10/2023] Open
Abstract
Importance Suicide remains an ongoing concern in the US military. Statistical models have not been broadly disseminated for US Navy service members. Objective To externally validate and update a statistical suicide risk model initially developed in a civilian setting with an emphasis on primary care. Design, Setting, and Participants This retrospective cohort study used data collected from 2007 through 2017 among active-duty US Navy service members. The external civilian model was applied to every visit at Naval Medical Center Portsmouth (NMCP), its NMCP Naval Branch Health Clinics (NBHCs), and TRICARE Prime Clinics (TPCs) that fall within the NMCP area. The model was retrained and recalibrated using visits to NBHCs and TPCs and updated using Department of Defense (DoD)-specific billing codes and demographic characteristics, including expanded race and ethnicity categories. Domain and temporal analyses were performed with bootstrap validation. Data analysis was performed from September 2020 to December 2022. Exposure Visit to US NMCP. Main Outcomes and Measures Recorded suicidal behavior on the day of or within 30 days of a visit. Performance was assessed using area under the receiver operating curve (AUROC), area under the precision recall curve (AUPRC), Brier score, and Spiegelhalter z-test statistic. Results Of the 260 583 service members, 6529 (2.5%) had a recorded suicidal behavior, 206 412 (79.2%) were male; 104 835 (40.2%) were aged 20 to 24 years; and 9458 (3.6%) were Asian, 56 715 (21.8%) were Black or African American, and 158 277 (60.7%) were White. Applying the civilian-trained model resulted in an AUROC of 0.77 (95% CI, 0.74-0.79) and an AUPRC of 0.004 (95% CI, 0.003-0.005) at NBHCs with poor calibration (Spiegelhalter P < .001). Retraining the algorithm improved AUROC to 0.92 (95% CI, 0.91-0.93) and AUPRC to 0.66 (95% CI, 0.63-0.68). Number needed to screen in the top risk tiers was 366 for the external model and 200 for the retrained model; the lower number indicates better performance. Domain validation showed AUROC of 0.90 (95% CI, 0.90-0.91) and AUPRC of 0.01 (95% CI, 0.01-0.01), and temporal validation showed AUROC of 0.75 (95% CI, 0.72-0.78) and AUPRC of 0.003 (95% CI, 0.003-0.005). Conclusions and Relevance In this cohort study of active-duty Navy service members, a civilian suicide attempt risk model was externally validated. Retraining and updating with DoD-specific variables improved performance. Domain and temporal validation results were similar to external validation, suggesting that implementing an external model in US Navy primary care clinics may bypass the need for costly internal development and expedite the automation of suicide prevention in these clinics.
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Affiliation(s)
- Michael A. Ripperger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jhansi Kolli
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Drew Wilimitis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Katelyn Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Carrie Reale
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Laurie L. Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Lalon M. Kasuske
- Daniel K. Inouye Graduate School of Nursing, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | | | | | - Colin G. Walsh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
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Kleiman EM, Glenn CR, Liu RT. The use of advanced technology and statistical methods to predict and prevent suicide. NATURE REVIEWS PSYCHOLOGY 2023; 2:347-359. [PMID: 37588775 PMCID: PMC10426769 DOI: 10.1038/s44159-023-00175-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 08/18/2023]
Abstract
In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.
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Affiliation(s)
- Evan M. Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | - Richard T. Liu
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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Shortreed SM, Walker RL, Johnson E, Wellman R, Cruz M, Ziebell R, Coley RY, Yaseen ZS, Dharmarajan S, Penfold RB, Ahmedani BK, Rossom RC, Beck A, Boggs JM, Simon GE. Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction. NPJ Digit Med 2023; 6:47. [PMID: 36959268 PMCID: PMC10036475 DOI: 10.1038/s41746-023-00772-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 02/07/2023] [Indexed: 03/25/2023] Open
Abstract
Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods.
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Affiliation(s)
- Susan M Shortreed
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA.
- Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA, 98195, USA.
| | - Rod L Walker
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
| | - Eric Johnson
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
| | - Robert Wellman
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
| | - Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
- Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA, 98195, USA
| | - Rebecca Ziebell
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
| | - R Yates Coley
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
- Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA, 98195, USA
| | - Zimri S Yaseen
- U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Robert B Penfold
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
| | - Brian K Ahmedani
- Center for Health Policy & Health Services Research, Henry Ford Health System, 1 Ford Place, Detroit, MI, 48202, USA
| | - Rebecca C Rossom
- HealthPartners Institute, Division of Research, 8170 33rd Ave S, Minneapolis, MN, 55425, USA
| | - Arne Beck
- Kaiser Permanente Colorado Institute for Health Research, 2550 S. Parker Road, Suite 200, Aurora, CO, 80014, USA
| | - Jennifer M Boggs
- Kaiser Permanente Colorado Institute for Health Research, 2550 S. Parker Road, Suite 200, Aurora, CO, 80014, USA
| | - Greg E Simon
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Ste 1600, Seattle, WA, 98101, USA
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Association of HLA class II alleles with suicidal behavior in a Transylvanian population. REV ROMANA MED LAB 2023. [DOI: 10.2478/rrlm-2023-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Abstract
Background: Suicide is a complex phenomenon determined by the interaction of various risk factors. The Major Histocompatibility Complex is the most polymorphic gene cluster of the entire human genome, being linked to both the regulation of the immune system and various psychiatric diseases. The aim of this study was to identify HLA-DQB1 and DRB1 alleles and genotypes susceptible to influence suicidal behavior.
Methods: We explored the association of HLA-DQB1 alleles with the suicidal behavior on a sample of 427 individuals (including 110 suicide attempters) from Transylvania, as well as the association of HLA-DRB1 alleles with the suicidal behavior on a sample of 271 individuals (including 50 suicide attempters), using the single specific primer-PCR (SSP-PCR) technique.
Results: We found that the HLA-DQB1*02, *03 and *06 alleles, the DQB1*02/*03, DQB1*02/*06, DRB1*12/*15 and DRB1*07/*13 genotypes, as well as the DQB1*06~DRB1*07 and DQB1*02~DRB1*13 haplotypes, were more frequent in suicide attempters. In contrast, the HLA-DQB1*04 and DQB1*13 alleles, the DQB1*02/*05 and DQB1*03/*05 genotypes and the DQB1*03~DRB1*13 haplotype were less frequent in the case group.
Conclusion: HLA-DQB1*02, *03 and *06 alleles and the DQB1*02/*03 and *02/*06 genotypes are susceptible to favor a suicide behavior, while the HLA-DQB1*04 and *13 alleles and the DQB1*02/*05 and *03/*05 genotypes were protective against such behavior. A similar analysis regarding the HLA-DRB1 alleles detected a possible risk for suicidal behavior among individuals possessing either the DRB1*12/*15 or the DRB1*07/*13 genotypes. DQB1*06~DRB1*07 and DQB1*02~DRB1*13 haplotypes were found susceptible to favor a suicidal behavior, while DQB1*03~DRB1*13 exhibited a protective influence.
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