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Papini S, Hsin H, Kipnis P, Liu VX, Lu Y, Girard K, Sterling SA, Iturralde EM. Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample. JAMA Psychiatry 2024; 81:700-707. [PMID: 38536187 PMCID: PMC10974695 DOI: 10.1001/jamapsychiatry.2024.0189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/16/2024] [Indexed: 07/04/2024]
Abstract
Importance Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. Objective To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. Design, Setting, and Participants This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Main Outcome and Measures Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. Results The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. Conclusions and Relevance In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.
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Affiliation(s)
- Santiago Papini
- Division of Research, Kaiser Permanente Division of Research, Oakland, California
- Department of Psychology, University of Hawaiʻi at Mānoa, Honolulu
| | - Honor Hsin
- The Permanente Medical Group, Kaiser Permanente, San Jose, California
| | - Patricia Kipnis
- Division of Research, Kaiser Permanente Division of Research, Oakland, California
| | - Vincent X. Liu
- Division of Research, Kaiser Permanente Division of Research, Oakland, California
| | - Yun Lu
- Division of Research, Kaiser Permanente Division of Research, Oakland, California
| | - Kristine Girard
- The Permanente Medical Group, Kaiser Permanente, San Jose, California
| | - Stacy A. Sterling
- Division of Research, Kaiser Permanente Division of Research, Oakland, California
| | - Esti M. Iturralde
- Division of Research, Kaiser Permanente Division of Research, Oakland, California
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Hesse M, Brummer JE. Suicide and Self-Harm-Toward the Full Picture From the Full Population. JAMA Netw Open 2024; 7:e2417049. [PMID: 38922622 DOI: 10.1001/jamanetworkopen.2024.17049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/27/2024] Open
Affiliation(s)
- Morten Hesse
- Center for Alcohol and Drug Research, Aarhus University, Aarhus, Denmark
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Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Transl Psychiatry 2024; 14:140. [PMID: 38461283 PMCID: PMC10925059 DOI: 10.1038/s41398-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.
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Affiliation(s)
- Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Domenico Madonna
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Pietro Pietrini
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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Somé NH, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Front Psychiatry 2024; 15:1291362. [PMID: 38501090 PMCID: PMC10944962 DOI: 10.3389/fpsyt.2024.1291362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Background Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Objective Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified. Methods A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. Results The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. Conclusion The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.
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Affiliation(s)
- Nibene H. Somé
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Pardis Noormohammadpour
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Shannon Lange
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Chen SC, Huang HC, Liu SI, Chen SH. Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm. OMEGA-JOURNAL OF DEATH AND DYING 2024; 88:1403-1429. [PMID: 34920680 DOI: 10.1177/00302228211060596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Suicidal risk has been a significant mental health problem. However, the predictive ability for repeated self-harm (SH) has not improved over the past decades. This study thus aimed to explore a potential tool with theoretical accommodation and clinical application by employing traditional logistic regression (LR) and newly developed machine learning, random forest algorithm (RF). Starting with 89 items from six commonly used scales (i.e., proximal suicide risk factors) as preliminary predictors, both LR and RF resulted in a better solution with much fewer items in two phases of item selections and analyses, with prediction accuracy 88.6% and 79.8%, respectively. A combination with 12 selected items, named LR-12, well predicted repeated self-harm in 6-month follow-up with satisfactory performance (AUC = 0.84, 95% CI: 0.76-0.92; cut-off point by 1/2 with sensitivity 81.1% and specificity 74.0%). The psychometrically appealing LR-12 could be used as a screening scale for suicide risk assessment.
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Affiliation(s)
- Shu-Chin Chen
- Department of Psychology, National Taiwan University, Taipei, Taiwan
- Suicide Prevention Center, MacKay Memorial Hospital, Taipei, Taiwan
| | - Hui-Chun Huang
- Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
| | - Shen-Ing Liu
- Department of Psychiatry, MacKay Memorial Hospital, Taipei, Taiwan
| | - Sue-Huei Chen
- Department of Psychology, National Taiwan University, Taipei, Taiwan
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Dutta R, Gkotsis G, Velupillai SU, Downs J, Roberts A, Stewart R, Hotopf M. Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records. Front Psychiatry 2023; 14:1217649. [PMID: 38152362 PMCID: PMC10752595 DOI: 10.3389/fpsyt.2023.1217649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/13/2023] [Indexed: 12/29/2023] Open
Abstract
Background Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators. Setting EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs. Objectives To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning. Methods The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2). Findings n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram). Conclusion EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.
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Affiliation(s)
- Rina Dutta
- King’s College London, IoPPN, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | | | | | - Johnny Downs
- King’s College London, IoPPN, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Angus Roberts
- King’s College London, IoPPN, London, United Kingdom
| | - Robert Stewart
- King’s College London, IoPPN, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Matthew Hotopf
- King’s College London, IoPPN, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
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Nielsen SD, Christensen RHB, Madsen T, Karstoft KI, Clemmensen L, Benros ME. Prediction models of suicide and non-fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers. Acta Psychiatr Scand 2023; 148:525-537. [PMID: 37961014 DOI: 10.1111/acps.13629] [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: 07/22/2023] [Revised: 10/06/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION To develop machine learning models capable of predicting suicide and non-fatal suicide attempt as separate outcomes in the first 30 days after discharge from a psychiatric inpatient stay. METHODS Prospective cohort study using nationwide Danish registry data. We included individuals who were 18 years or older, and all discharges from psychiatric hospitalizations in Denmark from 1995 to 2018. We trained predictive models using 10-fold cross validation on 80% of the data and did testing on the remaining 20%. RESULTS The best model for predicting non-fatal suicide attempt was an ensemble of predictions from gradient boosting (XGBoost) and categorical boosting (catBoost). The ROC-AUC for predicting suicide attempt was 0.85 (95% CI: 0.84-0.85). At a risk threshold of 4.36%, positive predictive value (PPV) was 11.0% and sensitivity was 47.2%. The best model for predicting suicide was an ensemble of predictions from random forest, XGBoost and catBoost. For suicide, the ROC-AUC was 0.71 (95% CI: 0.70-0.73). At a risk threshold of 0.15%, PPV was 0.34% and sensitivity was 56.0%. The most contributing predictors differed when predicting suicide and suicide attempt, indicating that separate models are needed. The ensemble model was fair across sex and age, and more so than the penalized logistic regression model. CONCLUSIONS We achieved good performance for predicting suicide attempts and demonstrated a clinical application of ensemble models. Our results indicate a difference in predictive performance for models predicting suicide and suicide attempt, respectively. Thus, we recommend that suicide and suicide attempt are treated as two separate endpoints, in particular for clinical application. We demonstrated that the ensemble model is fairer across sex and age compared with a penalized logistic regression, and therefore we recommend the use of well-tested ensembles despite a more complex explainability.
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Affiliation(s)
- Sara Dorthea Nielsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
- Copenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rune H B Christensen
- Copenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Trine Madsen
- Danish Research Institute of Suicide Prevention, Mental Health Center Copenhagen, Copenhagen, Denmark
- Section of Epidemiology, University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Karen-Inge Karstoft
- Department of Psychology, Faculty of Social Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Line Clemmensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michael E Benros
- Copenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Garriga R, Buda TS, Guerreiro J, Omaña Iglesias J, Estella Aguerri I, Matić A. Combining clinical notes with structured electronic health records enhances the prediction of mental health crises. Cell Rep Med 2023; 4:101260. [PMID: 37913776 PMCID: PMC10694623 DOI: 10.1016/j.xcrm.2023.101260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 07/12/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023]
Abstract
An automatic prediction of mental health crises can improve caseload prioritization and enable preventative interventions, improving patient outcomes and reducing costs. We combine structured electronic health records (EHRs) with clinical notes from 59,750 de-identified patients to predict the risk of mental health crisis relapse within the next 28 days. The results suggest that an ensemble machine learning model that relies on structured EHRs and clinical notes when available, and relying solely on structured data when the notes are unavailable, offers superior performance over models trained with either of the two data streams alone. Furthermore, the study provides key takeaways related to the required amount of clinical notes to add value in predictive analytics. This study sheds light on the untapped potential of clinical notes in the prediction of mental health crises and highlights the importance of choosing an appropriate machine learning method to combine structured and unstructured EHRs.
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Affiliation(s)
- Roger Garriga
- Koa Health, 08019 Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.
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Lagerberg T, Virtanen S, Kuja-Halkola R, Hellner C, Lichtenstein P, Fazel S, Chang Z. Predicting risk of suicidal behaviour after initiation of selective serotonin reuptake inhibitors in children, adolescents and young adults: protocol for development and validation of clinical prediction models. BMJ Open 2023; 13:e072834. [PMID: 37612105 PMCID: PMC10450049 DOI: 10.1136/bmjopen-2023-072834] [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: 02/15/2023] [Accepted: 07/31/2023] [Indexed: 08/25/2023] Open
Abstract
INTRODUCTION There is concern regarding suicidal behaviour risk during selective serotonin reuptake inhibitor (SSRI) treatment among the young. A clinically useful model for predicting suicidal behaviour risk should have high predictive performance in terms of discrimination and calibration; transparency and ease of implementation are desirable. METHODS AND ANALYSIS Using Swedish national registers, we will identify individuals initiating an SSRI aged 8-24 years 2007-2020. We will develop: (A) a model based on a broad set of predictors, and (B) a model based on a restricted set of predictors. For the broad predictor model, we will consider an ensemble of four base models: XGBoost (XG), neural net (NN), elastic net logistic regression (EN) and support vector machine (SVM). The predictors with the greatest contribution to predictive performance in the base models will be determined. For the restricted predictor model, clinical input will be used to select predictors based on the top predictors in the broad model, and inputted in each of the XG, NN, EN and SVM models. If any show superiority in predictive performance as defined by the area under the receiver-operator curve, this model will be selected as the final model; otherwise, the EN model will be selected. The training and testing samples will consist of data from 2007 to 2017 and from 2018 to 2020, respectively. We will additionally assess the final model performance in individuals receiving a depression diagnosis within 90 days before SSRI initiation.The aims are to (A) develop a model predicting suicidal behaviour risk after SSRI initiation among children and youths, using machine learning methods, and (B) develop a model with a restricted set of predictors, favouring transparency and scalability. ETHICS AND DISSEMINATION The research is approved by the Swedish Ethical Review Authority (2020-06540). We will disseminate findings by publishing in peer-reviewed open-access journals, and presenting at international conferences.
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Affiliation(s)
- Tyra Lagerberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Suvi Virtanen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Clara Hellner
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Seena Fazel
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Zheng Chang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Donnelly HK, Han Y, Kim S, Lee DH. Predictors of suicide ideation among South Korean adolescents: A machine learning approach. J Affect Disord 2023; 329:557-565. [PMID: 36828148 DOI: 10.1016/j.jad.2023.02.079] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 02/25/2023]
Abstract
BACKGROUND The current study developed a predictive model for suicide ideation among South Korean (Korean) adolescents using a comprehensive set of factors across demographic, physical and mental health, academic, social, and behavioral domains. The aim of this study was to address the pressing public health concerns of adolescent suicide in Korea and the methodological limitations of suicidal research. METHODS This study used machine learning methods (decision tree, logistic regression, naive Bayes classifier) to improve the accuracy of predicting suicidal ideation and related factors among a nationally representative sample of Korean middle school students (N = 6666). RESULTS Factors within all domains, including demographic characteristics, physical and mental health, and academic, social, and behavioral, were important in predicting suicidal thoughts among Korean adolescents, with mental health being the most important factor. LIMITATIONS The predictive model of the current research does not infer causality, and there may have been some loss of information due to measurement issues. CONCLUSIONS Study results provide insights for taking a multidimensional approach when identifying adolescents at risk of suicide, which may be used to further address their needs through intervention programs within the school setting. Considering the cultural stigma attached to disclosing suicidal ideation and behavior, the current study proposes the need for a preventive screening process based on the observation and assessment of adolescents' general characteristics and experiences in everyday life.
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Affiliation(s)
- Hayoung Kim Donnelly
- Boston University, Department of Counseling Psychology and Applied Human Development, USA.
| | - Yoonsun Han
- Seoul National University, Department of Social Welfare, South Korea.
| | - Suna Kim
- Seoul National University, Department of International Studies, South Korea.
| | - Dong Hun Lee
- Sungkyunkwan University, Traumatic Stress Center, Department of Education, South Korea.
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Sheu YH, Sun J, Lee H, Castro VM, Barak-Corren Y, Song E, Madsen EM, Gordon WJ, Kohane IS, Churchill SE, Reis BY, Cai T, Smoller JW. An efficient landmark model for prediction of suicide attempts in multiple clinical settings. Psychiatry Res 2023; 323:115175. [PMID: 37003169 PMCID: PMC10267893 DOI: 10.1016/j.psychres.2023.115175] [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: 11/20/2022] [Revised: 03/16/2023] [Accepted: 03/18/2023] [Indexed: 04/03/2023]
Abstract
Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from "temporal bias" (a bias that stems from using case-control sampling) or require training on all available patient visit data. Here, we adopt a "landmark model" framework that aligns with clinical practice for prediction of suicide-related behaviors (SRBs) using a large electronic health record database. Using the landmark approach, we developed models for SRB prediction (regularized Cox regression and random survival forest) that establish a time-point (e.g., clinical visit) from which predictions are made over user-specified prediction windows using historical information up to that point. We applied this approach to cohorts from three clinical settings: general outpatient, psychiatric emergency department, and psychiatric inpatients, for varying prediction windows and lengths of historical data. Models achieved high discriminative performance (area under the Receiver Operating Characteristic curve 0.74-0.93 for the Cox model) across different prediction windows and settings, even with relatively short periods of historical data. In short, we developed accurate, dynamic SRB risk prediction models with the landmark approach that reduce bias and enhance the reliability and portability of suicide risk prediction models.
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Affiliation(s)
- Yi-Han Sheu
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA
| | - Jiehuan Sun
- Department of Epidemiology and Biostatistics, University of Illinois Chicago, 1603W. Taylor St., Chicago, IL 60612, USA
| | - Hyunjoon Lee
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA
| | - Victor M Castro
- Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Yuval Barak-Corren
- Department of Pediatrics, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA; Schneider Children's Medical Center of Israel, 14 Kaplan Street, Petaẖ Tiqwa, Central, Israel
| | - Eugene Song
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA
| | - Emily M Madsen
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA
| | - William J Gordon
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Susanne E Churchill
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Ben Y Reis
- Department of Pediatrics, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA; Translational Data Science Center for a Learning Health System, Harvard University, 677 Huntington Avenue, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA.
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12
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Czyz EK, Koo HJ, Al-Dajani N, King CA, Nahum-Shani I. Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization. Psychol Med 2023; 53:2982-2991. [PMID: 34879890 PMCID: PMC9814182 DOI: 10.1017/s0033291721005006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/22/2021] [Accepted: 11/16/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Mobile technology offers unique opportunities for monitoring short-term suicide risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk factors were assessed daily following discharge to predict near-term suicidal ideation and inform decision algorithms for identifying elevations in daily level risk, with implications for real-time suicide-focused interventions. METHODS Adolescents (N = 78; 67.9% female) completed brief surveys texted daily for 4 weeks after discharge (n = 1621 observations). Using multi-level classification and regression trees (CARTSs) with repeated 5-fold cross-validation, we tested (a) a simple prediction model incorporating previous-day scores for each of 10 risk factors, and (b) a more complex model incorporating, for each of these factors, a time-varying person-specific mean over prior days together with deviation from that mean. Models also incorporated missingness and contextual (study week, day of the week) indicators. The outcome was the presence/absence of next-day suicidal ideation. RESULTS The best-performing model (cross-validated AUC = 0.86) was a complex model that included ideation duration, hopelessness, burdensomeness, and self-efficacy to refrain from suicidal action. An equivalent model that excluded ideation duration had acceptable overall performance (cross-validated AUC = 0.78). Models incorporating only previous-day scores, with and without ideation duration (cross-validated AUC of 0.82 and 0.75, respectively), showed relatively weaker performance. CONCLUSIONS Results suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts. Findings represent an important step in the development of decision tools identifying short-term risk as well as guiding timely interventions sensitive to proximal elevations in suicide risk in daily life.
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Affiliation(s)
- E. K. Czyz
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - H. J. Koo
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - N. Al-Dajani
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - C. A. King
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - I. Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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13
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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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14
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Garcia-Argibay M, Zhang-James Y, Cortese S, Lichtenstein P, Larsson H, Faraone SV. Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach. Mol Psychiatry 2023; 28:1232-1239. [PMID: 36536075 PMCID: PMC10005952 DOI: 10.1038/s41380-022-01918-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/05/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74-0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians' decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.
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Affiliation(s)
- Miguel Garcia-Argibay
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. .,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Yanli Zhang-James
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Samuele Cortese
- School of Psychology, University of Southampton, Southampton, UK.,Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK.,Solent NHS Trust, Southampton, UK.,Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, New York, NY, USA.,Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Larsson
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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15
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Weyh A, Gomez J, Kashat K, Fernandes R, Bunnell A. Self-inflicted craniomaxillofacial gunshot wounds: management, reconstruction, and outcomes. Int J Oral Maxillofac Surg 2023; 52:334-342. [PMID: 35773056 DOI: 10.1016/j.ijom.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 11/30/2022]
Abstract
Suicide by firearm remains one of the leading causes of violence-related injury death in the United States each year. The mortality rate from these injuries is high, resulting in a paucity of outcome data in the literature regarding injuries to the maxillofacial region. This has largely been attributed to a lack of funding for research in this area compared to other leading causes of mortality in the United States. The aim of this study was to detail the authors' experience and approach to complex maxillofacial reconstruction using both local reconstructive methods and microvascular free tissue transfer. A retrospective cohort study was designed, including patients who sustained self-inflicted gunshot wounds to the maxillofacial region between January 1, 2012 and May 1, 2020. Forty-one patients met the inclusion criteria. The majority of the patients were male (87.8%). Mean patient age was 44.2 ± 16.6 years. Alcohol or drugs, and a psychiatric history were present in a majority of the cases. The most involved anatomical region was the midface (75.6% of cases). Seven patients required free tissue transfer for reconstruction, with many needing multiple flaps. Self-inflicted gunshot wounds represent challenging reconstruction scenarios, often in the setting of severe psychological trauma, and require a multidisciplinary team to ensure the optimal outcome.
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Affiliation(s)
- A Weyh
- Department of Oral and Maxillofacial Surgery, University of Florida Jacksonville, Jacksonville, FL, USA.
| | - J Gomez
- Department of Oral and Maxillofacial Surgery, Ascension Macomb-Oakland Hospital, Detroit, MI, USA.
| | - K Kashat
- Department of Oral and Maxillofacial Surgery, University of Florida Jacksonville, Jacksonville, FL, USA.
| | - R Fernandes
- Department of Oral and Maxillofacial Surgery, University of Florida Jacksonville, Jacksonville, FL, USA.
| | - A Bunnell
- Department of Oral and Maxillofacial Surgery, University of Florida Jacksonville, Jacksonville, FL, USA.
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16
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Wang J, Qiu J, Zhu T, Zeng Y, Yang H, Shang Y, Yin J, Sun Y, Qu Y, Valdimarsdóttir UA, Song H. Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank. JMIR Public Health Surveill 2023; 9:e43419. [PMID: 36805366 PMCID: PMC9989910 DOI: 10.2196/43419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/21/2022] [Accepted: 01/12/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice. OBJECTIVE We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among individuals with different genetic risks of suicide. METHODS Based on the prospective cohort of UK Biobank, we included 223 (0.06%) eligible cases of suicide attempts or deaths, according to hospital inpatient or death register data within 1 year from baseline and randomly selected 4460 (1.18%) controls (1:20) without such records. We similarly identified 833 (0.22%) cases of suicidal behaviors 1 to 6 years from baseline and 16,660 (4.42%) corresponding controls. Based on 143 input features, mainly including sociodemographic, environmental, and psychosocial factors; medical history; and polygenic risk scores (PRS) for suicidality, we applied a bagged balanced light gradient-boosting machine (LightGBM) with stratified 10-fold cross-validation and grid-search to construct the full prediction models for suicide attempts or deaths within 1 year or between 1 and 6 years. The Shapley Additive Explanations (SHAP) approach was used to quantify the importance of input features, and the top 20 features with the highest SHAP values were selected to train the applicable models. The external validity of the established models was assessed among 50,310 individuals who participated in UK Biobank repeated assessments both overall and by the level of PRS for suicidality. RESULTS Individuals with suicidal behaviors were on average 56 years old, with equal sex distribution. The application of these full models in the external validation data set demonstrated good model performance, with the area under the receiver operating characteristic (AUROC) curves of 0.919 and 0.892 within 1 year and between 1 and 6 years, respectively. Importantly, the applicable models with the top 20 most important features showed comparable external-validated performance (AUROC curves of 0.901 and 0.885) as the full models, based on which we found that individuals in the top quintile of predicted risk accounted for 91.7% (n=11) and 80.7% (n=25) of all suicidality cases within 1 year and during 1 to 6 years, respectively. We further obtained comparable prediction accuracy when applying these models to subpopulations with different genetic susceptibilities to suicidality. For example, for the 1-year risk prediction, the AUROC curves were 0.907 and 0.885 for the high (>2nd tertile of PRS) and low (<1st) genetic susceptibilities groups, respectively. CONCLUSIONS We established applicable machine learning-based models for predicting both the short- and long-term risk of suicidality with high accuracy across populations of varying genetic risk for suicide, highlighting a cost-effective method of identifying individuals with a high risk of suicidality.
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Affiliation(s)
- Junren Wang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jiajun Qiu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yu Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Huazhen Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yanan Shang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jin Yin
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yajing Sun
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Unnur A Valdimarsdóttir
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Department of Epidemiology, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China.,Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
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17
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Sano M, Zhu CW, Neugroschl J, Grossman HT, Schimming C, Aloysi A. Agitation in Cognitive Disorders: Use of the National Alzheimer's Coordinating Center Uniform Data Set (NACC-UDS) to Evaluate International Psychogeriatric Association Definition. Am J Geriatr Psychiatry 2022; 30:1198-1208. [PMID: 35562259 DOI: 10.1016/j.jagp.2022.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Consensus-based definition of agitation by the International Psychogeriatric Association (IPA) has not been evaluated in community-based samples who are not preselected for behavioral disturbances. Here, we use a well-characterized cohort of community-dwelling older adults with cognitive impairment to assess the IPA criteria associated with agitation to evaluate the construction of this diagnostic entity. METHODS We used the National Alzheimer Coordinating Center Unified Data Set (NACC-UDS) to construct the IPA consensus-based provisional definition of agitation in cognitive impairment (N = 19,424). We used clinician diagnosis of agitation as a gold standard in those with mild cognitive impairment and dementia and used the Neuropsychiatric Inventory-Questionnaire to define agitation symptoms and standardized assessments of function (including the Functional Assessment Scale and Clinical Dementia Rating Scale Sum of Boxes) to assess "excess disability." We also examined patterns of psychiatric comorbidities to determine if they were consistent with IPA criteria. RESULTS There was agreement between the selected NPI measure of agitation and clinician judgment (sensitivity = 0.79, specificity = 0.69, Cohen's Kappa = 0.304). More than 84% of those with clinician judgment of agitation and 74% of those meeting the scale-based definition of agitation demonstrated excess social/functional disability. Comorbid psychiatric symptoms were present in 38% of the sample without agitation and higher in those with agitation by either definition. CONCLUSION Agitation ranges between 15% and 48% in those with cognitive impairment. The pattern of level of excess disability and the presence of comorbid psychiatric symptoms is consistent with the profile of published definitions.
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Affiliation(s)
- Mary Sano
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai (MS, JN, HTG, CS, AA), New York, NY; James J Peters VA Medical Center (MS, CWZ, HTG, CS), Bronx, NY
| | - Carolyn W Zhu
- James J Peters VA Medical Center (MS, CWZ, HTG, CS), Bronx, NY; Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai (CWZ), New York, NY.
| | - Judith Neugroschl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai (MS, JN, HTG, CS, AA), New York, NY
| | - Hillel T Grossman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai (MS, JN, HTG, CS, AA), New York, NY; James J Peters VA Medical Center (MS, CWZ, HTG, CS), Bronx, NY
| | - Corbett Schimming
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai (MS, JN, HTG, CS, AA), New York, NY; James J Peters VA Medical Center (MS, CWZ, HTG, CS), Bronx, NY
| | - Amy Aloysi
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai (MS, JN, HTG, CS, AA), New York, NY; Department of Neurology, Icahn School of Medicine at Mount Sinai (AA), New York, NY
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18
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Andersson HW, Mosti MP, Nordfjærn T. Suicidal ideation among inpatients with substance use disorders: Prevalence, correlates and gender differences. Psychiatry Res 2022; 317:114848. [PMID: 36116184 DOI: 10.1016/j.psychres.2022.114848] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 01/04/2023]
Abstract
We examined the prevalence of suicidal ideation (SI) among inpatients with substance use disorders (SUD) and investigated the association between SI and demographic (age, education, gender) and clinical factors (SUD, psychiatric disorders, anxiety/depression symptoms, substance use onset age). We collected medical record data including types of ICD-10 SUD and psychiatric diagnoses (i.e. mood: F30-39; anxiety: F40-48; personality: F60-F60.9; F61.0; F62; ADHD: F90-F90.9) and patient-reported data from 563 patients admitted to inpatient SUD treatment. Lifetime SI was measured by one question from the Addiction Severity Index (ASI). Gender differences in SI rates were examined using Chi-square tests. To determine variables that were uniquely associated with SI we conducted hierarchical regression analyses. The overall prevalence of SI was 50%, and it occurred more frequently among females (61.9%) than males (45.4%). SI was associated with female gender, younger age of substance use onset, mood and personality disorders, and higher anxiety/depression symptoms. Male gender accounted for the significant association between younger age of onset and SI. Diagnostic information on mood and personality disorders, and screening of patient-reported anxiety/depression symptoms at treatment intake may be useful for clinicians in identifying and providing personalized treatment for SUD inpatients who are at increased risk of SI.
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Affiliation(s)
- Helle Wessel Andersson
- Department of Research and Development, Clinic of Substance Use and Addiction Medicine, St. Olavs University Hospital, Pb 3250 Sluppen, Trondheim 7006, Norway.
| | - Mats P Mosti
- Department of Research and Development, Clinic of Substance Use and Addiction Medicine, St. Olavs University Hospital, Pb 3250 Sluppen, Trondheim 7006, Norway
| | - Trond Nordfjærn
- Department of Research and Development, Clinic of Substance Use and Addiction Medicine, St. Olavs University Hospital, Pb 3250 Sluppen, Trondheim 7006, Norway; Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
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19
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Nordin N, Zainol Z, Mohd Noor MH, Chan LF. Suicidal behaviour prediction models using machine learning techniques: A systematic review. Artif Intell Med 2022; 132:102395. [DOI: 10.1016/j.artmed.2022.102395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 08/12/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
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20
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Lyu J, Shi H, Zhang J, Norvilitis J. Prediction model for suicide based on back propagation neural network and multilayer perceptron. Front Neuroinform 2022; 16:961588. [PMID: 36059864 PMCID: PMC9435582 DOI: 10.3389/fninf.2022.961588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 07/15/2022] [Indexed: 12/18/2022] Open
Abstract
Introduction The aim was to explore the neural network prediction model for suicide based on back propagation (BP) and multilayer perceptron, in order to establish the popular, non-invasive, brief and more precise prediction model of suicide. Materials and method Data were collected by psychological autopsy (PA) in 16 rural counties from three provinces in China. The questionnaire was designed to investigate factors for suicide. Univariate statistical methods were used to preliminary filter factors, and BP neural network and multilayer perceptron were employed to establish the prediction model of suicide. Results The overall percentage correct of samples was 80.9% in logistic regression model. The total coincidence rate for all samples was 82.9% and the area under ROC curve was about 82.0% in the Back Propagation Neural Network (BPNN) prediction model. The AUC of the optimal multilayer perceptron prediction model was above 90% in multilayer perceptron model. The discrimination efficiency of the multilayer perceptron model was superior to BPNN model. Conclusions The neural network prediction models have greater accuracy than traditional methods. The multilayer perceptron is the best prediction model of suicide. The neural network prediction model has significance for clinical diagnosis and developing an artificial intelligence (AI) auxiliary clinical system.
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Affiliation(s)
- Juncheng Lyu
- School of Public Health, Weifang Medical University, Weifang, China
| | - Hong Shi
- Shandong Ikang Group, Weifang Ikang Guobin Medical Examination Center, Weifang, China
| | - Jie Zhang
- Department of Sociology, Central University of Finance Economics, Beijing, China
- Department of Sociology, State University of New York Buffalo State, Buffalo, NY, United States
| | - Jill Norvilitis
- Department of Sociology, State University of New York Buffalo State, Buffalo, NY, United States
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Hopkins D, Rickwood DJ, Hallford DJ, Watsford C. Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis. Front Digit Health 2022; 4:945006. [PMID: 35983407 PMCID: PMC9378826 DOI: 10.3389/fdgth.2022.945006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/29/2022] [Indexed: 11/23/2022] Open
Abstract
Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.
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Affiliation(s)
- Danielle Hopkins
- Faculty of Health, University of Canberra, Canberra, ACT, Australia
- *Correspondence: Danielle Hopkins
| | | | | | - Clare Watsford
- Faculty of Health, University of Canberra, Canberra, ACT, Australia
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22
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Trinhammer ML, Merrild ACH, Lotz JF, Makransky G. Predicting crime during or after psychiatric care: Evaluating machine learning for risk assessment using the Danish patient registries. J Psychiatr Res 2022; 152:194-200. [PMID: 35752071 DOI: 10.1016/j.jpsychires.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Structural changes in psychiatric systems have altered treatment opportunities for patients in need of mental healthcare. These changes are possibly associated with an increase in post-discharge crime, reported in the increase of forensic psychiatric populations. As current risk-assessment tools are time-consuming to administer and offer limited accuracy, this study aims to develop a predictive model designed to identify psychiatric patients at risk of committing crime leading to a future forensic psychiatric treatment course. METHOD We utilized the longitudinal quality of the Danish patient registries, identifying the 45.720 adult patients who had contact with the psychiatric system in 2014, of which 474 committed crime leading to a forensic psychiatric treatment course after discharge. Four machine learning models (Logistic Regression, Random Forest, XGBoost and LightGBM) were applied over a range of sociodemographic, judicial, and psychiatric variables. RESULTS This study achieves a F1-macro score of 76%, with precision = 57% and recall = 47% reported by the LightGBM algorithm. Our model was therefore able to identify 47% of future forensic psychiatric patients, while making correct predictions in 57% of cases. CONCLUSION The study demonstrates how a clinically useful initial risk-assessment can be achieved using machine learning on data from patient registries. The proposed approach offers the opportunity to flag potential future forensic psychiatric patients, while in contact with the general psychiatric system, hereby allowing early-intervention initiatives to be activated.
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Affiliation(s)
- M L Trinhammer
- Department of Psychology, University of Copenhagen, Øster Farimagsgade 2A, 1353, Copenhagen, Denmark.
| | - A C Holst Merrild
- DTU COMPUTE, Technical University of Denmark, Building 324, 2800, Kongens Lyngby, Denmark
| | - J F Lotz
- The ROCKWOOL Foundation, Ny Kongensgade 6, 1472, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark
| | - G Makransky
- Department of Psychology, University of Copenhagen, Øster Farimagsgade 2A, 1353, Copenhagen, Denmark
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Modeling Suicidality Risks and Understanding the Phenomenon of Suicidality Under the Loupe of Pandemic Context: National Findings of the COMET-G Study in the Russian Population. CONSORTIUM PSYCHIATRICUM 2022. [DOI: 10.17816/cp167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND: Suicidality is a complex clinical phenomenon reflecting vulnerability to suicidal behavior which can be explained via the biopsychosocial paradigm and in relationship with a variety of country-specific factors. Data on suicides within the Russian population are inconsistent (from 11.7 up to 25.1 per 100.000), whereas the populations suicidality risks have not been investigated in detail. Suicidality estimates during the multifactorial influence of the COVID-19 pandemic could serve as a basis to learn more about this mental health indicator.
METHODS: The current study is a part of the COMET-G international project (40 countries, n=55.589), which represents an analysis of data collected from Russias general population (n=7714, 3312 y.o., 61% female) to estimate suicidality using the Risk Assessment Suicidality Scale (RASS) and its relationships with socio-demographic, clinical, and life-habit characteristics during the COVID-19 pandemic. The evaluation of the statistical data (descriptive statistics, ANOVA, LASSO linear regression, significant at =0.05) was undertaken using TIBCO Statistica.
RESULTS: According to the RASS, at least 20.68%, and up to 29.15%, of the general population in Russia demonstrated increased risk of suicidality during the pandemic. Modelling these risks pointed to the key vulnerabilities related to mental and behavioral disorders, such as (i) current severe depression and a history of mental disorders, (ii) bipolar disorder, (iii) use of illicit drugs surprisingly outranking the alcohol misuse, and psychiatric compounds (hypnotics), highlighting sleep quality deterioration, (iv) a history of suicide attempts and self-harm though not self-reported changes in depression in response were predictors of the risk of suicidality, which can be explained by the phenomenon of learned suicidality, a habitual behavioral suicidality pattern completion accumulated over the background. Such (v) socio-demographic indicators as younger age (disregarding the gender factor), a marital status of single, having no children, living with fewer people in the household, a recent increase in family conflicts, increased need for emotional support, decreased need for communication, and not believing in precautionary measures against COVID-19, contributed to the increase of suicidality risk in the context of the pandemic.
CONCLUSIONS: The findings of this study revealed new suicide risk factors that should be taken into account in suicidality risk assessments for the Russian population and in the implementation of suicide prevention programs in the region.
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Machine learning model to predict mental health crises from electronic health records. Nat Med 2022; 28:1240-1248. [PMID: 35577964 PMCID: PMC9205775 DOI: 10.1038/s41591-022-01811-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/01/2022] [Indexed: 12/02/2022]
Abstract
The timely identification of patients who are at risk of a mental health crisis can lead to improved outcomes and to the mitigation of burdens and costs. However, the high prevalence of mental health problems means that the manual review of complex patient records to make proactive care decisions is not feasible in practice. Therefore, we developed a machine learning model that uses electronic health records to continuously monitor patients for risk of a mental health crisis over a period of 28 days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. A follow-up 6-month prospective study evaluated our algorithm’s use in clinical practice and observed predictions to be clinically valuable in terms of either managing caseloads or mitigating the risk of crisis in 64% of cases. To our knowledge, this study is the first to continuously predict the risk of a wide range of mental health crises and to explore the added value of such predictions in clinical practice. Machine learning applied on electronic health records can predict mental health crises 28 days in advance and become a clinically valuable tool for managing caseloads and mitigating the risk of crisis.
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Barberis N, Cannavò M, Cuzzocrea F, Verrastro V. Suicidal Behaviours During Covid-19 Pandemic: A Review. CLINICAL NEUROPSYCHIATRY 2022; 19:84-96. [PMID: 35601250 PMCID: PMC9112993 DOI: 10.36131/cnfioritieditore20220202] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Objective Novel COVID-19 disease has become a major concern worldwide, and a recent line of research warned that the context of the COVID-19 pandemic may be a major risk factor for developing severe suicidal behaviors. A broad systematic review is needed to cover the studies that have already assessed the potential underlying factors for suicidal behaviors in the context of the COVID-19 outbreak. Method A total of 52 studies met the inclusion criteria, and data were then described according to the subsequent categories: (1) countries where the studies were carried out; (2) factors impacting suicidal behaviors during the COVID-19 outbreak; and (3) examination of the observed populations. Results Findings of the current systematic review suggest that there is a certain amount of heterogeneity in factors impacting suicidal behaviors during the COVID-19 outbreak, with economic downturn, psychiatric vulnerability, isolation and quarantine, health concerns, and relational difficulties being the most prominent reasons for developing suicidal behaviors during the COVID-19 outbreak. Conclusions Timely interventions are needed to prevent suicidal behaviors in both the clinical and general populations, and in this regard, the creation of standard procedures may speed up the process.
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Abstract
Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.
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Lejeune A, Le Glaz A, Perron PA, Sebti J, Baca-Garcia E, Walter M, Lemey C, Berrouiguet S. Artificial intelligence and suicide prevention: a systematic review. Eur Psychiatry 2022; 65:1-22. [PMID: 35166203 PMCID: PMC8988272 DOI: 10.1192/j.eurpsy.2022.8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
Background Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings. Conclusions AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.
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Affiliation(s)
- Alban Lejeune
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | - Aziliz Le Glaz
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | | | - Johan Sebti
- Mental Health Department, French Polynesia Hospital, FFC3+H9G, Pirae, French Polynesia
| | | | - Michel Walter
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
| | - Christophe Lemey
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
- SPURBO, IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France
| | - Sofian Berrouiguet
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Brest, France
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28
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Andersson HW, Lilleeng SE, Ruud T, Ose SO. Suicidal ideation in patients with mental illness and concurrent substance use: analyses of national census data in Norway. BMC Psychiatry 2022; 22:1. [PMID: 34983462 PMCID: PMC8725289 DOI: 10.1186/s12888-021-03663-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/20/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Suicidal ideation may signal potential risk for future suicidal behaviors and death. We examined the prevalence of recent suicidal ideation in patients with mental illness and concurrent substance use and explored the clinical and sociodemographic factors associated with suicidal ideation in this patient subgroup, which represents a particular risk group for adverse psychiatric outcomes. METHODS We used national cross-sectional census data in Norway collected from 25,525 patients in specialized mental health services. The analytic sample comprised 3,842 patients with concurrent substance use, defined as having a co-morbid substance use disorder or who reported recent regular alcohol use/occasional illicit drug use. Data included suicidal ideation measured in relation to the current treatment episode, sociodemographic characteristics and ICD-10 diagnoses. Bivariate and multivariate analyses were used to examine differential characteristics between patients with and without suicidal ideation. RESULTS The prevalence of suicidal ideation was 25.8%. The suicidal ideation rates were particularly high for those with personality disorders, posttraumatic stress disorder, and depression, and for alcohol and sedatives compared with other substances. Patients with suicidal ideation were characterized by being younger, having single marital status, and having poorly perceived social relationships with family and friends. CONCLUSION Suicidal ideation in patients with mental illness and concurrent substance use was associated with a number of distinct characteristics. These results might help contribute to an increased focus on a subgroup of individuals at particular risk for suicidality and support suicide prevention efforts in specialized mental health services.
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Affiliation(s)
- Helle Wessel Andersson
- Department of Research and Development, Clinic of Substance Use and Addiction Medicine, St. Olavs University Hospital, PB 3250 Sluppen, 7006, Trondheim, Norway.
| | - Solfrid E. Lilleeng
- grid.461584.a0000 0001 0093 1110Department of Analysis and Performance Assessment, The Norwegian Directorate of Health, Holtermanns vei 70, 7031 Trondheim, Norway
| | - Torleif Ruud
- grid.411279.80000 0000 9637 455XAkershus University Hospital, Mental Health Services, PB 1000 1478 Lørenskog, Norway ,grid.5510.10000 0004 1936 8921Institute of Clinical Medicine, University of Oslo, PB 1171 Blindern, 0318 Oslo, Norway
| | - Solveig Osborg Ose
- grid.4319.f0000 0004 0448 3150Department of Health, SINTEF, Professor Brochs gate 2, 7030 Trondheim, Norway
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Balcombe L, De Leo D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Front Psychol 2022; 12:796371. [PMID: 35058855 PMCID: PMC8765720 DOI: 10.3389/fpsyg.2021.796371] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Suicidal men feel the need to be self-reliant and that they cannot find another way out of relationship or socioeconomic issues. Suicide prevention is of crucial importance worldwide. The much higher rate of suicide in men engenders action. The prelude is a subjective experience that can be very isolating and severely distressing. Men may not realize a change in their thinking and behaviors, which makes it more difficult to seek and get help, thereby interrupting a "downward spiral". Stoicism often prevents men from admitting to their personal struggle. The lack of "quality" connections and "non-tailored" therapies has led to a high number of men "walking out" on traditional clinical approaches. But there are complicated relationships in motivations and formative behaviors of suicide with regards to emotional state, psychiatric disorders, interpersonal life events and suicidal behavior method selection. Middle-aged and older men have alternated as the most at-risk of suicide. There is no one solution that applies to all men, but digital tools may be of assistance (e.g., video conferences, social networks, telephone calls, and emails). Digital interventions require higher levels of effectiveness for distress and suicidality but self-guided approaches may be the most suitable for men especially where linked with an integrated online suicide prevention platform (e.g., quick response with online chats, phone calls, and emails). Furthermore, technology-enabled models of care offer promise to advance appropriate linking to mental health services through better and faster understanding of the specific needs of individuals (e.g., socio-cultural) and the type and level of suicidality experienced. Long-term evidence for suicidality and its evaluation may benefit from progressing human computer-interaction and providing impetus for an eminent integrated digital platform.
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Affiliation(s)
- Luke Balcombe
- Australian Institute for Suicide Research and Prevention, School of Applied Psychology, Griffith University, Brisbane, QLD, Australia
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30
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Wei YX, Liu BP, Zhang J, Wang XT, Chu J, Jia CX. Prediction of recurrent suicidal behavior among suicide attempters with Cox regression and machine learning: a 10-year prospective cohort study. J Psychiatr Res 2021; 144:217-224. [PMID: 34700209 DOI: 10.1016/j.jpsychires.2021.10.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/28/2021] [Accepted: 10/18/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Research on predictors and risk of recurrence after suicide attempt from China is lacking. This study aims to identify risk factors and develop prediction models for recurrent suicidal behavior among suicide attempters using Cox proportional hazard (CPH) and machine learning methods. METHODS The prospective cohort study included 1103 suicide attempters with a maximum follow-up of 10 years from rural China. Baseline characteristics, collected by face-to-face interviews at least 1 month later after index suicide attempt, were used to predict recurrent suicidal behavior. CPH and 3 machine learning algorithms, namely, the least absolute shrinkage and selection operator, random survival forest, and gradient boosting decision tree, were used to construct prediction models. Model performance was accessed by concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve (AUC) value for discrimination, and time-dependent calibration curve along with Brier score for calibration. RESULTS The median follow-up time was 7.79 years, and 49 suicide attempters had recurrent suicidal behavior during the study period. Four models achieved comparably good discrimination and calibration performance, with all C-indexes larger than 0.70, AUC values larger than 0.65, and Brier scores smaller than 0.06. Mental disorder emerged as the most important predictor across all four models. Suicide attempters with mental disorders had a 3 times higher risk of recurrence than those without. History of suicide attempt (HR = 2.84, 95% CI: 1.34-6.02), unstable marital status (HR = 2.81, 95% CI: 1.38-5.71), and older age (HR = 1.51, 95% CI: 1.14-2.01) were also identified as independent predictors of recurrent suicidal behavior by CPH model. CONCLUSIONS We developed four models to predict recurrent suicidal behavior with comparable good prediction performance. Our findings potentially provided benefits in screening vulnerable individuals on a more precise scale.
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Affiliation(s)
- Yan-Xin Wei
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Bao-Peng Liu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Jie Zhang
- Shandong University Center for Suicide Prevention Research, China; Department of Sociology, State University of New York College at Buffalo, Buffalo, NY, 14222, USA
| | - Xin-Ting Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Jie Chu
- Shandong Center for Disease Prevention and Control, Jinan, 250014, Shandong, China
| | - Cun-Xian Jia
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China.
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Jiang T, Nagy D, Rosellini AJ, Horváth-Puhó E, Keyes KM, Lash TL, Galea S, Sørensen HT, Gradus JL. Suicide prediction among men and women with depression: A population-based study. J Psychiatr Res 2021; 142:275-282. [PMID: 34403969 PMCID: PMC8456450 DOI: 10.1016/j.jpsychires.2021.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/12/2021] [Accepted: 08/09/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Accurate identification of persons at risk of suicide is challenging because suicide is a rare outcome with a multifactorial origin. The purpose of this study was to predict suicide among persons with depression using machine learning methods. METHODS A case-cohort study was conducted in Denmark between January 1, 1995 and December 31, 2015. Cases were all persons who died by suicide and had an incident depression diagnosis in Denmark (n = 2,774). The comparison subcohort was a 5% random sample of all individuals in Denmark at baseline, restricted to persons with an incident depression diagnosis during the study period (n = 11,963). Classification trees and random forests were used to predict suicide. RESULTS In men with depression, there was a high risk of suicide among those who were prescribed other analgesics and antipyretics (i.e., non-opioid analgesics such as acetaminophen), prescribed hypnotics and sedatives, and diagnosed with a poisoning (n = 96; risk = 81%). In women with depression, there was an elevated risk of suicide among those who were prescribed other analgesics and antipyretics, anxiolytics, and hypnotics and sedatives, but were not diagnosed with poisoning nor cerebrovascular diseases (n = 338; risk = 58%). DISCUSSION Psychiatric disorders and their associated medications were strongly indicative of suicide risk. Notably, anti-inflammatory medications (e.g., acetaminophen) prescriptions, which are used to treat chronic pain and illnesses, were associated with suicide risk in persons with depression. Machine learning may advance our ability to predict suicide deaths.
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Affiliation(s)
- Tammy Jiang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
| | - David Nagy
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Anthony J. Rosellini
- Center for Anxiety and Related Disorders, Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
| | | | - Katherine M. Keyes
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Timothy L. Lash
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark,Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Sandro Galea
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA,Department of Family Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Henrik T. Sørensen
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA,Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Jaimie L. Gradus
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA,Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark,Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA
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32
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Bompelli A, Wang Y, Wan R, Singh E, Zhou Y, Xu L, Oniani D, Kshatriya BSA, Balls-Berry J(JE, Zhang R. Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review. HEALTH DATA SCIENCE 2021; 2021:9759016. [PMID: 38487504 PMCID: PMC10880156 DOI: 10.34133/2021/9759016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 06/28/2021] [Indexed: 03/17/2024]
Abstract
Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.
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Affiliation(s)
- Anusha Bompelli
- Department of Pharmaceutical Care & Health Systems, University of Minnesota, USA
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, USA
| | - Ruyuan Wan
- Department of Computer Science, University of Minnesota, USA
| | - Esha Singh
- Department of Computer Science, University of Minnesota, USA
| | - Yuqi Zhou
- Institute for Health Informatics and College of Pharmacy, University of Minnesota, USA
| | - Lin Xu
- Carlson School of Business, University of Minnesota, USA
| | - David Oniani
- Department of Computer Science and Mathematics, Luther College, USA
| | | | | | - Rui Zhang
- Institute for Health Informatics, Department of Pharmaceutical Care & Health Systems, University of Minnesota, USA
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Paul R, Tsuei T, Cho K, Belden A, Milanini B, Bolzenius J, Javandel S, McBride J, Cysique L, Lesinski S, Valcour V. Ensemble machine learning classification of daily living abilities among older people with HIV. EClinicalMedicine 2021; 35:100845. [PMID: 34027327 PMCID: PMC8129893 DOI: 10.1016/j.eclinm.2021.100845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 03/11/2021] [Accepted: 03/17/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND clinically relevant methods to identify individuals at risk for impaired daily living abilities secondary to neurocognitive impairment (ADLs) remain elusive. This is especially true for complex clinical conditions such as HIV-Associated Neurocognitive Disorders (HAND). The aim of this study was to identify novel and modifiable factors that have potential to improve diagnostic accuracy of ADL risk, with the long-term goal of guiding future interventions to minimize ADL disruption. METHODS study participants included 79 people with HIV (PWH; mean age = 63; range = 55-80) enrolled in neuroHIV studies at University California San Francisco (UCSF) between 2016 and 2019. All participants were virally suppressed and exhibited objective evidence of neurocognitive impairment. ADL status was defined as either normative (n = 39) or at risk (n = 40) based on a task-based protocol. Gradient boosted multivariate regression (GBM) was employed to identify the combination of variables that differentiated ADL subgroup classification. Predictor variables included demographic factors, HIV disease severity indices, brain white matter integrity quantified using diffusion tensor imaging, cognitive test performance, and health co-morbidities. Model performance was examined using average Area Under the Curve (AUC) with repeated five-fold cross validation. FINDINGS the univariate GBM yielded an average AUC of 83% using Wide Range Achievement test 4 (WRAT-4) reading score, self-reported thought confusion and difficulty reading, radial diffusivity (RD) in the left external capsule, fractional anisotropy (FA) in the left cingulate gyrus, and Stroop performance. The model allowing for two-way interactions modestly improved classification performance (AUC of 88%) and revealed synergies between race, reading ability, cognitive performance, and neuroimaging metrics in the genu and uncinate fasciculus. Conversion of Neuropsychological Assessment Battery Daily Living Module (NAB-DLM) performance from raw scores into T scores amplified differences between White and non-White study participants. INTERPRETATION demographic and sociocultural factors are critical determinants of ADL risk status among older PWH who meet diagnostic criteria for neurocognitive impairment. Task-based ADL assessment that relies heavily on reading proficiency may artificially inflate the frequency/severity of ADL impairment among diverse clinical populations. Culturally relevant measures of ADL status are needed for individuals with acquired neurocognitive disorders, including HAND.
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Affiliation(s)
- Robert Paul
- Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States
- Department of Psychological Sciences, University of Missouri-St. Louis, St. Louis, MO, United States
- Corresponding author at: Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States.
| | - Torie Tsuei
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Kyu Cho
- Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States
| | - Andrew Belden
- Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States
| | - Benedetta Milanini
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Jacob Bolzenius
- Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States
| | - Shireen Javandel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Joseph McBride
- Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States
| | - Lucette Cysique
- School of Psychology, University of New South Wales, Sydney, Australia
| | - Samantha Lesinski
- Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States
| | - Victor Valcour
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, United States
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Walsh CG, Johnson KB, Ripperger M, Sperry S, Harris J, Clark N, Fielstein E, Novak L, Robinson K, Stead WW. Prospective Validation of an Electronic Health Record-Based, Real-Time Suicide Risk Model. JAMA Netw Open 2021; 4:e211428. [PMID: 33710291 PMCID: PMC7955273 DOI: 10.1001/jamanetworkopen.2021.1428] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
IMPORTANCE Numerous prognostic models of suicide risk have been published, but few have been implemented outside of integrated managed care systems. OBJECTIVE To evaluate performance of a suicide attempt risk prediction model implemented in a vendor-supplied electronic health record to predict subsequent (1) suicidal ideation and (2) suicide attempt. DESIGN, SETTING, AND PARTICIPANTS This observational cohort study evaluated implementation of a suicide attempt prediction model in live clinical systems without alerting. The cohort comprised patients seen for any reason in adult inpatient, emergency department, and ambulatory surgery settings at an academic medical center in the mid-South from June 2019 to April 2020. MAIN OUTCOMES AND MEASURES Primary measures assessed external, prospective, and concurrent validity. Manual medical record validation of coded suicide attempts confirmed incident behaviors with intent to die. Subgroup analyses were performed based on demographic characteristics, relevant clinical context/setting, and presence or absence of universal screening. Performance was evaluated using discrimination (number needed to screen, C statistics, positive/negative predictive values) and calibration (Spiegelhalter z statistic). Recalibration was performed with logistic calibration. RESULTS The system generated 115 905 predictions for 77 973 patients (42 490 [54%] men, 35 404 [45%] women, 60 586 [78%] White, 12 620 [16%] Black). Numbers needed to screen in highest risk quantiles were 23 and 271 for suicidal ideation and attempt, respectively. Performance was maintained across demographic subgroups. Numbers needed to screen for suicide attempt by sex were 256 for men and 323 for women; and by race: 373, 176, and 407 for White, Black, and non-White/non-Black patients, respectively. Model C statistics were, across the health system: 0.836 (95% CI, 0.836-0.837); adult hospital: 0.77 (95% CI, 0.77-0.772); emergency department: 0.778 (95% CI, 0.777-0.778); psychiatry inpatient settings: 0.634 (95% CI, 0.633-0.636). Predictions were initially miscalibrated (Spiegelhalter z = -3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26). CONCLUSIONS AND RELEVANCE In this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system. Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems. The next step is careful pairing with low-cost, low-harm preventive strategies in a pragmatic trial of effectiveness in preventing future suicidality.
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Affiliation(s)
- 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 and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kevin B. Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michael Ripperger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sarah Sperry
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Joyce Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Nathaniel Clark
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Elliot Fielstein
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Laurie Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Katelyn Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - William W. Stead
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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Mansourian M, Khademi S, Marateb HR. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics (Basel) 2021; 11:393. [PMID: 33669114 PMCID: PMC7996506 DOI: 10.3390/diagnostics11030393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/13/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023] Open
Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.
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Affiliation(s)
- Mahsa Mansourian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Sadaf Khademi
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
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Liang S, Zhang J, Zhao Q, Wilson A, Huang J, Liu Y, Shi X, Sha S, Wang Y, Zhang L. Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder. Front Psychiatry 2021; 12:644038. [PMID: 34248696 PMCID: PMC8261285 DOI: 10.3389/fpsyt.2021.644038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 05/24/2021] [Indexed: 11/30/2022] Open
Abstract
Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population. Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model. Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer-Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set. Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.
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Affiliation(s)
- Sixiang Liang
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jinhe Zhang
- Peking University HuiLongGuan Clinical Medical School, Beijing HuiLongGuan Hospital, Beijing, China
| | - Qian Zhao
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Amanda Wilson
- Department of Psychology, Faculty of Health and Life Sciences, De Montfort University, Leicester, United Kingdom
| | - Juan Huang
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yuan Liu
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiaoning Shi
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yuanyuan Wang
- Department of Psychology, Faculty of Health and Life Sciences, De Montfort University, Leicester, United Kingdom
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China
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