Identifying Predictors of Suicide in Severe Mental Illness : A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

Senior, Morwenna; Burghart, Matthias; Yu, Rongqin; Kormilitzin, Andrey; Liu, Qiang; Vaci, Nemanja; Nevado-Holgado, Alejo; Pandit, Smita; Zlodre, Jakov; Fazel, Seena

Identifying Predictors of Suicide in Severe Mental Illness : A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

Dateien

Senior_2-1r125rxk9en0b2.pdfGröße: 886.59 KBDownloads: 88

Datum

2020

Autor:innen

Senior, Morwenna

Burghart, Matthias

Yu, Rongqin

Kormilitzin, Andrey

Liu, Qiang

Vaci, Nemanja

Nevado-Holgado, Alejo

Pandit, Smita

Zlodre, Jakov

Fazel, Seena

Open Access-Veröffentlichung

Open Access Gold

Sammlungen

Psychologie

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Frontiers in Psychiatry. Frontiers Research Foundation. 2020, 11, 268. eISSN 1664-0640. Available under: doi: 10.3389/fpsyt.2020.00268

Zusammenfassung

Background: Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.

Methods: In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).

Results: In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).

Conclusions: It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

Fachgebiet (DDC)

150 Psychologie

Schlagwörter

risk assessment, feasibility, OxMIS, suicide, schizophrenia, bipolar disorder, electronic health records, natural language processing

Zitieren

ISO 690

SENIOR, Morwenna, Matthias BURGHART, Rongqin YU, Andrey KORMILITZIN, Qiang LIU, Nemanja VACI, Alejo NEVADO-HOLGADO, Smita PANDIT, Jakov ZLODRE, Seena FAZEL, 2020. Identifying Predictors of Suicide in Severe Mental Illness : A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS). In: Frontiers in Psychiatry. Frontiers Research Foundation. 2020, 11, 268. eISSN 1664-0640. Available under: doi: 10.3389/fpsyt.2020.00268

BibTex

@article{Senior2020Ident-56609,
  year={2020},
  doi={10.3389/fpsyt.2020.00268},
  title={Identifying Predictors of Suicide in Severe Mental Illness : A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)},
  volume={11},
  journal={Frontiers in Psychiatry},
  author={Senior, Morwenna and Burghart, Matthias and Yu, Rongqin and Kormilitzin, Andrey and Liu, Qiang and Vaci, Nemanja and Nevado-Holgado, Alejo and Pandit, Smita and Zlodre, Jakov and Fazel, Seena},
  note={Article Number: 268}
}

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    <dcterms:abstract xml:lang="eng">Background: Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.&lt;br /&gt;&lt;br /&gt;Methods: In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).&lt;br /&gt;&lt;br /&gt;Results: In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).&lt;br /&gt;&lt;br /&gt;Conclusions: It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.</dcterms:abstract>
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