Mortality Risk and Related Adverse Outcomes Following Discharge from Inpatient Psychiatric Care

Editorial

27 September 2021

Editorial: Mortality Risk and Related Adverse Outcomes Following Discharge From Inpatient Psychiatric Care

Merete Nordentoft

Eric D. Caine

and

Roger T. Webb

1,149 views

0 citations

Editors

Roger Thomas Webb

The University of Manchester

Merete Nordentoft

University of Copenhagen

Eric D Caine

University of Rochester

Impact

Flowchart. *In 7% of cases (n = 38) and 1% of cases (n = 8) the suicide screening assessments were, respectively carried out by telephone or information was missing on how the assessment occurred, i.e., by home visit, physical meeting elsewhere or by telephone.

Brief Research Report

20 April 2021

Assessment of Suicide Risks During the First Week Immediately After Discharge From Psychiatric Inpatient Facility

Trine Madsen

, 3 more and

Merete Nordentoft

5,412 views

5 citations

Original Research

18 August 2020

Mortality Among Patients Discharged From an Acute Psychiatric Department: A 5-Year Prospective Study

Astrid Prestmo

, 3 more and

Ole Kristian Drange

3,587 views

6 citations

Systematic Review

23 July 2020

Suicides in Mood Disorders in Psychiatric Settings in Nordic National Register–Based Studies

Erkki T. Isometsä

6,381 views

18 citations

Original Research

06 May 2020

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System

Ronald C. Kessler

, 19 more and

Robert M. Bossarte

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010–2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79–.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%–32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%–9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

11,874 views

38 citations

Original Research

09 June 2020

Early Post-Discharge Suicide in Mental Health Patients: Findings From a National Clinical Survey

Lana Bojanić

, 4 more and

Pauline Turnbull

7,946 views

19 citations

Original Research

15 April 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)

Morwenna Senior

, 8 more and

Seena Fazel

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.

9,765 views

30 citations