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

Kessler, Ronald C.; Bauer, Mark S.; Bishop, Todd M.; Demler, Olga V.; Dobscha, Steven K.; Gildea, Sarah M.; Goulet, Joseph L.; Karras, Elizabeth; Kreyenbuhl, Julie; Landes, Sara J.; Liu, Howard; Luedtke, Alex; Mair, Patrick; McAuliffe, William H.B.; Nock, Matthew K.; Petukhova, Maria; Pigeon, Wilfred R.; Sampson, Nancy A.; Smoller, Jordan W.; Weinstock, Lauren M.; Bossarte, Robert M.

doi:10.3389/fpsyt.2020.00390

Cited by 36 publications

(40 citation statements)

References 114 publications

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“…Our random forests’ operating characteristics are comparable with those of a previous study that used machine learning to predict postdischarge suicide among veterans, 10 which found that the 5% of patients with the highest predicted risk accounted for 32% of suicides in the 1 month after psychiatric hospital discharge. In our study, persons in the highest 5% of predicted suicide risk accounted for 23% of all suicide deaths among men and 38% among women 1 month postdischarge.…”

Section: Discussionsupporting

confidence: 78%

“…This suggests that a prevention program delivered to only 5% of hospitalised patients with the highest predicted risk could capture a large proportion of patients who would otherwise die by suicide. 10…”

Section: Discussionmentioning

confidence: 99%

“…8 Kessler and colleagues developed algorithms to estimate suicide risk among USA army soldiers after a stay in hospital, and in the USA Veterans Health Administration system. 9,10 However, despite this contribution, several gaps remain. First, few studies have focused on predicting suicide in the relatively short 30-day window of interest to clinicians.…”

Section: Suicide Predictionmentioning

confidence: 99%

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Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark

et al. 2021

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Background Suicide risk is high in the 30 days after discharge from psychiatric hospital, but knowledge of the profiles of high-risk patients remains limited. Aims To examine sex-specific risk profiles for suicide in the 30 days after discharge from psychiatric hospital, using machine learning and Danish registry data. Method We conducted a case–cohort study capturing all suicide cases occurring in the 30 days after psychiatric hospital discharge in Denmark from 1 January 1995 to 31 December 2015 (n = 1205). The comparison subcohort was a 5% random sample of all persons born or residing in Denmark on 1 January 1995, and who had a first psychiatric hospital admission between 1995 and 2015 (n = 24 559). Predictors included diagnoses, surgeries, prescribed medications and demographic information. The outcome was suicide death recorded in the Danish Cause of Death Registry. Results For men, prescriptions for anxiolytics and drugs used in addictive disorders interacted with other characteristics in the risk profiles (e.g. alcohol-related disorders, hypnotics and sedatives) that led to higher risk of postdischarge suicide. In women, there was interaction between recurrent major depression and other characteristics (e.g. poisoning, low income) that led to increased risk of suicide. Random forests identified important suicide predictors: alcohol-related disorders and nicotine dependence in men and poisoning in women. Conclusions Our findings suggest that accurate prediction of suicide during the high-risk period immediately after psychiatric hospital discharge may require a complex evaluation of multiple factors for men and women.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Suicide Predictionmentioning

confidence: 99%

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Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark

et al. 2021

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“…The total number of potential suicide risk factors examined was 12 242 (mean per model = 135, s. d. = 387) after excluding the samples that used clinical judgement (in which the number of potential and included suicide risk factors could not be ascertained) and a single machine-learning study that used 8071 predictor variables. 62 The total number of included suicide risk factors in the suicide prediction models could not be ascertained exactly because some machine-learning studies did not clarify this precisely, but there were at least 777 (mean per model 8.7, s.d. = 9.9) of which 598 could be identified and tabulated (Supplementary Material Table S1, available at https://doi.org/10.1192/bjo.2020.162).…”

Section: Searches and Data Extractionmentioning

confidence: 99%

Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers

et al. 2021

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Background Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors. Aims To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions. Method Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title. Results In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7–8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0–22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1–10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors. Conclusions Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.

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“…In addition to the NLP-derived SBDH variables, we also identified social determinants from the structured data. We used the ICD-9 codes from patient diagnoses [ 33 ] to construct these 3 SBDH variables: (1) housing insecurity, (2) unemployment, and (3) social isolation. These were later integrated with the NLP-derived SBDH variables and prioritized in case of any mismatch.…”

Section: Methodsmentioning

confidence: 99%

Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study

Mitra¹,

Ahsan²,

Li³

et al. 2021

JMIR Med Inform

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Background Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem. Objective The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD. Methods We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR). Results The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes. Conclusions This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.

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

Cited by 36 publications

References 114 publications

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark

Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers

Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study

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