Predicting suicidal behavior and self-harm after general hospitalization of adults with serious mental illness

Abstract

Individuals with psychiatric disorders are vulnerable to adverse mental health outcomes following physical illness. This longitudinal cohort study defined risk profiles for readmission for suicidal behavior and self-harm after general hospitalization of adults with serious mental illness. Structured electronic health record data were analyzed from 15,644 general non-psychiatric index hospitalizations of individuals with depression, bipolar, and psychotic disorders admitted to an urban health system in the southwestern United States between 2006 and 2017. Using data from one-year prior to and including index hospitalization, supervised machine learning was implemented to predict risk of readmission for suicide attempt and self-harm in the following year. The Classification and Regression Tree algorithm produced a classification prediction with an area under the receiver operating curve (AUC) of 0.86 (95% confidence interval (CI) 0.74-0.97). Incidence of suicide-related behavior was highest after general non-psychiatric hospitalizations of individuals with prior suicide attempt or self-harm (18%; 69 cases/389 hospitalizations) and lowest after hospitalizations associated with very high medical morbidity burden (0 cases/3090 hospitalizations). Predictor combinations, rather than single risk factors, explained the majority of risk, including concomitant alcohol use disorder with moderate medical morbidity, and age ≤55-years-old with low medical morbidity. Findings suggest that applying an efficient and highly interpretable machine learning algorithm to electronic health record data may inform general hospital clinical decision support, resource allocation, and preventative interventions for medically ill adults with serious mental illness.

Keywords: Electronic health record; Hospitalization; Informatics; Physical illness; Self-harm; Suicide attempt.

Figure 1:. Decision tree for risk stratification of readmission for suicide attempt and self-harm following medical hospitalization.

The figure visually depicts a binary tree stratifying risk of readmission for suicide attempt or self-harm following medical hospitalization among individuals with serious mental illness (blue = low risk, red = high risk). Each path from root to leaf node can be translated into a series of ‘if-then’ rules that can be applied to classify observations. Each leaf node is associated with a decision rule, corresponding to the most frequent class label (i.e. attempt vs no attempt) of the observations belonging to that node. The denominator represents the number of total hospitalizations corresponding to that decision rule, and the numerator represents the number of readmissions for suicide attempt or self-injury in the year following the hospitalization. Elixhauser category diagnoses refers to the number of common disease conditions. In-hospital mortality score refers to the van Walraven score, a standardized method of condensing medical comorbidities into a single numeric score discriminating risk of death in the hospital. Top right diagram visually represents risk stratification, where circle size represents the size of the subgroup and shading represents the risk of suicide attempt or self-harm. An example of a tree path (right-most branch): 15,644 index hospitalizations were followed by an all-cause readmission, of these, 389 index hospitalizations were of individuals with a prior suicide attempt or self-harm, of these, 69 (18%) index hospitalizations were followed by a readmission for suicide attempt or self-harm. SMI = Serious Mental Illness.