Key Takeaways

From the Authors As published in the article
  1. Flexible data-mining methods derived from computer science, such as survival trees, have been developed to detect interactions among risk factors of suicide reattempt, allowing for identification of homogeneous subgroups of individuals with simple groupings of clinical features.
  2. Three subgroups of patients presented with an increased risk of suicide reattempt within 6 months after suicide attempt: patients with alcohol use disorder and a previous suicide attempt with acute alcohol use, patients with anxiety disorders, and patients with a history of more than 2 suicide attempts in the past 3 years.
  3. Risk factor combinations highlighted by a data-driven method of analyzing risk factors allow for a better evaluation of a subject’s suicide risk in clinical practice.
Extended Takeaways
  1. In this cohort, 117 of 880 participants with known 6-month status (13.3%) reattempted suicide and 11 (1.1%) died by suicide, underscoring how much clinically relevant recurrence is concentrated in the first 6 months after an index attempt.
  2. Among patients with alcohol use disorder, acute alcohol use during the index attempt sharply differentiated prognosis: 38 of 127 patients (29.9%) in the AUD plus acute alcohol use subgroup reattempted or died by suicide, whereas none of the 19 patients with AUD but without acute alcohol use reattempted.
  3. The lowest-risk subgroup was patients without AUD, with fewer than 2 previous suicide attempts in the past 3 years, and without anxiety disorder; only 23 of 361 reattempted, with RR (95% CI) of 0.36 (0.23 to 0.55) versus the root node.
  4. Recent repetition burden mattered more than lifetime history alone in this model: among 46 individuals without AUD and with more than 2 suicide attempts in the past 3 years, 12 reattempted, corresponding to RR (95% CI) of 2.11 (1.25 to 3.54).
  5. The simplified clinician-facing algorithm favored ruling out near-term recurrence over ruling it in, with sensitivity of 0.8 and negative predictive value of 0.94, but specificity was 0.47 and positive predictive value was 0.19.
  6. Performance of the simplified prediction tool was modest, with AUC of 0.63 and HR = 1.78; 95% CI, 1.42 to 2.24; P < .01 for the poor prognosis group, so it is best used to structure post-attempt risk stratification rather than as a stand-alone determinant of disposition.
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