Risk Factors for Reattempt and Suicide Within 6 Months After an Attempt in the French ALGOS Cohort: A Survival Tree Analysis. Demesmaeker, A., Chazard, E., Vaiva, G., & Amad, A. The Journal of Clinical Psychiatry, February, 2021. doi abstract bibtex OBJECTIVE: Understanding the cumulative effect of several risk factors involved in suicidal behavior is crucial for the development of effective prevention plans. The objective of this study is to provide clinicians with a simple predictive model of the risk of suicide attempts and suicide within 6 months after suicide attempt. METHODS: A prospective observational cohort of 972 subjects, included from January 26, 2010, to February 28, 2013, was used to perform a survival tree analysis with all sociodemographic and clinical variables available at inclusion. The results of the decision tree were then used to define a simple predictive algorithm for clinicians. RESULTS: The results of survival tree analysis highlighted 3 subgroups of patients with an increased risk of suicide attempt or death by suicide within 6 months after suicide attempt: patients with alcohol use disorder and a previous suicide attempt with acute alcohol use (risk ratio [RR] = 2.92; 95% CI, 2.08 to 4.10), patients with anxiety disorders (RR = 0.98; 95% CI, 0.69 to 1.39), and patients with a history of more than 2 suicide attempts in the past 3 years (RR = 2.11; 95% CI, 1.25 to 3.54). The good prognosis group comprised all other patients. CONCLUSIONS: By using a data-driven method, this study identified 4 clinical factors interacting together to reduce or increase the risk of recidivism. These combinations of risk factors allow for a better evaluation of a subject's suicide risk in clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01123174.
@article{demesmaeker_risk_2021,
title = {Risk {Factors} for {Reattempt} and {Suicide} {Within} 6 {Months} {After} an {Attempt} in the {French} {ALGOS} {Cohort}: {A} {Survival} {Tree} {Analysis}},
volume = {82},
issn = {1555-2101},
shorttitle = {Risk {Factors} for {Reattempt} and {Suicide} {Within} 6 {Months} {After} an {Attempt} in the {French} {ALGOS} {Cohort}},
doi = {10.4088/JCP.20m13589},
abstract = {OBJECTIVE: Understanding the cumulative effect of several risk factors involved in suicidal behavior is crucial for the development of effective prevention plans. The objective of this study is to provide clinicians with a simple predictive model of the risk of suicide attempts and suicide within 6 months after suicide attempt.
METHODS: A prospective observational cohort of 972 subjects, included from January 26, 2010, to February 28, 2013, was used to perform a survival tree analysis with all sociodemographic and clinical variables available at inclusion. The results of the decision tree were then used to define a simple predictive algorithm for clinicians.
RESULTS: The results of survival tree analysis highlighted 3 subgroups of patients with an increased risk of suicide attempt or death by suicide within 6 months after suicide attempt: patients with alcohol use disorder and a previous suicide attempt with acute alcohol use (risk ratio [RR] = 2.92; 95\% CI, 2.08 to 4.10), patients with anxiety disorders (RR = 0.98; 95\% CI, 0.69 to 1.39), and patients with a history of more than 2 suicide attempts in the past 3 years (RR = 2.11; 95\% CI, 1.25 to 3.54). The good prognosis group comprised all other patients.
CONCLUSIONS: By using a data-driven method, this study identified 4 clinical factors interacting together to reduce or increase the risk of recidivism. These combinations of risk factors allow for a better evaluation of a subject's suicide risk in clinical practice.
TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01123174.},
language = {eng},
number = {1},
journal = {The Journal of Clinical Psychiatry},
author = {Demesmaeker, Alice and Chazard, Emmanuel and Vaiva, Guillaume and Amad, Ali},
month = feb,
year = {2021},
pmid = {33999539},
}
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METHODS: A prospective observational cohort of 972 subjects, included from January 26, 2010, to February 28, 2013, was used to perform a survival tree analysis with all sociodemographic and clinical variables available at inclusion. The results of the decision tree were then used to define a simple predictive algorithm for clinicians. RESULTS: The results of survival tree analysis highlighted 3 subgroups of patients with an increased risk of suicide attempt or death by suicide within 6 months after suicide attempt: patients with alcohol use disorder and a previous suicide attempt with acute alcohol use (risk ratio [RR] = 2.92; 95% CI, 2.08 to 4.10), patients with anxiety disorders (RR = 0.98; 95% CI, 0.69 to 1.39), and patients with a history of more than 2 suicide attempts in the past 3 years (RR = 2.11; 95% CI, 1.25 to 3.54). The good prognosis group comprised all other patients. CONCLUSIONS: By using a data-driven method, this study identified 4 clinical factors interacting together to reduce or increase the risk of recidivism. These combinations of risk factors allow for a better evaluation of a subject's suicide risk in clinical practice. 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The objective of this study is to provide clinicians with a simple predictive model of the risk of suicide attempts and suicide within 6 months after suicide attempt.\nMETHODS: A prospective observational cohort of 972 subjects, included from January 26, 2010, to February 28, 2013, was used to perform a survival tree analysis with all sociodemographic and clinical variables available at inclusion. The results of the decision tree were then used to define a simple predictive algorithm for clinicians.\nRESULTS: The results of survival tree analysis highlighted 3 subgroups of patients with an increased risk of suicide attempt or death by suicide within 6 months after suicide attempt: patients with alcohol use disorder and a previous suicide attempt with acute alcohol use (risk ratio [RR] = 2.92; 95\\% CI, 2.08 to 4.10), patients with anxiety disorders (RR = 0.98; 95\\% CI, 0.69 to 1.39), and patients with a history of more than 2 suicide attempts in the past 3 years (RR = 2.11; 95\\% CI, 1.25 to 3.54). The good prognosis group comprised all other patients.\nCONCLUSIONS: By using a data-driven method, this study identified 4 clinical factors interacting together to reduce or increase the risk of recidivism. 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