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Dexamethasone Suppression Test May Predict More Severe/Violent Suicidal Behavior.

ABSTRACT: Introduction:Several studies demonstrated that the hypothalamic-pituitary-adrenal (HPA) axis is dysregulated in suicide attempters. Prospective studies found that people with an abnormal response at the dexamethasone suppression test (DST) are more likely to commit suicide. However, whether DST may predict suicide attempts remains less clear. A possible strategy to address this question is to consider the suicide attempt lethality. Objectives:(1) To compare the pre- and post-DST cortisol levels in serious/violent suicide attempters and in non-serious/non-violent suicide attempters, and (2) to investigate whether cortisol level can predict new suicide attempts or their lethality. Methods:The study included 70 recent suicide attempters (25 with a serious or violent attempt) who were followed for two years. Three saliva samples for cortisol measurement were obtained at 8a.m., 3p.m., and 9p.m. before the DST (pre-DST). Then, at 11 p.m., 1 mg of dexamethasone was given orally. The following day (post-DST), three saliva samples were collected at the same hours as before. The post-DST-pre-DST salivary cortisol ? index was calculated for each collection time. The Risk-Rescue Ratio Scale (RRRS) and the Suicidal Intent Scale (SIS) were used to characterize the suicide attempt at inclusion and those occurring during the follow-up. Results:Post-DST cortisol level at 9 p.m. was higher in patients with an initial violent or serious suicide attempt than in non-violent/non-serious attempters (p < .010). Higher post-DST cortisol at 9p.m. was associated with lower RRRS rescue score and higher clinical impression of suicide severity at inclusion. Among the 66 patients who completed the follow-up, 26 attempted suicide again at least once. Higher pre-DST cortisol at 8a.m. predicted new suicide attempts during the follow-up (OR = 2.15 [1.11, 4.15]), and higher cortisol ? index at 9p.m. was associated with higher suicide intent during the follow-up. Conclusions:Our results suggest that HPA axis hyper-reactivity monitored with the DST is a marker of violent/serious suicide attempt with lower rescue possibility. Furthermore, higher changes between pre-DST and post-DST cortisol levels may predict higher suicide intent. These findings might help to characterize the biological features of nearest suicide phenotypes.

SUBMITTER: Alacreu-Crespo A

PROVIDER: S-EPMC7065044 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Dexamethasone Suppression Test May Predict More Severe/Violent Suicidal Behavior.

Alacreu-Crespo Adrián A Olié Emilie E Guillaume Sebastien S Girod Chloé C Cazals Aurélie A Chaudieu Isabelle I Courtet Philippe P

Frontiers in psychiatry 20200304

<h4>Introduction</h4>Several studies demonstrated that the hypothalamic-pituitary-adrenal (HPA) axis is dysregulated in suicide attempters. Prospective studies found that people with an abnormal response at the dexamethasone suppression test (DST) are more likely to commit suicide. However, whether DST may predict suicide attempts remains less clear. A possible strategy to address this question is to consider the suicide attempt lethality.<h4>Objectives</h4>(1) To compare the pre- and post-DST c ...[more]

PMID: 32194449

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Project description:ImportanceThe weeks following discharge from psychiatric hospitalization are the highest-risk period for suicide attempts. Real-time monitoring of suicidal thoughts via smartphone prompts may be more indicative of short-term risk than a single, cross-sectional assessment.ObjectiveTo test whether modeling dynamic changes in real-time suicidal thoughts during psychiatric hospitalization can improve predictions of postdischarge suicide attempts vs using only baseline (ie, admission) data or using the mean level of real-time suicidal thoughts during hospitalization.Design, setting, and participantsIn this prognostic study, 83 adults recruited from the inpatient psychiatric unit at Massachusetts General Hospital completed ecological momentary assessment surveys of suicidal thinking 4 to 6 times per day during hospitalization as well as brief follow-up surveys assessing suicide attempts at 2 and 4 weeks after discharge. Participants completed at least 3 real-time monitoring surveys. Inclusion criteria included hospitalization for suicidal thoughts and/or behaviors and English fluency. Data were collected from January 2016 to December 2018 and analyzed from January to December 2020.Main outcomes and measuresThe primary outcome was suicide attempt in the month after discharge.ResultsOf 83 participants (mean [SD] age, 38.4 [13.6] years; 43 [51.8%] male participants; 69 [83.1%] White individuals), 9 (10.8%) made a suicide attempt in the month after discharge. Mean cross-validated AUC for elastic net models revealed predictive accuracy was fair for the model using baseline data (area under the curve [AUC], 0.71; first to third quartile, 0.55-0.88), good for the model using the mean level of real-time suicidal thoughts during hospitalization (AUC, 0.81; first to third quartile, 0.67-0.91), and best for the model using dynamic changes in real-time suicidal thoughts during hospitalization (AUC, 0.89; first to third quartile, 0.81-0.97); this pattern of results held for other classification metrics (eg, accuracy, positive predictive value, Brier score) and when using different cross-validation procedures. Features assessing rapid fluctuations in suicidal thinking emerged as the strongest predictors of posthospital suicide attempts. A final set of models incorporating percentage missingness further improved both the mean (mean AUC, 0.93; first to third quartile, 0.90-1.00) and dynamic feature (mean AUC, 0.93; first to third quartile, 0.88-1.00) models.Conclusions and relevanceIn this study, collecting real-time data about suicidal thinking during the course of hospitalization significantly improved short-term prediction of posthospitalization suicide attempts. Models including dynamic changes in suicidal thinking over time yielded the best prediction; features that captured rapid changes in suicidal thoughts were particularly strong predictors. Survey noncompletion also emerged as an important predictor of posthospitalization suicide attempts.

Project description:PurposeTreatment planning factors are known to affect the risk of severe acute esophagitis during thoracic radiation therapy. We tested a previously published model to predict the risk of severe acute esophagitis on an independent data set.Methods and materialsThe data set consists of data from patients who had recoverable treatment plans and received definitive radiation therapy for non-small cell carcinoma of the lung at a single institution between November 2004 and January 2010. Complete esophagus dose-volume and available clinical information was extracted using our in-house software. The previously published model was a logistic function with a combination of mean esophageal dose and use of concurrent chemotherapy. In addition to testing the previous model, we used a novel, machine learning-based method to build a maximally predictive model.ResultsNinety-four patients (81.7%) developed Common Terminology Criteria for Adverse Events, Version 4, Grade 2 or more severe esophagitis (Grade 2: n = 79 and Grade 3: n = 15). Univariate analysis revealed that the most statistically significant dose-volume parameters included percentage of esophagus volume receiving ?40 to 60 Gy, minimum dose to the highest 20% of esophagus volume (D20) to D35, and mean dose. Other significant predictors included concurrent chemotherapy and patient age. The previously published model predicted risk effectively with a Spearman's rank correlation coefficient (rs) of 0.43 (P < .001) with good calibration (Hosmer-Lemeshow goodness of fit: P = .537). A new model that was built from the current data set found the same variables, yielding an rs of 0.43 (P < .001) with a logistic function of 0.0853 × mean esophageal dose [Gy] + 1.49 × concurrent chemotherapy [1/0] - 1.75 and Hosmer-Lemeshow P = .659. A novel preconditioned least absolute shrinkage and selection operator method yielded an average rs of 0.38 on 100 bootstrapped data sets.ConclusionsThe previously published model was validated on an independent data set and determined to be nearly as predictive as the best possible two-parameter logistic model even though it overpredicted risk systematically. A novel, machine learning-based model using a bootstrapping approach showed reasonable predictive power.

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Dexamethasone Suppression Test May Predict More Severe/Violent Suicidal Behavior.

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Dexamethasone Suppression Test May Predict More Severe/Violent Suicidal Behavior.

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