Assessment of implicit self-associations with death relative to life, measured by a death/suicide implicit association test (d/s-IAT), has shown promise in the prediction of suicide risk. The current study examined whether the d/s-IAT reflects an individual's desire to die or a diminished desire to live and whether the predictive utility of implicit cognition is mediated by life-oriented beliefs. Four hundred eight undergraduate students (285 female; Mage = 20.36 years, SD = 4.72) participated. Participants completed the d/s-IAT and self-report measures assessing 6 indicators of suicide risk (suicide ideation frequency and intensity, depression, nonsuicidal self-harm thoughts frequency and intensity, and nonsuicidal self-harm attempts), as well as survival and coping beliefs and history of prior suicide attempts. The d/s-IAT significantly predicted 5 out of the 6 indicators of suicide risk above and beyond the strongest traditional indicator of risk, history of prior suicide attempts. However, the effect of the d/s-IAT on each of the risk indicators was mediated by individuals' survival and coping beliefs. Moreover, the distribution of d/s-IAT scores primarily reflected variability in self-associations with life. Implicit suicide-related cognition appears to reflect a gradual diminishing of the desire to live, rather than a desire to die. Contemporary theories of suicide and risk assessment protocols need to account for the dynamic relationship between both risk and life-oriented resilience factors, and intervention strategies aimed at enhancing engagement with life should be a routine part of suicide risk management.

Previous research suggests implicit cognition can predict suicidal behavior. This study examined the utility of the death/suicide implicit association test (d/s-IAT) in acute and prospective assessment of suicide risk and protective factors, relative to clinician and patient estimates of future suicide risk. Patients (N = 128; 79 female; 111 Caucasian) presenting to an emergency department were recruited if they reported current suicidal ideation or had been admitted because of an acute suicide attempt. Patients completed the d/s-IAT and self-report measures assessing three death-promoting (e.g., suicide ideation) and two life-sustaining (e.g., zest for life) factors, with self-report measures completed again at 3- and 6-month follow-ups. The clinician and patient provided risk estimates of that patient making a suicide attempt within the next 6 months. Results showed that among current attempters, the d/s-IAT differentiated between first time and multiple attempters; with multiple attempters having significantly weaker self-associations with life relative to death. The d/s-IAT was associated with concurrent suicidal ideation and zest for life, but only predicted the desire to die prospectively at 3 months. By contrast, clinician and patient estimates predicted suicide risk at 3- and 6-month follow-up, with clinician estimates predicting death-promoting factors, and only patient estimates predicting life-sustaining factors. The utility of the d/s-IAT was more pronounced in the assessment of concurrent risk. Prospectively, clinician and patient predictions complemented each other in predicting suicide risk and resilience, respectively. Our findings indicate collaborative rather than implicit approaches add greater value to the management of risk and recovery in suicidal patients. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Background: Delirium is a frequent diagnosis made by Consultation-Liaison Psychiatry (CLP). Numerous studies have demonstrated misdiagnosis prior to referral to CLP. Few studies have considered the factors underlying misdiagnosis using multivariate approaches. Objectives: To determine the number of cases referred to CLP, which are misdiagnosed at time of referral, to build an accurate predictive classifier algorithm, using input variables related to delirium misdiagnosis. Method: A retrospective observational study was conducted at Alfred Hospital in Melbourne, collecting data from a record of all patients seen by CLP for a period of 5 months. Data was collected pertaining to putative factors underlying misdiagnosis. A Machine Learning-Logistic Regression classifier model was built, to classify cases of accurate delirium diagnosis vs. misdiagnosis. Results: Thirty five of 74 new cases referred were misdiagnosed. The proposed predictive algorithm achieved a mean Receiver Operating Characteristic (ROC) Area under the curve (AUC) of 79%, an average 72% classification accuracy, 77% sensitivity and 67% specificity. CONCLUSIONS: Delirium is commonly misdiagnosed in hospital settings. Our findings support the potential application of Machine Leaning-logistic predictive classifier in health care settings.

Background: Investigating approaches for determining a functionally meaningful dorsolateral prefrontal cortex (DLPFC) stimulation site is imperative for optimising repetitive transcranial magnetic stimulation (rTMS) response rates for treatment-resistant depression. One proposed approach is neuro-cardiacguided rTMS (NCG-TMS) in which high frequency rTMS is applied to the DLPFC to determine the site of greatest heart rate deceleration. This site is thought to index a frontal-vagal autonomic pathway that intersects a key pathway believed to underlie rTMS response. Objective: We aimed to independently replicate previous findings of high-frequency NCG-TMS and extend it to evaluate the use of low-frequency rTMS for NCG-TMS. Methods: Twenty healthy participants (13 female; aged 38.6 ± 13.9) underwent NCG-TMS on frontal, fronto-central (active) and central (control) sites. For high-frequency NCG-TMS, three 5 s trains of 10 Hz were provided at each left hemisphere site. For low-frequency NCG-TMS, 60 s trains of 1 Hz were applied to left and right hemispheres and heart rate and heart rate variability outcome measures were analysed. Results: For high-frequency NCG-TMS, heart rate deceleration was observed at the left frontal compared with the central site. For low-frequency NCG-TMS, accelerated heart rate was found at the right frontal compared with central sites. No other site differences were observed. Conclusion: Opposite patterns of heart rate activity were found for high-and low-frequency NCG-TMS. The high-frequency NCG-TMS data replicate previous findings and support further investigations on the clinical utility of NCG-TMS for optimising rTMS site localisation. Further work assessing the value of lowfrequency NCG-TMS for rTMS site localisation is warranted.