Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.

Importance: Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. Objective: To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. Design, Setting, and Participants: This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Main Outcome and Measures: Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. Results: The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. Conclusions and Relevance: In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

The impact of physical environments on outpatient mental health recovery: A design-oriented qualitative study of patient perspectives.

The physical environment has been shown to affect the emotional states of patients receiving mental health treatment, yet it remains unknown whether physical space design may play a role in optimizing the delivery of mental health care. Principles of architectural design and human-centered co-design have been applied to enhance the patient experience of facility environments; however, little is known about how patients view the impact of physical spaces on their recovery. In this qualitative study, we aimed to understand patient perspectives of how physical environments contribute to mental wellbeing and personal experiences of recovery, in the context of informing future design efforts. Semi-structured telephone interviews were conducted with 13 participants receiving outpatient mental health treatment at the Kaiser Permanente San Jose Adult Psychiatry Clinic. Interviews were transcribed and themes were extracted that could inform future design concepts. The sample was comprised of nine female and three male participants, and one unidentified-gender participant, between the ages of 26-64, and across several self-reported racial/ethnic subgroups. We found four dimensions of physical environments that participants reported as impactful: 1) sensory design elements (colors, sounds, and textures), 2) engagement qualities (intensity of distracted activity such as crafting or commuting), 3) social relational aspects (privacy or connection), and 4) affective experiences evoked by being present in the space itself (feeling safe, calm, in control, self-aware, or creative was beneficial). Many of these elements were similarly noted across clinic and non-clinic environments. This study identifies key dimensions of physical environments that can serve as potential metrics of design success in supporting and facilitating mental health recovery. In the midst of the current COVID-19 pandemic, where mental health treatment has increasingly shifted outside of traditional clinics, our findings can support patients and clinicians seeking to harness potential in situ therapeutic benefits of physical environments.

Performance of a Prediction Model of Suicide Attempts Across Race and Ethnicity.

This study examines whether race disparities exist in the prediction of suicide attempts and if have they have detrimental effects on individuals and health care systems.

Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling.

BACKGROUND: Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. OBJECTIVE: This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. METHODS: A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). RESULTS: A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). CONCLUSIONS: This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

Perceived Utility and Characterization of Personal Google Search Histories to Detect Data Patterns Proximal to a Suicide Attempt in Individuals Who Previously Attempted Suicide: Pilot Cohort Study.

BACKGROUND: Despite decades of research to better understand suicide risk and to develop detection and prevention methods, suicide is still one of the leading causes of death globally. While large-scale studies using real-world evidence from electronic health records can identify who is at risk, they have not been successful at pinpointing when someone is at risk. Personalized social media and online search history data, by contrast, could provide an ongoing real-world datastream revealing internal thoughts and personal states of mind. OBJECTIVE: We conducted this study to determine the feasibility and acceptability of using personalized online information-seeking behavior in the identification of risk for suicide attempts. METHODS: This was a cohort survey study to assess attitudes of participants with a prior suicide attempt about using web search data for suicide prevention purposes, dates of lifetime suicide attempts, and an optional one-time download of their past web searches on Google. The study was conducted at the University of Washington School of Medicine Psychiatry Research Offices. The main outcomes were participants' opinions on internet search data for suicide prediction and intervention and any potential change in online information-seeking behavior proximal to a suicide attempt. Individualized nonparametric association analysis was used to assess the magnitude of difference in web search data features derived from time periods proximal (7, 15, 30, and 60 days) to the suicide attempts versus the typical (baseline) search behavior of participants. RESULTS: A total of 62 participants who had attempted suicide in the past agreed to participate in the study. Internet search activity varied from person to person (median 2-24 searches per day). Changes in online search behavior proximal to suicide attempts were evident up to 60 days before attempt. For a subset of attempts (7/30, 23%) search features showed associations from 2 months to a week before the attempt. The top 3 search constructs associated with attempts were online searching patterns (9/30 attempts, 30%), semantic relatedness of search queries to suicide methods (7/30 attempts, 23%), and anger (7/30 attempts, 23%). Participants (40/59, 68%) indicated that use of this personalized web search data for prevention purposes was acceptable with noninvasive potential interventions such as connection to a real person (eg, friend, family member, or counselor); however, concerns were raised about detection accuracy, privacy, and the potential for overly invasive intervention. CONCLUSIONS: Changes in online search behavior may be a useful and acceptable means of detecting suicide risk. Personalized analysis of online information-seeking behavior showed notable changes in search behavior and search terms that are tied to early warning signs of suicide and are evident 2 months to 7 days before a suicide attempt.

Creating boundaries to empower digital health technology.

SummaryThe potential of digital health tools such as smartphones and sensors to increase access to and enhance delivery of healthcare is well known. However, a lack of regulation and delineation between those technologies seeking to offer direct clinical diagnostics and treatments and those involving clinical care enhancements or direct-to-consumer resources has led to patient and clinician confusion about the appropriate use and role of digital health. Here, we propose that creating boundaries and better defining the scope of digital health technology will advance the field through matching the right use cases with the right tools. We further propose that ethical clinicians, as stewards of standard of care, are well suited to uphold these boundaries and to safeguard best practices in digital health.Declaration of interestH.H. is an employee of Verily Life Sciences and owns equity in this company. The views expressed here are those of the authors and are not official views of Verily Life Sciences.

'A more perfect arrangement of plants': the botanical model in psychiatric nosology, 1676 to the present day.

Psychiatric classification remains a complex endeavour; since the Enlightenment, nosologists have made use of various models and metaphors to describe their systems. Here we present the most common model, botanical taxonomy, and trace its history from the nosologies of Sydenham, Sauvages and Linnaeus; to evolutionary models; to the later contributions of Hughlings-Jackson, Kraepelin and Jaspers. Over time, there has been a shift from explicit attempts to pattern disease classification on botanical systems, to a more metaphorical use. We find that changes in the understanding of plants and plant relationships parallel changes in the conceptualization of mental illness. Not only have scientific discoveries influenced the use of metaphor, but the language of metaphor has also both illuminated and constrained psychiatric nosology.

Empowering the digital therapeutic relationship: virtual clinics for digital health interventions.

As "digital phenotyping" and monitoring technologies begin to unleash the potential of data insights for mental health care, we propose here a complementary concept of the "digital therapeutic relationship" to unleash the power of the patient-provider alliance in clinical care. In millions of clinics today, care decisions are made on a daily basis in the context of a relationship honed through professional training to be respectful, protective, and empowering of patients. Now as clinical care evolves toward online and especially mobile platforms, it is critical to not ignore the digital therapeutic relationship and instead to realize that supporting it will require new and innovative means of care delivery. Here, we propose that technology can be harnessed to facilitate, augment, and expand these relationships directly, and identify virtual clinics as the currently missing but necessary environment to unleash the true potential of digital medicine.

Transforming Psychiatry into Data-Driven Medicine with Digital Measurement Tools.

Psychiatry has been limited by historically rooted practices centered primarily on subjective observation. Fields such as oncology have progressed toward data-driven clinical decision-making that combines subjective clinical assessment of symptoms and preferences with biological measures such as genetics, biomarkers, imaging, and integrative physiology to derive quantitative risk scores and decision support. In contrast, psychiatry has just begun to scratch the surface of measurement-based care with validated clinical questionnaires. An opportunity exists to improve modern psychiatric care with novel data streams from digital sensors combined with clinical observation and subjective self-report. The prospect of integrating this complex information with modern computational and analytical methods could advance the field, both in research and clinical practice. Here we discuss this possibility and propose some key priorities to enable these innovations toward improving clinical outcomes in the future.

Phosphorylation of threonine-19 of PSD-95 by GSK-3ß is required for PSD-95 mobilization and long-term depression.

Activity of glycogen synthase kinase-3ß (GSK-3ß) is required for long-term depression (LTD) via molecular mechanisms that are incompletely understood. Here, we report that PSD-95, a major scaffold protein of the postsynaptic density (PSD) that promotes synaptic strength, is phosphorylated on threonine-19 (T19) by GSK-3ß. In cultured rat hippocampal neurons, phosphorylation of T19 increases rapidly with chemical LTD and is attenuated by pharmacologic or genetic suppression of GSK-3ß. In organotypic rat hippocampal slices, we find that a nonphosphorylatable PSD-95 mutant (T19A) tagged with photoactivatable green fluorescent protein (PAGFP) shows enhanced stability in dendritic spines versus wild-type PSD-95, whereas the phosphomimetic mutant (PSD-95-T19D) is more readily dispersed. Further, overexpression of PSD-95-T19A, but not WT-PSD-95, impairs AMPA receptor internalization and the induction of LTD. These data indicate that phosphorylation on T19 by GSK-3ß destabilizes PSD-95 within the PSD and is a critical step for AMPA receptor mobilization and LTD.

MINK and TNIK differentially act on Rap2-mediated signal transduction to regulate neuronal structure and AMPA receptor function.

Misshapen/NIKs (Nck-interacting kinases)-related kinase (MINK) and closely related TRAF2/Nck-interacting kinase (TNIK) are proteins that specifically bind to activated Rap2 and are thus hypothesized to relay its downstream signal transduction. Activated Rap2 has been found to stimulate dendritic pruning, reduce synaptic density and cause removal of synaptic AMPA receptors (AMPA-Rs) (Zhu et al., 2005; Fu et al., 2007). Here we report that MINK and TNIK are postsynaptically enriched proteins whose clustering within dendrites is bidirectionally regulated by the activation state of Rap2. Expression of MINK and TNIK in neurons is required for normal dendritic arborization and surface expression of AMPA receptors. Overexpression of a truncated MINK mutant unable to interact with Rap2 leads to reduced dendritic branching and this MINK-mediated effect on neuronal morphology is dependent upon Rap2 activation. While similarly truncated TNIK also reduces neuronal complexity, its effect does not require Rap2 activity. Furthermore, Rap2-mediated removal of surface AMPA-Rs from spines is entirely abrogated by coexpression of MINK, but not TNIK. Thus, although both MINK and TNIK bind GTP-bound Rap2, these kinases employ distinct mechanisms to modulate Rap2-mediated signaling. MINK appears to antagonize Rap2 signal transduction by binding to activated Rap2. We suggest that MINK interaction with Rap2 plays a critical role in maintaining the morphological integrity of dendrites and synaptic transmission.

Proline-rich tyrosine kinase 2 regulates hippocampal long-term depression.

Proline-rich tyrosine kinase 2 (PYK2), also known as cell adhesion kinase beta or protein tyrosine kinase 2b, is a calcium-dependent signaling protein involved in cell migration. Phosphorylation of residue Y402 is associated with activation of PYK2 and leads to the recruitment of downstream signaling molecules. PYK2 was previously implicated in long-term potentiation (LTP); however, the role of PYK2 in long-term depression (LTD) is unknown. Here, we report that PYK2 is activated by NMDA receptor stimulation (chemical LTD) in cultured neurons. Small hairpin RNA-mediated knockdown of PYK2 blocks LTD, but not LTP, in hippocampal slice cultures. We find that the Y402 residue and, to a lesser extent, PYK2 kinase activity contribute to PYK2's role in LTD. Knockdown experiments indicate that PYK2 is required to suppress NMDA-induced extracellular signal-regulated kinase (ERK) phosphorylation. Overexpression of PYK2 depresses NMDA-induced ERK phosphorylation and inhibits LTP, but not LTD. Our data indicate that PYK2 is critical for the induction of LTD, possibly in part by antagonizing ERK signaling in hippocampal neurons.

Impairment of NK cell function by NKG2D modulation in NOD mice.

Nonobese diabetic (NOD) mice, a model of insulin-dependent diabetes mellitus, have a defect in natural killer (NK) cell-mediated functions. Here we show impairment in an activating receptor, NKG2D, in NOD NK cells. While resting NK cells from C57BL/6 and NOD mice expressed equivalent levels of NKG2D, upon activation NOD NK cells but not C57BL/6 NK cells expressed NKG2D ligands, which resulted in downmodulation of the receptor. NKG2D-dependent cytotoxicity and cytokine production were decreased because of receptor modulation, accounting for the dysfunction. Modulation of NKG2D was mostly dependent on the YxxM motif of DAP10, the NKG2D-associated adaptor that activates phosphoinositide 3 kinase. These results suggest that NK cells may be desensitized by exposure to NKG2D ligands.

Rates of behavior and aging specified by mitochondrial function during development.

To explore the role of mitochondrial activity in the aging process, we have lowered the activity of the electron transport chain and adenosine 5'-triphosphate (ATP) synthase with RNA interference (RNAi) in Caenorhabditis elegans. These perturbations reduced body size and behavioral rates and extended adult life-span. Restoring messenger RNA to near-normal levels during adulthood did not elevate ATP levels and did not correct any of these phenotypes. Conversely, inhibiting respiratory-chain components during adulthood only did not reset behavioral rates and did not affect life-span. Thus, the developing animal appears to contain a regulatory system that monitors mitochondrial activity early in life and, in response, establishes rates of respiration, behavior, and aging that persist during adulthood.