Suicide attempts are a major public health problem, with an estimated 25 million nonfatal suicide attempts occurring each year worldwide (Centers for Disease Control and Prevention [CDC], 2016; World Health Organization, 2016). Beyond considerable economic and societal burdens associated with nonfatal attempts (Shepard, Gurewich, Lwin, Reed, & Silverman, 2016), nonfatal suicide attempts are among the strongest predictors of suicide death-a leading cause of death worldwide (Ribeiro et al., 2016a). The scope and seriousness of the problem have prompted substantial research attention (Franklin et al., 2017). Yet our ability to predict nonfatal attempts remains marginally above chance levels (Bentley et al., 2016; Chang et al., 2016; Franklin et al., 2017; Ribeiro et al., 2016a). Rates of nonfatal suicide attempts remain intractable, with recent estimates suggesting that nonfatal attempts may be on the rise (CDC, 2016). The purpose of the present study was to evaluate the accuracy and temporal variation of a potentially scalable suicide attempt risk detection strategy: machine learning applied to electronic health records (EHRs). Recent meta-analyses have shown that the ability to predict suicide attempts has been near chance for decades (Franklin et al., 2017). A major reason for this poor prediction is that the majority of studies tested predictors in isolation (e.g., a depression diagnosis), and even the best isolated predictors are inaccurate (Franklin et al., 2017; Ribeiro et al., 2016a). Accurate suicide attempt prediction may require complex combinations of hundreds of risk factors. Traditional statistical techniques are not ideal for such analyses; fortunately, machine learning (ML) techniques are well suited for such problems. These techniques can test a wide range of complex associations among large numbers of potential factors to produce algorithms that optimize prediction. Retrospective ML studies suggest that this approach may be promising 691560C PXXXX10.
The 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group was coordinated and supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health. It is designed to improve patient care and support informed decision making about asthma management in the clinical setting. This update addresses six priority topic areas as determined by the state of the science at the time of a needs assessment, and input from multiple stakeholders: Fractional Exhaled Nitric Oxide Testing Indoor Allergen Mitigation Intermittent Inhaled Corticosteroids Long-Acting Muscarinic Antagonists Immunotherapy in the Treatment of Allergic Asthma Bronchial Thermoplasty A rigorous process was undertaken to develop these evidence-based guidelines. The Agency for Healthcare Research and Quality’s (AHRQ) Evidence-Based Practice Centers conducted systematic reviews on these topics, which were used by the Expert Panel Working Group as a basis for developing recommendations and guidance. The Expert Panel used GRADE (Grading of Recommendations, Assessment, Development and Evaluation), an internationally accepted framework, in consultation with an experienced methodology team for determining the certainty of evidence and the direction and strength of recommendations based on the evidence. Practical implementation guidance for each recommendation incorporates findings from NHLBI-led patient, caregiver, and clinician focus groups. To assist clincians in implementing these recommendations into patient care, the new recommendations have been integrated into the existing Expert Panel Report-3 (EPR-3) asthma management step diagram format.
Machine learning on longitudinal clinical data may provide a scalable approach to broaden screening for risk of nonfatal suicide attempts in adolescents.
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10−4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10−2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10−5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34–0.81) as well as several psychiatric disorders (rg = 0.26–0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.
For decades, our ability to predict suicidal thoughts and behaviors (STBs) has been at near-chance levels. The objective of this study was to advance prediction by addressing two major methodological constraints pervasive in past research: (a) the reliance on long follow-ups and (b) the application of simple conceptualizations of risk. Participants were 1,021 high-risk suicidal and/or self-injuring individuals recruited worldwide. Assessments occurred at baseline and 3, 14, and 28 days after baseline using a range of implicit and self-report measures. Retention was high across all time points (> 90%). Risk algorithms were derived and compared with univariate analyses at each follow-up. Results indicated that short-term prediction alone did not improve prediction for attempts, even using commonly cited “warning signs”; however, a small set of factors did provide fair-to-good short-term prediction of ideation. Machine learning produced considerable improvements for both outcomes across follow-ups. Results underscore the importance of complexity in the conceptualization of STBs.
Objective Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models. Materials and Methods We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test’s properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions. Results In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance. Discussion Our test’s recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test. Conclusions This new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.
Aims Psychosocial factors amplify symptoms of Interstitial Cystitis (IC/BPS). While psychosocial self-management is efficacious in other pain conditions, its impact on an IC/BPS population has rarely been studied. The objective of this review is to learn the prevalence and impact of psychosocial factors on IC/BPS, assess baseline psychosocial characteristics, and offer recommendations for assessment and treatment. Method Following PRISMA guidelines, primary information sources were PubMed including MEDLINE, Embase, CINAHL, and GoogleScholar. Inclusion criteria included: (i) a clearly defined cohort with IC/BPS or with Chronic Pelvic Pain Syndrome provided the IC/BPS cohort was delineated with quantitative results from the main cohort; (ii) all genders and regions; (iii) studies written in English from 1995 to April 14, 2017; (iv) quantitative report of psychosocial factors as outcome measures or at minimum as baseline characteristics. Results Thirty-four of an initial 642 articles were reviewed. Quantitative analyses demonstrate the magnitude of psychosocial difficulties in IC/BPS, which are worse than average on all measures, and fall into areas of clinical concern for 7 out of 10 measures. Meta-analyses shows mean Mental Component Score of the Short-Form 12 Health Survey (MCS) of 40.80 (SD 6.25, N = 2912), where <36 is consistent with severe psychological impairment. Averaged across studies, the population scored in the range seen in clinical depression (CES-D 19.89, SD 13.12, N = 564) and generalized anxiety disorder (HADS-A 8.15, SD 4.85, N = 465). Conclusion The psychological impact of IC/BPS is pervasive and severe. Existing evidence of treatment is lacking and suggests self-management intervention may be helpful.
Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.
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