Prevalence of suicidal ideation in German psychotherapy outpatients: A large multicenter assessment

BACKGROUND

Suicidal ideation is a major concern in clinical practice. Yet, little is known about prevalence rates of suicidal ideation in patients undergoing outpatient psychotherapeutic treatment. Therefore, the aim of the current study is to assess the prevalence of suicidal ideation in a large sample of psychotherapy outpatients in Germany. The data analyzed in this study is taken from the KODAP-project on the coordination of data collection and analysis at German university-based research and training outpatient clinics for psychotherapy.

METHODS

A total of N = 10,357 adult outpatients (64.4 % female; age: M(SD) = 35.94 (13.54), range: 18-92 years of age) starting cognitive-behavioral therapy at one of 27 outpatient clinics in Germany were included in the current study. Prevalence of suicidal ideation was assessed with the Suicide Item (Item 9) of the Beck-Depression Inventory II.

RESULTS

Suicidal ideation was reported by 36.7 % (n = 3795) of the participants. Borderline Personality Disorder, Posttraumatic Stress Disorder, and recurrent Major Depression were the diagnoses most strongly associated with the presence and severity of suicidal ideation.

LIMITATION

Suicide ideation was assessed only with the respective item of the Beck Depression Inventory II.

CONCLUSION

Suicidal ideation is very common among adult patients who start psychotherapy in Germany. A well-founded knowledge of risk assessment in suicidal patients and suicide-specific treatment options is therefore highly relevant.

Symptom Reduction and Engagement in a Cognitive-Behavioral Mobile Phone App: A Study of User Profiling to Determine Prognostic Indicators

OBJECTIVE

We investigated the presence of latent transition profiles in a sample of users of a cognitive-behavioral mental health app for the general population. Users' baseline characteristics were used as predictors of the profiles. The role of engagement with the app in the transition profiles was examined.

METHOD

A total of 541 users completed the Patient Health Questionnaire-9 and the General Anxiety Disorder-7 when started using the app and 30 days after. Random-Intercept Latent Transition Analysis was implemented to identify users' profiles and transition patterns as classes. The age of the users and the Emotional Self-Awareness Scale-Revised (ESAS-R) were used as predictors of class membership at baseline. The Homework Rating Scale-Mobile Application (HRS-MA; as a measure of engagement) was used as a predictor of class membership at 30 days of app use.

RESULTS

A 3-class solution was obtained according to the severity of symptoms (from mild to moderately severe). Age and ESAS-R predicted class membership initially; the higher the age and ESAS-R, the higher the probability of starting using the app with lower distress levels. The HRS-MA predicted class membership at 30 days of app use; the higher the engagement for more symptomatic and younger users, the higher the probability of improvement. However, older users tended to engage less.

CONCLUSION

Our findings underpin the relevance of easily accessible digital interventions for young adults with mild to moderate mental health problems. Further studies and developments are required to enhance these apps for older cohorts.

Emotion dynamics of clients with test anxiety before and after an imagery-based treatment

BACKGROUND AND OBJECTIVES

Imagery-based techniques have become a promising means in the treatment of test anxiety (TA). Although previous studies have demonstrated the effectiveness of imagery-based treatment, not all clients seem to benefit from it. The present study compares clients' pre- as well as post-treatment emotion dynamics between responders and non-responders. Furthermore, it examines treatment-related changes in emotion dynamics in both subgroups.

METHODS

The results are based on 44 clients suffering from TA who underwent a six-session imagery-based treatment and include Ecological Momentary Assessment (EMA). Emotions were assessed with the Profile of Mood States four times a day over the course of two weeks before and after the treatment. Temporal networks were computed to index emotion dynamics.

RESULTS

Pre-treatment emotion dynamics differed between responders and non-responders. Similarly, post-treatment emotion dynamics differed as well between both groups. Some changes were also observed between pre-treatment and post-treatment networks: for responders, fatigue no longer predicted anger, and depression predicted itself; for non-responders, calmness predicted fatigue, anger, depression, contentment, and anxiety. In addition, fatigue no longer predicted itself and anxiety predicted vigor.

LIMITATIONS

The investigation is marked by several limitations: a liberal inclusion threshold of at least a 50% response to EMA prompts, and a relatively homogenous sample.

CONCLUSION

These results provide first evidence for the idea that emotion dynamics may be associated with response to treatment for TA. Furthermore, effective imagery-based treatments may be tied to changes within these dynamics.

Treatment credibility as a mechanism of change in cognitive behavioral therapy: Effects on depression and anxiety

OBJECTIVE

This study aimed to provide evidence for treatment credibility (TC) as a potential mechanism of change in cognitive behavioral therapy (CBT). Therefore, it focused on within-person effects that are free of the influence of stable characteristics and thus allow to exclude certain alternative explanations for the association under study.

METHOD

The sample included 1,423 patients receiving outpatient CBT, who presented a wide variety of psychiatric diagnoses (mostly affective and anxiety disorders). TC, depression, and anxiety were measured every fifth session from Session 5 to 25 using the Credibility Expectancy Questionnaire (CEQ), the Patient Health Questionnaire-9 (PHQ-9), and the General Anxiety Disorder-7 (GAD-7), respectively. Symptom severity was assessed every session using the Hopkins Symptom Checklist-11. Within- and between-person effects of TC, depression, and anxiety were analyzed using the latent curve model with structured residuals (LCM-SRs). In exploratory analyses, within-person effects of TC on next-session symptom severity were assessed using a modification of the LCM-SR.

RESULTS

LCM-SRs exhibited excellent fit in main analyses. There were significant negative correlations of both intercepts and slopes (between-person level) of CEQ and PHQ-9 as well GAD-7. No significant cross-lagged effects (within-person level) were found over the five-session interval. However, session-wise analyses revealed significant cross-lagged effects of CEQ on Hopkins Symptom Checklist-11.

CONCLUSIONS

This study is the first to find significant within-person effects of TC in session-wise analyses. This lends preliminary support to the notion of TC as a mechanism of change. The lack of significant findings at the five-session interval is discussed considering the specific design used in this study. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Towards a taxonomy of mechanisms of change? Findings from an expert survey on the association between common factors and specific techniques in psychotherapy

OBJECTIVE

In the present study, we used structural equation modeling (SEM) to investigate the complex relationship between common factors, i.e., mechanisms of change, and specific factors, i.e., therapeutic techniques.

METHOD

N = 256 psychotherapy experts were asked to rate the appropriateness of 14 techniques commonly used in psychotherapy to facilitate five different common factors - resource activation, motivational clarification, self-management & emotion regulation, social competence, and therapeutic relationship. Using SEM, we defined techniques as indicators and common factors as latent variables. Data were split randomly into two subsets. Indicators were selected if three a priori defined criteria were met based on training data (n = 128). Subsequently, the goodness of model fit was assessed in the test data (n = 128).

RESULTS

The proposed model revealed adequate fit. All factor loadings were theoretically sound and significant in magnitude. Findings suggest that psychotherapy experts discriminate between common factors by their various associations with therapeutic techniques.

CONCLUSION

Suggestions are made, how therapeutic techniques are to be used to facilitate desirable change in the patient. Our model is a step towards a taxonomy of mechanisms of change that may help to improve research-informed decision-making.

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy

OBJECTIVE

Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts.

METHOD

We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses.

RESULTS

The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination.

DISCUSSION

Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Where it all begins: Predicting initial therapeutic skills before clinical training in cognitive behavior therapy

To train novice students adequately, it is crucial to understand where they start and how they develop their skills. This study examined the impact of novice students' characteristics on their initial clinical micro-skills when treating simulated patients with cognitive behavior therapy. The sample consisted of 44 graduate psychology students treating seven simulated patients. Clinical micro-skills were measured both using video-based ratings in reaction to short video clips of simulated patients (via the Facilitative Interpersonal Skills (FIS) performance task) and by using video-based ratings within a session with a simulated patient (using the Inventory of Therapeutic Interventions and Skills; ITIS). Two separate LASSO regressions were performed using machine learning to select potential predictors for both skills assessments. Subsequently, a bootstrapping algorithm with 10,000 iterations was used to examine the variability of regression coefficients. Using LASSO regression, we identified two predictors for clinical micro-skills in standardized scenarios: extraversion (b = 0.10) and resilience (b = 0.09), both were not significantly associated with clinical micro-skills. Together, they explained 15% of the skill variation. Bootstrapping confirmed the stability of these predictors. For clinical micro-skills in sessions, only competitiveness was excluded by LASSO regression, and all predictors showed significant instability. The results provide initial evidence that trainees' resilience and extraversion should be promoted in the clinical training of cognitive behavior therapy. More studies on clinical micro-skills and training with larger sample sizes are needed to fully understand clinical development.

First-in-Human Study of the Reversible BTK Inhibitor Nemtabrutinib in Patients with Relapsed/Refractory Chronic Lymphocytic Leukemia and B-Cell Non-Hodgkin Lymphoma

Nemtabrutinib is an orally bioavailable, reversible inhibitor of Bruton tyrosine kinase (BTK) and C481S mutant BTK. We evaluated the safety, pharmacology, and antitumor activity of nemtabrutinib in relapsed/refractory hematologic malignancies. Forty-eight patients with chronic lymphocytic leukemia (CLL), B-cell non-Hodgkin lymphoma (NHL), or Waldenström macroglobulinemia (WM), relapsed/refractory after ≥2 prior therapies were enrolled in the open-label, single-arm, phase I MK-1026-001 study (NCT03162536) to receive nemtabrutinib 5 to 75 mg once daily in 28-day cycles. Dose finding progressed using a 3 + 3 dose escalation design. Primary endpoints were safety and the recommended phase II dose (RP2D). Among 47 treated patients, 29 had CLL, 17 had NHL, and 1 had WM. Grade ≥3 treatment-emergent adverse events occurred in 37 (89%), most commonly neutropenia (11; 23.4%), febrile neutropenia (7; 14.9%), and pneumonia (7; 14.9%). The RP2D was 65 mg daily. An overall response rate of 75% was observed in patients with CLL at 65 mg daily.

SIGNIFICANCE

This first-in-human phase I study demonstrates the safety and preliminary efficacy of nemtabrutinib in patients with relapsed/refractory B-cell malignancies. These data support further exploration of nemtabrutinib in larger clinical studies. This article is featured in Selected Articles from This Issue, p. 5.

Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension

Hypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)-dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysine N-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E). KMT2E-AS1 stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α-dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within a KMT2E intron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)-specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo, KMT2E-AS1 deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, the KMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.

[How Cooperation Instead of Coexistence in Psychotherapy Research can Improve Science, Practice and Continuing Education]

Die kontinuierliche Erhebung psychometrischer Daten vor, während und nach einer psychotherapeutischen Behandlung kann als Qualitätssicherungsmaßnahme Therapeut:innen in ihrer klinischen Arbeit unterstützen und zugleich eine belastbare Datengrundlage für die Psychotherapieforschung schaffen. Im Rahmen der Qualitätssicherung können die erhobenen Daten als zusätzliche Informationsquelle den klinischen Eindruck der Therapeut:innen erweitern und zur Evaluation der Behandlung am Einzelfall aber auch auf der Ebene des Versorgungssystems herangezogen werden. Darüber hinaus können prognostische Vorhersagen von Therapieergebnissen und Abbruchwahrscheinlichkeiten, Behandlungsempfehlungen sowie adaptive Behandlungsanpassungen während der Behandlung auf ihnen aufgebaut werden, die Therapeut:innen in ihren klinischen Entscheidungen unterstützen 1. Eine solche daten-gestützte und evidenzbasierte psychologische Psychotherapie kann die wissenschaftliche Fundierung der therapeutischen Herangehensweise und die Wirksamkeit der Behandlung verbessern. Dazu bedarf es umfangreicher Datenerhebungen, die verlässliche und aussagekräftige Forschungsbefunde ermöglichen 2.

Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling

Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?).

A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics.

There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes.

Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

Background

Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.

Methods

We collected Twitter data for four medications-fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.

Results

The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.

Conclusions

NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

Constructing a disease database and using natural language processing to capture and standardize free text clinical information

The ability to extract critical information about an infectious disease in a timely manner is critical for population health research. The lack of procedures for mining large amounts of health data is a major impediment. The goal of this research is to use natural language processing (NLP) to extract key information (clinical factors, social determinants of health) from free text. The proposed framework describes database construction, NLP modules for locating clinical and non-clinical (social determinants) information, and a detailed evaluation protocol for evaluating results and demonstrating the effectiveness of the proposed framework. The use of COVID-19 case reports is demonstrated for data construction and pandemic surveillance. The proposed approach outperforms benchmark methods in F1-score by about 1-3%. A thorough examination reveals the disease's presence as well as the frequency of symptoms in patients. The findings suggest that prior knowledge gained through transfer learning can be useful when researching infectious diseases with similar presentations in order to accurately predict patient outcomes.

Clinical Impact of Standardized TAVR Technique and Care Pathway: Insights From the Optimize PRO Study

BACKGROUND

Procedural success and clinical outcomes after transcatheter aortic valve replacement (TAVR) have improved, but residual aortic regurgitation (AR) and new permanent pacemaker implantation (PPI) rates remain variable because of a lack of uniform periprocedural management and implantation.

OBJECTIVES

The Optimize PRO study evaluates valve performance and procedural outcomes using an "optimized" TAVR care pathway and the cusp overlap technique (COT) in patients receiving the Evolut PRO/PRO+ (Medtronic) self-expanding valves.

METHODS

Optimize PRO, a nonrandomized, prospective, postmarket study conducted in the United States, Canada, Europe, Middle East, and Australia, is enrolling patients with severe symptomatic aortic stenosis and no pre-existing pacemaker. Sites follow a standardized TAVR care pathway, including early discharge and a conduction disturbance management algorithm, and transfemoral deployment using the COT.

RESULTS

A total of 400 attempted implants from the United States and Canada comprised the main cohort of this second interim analysis. The mean age was 78.7 ± 6.6 years, and the mean Society of Thoracic Surgeons predictive risk of mortality was 3.0 ± 2.4. The median length of stay was 1 day. There were no instances of moderate or severe AR at discharge. At 30 days, all-cause mortality or stroke was 3.8%, all-cause mortality was 0.8%, disabling stroke was 0.7%, hospital readmission was 10.1%, and cardiovascular rehospitalization was 6.1%. The new PPI rate was 9.8%, 5.8% with 4-step COT compliance. In the multivariable model, right bundle branch block and the depth of the implant increased the risk of PPI, whereas using the 4-step COT lowered 30-day PPI.

CONCLUSIONS

The use of the TAVR care pathway and COT resulted in favorable clinical outcomes with no moderate or severe AR and low PPI rates at 30 days while facilitating early discharge and reproducible outcomes across various sites and operators. (Optimize PRO; NCT04091048).

Public health emergency preparedness for infectious disease emergencies: a scoping review of recent evidence

BACKGROUND

The COVID-19 pandemic continues to demonstrate the risks and profound health impacts that result from infectious disease emergencies. Emergency preparedness has been defined as the knowledge, capacity and organizational systems that governments, response and recovery organizations, communities and individuals develop to anticipate, respond to, or recover from emergencies. This scoping review explored recent literature on priority areas and indicators for public health emergency preparedness (PHEP) with a focus on infectious disease emergencies.

METHODS

Using scoping review methodology, a comprehensive search was conducted for indexed and grey literature with a focus on records published from 2017 to 2020 onward, respectively. Records were included if they: (a) described PHEP, (b) focused on an infectious emergency, and (c) were published in an Organization for Economic Co-operation and Development country. An evidence-based all-hazards Resilience Framework for PHEP consisting of 11 elements was used as a reference point to identify additional areas of preparedness that have emerged in recent publications. The findings were analyzed deductively and summarized thematically.

RESULTS

The included publications largely aligned with the 11 elements of the all-hazards Resilience Framework for PHEP. In particular, the elements related to collaborative networks, community engagement, risk analysis and communication were frequently observed across the publications included in this review. Ten emergent themes were identified that expand on the Resilience Framework for PHEP specific to infectious diseases. Planning to mitigate inequities was a key finding of this review, it was the most frequently identified emergent theme. Additional emergent themes were: research and evidence-informed decision making, building vaccination capacity, building laboratory and diagnostic system capacity, building infection prevention and control capacity, financial investment in infrastructure, health system capacity, climate and environmental health, public health legislation and phases of preparedness.

CONCLUSION

The themes from this review contribute to the evolving understanding of critical public health emergency preparedness actions. The themes expand on the 11 elements outlined in the Resilience Framework for PHEP, specifically relevant to pandemics and infectious disease emergencies. Further research will be important to validate these findings, and expand understanding of how refinements to PHEP frameworks and indicators can support public health practice.

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

BACKGROUND

Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.

OBJECTIVE

This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.

METHODS

The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.

RESULTS

The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.

CONCLUSIONS

A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.

Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes

OBJECTIVE

The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout predictions are usually limited by imbalanced datasets and the size of the sample. This paper aims to improve dropout prediction by comparing ML algorithms, sample sizes and resampling methods.

METHOD

Twenty ML algorithms were examined in twelve subsamples (drawn from a sample of N = 49,602) using four resampling methods in comparison to the absence of resampling and to each other. Prediction accuracy was evaluated in an independent holdout dataset using the F₁-Measure.

RESULTS

Resampling methods improved the performance of ML algorithms and down-sampling can be recommended, as it was the fastest method and as accurate as the other methods. For the highest mean F₁-Score of .51 a minimum sample size of N = 300 was necessary. No specific algorithm or algorithm group can be recommended.

CONCLUSION

Resampling methods could improve the accuracy of predicting dropout in psychological interventions. Down-sampling is recommended as it is the least computationally taxing method. The training sample should contain at least 300 cases.

Is it me, is it you or is it both of US? Applying the social relations model to disentangle the components of the therapeutic bond

OBJECTIVE

The study investigated the contribution of therapists and patients to the therapeutic bond and their associations (at the within and between levels) to treatment outcome. On this aim, the social relations model (SRM, aimed to analyze dyadic interpersonal data) was implemented.

METHOD

A novel design for individual psychotherapy studies was adopted, a many-with-many asymmetrical block dyadic design, in which several patients interact with several therapists. Hierarchical linear models were computed to study through variance partitioning the different components of the SRM and their association to treatment outcome.

RESULTS

All SRM components (with significant effects at therapist- and patient- within and between levels) resulted in significant contributions to the bond. However, only components at the within- and between-therapist, and within-patient levels resulted in significant associations with outcome.

CONCLUSION

Given the dyadic nature of the bond, our results support not only studying and offering clinical training on interpersonal therapeutic skills but also on constant monitoring and feedback of the relationship at the more idiosyncratic level.

Inefficient Batteries in Heart Failure: Metabolic Bottlenecks Disrupting the Mitochondrial Ecosystem

Mitochondrial abnormalities have long been described in the setting of cardiomyopathies and heart failure (HF), yet the mechanisms of mitochondrial dysfunction in cardiac pathophysiology remain poorly understood. Many studies have described HF as an energy-deprived state characterized by a decline in adenosine triphosphate production, largely driven by impaired oxidative phosphorylation. However, impairments in oxidative phosphorylation extend beyond a simple decline in adenosine triphosphate production and, in fact, reflect pervasive metabolic aberrations that cannot be fully appreciated from the isolated, often siloed, interrogation of individual aspects of mitochondrial function. With the application of broader and deeper examinations into mitochondrial and metabolic systems, recent data suggest that HF with preserved ejection fraction is likely metabolically disparate from HF with reduced ejection fraction. In our review, we introduce the concept of the mitochondrial ecosystem, comprising intricate systems of metabolic pathways and dynamic changes in mitochondrial networks and subcellular locations. The mitochondrial ecosystem exists in a delicate balance, and perturbations in one component often have a ripple effect, influencing both upstream and downstream cellular pathways with effects enhanced by mitochondrial genetic variation. Expanding and deepening our vantage of the mitochondrial ecosystem in HF is critical to identifying consistent metabolic perturbations to develop therapeutics aimed at preventing and improving outcomes in HF.

Trends in Ischemic Evaluation in New-Onset Heart Failure Without Known Coronary Artery Disease

BACKGROUND

Guidelines recommend consideration of an ischemic evaluation (Class IIa-IIb) in new-onset heart failure (HF), but it is not well-known how often this is performed and leads to revascularization.

OBJECTIVES

The authors investigated temporal trends in ischemic evaluation and revascularization within 90 days of HF onset in Denmark 2008-2018.

METHODS

From the Danish nationwide administrative registries, diagnostic tests and revascularizations within 90 days were identified among patients with new-onset HF between 2008 and 2018, alive 90 days after diagnosis.

RESULTS

Of 61,475 patients (mean age: 72.6 ± 13.8 years, 46% women), 12,503 (20%) underwent an ischemic evaluation, of whom 10,547 (84%) underwent invasive coronary angiography, and 1,956 (16%) underwent an initial noninvasive test, most frequently coronary computed tomographic angiography (n = 1,813, 93%). Of those who were initially referred for coronary computed tomographic angiography, 374 (21%) had a subsequent invasive coronary angiogram undertaken. Among individuals undergoing ischemic testing, percutaneous coronary intervention and coronary artery bypass graft surgery were performed in 1,354 (11%) and 619 (5%), respectively, corresponding to 2.2% and 1.0% of the entire sample. Between 2008 and 2018, the number of patients referred for ischemic evaluations increased, adjusted OR for 1.07 (95% CI: 1.06-1.07) per year high, and was greater among older versus younger individuals (OR: 1.01 [95% CI: 0.99-1.03], OR: 1.04 [95% CI: 1.03-1.06], OR: 1.06 [95% CI: 1.05-1.07], OR: 1.11 [95% CI: 1.09-1.12], and OR: 1.14 [95% CI: 1.10-1.18] per year increase for age group <50, 51-60, 61-75, 76-85, and >85 years, respectively, P for interaction <0.0001).

CONCLUSIONS

In clinical practice, few patients with new-onset HF are referred for an ischemic evaluation and a minority undergo revascularization. Studies are needed to establish the appropriateness of this practice.

Utilizing digital predictive biomarkers to identify Veteran suicide risk

Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.

Cardiovascular Morbidity in Monoclonal Gammopathy of Undetermined Significance: A Danish Nationwide Study

Background

Monoclonal gammopathy of undetermined significance (MGUS) is associated with renal dysfunction, inflammation, and increased cardiovascular mortality, but the cardiovascular risks are not fully understood.

Objectives

The authors explored the association of MGUS with a spectrum of cardiovascular diseases using the Danish nationwide databases.

Methods

Between 1995 and 2018, patients 18 years and older with MGUS were age- and sex-matched (1:10) with control patients and followed prospectively until December 31, 2018, for the occurrence of cardiovascular diseases. Patients diagnosed with multiple myeloma, lymphoma, or amyloidosis were excluded. Multivariable adjusted hazard ratios (HRs) for cardiovascular outcomes were estimated using Cox proportional hazard regression.

Results

Patients with MGUS (n = 8,189; mean age 69.8 ± 11.7 years; 51.2% male) had higher prevalence of cardiovascular risk factors at baseline, including hypertension (48.0% vs 38.5%) and type 2 diabetes (13.0% vs 9.3%), compared with control patients. Outcomes included an increased risk of heart failure (HR: 1.55; 95% CI: 1.41-1.69), acute myocardial infarction (HR: 1.22; 95% CI: 1.06-1.40), ischemic stroke (HR: 1.16; 95% CI: 1.03-1.30), atrial fibrillation (HR: 1.32; 95% CI: 1.23-1.42), aortic aneurysm (HR: 1.55; 95% CI: 1.28-1.89), aortic stenosis (HR: 1.60; 95% CI: 1.41-1.82), aortic regurgitation (HR: 1.67; 95% CI: 1.34-2.07), heart block (HR: 1.32; 95% CI: 1.08-1.61), peripheral artery disease (HR: 1.69; 95% CI: 1.47-1.95), cor pulmonale (HR: 2.06; 95% CI: 1.55-2.73), and venous thromboembolism (HR: 1.43; 95% CI: 1.24-1.65). A sensitivity analysis including only patients without certain comorbidities (type 2 diabetes, hypertension, acute myocardial infarction, and chronic kidney disease) yielded similar results.

Conclusions

MGUS is associated with a broad spectrum of cardiovascular diseases, with greater relative risks observed for diseases previously associated with infiltrative and inflammatory disorders. Further studies are warranted to explore the underlying mechanisms.

Abstract 3454: Adenosine receptor antagonists A2AR (TT-10) and A2BR (TT-4) demonstrate anti-tumor activity in 4T1-induced syngeneic breast cancer mouse model

Background: Extracellular adenosine has been shown to negatively regulate anti-tumor immunity. Adenosine is produced at high levels in the TME as a result of hypoxia-driven tumor cell death. As a result, ATP is released from dying cells generating adenosine via enzymatic dephosphorylation, mainly by ectonucleotidases CD39 & CD73. This results in high extracellular adenosine levels, being elevated 100-500x in the TME. The free adenosine proceeds to binding one of its four known receptors A1, A2a, A2b & A3. The binding of adenosine to A2AR & A2BR contributes to immunosuppression via cyclic AMP pathway. It has been observed that bound A2AR suppress T cell function (CD8+T & NK) while A2BR dampens myeloid cell function and promotes chemotaxis. These concerted actions thereby enhance tumor growth and metastasis. Blocking the binding of adenosine to the A2AR & A2BR results in T-cell activation and infiltration of immune cells in the TME. TT-10 & TT-4 are potent and selective antagonists of A2AR & A2BR. Both agents are being developed for the treatment of advanced cancers individually and as dual inhibitors.

Methods: Balb/c mice were implanted with 5 x 104 4T1 cells and randomly assigned to 6 groups per study; (1) Vehicle Control-VC, (2) TT-10, 1 mg/kg, PO, BID (3) TT-4, 3 mg/kg, PO, BID (4) Anti-mPD-1, 200 ug, IP (D4, 8, 14) (5) 1 mg/kg TT-10 + 200 ug Anti-mPD-1 (D4, 8, 14) and (6) TT-4, 3 mg/kg, PO, BID + 200 ug Anti-mPD-1 (D4, 8, 14). Mice were dosed for 21 days, and tumor volume was measured 2x a week. After 21 days post initiation of treatment (31 days post 4T1 cell inoculation), mice were sacrificed, TIL populations were isolated from 4T1 induced tumors and interrogated via Flow. Markers for surface staining included CD45, CD3, CD4, CD8, CD11b, CD49, Ly6G.

Results: All implanted mice developed measurable tumors. Mean suppression of tumor growth was observed to be greater in single agent TT-10 (p&lt;0.05) & TT-4 (p&lt;0.05), over single agent anti-PD-1 inhibitor when compared to the control on day 31, post 4T1 cell inoculation. Analysis of the TIL at the end of the study further supported the observation of tumor growth suppression, from increased T-cell activity seen for both TT-10 & TT-4 when compared to the VC. Furthermore, striking reductions in MDSC populations were also observed in both TT-10 & TT-4 treated mice, when compared to single agent anti-PD-1 and the VC.

Conclusions: TT-10 & TT-4 alone was superior (p&lt;0.05) to the VC as well as single agent anti-PD-1 in slowing tumor growth, which was further supported by the increase in T-cell activity observed in the end of study TIL analysis. Lastly, striking reductions in MDSC populations were also observed in both TT-10 & TT-4 treated mice, when compared to single agent anti-PD-1 and the VC.

Citation Format: Desa Rae E. Pastore, Kasim Mookhtiar, Brian Schwartz, Sushant Kumar, Ranganayaki Nagaraj, Ashwinkumar V. Meru. Adenosine receptor antagonists A2AR (TT-10) and A2BR (TT-4) demonstrate anti-tumor activity in 4T1-induced syngeneic breast cancer mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3454.

CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice

Background

Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time.

Methods

This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics.

Results

Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%.

Conclusion

CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04751-6.

Incidence rates of dilated cardiomyopathy in adult first-degree relatives versus matched controls

Background

The incidence rates and importance of traditional risk factors in dilated cardiomyopathy among first-degree relatives are unknown.

Methods and Results

We identified all probands with dilated cardiomyopathy (n = 13,714, mean age at diagnosis 63 years) from the Danish nationwide registries between 1994 and 2017. Incidence rates among first-degree relatives (n = 29,671, mean age 38 years) and for up to 10 age- and sex-matched controls were calculated. Totally 233 (0.8%) first-degree relatives and 285 (0.1%) controls developed dilated cardiomyopathy during a median follow-up of 8.2 (Q1-Q3 4.4-13.3) years. Incidence rates (per 100,000 person-years) were 86.4 (95% confidence interval 73.9-101.0) and 111.1 (79.4-128.7) for first-degree relatives aged < 50 and ≥ 50 years, respectively, versus 7.5 (6.4-8.9) and 19.7 (16.8-23.2) for controls. Atrial fibrillation, diabetes, ischemic heart disease, and hypertension were associated with increased risks of developing dilated cardiomyopathy both in first-degree relatives and controls. Population attributable fractions for the 4 risk factors were 27.7% for first-degree relatives and 37.3% for controls aged < 50 years, and 46.4% versus 58.4% for first-degree relatives and controls among people aged ≥ 50 years, respectively.

Conclusions

The absolute incidence rates of dilated cardiomyopathy in first-degree relatives to patients with dilated cardiomyopathy were low, but significantly higher than in matched controls and elevated by the presence of additional risk factors, especially atrial fibrillation. Additional investigations are warranted to assess whether aggressive treatment of risk factors translates into a reduction of dilated cardiomyopathy in first-degree relatives.

Within and between associations of clinical microskills and correct application of techniques/strategies: A longitudinal multilevel approach

OBJECTIVE

The therapist effect has been demonstrated in various studies. However, studies on putative therapist characteristics show heterogeneous results. Although the majority of studies have solely examined effects between therapists, a growing interest in effects within therapists has emerged. However, it remains unclear whether therapist characteristics are rather a state-like than a trait-like phenomenon. The main aim of the present study is to test whether clinical microskills as well as correct application of techniques and strategies (both between and within therapists) predict across-session change of symptom severity in a large naturalistic data set.

METHOD

The results are based on 398 patients and 48 therapists who treated 5-17 patients each. Clinical microskills and correct application of techniques/strategies were rated using the Inventory of Therapeutic Interventions and Skills, and symptom severity was assessed with the Hopkins Symptom Checklist-11.

RESULTS

Results demonstrated significant within- and between-therapist variability in clinical microskills and correct application of techniques/strategies, and no change over the course of treatment in both variables. Moreover, correct application of techniques/strategies was significantly associated with symptom improvement within, but not between therapists. In addition, higher treatment difficulty as well as an interaction between treatment difficulty and correct application of techniques/strategies were significantly associated with symptom improvement. Clinical microskills were neither predictive of symptom improvement within nor between therapists.

CONCLUSIONS

These results provide initial evidence that not the therapist's average correct application of techniques/strategies is important, but rather how correctly they use a technique with a specific patient. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

MO212: Updated Interim Results of A Phase 1/2 Study to Investigate the Safety, Tolerability, Pharmacokinetics, Pharmacodynamics and Clinical Activity of BION-1301 in Patients With IGA Nephropathy

BACKGROUND AND AIMS

Immunoglobulin A nephropathy (IgAN) is the leading cause of primary glomerulonephritis worldwide with limited treatment options, especially for high-risk patients [1]. BION-1301 is a novel humanized monoclonal antibody that blocks a proliferation-inducing ligand (APRIL), a soluble factor that has been shown to be elevated in patients with IgAN and is correlated with poorer outcomes, including increased proteinuria and decreased eGFR [2, 3]. APRIL promotes IgA class switching, the survival of IgA-secreting plasma cells and the excess production of a galactose-deficient variant form of IgA1 (Gd-IgA1), which is an initiating step in IgAN pathogenesis. This leads to the generation of anti-Gd-IgA1 autoantibodies, considered to be the first ‘hit’ in the multi-hit pathogenesis of IgAN, and the formation of nephritogenic immune complexes that deposit in the kidney, resulting in inflammation and damage [2–4]. Blocking APRIL with BION-1301 is a novel approach to address the underlying pathogenesis of IgAN by reducing circulating levels of Gd-IgA1 and preventing the formation of pathogenic immune complexes. In a Phase 1/2 study of BION-1301 in healthy volunteers (HV), BION-1301 was well-tolerated with no serious adverse events (SAEs), demonstrated a pharmacokinetic (PK) half-life &gt; 30 days and durable dose-dependent reductions in free APRIL (fAPRIL), Gd-IgA1, IgA and IgM, with a lesser effect on IgG [5]. Here we present updated interim results from Part 3 of the study that characterize the safety, PK/PD profile and preliminary efficacy of BION-1301 initially administered intravenously (IV), then subcutaneously (SC), in patients with IgAN.

METHOD

Parts 1 and 2 of the Phase 1/2 study (NCT03945318) assessing single and multiple ascending doses of BION-1301 in HV are complete. Part 3 is an ongoing, open-label, multicohort design in patients with IgAN treated with BION-1301 for up to 1 year. Key eligibility criteria for Part 3 include: (i) biopsy-verified diagnosis of IgAN within the past 10 years, (ii) baseline urine protein excretion ≥ 0.5 g/24 h or UPCR ≥ 0.5 g/g and (iii) stable/optimized dose of ACE-I/ARB (or intolerant). Cohort 1 receives 450 mg of BION-1301 administered IV every 2 weeks. After at least 24 weeks of IV dosing, patients’ transition to 600 mg of BION-1301 administered SC every 2 weeks. Cohort 2 receives 600 mg of BION-1301 SC every 2 weeks. To evaluate PK/PD effects of BION-1301, serum levels of BION-1301, fAPRIL, anti-drug antibodies (ADA), neutralizing antibodies (NAbs) and Gd-IgA1 were quantitated using ELISA-based immunoassays.

RESULTS

Updated data from Cohort 1 will be reported. BION-1301 was well-tolerated in patients with IgAN receiving a 450 mg IV dose every 2 weeks for at least 24 weeks, with no SAEs or early terminations due to AEs. Durable reductions in serum levels of fAPRIL and immunoglobulins were observed in patients with IgAN. Clinically meaningful reductions in proteinuria were seen as early as 12 weeks and were associated with the reduction in Gd-IgA1 levels.

CONCLUSION

BION-1301 offers disease-modifying potential by directly targeting the initiating mechanisms underlying the multi-hit immune pathogenesis of IgAN, which is not addressed with currently available treatments. Promising early mechanistic biomarker and clinical activity responses support the therapeutic potential of BION-1301 in IgAN.

MO207: A Phase 1/2, Multicenter Study to Investigate the Safety, Tolerability, Pharmacokinetics and Pharmacodynamics of BION-1301 in Healthy Volunteers and Adults with IGA Nephropathy

BACKGROUND AND AIMS

Immunoglobulin A nephropathy (IgAN) is the leading cause of primary glomerulonephritis worldwide with limited treatment options, especially for high-risk patients [1]. BION-1301 is a novel humanized monoclonal antibody that blocks a proliferation-inducing ligand (APRIL), a soluble factor that has been shown to be elevated in patients with IgAN and is correlated with poorer outcomes, including increased proteinuria and decreased eGFR [2, 3]. APRIL promotes IgA class switching, the survival of IgA-secreting plasma cells and the excess production of a galactose-deficient variant form of IgA1 (Gd-IgA1), which is an initiating step in the multi-hit pathogenesis of IgAN. This leads to the generation of anti-Gd-IgA1 autoantibodies and the formation of nephritogenic immune complexes that deposit in the kidney, resulting in inflammation and damage [2–4]. Blocking APRIL with BION-1301 is a novel approach to address the underlying pathogenesis of IgAN by reducing circulating levels of Gd-IgA1 and preventing the formation of pathogenic immune complexes. The primary objective of this Phase 1/2 study is to assess the safety and tolerability of BION-1301 in healthy volunteers (HV) and patients with IgAN, and secondarily to assess the PK, PD, immunogenicity and preliminary clinical activity.

METHOD

The Phase 1/2 study (NCT03945318) is comprised of three parts. Parts 1 and 2 were blinded, placebo-controlled single and multiple ascending dose designs in HV and have been completed. Part 3 is a multicenter (USA, UK, South Korea), multicohort, open-label study in up to 40 patients with IgAN. Patients in Cohort 1 receive 450 mg of BION-1301 administered IV every 2 weeks for up to 1 year. After completing at least 24 weeks of IV dosing, patients in Cohort 1 transitioned from receiving 450 mg of BION-1301 IV to receiving 600 mg of BION-1301 SC every 2 weeks. Patients in Cohort 2 receive 600 mg of BION-1301 SC every 2 weeks for up to 1 year. Additional cohorts may be added to explore other doses and dosing schedules. Key eligibility criteria for Part 3 include: (1) biopsy-verified diagnosis of IgAN within 10 years, (2) baseline urine protein excretion ≥ 0.5 g/24 h or UPCR ≥ 0.5 g/g and (3) stable/optimized dose of ACE-I/ARB (or intolerant).

RESULTS

Final HV data from Parts 1 and 2 have been presented at earlier conferences [5]. Part 3 is on-going and updated interim data from patients with IgAN in Cohort 1 who received BION-1301 IV as well as patients who transitioned to SC dosing are planned to be presented at the 59th ERA Congress.

CONCLUSION

The current design of the Phase 1/2 study incorporating SC dosing provides for an improved patient experience and will enable the generation of extended safety, PK, PD, immunogenicity and preliminary efficacy data for the potential use of BION-1301 in patients with IgAN.

How do internal medicine subspecialty societies support clinician-educator careers? A qualitative exploratory study

BACKGROUND

Internal Medicine (IM) subspecialty professional societies can provide valuable community, recognition, resources, and leadership opportunities that promote career success. Historically, this support focused on clinical and research dimensions of academic careers, but educational dimensions have gained more attention recently. This study explores how IM subspecialty professional societies support their clinician-educator members.

METHODS

Using a qualitative study with two phases, the authors collected information from each IM subspecialty society's website about support for medical education. Using information from the first phase, we developed an interview guide for subspecialty society leaders. We used inductive thematic analysis to analyze interview transcripts.

RESULTS

Website analysis identified various mechanisms used by several IM subspecialty societies to promote medical education. These included websites focused on medical education, dedicated medical education poster/abstract sessions at annual meetings, and strategies to promote networking among clinician-educators. Interviews with eight subspecialty society leaders about the professional societies' roles with respect to medical education yielded four main themes: [1] varying conceptions of "medical education" in relation to the society [2] strategies to advance medical education at the society level [3] barriers to recognizing medical education [4] benefits of clinician-educators to the societies. Integrating these themes, we describe recommended strategies for professional societies to better serve clinician-educators.

CONCLUSIONS

We explore how IM subspecialty societies attend to a growing constituency of clinician-educators, with increasing recognition and support of the career path but persistent barriers to its formalization. These conversations shed light on opportunities for professional subspecialty societies to better serve the needs of their clinician-educator members while also enabling these members to make positive contributions in return.

Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms

BACKGROUND

About 30% of patients drop out of cognitive-behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous.

AIMS

This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables.

METHOD

The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation.

RESULTS

The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = -2.93, 95% CI (-3.95, -1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale.

CONCLUSIONS

Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

Rising through the pandemic: a scoping review of quality improvement in public health during the COVID-19 pandemic

Background

The COVID-19 pandemic generated a growing interest in and need for evidence-based tools to facilitate the implementation of emergency management strategies within public health practice. Quality improvement (QI) is a key framework and philosophy to guide organizational emergency response efforts; however, the nature and extent to which it has been used in public health settings during the COVID-19 pandemic remains unclear.

Methods

We conducted a scoping review of literature published January 2020 – February 2021 and focused on the topic of QI at public health agencies during the COVID-19 pandemic. The search was conducted using four bibliographic databases, in addition to a supplementary grey literature search through custom Google search engines and targeted website search methods. Of the 1,878 peer-reviewed articles assessed, 15 records met the inclusion criteria. An additional 11 relevant records were identified during the grey literature search, for a total of 26 records included in the scoping review.

Results

Records were organized into five topics: 1) collaborative problem solving and analysis with stakeholders; 2) supporting learning and capacity building in QI; 3) learning from past emergencies; 4) implementing QI methods during COVID-19; and 5) evaluating performance using frameworks/indicators.

Conclusions

The literature indicates that QI-oriented activities are occurring at the organizational and program levels to enhance COVID-19 response. To optimize the benefits that QI approaches and methodologies may offer, it is important for public health agencies to focus on both widespread integration of QI as part of an organization’s management philosophy and culture, as well as project level activities at all stages of the emergency management cycle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12631-0.

Movement-based patient-therapist attunement in psychological therapy and its association with early change

Objective

Attunement is a novel measure of nonverbal synchrony reflecting the duration of the present moment shared by two interaction partners. This study examined its association with early change in outpatient psychotherapy.

Methods

Automated video analysis based on motion energy analysis (MEA) and cross-correlation of the movement time-series of patient and therapist was conducted to calculate movement synchrony for N = 161 outpatients. Movement-based attunement was defined as the range of connected time lags with significant synchrony. Latent change classes in the HSCL-11 were identified with growth mixture modeling (GMM) and predicted by pre-treatment covariates and attunement using multilevel multinomial regression.

Results

GMM identified four latent classes: high impairment, no change (Class 1); high impairment, early response (Class 2); moderate impairment (Class 3); and low impairment (Class 4). Class 2 showed the strongest attunement, the largest early response, and the best outcome. Stronger attunement was associated with a higher likelihood of membership in Class 2 (b = 0.313, p = .007), Class 3 (b = 0.251, p = .033), and Class 4 (b = 0.275, p = .043) compared to Class 1. For highly impaired patients, the probability of no early change (Class 1) decreased and the probability of early response (Class 2) increased as a function of attunement.

Conclusions

Among patients with high impairment, stronger patient-therapist attunement was associated with early response, which predicted a better treatment outcome. Video-based assessment of attunement might provide new information for therapists not available from self-report questionnaires and support therapists in their clinical decision-making.

Validation and application of the Non-Verbal Behavior Analyzer: An automated tool to assess non-verbal emotional expressions in psychotherapy

BACKGROUND

Emotions play a key role in psychotherapy. However, a problem with examining emotional states via self-report questionnaires is that the assessment usually takes place after the actual emotion has been experienced which might lead to biases and continuous human ratings are time and cost intensive. Using the AI-based software package Non-Verbal Behavior Analyzer (NOVA), video-based emotion recognition of arousal and valence can be applied in naturalistic psychotherapeutic settings. In this study, four emotion recognition models (ERM) each based on specific feature sets (facial: OpenFace, OpenFace-Aureg; body: OpenPose-Activation, OpenPose-Energy) were developed and compared in their ability to predict arousal and valence scores correlated to PANAS emotion scores and processes of change (interpersonal experience, coping experience, affective experience) as well as symptoms (depression and anxiety in HSCL-11).

MATERIALS AND METHODS

A total of 183 patient therapy videos were divided into a training sample (55 patients), a test sample (50 patients), and a holdout sample (78 patients). The best ERM was selected for further analyses. Then, ERM based arousal and valence scores were correlated with patient and therapist estimates of emotions and processes of change. Furthermore, using regression models arousal and valence were examined as predictors of symptom severity in depression and anxiety.

RESULTS

The ERM based on OpenFace produced the best agreement to the human coder rating. Arousal and valence correlated significantly with therapists' ratings of sadness, shame, anxiety, and relaxation, but not with the patient ratings of their own emotions. Furthermore, a significant negative correlation indicates that negative valence was associated with higher affective experience. Negative valence was found to significantly predict higher anxiety but not depression scores.

CONCLUSION

This study shows that emotion recognition with NOVA can be used to generate ERMs associated with patient emotions, affective experiences and symptoms. Nevertheless, limitations were obvious. It seems necessary to improve the ERMs using larger databases of sessions and the validity of ERMs needs to be further investigated in different samples and different applications. Furthermore, future research should take ERMs to identify emotional synchrony between patient and therapists into account.

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

85. A Virtual Platform for Mentoring Clinician Educators at IDWeek is as Effective as In-Person

Background

Career mentorship for clinician educators (CE) may be difficult to obtain within one’s home institution. During IDWeek 2018 and 2019, a mentoring program pairing junior faculty pursuing careers as CEs with more experienced CEs from other institutions was found to be feasible and effective. During IDWeek 2020, the program was transitioned to a virtual format. We assessed the feasibility and efficacy of this virtual mentoring program.

Methods

Junior and established CEs were recruited through the IDSA listserv and Medical Education Community of Practice and paired. Mentees completed an individual development plan (IDP) and identified discussion topics for their meeting. Mentors received training on successful mentoring and their mentee’s IDP and CV prior to meeting. Mentor and mentees met via videoconference for one hour during IDWeek 2020, created an action plan, and scheduled a follow-up call. Post-participation surveys were sent to mentees and mentors.

Results

30 mentor and mentee pairs were matched; 1 pair did not meet. Compared to IDWeek 2018 (17) and 2019 (20), the 2020 program had more mentees (30). 24 (80%) mentees completed the pre-session survey; 17 (59%) mentees and 20 (69%) mentors completed the post-session survey. When compared to survey results from mentees in 2018-19 who met in-person, mentees in the virtual format reported similarly high rates of satisfaction, planned to make changes at work, had an increase in confidence, and felt it was a valuable experience (Table 1). Mentors also reported high rates of satisfaction with the experience in 2020 and were likely to participate in the program next year (Table 2). Only 1 (6%) mentee reported that the virtual format negatively impacted their experience, although 6 (30%) mentors reported some negative impact of the virtual format (Table 3).

Conclusion

A virtual mentoring program for CEs was feasible and as effective for mentees as an in-person format. Some mentors felt that the virtual nature did negatively impact the experience although it had minimal negative impact on mentees.

Disclosures

David J. Riedel, MD, MPH , Gilead (Advisor or Review Panel member)ViiV (Advisor or Review Panel member) Vera Luther, MD, Nothing to disclose Wendy Armstrong, MD, Nothing to disclose Brian Schwartz, MD, Nothing to disclose

Abstract P042: Phase I/II first-in-human study of TT-10 (A2AB inhibitor) as a single agent in subjects with advanced selected solid tumors

Immunotherapy has emerged as a potent tool for cancer treatment by activating the immune system to recognize and attack cancer cells. Immune checkpoint blockage has shown notable clinical success in several malignancies such as advanced non-small cell lung cancer, melanoma, advanced kidney cancer and several other indications, as demonstrated with the approval of CTLA-4 and PD-1/PD-L1 checkpoint inhibitors. However, the efficacy of checkpoint inhibitors to date is limited to only a subset of patients, even within the same cancer type. Thus, the continued focus in the cancer immunotherapy field is to identify new immunosuppressive pathways that allow cancer cells to evade immune surveillance. One emerging pathway of interest is the purinergic pathway, in which adenosine is recognized as a key metabolic checkpoint, and high levels of extracellular adenosine have been evidenced to mediate profound tumor resistance. In this first-in-human Phase I/II study, we explore selective inhibition of the adenosine A2A receptor (A2AR) with TT-10, an oral immune-oncology agent, as a treatment option for subjects with select solid tumors. TT-10 is being evaluated as a single agent, in an open-label, non-randomized, multicenter, dose-escalation (phase 1) and dose-expansion (phase 2) study in subjects with renal cell cancer (RCC), castrate resistant prostate cancer (CRPC) and non-small cell lung cancer (NSCLC) (NCT# NCT04969315). Phase 1 (Cohort A) will explore ascending doses of TT-10, taken orally (10, 20, 50, 100, 200 mg, BID), utilizing a 3+3 design in 28-day cycles to identify the maximum tolerated dose (MTD) and/or recommended Phase 2 dose (R2PD). Enrollment will be open to all three indications. Phase 2 will begin once RP2D is determined and will enroll up to 60 subjects, with approximately 15 subjects per cohort (B.) RCC, (C.) CRPC, (D.) NSCLC (E.) Exploratory Tumor Biopsy). The primary objectives of this study are to evaluate the safety, tolerability, and MTD or the RP2D of TT-10 monotherapy. Secondary objectives are to obtain a preliminary estimate of efficacy, evaluate PK and anti-tumor activity of TT-10 monotherapy, with exploratory objectives inclusive of identification of candidate biomarkers and biomarkers that might predict response, as well as, exploring if adenosine signature and/or receptor expression in tumor tissue at baseline might predict response. Subjects must have locally advanced, recurrent or metastatic neoplastic disease that is not curable by currently available, failure to respond to standard therapy or for whom no appropriate therapies are available, and have a life expectancy of ≥ 3 months to be eligible for the trail. Select subjects that would like to participate in Cohort E.) Exploratory Tumor Biopsy must have an accessible tumor for pre and post dose biopsies. Enrollment of phase 1 is expected to start October 2021.

Citation Format: Sushant Kumar, Kasim Mookhtiar, Desa Rae Pastore, Brian Schwartz, Vijay Reddy. Phase I/II first-in-human study of TT-10 (A2AB inhibitor) as a single agent in subjects with advanced selected solid tumors [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P042.

Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network

Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, grass weeds, spurge, sedge, and no weeds at a precision between 0.68 and 0.87, 0.57 and 0.82, 0.68 and 0.83, 0.66 and 0.90, and 0.80 and 0.88, respectively, when using UAS images at 0.57 cm-1.28 cm pixel^-1 resolution. Recall ranges for the five classes were 0.78-0.93, 0.65-0.87, 0.82-0.93, 0.52-0.79, and 0.94-0.99. Additionally, this study demonstrates that a CNN can achieve precision and recall above 0.9 at detecting different types of weeds during turf establishment when the weeds are mature. The CNN is limited by the image resolution, and more than one model may be needed in practice to improve the overall performance of weed mapping.

Lifetime Risk of Heart Failure and Trends in Incidence Rates Among Individuals With Type 2 Diabetes Between 1995 and 2018

Background

There are limited data on the lifetime risk of heart failure (HF) in people with type 2 diabetes and how incidence has changed over time. We estimated the cumulative incidence and incidence rates of HF among Danish adults with type 2 diabetes between 1995 and 2018 using nationwide data.

Methods and Results

In total, 398 422 patients (49% women) with type 2 diabetes were identified. During follow‐up, 36 400 (9%) were diagnosed with HF and 121 459 (30%) were censored due to death. Using the Aalen‐Johansen estimators, accounting for the risk of death, the estimated residual lifetime risk of HF at age 50 years was calculated as 24% (95% CI 22%–27%) in women and 27% (25%–28%) in men. During the observational period, the proportion of patients treated with statins, angiotensin‐converting enzyme inhibitors or angiotensin II receptor blockers, and metformin increased from <30% to >60%. Similarly, the annual incidence rates of HF decreased significantly, with declines being greater in older versus younger individuals (5% versus 2% in age >50 versus ≤50 years, respectively; P<0.0001) and in women versus men (5% versus 4%, P=0.02), but similar in patients with and without IHD (4% versus 4%, P=0.53).

Conclusions

The current lifetime risk of HF in type 2 diabetes approximates 1 in 4 for men and women. Paralleled by an increase in use of evidence‐based pharmacotherapy over the past decades, the risk of developing HF has declined across several subgroups and regardless of underlying IHD, suggesting that optimal diabetes treatment can mitigate HF risk.

257 Combination of adenosine antagonists A2AR (TT-10) and A2BR (TT-4) with checkpoint inhibitors demonstrate anti-tumor activity in CT-26 murine colon tumor allograft model

Background

TT-10 and TT-4 are potent and selective antagonists of adenosine A2A receptor (A2AR) and A2B receptor (A2BR) respectively. Both agents are being developed for the treatment of advanced cancers initially as monotherapy, using high levels of adenosine receptor expression in tumor tissue as biomarker.

Methods

Balb/c mice were implanted with CT-26 cells and randomly assigned to 8 groups per study; (1) vehicle control, (2) adenosine antagonist - 1 mg/kg A2AR (TT-10) 1 mg/kg or 3 mg/kg A2BR (TT-4), (3) 10 mg/kg Anti-mPD-1, (4) 5 mg/kg Anti-mCTLA-4, (5) 100 mg/kg Irinotecan, (6) adenosine antagonist + Anti-mPD-1, (7) adenosine antagonist + Anti-mCTLA-4, (8) adenosine antagonist+ Irinotecan. Adenosine antagonists and control were given daily by oral gavage, Anti-mPD-1, Anti-mCTLA-4 and Irinotecan were administered Intraperitoneal. Treatment was started on day 1 post implant and mice were followed until individual tumor volume reached 2,000 or 3000 mm3 (as defined by protocol) or moribund. Tumor measurements and weights were taken every 2 to 3 days. In addition, a subset of mice were investigated for changes in peripheral whole blood and intra-tumor analysis on days 3 and 10 via flow cytometry. The populations of interest included CD223+, CD3+, CD4+, CD8+, CD25+FoxP3+, CD25-CD69+ and CD44+CD62L.

Results

All implanted mice developed measurable tumors. Mean suppression of tumor growth was observed to be greater in single agent adenosine antagonists TT-10 and TT-4 when compared to the vehicle control and was observed to show overall greater suppression of tumor growth when combined with anti-mPD-1 or anti-m-CTLA-4. Tumor infiltrating lymphocyte analysis by flow cytometry, showed higher amounts of CD25+FoxP3+ present in control mice at day 3, than was observed in mice that were treated with A2AR alone, A2AR + anti-mPD-1 and A2AR + anti-m-CTLA-4.

Conclusions

TT-10 and TT-4 alone was superior to vehicle control in slowing tumor growth. However, the combination of TT-10 + Anti-mPD-1 and TT-4 + Anti-mCTLA-4 showed the greatest tumor response and growth suppression. Furthermore, a striking reduction of CD25+FoxP3+ within the tumor was observed at day 3 in mice treated with A2AR alone, A2AR + anti-mPD-1 and A2AR + anti-m-CTLA-4 when compared to the vehicle control.

Long-Term Mortality Associated With Use of Carvedilol Versus Metoprolol in Heart Failure Patients With and Without Type 2 Diabetes: A Danish Nationwide Cohort Study

Background

Carvedilol may have favorable glycemic properties compared with metoprolol, but it is unknown if carvedilol has mortality benefit over metoprolol in patients with type 2 diabetes (T2D) and heart failure with reduced ejection fraction (HFrEF).

Methods and Results

Using Danish nationwide databases between 2010 and 2018, we followed patients with new‐onset HFrEF treated with either carvedilol or metoprolol for all‐cause mortality until the end of 2018. Follow‐up started 120 days after initial HFrEF diagnosis to allow initiation of guideline‐directed medical therapy. There were 39 260 patients on carvedilol or metoprolol at baseline (mean age 70.8 years, 35% women), of which 9355 (24%) had T2D. Carvedilol was used in 2989 (32%) patients with T2D and 10 411 (35%) of patients without T2D. Users of carvedilol had a lower prevalence of atrial fibrillation (20% versus 35%), but other characteristics appeared well‐balanced between the groups. Totally 11 306 (29%) were deceased by the end of follow‐up. We observed no mortality differences between carvedilol and metoprolol, multivariable‐adjusted hazard ratio (HR) 0.97 (0.90–1.05) in patients with T2D versus 1.00 (0.95–1.05) for those without T2D, P for difference =0.99. Rates of new‐onset T2D were lower in users of carvedilol versus metoprolol; age, sex, and calendar year adjusted HR 0.83 (0.75–0.91), P<0.0001.

Conclusions

In a contemporary clinical cohort of HFrEF patients with and without T2D, carvedilol was not associated with a reduction in long‐term mortality compared with metoprolol. However, carvedilol was associated with lowered risk of new‐onset T2D supporting the assertion that carvedilol has a more favorable metabolic profile than metoprolol.

[Convergence of Scales to Measure Social Anxiety: An IRT Linking Approach]

OBJECTIVE

The Liebowitz Social Anxiety Scale (LSAS) and the Social Phobia Inventory (SPIN) are established measures in the investigation of social anxiety. Furthermore, the subscale Interpersonal Sensitivity of the Brief Symptom Inventory (BSI-53) is frequently used to screen social anxiety. All three scales claim to capture the same construct, which raises the question of the convergence of these scales. To make research findings comparable by a cross-questionnaire factor (common factor), an item response theory (IRT) linking approach is used in the present study.

METHODS

64 German-speaking psychiatric patients and 295 healthy subjects completed the three questionnaires. Different IRT models, including Graded Response Models (GRM), were constructed, and their model fit compared. Regression analyses were performed based on the best-fit model. The common factor was predicted from the questionnaire total values.

RESULTS

The relationship between the different scales was best explained by a bifactor GRM with one common factor and three domain-specific factors (RMSEA=0.036, CFI=0.977, WRMR=1.061). Based on the results of the regression analyses, three equations were derived for the transformation of questionnaire's total values.

CONCLUSION

The IRT linking approach allows the derivation of a general factor of social anxiety, taking into account commonalities and differences between the instruments used. This has advantages for both research and practice. A replication of this study as well as the implementation of further instruments are recommended to verify the validity of this approach and to generalize the results.