Predicting Therapy Outcomes in Patients With Stress-Related Disorders: Protocol for a Predictive Modeling Study.

Guilt by Association: Inflammation and Shared Genetic Risk Between Stress-Related and Immune Disorders

Background: Prolonged exposure to stress can lead to substantial suffering, impairment and societal costs. However, access to psychological treatment is limited. Internet-based cognitive behavioral therapy (ICBT) can be effective in reducing symptoms of stress, but little is known of its effects in clinical samples. The aim of this study was to investigate the efficacy of ICBT for patients suffering from chronic stress, operationalized as adjustment disorder (AD) and exhaustion disorder (ED). Methods: A total of 100 adults diagnosed with AD or ED were randomly assigned to a 12-week ICBT (n = 50) or waitlist control condition (n = 50). Primary outcome was the level of perceived stress (PSS). Secondary outcomes included several mental health symptom domains as well as functional impairment and work ability. All outcomes were assessed at baseline, after treatment and at the 6-month follow-up. The study was preregistered at Clinicaltrials.gov: NCT02540317. Results: Compared to the control condition, patients in the ICBT group made large and significant improvements on the PSS (d = 1.09) and moderate to large improvements in secondary symptom domains. Effects were maintained at the 6-month follow-up. There was no significant between-group effect on functional impairment or work ability. Conclusions: A relatively short ICBT is indicated to be effective in reducing stress-related symptoms in a clinical sample of patients with AD and ED, and has the potential to substantially increase treatment accessibility. Results must be replicated, and further research is needed to understand the relationship between symptom reduction, functional impairment and work ability.

Background People diagnosed with stress-related exhaustion disorder report high levels of cognitive symptoms. This study aimed to explore how persons diagnosed with Exhaustion disorder (ED) experienced cognitive functioning and recovery 6-10 years after participating in a rehabilitation programme. Specifically, it investigated the experiences of current functioning, change over time, and what had been barriers or facilitators for cognitive recovery.MethodsSemi‐structured interviews were conducted for 38 persons previously diagnosed with ED (Mean age: 52; Females: 32) and explored using thematic analysis.ResultsThe analysis resulted in four themes: “’It’s different now’: Remaining cognitive symptoms”, “The bigger picture: Cognitive recovery in context”, “Overcoming challenges: Strategies for coping with cognitive symptoms”, and “The approach towards cognition matters”. The participants’ experiences varied but included descriptions on how cognitive functioning had become better with some remaining symptoms. These difficulties were reported across cognitive domains, yet often centred around upholding executive control. Cognitive recovery was seen in the context of overall well-being and recovery which differed between the participants. Facilitators and barriers thus varied between persons, and included both restorative and compensatory strategies, external conditions, the degree of worry, and development of a more acceptant or self-compassionate view on cognition and oneself.ConclusionsThe results show that cognitive recovery in ED is multifaceted. 6-10 years after rehabilitation, experiences included improvement of everyday cognitive functioning, but also lingering challenges, often related to maintenance of executive control. Recovery was influenced by factors such as general well-being, restorative or compensatory strategies, worrying, or the adaptation of more accepting or self-compassionate perspectives. The findings indicate a need for tailored, person-centred approaches to supporting cognitive recovery.Trial registrationParticipants were recruited as part of the Rehabilitation for Improved Cognition (RECO) trial (ClinicalTrials.gov: NCT03073772, date of registration: 8 March, 2017). This study was preregistered on the Open Science Framework (osf.io: https://doi.org/10.17605/OSF.IO/S2W6X).

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

BackgroundAdjustment and stress-related disorders are prevalent among psychiatric service users. Despite their prevalence, little is known about their prognosis. To reduce that gap, the present article documents the service use and diagnostic outcomes of people with adjustment or stress-related disorders presenting at Singapore’s largest psychiatric emergency department.MethodsAdministrative data from 2014 to 2021 was retrieved to follow a group of 683 service users whose first-ever psychiatric presentation in 2014 warranted a diagnosis of adjustment or stress-related disorder. People were grouped a priori depending on whether different diagnoses were recorded within 7 days, 9 months, after 9 months or not at all. Survival curves characterized conversion to other diagnoses and engagement with healthcare services. Service use outcomes include the number of hospitalizations, outpatient appointments, emergency department visits, and prescriptions.ResultsSixty-one percent (n = 417) never received another diagnosis over the 8-year period. This group used emergency services most and received the most pharmacotherapy shortly after their first visit. Of those who received another diagnosis, depression, personality disorders, and psychotic disorders were the most common. Those who received another diagnosis within 7 days (n = 70, 10%) received it on their first day of hospitalization (IQR 1–1), making the most use of inpatient services. The group who received another diagnosis within 9 months (n = 105, 15%) did so after 42 days (IQR 26–84) and had the highest relative number of deaths. Those who received another diagnosis after 9 months (n = 91, 13%) did so after 1,134 days (IQR 613–1,823) and had the longest period of engagement but made the least use of any psychiatric service, potentially suggesting a group whose early index diagnosis heralded vulnerability to future disorders.ConclusionsA large group of service users with acute stress or adjustment disorders will likely never be given another psychiatric diagnosis and appear to disengage following an initial period of high-intensity service use. The group that received a different diagnosis after the 9-month mark had prolonged contact with services but low intensity of service use and may represent a target for preventative intervention to help them improve their stress-managing skills and avoid developing other disorders.

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.

Emotional disorders such as depression and anxiety affect millions globally and pose a significant burden on public health. Personalized treatment approaches using machine learning (ML) to predict treatment response could revolutionize treatment strategies. However, there is limited evidence as to whether ML is successful in predicting treatment outcomes. This meta-analysis aims to evaluate the accuracy of ML algorithms in predicting binary treatment response (responder vs. non-responder) to evidence-based psychotherapies, pharmacotherapies, and other treatments for emotional disorders, and to examine moderators of prediction accuracy. Following PRISMA guidelines, a comprehensive literature search was conducted across PubMed and PsycINFO from January 1st, 2010 to March 27th, 2025. Studies were included if they used ML methods to predict treatment response in patients with emotional disorders. Data were extracted on sample size, type of treatment, predictors used, ML methods, and prediction accuracy. Meta-analytic techniques were used to synthesize findings and identify moderators of prediction accuracy. Out of 3816 non-duplicate records, 155 studies met inclusion criteria. The overall mean prediction accuracy was 0.76 (95% CI: 0.74-0.78), and the mean area under the curve was 0.80 indicating good discrimination. The average sensitivity and specificity were 0.73 and 0.75, respectively. Moderator analyses indicated that studies using more robust cross-validation procedures exhibited higher prediction accuracy. Neuroimaging data as predictors were associated with higher accuracy compared to clinical and demographic data. Moreover, results indicated that studies with larger responder rates, as well as those that did not correct for imbalances in outcome rates, were associated with higher prediction accuracy. ML methods show promise in predicting treatment response for emotional disorders, with varying degrees of accuracy depending on the type of predictors used and the rigor of methodological procedures implemented. Future research should focus on improving methodological integrity and exploring the integration of multimodal data to enhance prediction accuracy.

The aim of this study was to evaluate specific effects for patients with adjustment or exhaustion disorder, the Stress subgroup (n=152), regarding symptom severity and sick leave after CBT, a return-to-work intervention (RTW-I), and a combination of them (COMBO), using data from a randomized trial. In the original study, primary care patients on sick leave (N=211) were randomized to CBT (n=64), RTW-I (n=67), or COMBO (n=80). Blinded Clinician Severity Rating (CSR) of symptoms and sick leave registry data were primary outcomes. Subgroup analyses showed that for the Stress subgroup, CBT led to greater reduction of symptoms than RTW-I posttreatment, but COMBO did not differ from CBT or RTW-I. Regarding sick leave, there was no difference between treatments in the Stress subgroup. An exploratory analysis of the treatment effects in a subgroup of patients with depression, anxiety or insomnia indicates that RTW-I reduced sick leave faster than CBT. We conclude that CBT may be promising as an effective treatment of stress and exhaustion disorder.

The adjustment disorder (AD) diagnosis has clinical appeal to both doctors and patients. The idea of temporary emotional symptoms resulting directly from a stressful life event is viewed as a more normal human reaction than an idiopathic pathologic psychiatric state and is therefore less stigmatizing. Additionally, the disorder's more benign course (especially in adults) encourages a clinician to be more prognostically optimistic. This chapter discusses the prevalence, epidemiology, course and prognosis, and etiology of ADs. Also reviewed are AD subtypes proposed but not accepted for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), including the acute stress disorder/posttraumatic stress disorder subtype and the bereavement-related subtype. Treatment options are covered, including psychotherapy, pharmacotherapy, and primary care. Tables outline diagnostic criteria for ADs in DSM-IV, stress-related disorders in DSM-5, DSM classifications, ADs in mental illness and medical settings, and subtypes of DSM-IV, text revision adjustment disorders. Graphs categorize patients diagnosed with ADs according to type of illness and the prognosis for recovery from ADs in adolescents and adults.This chapter contains 2 highly rendered figures, 6 tables, 109 references, and 5 MCQs.

BackgroundAsthma is a chronic disease that exacerbates due to various risk factors, including the patient’s biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model’s performance, and determine the need to improve research in this field.MethodA systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article’s quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review.ResultFrom 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field.ConclusionAsthma attack predictive models become more significant when using both patient’s biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

Background and Aims Psychiatric reactions to life stressors are common in the general population and may increase the risk of acute (AKI) and chronic kidney disease (CKD). The aim of this study is to evaluate the association between stress related disorders and subsequent risk of AKI or CKD progression. Method Population-matched retrospective cohort study conducted in the Stockholm CREAtinine Measurement project (SCREAM), Sweden from 2006 to 2012. The cohort included 31,137 exposed patients with stress related disorders (ie, posttraumatic stress disorder, acute stress reaction, adjustment disorder, and other stress reactions, defined by 10th international Classification of diseases, ICD-10: F43) and known kidney function (estimated glomerular filtration rate, eGFR), and 117,144 unexposed matched by age, sex and eGFR category. The primary outcome was CKD progression, defined as a sustained relative decline in eGFR of more than 40% or commencement of kidney replacement therapy. The secondary outcome was acute kidney injury (AKI), defined by death or hospitalization attributed to AKI or detected transient plasma creatinine elevations (KDIGO classification). Conditional Cox models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Results During a medium follow-up of 3.3 years and compared with the unexposed population, patients with stress related disorders were at increased risk of CKD progression (HR 1.16, 95% CI 1.04-1.30) and AKI (HR 1.17, 95% CI 1.01-1.37). While the relative risk of CKD progression remained constant over follow-up, the association with AKI was most evident during the first year after stress diagnosis (HR: 1.31, 95% CI 1.04-1.66), disappearing thereafter. Among 9 prespecified subgroups, there was suggestion of heterogeneity with possible lack of association in patients with psychiatric comorbidity. Conclusion Exposure to a stress related disorder was associated with the long-term risk of CKD progression and the short-term risk of AKI. Studies are needed to better understand underlying mechanisms.

Systematic Review of Currently Available Predictive Machine Learning Models for Multiple Myeloma

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Despite its high prevalence and strong linkages with dangerous health outcomes, research on Adjustment Disorder (AjD) is hindered by lack of diagnostic clarity. AjD is categorized as a stress-related disorder, highlighting the important role of the stressor(s) on AjD symptom onset and severity. The military community shows increased risk for AjD, and existing tools do not capture the stressors most relevant and appropriate to this unique community. A diagnostic assessment tool developed specifically for this specialized population may provide critical capability to clinical assessment. A Delphi method was used to create a military-specific version of the standard assessment for Adjustment Disorders (ADNM-20), named ADNM-20-MIL. This tool was pilot-tested in a sample of U.S. Active Duty Service Members (ADSMs) with AjD diagnoses. Throughout the Delphi process, military-specific stressors were identified and integrated into the ADNM-20-MIL, then refined and validated, ensuring their applicability and relevance to the military context. The ADNM-20-MIL will enable timely diagnosis and targeted treatment for AjD, which remains a highly prevalent and destabilizing diagnosis in ADSMs.

Traditional workflows in chemical engineering, from design to characterization, have served the field well. Emerging technologies, such as machine learning, now offer opportunities for faster design iterations and better integration of the various development stages. This study demonstrates how machine learning (ML) can reduce this gap by directly integrating experimental data into predictive models. Focusing on a Additive Manufactured helical micro-distillation column design for separating liquid mixtures, several ML models were trained to predict the number of theoretical stages based on geometric parameters and operating conditions. 11 variants were designed, built, and tested to collect 197 experimental data sets to assess the feasibility of five predictive ML models from experimental data. Among the algorithms tested, the Gradient Boosting Regressor achieved the best performance with a coefficient of determination R 2 = 0 . 7934 . The work highlights the behavior of the model across different regimes, identifies key sources of error, and proposes a hybrid experimental–ML workflow for rapid screening of distillation designs. This approach accelerates process development and reduces the need for extensive experimentation, especially in time-consuming tasks such as distillation. • ML predicts KPIs for complex AM helical distillation columns using real data. • Multiple supervised ML models benchmarked for AM-induced geometric effects. • Multi-output modeling enhances stability and captures physical correlations. • Hybrid ML–experimental workflow identifies high-value regions for testing. • Framework accelerates design and screening of AM-enabled separation units.