Enhanced clinical decisions for management of benign prostatic hyperplasia using patient-reported outcomes: protocol for a prospective observational study.

BACKGROUND: Lower urinary tract symptoms (LUTS) due to benign prostatic hyperplasia (BPH) significantly impact quality of life among older men. Despite the prevalent use of the American Urological Association Symptom Index (AUA-SI) for BPH, this measure overlooks key symptoms such as pain and incontinence, underscoring the need for more comprehensive patient-reported outcome (PRO) tools. This study aims to integrate enhanced PROs into routine clinical practice to better capture the spectrum of LUTS, thereby improving clinical outcomes and patient care. METHODS: This prospective observational study will recruit men with LUTS secondary to BPH aged ≥ 50 years from urology clinics. Participants will be stratified into medical and surgical management groups, with PRO assessments scheduled at regular intervals to monitor LUTS and other health outcomes. The study will employ the LURN Symptom Index (SI)-29 alongside the traditional AUA-SI and other non-urologic PROs to evaluate a broad range of symptoms. Data on comorbidities, symptom severity, and treatment efficacy will be collected through a combination of electronic health records and PROs. Analyses will focus on the predictive power of these tools in relation to symptom trajectories and treatment responses. Aims are to: (1) integrate routine clinical tests with PRO assessment to enhance screening, diagnosis, and management of patients with BPH; (2) examine psychometric properties of the LURN SIs, including test-retest reliability and establishment of clinically meaningful differences; and (3) create care-coordination recommendations to facilitate management of persistent symptoms and common comorbidities measured by PROs. DISCUSSION: By employing comprehensive PRO measures, this study expects to refine symptom assessment and enhance treatment monitoring, potentially leading to improved personalized care strategies. The integration of these tools into clinical settings could revolutionize the management of LUTS/BPH by providing more nuanced insights into patient experiences and outcomes. The findings could have significant implications for clinical practices, potentially leading to updates in clinical guidelines and better health management strategies for men with LUTS/BPH. TRIAL REGISTRATION: This study is registered in ClinicalTrials.gov (NCT05898932).

The new paradigm in machine learning - foundation models, large language models and beyond: a primer for physicians.

Foundation machine learning models are deep learning models capable of performing many different tasks using different data modalities such as text, audio, images and video. They represent a major shift from traditional task-specific machine learning prediction models. Large language models (LLM), brought to wide public prominence in the form of ChatGPT, are text-based foundational models that have the potential to transform medicine by enabling automation of a range of tasks, including writing discharge summaries, answering patients questions and assisting in clinical decision-making. However, such models are not without risk and can potentially cause harm if their development, evaluation and use are devoid of proper scrutiny. This narrative review describes the different types of LLM, their emerging applications and potential limitations and bias and likely future translation into clinical practice.

[Interpreting a randomized trial in oncology: Key points for the clinician]. / Interpréter un essai randomisé en oncologie : éléments clés en clinique.

The analysis of randomized clinical trials presents a challenge for clinicians. A set of critical elements can facilitate their interpretation. One must question whether the inclusion and exclusion criteria accurately mirror clinical practice. Does the control arm align with what is currently recognized as best practice? Do patients in the control group have access to the best options when the cancer progresses or recurs? The degree of confidence with which phase II trial results can be interpreted also warrants consideration. Finally, informative censoring can be searched for by comparing early censoring rates between treatment arms. Faced with the challenges of interpreting scientific literature, these keys can help the clinician and guide the eventual integration of new results into shared medical decision-making.

L'analyse d'essais cliniques randomisés est un défi pour le clinicien. Une série d'éléments clés peuvent toutefois aider à l'interprétation. Tout d'abord, les critères d'inclusion et d'exclusion reflètent-ils la pratique quotidienne ? Ensuite, le bras contrôle correspond-il aux meilleures pratiques reconnues ? Est-ce que les patients du groupe contrôle ont un accès aux meilleures options lorsque le cancer progresse ou récidive ? Avec quelle confiance interpréter des résultats de phase II ? Enfin, la censure informative peut être recherchée en comparant les taux de censure précoce entre les bras de traitements. Face aux défis de l'interprétation de la littérature scientifique, ces clés peuvent être une aide pour le clinicien et guider l'intégration éventuelle de nouveaux résultats dans la décision médicale partagée.

Co-creation of a toolkit to assist risk communication and clinical decision-making in severe preeclampsia: SPOT-Impact study design.

Globally, the incidence of hypertensive disorders of pregnancy, especially preeclampsia, remains high, particularly in low- and middle-income countries. The burden of adverse maternal and perinatal outcomes is particularly high for women who develop a hypertensive disorder remote from term (<34 weeks). In parallel, many women have a suboptimal experience of care. To improve the quality of care in terms of provision and experience, there is a need to support the communication of risks and making of treatment decision in ways that promote respectful maternity care. Our study objective is to co-create a tool(kit) to support clinical decision-making, communication of risks and shared decision-making in preeclampsia with relevant stakeholders, incorporating respectful maternity care, justice, and equity principles. This qualitative study detailing the exploratory phase of co-creation takes place over 17 months (Nov 2021-March 2024) in the Greater Accra and Eastern Regions of Ghana. Informed by ethnographic observations of care interactions, in-depth interviews and focus group and group discussions, the tool(kit) will be developed with survivors and women with hypertensive disorders of pregnancy and their families, health professionals, policy makers, and researchers. The tool(kit) will consist of three components: quantitative predicted risk (based on external validated risk models or absolute risk of adverse outcomes), risk communication, and shared decision-making support. We expect to co-create a user-friendly tool(kit) to improve the quality of care for women with preeclampsia remote from term which will contribute to better maternal and perinatal health outcomes as well as better maternity care experience for women in Ghana.

Adverse maternal and perinatal outcomes is high for women who develop preeclampsia remote from term (<34 weeks). To improve the quality of provision and experience of care, there is a need to support communication of risks and treatment decisions that promotes respectful maternity care.This article describes the methodology deployed to cocreate a user-friendly tool(kit) to support risk communication and shared decision-making in the context of severe preeclampsia in a low resource setting.

High throughput screening aids clinical decision-making in refractory acute myeloid leukaemia.

BACKGROUND: Despite advances in therapeutics for adverse-risk acute myeloid leukaemia (AML), overall survival remains poor, especially in refractory disease. Comprehensive tumour profiling and pre-clinical drug testing can identify effective personalised therapies. CASE: We describe a case of ETV6-MECOM fusion-positive refractory AML, where molecular analysis and in vitro high throughput drug screening identified a tolerable, novel targeted therapy and provided rationale for avoiding what could have been a toxic treatment regimen. Ruxolitinib combined with hydroxyurea led to disease control and enhanced quality-of-life in a patient unsuitable for intensified chemotherapy or allogeneic stem cell transplantation. CONCLUSION: This case report demonstrates the feasibility and role of combination pre-clinical high throughput screening to aid decision making in high-risk leukaemia. It also demonstrates the role a JAK1/2 inhibitor can have in the palliative setting in select patients with AML.

Clinical decision making for VA ECMO weaning in patients with cardiogenic shock A formative qualitative study.

Weaning and liberation from VA ECMO in cardiogenic shock patients comprises a complex process requiring a continuous trade off between multiple clinical parameters. In the absence of dedicated international guidelines, we hypothesized a great heterogeneity in weaning practices among ECMO centers due to a variety in local preferences, logistics, case load and individual professional experience. This qualitative study focused on the appraisal of clinicians' preferences in decision processes towards liberation from VA ECMO after cardiogenic shock while using focus group interviews in 4 large hospitals. The goal was to provide novel and unique insights in daily clinical weaning practices. As expected, we found we a great heterogeneity of weaning strategies among centers and professionals, although participants appeared to find common ground in a clinically straightforward approach to assess the feasibility of ECMO liberation at the bedside. This was shown in a preference for robust, easily accessible parameters such as arterial pulse pressure, stable cardiac index ≥2.1 L/min, VTI LVOT and 'eyeballing' LVEF.

Effects of explainable artificial intelligence in neurology decision support.

OBJECTIVE: Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. METHODS: We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. RESULTS: We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. INTERPRETATION: xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.

BACKGROUND: General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS). METHODS: We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure. RESULTS: Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance. DISCUSSION: Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice. HIGHLIGHTS: We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes.

Clinical Guideline for Preimplantation Genetic Testing in Inherited Cardiac Diseases.

BACKGROUND: Preimplantation genetic testing (PGT) is a reproductive technology that selects embryos without (familial) genetic variants. PGT has been applied in inherited cardiac disease and is included in the latest American Heart Association/American College of Cardiology guidelines. However, guidelines selecting eligible couples who will have the strongest risk reduction most from PGT are lacking. We developed an objective decision model to select eligibility for PGT and compared its results with those from a multidisciplinary team. METHODS: All couples with an inherited cardiac disease referred to the national PGT center were included. A multidisciplinary team approved or rejected the indication based on clinical and genetic information. We developed a decision model based on published risk prediction models and literature, to evaluate the severity of the cardiac phenotype and the penetrance of the familial variant in referred patients. The outcomes of the model and the multidisciplinary team were compared in a blinded fashion. RESULTS: Eighty-three couples were referred for PGT (1997-2022), comprising 19 different genes for 8 different inherited cardiac diseases (cardiomyopathies and arrhythmias). Using our model and proposed cutoff values, a definitive decision was reached for 76 (92%) couples, aligning with 95% of the multidisciplinary team decisions. In a prospective cohort of 11 couples, we showed the clinical applicability of the model to select couples most eligible for PGT. CONCLUSIONS: The number of PGT requests for inherited cardiac diseases increases rapidly, without the availability of specific guidelines. We propose a 2-step decision model that helps select couples with the highest risk reduction for cardiac disease in their offspring after PGT.

Adolescents with Rett syndrome at critical care pathway junctures: Examining clinicians' decision to initiate invasive long-term ventilation.

BACKGROUND: The initiation of invasive long-term ventilation (I-LTV) for an adolescent with Rett Syndrome (RTT) involves many serious bioethical considerations. In moving towards a more inclusive model of patient participation, transparency surrounding the main influencing factors around this decision is important. OBJECTIVE: We aimed to identify the main drivers influencing a clinician's decision to support initiation of I-LTV for an adolescent with RTT. METHOD: We used an anonymous online vignette-based factorial survey. The survey was distributed internationally through eight professional multi-disciplinary organisations to reach clinicians working in paediatrics. RESULTS: We analysed 504 RTT vignettes completed by 246 clinicians using mixed effect regression modelling. The main three significant influencing factors identified were: parental agreement with the decision to support initiation, the family's support network, and proximity to a tertiary care centre. Additional comments from participants focused on family support, and the importance of on-going communication with the family. CONCLUSION: As the rights of those with disabilities improve and participation of adolescents in decision-making becomes more established, effective communications with the family around goals of care and particular sensitivity and reflective practice around methods of consensus building will likely contribute to a positive decision-making process at this difficult time.

The process of clinical decision-making in chronic wound care: A scenario-based think-aloud study.

AIMS: To undertake a comprehensive investigation into both the process of information acquisition and the clinical decision-making process utilized by primary care nurses in the course of treating chronic wounds. DESIGN: Scenario-based think-aloud method, enriched by the integration of information processing theory. The study was conducted within the framework of home care nursing organizations situated in Flanders, the Flemish speaking part of Belgium. A cohort of primary care nurses (n = 10), each possessing a minimum of one year of nursing experience, was recruited through the collaboration of three home care nursing organizations. METHODS: Two real-life clinical practice scenarios were employed for the interviews, with the researcher adopting the roles of either the patient or another clinician to enhance the realism of the think-aloud process. Each think-aloud session was promptly succeeded by a subsequent follow-up interview. The Consolidated criteria for Reporting Qualitative research checklist was followed to guarantee a consistent and complete report of the study. RESULTS: Amidst noticeable variations, a discernible pattern surfaced, delineating three sequential concepts: 1. gathering overarching information, 2. collecting and documenting wound-specific data, and 3. interpreting information to formulate wound treatment strategies. These concepts encompassed collaborative discussions with stakeholders, while the refinement of wound treatment strategies was interwoven within both concepts 2 and 3. CONCLUSIONS: Evident variations were identified in chronic wound care clinical decision-making, regardless of educational background or experience. These insights hold the potential to inform the development of clinical decision support systems for chronic wound management and provide guidance to clinicians in their decision-making endeavours.

3D analysis of the preoperative deformity in AIS can be used to guide surgical treatment decisions for selective thoracic fusion.

PURPOSE: To identify 3D measures of scoliosis from preoperative imaging that are associated with optimal radiographic outcomes after selective thoracic fusion (STF) for adolescent idiopathic scoliosis (AIS). METHODS: Subjects with primary thoracic curves (Lenke 1-4, B or C modifiers) fused selectively (L1 or above) who had preoperative 3D reconstructions and minimum 2 years of follow-up were included. An optimal outcome at 2 years was defined as having 4 of 5 parameters previously defined in the literature: (1) lumbar curve < 26º, (2) deformity flexibility quotient < 4, (3) C7-CSVL < 2 cm, (4) lumbar prominence < 5º and (5) trunk shift < 1.5 cm. Univariate and CART analyses were performed to identify preoperative variables associated with achieving an optimal outcome 2 years postoperatively. RESULTS: Ninety-nine (88F, 11 M) patients met inclusion. Mean age was 15 ± 2 years. Fifty-one subjects (52%) had an optimal outcome. Seven preoperative deformity measures representing smaller thoracolumbar/lumbar deformity in the optimal group were found to be significant on univariate analysis. CART analysis identified the following variables associated with optimal outcomes: difference in apical rotation > 30° = 27% optimal outcomes, difference in apical rotation ≤ 30° and coronal vertebral wedging of lumbar apex > 3° = 46% optimal outcomes, and difference in apical rotation ≤ 30° and coronal vertebral wedging of lumbar apex ≤ 3° = 80% optimal outcomes (p < 0.05). CONCLUSION: Optimal outcomes after STF were associated with a preoperative difference in apical vertebral rotation in the axial plane less than 30° between thoracic and lumbar curves as well as coronal plane vertebral wedging of the lumbar apical vertebra less than 3°.

Understanding Social, Cultural, and Religious Factors Influencing Medical Decision-Making on BRCA1/2 Genetic Testing in the Orthodox Jewish Community.

INTRODUCTION: Although the prevalence of a pathogenic variant in the BRCA1 and BRCA2 genes is about 1:400 (0.25%) in the general population, the prevalence is as high as 1:40 (2.5%) among the Ashkenazi Jewish population. Despite cost-effective preventive measures for mutation carriers, Orthodox Jews constitute a cultural and religious group that requires different approaches to BRCA1 and BRCA2 genetic testing relative to other groups. This study analyzed a dialog of key stakeholders and community members to explore factors that influence decision-making about BRCA1 and BRCA2 genetic testing in the New York Orthodox Jewish community. METHODS: Qualitative research methods, based on Grounded Theory and Narrative Research, were utilized to analyze the narrative data collected from 49 key stakeholders and community members. A content analysis was conducted to identify themes; inter-rater reliability was 71%. RESULTS: Facilitators of genetic testing were a desire for preventive interventions and education, while barriers to genetic testing included negative emotions, feared impact on family/romantic relationships, cost, and stigma. Views differed on the role of religious leaders and healthcare professionals in medical decision-making. Education, health, and community were discussed as influential factors, and concerns were expressed about disclosure, implementation, and information needs. CONCLUSION: This study elicited the opinions of Orthodox Jewish women (decision-makers) and key stakeholders (influencers) who play critical roles in the medical decision-making process. The findings have broad implications for engaging community stakeholders within faith-based or culturally distinct groups to ensure better utilization of healthcare services for cancer screening and prevention designed to improve population health.

Diagnostic uncertainty and decision-making in home-based primary care: A qualitative study of antibiotic prescribing.

BACKGROUND: Evaluating infection in home-based primary care is challenging, and these challenges may impact antibiotic prescribing. A refined understanding of antibiotic decision-making in this setting can inform strategies to promote antibiotic stewardship. This study investigated antibiotic decision-making by exploring the perspectives of clinicians in home-based primary care. METHODS: Clinicians from the Department of Veterans Affairs Home-Based Primary Care Program were recruited. Semi-structured interviews were conducted from June 2022 through September 2022 using a discussion guide. Transcripts were analyzed using grounded theory. The constant comparative method was used to develop a coding structure and to identify themes. RESULTS: Theoretical saturation was reached after 22 clinicians (physicians, n = 7; physician assistants, n = 2, advanced practice registered nurses, n = 13) from 19 programs were interviewed. Mean age was 48.5 ± 9.3 years, 91% were female, and 59% had ≥6 years of experience in home-based primary care. Participants reported uncertainty about the diagnosis of infection due to the characteristics of homebound patients (atypical presentations of disease, presence of multiple chronic conditions, presence of cognitive impairment) and the challenges of delivering medical care in the home (limited access to diagnostic testing, suboptimal quality of microbiological specimens, barriers to establishing remote access to the electronic health record). When faced with diagnostic uncertainty about infection, participants described many factors that influenced the decision to prescribe antibiotics, including those that promoted prescribing (desire to avoid hospitalization, pressure from caregivers, unreliable plans for follow-up) and those that inhibited prescribing (perceptions of antibiotic-associated harms, willingness to trial non-pharmacological interventions first, presence of caregivers who were trusted by clinicians to monitor symptoms). CONCLUSIONS: Clinicians face the difficult task of balancing diagnostic uncertainty with many competing considerations during the treatment of infection in home-based primary care. Recognizing these issues provides insight into strategies to promote antibiotic stewardship in home care settings.

Tracheostomy and long-term invasive ventilation decision-making in children: A scoping review.

An increasing number of children are surviving critical illnesses requiring tracheostomy/long-term ventilation (LTV). This scoping review seeks to collate the available evidence on decision-making for tracheostomy/LTV in children. Systematic searches of electronic databases and websites were conducted for articles and reports. Inclusion criteria included: (1) children 0-18 years old; (2) described use of tracheostomy or tracheostomy/LTV; and (3) information on recommendations for tracheostomy decision-making or decision-making experiences of family-caregivers or health care providers. Articles not written in English were excluded. Of the 4463 records identified through database search and other methods, a total of 84 articles, 2 dissertations, 1 book chapter, 3 consensus statement/society guidelines, and 8 pieces of grey literature were included. Main thematic domains identified were: (1) legal and moral standards for decision-making; (2) decision-making models, roles of decision-makers, and decisional aids towards a shared decision-making model; (3) experiences and perspectives of decision-makers; (4) health system and society considerations; and (5) conflict resolution and legal considerations. A high degree of uncertainty and complexity is involved in tracheostomy/LTV decision-making. There is a need for a standardized decision-support process that is consistent with a child's best interests and shared decision-making. Strategies for optimizing communication and mechanism for managing disputes are needed.

Evaluation of ChatGPT and Google Bard Using Prompt Engineering in Cancer Screening Algorithms.

Large language models (LLMs) such as ChatGPT and Bard have emerged as powerful tools in medicine, showcasing strong results in tasks such as radiology report translations and research paper drafting. While their implementation in clinical practice holds promise, their response accuracy remains variable. This study aimed to evaluate the accuracy of ChatGPT and Bard in clinical decision-making based on the American College of Radiology Appropriateness Criteria for various cancers. Both LLMs were evaluated in terms of their responses to open-ended (OE) and select-all-that-apply (SATA) prompts. Furthermore, the study incorporated prompt engineering (PE) techniques to enhance the accuracy of LLM outputs. The results revealed similar performances between ChatGPT and Bard on OE prompts, with ChatGPT exhibiting marginally higher accuracy in SATA scenarios. The introduction of PE also marginally improved LLM outputs in OE prompts but did not enhance SATA responses. The results highlight the potential of LLMs in aiding clinical decision-making processes, especially when guided by optimally engineered prompts. Future studies in diverse clinical situations are imperative to better understand the impact of LLMs in radiology.

Ultra-fast deep-learned CNS tumour classification during surgery.

Central nervous system tumours represent one of the most lethal cancer types, particularly among children1. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity2,3. However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery4. Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries.

Variables Associated with Emotional Symptom Severity in Primary Care Patients: The Usefulness of a Logistic Regression Equation to Help Clinical Assessment and Treatment Decisions

The aim of this study is to contribute to the evidence regarding variables related to emotional symptom severity and to use them to exemplify the potential usefulness of logistic regression for clinical assessment at primary care, where most of these disorders are treated. Cross-sectional data related to depression and anxiety symptoms, sociodemographic characteristics, quality of life (QoL), and emotion-regulation processes were collected from 1,704 primary care patients. Correlation and analysis of variance (ANOVA) tests were conducted to identify those variables associated with both depression and anxiety. Participants were then divided into severe and nonsevere emotional symptoms, and binomial logistic regression was used to identify the variables that contributed the most to classify the severity. The final adjusted model included psychological QoL (p < .001, odds ratio [OR] = .426, 95% CI [.318, .569]), negative metacognitions (p < .001, OR = 1.083, 95% CI [1.045, 1.122]), physical QoL (p < .001, OR = .870, 95% CI [.841, .900]), brooding rumination (p < .001, OR = 1.087, 95% CI [1.042, 1.133]), worry (p < .001, OR = 1.047, 95% CI [1.025, 1.070]), and employment status (p = .022, OR [.397, 2.039]) as independent variables, ρ2 = .326, area under the curve (AUC) = .857. Moreover, rumination and psychological QoL emerged as the best predictors to form a simplified equation to determine the emotional symptom severity (ρ2 = .259, AUC = .822). The use of statistical models like this could accelerate the assessment and treatment-decision process, depending less on the subjective point of view of clinicians and optimizing health care resources. (AU)