Current Diagnostic Paradigm Research Articles

The interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach

The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness. Numerous studies have sought to use RDoC to understand the Diagnostic and Statistical Manual of Mental Disorders (DSM) categories from a qualified perspective, but few studies have examined the distribution variations and interaction characteristics of RDoC within various DSM categories through retrospective analyses. Therefore, we employed unsupervised learning to translate five domains of eRDoC scores derived from electronic medical records (EMR) of patients diagnosed with Major Depressive Disorder (MDD), Schizophrenia (SCZ), and Bipolar Disorder (BD) at West China Hospital between 2008 and 2021. The distribution characteristics, interaction networks, and potential clinical effectiveness of RDoC domains were analyzed. Using non-parametric statistical tests, we found that MDD had the highest score in Negative Valence System (NVS) (4.1, p < 0.001), while BD exhibited the highest score in Positive Valence System (PVS) score (4.9, p < 0.001) and Arousal System (AS) (4.4, p < 0.001). SCZ demonstrated the highest scores in Cognitive Systems (CS) (5.8, p < 0.001) and Social Processes Systems (SPS) (4.6, p < 0.001). Through Bayesian network (BN) analysis, we identified relatively consistent interaction relationships among various RDoC domains (NVS → AS, NVS → CS, NVS → PVS, as well as CS → SPS; parameter range = 0.156 to 0.635, p < 0.001). Lastly, using logistic regression and Cox proportional hazards models, we demonstrated that AS was significantly associated with the length of hospital stay (-0.21, p < 0.05) and 30-day readmission risk (adjusted odds ratio [aOR] = 0.91, 95% confidence interval [CI] 0.91–0.99) to some extent. In conclusion, we suggest that the eRDoC characteristics varied in different DSM. By Bayesian Network, we found NVS and CS might be potential source in interacting with other system. Furthermore, CS, SPS and AS were associated with the length of stay and 30-days readmission, making them effective for predicting prognosis of psychiatric disorders.

Metastatic brain tumors: from development to cutting-edge treatment.

Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting-edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody-drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood-brain barrier to achieve high intracranial concentrations, including small-molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.

Non-Invasive Markers for the Detection of Gastric Precancerous Conditions

Gastric cancer (GC) is still one of the most prevalent cancers worldwide, with a high mortality rate, despite improvements in diagnostic and therapeutic strategies. To diminish the GC burden, a modification of the current diagnostic paradigm, and especially endoscopic diagnosis of symptomatic individuals, is necessary. In this review article, we present a broad review and the current knowledge status on serum biomarkers, including pepsinogens, gastrin, Gastropanel®, autoantibodies, and novel biomarkers, allowing us to estimate the risk of gastric precancerous conditions (GPC)—atrophic gastritis and gastric intestinal metaplasia. The aim of the article is to emphasize the role of non-invasive testing in GC prevention. This comprehensive review describes the pathophysiological background of investigated biomarkers, their status and performance based on available data, as well as their clinical applicability. We point out future perspectives of non-invasive testing and possible new biomarkers opportunities.

Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies.

Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acquired factors. Current diagnostic paradigms, primarily relying on a combination of clinical assessment and targeted laboratory tests, often fail to capture the complex interplay of factors contributing to thrombophilia risk. This paper proposes an innovative artificial intelligence (AI)-based methodology aimed to enhance the prediction of thrombophilia risk. The designed multidimensional risk assessment model integrates and elaborates through AI a comprehensive collection of patient data types, including genetic markers, clinical parameters, patient history, and lifestyle factors, in order to obtain advanced and personalized explainable diagnoses.

Robust metabolic syndrome risk score based on triangular areal similarity.

One of the limitations of currently-used metabolic syndrome (MetS) risk calculations is that they often depend on sample characteristics. To address this, we introduced a novel sample-independent risk quantification method called 'triangular areal similarity' (TAS) that employs three-axis radar charts constructed from five MetS factors in order to assess the similarity between standard diagnostic thresholds and individual patient measurements. The method was evaluated using large datasets of Korean (n = 72,332) and American (n = 11,286) demographics further segmented by sex, age, and race. The risk score exhibited a strong positive correlation with the number of abnormal factors and was closely aligned with the current diagnostic paradigm. The proposed score demonstrated high diagnostic accuracy and robustness, surpassing previously reported risk scores. This method demonstrated superior performance and stability when tested on cross-national datasets. This novel sample-independent approach has the potential to enhance the precision of MetS risk prediction.

Filamin A Is a Prognostic Serum Biomarker for Differentiating Benign Prostatic Hyperplasia from Prostate Cancer in Caucasian and African American Men.

Prostate cancer represents a significant health risk to aging men, in which diagnostic challenges to the identification of aggressive cancers remain unmet. Prostate cancer screening is driven by the prostate-specific antigen (PSA); however, in men with benign prostatic hyperplasia (BPH) due to an enlarged prostate and elevated PSA, PSA's screening utility is diminished, resulting in many unnecessary biopsies. To address this issue, we previously identified a cleaved fragment of Filamin A (FLNA) protein (as measured with IP-MRM mass spectrometry assessment as a prognostic biomarker for stratifying BPH from prostate cancer and subsequently evaluated its expanded utility in Caucasian (CA) and African American (AA) men. All men had a negative digital rectal examination (DRE) and PSA between 4 and 10 ng/mL and underwent prostate biopsy. In AA men, FLNA serum levels exhibited diagnostic utility for stratifying BPH from patients with aggressive prostate cancer (0.71 AUC and 12.2 OR in 48 men with BPH and 60 men with PCa) and outperformed PSA (0.50 AUC, 2.2 OR). In CA men, FLNA serum levels also exhibited diagnostic utility for stratifying BPH from patients with aggressive prostate cancer (0.74 AUC and 19.4 OR in 191 men with BPH and 109 men with PCa) and outperformed PSA (0.46 AUC, 0.32 OR). Herein, we established FLNA alone as a serum biomarker for stratifying men with BPH vs. those with high Gleason (7-10) prostate cancers compared to the current diagnostic paradigm of using PSA. This approach demonstrates clinical actionability of FLNA alone without the requirement of prostate volume measurement as a test with utility in AA and CA men and represents a significant opportunity to decrease the number of unnecessary biopsies in aggressive prostate cancer diagnoses.

Direct Detection of Tobacco Mosaic Virus in Infected Plants with SERS-Sensing Hydrogels.

The impact of plant pathogens on global crop yields is a major societal concern. The current agricultural diagnostic paradigm involves either visual inspection (inaccurate) or laboratory molecular tests (burdensome). While field-ready diagnostic methods have advanced in recent years, issues remain with detection of presymptomatic infections, multiplexed analysis, and requirement for in-field sample processing. To overcome these issues, we developed surface-enhanced Raman scattering (SERS)-sensing hydrogels that detect pathogens through simple contact with a leaf. In this work, we developed a novel reagentless SERS sensor for the detection of tobacco mosaic virus (TMV) and embedded it in an optimized hydrogel material to produce sensing hydrogels. To test the diagnostic application of our sensing hydrogels, we demonstrate their use to detect TMV infection in tobacco plants. This technology has the potential to shift the current agricultural diagnostic paradigm by offering a field-deployable tool for presymptomatic and multiplexed molecular identification of pathogens.

High Prevalence of Pfhrp2/3 Gene Deletions and Major Threat to Malaria Control Programs in Ethiopia.

Background: Rapid diagnostic tests (RDTs) targeting pfhistidine-rich protein 2 (Pfhrp2) are widely used for diagnosis of Plasmodium falciparum infections in resource-limited malaria endemic countries. However, test results are affected by deletions of the Pfhrp2, Pfhrp3, and flanking genes and associated negative results from rapid diagnostic devices were previously reported. Therefore, the aim of this study was to reveal the existing genetic profile of Pfhrp2 and Pfhrp3 genes of P. falciparum-infected patients in northwestern Ethiopia. Methods: A total number of 302 blood samples were collected from children at Chilga (Aykel, Negade Bahir), and Sanja health centers in northwestern Ethiopia. Thirty-three (10.9%) samples tested positive for P. falciparum malaria. The Pfhrp2, Pfhrp3, and flanking genes (MAL7P1_228 and MAL7P1_230 for Pfhrp2, and MAL13P1_475 and MAL13P1_485 for Pfhrp3) were amplified using standard nested-PCR. Results: Pfhrp2 and both of its flanking genes were found to be present in 12 (36.4%) out of the 33 samples. Twenty-one (63.6%) samples tested negative for the Pfhrp2 gene and 19 samples (57.6%) tested positive for at least one of the flanking genes. Five (15.2%) samples gave positive results for the Pfhrp3 gene and both of its flanking genes, whereas 16 (48.5%) tested negative for all three. Conclusions: Our study provides widespread deletions in the Pfhrp2 and Pfhrp3 genes in Ethiopia, thereby confirming anecdotal reports of diagnostic failure with Pfhrp2-based RDTs in the region. The implications of our finding for the current diagnostic paradigm, which relies on the detection of P. falciparum by Pfhrp2-based RDTs in remote areas, may need rethinking.

Screening of Mild Cognitive Impairment Subtypes Through the Training of 1D Convolutional Neural Network with QEEG Features

AbstractBackgroundThe pre‐clinical stage of dementia, mild cognitive impairment (MCI) carries various pathological pathways and respective prognoses. The two foremost causes of MCI and dementia that have been identified through autopsy diagnoses are Amyloid plaques and Lewy body, commonly referred to as Alzheimer’s disease (AD) and Lewy body disease (LBD) respectively. Although correct identification of pathological pathways plays a crucial role in effective designing of treatment methods, MCI subtypes are underexploited in the current diagnostic paradigm due to multiple reasons, mainly cost‐effectiveness. Hence, we utilized quantitative electroencephalography (QEEG) that are cheap and convenient to record in the distinguishment of MCI subtypes.MethodA total of 151 MCI patients’ EEG data were aggregated into 3 groups, pure AD (n = 29), pure LBD (n = 88), and mixed (n = 34). The mixed type was further subdivided into main AD (n = 31) and main LBD (n = 3) groups, in accordance with AD/LBD tendency exhibited by the patients. The clinical labelling was brought by the experienced experts of Yonsei Severance hospital, South Korea.1D SE‐ResNet‐based classification model was established for quantitative investigation of AD and LBD propensity. The power spectrum density (PSD) in dB/Hz scale were computed from the EEG data and were used to train the model. Due to the small number of data, augmentation was applied to the training data. The final dataset was split into 8 to 1 to 1 ratio (Train n = 7092: 2274 pure AD + main AD; 3694 pure LBD + main LBD, Validation n = 14: 6 pure AD + main AD; 8 Pure LBD + main LBD, Test n = 15: 6 pure AD + main AD; 9 pure LBD + main LBD).ResultThe classification model showed validation results of 85.7% accuracy, 83.3% AD sensitivity and 87.5% LBD sensitivity. Test results were at 92.8% accuracy, 83.3% AD sensitivity and 88.9% LBD sensitivity.ConclusionQEEG‐based deep learning classifier developed in this study successfully distinguished the two main causes of cognitive degeneration in MCI patients. Our model can help identify subtype‐specific spectral trends, which could also make contributions in the establishment of effective treatment methods for MCI.

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT.

Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.

Love Addiction - Current Diagnostic and Therapeutic Paradigms in Clinical Psychology. (v2).

We have investigated and analyzed the latest research on love addiction in the fields of clinical psychology, diagnosis, psychotherapy and treatment. From 30 November 2021 to July 2022 a review was conducted aimed at identifying the current diagnostic paradigms associated with this new behavioral dependence, investigating in depth what has not yet emerged, searching for strong and weak correlations with related theoretical constructs, comorbidities, investigate which evaluation scales are currently used and outline a guiding design that allows you to move between current scientific discoveries, searching for relevant studies in the databases PubMed, National Center for Biotechnology Information (NCBI), PsycINFO, MDPI, APA, ScienceDirect, and ReserchGate. We identified a total of 102 unique articles. Among these, 22 full-text articles were assessed for eligibility and 5 of these met the eligibility criteria and were, therefore, included in the final systematic review. Group psychotherapy has proved to be a valid alternative, in fact the scientific landscape suggests that most group therapies are successful because these dynamics involve the reward and attachment systems in the brain in most subjects. Although there is currently no official classification for this type of addiction, the continuing interests of clinical psychology open up new scenarios for achieving greater psychophysical well-being.

Simplifying the complex: accessible microfluidic solutions for contemporary processes within in vitro diagnostics.

In vitro diagnostics (IVDs) form the cornerstone of modern medicine. They are routinely employed throughout the entire treatment pathway, from initial diagnosis through to prognosis, treatment planning, and post-treatment surveillance. Given the proven links between high quality diagnostic testing and overall health, ensuring broad access to IVDs has long been a focus of both researchers and medical professionals. Unfortunately, the current diagnostic paradigm relies heavily on centralized laboratories, complex and expensive equipment, and highly trained personnel. It is commonly assumed that this level of complexity is required to achieve the performance necessary for sensitive and specific disease diagnosis, and that making something affordable and accessible entails significant compromises in test performance. However, recent work in the field of microfluidics is challenging this notion. By exploiting the unique features of microfluidic systems, researchers have been able to create progressively simple devices that can perform increasingly complex diagnostic assays. This review details how microfluidic technologies are disrupting the status quo, and facilitating the development of simple, affordable, and accessible integrated IVDs. Importantly, we discuss the advantages and limitations of various approaches, and highlight the remaining challenges within the field.

Radiofrequency Ablation for Chronic Posterior Sacroiliac Joint Complex Pain: A Comprehensive Review.

Radiofrequency ablation of the sacral lateral branches targets the innervation of the posterior sacroiliac ligaments and posterior portion of the sacroiliac joint. These structures are also collectively referred to as the posterior sacroiliac joint complex. This review will discuss current diagnostic block paradigms and selection criteria for sacral lateral branch radiofrequency ablation, varying techniques and technologies utilized for sacral lateral branch radiofrequency ablation, and updates on the clinical outcome literature. The current evidence suggests that sacral lateral branch radiofrequency ablation can provide relief for posterior sacroiliac joint complex pain, but the literature is limited by variability in selection criteria, the specific nerves targeted by radiofrequency ablation, and the types of radiofrequency ablation technology and techniques utilized in clinical outcome studies.

Re-Examining Myofascial Pain Syndrome: Toward Biomarker Development and Mechanism-Based Diagnostic Criteria.

We discuss the need for a mechanism-based diagnostic framework with a focus on the development of objective measures (e.g., biomarkers) that can potentially be added to the diagnostic criteria of the syndrome. Potential biomarkers are discussed in relation to current knowledge on the pathophysiology of myofascial pain syndrome (MPS), including alterations in redox status, inflammation, and the myofascial trigger point (MTrP) biochemical milieu, as well as imaging and neurophysiological outcomes. Finally, we discuss the long-term goal of conducting a Delphi survey, to assess the influence of putative MPS biomarkers on clinician opinion, in order to ultimately develop new criteria for the diagnosis of MPS. Myofascial pain syndrome (MPS) is a prevalent healthcare condition associated with muscle weakness, impaired mood, and reduced quality of life. MPS is characterized by the presence of myofascial trigger points (MTrPs): stiff and discrete nodules located within taut bands of skeletal muscle that are painful upon palpation. However, physical examination of MTrPs often yields inconsistent results, and there is no gold standard by which to diagnose MPS. The current MPS diagnostic paradigm has an inherent subjectivity and the absence of correlation with the underlying pathophysiology. Recent advancements in ultrasound imaging, systemic biomarkers, MTrP-specific biomarkers, and the assessment of dysfunction in the somatosensorial system may all contribute to improved diagnostic effectiveness of MPS.

Biomarkers and Tools for Predicting Alzheimer's Disease in the Preclinical Stage.

Alzheimer's disease (AD) is the only leading cause of death for which no disease-modifying therapy is currently available. Over the past decade, a string of disappointing clinical trial results has forced us to shift our focus to the preclinical stage of AD, which represents the most promising therapeutic window. However, the accurate diagnosis of preclinical AD requires the presence of brain β- amyloid deposition determined by cerebrospinal fluid or amyloid-positron emission tomography, significantly limiting routine screening and diagnosis in non-tertiary hospital settings. Thus, an easily accessible marker or tool with high sensitivity and specificity is highly needed. Recently, it has been discovered that individuals in the late stage of preclinical AD may not be truly "asymptomatic" in that they may have already developed subtle or subjective cognitive decline. In addition, advances in bloodderived biomarker studies have also allowed the detection of pathologic changes in preclinical AD. Exosomes, as cell-to-cell communication messengers, can reflect the functional changes of their source cell. Methodological advances have made it possible to extract brain-derived exosomes from peripheral blood, making exosomes an emerging biomarker carrier and liquid biopsy tool for preclinical AD. The eye and its associated structures have rich sensory-motor innervation. In this regard, studies have indicated that they may also provide reliable markers. Here, our report covers the current state of knowledge of neuropsychological and eye tests as screening tools for preclinical AD and assesses the value of blood and brain-derived exosomes as carriers of biomarkers in conjunction with the current diagnostic paradigm.

Biosensor for electrochemical detection of biomarkers of osteoarthritis

Biosensor for electrochemical detection of biomarkers of osteoarthritis

COVID-19, myocarditis, and the other side of the bed.

COVID-19, myocarditis, and the other side of the bed.

Pediatric recurrent fever and autoinflammation from the perspective of an allergist/immunologist

Autoinflammatory diseases are monogenic and polygenic disorders due to dysregulation of the innate immune system. The inherited conditions have been clustered with primary immunodeficiencies in the latest practice parameters; however, these diseases have unique clinical presentations, genetics, and available therapies. Given the presentation of fevers, rashes, and mucosal symptoms observed in many of these syndromes, patients are likely to present to an allergist/immunologist. Although there has been attention in the literature to diagnosis and treatment of rare, genetically defined autoinflammatory disorders, physicians are challenged by increasing numbers of patients with intermittent or periodic fevers who face unnecessary morbidities due to a lack of a diagnosis. The broad differential of diseases presenting with fever includes autoinflammatory syndromes, infections associated with immunodeficiency and/or allergies complicated by infection, and less commonly, autoimmune disorders or malignancy. To address this challenge, we review the history of the medical approach to fever, current diagnostic paradigms, and controversies in management. We describe the spectrum of disorders referred to a recurrent fever disorders clinic established in an Allergy/Immunology division at a tertiary pediatric care center. Finally, we provide practical recommendations including historical features and initial laboratory investigations that can help clinicians appropriatelymanage these patients.

Radiation-related caries: current diagnostic, prognostic, and management paradigms.

This narrative review summarizes the current state of art of radiation-related caries (RC), an aggressive disease that affects approximately 30% of post-head and neck radiotherapy (HNRT) patients. RC mainly affects the tooth cervical areas and incisal/cuspal tips and develops 6 to 12 months after HNRT. Early RC signs include black/brownish tooth discoloration and enamel cracks, which progress to enamel delamination, exposing underlying dentin to a highly cariogenic oral environment and rapid tooth destruction/dental crown amputation. As RC advances and renders the tooth nonrestorable, it may lead to osteoradionecrosis spontaneously or upon extraction if the tooth is in a highly irradiated field of the oral cavity. This requires aggressive treatment, which would have a negative impact on a cancer survivor's quality of life and contribute to the incremental cost of cancer care. Chlorhexidine mouth rinses and topical fluoride applications are effective agents used in RC prevention; however, there are no well-established treatment protocols. Once RC progresses, dental restorations should be performed with adhesive materials in association with systematic fluoride application as illustrated in the clinical case presented in this review. Post-HNRT patients should be closely followed up for optimal RC prevention, early diagnosis, and prompt treatment. Future clinical studies are necessary to establish a contemporary, clinically validated protocol for RC management.

O3.6. PREVALENCE OF AND RECOVERY FROM COMMON MENTAL DISORDERS INCLUDING PSYCHOTIC EXPERIENCES

BackgroundSystematic reviews indicate that approximately one third of people with at-risk mental states for psychosis (ARMS) will transition to a psychotic disorder. Research in non-specialised services, such as primary care settings, has shown that far fewer make such a conversion. Nonetheless, psychotic experiences (PE) may also be linked to common mental disorders (CMD), particularly depression and anxiety, and still predict poor outcomes. Population studies modelling the co-occurrence of CMD and PE have found an underlying unitary psychopathological factor, with PE emerging towards its more severe end.We know little about the prevalence of and recovery from PE in primary mental health care, where most CMD are treated. One example of primary mental health care setting in England is the Improving Access to Psychological Therapies (IAPT) programme (https://www.england.nhs.uk/mental-health/adults/iapt/). The IAPT programme provides evidence-based psychological therapies for mild to moderate CMD across the UK National Health Service (NHS). IAPT services adhere to current diagnostic paradigms and, therefore, do not either measure or treat PE.We aimed to establish the prevalence of PE in a large sample of patients with CMD from the IAPT programme and compare recovery rates between patients with CMD and PE (CMD-P) and those without PE.MethodsWe used the Community Assessment of Psychic Experiences - Positive 15-item Scale (CAPE-P15) to determine the prevalence of PE in patients with CMD receiving treatment from IAPT services across England. We employed the CAPE-P15 threshold score of 1.47, which identifies individuals with ARMS, and also a lower threshold of 1.30, chosen as within one standard error of measurement, in order to explore threshold effects in the association between PE and recovery.Patient-reported measures of depression (PHQ-9) and anxiety (GAD-7) are routinely collected in IAPT services and determine ‘caseness’ before, during and after therapy. Using recovery rates (moving from ‘caseness’ to recovery) monitored nationally in the IAPT programme, we stratified patients according to the absence and presence of PE. Multi-group growth models estimated improvement trajectories for each group.Results2,042 patients with CMD completed the CAPE-P15. The mean age was 39.8. The overall prevalence of CMD-P was 29.68% at CAPE-P15 threshold score for ARMS, i.e. 1.47, and 48.09% at threshold score 1.30.The overall recovery rate at threshold of 1.47 was 27.87% and 36.3% at 1.30. Recovery rates for those without PE were 58.92% and 62.43% for thresholds 1.47 and 1.30, respectively. Although patients with or without PE shared similar improvement trajectories, the initial severity of patients with CMD-P impeded their likelihood of recovery during treatment.DiscussionAt least one in four patients receiving treatment from IAPT services in primary care experience CMD-P. This significant group of people experience a lower recovery rate, with adverse implications not only for them but also for efficiency of services. Although recovery trajectories for this group showed improvement over therapy sessions, remittance of symptoms was insufficient to meet national IAPT standards of recovery. This patient group is not well-served by current interventions in primary care.This work forms part of a nation-wide NIHR research programme (TYPPEX; https://www.nihr.ac.uk/news/innovative-mental-health-study-launchesin-eastern-region) aiming to develop innovative therapies for people with CMD-P in primary care. Preliminary results related to feasibility and effectiveness of new therapeutic approaches will also be presented.