Clinical Decision-making Strategies Research Articles

Biventricular modelling for investigating ventricular arrhythmias in silico

Abstract Background Ventricular arrhythmias have a significant incidence rate worldwide and may potentially lead to sudden cardiac death in severe cases. Despite therapeutic advancements, the intricate mechanisms dictating these arrhythmias remain elusive. Biventricular modelling and deep learning provide a way to investigate the complex electrophysiological substrates underlying cardiac arrhythmogenesis. Purpose This study aims to develop and validate a patient-specific biventricular computational modelling pipeline for simulating ventricular arrhythmias in silico. We provide a comprehensive platform for understanding the initiation, propagation, and treatment response of various ventricular arrhythmias. Methods To facilitate large-scale computational experiments and personalised model predictions within clinical timeframes, it is imperative to establish a rapid and reproducible modelling framework. First, we constructed biventricular volumetric models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) data from the Cardiac Atlas Project (2018 Atria Segmentation Challenge), and then we calculate biventricular coordinates to facilitate data registration between measures, and fibre and fibrosis inclusion. We also apply the pipeline to cardiac magnetic resonance (CMR) data and electrocardiogram recordings from UK Biobank. Results We have successfully developed a pipeline for constructing biventricular volumetric models and efficiently processed biventricular coordinates (Figure). By systematically exploring interactions between ventricular regions, the model may identify critical factors contributing to arrhythmia susceptibility and guide personalised therapeutic strategies. Furthermore, the model may provide insights into the efficacy of antiarrhythmic interventions, ablation techniques, and device therapies, facilitating the development of precision medicine approaches. Conclusions We demonstrate a pipeline for constructing patient-specific biventricular computational models at scale from imaging data and apply this to LGE-MRI data and UK Biobank CMR data. These biventricular models enable investigation of the complex mechanisms of ventricular arrhythmias, providing novel insights for clinical decision-making and treatment strategies.

Predictive Ability of the Pedal Medial Arterial Calcification Score for Major Adverse Limb Events Among Patients with Chronic Limb-Threatening Ischemia

ObjectivePedal medial arterial calcification (pMAC) is a potential predictor of major adverse limb events (MALE) among patients with critical limb-threatening ischemia (CLTI). This study aims to validate the prognostic value of the pMAC score in predicting major lower limb amputation (MLLA) and need for re-do revascularisation. MethodsA single centre study involving 196 patients diagnosed with CLTI, reviewed between 2012 and 2022. Patients’ pMAC scores were obtained through X-ray assessments. Correlations between pMAC scores and the incidence of MALE and re-do revascularisation, were analysed. A power analysis using G* Power 3 software determined a needed sample size of 157 participants. Statistical analysis employed SPSS 26, utilising descriptive statistics, Chi Square, Mann-Whitney, Kaplan-Meier with log-rank test, and Cox regression for predictor identification. ResultsThe cohort was predominantly male (79.1%) and diabetic (59.2%). Re-do revascularisation need was 15.3%, increasing with pMAC scores: 3.3% (low), 24.5% (medium), and 50% (high) (p<0.001). MLLA frequency also rose with pMAC scores: 6.7%, 27.5%, and 50% respectively (p<0.001). Higher pMAC scores correlated with a higher incidence of MALE (p=0.029). Multivariate analysis showed medium pMAC (HR 4.159, p=0.002), high pMAC (HR 7.366, p=0.015), and GLASS stage 3 (HR 3.529, p=0.011) as independent MLLA predictors. Re-do revascularisation predictors included female sex (HR 2.106, p=0.063), Black race (HR 64.498, p=0.001), medium pMAC (HR 6.812, p=0.002), high pMAC (HR 11.455, p=0.008), and GLASS stage 3 (HR 2.899, p=0.037). ConclusionThe pMAC score effectively predicts MALE in CLTI patients and enhances risk stratification. Using both, pMAC scores and GLASS stages enhances predictive accuracy, identifying patients at higher risk for MLLA, Informing clinical decision-making and therapeutic strategies.

Comprehensive approach to clinical decision-making strategy, illustrated by the Gulf War.

Throughout the history of medicine, clinical decision-making strategies have largely been dependent on the implementation of novel technologies. Artificial intelligence (AI) has not only made a leap into a new dimension of medical measures & decisions' shaping but has also served as a strategic backup of medical practice. Due to its nature, and military technologies in use, the Gulf War (1990-1991) is considered to be the verge of the contemporary warfare era. Soldiers had been engaged into a highly complex military operation theatre contaminated with both chemical and radiological noxious agents, and burdened with side-effects of prophylactic measurements. The aim of this review is to present a comprehensive approach to clinical decision-making strategy using theGulf War veterans' syndrome as an example. The model is based on the processing of all data coming from the military operation theatre and their unification with medical data, so as to obtain the final product, i.e.,the decision needed for a personalised therapeutic approach. Due to the complexity and a huge amount of data gathered at a given location in a given time, the functioning of this model unanimously calls for the interlace between military system AI and that of the medical sector.

Comparing One-Year Survival in Anterior Versus Inferior ST-Elevation Myocardial Infarction Patients: Results of a Cohort Study in Western Iran

Background: The site of acute myocardial infarction (MI) plays a pivotal role in determining the prognosis and risk assessment for patients experiencing their first ST-segment elevation MI (STEMI). Objectives: This study aims to compare one-year survival rates in patients with anterior versus inferior ST-elevation myocardial infarction. Methods: This registry-based cohort study was conducted from July 2018 to December 2019, examining data from STEMI patients. A total of 643 patients diagnosed with STEMI who met the inclusion criteria were enrolled. Patients were categorized based on the location of their myocardial infarction (MI) into two groups: Those with anterior MI and those with inferior MI. Their progress was meticulously followed over the course of one year. For data analysis, Cox proportional hazards models were used to calculate two sets of hazard ratio estimates: The initial unadjusted (crude) hazard ratios and the fully adjusted hazard ratios, which accounted for potential confounding factors. Along with these hazard ratio estimates, their corresponding 95% confidence intervals (HR, 95% CI) were obtained. All statistical analyses were performed using R software version 4.2.1. Results: Throughout the follow-up period, totaling 598 patients and 4,109 person-days, only 7 patients (1.09%) were lost to follow-up. The analysis revealed no significant difference in one-year mortality rates between the inferior and anterior STEMI groups, with rates of 37 (8.39%) versus 15 (7.69%), respectively, yielding a P-value of 0.767. However, it is noteworthy that the mortality risk trended higher in the inferior MI group, with a hazard ratio of 1.093 (95% CI: 0.60 - 1.99). Conclusions: In conclusion, our study highlights the heightened mortality risk associated with inferior wall MI. These results underscore the prognostic value of MI location, shedding light on its potential role in predicting the severity and extent of infarction, thereby guiding clinical decision-making and risk management strategies in STEMI patients.

Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning.

Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 78.94%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.

A Comparison of Preoperative Predictive Scoring Systems for Postoperative Pancreatic Fistula after Pancreaticoduodenectomy Based on a Single-Center Analysis.

Background: Postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is associated with major postoperative morbidity and mortality. Several scoring systems have been described to stratify patients into risk groups according to the risk of POPF. The aim of this study was to compare scoring systems in patients who underwent a PD. Methods: A total of 196 patients undergoing PD from July 2019 to June 2022 were identified from a prospectively maintained database of the University Hospital Ghent. After performing a literature search, four validated, solely preoperative risk scores and the intraoperative Fistula Risk Score (FRS) were included in our analysis. Furthermore, we eliminated the variable blood loss (BL) from the FRS and created an additional score. Univariate and multivariate analyses were performed for all risk factors, followed by a ROC analysis for the six scoring systems. Results: All scores showed strong prognostic stratification for developing POPF (p < 0.001). FRS showed the best predictive accuracy in general (AUC 0.862). FRS without BL presented the best prognostic value of the scores that included solely preoperative variables (AUC 0.783). Soft pancreatic texture, male gender, and diameter of the Wirsung duct were independent prognostic factors on multivariate analysis. Conclusions: Although all predictive scoring systems stratify patients accurately by risk of POPF, preoperative risk stratification could improve clinical decision-making and implement preventive strategies for high-risk patients. Therefore, the preoperative use of the FRS without BL is a potential alternative.

Modelling midline shift and ventricle collapse in cerebral oedema following acute ischaemic stroke.

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema.

A Mathematical Model Based on Stratifying the Severity of Medical Errors for Building Scenarios for Clinical Cases With Branching.

Background There are no mathematical models or score systems available for assessing and creating clinical case simulations based on branching scenario scripts. Objective This study aimed to develop a mathematical model based on stratifying the severity of medical errors for building clinical cases with branching scenarios for clinical simulation. Methods This study was undertaken from August 2020 to August 2023. To build a mathematical model for building scenarios of clinical cases with branching, the classification of the seriousness of medication errors was used. A mathematical model was built for predicting and modeling the development of a clinical situation and as an assessment strategy.The study recruited a total of 34 participants, with 16 participants assigned to the branching scenarios without the mathematical model group and 18 participants assigned to the branching scenarios with the mathematical model group. Results A simple diagram of score based on stratification of the severity of medical errors and correct decisions in clinical practice for building interactive training scenarios with branching was proposed. According to this score system algorithm, each clinical decision-making step is scored points with plus or minus, from 0 to 10.The sum of the points for each block in the decision-making process is then added up. Each step in the overall clinical decision-making strategy is stratified by the proposed algorithm, and finally, the results of internal validation and implementation are presented. Conclusion A mathematical model and score system for building clinical case scenarios based on branching and classification of the seriousness of medication errors was developed. This system could help in the prediction and modeling of the development of events in particular clinical situations and the assessment of competency formation in medical simulation as well.

757 Machine Learning-Driven Web Application for Enhanced Clinical Decision Support in Thoracolumbar Spinal Cord Injuries

INTRODUCTION: Thoracolumbar spinal cord injuries (tlSCI) can result in negative short-term in-hospital outcomes, underscoring the importance of precise prediction tools. METHODS: This retrospective cohort study included 147,826 patients from the American College of Surgeons Trauma Quality Program database. XGBoost, LightGBM, CatBoost, and Random Forest algorithms were utilized in combination with the Optuna optimization library for hyperparameter tuning. Models were evaluated graphically with receiver operating characteristic (ROC) curves, precision-recall curves, and numerically with precision, recall, F1 score, accuracy, area under ROC (AUROC), and area under PRC. The best-performing models were integrated into an open-access web application for individualized predictions. RESULTS: The best-performing models for each outcome were as follows: for predicting mortality, Random Forest achieved the highest AUROC (0.865, 95% CI 0.848-0.879); in predicting non-home discharges, LightGBM had the highest AUROC (0.801, 95% CI 0.794-0.804); and for predicting major complications, Random Forest performed best with an AUROC of 0.733 (95% CI 0.642-0.808). Algorithms' receiver operator curves for the outcomes: (A) mortality, (B) non-home discharges, and (C) major complications. CONCLUSIONS: This study successfully developed ML models to predict critical in-hospital outcomes for patients with tlSCI. By incorporating the top-performing algorithms into a user-friendly web application, these models have the potential to improve clinical decision-making and management strategies for tlSCI patients, ultimately enhancing patient care and outcomes.

Morules and β-catenin predict POLE mutation status in endometrial cancer: A pathway to more cost-effective diagnostic procedures.

The characterization of DNA polymerase epsilon (POLE) mutations has transformed the classification of endometrial endometrioid carcinomas (EECs), highlighting the need for efficient identification methods. This study aims to examine the relationship between distinct morphologic features-namely, squamous morules and squamous differentiation (SD), as well as β-catenin expression-and the POLE mutation status in endometrial cancer (EC). Our study included 35 POLE-mutated (POLEmut) EC cases and 395 non-POLEmut EEC cases. Notably, we observed no presence of morules in POLEmut cases, while SD was identified in 20% of instances. Conversely, morules and SD were identified in 12.7% and 26.1% of non-POLEmut EC cases, respectively, with morules consistently linked to a POLE wild-type status. The nuclear β-catenin expression is typically absent in tumors with wild-type POLE (wt-POLE) status. Our findings suggest that the presence of either morules or nuclear β-catenin expression in EEC could practically rule out the presence of POLE mutations. These morphologic and immunohistochemical features can be used as preliminary screening tools for POLE mutations, offering significant savings in time and resources and potentially enhancing clinical decision-making and patient management strategies. However, further validation in larger, multi-institutional studies is required to fully understand the implications of these findings on clinical practice.

DIAGNOSTIC ACCURACY OF TIRADS CLASSIFICATION IN DIFFERENTIATING BENIGN AND MALIGNANT THYROID NODULES KEEPING FINE NEEDLE ASPIRATION CYTOLOGY (FNAC) AS GOLD STANDARD

Thyroid nodules represent a joint clinical presentation of various benign and malignant thyroid diseases. Diagnostic ultrasound, particularly utilizing the Thyroid Imaging Reporting and Data System (TI-RADS), has emerged as a pivotal tool for stratifying the risk of malignancy in thyroid nodules and guiding decisions regarding fine-needle aspiration biopsy (FNAC). However, comprehensive validation studies are warranted to assess the diagnostic accuracy of ultrasound-based TI-RADS classification compared to FNAC as the gold standard. This cross-sectional validation study, conducted at the Department of Radiology, CPE Institute of Cardiology Multan from August 2, 2021, to February 1, 2022, enrolled 243 patients presenting with thyroid nodules, including multiple and solitary nodules. Baseline demographic variables, including age, gender, and nodule duration, were recorded for each participant. Ultrasonography was performed in all patients, and TI-RADS scores were calculated. Subsequently, fine-needle aspiration biopsies, guided by ultrasound, were obtained in the radiology department and sent to the histopathology department for confirmation of malignancy. The mean age of the cohort was 42.05 ± 12.27 years, with a mean nodule duration of 6.87 ± 3.56 months. The mean TI-RADS score was 3.46 ± 2.00. Of the participants, 176 (72.43%) were male and 67 (27.57%) were female. Malignancy was detected in 84 (34.57%) patients based on TI-RADS score, whereas FNAC confirmed malignancy in 50 (20.58%) patients. Evaluating diagnostic accuracy with FNAC as the gold standard, the TI-RADS score demonstrated a sensitivity of 84.0%, specificity of 78.2%, positive predictive value (PPV) of 50.0%, and negative predictive value (NPV) of 95.0%. Our findings indicate that the TI-RADS scoring system is reliable in predicting malignancy in patients with thyroid nodules. With a sensitivity of 84.0% and specificity of 78.2%, TI-RADS represents a valuable adjunctive tool in the diagnostic algorithm for thyroid nodules, offering considerable potential for improved clinical decision-making and patient management strategies.

Contribution to an Advanced Clinical Aided Tool Dedicated to Explore ASPECTS Score of Ischemic Stroke

The Alberta Stroke Program Early CT Score (ASPECTS) is a simple and reliable systematic method used to quantify and explore acute ischemic stroke. It was initially developed to standardize the assessment of the early ischemic changes’ extent within the Middle Cerebral Artery (MCA). The ASPECTS assessment has been increasingly incorporated into treatment decision-making and has been used in several randomized clinical trials for endovascular treatment decision-making. The e-ASPECTS software is a tool for the automated use of ASPECTS. The purpose of this paper is twofold: The first objective is to present an advanced clinical that streamlines the extraction of ASPECTS regions of interest. This tool aids neuro-physicians by automating the segmentation Department process through preprocessing steps involving skull bone stripping, edge detection, and thresholding. The second objective is to propose an automated semi-quantitative method using Non-Contrast Computed Tomography (NCCT), enabling neuro-physicians to accurately diagnose and evaluate acute ischemic stroke. This comprehensive approach improves the exploration, diagnosis, and evaluation of acute ischemic stroke, bolstering clinical decision-making and treatment strategies. Experimental results were promising and depicted an interesting accuracy level ranging from 0.81 (internal capsule) to 0.98 (caudate), with a greater agreement for cortical areas. The proposed automated ASPECTS method presents an independent predictor for clinical practice and ischemic core judgment and treatment selection.

Stroke vs. Preeclampsia: Dangerous Liaisons of Hypertension and Pregnancy.

Stroke during pregnancy and preeclampsia are two distinct but interrelated medical conditions, sharing a common denominator-blood control failure. Along with cardiovascular diseases, diabetes, dyslipidemia, and hypercoagulability, hypertension is undoubtedly a major risk factor associated with stroke. Even though men have higher age-specific stroke rates, women are facing higher life-long stroke risk, primarily due to longer life expectancy. Sex hormones, especially estrogen and testosterone, seem to play a key link in the chain of blood pressure control differences between the genders. Women affected with stroke are more susceptible to experience some atypical stroke manifestations, which might eventually lead to delayed diagnosis establishment, and result in higher morbidity and mortality rates in the population of women. Preeclampsia is a part of hypertensive disorder of pregnancy spectrum, and it is common knowledge that women with a positive history of preeclampsia are at increased stroke risk during their lifetime. Preeclampsia and stroke display similar pathophysiological patterns, including hypertension, endothelial dysfunction, dyslipidemia, hypercoagulability, and cerebral vasomotor reactivity abnormalities. High-risk pregnancies carrying the burden of hypertensive disorder of pregnancy have up to a six-fold higher chance of suffering from stroke. Resemblance shared between placental and cerebral vascular changes, adaptations, and sophisticated auto-regulatory mechanisms are not merely coincidental, but they reflect distinctive and complex cardiovascular performances occurring in the maternal circulatory system during pregnancy. Placental and cerebral malperfusion appears to be in the midline of both of these conditions; placental malperfusion eventually leads to preeclampsia, and cerebral to stoke. Suboptimal performances of the cardiovascular system are proposed as a primary cause of uteroplacental malperfusion. Placental dysfunction is therefore designated as a secondary condition, initiated by the primary disturbances of the cardiovascular system, rather than an immunological disorder associated with abnormal trophoblast invasion. In most cases, with properly and timely applied measures of prevention, stroke is predictable, and preeclampsia is a controllable condition. Understanding the differences between preeclampsia and stroke in pregnancy is vital for healthcare providers to enhance their clinical decision-making strategies, improve patient care, and promote positive maternal and pregnancy outcomes. Management approaches for preeclampsia and stroke require a multidisciplinary approach involving obstetricians, neurologists, and other healthcare professionals.

Cardiac Magnetic Resonance in HCM Phenocopies: From Diagnosis to Risk Stratification and Therapeutic Management.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease characterized by the thickening of the heart muscle, which can lead to symptoms such as chest pain, shortness of breath, and an increased risk of sudden cardiac death. However, not all patients with HCM have the same underlying genetic mutations, and some have conditions that resemble HCM but have different genetic or pathophysiological mechanisms, referred to as phenocopies. Cardiac magnetic resonance (CMR) imaging has emerged as a powerful tool for the non-invasive assessment of HCM and its phenocopies. CMR can accurately quantify the extent and distribution of hypertrophy, assess the presence and severity of myocardial fibrosis, and detect associated abnormalities. In the context of phenocopies, CMR can aid in the differentiation between HCM and other diseases that present with HCM-like features, such as cardiac amyloidosis (CA), Anderson-Fabry disease (AFD), and mitochondrial cardiomyopathies. CMR can provide important diagnostic and prognostic information that can guide clinical decision-making and management strategies. This review aims to describe the available evidence of the role of CMR in the assessment of hypertrophic phenotype and its diagnostic and prognostic implications.

Perinatal mood and anxiety disorders: biomarker discovery using plasma proteomics

Perinatal mood and anxiety disorders encompass a range of mental health disorders that occur during pregnancy and up to 1 year postpartum, affecting approximately 20% of women. Traditional risk factors, such as a history of depression and pregnancy complications including preeclampsia, are known. Their predictive utility, however, is not specific or sensitive enough to inform clinical decision-making or prevention strategies for perinatal mood and anxiety disorders. Better diagnostic and prognostic models are needed for early identification and referral to treatment. This study aimed to determine if a panel of novel third-trimester plasma protein biomarkers in pregnant women can be used to identify those who have a high predisposed risk for perinatal mood and anxiety disorders within 3 months postpartum. We studied 52 women (n=34 with a risk for perinatal mood and anxiety disorders and n=18 controls) among whom mental health screening was conducted at 2 time points, namely in the third trimester and again at 3 months postdelivery. An elevated perinatal mood and anxiety disorder risk was identified by screening individuals with above-validated cutoffs for depression (Edinburgh Postnatal Depression Scale ≥12), anxiety (Overall Anxiety Severity and Impairment Scale ≥7), and/or posttraumatic stress disorder (Impact of Events Scale >26) at both time points. Plasma samples collected in the third trimester were screened using the aptamer-based SomaLogic SomaScan proteomic assay technology to evaluate perinatal mood and anxiety disorder-associated changes in the expression of 1305 protein analytes. Ingenuity Pathway Analysis was conducted to highlight pathophysiological relationships between perinatal mood and anxiety disorder-specific proteins found to be significantly up- or down-regulated in all subjects with perinatal mood and anxiety disorder and in those with perinatal mood and anxiety disorders and no preeclampsia. From a panel of 53 significant perinatal mood and anxiety disorder-associated proteins, a unique 20-protein signature differentiated perinatal mood and anxiety disorder cases from controls in a principal component analysis (P<.05). This protein signature included NCAM1, NRCAM, and NTRK3 that converge around neuronal signaling pathways regulating axonal guidance, astrocyte differentiation, and maintenance of GABAergic neurons. Interestingly, when we restricted the analysis to subjects without preeclampsia, a 30-protein signature differentiated perinatal mood and anxiety disorder cases from all controls without overlap on the principal component analysis (P<.001). In the nonpreeclamptic perinatal mood and anxiety disorder group, we observed increased expression of proteins, such as CXCL11, CXCL6, MIC-B, and B2MG, which regulate leucocyte migration, inflammation, and immune function. Participants with perinatal mood and anxiety disorders had a unique and distinct plasma protein signature that regulated a variety of neuronal signaling and proinflammatory pathways. Additional validation studies with larger sample sizes are needed to determine whether some of these molecules can be used in conjunction with traditional risk factors for the early detection of perinatal mood and anxiety disorders.

Clinical decision-making and adaptive expertise in residency: a think-aloud study

Clinical decision-making (CDM) is the ability to make clinical choices based on the knowledge and information available to the physician. It often refers to individual cognitive processes that becomes more dependent with the acquisition of experience and knowledge. Previous research has used dual-process theory to explain the cognitive processes involved in how physicians acquire experiences that help them develop CDM. However, less is known about how CDM is shaped by the physicians’ situated cognition in the clinical environment. This is especially challenging for novice physicians, as they need to be adaptive to compensate for the lack of experience. The adaptive expert framework has been used to explain how novice physicians learn, but it has not yet been explored, how adaptive expertise is linked to clinical decision-making amongst novice physicians.This study aimed to analyse how residents utilize and develop adaptive expert cognition in a natural setting. By describing cognitive processes through verbalization of thought processes, we sought to explore their CDM strategies considering the adaptive expert framework.We used concurrent and retrospective think-aloud interviews in a natural setting of an emergency department (ED) at a university hospital, to query residents about their reasoning during a patient encounter. We analysed data using protocol analysis to map cognitive strategies from these verbalizations. Subsequently in a narrative analysis, we compared these strategies with the literature on adaptive expertise.Fourteen interviews were audio recorded over the course for 17 h of observation. We coded 78 informational concepts and 46 cognitive processes. The narrative analysis demonstrated how epistemic distance was prevalent in the initial CDM process and self-regulating processes occurred during hypothesis testing. However, residents who too quickly moved on to hypothesis testing tended to have to redirect their hypothesis more often, and thus be more laborious in their CDM. Uncertainty affected physicians’ CDM when they did not reconcile their professional role with being allowed to be uncertain. This allowance is an important feature of orientation to new knowledge as it facilitates the evaluation of what the physician does not know.For the resident to learn to act as an adaptive decision-maker, she relied on contextual support. The professional role was crucial in decisional competency. This supports current literature, which argues that role clarification helps decisional competency. This study adds that promoting professional development by tolerating uncertainty may improve adaptive decisional competency.

The predictive value of patient-reported outcomes on the impact of breast cancer treatment-related quality of life.

PurposePatient-reported outcomes (PROs) have been widely used to measure breast cancer (BC) treatment outcomes. However, evidence is still limited on using routinely PROs to personalize treatment decision-making, including or not chemotherapy, targeted therapy, and radiotherapy. Using patient baseline PRO scores, we aimed to use PROs before treatment initiation to predict improvement or decline in health-related quality of life (HRQoL) due to treatment that they receive.MethodsIn two French cancer sites, women with non-metastatic BC completed the EORTC QLQ-C30 and QLQ-BR23 and BREAST-Q questionnaires to assess their PROs at baseline and again at 6 months. The outcome measured was post-operative change in PROs with minimal important difference for QLQ-C30 domains. We performed multivariate ordinal logistic regression to estimate the incremental probability of post-operative PRO improvements and deteriorations depending upon treatment options and baseline HRQoL.ResultsOne hundred twenty-seven women completed questionnaires. Chemotherapy had significant negative impacts on Global health status (GHS) and on physical and social functioning. Chemotherapy and radiotherapy increased patient fatigue scores after adjusting for clinical factors (p< 0.01 and p< 0.05, respectively). The incremental probability of GHS deteriorations for chemotherapy was +0.3, +0.5, and +0.34 for patients with baseline GHS scores of 40, 70, and 100, respectively. This showed that different pre-treatment PROs might predict differential effects of chemotherapy on women change in HRQoL.ConclusionPatients with different baseline PRO scores may experience dissimilar impacts from BC treatments on post-operative PROs in terms of improvements and deteriorations. Oncologists might decide to adapt the treatment option based on a given level of the negative impact. Future studies should concentrate on incorporating this information into routine clinical decision-making strategies to optimize the treatment benefit for patients.

A liquid biopsy signature predicts lymph node metastases in T1 oesophageal squamous cell carcinoma: implications for precision treatment strategy.

The treatment strategies for T1 oesophageal squamous cell carcinoma (ESCC) patients with or without lymph node metastasis (LNM) are different. Given the advantages of the minimally invasive, sensitive and real-time detection, liquid biopsy has become an important cancer diagnostic and prognostic tool. MiRNA array and small-RNA sequencing were performed. Then, 222 formalin-fixed and paraffin-embedded tumour samples and 229 pretreatment serum samples from T1 ESCC patients were used to verify and evaluate the results. We demonstrated that serum miR-20b-5p could predict LNM in T1 ESCC patients. The AUC for serum miR-20b-5p was higher (0.827) than those for lymphovascular invasion (LVI) (0.751, P = 0.2128), invasion depth (0.662, P = 0.0027) and tumour differentiation grade (0.634, P = 0.0019). A nomogram for predicting LNM with three independent significant predictors (miR-20b-5p, LVI and invasion depth) was constructed with a concordance index of 0.931. Serum miR-20b-5p was also significantly correlated with disease-free survival (P < 0.001). An algorithm of improved T1 ESCC treatment strategy after biopsy and/or after endoscopic resection based on serum miR-20b-5p level was constructed. This study suggests that serum miR-20b-5p is a potential biomarker for predicting LNM and can be helpful for precise clinical decision-making strategies and improve treatment outcomes for T1 ESCC patients.

Signature construction and molecular subtype identification based on cuproptosis-related genes to predict the prognosis and immune activity of patients with hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies in the world, with high incidence, high malignancy, and low survival rate. Cuproptosis is a novel form of cell death mediated by lipoylated TCA cycle proteins-mediated novel cell death pathway and is highly associated with mitochondrial metabolism. However, the relationship between the expression level of cuproptosis-related genes (CRGs) and the prognosis of HCC is still unclear.MethodsCombining the HCC transcriptomic data from The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus (GEO) databases, we identified the differentially expressed cuproptosis-related genes (DECRGs) and obtained the prognosis-related DECRGs through univariate regression analysis.LASSO and multivariate COX regression analyses of these DECRGs yielded four genes that were used to construct the signature. Next, we use ROC curves to evaluate the performance of signatures. The tumor microenvironment, immune infiltration, tumor mutation load, half-maximum suppression concentration, and immunotherapy effects were also compared between the low-risk and high-risk groups. Finally, we analyzed the expression level, prognosis, and immune infiltration correlation on the four genes that constructed the model.ResultsFour DECRGs s were used to construct the signature. The ROC curves indicated that signature can better assess the prognosis of HCC patients. Patients were grouped according to the signature risk score. Patients in the low-risk group had a significantly longer survival time than those in the high-risk group. Furthermore, the tumor mutation burden (TMB) values were associated with the risk score and the higher-risk group had a higher proportion of TP53 mutations than the low-risk group.ESTIMATE analysis showed significant differences in stromal scores between the two groups.N6-methyladenosine (m6A) and multiple immune checkpoints were expressed at higher levels in the high-risk group. Then, we found that signature score correlated with chemotherapeutic drug sensitivity and immunotherapy efficacy in HCC patients. Finally, we further confirmed that the four DECRGs genes were associated with the prognosis of HCC through external validation.ConclusionsWe studied from the cuproptosis perspective and developed a new prognostic feature to predict the prognosis of HCC patients. This signature with good performance will help physicians to evaluate the overall prognosis of patients and may provide new ideas for clinical decision-making and treatment strategies.

The Hypoxic Landscape Stratifies Gastric Cancer Into 3 Subtypes With DistinctM6a Methylation and Tumor Microenvironment Infiltration Characteristics.

The interaction between hypoxia and RNA N6-methyladenosine (m6A) is an emerging focus of investigation. However, alterations in m6A modifications at distinct hypoxia levels remain uncharacterized in gastric cancer (GC). Unsupervised hierarchical clustering was performed to stratify samples into different clusters. Differentially expressed gene analysis, univariate Cox proportional hazards regression analysis, and hazard ratio calculations were used to establish an m6A score to quantify m6A regulator modification patterns. After using an algorithm integrating Least absolute shrinkage and selection operator (LASSO) and bootstrapping, we identified the best candidate predictive genes. Thence, we established an m6A-related hypoxia pathway gene prognostic signature and built a nomogram to evaluate its predictive ability. The area under the curve (AUC) value of the nomogram was 0.811, which was higher than that of the risk score (AUC=0.695) and stage (AUC=0.779), suggesting a high credibility of the nomogram. Furthermore, the clinical response of anti-PD-1/CTLA-4 immunotherapy between high- and low-risk patients showed a significant difference. Our study successfully explored a brand-new GC pathological classification based on hypoxia pathway genes and the quantification of m6A modification patterns. Comprehensive immune analysis and validation demonstrated that hypoxia clusters were reliable, and our signature could provide a new approach for clinical decision-making and immunotherapeutic strategies for GC patients.