Clinical Decision-making Strategies Research Articles

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.

The Prevalence of Coexisting Lumbar Spondylosis and Knee Osteoarthritis: A Systematic Review and Meta-Analysis.

A systematic review. This study aimed to evaluate the prevalence of coexisting lumbar spondylosis (LS) and knee osteoarthritis (KOA), which has clinical implications on the screening, diagnosis, and management of orthopedic patients. Due to current global health trends, the number of affected patients is expected to increase substantially. However, no prior systematic reviews have discussed this topic. A systematic literature search was conducted in June 2021 in the PubMed, Embase, Scopus, CINAHL, and Cochrane CENTRAL databases. Clinical and epidemiological studies that reported quantitative data on the prevalence of coexisting LS and KOA were included. Studies which reported data on only LS or KOA alone were excluded. Odds ratios (ORs) and 95% confidence intervals (CI) for LS or KOA were retrieved or calculated for meta-analysis. Fixed-effects and random-effects models were used, and statistical significance was considered when p<0.05. Heterogeneity was evaluated using Cochran's Q test and the I2 statistic. Risk of bias was assessed using the MINORs (methodological index for nonrandomized studies) criteria. This review included nine studies (5,758 patients). Four studies (4,164 patients) defined KOA and LS by a Kellgren-Lawrence (KL) grade of ≥2 and were included in the meta-analysis. Two other studies defined KOA and LS by a joint space narrowing grade of ≥2. The remaining three studies reported other outcomes. The combined ORs of having KOA of KL grade ≥2 due to LS was 1.75 (95% CI, 1.22-2.50; p=0.002), while the combined OR of having LS of KL grade ≥2 due to KOA was 1.84 (95% CI, 1.23-2.77; p=0.003). In patients with either KOA or LS, the odds of having a concurrent knee-spine presentation are significantly increased. This may have implications for clinical decision-making and treatment strategies. Further high-level studies with larger patient populations are required to confirm these results in specific populations.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma.

BackgroundGenomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.MethodsCombining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.ResultsFive GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.ConclusionOur research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices.

This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts (n = 20) and novices (n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants' rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Gestational Diabetes and Risk Assessment of Adverse Perinatal Outcomes and Newborns Early Motoric Development.

Background and Objectives: The aim of this study was to analyze the presence of gestational diabetes mellitus (GDM) on maternal and fetal perinatal parameters, as well to evaluate the influence of GDM on neonatal early motoric development. Materials and Methods: In this prospective study, we evaluated 203 eligible participants that were admitted to obstetrics department for a labor. GDM was assessed by evaluation of maternal parameters, fetal parameters, as well its impact on infants early motoric development (Alberta Infant Motor Scale—AIMS). Results: Presence of GDM was significantly positively associated with: pre-pregnancy weight, obesity degree, weight at delivery, gestational weight gain (GWG), body mass index (BMI) at delivery, GWG and increased pre-pregnancy BMI, glucose levels in mother’s venous blood after the delivery, positive family history for cardiovascular disease, pregnancy-related hypertension, congenital thrombophilia, drug use in pregnancy, large for gestational age (LGA), mode of delivery (Cesarean section and instrumental delivery). Likewise, GDM association was detected for tested ultrasound parameters (biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femoral length (FL)), length at birth, birth weight, newborn’s head circumference, newborn’s chest circumference, AIMS supination and pronation at three months, AIMS supination, pronation, sitting and standing at six months. Only Amniotic Fluid Index and AIMS supination at three months of infant’s age remained significantly associated in multivariate regression model. Conclusions: The presence of significant positive association of numerous tested parameters in our study on perinatal outcomes and early motoric development, points to the necessity of establishing appropriate clinical decision-making strategies for all pregnant woman at risk and emphasize the importance of providing adequate glycaemia control options and further regular follow ups during the pregnancy.

P118 External validation of model predicting early-onset surgery in Crohn’s Disease patients

Abstract Background Screening Crohn’s disease (CD) patients with high risk of early-onset surgery is crucial in launching therapeutic strategies. We have already identified disease behavior, smoking, body mass index, C-reactive protein level at diagnosis, previous perianal or intestinal surgery, maximum bowel wall thickness, use of biologics, and exclusive enteral nutrition as independent significant factors associated with 1-year surgery surgeries risk and further established a prognostic model (Fig.1,World J Gastroenterol. 2020;26(5):524–534). We aimed to validate this model using external cohort. Methods This retrospective study was conducted from Jan, 1, 2017, to Dec, 31, 2019 in three tertiary referral centers including Sixth Affiliated Hospital of Sun Yat-Sen University, Second Affiliated Hospital of Zhejiang University, and Second Affiliated Hospital of Military Medical University. Data of patients with a confirmed diagnosis of CD were collected through hospital electronic system. The published model was validated with calibration using the Hosmer-Lemeshow goodness-of-fit test, and discrimination was assessed using areas under the curve (AUC). Results A total of 756 patients were enrolled in our study with 101 (13.4%) excluded for the sake of incomplete data and loss of follow-up. Of the enrolled patients, 74.8% were male (n = 490) at the mean age of 28.4 ± 11.0 years, with the mean follow-up period of 21.8 ± 8.1 months. An ideal predictive ability of this model was confirmed by receiver operating characteristic curves and AUC as high as 94.5%. Besides, acceptable sensitivity of 69.5% and excellent specificity of 97.0% supported further clinical promotion and application of this model. Conclusion This model owns ideal ability to predict 1-year surgery risk in CD patients, which definitely help clinical decision-making and acid therapeutic strategies launching.