Clinical Decision Tool Research Articles

Age-related nomogram revealed optimal therapeutic option for older patients with primary liver cancer: less is more.

Age bias in therapeutic decisions for older patients with cancer exists. There is a clear need to individualize such decisions. Based on the Surveillance, Epidemiology and End Results (SEER) database, 5081 primary liver cancer (PLC) patients between 2010 and 2014 were identified and divided into <64, 64-74 and >74 years group. Each group was randomly divided into training and internal validation cohorts, and patients who were diagnosed between 2015 and 2016 were included as an external validation. The nomogram model predicting overall survival (OS) was generated and evaluated based on the Cox regression for the influencing factors in prognosis. The K-M analysis was used to compare the difference among different treatments. KM analysis showed a significant difference for OS in three age groups (P < 0.001). At the same time, we also found different prognostic factors and their importance in different age groups. Therefore, we created three nomograms based on the results of Cox regression results for each age group. The c-index was 0.802, 0.766, 0.781 respectively. The calibration curve and ROC curve show that our model has a good predictive efficacy and the reliability was also confirmed in the internal and external validation set. An available online page was established to simplify and visualize our model (http://124.222.247.135/). The results of treatment analysis revealed that the optimal therapeutic option for PLCs was surgery alone. The optimal therapeutic option for older PLCs was surgery alone. The generated dynamic nomogram in this study may be a useful tool for personalized clinical decisions.

Clinical, demographic, and contextual determinants of older breast cancer survivors' reports of discussions about exercise with healthcare providers.

e22549 Background: Exercise offers various benefits to older breast cancer survivors, but studies report that healthcare providers do not frequently engage in discussions about exercise in clinical practice. The purpose of this study was to evaluate the clinical, demographic, and contextual determinants of receiving exercise advice from healthcare providers among older breast cancer survivors in the U.S. Methods: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) cancer registries linked to the Medicare Health Outcomes Survey (MHOS). Participants included women diagnosed with histologically confirmed invasive breast cancer from 2008-2015 who were 65+ years of age and had completed a MHOS survey at least two years after diagnosis. Descriptive statistics were calculated. We conducted a multivariable logistic regression analysis to identify clinical, demographic, and contextual variables associated with the likelihood of receiving exercise advice from a healthcare provider. Results: The sample of breast cancer survivors (n = 1,836) included 960 who received exercise advice and 876 who did not. The median age of the women in the sample was 76.2 years (range: 67.0-100.6). Survivors who were American Indian/Alaska Native/Asian or Pacific Islander (Odds Ratio (OR): 1.54; 95% Confidence Interval (CI): 1.09-2.18) or who had overweight (OR: 1.50; 95% CI: 1.17-1.91) or obesity (OR: 1.71; 95% CI: 1.31-2.23) were significantly more likely to receive exercise advice. In contrast, survivors who were never married (OR: 0.60; 95% CI: 0.37-0.96), had a high school degree (OR: 0.73; 95% CI: 0.58-0.92) or less (OR: 0.55; 95% CI: 0.42-0.73), were underweight (OR: 0.59; 95% CI: 0.36-0.95), or who were older (OR: 0.98; 95% CI: 0.96-0.99) had lower odds of receiving advice about exercise. The associations varied by type of comorbidity. Conclusions: Individual clinical and contextual characteristics of breast cancer survivors were associated with the likelihood of receiving exercise advice in a clinical setting. The consideration of the patients’ full range of circumstances when discussing and advising about exercise could potentially improve the overall quality of survivorship care. A personalized clinical decision tool could support individualized discussions about exercise in clinical settings.

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes.

Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.

Situational analysis of hypertension management at primary health care level in São Paulo, Brazil: population, healthcare professional and health system perspectives

BackgroundGovernment-led, population-wide initiatives are crucial for advancing the management of hypertension − a leading cause of cardiovascular disease (CVD) morbidity and mortality. An urban population health initiative was conducted against this backdrop, focussing on hypertension in the primary health system in São Paulo, Brazil. Within the frame of the initiative and under the supervision and leadership of the municipal health authorities, a situational analysis was conducted on the needs in hypertension management, marking the first phase of a Design Thinking process. This article describes the situational analysis process and presents the identified elements to be strengthened considering hypertension diagnosis, treatment and control.MethodsFirst, a mixed-methods approach was used, starting with a literature review of municipal hypertension data followed by meetings (N = 20) with the local public health administration to assess health system level components. To investigate activities on hypertension diagnosis, treatment and control, nine primary healthcare units were selected from two districts of São Paulo city– Itaquera and Penha– which received an online form addressed to managers, participated in conversation circles of staff and patients, and underwent shadowing of community health agents.ResultsData gave rise to two main outputs: (i) a patient care journey map; and (ii) a matrix summarizing the identified needs at patient, healthcare professional and health system level for diagnosis, treatment and control of hypertension. Patient awareness and knowledge of hypertension was found to be insufficient and its management needs to be improved. For health professionals, disease awareness, technical training, more time dedicated to patients, and simplified guidelines and clinical decision-making tools for hypertension management were identified as principal needs. The situational analysis found that the healthcare systems efficiency might be improved by establishing defined treatment and care delivery goals with a focus on outcomes and implemented through action plans.ConclusionsThis situational analysis identified several needs related to hypertension control in São Paulo that are in line with global challenges to improve the control of CVD risk factors. Findings were also confirmed locally in an expansion phase of this situational analysis to additional primary care facilities. As a consequence, solutions were designed, promptly taken up and implemented by the municipal health secretariat.

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans

This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

Cribriform versus Intraductal: How to Determine the Difference.

Over the years, our understanding of cribriform and intraductal prostate cancer (PCa) has evolved significantly, leading to substantial changes in their classification and clinical management. This review discusses the histopathological disparities between intraductal and cribriform PCa from a diagnostic perspective, aiming to aid pathologists in achieving accurate diagnoses. Furthermore, it discusses the ongoing debate surrounding the different recommendations between ISUP and GUPS, which pose challenges for practicing pathologists and complicates consensus among them. Recent studies have shown promising results in integrating these pathological features into clinical decision-making tools, improving predictions of PCa recurrence, cancer spread, and mortality. Future research efforts should focus on further unraveling the biological backgrounds of these entities and their implications for clinical management to ultimately improve PCa patient outcomes.

HEART + score: integrating carotid ultrasound to chest pain assessment in the emergency department.

The HEART score is a clinical decision tool that stratifies patients into categories of low, moderate, and high-risk of major adverse cardiac events in the emergency department (ED) but cannot identify underlying cardiovascular disease in patients without prior history. The presence of atherosclerosis can easily be detected at the bedside using carotid ultrasound. Plaque quantification is well established, and plaque composition can be assessed using ultrasound grayscale pixel distribution analysis. This study aimed to determine whether carotid plaque burden and/or composition correlated with risk of events and could improve the sensitivity of the HEART score in risk stratifying ED patients with chest pain. The HEART score was calculated based on history, electrocardiogram, age, risk factors, and initial troponin in patients presenting to the ED with chest pain (n = 321). Focused carotid ultrasound was performed, and maximum plaque height and total plaque area were used to determine plaque burden (quantity). Plaque composition (% blood, fat, muscle, fibrous, calcium-like tissue) was assessed by pixel distribution analysis. Carotid plaque height and area increased with HEART score (p < 0.0001). Carotid plaque % fibrous and % calcium also increased with HEART score. The HEART score had a higher area under the curve (AUC = 0.84) in predicting 30-day events compared to the plaque variables alone (AUCs < 0.70). Integrating plaque quantity into the HEART score slightly increased test sensitivity (62-69%) for 30-day events and reclassified 11 moderate-risk participants to high-risk (score 7-10). Plaque burden with advanced composition features (fibrous and calcium) was associated with increased HEART score. Integrating plaque assessment into the HEART score identified subclinical atherosclerosis in moderate-risk patients.

Implementation of a Novel Prehospital Clinical Decision Tool and ECG Transmission for STEMI Significantly Reduces Door-to-Balloon Time and Sex-Based Disparities

Background An important method employed to reduce door to balloon time (DTBT) for ST segment elevation Myocardial Infarctions (STEMIs) is a prehospital MI alert. The purpose of this retrospective study was to examine the effects of an educational intervention using a novel decision support method of STEMI notification and prehospital electrocardiogram (ECG) transmission on DTBT. Methods An ongoing database (April 4, 2000 – present) is maintained to track STEMI alerts. In 2007, an MI alert program began; emergency medicine physicians could activate a “prehospital MI alert”. In October 2015, modems were purchased for Emergency Medical Services personnel to transmit ECGs. There was concurrent implementation of a decision support tool for identifying STEMI. Sex was assigned as indicated in the medical record. Data were analyzed in two groups: Pre-2016 (PRE) and 2016–2022 (POST). Results In total, 3,153 patients (1,301 PRE; 1,852 POST) were assessed; the average age was 65.2 years, 32.6% female, 87.7% white with significant differences in age and race between the two cohorts. Of the total 3,153 MI alerts, 239 were false activations, leaving 2,914 for analysis. 2,115 (72.6%) had cardiac catheterization while 16 (6.7%) of the 239 had a cardiac catheterization. There was an overall decrease in DTBT of 27.5% PRE to POST of prehospital ECG transmission (p < 0.001); PRE median time was 74.5 min vs. 55 min POST. There was no significant difference between rates of cardiac catheterization PRE and POST for all patients. After accounting for age, race, and mode of arrival, DTBT was 12.2% longer in women, as compared to men (p < 0.001) PRE vs. POST. DTBT among women was significantly shorter when comparing PRE to POST periods (median 77 min vs. 60 min; p = 0.0001). There was no significant sex difference in the proportion of those with cardiac catheterization between the two cohorts (62.5% vs. 63.5%; p = 0.73). Conclusion Introduction of a decision support tool with prehospital ECG transmission with prehospital ECG transmission decreased overall DTBT by 20 min (27.5%). Women in the study had a 17-minute decrease in DTBT (22%), but their DTBT remained 12.2% longer than men for reasons that remain unclear.

Development and validation of a nomogram integrating marital status for 5-year overall survival of chondrosarcoma: a population-based study.

The objective of this study was to evaluate the influence of marital status on overall survival (OS) and develop a nomogram for predicting 5-year OS in chondrosarcoma (CHS) patients. We utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify CHS patients diagnosed between 2010 and 2018. Survival rates were calculated using Kaplan-Meier analysis. Prognostic factors were identified through univariate and multivariate analyses. An independent cohort was used for external validation of the nomogram. Performance evaluation of the nomogram was conducted using Harrell's concordance index (C-index), calibration plot, and decision curve analysis (DCA). In the SEER cohort, Kaplan-Meier analysis showed significant differences in OS among CHS patients with different marital statuses (P < 0.001), with widowed patients having the lowest OS. In terms of gender, there were significant survival differences based on marital status in females (P < 0.001), but not in males (P = 0.067). The OS of married and single females is significantly higher than that of married (P < 0.001) and single male (P = 0.006), respectively. Kaplan-Meier curves showed no significant difference in OS between groups stratified by either gender or marital status in the external cohort. Univariate and multivariate analyses confirmed that age at diagnosis, gender, marital status, tumor size, histological type, tumor grade, SEER stage, and surgery were independent prognostic factors for OS. The nomogram demonstrated high internal and external validation C-indexes of 0.818 and 0.88, respectively. Calibration plots, DCA curve, and Kaplan-Meier curve (P < 0.001) confirmed the excellent performance and clinical utility of the nomogram. Marital status was an independent factor influencing OS in CHS patients, with widowed patients having the worst prognosis. The OS of both married and single females is significantly higher than that of their male counterparts. However, these findings require further validation in a large independent cohort. While the contribution of marital status on predicting OS appears modest, our nomogram accurately predicted 5-year OS and identified high-risk groups, providing a valuable tool for clinical decision-making.

Enablers and Barriers to Home Management for Children with Gastroenteritis: Systematic Review

ObjectiveTo identify enablers and barriers to home management for children with acute gastroenteritis perceived by healthcare professionals and caregivers. Study designA systematic review was conducted using the following databases: PubMed, Embase, Web of Science and CINAHL. Studies from high-income countries published from 2003 to 2023 who included children with acute gastroenteritis under six years, treated via home management, and addressed enablers or barriers from the perspective of healthcare professionals or caregivers, were eligible for inclusion. Studies were independently reviewed for inclusion, data extraction, and quality assessment. Data synthesis was conducted utilizing the Theoretical Domains Framework (TDF) and Capability, Opportunity, Motivation-Behavior (COM-B) model . Results4476 studies were screened with 16 meeting the inclusion criteria. The commonest enablers for healthcare professionals concerned the ‘opportunity’ component (i.e., access to clinical decision tools, protocols, provision of free oral rehydration therapy (ORT)), followed by their ‘capability’ component (i.e., knowledge about guidelines, ORT and ondansetron) to initiate home management. Conversely, caregivers’ factors relied more on internal factors within the ‘motivation’ component (i.e., emotions, insecurity, need for reassurance), while ‘opportunity’ components (i.e., information sheets, monitoring calls) could assist them in managing their child with gastroenteritis at home. ConclusionsHealthcare professionals could benefit from enhanced capabilities and clinical decision support systems, while caregivers may require access to information resources and support for positive emotions and beliefs in their capabilities. Addressing these aspects could optimize home management, potentially allowing more children with acute gastroenteritis to be treated at home.

A novel web-based tool for lung transplant patients undergoing extracorporeal photopheresis

IntroductionExtracorporeal photopheresis (ECP) is considered an emerging rescue therapy for patients with CLAD (chronic lung allograft dysfunction). The aim of the study was to set up a web-based data collection tool for lung transplant patients with CLAD undergoing ECP. MethodologyThe web-based tool was developed using Oracle MySQL and coded in HTML, JavaScript and CSS (Cascading Style Sheets) and was set up with pre and post transplant data of possible interest in CLAD. The functionality assessment of the software was carried out using data from a cohort of CLAD patients undergoing ECP treatment. ResultsThe software consists of seven major sections. The validation cohort consisted of 25 lung transplant patients (13 men and 12 women, median age at transplant 51 years). A significant improvement in the rate of decline of FEV1, FVC and FEV1/FVC after introduction of ECP was observed. Forty-four percent of patients showed a <10% decline in FEV1 at 6 months. Patients with recurrent respiratory infections showed less probability of responding to ECP. ConclusionsToday informatics is an integral part of medical science and an essential tool for clinical decision-making under many circumstances, reducing costs and improving patient outcomes. The “Siena ECP Database” allowed us to identify major functional trajectories after the introduction of ECP. It showed good data collection capacity, providing significant pre- and post-transplant information associated with ECP response. Although no clear clinical profile of responders has yet been defined, BOS phenotype and absence of recurrent respiratory infections before diagnosis of CLAD seem to be associated with a positive response to ECP therapy.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator

PurposeThe aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use.MethodsA retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator.ResultsTraining and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment (https://nicolazhang.shinyapps.io/refracture_shap/).ConclusionsThe SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

Clinical significance of combined tumour-infiltrating lymphocytes and microsatellite instability status in colorectal cancer: a systematic review and network meta-analysis

Microsatellite instability (MSI) status and tumour-infiltrating lymphocytes (TIL) are established prognostic factors in colorectal cancer. Previous studies evaluating the combination of TIL and MSI status identified distinct colorectal cancer subtypes with unique prognostic associations. However, these studies were often limited by sample size, particularly for MSI-high (MSI-H) tumours, and there is no comprehensive summary of the available evidence. We aimed to review the literature to compare the survival outcomes associated with the subtypes derived from the integrated MSI-TIL classification in patients with colorectal cancer. In this systematic review and network meta-analysis, we searched PubMed, Embase, Scopus, and the Cochrane Library without language restrictions, for articles published between Jan 1, 1990, and March 13, 2024. Patient cohorts comparing different combinations of TIL (high or low) and MSI status (MSI or microsatellite stable [MSS]) in patients with surgically resected colorectal cancer were included. Studies were excluded if they focused on neoadjuvant therapy or on other immune markers such as B cells or macrophages. Methodological quality assessment was done with the Newcastle-Ottawa scale; data appraisal and extraction was done independently by two reviewers. Summary estimates were extracted from published reports. The primary outcomes were overall survival, disease-free survival, and cancer-specific survival. A frequentist network meta-analysis was done to compare hazard ratios (HRs) and 95% CI for each outcome. The MSI-TIL subgroups were prognostically ranked based on P-score, bias, magnitude, and precision of associations with each outcome. The protocol is registered with PROSPERO (CRD42023461108). Of 302 studies initially identified, 21 studies (comprising 14 028 patients) were included in the systematic review and 19 (13 029 patients) in the meta-analysis. Nine studies were identified with a low risk of bias and the remaining ten had a moderate risk of bias. The MSI-TIL-high (MSI-TIL-H) subtype exhibited longer overall survival (HR 0·45, 95% CI 0·34-0·61; I2=77·7%), disease-free survival (0·43, 0·32-0·58; I2=61·6%), and cancer-specific survival (0·53, 0·43-0·66; I2=0%), followed by the MSS-TIL-H subtype for overall survival (HR 0·53, 0·41-0·69; I2=77·7%), disease-free survival (0·52, 0·41-0·64; I2=61·6%), and cancer-specific survival (0·55, 0·47-0·64; I2=0%) than did patients with MSS-TIL-low tumours (MSS-TIL-L). Patients with the MSI-TIL-L subtype had similar overall survival (0·88, 0·66-1·18; I2=77·7%) and disease-free survival (0·93, 0·69-1·26; I2=61·6%), but a modestly longer cancer-specific survival (0·72, 0·57-0·90; I2=0%) than did the MSS-TIL-L subtype. Results from the direct and indirect evidence were strongly congruous. The findings from this network meta-analysis suggest that better survival was only observed among patients with TIL-H colorectal cancer, regardless of MSI or MSS status. The integrated MSI-TIL classification should be further explored as a predictive tool for clinical decision-making in early-stage colorectal cancer. German Research Council (HO 5117/2-2).

ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study.

Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments. The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts. A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based "chatbot" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions. Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards. Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.

Computed tomography pulmonary angiogram ordering, adherence to decision rules and yield in the emergency department: An observational study.

Pulmonary embolism (PE) frequently requires diagnosis through CT pulmonary angiogram (CTPA). Appropriate application of evidence-based clinical decision tools can reduce unnecessary CTPAs. This study assessed adherence to and the efficacy of various aspects of the Queensland Health suspected PE diagnostic pathway, including Wells score, PE rule out criteria (PERC) and age-adjusted D-dimer interpretation. Retrospective study of CTPAs ordered from 1 January to 30 April 2023 in a tertiary and urban ED in Southeast Queensland. Data on clinical variables, D-dimer and CTPA results were collected through medical record and radiology database review. Descriptive analyses were used to determine adherence to Queensland guidelines and performance of D-dimer interpretation tools (including comparison of age-adjusted PE with a new pre-test probability [PTP]-based model using D-dimer cut-off <1000 ng/mL for Wells score ≤4 and 500 ng/mL for Wells score 4.5-6). A total of 573 CTPAs were available for analysis with a 12.4% (95% confidence interval 10.0-15.4) diagnostic yield. Stratification by Wells score showed yields of 4.0%, 18.5% and 41% for low-, moderate- and high-risk patients, respectively. Twenty-five patients with low-PTP who received CTPA could have been excluded with the PERC rule. Age-adjusted D-dimer interpretation may have prevented 26 CTPAs with no false negatives, whereas PTP approach may have prevented 128 CTPAs with four false negatives. Guideline adherence can be improved, and adherence to existing clinical decision tools may reduce unnecessary CTPA ordering and increase diagnostic yield. The use of the age-adjusted D-dimer had good sensitivity, whereas the new PTP approach will require further prospective research.

Algorithms and clinical decision-making tools for ruling out acute aortic syndrome in the emergency department: a narrative review

Algorithms and clinical decision-making tools for ruling out acute aortic syndrome in the emergency department: a narrative review

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.

This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

Haemostatic gene variations in cervical cancer-associated venous thrombosis: considerations for clinical strategies.

Venous thromboembolism (VTE) is a life-threatening haemostatic disease frequently diagnosed among the cancer population. The Khorana Score is currently the primal risk assessment model to stratify oncological patients according to their susceptibility to VTE, however, it displays a limited performance. Meanwhile, intensive research on VTE pathophysiology in the general population has uncovered a range of single-nucleotide polymorphisms (SNPs) associated with the condition. Nonetheless, their predictive ability concerning cancer-associated thrombosis (CAT) is controversial. Cervical cancer (CC) patients undergoing chemoradiotherapy often experience VTE, which negatively affects their survival. Thus, aiming for an improvement in thromboprophylaxis, new thrombotic biomarkers, including SNPs, are currently under investigation. In this study, the predictive capability of haemostatic gene SNPs on CC-related VTE and their prognostic value regardless of VTE were explored. Six SNPs in haemostatic genes were evaluated. A total of 401 CC patients undergoing chemoradiotherapy were enrolled in a retrospective cohort study. The implications for the time to VTE occurrence and overall survival (OS) were assessed. CAT considerably impacted the CC patients' OS (log-rank test, P < 0.001). SERPINE1 rs2070682 (T > C) showed a significant association with the risk of CC-related VTE (CC/CT vs. TT, log-rank test, P = 0.002; C allele, Cox model, hazard ratio (HR) = 6.99 and P = 0.009), while F2 rs1799963 (G > A) demonstrated an important prognostic value regardless of VTE (AA/AG vs. GG, log-rank test, P = 0.020; A allele, Cox model, HR = 2.76 and P = 0.026). For the remaining SNPs, no significant associations were detected. The polymorphisms SERPINE1 rs2070682 and F2 rs1799963 could be valuable tools in clinical decision-making, aiding in thromboprophylaxis and CC management, respectively.

Diagnosis and management of acute aortic dissection in the emergency department.

Acute aortic dissection is often misdiagnosed as a result of its atypical presentations. It affects 4000 patients a year in the UK of all ages, not just older patients, with increasing numbers of cases expected in the future because of the ageing population. Dissection of the aortic wall leads to sudden, severe pain, and commonly end-organ symptoms which must be recognised. Acute aortic dissection can be challenging to diagnose in the emergency department because of the multitude of possible presentations and the need for selective testing with Computed Tomography Angiography (CTA). Clinicians often miss acute aortic dissection because it is not considered in the differential diagnosis, and the challenge lies in identifying acute aortic dissection in a sea of complaints of chest, back and abdominal pain. There are several ways to improve diagnosis, including awareness campaigns, better education about patients in which to consider acute aortic dissection, and improved detection strategies including which patients should receive CTA. Clinical decision tools and biomarkers could help, but further research is required and is a research focus in emergency medicine. Once diagnosed, blood pressure control, analgesia and urgent surgery or transfer to enable this to occur with minimal delay is required.

Identification and validation of a novel anoikis-related prognostic model for prostate cancer.

Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis-related prognostic model for prostate cancer (PCa). We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis-related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high-risk group patients. Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP-derived and four PRISM-derived compounds were identified for high-risk patients. The anoikis-related prognostic model developed in this study could be a useful tool for clinical decision-making. This study may provide a new perspective for the treatment of anoikis-related PCa.