Clinical Decision-making Process Research Articles

Computed Simulation of Transcatheter Edge-to-Edge Mitral Valve Repair - A Proof of Concept Study.

As transcatheter mitral valve (MV) interventions are expanding and more device types and sizes become available, a tool supporting operators in preprocedural planning and the clinical decision-making process is highly desirable. We sought to develop a finite element (FE) computational simulation model to predict results of transcatheter edge-to-edge (TEER) interventions. We prospectively enrolled patients with secondary mitral regurgitation (MR) referred for a clinically indicated TEER. Three-dimensional (3D) transesophageal echocardiograms performed at the beginning of the procedure were used to perform the simulation. On the 3D dynamic model of the MV that was first obtained, we simulated the clip implantation using the same clip(s) type, size, number, and implantation location that was used during the intervention. The 3D model of the MV obtained after simulation of the clip implantation was compared to the clinical results obtained at the end of the intervention. We analyzed the degree and location of residual MR and the shape and area of the diastolic mitral valve area. We performed computational simulation on 5 patients. Overall, the simulated models predicted well the degree and location of the residual regurgitant orifice(s) but tended to underestimate the diastolic mitral orifice area. In this proof-of-concept study, we present preliminary results on our algorithm simulating clip implantation in 5 patients with functional MR. We show promising results regarding the feasibility and accuracy in terms of predicting residual MR and the need to improve the estimation of the diastolic mitral valve area.

#691 Validation of a prognostic model for adverse events in advanced chronic kidney disease in the Chinese population

Abstract Background and Aims Currently, the Grams model has been developed to estimate risks of adverse events among advanced chronic kidney disease (CKD) patients in western populations. Its utility is noteworthy as it predicts multiple adverse events within a single model while taking competing risks into account. While no external validation has been conducted among Asian, especially Chinese population, restricting further practical application and clinical benefits evaluation. Hence, we aimed to validate the performance of Grams model for estimating risks of major adverse events among Chinese CKD population. Method The Grams model was validated in the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE) study, a multicenter prospective cohort study which involved 39 renal departments of 39 hospitals from 28 cities of 22 provinces in China. Participants with estimated glomerular filtration rate (eGFR) <30 ml/min/1.73 m2 during baseline and follow-up were enrolled. The same predictors as those selected in the Gram model were considered, including age, sex, race, history of cardiovascular disease(CVD), smoking status, systolic blood pressure, diabetes mellitus, eGFR, and urine albumin-to-creatinine ratio (uACR). The outcomes included kidney replacement therapy(KRT), CVD and death within 2- and 4- year follow-up periods. The discrimination and calibration of the model were evaluated using the concordance index (C index), the ratio of observed and expected outcomes (O/E ratio), and the calibration curve. The clinical utility of the model was assessed through decision curve analysis. Intercept recalibration was used to update the model. Results Among 1333 included participants (54 (IQR, 43-64) years, 699(52.4%) male), 386(29.0%) developed KRT, 118(8.9%) developed CVD, 88(6.6%) died during the follow-up period. The Grams model showed a moderate to good discrimination for three outcomes, with the C index ranging from 0.656 to 0.720. According to the O/E ratios and calibration curves, the calibration for KRT was relatively accurate, but the model overestimated the predicted probability of CVD and death. Intercept recalibration led to closer alignment of observed and predicted risks. Decision curve analyses revealed the superior net benefit of the Grams model in guiding KRT preparation. Conclusion In summary, the Grams model exhibited moderate discrimination for predicting outcomes, with relatively accurate calibration for KRT but an overestimation of predicted probabilities for CVD and death, intercept recalibration improved the calibration of the model. Despite its limitations, the model offered a superior net benefit in guiding KRT preparation, suggesting its potential use in clinical decision-making process. Further investigations are warranted to improve the performance of the Grams model in Chinese population.

Attitudes and perceived barriers to evidence-based practice among occupational therapists in Jordan.

Evidence-based practice (EBP) refers to the clinical decision-making process incorporating the best available evidence from research, therapists' clinical experience, and patient values. The current study aimed to examine the experience of Jordanian occupational therapy practitioners (OTs) in using EBP and to identify the perceived barriers to implementing EBP among OTs in Jordan. The study utilized a cross-sectional descriptive study design. A questionnaire was emailed to OTs who have been working in Jordan for the last six months of their practice in a clinical setting. The majority of the participants had a positive attitude toward EBP. However, they reported several barriers to implementing EBP, including a lack of tools and equipment in clinical settings as a major barrier (65.8%). National collaborative actions are needed to develop strategies to improve the utilization of EBP in occupational therapy (OT) practice and to overcome the barriers therapists experience with implementing EBP.

Conventional Radiography Assessment of the Pediatric Knee: Pearls and Pitfalls.

Knee pain is one of the most common indications for radiography in the evaluation of musculoskeletal disorders in children and adolescents. According to international guidelines, knee radiographs should be obtained when there is the suspicion of an effusion, limited motion, pain to palpation, inability to bear weight, mechanical symptoms (such as "locking"), and persistent knee pain after therapy. When indicated, radiographs can provide crucial information for the clinical decision-making process. Because of the developmental changes occurring in the knee during growth, the assessment of knee radiographs can be challenging in children and adolescents. Radiologists unfamiliar with the appearance of the knee on radiographs during skeletal maturation risk overcalling or overlooking bone lesions. Image acquisition techniques and parameters should be adapted to children. This article describes the most common challenges in distinguishing pathology from the normal appearance of knee radiographs in the pediatric population, offering some pearls and pitfalls that can be useful in clinical practice.

Meta-analysis of the efficacy and adverse effects of acalabrutinib in the management of relapsed/refractory chronic lymphocytic leukemia

The advent of Bruton tyrosine kinase inhibitor (BTKi) therapy with ibrutinib introduced a highly effective targeted therapy in the management of chronic lymphocytic leukemia (CLL). However, due to the adverse effect profile some patients cannot tolerate this novel therapy. Newer, more potent and targeted BTK inhibitors such as acalabrutinib have been developed. Acalabrutinib is an irreversible and second generation BTKi that covalently inhibits BTK with greater selectivity than ibrutinib. As novel BTKis are developed, a greater understanding of their efficacy and adverse effect rates can assist clinicians and patients in the shared clinical decision-making process. A search was conducted using the PICOS model and PRISMA guidelines. PubMeb, Embase, and Cochrane Library databases were searched using the keywords: Acalabrutinib, Acalabrutinib Monotherapy, Tyrosine Kinase Inhibitor, and Relapsed/Refractory (R/R) CLL. After initial literature review 12 studies were chosen for evaluation in this meta-analysis. Meta-analysis and follow up meta-regression models were completed. The results were as follows: ORR 82% (95% CI 74%–90%, I2 = 84.14%, p < 0.01), CR 4% (95% CI 2%–6%, I2 = 0.00%, p = 0.99), mortality rate 12% (95% CI 6%–19%, I2 = 87.23%, p < 0.01), mortality rate due to adverse effect 7% (95% CI 3%–10%, I2 = 67.67%, p = 0.01), mortality due to pneumonia 2% (95% CI 1%–3%, I2 = 0.00%, p = 0.43), mortality due to CLL progression 4% (95% CI 2%–6%, I2 = 61.03%, p = 0.04), neutropenia (≥ grade 3) 18% (95% CI 15%–20%, I2 = 0.00%, p = 0.70), thrombocytopenia (≥ grade 3) 7% (95% CI 4%–11%, I2 = 54%, p = 0.09), anemia (≥ grade 3) 9% (95% CI 6%–12%, I2 = 36.93%, p = 0.18), pneumonia (≥ grade 3) 10% (95% CI 6%–14%, I2 = 66.37%, p = 0.02) and atrial fibrillation 7% (95% CI 3%–11%, I2 = 80.13%, p = 0.00). The results demonstrate that acalabrutinib shows efficacy in the treatment of R/R CLL with tolerable adverse reaction rates.

Examining Adolescent Language Performance in Discourse Production Across Four Elicitation Tasks.

Comprehensive spoken language assessment should include the evaluation of language use in naturalistic contexts. Discourse elicitation and analysis provides the opportunity for such an evaluation to occur. In this article, our overall aim was to describe adolescents' language performance on four elicitation tasks and determine if there are task-related differences across the elicitation tasks. Forty-four typically developing adolescents with ages ranging from 12;2 to 17;11 (years;months; M = 15;2; 21 boys and 23 girls) participated in the study. They completed four spoken discourse tasks: (a) story generation using a wordless picture book, (b) fable retell, (c) six personal narratives in response to emotion-based prompts, and (d) monologic response to two stories that contained a moral dilemma. Responses were transcribed and analyzed for four language performance measures tapping into language productivity, syntactic complexity, lexical diversity, and verbal facility. Despite individual variability in performance, mean scores were close to median scores for most measures, suggesting a symmetrical distribution. As expected, all four language performance measures were significantly different across the four elicitation tasks. The personal narrative task elicited the longest samples, with the highest verbal fluency. In contrast, both lexical diversity and syntactic complexity were the strongest in response to the fable retell and the moral dilemma tasks. This investigation provides speech-language pathologists with an overview of how task-related factors may impact adolescent language performance. These findings may be used to support their clinical decision-making processes in choosing a suitable discourse task when conducting a comprehensive spoken language assessment. Three hypothetical case examples are used to illustrate the decision-making process. https://doi.org/10.23641/asha.25761768.

Parallel Hierarchical Multi-View Feature Fusion Based on Canonical Correlation Analysis for Mammogram Retrieval

Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to revolutionize healthcare by augmenting the expertise of clinicians, improving overall diagnostic accuracy and helping experts in the clinical decision-making process by classifying and retrieving similar annotated clinical images to a given query. In the context of multi-view mammography interpretation, the concept of multi-view feature fusion has recently been studied to improve retrieval performance while effectively guaranteeing the complementarity of both MLO and CC views. However, conventional multi-view feature fusion makes descriptors long and lacks to take into consideration the relationship between descriptors. To deal with this issue, we propose two hierarchical multi-view feature fusion methods, for multi-view mammogram retrieval, based on the Canonical Correlation Analysis (CCA), which is the most commonly used multivariate parametric test. In fact, we have adapted CCA to determine the relationship between two descriptors by processing latent correlation factors. Moreover, after extracting descriptors for each view, a comparative study of texture and shape fusion descriptors is proposed in order to identify the more discriminative features for multi-view mammogram retrieval. Then, a query-dependent distance metric preserving both visual resemblance and semantic similarity is carried out to dynamically determine the more appropriate distance measure for each query image. Extensive experiments on the challenging Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) have demonstrated the effectiveness of the proposed hierarchical multi-view feature fusion for mammogram retrieval, which outperforms the performance achieved either by conventional fused information or by single view information. To improve the transparency of our paper, the source code of the proposed method and the related dataset (including readme files) are publicly accessible through the following GitHub link: https://github.com/ABDERRAHIMMAR/Multi-View-Feature-Fusion-for-Mammogram-Retrieval . This open-access resource empowers researchers and practitioners to delve deeper into our methodology, fostering collaboration and advancements in the field of computer-aided diagnosis and medical image analysis.

Human dignity and autonomy in medicoethical decisions at the end of life

Respect of human dignity is an unquestionable part of daily clinical routine as well as of evaluations in medical ethics. Some aspects of human dignity are asubject of transformation in the course of alifetime. If the ability to form a will is reduced, it might be difficult to satisfy the individual perception of adignified treatment, which is of particular interest in terminal care. As illustrated by the presented case report, moral problems can arise when the patient's present verbal or non-verbal expressions are opposed to what is documented or what is estimated by dependants to be the individual's will. In clinical decision-making processes, the determination of the will via an advance directive can be overstated, which can go so far that human dignity is reduced to autonomy. Aconcept of dignity reduced to autonomy misses the point not only of adignity-centered medical care but also of shared decision-making. In situations at the end of life other aspects, such as authenticity or self-actualization should be considered when respecting the individual's dignity. Medicine should as such not marginalize death but provide acare based on principles that focus on human interaction and respect for acomprehensive concept of dignity.

Unraveling the Interplay of KRAS, NRAS, BRAF, and Micro-Satellite Instability in Non-Metastatic Colon Cancer: A Systematic Review.

Microsatellite Instability (MSI-H) occurs in approximately 15% of non-metastatic colon cancers, influencing patient outcomes positively compared to microsatellite stable (MSS) cancers. This systematic review focuses on the prognostic significance of KRAS, NRAS, and BRAF mutations within MSI-H colon cancer. Through comprehensive searches in databases like MEDLINE, EMBASE, and others until 1 January 2024, we selected 8 pertinent studies from an initial pool of 1918. These studies, encompassing nine trials and five observational studies involving 13,273 patients, provided insights into disease-free survival (DFS), survival after recurrence, and overall survival. The pooled data suggest that while KRAS and BRAF mutations typically predict poorer outcomes in MSS colorectal cancer, their impact is less pronounced in MSI contexts, with implications varying across different stages of cancer and treatment responses. In particular, adverse effects of these mutations manifest significantly upon recurrence rather than affecting immediate DFS. Our findings confirm the complex interplay between genetic mutations and MSI status, emphasizing the nuanced role of MSI in modifying the prognostic implications of KRAS, NRAS, and BRAF mutations in colon cancer. This review underscores the importance of considering MSI alongside mutational status in the clinical decision-making process, aiming to tailor therapeutic strategies more effectively for colon cancer patients.

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

Assessing Evidence-Based Practice in Operating Room Nursing Students: A Cross-Sectional Study in the Southwest of Iran.

Evidence-based practice (EBP) is a clinical decision-making process that is grounded in the utilization of the most reliable and up-to-date evidence. It involves utilizing resources and evidence to enhance patient management. The application of evidence-based decisions in patient care and education is of utmost importance in the performance of health sciences students. However, the emphasis on this concept among operating room nursing students has been lacking. Hence, the objective of this research is to assess the implementation of evidence-based practice among operating room nursing students studying in the operating room department at the Shiraz University of Medical Sciences in Iran. The study follows a descriptive, cross-sectional design, with a sample of 148 operating room nursing students selected through census sampling based on the student list. After obtaining informed consent, participants completed a demographic information form and the Rubin-Parrish evidence-based practice questionnaire. The data that were gathered underwent analysis through the utilization of SPSS version 22 software, employing descriptive statistics, T-tests, and Pearson correlation coefficient tests. The results revealed that the overall average score of evidence-based practice among the students exceeded the standard scores (172.66 ± 14.74). There was a significant association between the evidence-based practice score and prior familiarity with evidence-based practice, interest in the field of study, research experience, intention to participate in the Master's exam, and the participants' grade point average (GPA) (p < 0.05). These findings indicate that operating room nursing students displayed an average level of evidence-based practice, emphasizing the need for effective plans and strategies to improve their performance. Addressing the identified factors from this study becomes crucial in this regard.

DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.

The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. The intent is to offer valuable diagnostic support to clinicians in their clinical decision-making processes. The construction of the deep learning model involved utilizing a dataset comprising abdominal magnetic resonance T2-weighted images obtained from patients diagnosed with pancreatic cystic tumors at Changhai Hospital. The dataset comprised 207 patients with SCN and 93 patients with MCN, encompassing a total of 1761 images. The foundational architecture employed was DenseNet-161, augmented with a hybrid attention mechanism module. This integration aimed to enhance the network's attentiveness toward channel and spatial features, thereby amplifying its performance. Additionally, clinical features were incorporated prior to the fully connected layer of the network to actively contribute to subsequent decision-making processes, thereby significantly augmenting the model's classification accuracy. The final patient classification outcomes were derived using a joint voting methodology, and the model underwent comprehensive evaluation. Using the five-fold cross validation, the accuracy of the classification model in this paper was 92.44%, with an AUC value of 0.971, a precision rate of 0.956, a recall rate of 0.919, a specificity of 0.933, and an F1-score of 0.936. This study demonstrates that the DenseNet model, which incorporates hybrid attention mechanisms and clinical features, is effective for distinguishing between SCN and MCN, and has potential application for the diagnosis of pancreatic cystic tumors in clinical practice.

Prediction of 6-month poststroke spasticity in patients with acute first-ever stroke by machine learning.

Poststroke spasticity (PSS) reduces arm function and leads to low levels of independence. This study suggested applying machine learning (ML) from routinely available data to support the clinical management of PSS. 172 patients with acute first-ever stroke were included in this prospective cohort study. Twenty clinical information and rehabilitation assessments were obtained to train various ML algorithms for predicting 6-month PSS defined by a modified Ashworth scale (MAS) score ≥ 1. Factors significantly relevant were also defined. The study results indicated that multivariate adaptive regression spline (area under the curve (AUC) value: 0.916; 95% confidence interval (CI): 0.906-0.923), adaptive boosting (AUC: 0.962; 95% CI: 0.952-0.973), random forest (RF) (AUC: 0.975; 95% CI: 0.968-0.981), support vector machine (SVM) (AUC: 0.980; 95% CI: 0.970-0.989) outperformed the traditional logistic model (AUC: 0.897; 95% CI: 0.884-0.910) (P < 0.05). Among all of the algorithms, the RF and SVM models outperformed the others (P < 0.05). FMA score, days in hospital, age, stroke location, and paretic side were the most important features. These findings suggest that ML algorithms can help augment clinical decision-making processes for the assessment of PSS occurrence, which may enhance the efficacy of management for patients with PSS in the future.

Role of the systemic immune-inflammation index in predicting spontaneous stone passage in patients with renal colic

ABSTRACT Objectives Renal colic (RC) is one of the most frequent reasons for presentation to the emergency department (ED) and creates a high economic and medical burden. Management strategies for RC range from waiting for spontaneous passage to surgical intervention. However, factors determining spontaneous stone passage (SSP) are still poorly understood. Therefore, in this study, we aimed to investigate the role of the systemic immune-inflammatory index (SII) in predicting SSP. Methods We retrospectively analyzed the data of 924 patients aged over 18 years, who were diagnosed with RC in our clinic between 1 January 2019, and 30 May 2022, and had ureteral stones of ≤ 10 mm. The patients were divided into two groups according to whether they had SSP. The clinical and laboratory characteristics of the patients in the ED were evaluated. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and SII (neutrophil count x platelet count/lymphocyte count) values of the patients were calculated. Stone size and location were also recorded. Results In the univariate analysis of SSP, a ureteral stone size of ≤ 5 mm (p < 0.001), distal ureteral location (p < 0.001), SII (p < 0.001), NLR (p < 0.001), and PLR (p = 0.036) were significantly correlated with SSP. ROC analysis showed that an SII level < 721.8 (Sensitivity %82.6, Specificity %74.7, p < 0.001) was an independent predictor of SSP. Conclusion Our findings showed that a low SII level was associated with SSP and could be used as a predictive marker of SSP as a more valuable parameter than NLR. SII and NLR, together with other indicators, are inflammatory markers that can be used in the clinical decision-making process for ureteral stone treatment.

External Validation of Updated Prediction Models for Neurological Outcomes at 90 Days in Patients With Out-of-Hospital Cardiac Arrest.

Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset. We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/). The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.

Abstract PO4-22-05: Changes in Treatment Recommendation for Patients with Ductal Carcinoma In Situ Using a 7-Gene Predictive Biosignature: Analysis of the PREDICT Australia Study

Abstract Background: The role of adjuvant radiotherapy (RT) following breast-conserving surgery (BCS) for women with ductal carcinoma in situ (DCIS) remains controversial. Although there is Level 1 evidence supporting the use of RT to reduce the risk of local recurrence, prognostic and predictive tools are needed to better stratify the individual risks and benefits of RT. The 7-gene predictive DCIS biosignature provides a validated score (DS) for women receiving BCS that assesses 10-year risk of DCIS recurrence and development of invasive breast cancer with and without adjuvant RT. We established a registry to evaluate the decision impact of the 7-gene predictive biosignature on DCIS treatment recommendations in an Australian setting. Methods: The PREDICT study is a prospective, multi-institutional registry for patients who received DCISionRT testing as part of their routine care. The registry includes females 26 and older who are diagnosed with DCIS and are candidates for BCS and eligible for RT. Treating physicians completed treatment recommendation forms before and after receiving test reports to capture surgical, radiation and hormonal treatment (HT) recommendations and patient preferences. Results: This planned interim analysis was performed in 483 patients with complete data treated at 43 clinical sites in Australia. The median age of patients was 61 years, 19% were 50 or younger, nuclear grade was high in 51%, and tumor size was 2.5 cm or greater in 15%. Overall, RT recommendation (yes/no) was changed for 41% of women and HT recommendation was changed for 9% after testing with a net reduction in recommended RT of 14% (66% pre-assay to 52% post-assay p&amp;lt; 0.001). Of patients recommended to receive RT pre-test, 42% were recommended to not receive RT post-test and of the patients recommended to not receive RT pre-test, 40% were recommended to receive RT post-test. The post-test RT recommendation rate increased with increasing DS score (&amp;lt; 2, 2-4, &amp;gt;4), with 9% of patients recommended RT for DS&amp;lt; 2, 62% for DS 2-4, and 100% for DS 4-10. The use of the test resulted in different RT recommendations than with clinicopathology alone, where RT recommendations were changed for 49%, 37%, and 34% for women of age&amp;lt; 50 yrs, with Grade 3 DCIS, or with DCIS &amp;gt; 2.5 cm, respectively. Collectively, this suggests that physicians had a high confidence in the test results when making their final treatment recommendations with the test results. Conclusions: This analysis demonstrates that the use of the 7-gene predictive biosignature resulted in significant changes in recommendations to add or omit RT in this study of 483 women. The integration of DCISionRT into the clinical decision-making processes has a substantial impact on recommendations to personalize care and prevent over- or under-treatment. Table 1: Impact of DCISionRT on Adjuvant Radiation Recommendations by Clinicopathologic Features Citation Format: Yvonne Zissiadis, Bruce Mann, David Speakman, Christobel Saunders, Marcus Dreosti, Christopher Pyke, Daniel De Viana, Melissa Bochner, James French, Sally Baron-Hay, Karuna Mittal, Steven Shivers, Troy Bremer. Changes in Treatment Recommendation for Patients with Ductal Carcinoma In Situ Using a 7-Gene Predictive Biosignature: Analysis of the PREDICT Australia Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-22-05.

Development and Validation of a Nomogram for Predicting Heterotopic Ossification Following Spinal Cord Injury

ObjectiveHeterotopic ossification (HO) following spinal cord injury (SCI) can severely compromise patient mobility and quality of life. Precise identification of SCI patients at an elevated risk for HO is crucial for implementing early clinical interventions. While the literature presents diverse correlations between HO onset and purported risk factors, the development of a predictive model to quantify these risks is likely to bolster preventive approaches. This study is designed to develop and validate a nomogram-based predictive model that estimates the likelihood of HO in SCI patients, utilizing recognized risk factors to expedite clinical decision-making processes. MethodsWe recruited a total of 145 patients with SCI and presenting with HO who were hospitalized at the China Rehabilitation Research Center, Beijing Boai Hospital, from June 2016 to December 2022. Additionally, 337 patients with SCI without HO were included as controls. Comprehensive data were collected for all study participants, and subsequently, the dataset was randomly partitioned into training and validation groups. Using Least Absolute Shrinkage and Selection Operator regression, variables were meticulously screened during the pretreatment phase to formulate the predictive model. The efficacy of the model was then assessed using metrics including receiver-operating characteristic (ROC) analysis, calibration assessment, and decision curve analysis. ResultsThe final prediction model incorporated age, sex, complete spinal cord injury status, spasm occurrence, and presence of deep vein thrombosis (DVT). Notably, the model exhibited commendable performance in both the training and validation groups, as evidenced by areas under the ROC curve (AUCs) of 0.756 and 0.738, respectively. These values surpassed the AUCs obtained for single variables, namely age (0.636), sex (0.589), complete spinal cord injury (0.681), spasm occurrence (0.563), and DVT presence (0.590). Furthermore, the calibration curve illustrated a congruence between the predicted and actual outcomes, indicating the high accuracy of the model. The decision curve analysis indicated substantial net benefits associated with the application of the model, thereby underscoring its practical utility. ConclusionsHO following SCI correlates with several identifiable risk factors, including male gender, youthful age, complete SCI, spasm occurrence and DVT. Our predictive model effectively estimates the likelihood of HO development by leveraging these factors, assisting physicians in identifying patients at high risk. Subsequently, correct positioning to prevent spasm-related deformities and educating healthcare providers on safe lower limb mobilization techniques are crucial to minimize muscle injury risks from rapid iliopsoas muscle extension. Additionally, the importance of early DVT prevention through routine screening and anticoagulation is emphasized to further reduce the incidence of HO.

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

ObjectiveThe purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).MethodsClinicopathologic data of 286 patients with RGC undergoing operation (radical resection and palliative resection) from a multi-institution database were enrolled and analyzed retrospectively. These individuals were split into training (80%) and test cohort (20%) by using random allocation. Nine commonly used ML methods were employed to construct survival prediction models. Algorithm performance was estimated by analyzing accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrices, five-fold cross-validation, decision curve analysis (DCA), and calibration curve. The best model was selected through appropriate verification and validation and was suitably explained by the SHapley Additive exPlanations (SHAP) approach.ResultsCompared with the traditional methods, the RGC survival prediction models employing ML exhibited good performance. Except for the decision tree model, all other models performed well, with a mean ROC AUC above 0.7. The DCA findings suggest that the developed models have the potential to enhance clinical decision-making processes, thereby improving patient outcomes. The calibration curve reveals that all models except the decision tree model displayed commendable predictive performance. Through CatBoost-based modeling and SHAP analysis, the five-year survival probability is significantly influenced by several factors: the lymph node ratio (LNR), T stage, tumor size, resection margins, perineural invasion, and distant metastasis.ConclusionsThis study established predictive models for survival probability at five years in RGC patients based on ML algorithms which showed high accuracy and applicative value.

Investigating pharmacy students clinical reasoning of antimicrobial stewardship cases using a qualitative, think-aloud methodology

Abstract Introduction Future healthcare workers’ education must include antimicrobial stewardship (AMS) clinical decision-making to achieve optimal clinical outcomes and to treat and prevent infection in a manner that minimises the effects of antimicrobial resistance (AMR) and is safe for patients. Clinical reasoning is an important cognitive process that is essential to evaluate and manage a patient’s medical problem.[1] This study aimed to evaluate the cognitive processes and clinical reasoning skills of pharmacy students when they review AMS cases, as well as identify any existing knowledge gaps in this area that need to be addressed. Methods This qualitative study used a case study approach, in which a sample (n=13) of Master of Pharmacy (MPharm) students in an Irish University were recruited to interpret three patient infection cases. Convenience sampling of MPharm students by email invitation was conducted and students took part voluntarily. Semi-structured interviews were conducted, recorded and transcribed verbatim. A think-aloud procedure with verbal protocol analysis was adopted to determine students’ decision-making processes and clinical reasoning.[2] Thematic analysis was used to analyse interview data. Results Interviews were conducted with 13 students (5 male, 8 female), all aged 22-27 years old. Overall, students interpreted and reasoned through the cases following a structured approach. Some students adopted different approaches to clinical decision-making and did not always follow all the steps outlined in the clinical reasoning cycle. Within the clinical decision-making process, common themes were identified, as outlined in Table 1. Some knowledge gaps were reported by students, including antibiotic prophylaxis, microorganisms, AMS measures, guideline use, soft tissue infection management, and IV-to-oral switch. Conclusion This study provides interesting findings on students’ interpretation and review of infection management and AMS principles which could inform the curriculum development of pharmacy education in AMR and AMS. Educators should consider introducing opportunities to develop clinical reasoning skills to support learning on AMS concepts, particularly in challenging areas such as microbiology, and interpretation and application of guidelines. A limitation of the study is that it was conducted with pharmacy students from one university programme. A strength of the study is the think-aloud methodology which provides in-depth detail on students clinical reasoning thought processes and skills.

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions

Objective. To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques. Approach. The primary focus is on developing a transfer learning-based meningioma feature extraction model (MFEM) that leverages both vision transformer (ViT) and convolutional neural network (CNN) architectures. Additionally, the study explores the significance of the PTE region in enhancing the grading process. Main results. The proposed method demonstrates excellent grading accuracy and robustness on a dataset of 98 meningioma patients. It achieves an accuracy of 92.86%, precision of 93.44%, sensitivity of 95%, and specificity of 89.47%. Significance. This study provides valuable insights into preoperative meningioma grading by introducing an innovative method that combines radiomics and deep learning techniques. The approach not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making processes.