Clinical Decision Research Articles

Roles Caregivers Take on in Pediatric Rehabilitation Telehealth Services: A Scoping Review.

Caregivers and children continue to use pediatric rehabilitation telehealth services (PRTS) post-pandemic. Understanding how caregivers support children during PRTS can help us fully integrate caregivers for positive outcomes. This scoping review describes the roles caregivers take on during PRTS. We searched MEDLINE, CINAHL, PsycINFO, and ERIC for research published January 2005 to July 2023. We included English articles on the effect of PRTS. We excluded qualitative and non-peer-reviewed literature. Thirty-four articles met inclusion criteria. Caregivers were gatekeepers, technicians, implementers, trainees, collaborators, telehealth consumers, and supervisors in PRTS. Caregivers took on roles throughout PRTS, and roles varied by type of PRTS delivered. Caregivers take on multiple roles to support children during PRTS. Our results can inform clinical decisions about how to integrate and support caregivers in PRTS. Future research should explore strategies that support caregiver role taking to maximize caregiver involvement and outcomes in PRTS.

Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD’s molecular pathways will be better understood, leading to more precise treatment options.

Intraoperative Invasive Coronary Angiography after Coronary Artery Bypass Grafting.

The aim of this study was to prospectively evaluate the feasibility and safety of intraoperative invasive coronary angiography (ICA) following coronary artery bypass grafting using a mobile angiography C-arm. Between August 2020 and December 2021, 18 patients were enrolled for intraoperative ICA following coronary artery bypass grafting. After skin closure, ICA was performed including angiography of all established bypass grafts via a mobile angiography system by an interventional cardiologist. Data on graft patency, stenosis, and kinking were assessed. Grafts were rated on an ordinal scale ranging from very poor (1) to excellent (5). Furthermore, the impact of ICA compared with flow measurement was assessed using the ordinal Likert scale ranging from (I) worse to (V) much better. The ICA was considered better (V) compared with transient flow measurement in 38 (93%) and comparable (III) in 3 (7%) distal anastomoses. ICA impacted clinical or surgical decision-making in three patients (17%). In one patient, dual antiplatelet therapy for 6 months was initiated and rethoracotomy was needed in two (11%) patients with bypass graft revision and additional bypass grafting for graft occlusion. There were no cerebral and distal embolic events or access vessel complications observed and no postoperative acute kidney injury occurred. Intraoperative angiography after coronary bypass grafting is safe. Using a mobile angiographic device, graft patency, and function assessment was superior to transit time flow measurement leading to further consequences in a relevant number of patients. Therefore, it has the potential to reduce postoperative myocardial injury and improve survival.

Volumetric Integrated Classification Index: An Integrated Voxel-Based Morphometry and Machine Learning Interpretable Biomarker for Post-Traumatic Stress Disorder.

PTSD is a complex mental health condition triggered by individuals' traumatic experiences, with long-term and broad impacts on sufferers' psychological health and quality of life. Despite decades of research providing partial understanding of the pathobiological aspects of PTSD, precise neurobiological markers and imaging indicators remain challenging to pinpoint. This study employed VBM analysis and machine learning algorithms to investigate structural brain changes in PTSD patients. Data were sourced ADNI-DoD database for PTSD cases and from the ADNI database for healthy controls. Various machine learning models, including SVM, RF, and LR, were utilized for classification. Additionally, the VICI was proposed to enhance model interpretability, incorporating SHAP analysis. The association between PTSD risk genes and VICI values was also explored through gene expression data analysis. Among the tested machine learning algorithms, RF emerged as the top performer, achieving high accuracy in classifying PTSD patients. Structural brain abnormalities in PTSD patients were predominantly observed in prefrontal areas compared to healthy controls. The proposed VICI demonstrated classification efficacy comparable to the optimized RF model, indicating its potential as a simplified diagnostic tool. Analysis of gene expression data revealed significant associations between PTSD risk genes and VICI values, implicating synaptic integrity and neural development regulation. This study reveals neuroimaging and genetic characteristics of PTSD, highlighting the potential of VBM analysis and machine learning models in diagnosis and prognosis. The VICI offers a promising approach to enhance model interpretability and guide clinical decision-making. These findings contribute to a better understanding of the pathophysiological mechanisms of PTSD and provide new avenues for future diagnosis and treatment.

A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer

PurposeTo investigate the independent risk factors associated with the development of lymph node metastasis (LNM) in patients with colorectal cancer (CRC), focusing on preoperative systemic inflammatory indicators, and to construct a corresponding risk predictive model.Materials and MethodsThe clinical data of 241 patients with CRC who underwent surgery after the first diagnosis between January 2012 and December 2017 at our hospital were reviewed. A best logistic regression model was constructed by Lasso regression for multivariate analysis, from which a Nomogram was derived. Using bootstrap to conduct internal validation. The model’s predictive performance and clinical practicability were evaluated using the receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis (DCA). External validation was conducted using retrospective data from 170 patients who underwent surgery between January 2020 and May 2022 at another hospital.ResultsCross-validation indicated smoking history, neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), fibrinogen-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. These factors were included in the logistic regression, and multivariate analysis confirmed that smoking history, NLR, LMR, FAR, and FOB were independent risk factors (P < 0.05). The ROC and calibration curve of the original model and external validation indicated strong predictive power of the model. DCA suggested the model’s favorable clinical utility.ConclusionsThe model constructed in this study has robust predictive performance and clinical utility for the preoperative determination of CRC LMN, offering significant for clinical decision-making in patients with CRC.

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

ObjectivesOur research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.MethodsThis study collected 210 patients underwent IP resection surgery at a university hospital from January 2010 to December 2023. Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. Shapley Additive Explanations (SHAP) values were used to explain the importance of various features in predicting IP recurrence.ResultsWe found that the recurrence rate of IP patients is 20.00%, with a median recurrence time of 35.5 months. Multivariate Cox regression analysis identified mild or moderate dysplasia as an independent risk factor for recurrence. The EST model performs the best in predicting postoperative recurrence of IP, with C-index of 0.968 and 0.878 in the training and testing sets. SHAP emphasizes five important predictive factors for recurrence, including bone defects, orbital involvement, smoking, no processing of tumor attachment sites and drinking.ConclusionsTo our knowledge, this is the first study to use multiple ML models to predict postoperative recurrence of IP. The EST model has the best predictive performance, with SHAP emphasizing several key predictive factors for IP recurrence. This study emphasizes the practicality of machine learning algorithms in predicting IP clinical outcomes, providing valuable insights into the potential for improving clinical decision-making.

Development and validation of a model for predicting in-hospital mortality in patients with sepsis-associated kidney injury receiving renal replacement therapy: a retrospective cohort study based on the MIMIC-IV database

BackgroundSAKI is a common and serious complication of sepsis, contributing significantly to high morbidity and mortality, especially in patients requiring RRT. Early identification of high-risk patients enables timely interventions and improvement in clinical outcomes. The objective of this study was to develop and validate a predictive model for in-hospital mortality in patients with SAKI receiving RRT.MethodsPatients with SAKI receiving RRT from the MIMIC-IV database were retrospectively enrolled and randomly assigned to either the training cohort or the testing cohort in a 7:3 ratio. LASSO regression and Boruta algorithm were utilized for feature selection. Subsequently, three machine learning models—CART, SVM and LR—were constructed, and their predictive efficacy was assessed using a comprehensive set of performance indicators. Feature importance analysis was performed to determine the contribution of each feature to a model’s predictions. Finally, DCA was employed to evaluate the clinical utility of the prediction models. Additionally, a clinical nomogram was developed to facilitate the interpretation and visualization of the LR model.ResultsA total of 1663 adults were ultimately enrolled and randomly allocated into the training cohort (n = 1164) or the testing cohort (n = 499). Twenty-eight variables were evaluated for feature selection, with eight ultimately retained in the final model: age, MAP, RR, lactate, Cr, PT-INR, TBIL and CVP. The LR model demonstrated commendable performance, exhibiting robust discrimination in both the training cohort (AUROC: 0.73 (95% CI 0.70–0.76); AUPRC: 0.75 (95% CI 0.72–0.79); accuracy: 0.66 (95% CI 0.63–0.68)) and the testing cohort (AUROC: 0.72 (95% CI 0.68-0.76); AUPRC: 0.73 (95% CI 0.67–0.79); accuracy: 0.65 (95% CI 0.61–0.69)). Furthermore, there was good concordance between predicted and observed values in both the training cohort (χ2 = 4.41, p = 0.82) and the testing cohort (χ2 = 4.16, p = 0.84). The results of the DCA revealed that the LR model provided a greater net benefit compared to other prediction models.ConclusionsThe LR model exhibited superior performance in predicting in-hospital mortality in patients with SAKI receiving RRT, suggesting its potential utility in identifying high-risk patients and guiding clinical decision-making.

Risk-stratification tools for emergency department patients with syncope: A systematic review and meta-analysis of direct evidence for SAEM GRACE.

Approximately 10% of patients with syncope have serious or life-threatening causes that may not be apparent during the initial emergency department (ED) assessment. Consequently, researchers have developed clinical decision rules (CDRs) to predict adverse outcomes and risk stratify ED syncope patients. This systematic review and meta-analysis (SRMA) aims to cohere and synthesize the best current evidence regarding the methodological quality and predictive accuracy of CDRs for developing an evidence-based ED syncope management guideline. We conducted a systematic literature search according to the patient-intervention-control-outcome question: In patients 16 years of age or older who present to the ED with syncope for whom no underlying serious/life-threatening condition was found during the index ED visit (population), are risk stratification tools (intervention), better than unstructured clinical judgment (i.e., usual care; comparison), for providing accurate prognosis and aiding disposition decision for outcomes within 30 days (outcome)? Two reviewers independently assessed articles for inclusion and methodological quality. We performed statistical analysis using Meta-DiSc. We used GRADEPro GDT software to determine the certainty of the evidence and create a summary of the findings (SoF) tables. Of 2047 publications obtained through the search strategy, 31 comprising 13 CDRs met the inclusion criteria. There were 13 derivation studies (17,578 participants) and 24 validation studies (14,845 participants). Only three CDRs were validated in more than two studies. The San Francisco Syncope Rule (SFSR) was validated in 12 studies: positive likelihood ratio (LR+) 1.15-4.70 and negative likelihood ratio (LR-) 0.03-0.64. The Canadian Syncope Risk Score (CSRS) was validated in five studies: LR+ 1.15-2.58 and LR- 0.05-0.50. The Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL) risk score was validated in five studies: LR+ 1.16-3.32 and LR- 0.14-0.46. Most CDRs for ED adult syncope management have low-quality evidence for routine clinical practice use. Only three CDRs (SFSR, CSRS, OESIL) are validated by more than two studies, with significant overlap in operating characteristics.

Pregnancy monitoring and parturition in dogs

Management of term pregnant dogs is a major and frequent clinical situation in small animal reproductive veterinary practices, and can be challenging. Whether a pregnancy is planned or unplanned, these cases require careful monitoring to prevent maternal distress and neonatal loss. Reproductive system evaluation of female dogs is useful for determination of ovulation during breeding management, pregnancy diagnosis, and for timing of cesarean surgery, among other clinical scenarios. Basic understanding of female dog’s reproductive physiology is mandatory to interpret relevant hormonal assays and other complementary diagnostics such as vaginal cytological evaluation and imaging (ultrasonography and radiography) for clinical decisions. This review covers physiology, diagnostics, and clinical outcomes relevant for pregnancy monitoring and parturition.

What role does antibiotic resistance play in secondary endodontic infections?

The systematic review utilised multiple electronic databases to gather relevant literature, including PubMed, Scopus, EMBASE, Web of Science, and Medline. The search was conducted focusing on clinical studies where Enterococcus strains were isolated to assess their antimicrobial resistance. The search strategy was designed based on the PICO model, targeting patients with secondary/persistent endodontic infections (SPEI) and evaluating the resistance profiles of Enterococci to systemic antibiotics. A total of 584 studies were initially identified through database searches. After removing duplicates, 517 studies were excluded based on title and abstract screening, leading to the final inclusion of 11 clinical trials that met the eligibility criteria. The studies were conducted in various countries, including Brazil, Lithuania, Poland, Germany, Sweden, the United States, and Japan. The focus was on permanent teeth with post-treatment apical periodontitis undergoing endodontic retreatment. Data extraction was performed independently by two reviewers, who compiled details such as authorship, year of publication, country of study, sample groups, bacterial strains tested, antibiotics used, and resistance outcomes. The risk of bias was assessed using the ROB-2 platform, and all studies were considered to have a low risk of bias, with occasional minor deficiencies noted. The analysis revealed fluctuating resistance profiles of Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) to various antibiotics over time. E. faecalis isolated from teeth with SPEI showed intermediate resistance to 16 antibiotics. The most effective antibiotics identified were the combination of amoxicillin and clavulanate, followed by amoxicillin and benzylpenicillin. In patients with penicillin allergies, moxifloxacin and azithromycin were suggested as alternatives, albeit with caution due to their varying resistance levels. The antibiotics demonstrating the highest resistance patterns against E. faecalis included clindamycin, gentamicin, metronidazole, and rifampicin, which are contraindicated in SPEI cases. Ongoing monitoring of antibiotic resistance patterns is crucial for effective endodontic treatment protocols. The emergence of resistance among Enterococci, particularly E. faecalis, necessitates regular susceptibility testing to inform clinical decisions and improve treatment outcomes. The findings underscore the need for health professionals to stay informed about the evolving resistance landscape to ensure safe and effective therapeutic regimens in endodontics.

Predicting cancer detection rates from multiparametric prostate MRI

Introduction: Although the Prostate Imaging-Reporting and Data System (PI-RADS) categorization represents the standard method for assessing the risk of prostate cancer using prostate magnetic resonance imaging (MRI), there exists wide variation in cancer detection rates (CDRs) in real-world practice. We therefore evaluated the association of clinical and radiographic features with CDRs and developed a predictive model to improve clinical management. Methods: We identified men aged 18–89 years with elevated prostate-specific antigen (PSA) or on active surveillance for prostate cancer who underwent MRI-ultrasound (US) fusion biopsy or in-bore MRI-targeted biopsy. The associations of features with the per-lesion CDR (Gleason 6–10) and clinically significant (cs) CDR (Gleason 7–10) were examined using logistic regression, and results were operationalized into a predictive model. Results: Targeted biopsy was performed for 347 lesions in 281 patients. Overall, the CDR was 49.0% and the csCDR was 28.0%. On multivariable analysis, increasing PI-RADS category, no prior prostate biopsies, smaller prostate size, and increasing PSA density were independently associated with higher CDR, while 0–1 prior prostate biopsies, and a solitary PI-RADS 3–5 lesion were associated with higher csCDR. A predictive model provided a greater net benefit than a strategy of performing biopsy in all PI-RADS 3–5 lesions across a wide range of threshold probabilities. Conclusions: Several clinical and radiographic features are independently associated with the risk of prostate cancer in men undergoing MRI-targeted biopsy. A predictive model based on these features can improve clinical decisions regarding biopsy compared to the conventional strategy of performing biopsy for all PI-RADS 3–5 lesions.

Extended Monitoring for Transition to Oral Corticosteroids in Acute Severe Ulcerative Colitis May Be Unnecessarily Prolonging Length of Stay.

There is no guideline regarding whether patients treated with intravenous corticosteroids for acute severe ulcerative colitis (ASUC) should be monitored in the hospital after transitioning to oral steroids. Our study aimed to: (1) compare rates of oral steroid transition failure and 30-day readmission between ASUC hospitalizations with extended inpatient monitoring compared to accelerated inpatient monitoring, and (2) identify predictors of oral steroid transition failure. A retrospective cohort study of ulcerative colitis (UC) related admissions at UCSF from 2014 to 2022 was conducted comparing rates of steroid transition failures in extended inpatient monitoring (≥ 24h on oral steroids prior to discharge) to accelerated inpatient monitoring (< 24h on oral steroids). Steroid transition failure was defined as worsening colitis activity with the need to return to IV steroids or undergo colectomy. Data analysis incorporated demographics, disease features, and treatment history. Transition failures from intravenous to oral corticosteroids occurred in 8% of all UC-related admissions. There was a significant difference in transition failure observed between the extended and accelerated monitoring groups, 13 vs 3% (p = 0.03), respectively, with 83.3% of total transition failures occurring within the extended monitoring group. The 30-day readmission rate was 6% in each group (p = 0.93). No significant predictors of transition failures were identified. Transition failures from IV to oral steroids are uncommon in ASUC hospitalizations. Transition failures were more likely to occur with extended monitoring, suggesting potential predictors and/or patient selection bias within this group. Further studies are needed to investigate the parameters driving clinician decision-making regarding oral steroid transitioning.

Quality of Life in Mild Cognitive Impairment and Mild Dementia Associated with Alzheimer's Disease: A Systematic Review.

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) have a profound impact on patients' quality of life (QoL), with progressive declines occurring as the disease advances. This systematic review aims to summarize the published evidence on patient-reported outcomes (PROs) in individuals with MCI due to AD and mild AD dementia. Comprehensive searches were conducted across five major databases to identify studies reporting on utility values, disutilities, and QoL measures in these patient populations. A total of 23 studies were included that utilized various QoL assessment tools, including EQ-5D (n = 14), SF-36/SF-12 (n = 4), and QOL-AD (n = 11). Reported EQ-5D scores ranged from 0.81 to 0.92 for patients with MCI and from 0.67 to 0.85 for those with mild AD, indicating a noticeable decline in QoL as the disease progresses. QOL-AD scores ranged from 33.8 to 42.5 for MCI and from 32.4 to 38.1 for mild AD, equally reflecting the greater impairment in QoL with disease advancement. Interventions were generally associated with smaller declines in PROs compared to placebo, suggesting a positive impact of treatment in mitigating QoL deterioration. The findings underscore the significant QoL differences between MCI and mild AD, emphasizing the potential benefit of early intervention to preserve QoL and delay disease progression. This review highlights the importance of continued research to better understand QoL in patients with MCI and mild AD dementia, particularly in terms of capturing comprehensive patient-reported outcomes and evaluating the effectiveness of interventions over time. These findings can contribute to a more informed approach in clinical practice and support decision-making in the management of early-stage AD.

A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making

Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. This study presents a real-time end-to-end framework tailored for pediatric ultrasound video analysis for CSD decision-making. The framework employs an advanced real-time architecture based on You Only Look Once (Yolo) techniques for CSD decision-making with high accuracy. Leveraging the state of the art with the Yolov8l (large) architecture, the proposed model achieves a robust performance in real-time processes. It can be observed that the experiment yielded a mean average precision (mAP) exceeding 89%, indicating the framework’s effectiveness in accurately diagnosing CSDs from ultrasound (US) videos. The Yolov8l model exhibits precise performance in the real-time testing of pediatric patients from Mohammad Hoesin General Hospital in Palembang, Indonesia. Based on the results of the proposed model using 222 US videos, it exhibits 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity. During real-time testing in the hospital, the model exhibits a 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity; only 3 out of the 53 US videos in the real-time process were diagnosed incorrectly. This comprehensive approach holds promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology.

Association between triglyceride-glucose index and fractional exhaled nitric oxide in adults with asthma from NHANES 2007–2012

BackgroundSeveral studies have shown a potential relationship between triglyceride-glucose index (TGI) and asthma. However, limited research has been conducted on the relationship between TGI and fractional exhaled nitric oxide (FeNO).MethodsA total of 1,910 asthmatic individuals from the National Health and Nutrition Examination Survey (NHANES) database were included in this study. Linear regression analyses were used to investigate the relationship between TGI and FeNO in patients with asthma. Subsequently, a trend test was applied to verify whether there was a linear relationship between the TGI and FeNO. Finally, a subgroup analysis was performed to confirm the relationship among the different subgroup populations.ResultsMultivariable linear regression analyses showed that TGI was linearly related to FeNO in the asthmatic population. The trend test additionally validated the positive linear relationship between TGI and FeNO. The result of XGBoost revealed the five most influential factors on FeNO in a ranking of contrasted importance: eosinophil (EOS), body mass index (BMI), poverty-to-income ratio (PIR), TGI, and white blood cell count (WBC).ConclusionsThis investigation revealed a positive linear relationship between TGI and FeNO in patients with asthma. This finding suggests a potential relationship between TGI and airway inflammation in patients with asthma, thereby facilitating the prompt identification of irregularities and providing a basis for clinical decision making. This study provides a novel perspective on asthma management.

Designing Culturally Competent Clinical Decision Support Systems to Reduce Health Disparities in Underserved Populations

Income inequality is associated with poor health. Both the cost of medical care and the utilization of services vary. The high expense of health care pushes a significant portion of Indian households into poverty. The surroundings in which people live, grow, work, and interact with one another are known as social determinants, and they have a big influence on people's well-being which can be reduced by computerized clinical decision support systems. In today's healthcare, computerized clinical decision support systems, or CDSS, represent a paradigm change. Clinicians can use CDSS to support them in making complicated decisions. Rapid advancements have been made in CDSS since they were used in the 1980s. Due to the growing worldwide usage of advanced electronic medical records, they are frequently administered using computerized clinical processes and electronic medical records.

Exploring the Role of Genetic Testing in Decisions to Redirect Care in Critically Ill Infants.

Genetic disorders are a major determinant of morbidity and mortality within neonatal intensive care units (NICUs). Studies have found genetic testing in critically ill infants may lead to changes in clinical decisions such as pursuing end of life care. This study surveyed palliative care providers to explore the influence of genetic testing on decisions to redirect care in critically ill infants. This study was conducted retrospectively on cases who were admitted to the Level IV NICU at Primary Children's Hospital, underwent redirection to end of life care, and whose death date was between 2019-2023. A review of the case's electronic medical record was performed to construct a clinical case summary. The clinical summary and questionnaire were sent to the case's palliative care provider. Fifty-six cases were included in this study and 73% had genetic testing completed. Our results suggest the information from genetic testing played a relatively minor role in the decision to redirect care for cases with negative or uncertain genetic testing and positive genetic testing results, although the influence appeared higher in the latter group. Our results suggest the assumptions of several studies that genetic testing is responsible for changes in clinical management and cost savings, especially in cases of redirection of care may have been overestimated. Our results fill a critical gap in current literature and demonstrate the need for further investigation to clarify the direct role of genetic testing in clinical decisions in the NICU, especially related to redirection of care.

Clinical Decision-Making of Repair vs. Replacement of Defective Direct Dental Restorations: A Multinational Cross-Sectional Study With Meta-Analysis.

This web-based survey, conducted across multiple countries, sought to explore the factors that impact the decision-making of clinicians when it comes to managing defective direct restorations. A survey consisting of 14 questions was sent out to dentists in 21 countries through various online platforms. The survey consisted of two sections. The first contained five questions about demographic information, while the second involved eight clinical scenarios. In the second part, participants were tasked with deciding whether to repair or replace defective composite and amalgam restorations. Three thousand six hundred eighty dental practitioners completed the survey. For composite restorations, repair was preferred in scenarios like partial loss or fracture (RR:0.72; 95% CI: 0.58, 0.89; p = 0.002), whereas replacement was favored for secondary caries (RR:2.43; 95% CI: 1.87, 3.16; p < 0.001) and open/defective margins (RR:3.93; 95% CI: 2.68, 5.76;p < 0.001). Amalgam restorations were mostly replaced across all scenarios. The main factors influencing decision-making were caries risk, restoration size, and patient oral hygiene. Substantial heterogeneity was observed across countries. This study underscores the complexity of the decision-making process and the need for evidence-based guidelines to inform clinicians' decisions regarding restoration management. Patient-level factors predominantly influence decision-making, emphasizing the need for individualized approaches. The study reveals that the material type in the original restoration is a critical determinant, with composite restorations being repaired in specific scenarios, while amalgam restorations are consistently replaced across different countries. Key patient and tooth-level factors, such as high caries risk, poor oral hygiene, and restoration size, significantly impact clinicians' decisions, often favoring replacement over repair. These findings underscore the necessity for evidence-based guidelines to assist clinicians in making informed choices, ultimately enhancing the quality of patient care.

Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on 18F-FDG PET/CT

ObjectiveTo investigate the value of 18F-FDG PET/CT-based intratumoral and peritumoral radiomics in predicting the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer.Methods190 patients who met the inclusion and exclusion criteria from 2017 to 2022 were studied. Features were extracted from the PET/CT intratumoral and peritumoral regions, feature selection was performed through the correlation analysis, t-tests, and least absolute shrinkage and selection operator regression (LASSO). Four classifiers, support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), and naive bayes (NB) were used to build the prediction models. The receiver operating characteristic (ROC) curves were plotted to measure the predictive performance of the models. Concurrent stratified analysis was conducted to establish subtype-specific features for each molecular subtype.ResultsCompared to intratumoral features alone, intratumoral + peritumoral features achieved higher AUC values in each classifier. The SVM model constructed with intratumoral + peritumoral features achieved the highest AUC values in both the train and test set (train set: 0.95 and test set: 0.83). Subtype-specific features improve performance in predicting the efficacy of NAC (luminal group: 0.90; HER2 + group: 0.86; triple negative group: 0.92).ConclusionIntratumoral and peritumoral radiomics models based on 18F-FDG PET/CT can reliably forecast the efficacy of NAC, thereby assisting clinical decision-making.

Development of an ultrasound-based clinical decision rule to rule-out diverticulitis

The concern for diverticulitis often leads to the use of computed tomography (CT) scans for diagnosis. We aim to develop an ultrasound-based clinical decision rule (CDR) to confidently rule-out the disease without requiring a CT scan. We analyzed data from a prospective study of adult emergency department (ED) patients with suspected diverticulitis who underwent both bedside ultrasound (US) and CT. Patient history, physical examination, laboratory findings, and US results were used to create a CDR via a recursive partitioning model designed to prioritize sensitivity, with a loss matrix heavily penalizing false negatives. We calculated the test characteristics for this CDR (TICS-Rule) and assessed the potential reduction in CT scans and ED length of stay. Data from 149 patients (84 female; mean age 58 ± 16) were used to develop the TICS-Rule. The final model integrates US diagnosis of simple and complicated diverticulitis, along with variables of heart rate, age, history of diverticulosis, vomiting, and leukocytosis. Negative US results and a heart rate below 100 effectively excluded diverticulitis. The sensitivity increased from 54.5% (32.2–75.6) in the US alone to 100% (84.6–100%) for complicated diverticulitis in the model. The TICS-Rule missed no cases of complicated diverticulitis but one case of simple diverticulitis. The median time from ED greeting to US interpretation was 103 min (IQR 62–169), compared to 285 min (IQR 229–372) for CT. The TICS-Rule uses a combination of negative US and heart rate less thanQ1 100 to exclude diverticulitis without the need for a CT scan. Integration of the TICS-Rule offers a promising enhancement to clinical decision-making while reducing both CT use and ED length of stay.