Patient Data Research Articles

Changes in nutritional status and the development of obesity and metabolic syndrome following pediatric heart transplantation.

Nutritional status in pediatric patients undergoing heart transplantation (HT) is frequently a focus of clinical management and requires high resource utilization. Pre-operative nutrition status has been shown to affect post-operative mortality but no studies have been performed to assess how nutritional status may change and the risk of developing nutritional comorbidities long-term in the post-transplant period. A single-center retrospective chart review of patients ≥2 years of age who underwent heart transplantation between 1/1/2005 and 4/30/2020 was performed. Patient data were collected at listing, time of transplant, 1-year, and 3-year follow-up post-transplant. Nutrition status was classified based on body mass index (BMI) percentile in the primary analysis. Alternative nutritional indices, namely the nutrition risk index (NRI), prognostic nutrition index (PNI), and BMI z-score, were utilized in secondary analyses. Of the 63 patients included, the proportion of patients with overweight/obese status increased from 21% at listing to 41% at 3-year follow-up. No underweight patients at listing became overweight/obese at follow-up. Of patients who were overweight/obese at listing, 88% maintained that status at 3-year follow-up. Overweight/obese status at listing, 1-year, and 3-year post-transplantation were significantly associated with developing metabolic syndrome. In comparison to the alternative nutritional indices, BMI percentile best predicted post-transplant metabolic syndrome. The results suggest that pediatric patients who undergo heart transplantation are at risk of developing overweight/obesity and related nutritional sequelae (ie, metabolic syndrome). Improved surveillance and interventions targeted toward overweight/obese HT patients should be investigated to reduce the burden of associated comorbidities.

A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients

Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.

The effect of COVID‐19 lockdown periods and societal restrictions on blood product usage in adult trauma patients: A retrospective observational study

Background. Blood products, though potentially lifesaving, are a scarce and costly resource. Trauma injuries, which contribute to the demand for these resources, increase in proportion to alcohol consumption. South Africa’s COVID-19 lockdown measures included a ban on alcohol sales and curfews.Objective. To investigate the effect of these societal measures on blood product usage. Methods. We retrospectively compared medical patient data from the South African National Blood Service database of red cell concentrate (RCC) usage in four disciplines during the COVID-19 period with data from the 3 years preceding the pandemic. We also examined trauma case patterns using an institutional database from the Harry Gwala Regional Hospital (HGRH) Emergency Department during the same periods. Results. In total, 16 922 RCCs at HGRH and 528 734 RCCs in KwaZulu-Natal were issued across the four disciplines, from March 2017 - March 2021. RCC usage significantly decreased among trauma patients at institutional and provincial levels during the COVID-19 period (provincial pre-lockdown mean 10.99 units per day; SD 6.89, 95% CI 10.63 - 11.35 v. lockdown 7.46 units per day; SD 5.42, 95% CI 6.14 - 8.79; p<0.01). Further, RCC usage significantly increased during the weekend immediately following the lifting of the first alcohol ban, along with a significant decrease in the number of trauma patients seen during hard lockdown periods. Conclusions. Societal restrictions, including curfews and banning alcohol consumption, decreased the use of RCCs in trauma patients. Government policies aimed at reducing alcohol-related traumatic injuries would likely have a significant impact on RCC usage.

Efficacy-effectiveness analysis on survival in a population-based real-world study of BRAF-mutated metastatic colorectal cancer patients treated with encorafenib-cetuximab.

Encorafenib-cetuximab has been approved for pretreated BRAFV600E-mutated metastatic colorectal cancer (mCRC) patients based on efficacy demonstrated in the randomized phase III BEACON trial. The aim of this real-world effectiveness study is to improve knowledge on the generalizability of trial results. This population-based real-world study includes all mCRC patients in the Netherlands treated with encorafenib-cetuximab since approval. Individual patient data and pathology reports were collected. Overall survival (OS) was compared to BEACON and subgroup analyses were conducted for patients who would have been eligible and ineligible for BEACON. 166 patients were included with a median follow-up time of 14.5 months. Median OS was 6.7 months (95% CI:6.0-8.3) and differed from BEACON (9.3 months; 95% CI:8.0-11.3, p-value 0.002). Thirty-six percent of real-world patients would have been ineligible for the BEACON trial. Trial ineligible subgroups with symptomatic brain metastases and WHO performance status ≥2 had the poorest median OS of 5.0 months (95% CI:4.0-NR) and 3.9 months (95% CI:2.4-NR). This real-world cohort of mCRC patients treated with encorafenib-cetuximab showed a clinically relevant efficacy-effectiveness gap for OS. The chance of survival benefit from encorafenib-cetuximab in patients with brain metastases and/or WHO performance status ≥2 is negligible as neither efficacy nor effectiveness has been demonstrated.

APPLICATION OF MATHEMATICAL MODELS IN THE DIAGNOSIS OF DISEASES OF INTERNAL ORGANS

The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems.

Use of the FallAkte Plus System as an IT Infrastructure for the North Rhine-Westphalian General Practice Research Network: Mixed Methods Usability Study.

Primary care research networks can generate important information in the setting where most patients are seen and treated. However, this requires a suitable IT infrastructure (ITI), which the North Rhine-Westphalian general practice research network is looking to implement. This mixed methods research study aims to evaluate (study 1) requirements for an ITI and (study 2) the usability of an IT solution already available on the market, the FallAkte Plus (FA+) system for the North Rhine-Westphalian general practice research network, which comprises 8 primary care university institutes in Germany's largest state. In study 1, a survey was conducted among researchers from the institutes to identify the requirements for a suitable ITI. The questionnaire consisted of standardized questions with open-ended responses. In study 2, a mixed method approach combining a think-aloud approach and a quantitative survey was used to evaluate the usability and acceptance of the FA+ system among 3 user groups: researchers, general practitioners, and practice assistants. Respondents were asked to assess the usability with the validated system usability scale and to test a short questionnaire on vaccination management through FA+. In study 1, five of 8 institutes participated in the requirements survey. A total of 32 user requirements related primarily to study management were identified, including data entry, data storage, and user access management. In study 2, a total of 36 participants (24 researchers and 12 general practitioners or practice assistants) were surveyed in the mixed methods study of an already existing IT solution. The tutorial video and handouts explaining how to use the FA+ system were well received. Researchers, unlike practice personnel, were concerned about data security and data protection regarding the system's emergency feature, which enables access to all patient data. The median overall system usability scale rating was 60 (IQR 33.0-85.0), whereby practice personnel (median 82, IQR 58.0-94.0) assigned higher ratings than researchers (median 44, IQR 14.0-61.5). Users appreciated the option to integrate data from practices and other health care facilities. However, they voted against the use of the FA+ system due to a lack of support for various study formats. Usability assessments vary markedly by professional group and role. In its current stage of development, the FA+ system does not fully meet the requirements for a suitable ITI. Improvements in the user interface, performance, interoperability, security, and advanced features are necessary to make it more effective and user-friendly. Collaborating with end users and incorporating their feedback are crucial for the successful development of any practice network research ITI.

Exposure to maternal cannabis use disorder and risk of autism spectrum disorder in offspring: A data linkage cohort study

This study aimed to investigate the association between pre-pregnancy, prenatal and perinatal exposures to cannabis use disorder (CUD) and the risk of autism spectrum disoder (ASD) in offspring. Data were drawn from the New South Wales (NSW) Perinatal Data Collection (PDC), population-based, linked administrative health data encompassing all-live birth cohort from January 2003 to December 2005. This study involved 222 534 mother-offspring pairs. . The exposure variable (CUD) and the outcome of interest (ASD) were identified using the 10th international disease classification criteria, Australian Modified (ICD-10-AM). We found a three-fold increased risk of ASD in the offspring of mothers with maternal CUD compared to non-exposed offspring. In our sensitivity analyses, male offspring have a higher risk of ASD associated with maternal CUD than their female counterparts. In conclusion, exposure to maternal CUD is linked to a higher risk of ASD in offspring, with a stronger risk in male offspring. Further research is needed to understand these gender-specific effects and the relationship between maternal CUD and ASD risk in children.

Medical calculators derived synthetic cohorts: a novel method for generating synthetic patient data

This study shows that we can use synthetic cohorts created from medical risk calculators to gain insights into how risk estimations, clinical reasoning, data-driven subgrouping, and the confidence in risk calculator scores are connected. When prediction variables aren't evenly distributed in these synthetic cohorts, they can be used to group similar cases together, revealing new insights about how cohorts behave. We also found that the confidence in predictions made by these calculators can vary depending on patient characteristics. This suggests that it might be beneficial to include a "normalized confidence" score in future versions of these calculators for healthcare professionals. We plan to explore this idea further in our upcoming research.

Matched vs Nonmatched Placebos in a Randomized Trial of COVID-19 Treatments

Matched placebo interventions are complex and resource intensive. Recent evidence suggests matched placebos may not always be necessary. Previous studies have predominantly evaluated potential bias of nonmatched placebos (ie, differing on dose, frequency of administration, or formulation) in pain and mental health, but to date no systematic examination has been conducted in infectious disease. To test for differences between nonmatched and matched placebo arms with respect to clinical outcome measures across multiple therapeutics for COVID-19. In a comparative effectiveness research study, a post hoc analysis was conducted of data on individual patients enrolled in a large, multiarm, platform randomized clinical trial in symptomatic adult outpatients with COVID-19 between January 15, 2021, to September 28, 2023, in which the outcomes of both matched and nonmatched placebo groups were reported. Bayesian and frequentist covariate-adjusted techniques were compared with 7 intervention-placebo pairs. Seven matched and nonmatched placebo pairs (for a total of 7 comparisons) were evaluated throughout the primary platform trial. Comparisons were made between treatment and its associated matched (concurrent) placebo, as well as with nonmatched placebo (alone and in combination) assessed at a similar time point. Outcomes assessed included hospitalizations, EuroQol 5-Dimension 5-level scores, and PROMIS Global-10 scores. A total of 7 intervention-control pairs (N = 2684) were assessed, including 1620 (60.4%) women, with mean (SD) age, 47 (15.2) years; the most common comorbidities were obesity (41.9%) and hypertension (37.9%). In a meta-analysis with decoupled SEs, accounting for overlapping placebo patients, the overall odds ratio (OR) of nonmatched compared with matched placebo was 1.01 (95% credible interval, 0.77-1.32), with posterior probability of equivalence, defined as 0.8 ≤ OR ≤ 1.2 (a deviation from perfect equivalence ie, OR = 1, by no more than 0.2) of 85.4%, implying no significant difference. Unadjusted analysis of the event rate difference between all nonmatched and matched placebo groups did not identify any notable differences across all 7 treatment-placebo combinations assessed. Similar analysis that was conducted for patient-reported quality of life outcomes did not yield statistically significant differences. In this post hoc study of a randomized clinical platform trial, pooling matched and nonmatched placebo patient data did not lead to inconsistencies in treatment effect estimation for any of the investigational drugs. These findings may have significant implications for future platform trials, as the use of nonmatched placebo may improve statistical power, or reduce barriers to placebo implementation.

Smart Report for Healthcare System

In the ever-evolving landscape of healthcare, the effective interpretation and presentation of pathology patient data play a pivotal role in diagnosis, treatment, and research. Recognizing the transformative power of advanced data visualization techniques, this project endeavors to forge a comprehensive and user-friendly a platform dedicated to illuminating pathology patient data. By leveraging cutting-edge visualization tools, our the initiative aspires to furnish healthcare professionals, researchers, and patients with an intuitive and holistic representation of intricate pathology data. As healthcare continues to evolve, the potential benefits of Smart Healthcare Systems are evident, including enhanced patient outcomes, reduced costs, and improved healthcare delivery. In conclusion, this report underscores the profound impact of Smart Healthcare Systems on the future of medical services. It provides valuable insights for healthcare professionals, policymakers, and stakeholders to navigate the changing landscape and maximizing the benefits of smart technologies in healthcare.

Pulmonary vein isolation by cryoablation on patients with paroxysmal atrial fibrillation

Aim: Atrial fibrillation is just one of many arrhythmias that affects the cardiac rhythm and the heart as a pump which consequently increases the chance of an embolic insult. Due to the electric stimuli that originate from pulmonary vein ostia and their lumen, an isolation procedure is initiated, mostly by cryoablation. The main goals of this study were to investigate the relationship between measured nadir temperatures during cryoablation of each individual pulmonary vein and to compare the success of first-time cryoablation between left and right pulmonary veins. Participants and methods: The study involved 40 participants who underwent pulmonary vein isolation by cryoablation due to paroxysmal atrial fibrillation, irrespective of gender and age. The procedures were carried out throughout the year 2022 at the University Hospital Centre Zagreb, where the data were collected. All necessary patient data were permanently recorded and saved in a separate cryoconsole used to dictate the entire procedure. Results: Given the 40 participants in the study, a total of 160 pulmonary veins were analyzed. The t-test shows that the left upper pulmonary vein was the most advantageous vein to isolate, and the lower right pulmonary vein was the least advantageous. p-value analysis has also shown significant differences between left and right pulmonary veins, with that difference being lower in value between the left-sided pulmonary veins. Additionally, the chi-squared test confirmed a statistically significant difference between successful cryoablations of left-sided and right-sided pulmonary veins. This has shown that the left-sided pulmonary veins are easier to isolate on first cryoablation, rather than their right-sided counterparts. Conclusion: Pulmonary vein isolation by cryoablation is an excellent proof of incorporating fundamental physical principles in medicinal purposes. This study keeps the doors open for further research into optimal approaches in atrial fibrillation treatment. Therefore, comparing our study with recent publications and literature suggests higher success in isolating left pulmonary veins compared to right pulmonary veins.

The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis

This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management.

Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.

Delirium is the most common neuropsychological complication among older adults admitted to the intensive care unit (ICU) and is often associated with a poor prognosis. This study aimed to construct and validate an interpretable machine learning (ML) for early delirium prediction in older ICU patients. This was a retrospective observational cohort study and patient data were extracted from the Medical Information Mart for Intensive Care-IV database. Feature variables associated with delirium, including predisposing factors, disease-related factors, and iatrogenic and environmental factors, were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, decision trees, support vector machines, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, F1 score, calibration plot, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to improve the interpretability of the final model. Nine thousand seven hundred forty-eight adults aged 65 years or older were included for analysis. Twenty-six features were selected to construct ML prediction models. Among the models compared, the XGBoost model demonstrated the best performance including the highest AUC (0.836), accuracy (0.765), sensitivity (0.713), recall (0.713), and F1 score (0.725) in the training set. It also exhibited excellent discrimination with AUC of 0.810, good calibration, and had the highest net benefit in the validation cohort. The SHAP summary analysis showed that Glasgow Coma Scale, mechanical ventilation, and sedation were the top three risk features for outcome prediction. The SHAP dependency plot and SHAP force analysis interpreted the model at both the factor level and individual level, respectively. ML is a reliable tool for predicting the risk of critical delirium in elderly patients. By combining XGBoost and SHAP, it can provide clear explanations for personalized risk prediction and more intuitive understanding of the effect of key features in the model. The establishment of such a model would facilitate the early risk assessment and prompt intervention for delirium.

Abstract IA001: Cellular and molecular underpinnings of field cancerization and progression to invasive carcinoma in bladder cancer

Abstract Two major concerns in clinical management of bladder cancer are: (i) the high frequency of recurrence; and (ii) the morbidity and mortality associated with progression to invasive disease (invasive urothelial carcinoma, IUC). To gain a better understanding of these issues we have used the murine BBN (N-butyl-N-(4-hydroxylbutyl) nitrosamine) experimental model of bladder cancer, as well as tissue samples from normal human bladders and from bladders of patients at various stages of bladder cancer formation and progression. We have documented in the BBN model a CIS-like stage, prior to invasion, in which clonal expansion from a single cell occupies a significant fraction of the urothelium. With this experimental murine model alongside observations from patient samples and data from The Cancer Genome Atlas (TCGA), we have ascertained that a key event in progression of pre-invasive to invasive carcinoma is the silencing of the Sonic hedgehog (Shh) member of the Hedgehog gene family. Silencing-associated invasion appears due to the loss of SHH-induced differentiation signals, which when present slow growth and block invasion. Despite the consistent loss of SHH expression in invasive murine and human bladder cancers, however, TCGA data show few if any mutations in the SHH gene of IUC cells. Instead, our studies of patient samples and cancer-derived cell lines reveal that the loss of SHH expression is due to tighter chromosomal packaging and reduced accessibility of a bladder-specific enhancer located ~200 kb upstream of the SHH transcription start site. We have found that inhibitors of certain chromatin-modifying enzymes, including an FDA-approved drug, are capable of re-activating SHH expression, and are testing the possibility that these drugs may block or reverse invasion of urothelial carcinoma. The high frequency of bladder cancer recurrence is thought to result from field cancerization, that is, an increased probability of new cancer formation in apparent normal urothelium, even after successful resection of the primary tumor. To understand the basis for this field effect, we compared apparent normal urothelium from cancer patients to truly normal urothelium from bladders never affected by malignancy. We found that apparent normal samples display a dramatically increased basal character in transcriptomic assays, using as a metric of basalization an index constructed from single cell RNA-seq analysis of normal urothelium. We and others have demonstrated previously by lineage tracing and cell ablation studies in the BBN model that IUC arises from basal stem cells of the urothelium. The basalization of urothelium distant from the primary tumor that we observe thus may account for the increased probability of cancer formation in the apparent normal urothelium of bladder cancer patients. We have found that several FDA-approved antagonists of EGF signaling can reverse this basalization in the mouse BBN model as well as in organoid culture, and that these drugs dramatically reduce the formation of tumors following BBN exposure. Citation Format: Philip A. Beachy, Kris B. Prado, Aaron M. Kershner, Karim Mrouj, Bernard M. Kiss, Hua Dong, Ozgu Aydogdu, Ye Tian, Joseph C. Liao. Cellular and molecular underpinnings of field cancerization and progression to invasive carcinoma in bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA001.

Incidence of Surgically Managed Post-Tonsillectomy Hemorrhage Associated With NSAID Prescribing for Postoperative Pain Management.

Tonsillectomy ranks high among the most common pediatric surgical procedures in the United States. Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), such as ibuprofen, are routinely prescribed to manage post-tonsillectomy pain, but may carry the risk of hemorrhage. This retrospective, longitudinal, secondary-data analysis study compared the incidence of surgically managed post-tonsillectomy hemorrhage (sPTH) in pediatric patients prescribed ibuprofen at Brooke Army Medical Center (BAMC) after tonsillectomy compared to a similar cohort of pediatric patients at the Children's Hospital of Philadelphia (CHOP) not prescribed ibuprofen. Additional regression analysis examined predictors of sPTH at BAMC. The odds of sPTH was lower in patients who were prescribed ibuprofen at BAMC, relative to patients who were not at CHOP (OR 0.57, 95% CI, 0.37, 0.87; P < 0.01). In a generalized linear model evaluating BAMC patient data, there was a lack of a relationship between reason for tonsillectomy (tonsillitis versus tonsillar obstruction), primary procedure (tonsillectomy-only versus tonsillectomy with adenoidectomy), and presence of a co-occurring procedure. Post-tonsillectomy ibuprofen prescribing practices were not associated with an elevated risk of sPTH, relative to patients at CHOP not exposed to ibuprofen.

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.

Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited. This study aimed to assess the longitudinal changes in the accuracy of differential diagnosis lists created by an AI-based symptom checker used in the real world. This was a single-center, retrospective, observational study. Patients who visited an outpatient clinic without an appointment between May 1, 2019, and April 30, 2022, and who were admitted to a community hospital in Japan within 30 days of their index visit were considered eligible. We only included patients who underwent an AI-based symptom checkup at the index visit, and the diagnosis was finally confirmed during follow-up. Final diagnoses were categorized as common or uncommon, and all cases were categorized as typical or atypical. The primary outcome measure was the accuracy of the differential diagnosis list created by the AI-based symptom checker, defined as the final diagnosis in a list of 10 differential diagnoses created by the symptom checker. To assess the change in the symptom checker's diagnostic accuracy over 3 years, we used a chi-square test to compare the primary outcome over 3 periods: from May 1, 2019, to April 30, 2020 (first year); from May 1, 2020, to April 30, 2021 (second year); and from May 1, 2021, to April 30, 2022 (third year). A total of 381 patients were included. Common diseases comprised 257 (67.5%) cases, and typical presentations were observed in 298 (78.2%) cases. Overall, the accuracy of the differential diagnosis list created by the AI-based symptom checker was 172 (45.1%), which did not differ across the 3 years (first year: 97/219, 44.3%; second year: 32/72, 44.4%; and third year: 43/90, 47.7%; P=.85). The accuracy of the differential diagnosis list created by the symptom checker was low in those with uncommon diseases (30/124, 24.2%) and atypical presentations (12/83, 14.5%). In the multivariate logistic regression model, common disease (P<.001; odds ratio 4.13, 95% CI 2.50-6.98) and typical presentation (P<.001; odds ratio 6.92, 95% CI 3.62-14.2) were significantly associated with the accuracy of the differential diagnosis list created by the symptom checker. A 3-year longitudinal survey of the diagnostic accuracy of differential diagnosis lists developed by an AI-based symptom checker, which has been implemented in real-world clinical practice settings, showed no improvement over time. Uncommon diseases and atypical presentations were independently associated with a lower diagnostic accuracy. In the future, symptom checkers should be trained to recognize uncommon conditions.

Computational Lower Limb Simulator Boundary Conditions to Reproduce Measured TKA Loading in a Cohort of Telemetric Implant Patients.

Recent advancements in computational modeling offer opportunities to refine total knee arthroplasty (TKA) design and treatment strategies. This study developed patient-specific simulator external boundary conditions (EBCs) using a PID-controlled lower limb finite element (FE) model. Calibration of the external actuation required to achieve measured patient-specific joint loading and motion was completed for nine patients with telemetric implants during gait, stair descent, and deep knee bend. The study also compared two EBC scenarios: activity-specific hip AP motion and pelvic rotation (that was averaged across all patients for an activity) and patient-specific hip AP motion and pelvic rotation. Including patient-specific data significantly improved reproduction of joint-level loading, reducing root mean squared error between the target and achieved loading by 28.7% and highlighting the importance of detailed patient data in replicating joint kinematics and kinetics. The principal component analysis (PCA) of the EBCs for the patient dataset showed that one component represented 77.8% of the overall variation, while the first three components represented 97.8%. Given the significant loading variability within the patient cohort, this group of patient-specific models can be run individually to provide insight into expected TKA mechanics variability, and the PCA can be utilized to further create reasonable EBCs that expand the variability evaluated.

Distinct characteristics of distant metastasis in early-onset gastric cancer patients compared to late-onset patients: An observational study.

Presently, there is limited understanding of the features of distant metastasis in early-onset gastric cancer (GC). To explore these disparities, a retrospective study utilizing the Surveillance, Epidemiology, and End Results (SEER) database was undertaken. The SEER database was utilized to extract patient data, and multivariate logistic regression analysis was employed to identify the risk factors associated with distant metastasis and liver metastasis. Propensity score matching (PSM) was used to compare the occurrence of liver metastasis among patients based on their age at diagnosis. The study included 2684 early-onset GC patients and 33,289 late-onset GC patients. Preliminary data analysis indicated that early-onset GC patients exhibited more aggressive characteristics such as poor cell differentiation, advanced T stage, and a higher incidence of distant metastasis, excluding liver metastasis. Multivariate logistic regression analysis identified younger age as an independent risk factor for distant metastasis, along with T stage, lymph node metastasis (LNM), and tumor size (>3 cm). Another regression analysis revealed that younger age, diffuse type, and female gender were protective factors against liver metastasis. Through PSM, 3276 early-onset GC patients were matched with an equal number of late-onset GC patients, revealing that patients with early-onset GC had fewer instances of liver metastasis but a higher prevalence of distant metastasis. Our findings suggest that early-onset serves as a protective factor against liver metastasis in GC, while it poses a risk for distant metastasis, likely influenced by the increased prevalence of diffuse-type GC in early-onset patients.

Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast.

In a typical MR session, several contrasts are acquired. Due to the sequential nature of the data acquisition process, the patient may experience some discomfort at some point during the session, and start moving. Hence, it is quite common to have MR sessions where some contrasts are well-resolved, while other contrasts exhibit motion artifacts. Instead of repeating the scans that are corrupted by motion, we introduce a reference-guided retrospective motion correction scheme that takes advantage of the motion-free scans, based on a generalized rigid registration routine. We focus on various existing clinical 3D brain protocols at 1.5 Tesla MRI based on Cartesian sampling. Controlled experiments with three healthy volunteers and three levels of motion are performed. Radiological inspection confirms that the proposed method consistently ameliorates the corrupted scans. Furthermore, for the set of specific motion tests performed in this study, the quality indexes based on PSNR and SSIM shows only a modest decrease in correction quality as a function of motion complexity. While the results on controlled experiments are positive, future applications to patient data will ultimately clarify whether the proposed correction scheme satisfies the radiological requirements.

Evaluation of the Impact of ChatGPT on the Selection of Surgical Technique in Bariatric Surgery.

With the growing interest in artificial intelligence (AI) applications in medicine, this study explores ChatGPT's potential to influence surgical technique selection in metabolic and bariatric surgery (MBS), contrasting AI recommendations with established clinical guidelines and expert consensus. Conducting a single-center retrospective analysis, the study involved 161 patients who underwent MBS between January 2022 and December 2023. ChatGPT4 was used to analyze patient data, including demographics, pathological history, and BMI, to recommend the most suitable surgical technique. These AI recommendations were then compared with the hospital's algorithm-based decisions. ChatGPT recommended Roux-en-Y gastric bypass in over half of the cases. However, a significant difference was observed between AI suggestions and actual surgical techniques applied, with only a 34.16% match rate. Further analysis revealed any significant correlation between ChatGPT recommendations and the established surgical algorithm. Despite ChatGPT's ability to process and analyze large datasets, its recommendations for MBS techniques do not align closely with those determined by expert surgical teams using a high success rate algorithm. Consequently, the study concludes that ChatGPT4 should not replace expert consultation in selecting MBS techniques.