Clinical Metrics Research Articles

Comparison of deep-learning multimodality data fusion strategies in mandibular osteoradionecrosis NTCP modelling using clinical variables and radiation dose distribution volumes

Objective.Normal tissue complication probability (NTCP) modelling is rapidly embracing deep learning (DL) methods, acknowledging the importance of spatial dose information. Finding effective ways to combine information from radiation dose distribution maps (dosiomics) and clinical data involves technical challenges and requires domain knowledge. We propose different multi-modality data fusion strategies to facilitate future DL-based NTCP studies.Approach.Early, joint and late DL multi-modality fusion strategies were compared using clinical and mandibular radiation dose distribution volumes. These were contrasted with single-modality models: a random forest trained on non-image data (clinical, demographic and dose-volume metrics) and a 3D DenseNet-40 trained on image data (mandibular dose distribution maps). The study involved a matched cohort of 92 osteoradionecrosis cases and 92 controls from a single institution.Main results.The late fusion model exhibited superior discrimination and calibration performance, while the join fusion achieved a more balanced distribution of the predicted probabilities. Discrimination performance did not significantly differ between strategies. Late fusion, though less technically complex, lacks crucial inter-modality interactions for NTCP modelling. In contrast, joint fusion, despite its complexity, resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation.Significance.This study is a pioneering effort in comparing different strategies for including image data into DL-based NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. Multiple data fusion strategies should be compared and reported in multi-modality NTCP modelling using DL.

Interpretable machine learning models for predicting short-term prognosis in AChR-Ab+ generalized myasthenia gravis using clinical features and systemic inflammation index.

Myasthenia Gravis (MG) is an autoimmune disease that causes muscle weakness in 80% of patients, most of whom test positive for anti-acetylcholine receptor (AChR) antibodies (AChR-Abs). Predicting and improving treatment outcomes are necessary due to varying responses, ranging from complete relief to minimal improvement. Our study aims to develop and validate an interpretable machine learning (ML) model that integrates systemic inflammation indices with traditional clinical indicators. The goal is to predict the short-term prognosis (after 6 months of treatment) of AChR-Ab+ generalized myasthenia gravis (GMG) patients to guide personalized treatment strategies. We performed a retrospective analysis on 202 AChR-Ab+ GMG patients, dividing them into training and external validation cohorts. The primary outcome of this study was the Myasthenia Gravis Foundation of America (MGFA) post-intervention status assessed after 6 months of treatment initiation. Prognoses were classified as "unchanged or worse" for a poor outcome and "improved or better" for a good outcome. Accordingly, patients were categorized into "good outcome" or "poor outcome" groups. In the training cohort, we developed and internally validated various ML models using systemic inflammation indices, clinical indicators, or a combination of both. We then carried out external validation with the designated cohort. Additionally, we assessed the feature importance of our most effective model using the Shapley Additive Explanations (SHAP) method. In our study of 202 patients, 28.7% (58 individuals) experienced poor outcomes after 6 months of standard therapy. We identified 11 significant predictors, encompassing both systemic inflammation indexes and clinical metrics. The extreme gradient boosting (XGBoost) model demonstrated the best performance, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.944. This was higher than that achieved by logistic regression (Logit) (AUC: 0.882), random forest (RF) (AUC: 0.917), support vector machines (SVM) (AUC: 0.872). Further refinement through SHAP analysis highlighted five critical determinants-two clinical indicators and three inflammation indexes-as crucial for assessing short-term prognosis in AChR-Ab+ GMG patients. Our analysis confirms that the XGBoost model, integrating clinical indicators with systemic inflammation indexes, effectively predicts short-term prognosis in AChR-Ab+ GMG patients. This approach enhances clinical decision-making and improves patient outcomes.

Respiratory pathogen analysis in pediatric inpatients unraveled the infection pattern of Mycoplasma pneumoniae post the COVID-19 pandemic.

To counteract the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) were implemented globally, exerting a profound influence on a wide spectrum of infectious diseases, encompassing respiratory tract infections (RTIs). Subsequent to the easing of NPIs, China experienced a significant outbreak of Mycoplasma pneumoniae (MP). Over a decade from 2015 to 2024, our study scrutinized 12 common infectious diseases among pediatric children. Etiologically diagnostic data and clinical outcome metrics of children with RTIs, tested for 13 pathogens, were analyzed to evaluate changes during and after the pandemic compared to pre-pandemic periods, with a notable emphasis on age profile and coinfection patterns of MP. Among 57,471 hospitalized children, 23,178 were diagnosed with infectious diseases. Under NPIs, most respiratory infections declined compared to pre-pandemic levels, rebounding by 69.64% in 2023. While the infection rate of common respiratory pathogens decreased, cases of respiratory syncytial virus increased during the period of extensive NPI implementation. In 2023, pediatric intensive care unit durations for these pathogens increased, suggesting greater severity of illness compared to 2019. MP exhibited the highest infection rate (31.38% average), with a notable outbreak post-pandemic due to severity increase in <3 year olds and rise among older children. NPIs reduced MP coinfections and mitigated their severity, while exerting a significant influence on bacterial coinfections with MP over the span of 5 years, in contrast to their impact on viral pathogens. NPIs effectively curb transmission of respiratory infections by most pathogens, resulting in increased average age of MP infections and altered patterns of coinfection post-pandemic.

A framework for three-dimensional statistical shape modeling of the proximal femur in Legg-Calvé-Perthes disease.

The pathomorphology of Legg-Calvé-Perthes disease (LCPD) is a key contributor to poor long-term outcomes such as hip pain, femoroacetabular impingement, and early-onset osteoarthritis. Plain radiographs, commonly used for research and in the clinic, cannot accurately represent the full extent of LCPD deformity. The purpose of this study was to develop and evaluate a methodological framework for three-dimensional (3D) statistical shape modeling (SSM) of the proximal femur in LCPD. We developed a framework consisting of three core steps: segmentation, surface mesh preparation, and particle-based correspondence. The framework aims to address challenges in modeling this rare condition, characterized by highly heterogeneous deformities across a wide age range and small sample sizes. We evaluated this framework by producing a SSM from clinical magnetic resonance images of 13 proximal femurs with LCPD deformity from 11 patients between the ages of six and 12 years. After removing differences in scale and pose, the dominant shape modes described morphological features characteristic of LCPD, including a broad and flat femoral head, high-riding greater trochanter, and reduced neck-shaft angle. The first four shape modes were chosen for the evaluation of the model's performance, together describing 87.5% of the overall cohort variance. The SSM was generalizable to unfamiliar examples with an average point-to-point reconstruction error below 1mm. We observed strong Spearman rank correlations (up to 0.79) between some shape modes, 3D measurements of femoral head asphericity, and clinical radiographic metrics. In this study, we present a framework, based on SSM, for the objective description of LCPD deformity in three dimensions. Our methods can accurately describe overall shape variation using a small number of parameters, and are a step toward a widely accepted, objective 3D quantification of LCPD deformity.

Contrastive Functional Connectivity Defines Neurophysiology-informed Symptom Dimensions in Major Depression.

Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity. We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients. Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent "internalizing-externalizing" symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This "depression-protective personality" dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control. Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.

7696 Ovarian Steroid Cell Tumor Presenting as Non-classical Adrenal Hyperplasia

Abstract Disclosure: V. Reddy: None. A. Gowda: None. V. Vattipally: None. D. Kunduru: None. IntroductionSteroid cell tumors(SCT) of the ovary make up less than 0.1% of all ovarian tumors. The three subtypes are stromal luteoma, Leydig cell tumor, and steroid cell tumor-not otherwise specified (NOS) which is the commonest subtype. Androgen excess is seen in 75% tumors and presents with virilization. Tumors are usually benign but maybe malignant in 25-43% cases. Factors predicting malignancy include size &amp;gt;7 cm, mitotic activity, necrosis, hemorrhage, and nuclear atypia. Case ReportA 29 y/o Caucasian female presented with secondary amenorrhea and hirsutism. History includes DM2,hypothyroidism,depression and morbid obesity. She reported early pubarche by age 8,menarche at age 13 and developed hirsutism and irregular menstrual cycles from age 15. No workup due to poor follow up until she saw PCP at age 21.She was diagnosed with PCOS and started on Metformin. Eventually referred to endocrinology at age 27, since amenorrhea and hirsutism persisted. On exam she had significant male pattern hair loss and hirsutism over face and body.She reported three first trimester miscarriages.Labs showed testosterone 370 ng/dl, 17 OHP 808 ng/dl, DHEAS 47 ng/dl, Estradiol 117 pg/ml, FSH 2.2 mIU/ml, LH 2.5 mIU/ml, androstenedione 319 ng/dl.Transvaginal US was unremarkable. She was diagnosed with non classical adrenal hyperplasia (NCAH) and started on treatment with prednisone 5 mg daily, drospirenone and spironolactone 100 mg daily.3 months later, labs showed 50% lowering of testosterone to 199 ng/dl and 17 OHP to 407 ng/dl.There was continued biochemical and clinical improvement at the next visit with 17 OHP 73 ng/dl and testosterone 55 ng/dl. Labs done 21 months after initial presentation, showed sudden dramatic rise in 17 OHP to 1148 ng/dl and testosterone to 639 ng/dl. Normal AFP, HCG, CEA, CA125, CA19-9, Inhibin A. CT showed 10.4 cm solid right ovarian mass and underwent right salpingo-oophorectomy. Pathology showed steroid cell tumor, NOS. Labs 1 month postoperatively showed complete normalization of testosterone to &amp;lt;3 ng/dl, 17 OHP to 11 ng/dl and DHEAS 0.66 ng/dl. Steroid therapy was tapered and discontinued. DiscussionWe present the case of a patient with ovarian SCT presenting as NCAH.SCTs are very rare accounting for only 0.1% of all ovarian tumors. Since they commonly secrete androgens, they present similarly to CAH or PCOS and may rarely even co-exist with CAH. Differentiating between NCAH and SCT is difficult especially when there is initial response to steroid therapy and imaging is unremarkable as with our patient. It is important to consider SCT in patients with hyperandrogenism even in the setting of elevated 17 OHP levels. Care should be taken to follow up both clinical and metabolic metrics closely and investigate if any change is noticed. Early diagnosis can prevent irreversible symptoms of hyperandrogenism. Presentation: 6/1/2024

7696 Ovarian Steroid Cell Tumor Presenting As Non-classical Adrenal Hyperplasia

Abstract Disclosure: V. Reddy: None. A. Gowda: None. V. Vattipally: None. D. Kunduru: None. Ovarian Steroid Cell Tumor Presenting as Non-classical Adrenal Hyperplasia IntroductionSteroid cell tumors(SCT) of the ovary make up less than 0.1% of all ovarian tumors. The three subtypes are stromal luteoma, Leydig cell tumor, and steroid cell tumor-not otherwise specified (NOS) which is the commonest subtype. Androgen excess is seen in 75% tumors and presents with virilization. Tumors are usually benign but maybe malignant in 25-43% cases. Factors predicting malignancy include size &amp;gt;7 cm, mitotic activity, necrosis, hemorrhage, and nuclear atypia. Case ReportA 29 y/o Caucasian female presented with secondary amenorrhea and hirsutism. History includes DM2,hypothyroidism,depression and morbid obesity. She reported early pubarche by age 8,menarche at age 13 and developed hirsutism and irregular menstrual cycles from age 15. No workup due to poor follow up until she saw PCP at age 21.She was diagnosed with PCOS and started on Metformin. Eventually referred to endocrinology at age 27, since amenorrhea and hirsutism persisted. On exam she had significant male pattern hair loss and hirsutism over face and body.She reported three first trimester miscarriages.Labs showed testosterone 370 ng/dl, 17 OHP 808 ng/dl, DHEAS 47 ng/dl, Estradiol 117 pg/ml, FSH 2.2 mIU/ml, LH 2.5 mIU/ml, androstenedione 319 ng/dl.Transvaginal US was unremarkable. She was diagnosed with non classical adrenal hyperplasia (NCAH) and started on treatment with prednisone 5 mg daily, drospirenone and spironolactone 100 mg daily.3 months later, labs showed 50% lowering of testosterone to 199 ng/dl and 17 OHP to 407 ng/dl.There was continued biochemical and clinical improvement at the next visit with 17 OHP 73 ng/dl and testosterone 55 ng/dl. Labs done 21 months after initial presentation, showed sudden dramatic rise in 17 OHP to 1148 ng/dl and testosterone to 639 ng/dl. Normal AFP, HCG, CEA, CA125, CA19-9, Inhibin A. CT showed 10.4 cm solid right ovarian mass and underwent right salpingo-oophorectomy. Pathology showed steroid cell tumor, NOS. Labs 1 month postoperatively showed complete normalization of testosterone to &amp;lt;3 ng/dl, 17 OHP to 11 ng/dl and DHEAS 0.66 ng/dl. Steroid therapy was tapered and discontinued. DiscussionWe present the case of a patient with ovarian SCT presenting as NCAH.SCTs are very rare accounting for only 0.1% of all ovarian tumors. Since they commonly secrete androgens, they present similarly to CAH or PCOS and may rarely even co-exist with CAH. Differentiating between NCAH and SCT is difficult especially when there is initial response to steroid therapy and imaging is unremarkable as with our patient. It is important to consider SCT in patients with hyperandrogenism even in the setting of elevated 17 OHP levels. Care should be taken to follow up both clinical and metabolic metrics closely and investigate if any change is noticed. Early diagnosis can prevent irreversible symptoms of hyperandrogenism. Presentation: 6/1/2024

Subjective and functional outcome measures to evaluate performance of pediatric cochlear implant

BackgroundThe majority of research on the effects of cochlear implants has been on clinical evaluations of efficacy (hearing and speech skills and auditory thresholds). But it is unclear how much clinical efficacy metrics (speech, hearing, and language tests) actually demonstrate how beneficial cochlear implants (CI) are in everyday circumstances. In this study, we aim to assess the performance of a child’s current CI with both subjective and functional outcomes measures. Also, we aimed to assess factors affecting these outcomes after CI.MethodsThe study group included 45 cochlear-implanted children, and their chronological age ranges between 4 and 12 years. Children were divided according to their language age into three groups: group 1 (G1) includes15 children with a language age of less than 2 years, group 2 (G2) includes 15 children with a language age of 2 to less than 4 years, and group 3 (G3) includes 15 children with language age 4 to 6 years. Assessment was done in two sessions: one for phoniatric evaluation and another for audiological evaluation.ResultsCI enabled children to develop speech perception in a closed manner evidenced by the early speech perception (ESP) test with pattern perception being the easiest and monosyllabic words being the most difficult and the least to develop. Most children with CI were able to control their voice, to a lesser extent to use speech in meaningful use in social situations and to develop communication strategies in oral situations which were the least to develop, and this was evident by Meaningful Use of Speech Scale. Children with CI were able to acquire language, and their receptive language was better than expressive language, but their language age was below their chronological age.ConclusionThe cochlear implant is an effective rehabilitative tool for the treatment of prelingually deafened children, and this was evident by our results. A hierarchal approach of speech assessment according to the language age of the child and selection of age-appropriate speech perception test is very important to assess cochlear-implanted children Speech recognition had a high degree of variability. Early implantation age and increased duration of implant experience were the most important factors associated with good performance with CI, better speech perception, and better language outcomes.

Anti-inflammatory diet mitigate cardiovascular risks due to particulate matter exposure in women during pregnancy: A perspective cohort study from China

BackgroundParticulate matter (PM) exposure during pregnancy may increase cardiovascular risk (CVR). However, the specific time windows of exposure contributing to this association and the potential biological mechanisms underlying it remain unclear. ObjectiveTo determine the sensitive time window for CVR related to PM exposure. We investigated whether levels of inflammatory biomarkers mediate the relationship between PM exposure and CVR, and examined the potential impact of an anti-inflammatory diet on this association. MethodsFrom 2015 to 2021, 9294 pregnant women from three Hefei hospitals were included. We used a 1 × 1 km satellite dataset to assess PM1, PM2.5, and PM10 exposure. High-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured as inflammatory biomarkers. The empirical dietary inflammatory pattern (EDIP) score, based on a validated food frequency questionnaire. The CVR score was calculated using five clinical metrics based on American Heart Association criteria. ResultsWe found a significant association between PM exposure and increased CVR score, especially during the 2nd to 8th weeks of the first trimester. For every increase of 10-μg/m3 of PM1, PM2.5, and PM10, there was an associated increase in CVR of 0.51 (95%CI: 0.21, 082), 0.25 (95% CI: 0.11 to 0.39), and 0.29 (95% CI: 0.09 to 0.37), respectively. Mediation analysis revealed that the proportion of the association between PM1, PM2.5, and PM10 exposure and CVR mediated by inflammatory biomarkers was 24.3%, 22.4%, and 20.1%, respectively. Stratified analyses showed no positive correlation between PM exposure and CVR in the anti-inflammatory diet (low EDIP) group. The β coefficients were 0.52 for PM1 (95% CI: -0.06 to 1.11), 0.31 for PM2.5 (95% CI: -0.04 to 0.79), and 0.25 for PM10 (95% CI: -0.03 to 0.54). ConclusionsPM exposure, particularly during weeks 2–8 of pregnancy, correlates with CVR, partly mediated by levels of inflammatory biomarkers. An anti-inflammatory diet mitigates CVR associated with PM exposure.

Prediction of dose distributions for non-small cell lung cancer patients using MHA-ResUNet.

The current level of automation in the production of radiotherapy plans for lung cancer patients is relatively low. With the development of artificial intelligence, it has become a reality to use neural networks to predict dose distributions and provide assistance for radiation therapy planning. However, due to the significant individual variability in the distribution of non-small cell lung cancer (NSCLC) planning target volume (PTV) and the complex spatial relationships between the PTV and organs at risk (OARs), there is still a lack of a high-precision dose prediction network tailored to the characteristics of NSCLC. To assist in the development of volumetric modulated arc therapy (VMAT) plans for non-small cell lung cancer patients, a deep neural network is proposed to predict high-precision dose distribution. This study has developed a network called MHA-ResUNet, which combines a large-kernel dilated convolution module and multi-head attention (MHA) modules. The network was trained based on 80 VMAT plans of NSCLC patients. CT images, PTV, and OARs were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks' performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics. The MAE between the predicted dose distribution and the manually planned dose distribution within the PTV is 1.43Gy, and the D95 error is less than 1Gy. Compared with the other three commonly used networks, the dose error of the MHA-ResUNet is the smallest in PTV and OARs. The proposed MHA-ResUNet network improves the receptive field and filters the shallow features to learn the relative spatial relation between the PTV and the OARs, enabling accurate prediction of dose distributions in NSCLC patients undergoing VMAT radiotherapy.

Effect of implementing an integrated oncology dashboard on clinical and operational quality in a community-based cancer network.

420 Background: MaineHealth Cancer Care Network (MHCCN) encompasses nine regional cancer centers and one affiliate cancer center located in rural south-central Maine and New Hampshire. MHCCN developed an integrated operational dashboard to improve clinical and operational performance. In this report, we detail improvements in a variety of quality metrics after implementation of the dashboard. Methods: Extracts from various non-interoperable data sources were integrated using a MS Power BI platform to create the dashboard. Divided into pages— summary, growth, service excellence, innovation and transformation —the dashboard provides searchable and regularly updated snapshots of numerous relevant metrics in these domains. Access to the dashboard was extended to all clinical and operational staff, with data reviewed bi-monthly in provider and operational meetings. The dashboard played a pivotal role in identifying MHCCN’s 2023 key performance indicator, specifically the electronic health record (EHR)-based TNM staging, along with notable care gaps such as underutilization of the NCCN distress thermometer (DT) for psychosocial distress assessment. Bottlenecks to timely care delivery were similarly identified by leveraging time to treatment data in the service excellence page. Targeted quality improvement initiatives were devised to address the gaps. Results: EHR-based structured TNM staging by providers improved from 29% in Fiscal Year (FY) 2022 to 47% in FY 2023 and 64% in FY 2024 year-to-date (YTD). Use of the NCCN DT improved from 1.1% in FY 2022 to 30.9% in FY 2023 and 49.9% in FY 2024 YTD. Process changes resulting from identifying delayed scheduling of PET scans as a bottleneck reduced the lead time from scan ordering to scan appointment from 18 days (January-July 2023) to 9 days (January-April 2024). Leveraging access to monthly productivity data through the dashboard, provider compensation reform incorporating production-based incentives was launched in July 2023, improving throughput and access as well as provider recruitment and retention. Information transparency from a freely accessible dashboard contributed to a 0.67-point gain in the 2023 Gallup provider core engagement CE3 score, reaching 3.45 (on a five-point scale). Conclusions: Clinical and operational metrics are often fragmented across different platforms, leading to inefficiencies and limited insights. Creating a system-wide integrated quality dashboard improved data visibility, provider engagement and led to measurable improvement in quality metrics across various domains.

Augmenting Prognostication: Utilizing Activity Trackers to Enhance Survival Prediction in Metastatic Non-Small Cell Lung Cancer

Introduction/BackgroundPrognostication by Performance Status (PS) assessment is a fundamental element of treatment decisions and clinical trial design in oncology, but it is limited by subjectivity and potential miscommunication between patient, physician, and family. Activity tracker offers the potential to collect a broad range of patient-generated data to supplement the assessment of PS. Patients and MethodsPatients with metastatic NSCLC (mNSCLC) participated in a single institute, prospective, observational feasibility study conducted at Huntsman Cancer Institute. Patients were given a Fitbit® activity tracker, which collects their steps taken, distance moved, heart rate, and activity intensity. At baseline, PS was assessed by physicians and patients, and demographics and clinical data were collected. We defined novel indices of health: Heart rate Activity zone Mismatch (HAM) and excessive Sedentary Heart Rate (eSHR). We used multivariable Cox proportional hazards models adjusted for age, sex, and treatment line to estimate and test the prognostic ability of clinical and fitness metrics on overall survival (OS). Each prognostic model was evaluated using Harrell's concordance index (C-index). ResultsFifty-five patients with mNSCLC were enrolled. The median OS was 10.4 months (95% CI: 7.2, 15.2). PS-physician (HR=2.0; p<0.001) and Fitbit® metrics were associated with OS, including daily total steps (1,000-steps) (HR=0.8; p=0.004), HAM (HR=2; p=0.02), eSHR (HR=0.3; p=0.001). The prognostic model that includes PS-physician was associated with the best concordance (C-index=0.75), followed by daily total distance (C-index=0.74) and steps (C-index=0.73) ConclusionsTracker-based measures were prognostic of survival in mNSCLC and may be useful as a supplement or alternative to PS in practice and clinical trials. Micro AbstractPerformance status (PS) is the foundation for clinical trial eligibility and the basis for treatment decisions in thoracic oncology, but it is limited by subjectivity and potential miscommunication between patient, physician, and family. Activity trackers offer a collection of a broad range of patient-generated data that can be used to supplement PS in clinical trials and practice while minimizing subjectivity.

Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot.

Despite the improved survival rates of children with tetralogy of Fallot (TOF), various degrees of neurodevelopmental disorders persist. Currently, there is a lack of quantitative and objective imaging markers to assess the neurodevelopment of individuals with TOF. This study aimed to noninvasively examine potential quantitative imaging markers of TOF neurodevelopment by combining radiomics signatures and morphological features and to further clarify the relationship between imaging markers and clinical neurodevelopment metrics. This study included 33 preschool children who had undergone surgical correction for TOF and 29 healthy controls (36 in the training cohort and 26 in the testing cohort), all of whom underwent three-dimensional T1-weighted high-resolution (T1-3D) head magnetic resonance imaging (MRI). Radiomics features were extracted by Pyradiomics to construct radiomics models, while surface morphometry (surface and volumetric) features were analyzed to build morphometry models. Merged models integrating radiomics and morphometry features were subsequently developed. The optimal discriminative radiomics signatures were identified via least absolute shrinkage and selection operator (LASSO). Machine learning classification models include support vector machine (SVM) with radial basis function (RBF) and multivariable logistic regression (MLR) models, both of which were used to evaluate the potential imaging biomarkers. Performances of models were evaluated based on their calibration and classification metrics. The area under the receiver operating characteristic curves (AUCs) of the models were evaluated using the Delong test. Neurodevelopmental assessments for children with corrected TOF were conducted with the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV). Furthermore, the correlation of the significant discriminative indicators with clinical metrics and neurodevelopmental scales was evaluated. Twelve discriminative radiomics signatures, optimized for classification, were identified. The performance of the merged model (AUCs of 0.922 and 0.917 for the training set and test set with SVM, respectively) was superior to that of the single radiomics model (AUCs of 0.915 and 0.917 for the training set and test set with SVM, respectively) and that of the single morphometric models (AUCs of 0.803 and 0.756 for the training set and test set with SVM, respectively). The radiomics model demonstrated higher significance than did the morphometric models in training set with SVM (AUC: 0.915 vs. 0.803; P<0.001). Additionally, the significant indicators showed a correlation with clinical indicators and neurodevelopmental scales. MRI-based radiomics features combined with morphometry features can provide complementary information to identify neurodevelopmental abnormalities in children with corrected TOF, which will provide potential evidence for clinical diagnosis and treatment.

A Kinematic Deviation Index (KDI) for Evaluation of Forelimb Function in Rodents.

Rodent models are widely used to study neurological conditions and assess forelimb movement to measure function performance, deficit, recovery and treatment effectiveness. Traditional assessment methods based on endpoints such as whether the task is accomplished, while easy to implement, provide limited information on movement patterns important to assess different functional strategies. On the other side, detailed kinematic analysis provides granular information on the movement patterns but is difficult to compare across laboratories, and may not translate to clinical metrics of upper limb function. To address these limitations, we developed and validated a kinematic deviation index (KDI) for rodents that mimics current trends in clinical research. The KDI is a unitless summary score that quantifies the difference between an animal movement during a task and its optimal performance derived from spatiotemporal marker sequences without pre-specifying movements. We demonstrate the utility of KDI in assessing reaching and grasping in mice and validate its discrimination between trial endpoints in healthy animals. Furthermore, we show KDI sensitivity to interventions, including acute and chronic spinal cord injury and optogenetic disruption of sensorimotor circuits. The KDI provides a comprehensive measure of motor function that bridges the gap between detailed kinematic analysis and simple success/failure metrics, offering a valuable tool for assessing recovery and compensation in rodent models of neurological disorders.

EHealth Interventions of Health Literacy for Stroke Survivors: Systematic Review and Meta-Analysis.

eHealth literacy interventions have emerged as a new approach in management of stroke survivors. Its effect on knowledge and clinical outcomes presents an inconsistent result in literature. We aim to evaluate the impact of eHealth interventions on health literacy, clinical metrics, adherence to healthy behaviors, stroke recurrence, mortality, and health-related quality of life in stroke survivors. We systematically searched six databases (PubMed, Cochrane Library, CINAHL, EMBASE, Web of Science, ProQuest) up to February 21, 2024, selecting articles that meet these criteria: (i) patients with stroke; (ii) intervention with education content; (iii) eHealth interventions included telehealth, mobile phone, internet or computer; (iv) randomized controlled trials. The mean differences (MD) and standardized mean differences (SMD) between groups were measured. Risk of bias was evaluated using the Cochrane tool. From 16 studies involving 9646 participants, we observed that eHealth interventions significantly improved systolic blood pressure (MD = -2.78mmHg, 95% confidence interval (CI) [-4.67 to -0.88], p = 0.004), medication adherence (SMD = 0.28, 95% CI [0.04 to 0.52], p = 0.023), and health-related quality of life (SMD = 0.21, 95% CI [0.04 to 0.37], p = 0.013). Meta-regression found that age modified the effect size of systolic blood pressure (p = 0.027). There was insufficient evidence to conclude effects on other outcomes. The quality of evidence was moderate. Outcome variation may be due to the diversity in eHealth intervention approaches. The limited number of studies precluded the subgroup analysis. More interactive interventions with longer follow-ups were more effective. eHealth interventions may benefit stroke survivors in terms of blood pressure, medication adherence, and health-related quality of life. eHealth literacy interventions could be implemented to improve the management of stroke survivors, especially in the context of resource limitations. PROSPERO registration number: CRD42024502470.

Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach.

Although the involvement of progressive brain alterations in epilepsy was recently suggested, individual patients' trajectories of white matter (WM) disruption are not known. We investigated the disease progression patterns of WM damage and its associations with clinical metrics. We examined the cross-sectional diffusion tensor imaging (DTI) data of 155 patients with unilateral temporal lobe epilepsy (TLE) and 270 age/gender-matched healthy controls, and we then calculated the average fractional anisotropy (FA) values within 20 WM tracts of the whole brain. We used the Subtype and Stage Inference (SuStaIn) program to detect the progression trajectory of FA changes and investigated its association with clinical parameters including onset age, disease duration, drug-responsiveness, and the number of anti-seizure medications (ASMs). The SuStaIn algorithm identified a single subtype model in which the initial damage occurs in the ipsilateral uncinate fasciculus (UF), followed by damage in the forceps, superior longitudinal fasciculus (SLF), and anterior thalamic radiation (ATR). This pattern was replicated when analyzing TLE with hippocampal sclerosis (n = 50) and TLE with no lesions (n = 105) separately. Further-progressed stages were associated with longer disease duration (p < 0.001) and a greater number of ASMs (p = 0.001). the disease progression model based on WM tracts may be useful as a novel individual-level biomarker.

Acceptable outcomes of liver transplantation in uninsured patients under the coverage of a state assistance program.

The lack of health insurance is a major barrier in the access to healthcare even in case of life-saving procedures such as liver transplantation (LT). Concerns about worse outcomes in uninsured patients have also discouraged the evaluation and transplantation of patients without adequate health insurance coverage. The aim of this study is to evaluate outcomes from the largest cohort of uninsured patients who underwent LT with the support of a state payment assistance program (also called charity care). This study included all consecutive patients who underwent LT at a single center from 2002 to 2020. Demographic, clinical, social variables and outcome metrics were collected and compared between insured and uninsured patients. Among a total of 978 LT recipients, 594 had private insurance, 324 government insurance (Medicare/Medicaid), 60 were uninsured and covered under a state charity care program. In the charity care group, there was a higher proportion of Hispanic subjects, single marital status, younger age, and high-MELD score patients. The 1- and 3-year patient survival rates were 89.0% and 81.8% in private insurance patients, 88.8% and 80.1% in government insurance recipients, and 93.3% and 79.6% in those with charity care (p=0.49). There was no difference in graft survival between insured and uninsured patients (p=0.62). The three insurance groups presented similar hospital length-of-stay and 30-day readmission rates. In both univariate and multivariate analysis, uninsured status (charity care) was not associated with worse patient survival (HR:1.23, 95%CI: 0.84-1.80, p=0.29) or graft survival (HR:1.22, 95%CI:0.84-1.78, p=0.29). In conclusion, there was no difference in outcomes after LT between insured and uninsured patients. A charity care program may be an effective tool to mitigate socio-economic disparities in both outcomes and access to LT.

The Fog has not Lifted: No Reduction in Complications for Partial REBOA in the AAST AORTA Registry

Background: Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) is a potentially lifesaving but polarizing therapy due to the associated morbidity and uncertainty of who might benefit. Techniques such as partial (p)REBOA that provide hemodynamic support while reducing distal ischemia are now captured in the Aortic Resuscitation in Trauma and Acute Care (AORTA) registry. We hypothesized that pREBOA would be associated with improved mortality and fewer adverse outcomes.Methods: The AORTA registry was queried for adult patients who received complete (c)REBOA or pREBOA between 2020 and 2022. Patients were excluded if they had a head Abbreviated Injury Scale (AIS) ≥three or an AIS of six in any body region. Outcome measures were complications and mortality. Poisson regression analyses identified the independent effect of the type of approach on outcomes.Results: 164 patients met the inclusion criteria, with pREBOA used in 36% of cases and no significant difference in patient demographics, injury characteristics, or injury severity between pREBOA and cREBOA. There was no difference in mortality rate (44.1% vs 45.7%). After adjusting for potential confounders, no statistically significant difference in complications was detected between the two approaches [adjusted IRR (95% CI): 1.11 (0.54–2.27), p = 0.777]. This association persisted after subgroup analysis of aortic Zone one vs Zone three deployment.Conclusions: In this registry analysis, pREBOA did not reduce morbidity or mortality compared to cREBOA. Improving the granularity of clinical metrics in the AORTA registry is essential to understanding whether patients will benefit from pREBOA, and how to best implement this controversial resuscitation adjunct.

Correlating plasma protein profiles with symptomatology and treatment response in acute phase and early remission of major depressive disorder.

This study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode. In this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID). Five distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder's trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms. This research points to a complex interplay between the plasma proteome and MDD's clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools. The study's limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability.

Analyzing the distribution of progression-free survival for combination therapies: A study of model-based translational predictive methods in oncology

Progression-free survival (PFS) is an important clinical metric in oncology and is typically illustrated and evaluated using a survival function. The survival function is often estimated post-hoc using the Kaplan-Meier estimator but more sophisticated techniques, such as population modeling using the nonlinear mixed-effects framework, also exist and are used for predictions. However, depending on the choice of population model PFS will follow different distributions both quantitatively and qualitatively. Hence the choice of model will also affect the predictions of the survival curves.In this paper, we analyze the distribution of PFS for a frequently used tumor growth inhibition model with and without drug-resistance and highlight the translational implications of this. Moreover, we explore and compare how the PFS distribution for combination therapy differs under the hypotheses of additive and independent-drug action.Furthermore, we calibrate the model to preclinical data and use a previously calibrated clinical model to show that our analytical conclusions are applicable to real-world setting. Finally, we demonstrate that independent-drug action can effectively describe the tumor dynamics of patient-derived xenografts (PDXs) given certain drug combinations.