ObjectiveAs available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit.MethodsUsing data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration.ResultsOverall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year.ConclusionOverall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.

SUMMARYCellular heterogeneity is frequently observed in cancer, but the biological significance of heterogeneous tumor clones is not well defined. Using multicolor reporters and CRISPR-Cas9 barcoding, we trace clonal dynamics in a mouse model of sarcoma. We show that primary tumor growth is associated with a reduction in clonal heterogeneity. Local recurrence of tumors following surgery or radiation therapy is driven by multiple clones. In contrast, advanced metastasis to the lungs is driven by clonal selection of a single metastatic clone (MC). Using RNA sequencing (RNA-seq) and in vivo assays, we identify candidate suppressors of metastasis, namely, Rasd1, Reck, and Aldh1a2. These genes are downregulated in MCs of the primary tumors prior to the formation of metastases. Overexpression of these suppressors of metastasis impair the ability of sarcoma cells to colonize the lungs. Overall, this study reveals clonal dynamics during each step of tumor progression, from initiation to growth, recurrence, and distant metastasis.

Objective Phenotyping patients using electronic health record (EHR) data conventionally requires labeled cases and controls. Assigning labels requires manual medical chart review and therefore is labor intensive. For some phenotypes, identifying gold-standard controls is prohibitive. We developed an accurate EHR phenotyping approach that does not require labeled controls. Materials and Methods Our framework relies on a random subset of cases, which can be specified using an anchor variable that has excellent positive predictive value and sensitivity independent of predictors. We proposed a maximum likelihood approach that efficiently leverages data from the specified cases and unlabeled patients to develop logistic regression phenotyping models, and compare model performance with existing algorithms. Results Our method outperformed the existing algorithms on predictive accuracy in Monte Carlo simulation studies, application to identify hypertension patients with hypokalemia requiring oral supplementation using a simulated anchor, and application to identify primary aldosteronism patients using real-world cases and anchor variables. Our method additionally generated consistent estimates of 2 important parameters, phenotype prevalence and the proportion of true cases that are labeled. Discussion Upon identification of an anchor variable that is scalable and transferable to different practices, our approach should facilitate development of scalable, transferable, and practice-specific phenotyping models. Conclusions Our proposed approach enables accurate semiautomated EHR phenotyping with minimal manual labeling and therefore should greatly facilitate EHR clinical decision support and research.

Enchondromas and chondrosarcomas are common cartilage neoplasms that are either benign or malignant, respectively. The majority of these tumors harbor mutations in either IDH1 or IDH2. Glutamine metabolism has been implicated as a critical regulator of tumors with IDH mutations. Using genetic and pharmacological approaches, we demonstrated that glutaminase‐mediated glutamine metabolism played distinct roles in enchondromas and chondrosarcomas with IDH1 or IDH2 mutations. Glutamine affected cell differentiation and viability in these tumors differently through different downstream metabolites. During murine enchondroma‐like lesion development, glutamine‐derived α‐ketoglutarate promoted hypertrophic chondrocyte differentiation and regulated chondrocyte proliferation. Deletion of glutaminase in chondrocytes with Idh1 mutation increased the number and size of enchondroma‐like lesions. In contrast, pharmacological inhibition of glutaminase in chondrosarcoma xenografts reduced overall tumor burden partially because glutamine‐derived non‐essential amino acids played an important role in preventing cell apoptosis. This study demonstrates that glutamine metabolism plays different roles in tumor initiation and cancer maintenance. Supplementation of α‐ketoglutarate and inhibiting GLS may provide a therapeutic approach to suppress enchondroma and chondrosarcoma tumor growth, respectively. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Cognitive distortions are counterproductive patterns of thinking that are one of the targets of cognitive behavioral therapy (CBT). These can be challenging for clinicians to detect, especially those without extensive CBT training or supervision. Text classification methods can approximate expert clinician judgment in the detection of frequently occurring cognitive distortions in text-based therapy messages. However, performance with infrequent distortions is relatively poor. In this study, we address this sparsity problem with two approaches: Data Augmentation and Domain-Specific Model. The first approach includes Easy Data Augmentation, back translation, and mixup techniques. The second approach utilizes a domain-specific pretrained language model, MentalBERT. To examine the viability of different data augmentation methods, we utilized a real-world dataset of texts between therapists and clients diagnosed with serious mental illness that was annotated for distorted thinking. We found that with optimized parameter settings, mixup was helpful for rare classes. Performance improvements with an augmented model, MentalBERT, exceed those obtained with data augmentation.