Equivalent thrombotic risk with Warfarin, Dabigatran, or Enoxaparin after failure of initial direct oral anticoagulation (DOAC) therapy.

BACKGROUND: The direct oral anticoagulants (DOACs) are now commonly regarded as first line anticoagulants in most cases of venous thromboembolism (VTE). However, the optimal choice of subsequent anticoagulant in instances of first line DOAC failure is unclear. OBJECTIVES: To describe and compare outcomes with second line anticoagulants used after DOAC failure. METHODS: Patients seen at an urban hospital system for an episode of acute VTE initially treated with either apixaban or rivaroxaban who experienced a subsequent recurrent thrombosis while on anticoagulation (1st recurrent thrombosis) were included. RESULTS: In total, 166 patients after apixaban or rivaroxaban failure were included. Following DOAC failure (1st recurrent thrombosis), the subsequent anticoagulant was warfarin in 60 patients (36%), dabigatran in 42 patients (25%), and enoxaparin in 64 patients (39%). Enoxaparin was preferentially prescribed in patients with a malignancy-associated etiology for 1st recurrent thrombosis (p < 0.01). The median follow-up time in our cohort was 16 months. There was no difference in 2nd recurrent thrombosis-free survival (p = 0.72) or risk for major bleeding event (p = 0.30) among patients treated with dabigatran, warfarin, or enoxaparin. CONCLUSIONS: In this retrospective analysis of patients failing first line DOAC therapy, rates of 2nd recurrent thrombosis and bleeding did not differ among subsequently chosen anticoagulants. Our study provides evidence that the optimal 2nd anticoagulant is not clear, and the choice of 2nd anticoagulant should continue to balance patient preference, cost, and provider experience.

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization.

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.

Deep learning generates synthetic cancer histology for explainability and education.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence.

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

Predictors and clinical complications associated with antiphospholipid antibodies in sickle cell disease.

Although a higher prevalence of antiphospholipid autoantibodies (aPL) has been observed in some cohorts of sickle cell disease (SCD) patients, the clinical risk factors for the development of aPL and its associated complications remain unclear. In a retrospective study of 63 SCD patients, a lower hemoglobin concentration and higher white blood cell count were independently associated with an elevated aPL. SCD patients with elevated aPL had increased pregnancy complications (≥3 miscarriages, preterm delivery, pre-eclampsia) and venous thrombotic events. Our findings suggest that SCD may predispose to the generation of aPL and that aPL itself may contribute to the vasculopathy of SCD. Prospective testing for aPL is warranted in patients with SCD.

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.

Evaluation of point-of-care International Normalized Ratio in sickle cell disease.

BACKGROUND: Point-of-care (POC) International Normalized Ratio (INR) measurement provides efficient monitoring of warfarin therapy; however, its reliability may be affected in patients with anemia, such as those with sickle cell disease (SCD). OBJECTIVES: To evaluate the correlation of POC-INR to clinical laboratory INR (CL-INR) in SCD and use of a correction factor. PATIENT/METHODS: In this retrospective study, the accuracy of POC-INR compared to CL-INR was evaluated in a cohort of patients with SCD and in a non-SCD Black cohort. RESULTS: Despite the difference in anemia, the SCD cohort showed a similar percentage of in-range POC-INR values as observed in the non-SCD cohort (37% vs 42%). The SCD cohort was randomly divided to form discovery and validation cohorts. In the discovery cohort, 86% of POC-INRs were in range when the POC-INRs were ˂4.0, but only 24% were in range if POC-INRs were ≥4.0. A linear regression of CL-INR versus POC-INR for POC-INR values ≥4.0 yielded a coefficient of 0.72 (95% confidence interval, 0.69-0.75); Multiplying POC-INR by this correction factor, rounded to 0.7 for ease of use in clinical practice, improved the proportion of in-range POC-INR values ≥4.0 from 24% to 100% in the SCD discovery cohort and from 19% to 95% in the SCD validation cohort. Similar findings applied to analyses of the non-SCD cohort. CONCLUSIONS: POC-INR and CL-INR in patients with SCD are similar when POC-INR is <4.0, and the accuracy of POC-INR values ≥4.0 can be improved by applying an institution-specific correction factor.

Antimicrobial resistance is a risk factor for mortality in adults with sickle cell disease.

Not available.

Clinical, laboratory, and genetic risk factors for thrombosis in sickle cell disease.

Sickle cell disease (SCD) patients are at a four- to 100-fold increased risk for thrombosis compared with the general population, although the mechanisms and risk factors are not clear. We investigated the incidence and predictors for thrombosis in a retrospective, longitudinal cohort of 1193 pediatric and adult SCD patients treated at our institution between January 2008 and December 2017. SCD diagnosis and thrombotic complications were identified using International Classification of Diseases coding and verified through medical chart review. Clinical and laboratory data were extracted from the medical records. With a median follow-up of 6.4 years, 208 (17.4%) SCD patients experienced 352 thrombotic events (64 strokes, 288 venous thromboembolisms [VTE]). Risk factors for stroke included older age and HbSS/Sß0-genotype and a lower hemoglobin (Hb) F% in the subset of HbSS/Sß0-genotype patients (P < .05). VTE risk was independently associated with lower estimated glomerular filtration rate, hydroxyurea (HU) use, HbSS/Sß0 genotype, and higher white blood cell (WBC) counts and Hb (P ≤ .03). Two thrombomodulin gene variants previously associated with thrombosis in the general African American population, THBD rs2567617 (minor allele frequency [MAF] 0.25; odds ratio [OR], 1.5; P = .049) and THBD rs1998081 (MAF, 0.24; OR, 1.5; P = .059), were associated with thrombosis in this cohort. In summary, thrombotic complications are common, and several traditional and SCD-specific risk factors are associated with thrombotic risk. Future studies integrating clinical, laboratory, and genetic risk factors may improve our understanding of thrombosis and guide intervention practices in SCD.

Pan-cancer image-based detection of clinically actionable genetic alterations.

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.

Accounting for early job turnover in recent pediatric surgery fellowship graduates: An American Pediatric Surgical Association Membership and Credentials Committee study.

PURPOSE: Employment opportunities for graduating pediatric surgeons vary from year to year. Significant turnover among new employees indicates fellowship graduates may be unsophisticated in choosing job opportunities which will ultimately be satisfactory for themselves and their families. The purpose of this study was to assess what career, life, and social factors contributed to the turnover rates among pediatric surgeons in their first employment position. METHODS: American Pediatric Surgical Association members who completed fellowship training between 2011 and 2016 were surveyed voluntarily. Only those who completed training in a pediatric surgery fellowship sanctioned by the American Board of Surgery and whose first employment involved the direct surgical care of patients were included. The survey was completed electronically and the results were evaluated using chi-squared analysis to determine which independent variables contributed to a dependent outcome of changing place of employment. RESULTS: 110 surveys were returned with respondents meeting inclusion criteria. 13 (11.8%) of the respondents changed jobs within the study period and 97 (88.2%) did not change jobs. Factors identified that likely contributed to changing jobs included a perceived lack of opportunity for career [p = <0.001] advancement and the desire to no longer work at an academic or teaching facility [p = 0.013]. Others factors included excessive case load [p = 0.006]; personal conflict with partners or staff [p = 0.007]; career goals unfulfilled by practice [p = 0.011]; lack of mentorship in partners [p = 0.026]; and desire to be closer to the surgeon's or their spouse's family [p = 0.002]. CONCLUSIONS: Several factors appear to play a role in motivating young pediatric surgeons to change jobs early in their careers. These factors should be taken into account by senior pediatric fellows and their advisors when considering job opportunities. TYPE OF STUDY: Survey. LEVEL OF EVIDENCE: IV.

Reduced skeletal muscle function is associated with decreased fiber cross-sectional area in the Cy/+ rat model of progressive kidney disease.

BACKGROUND: The combination of skeletal muscle wasting and compromised function plays a role in the health decline commonly observed in chronic kidney disease (CKD) patients, but the pathophysiology of muscle mass/strength changes remains unclear. The purpose of this study was to characterize muscle properties in the Cy/+ rat model of spontaneously progressive CKD. METHODS: Leg muscle function and serum biochemistry of male Cy/+ (CKD) rats and their nonaffected littermates (NLs) were assessed in vivo at 25, 30 and 35 weeks of age. Architecture and histology of extensor digitorum longus (EDL) and soleus (SOL) muscles were assessed ex vivo at the conclusion of the experiment. We tested the hypothesis that animals with CKD have progressive loss of muscle function, and that this functional deficit is associated with loss of muscle mass and quality. RESULTS: Thirty-five-week-old CKD rats produced significantly lower maximum torque in ankle dorsiflexion and shorter time to maximum torque, and longer half relaxation time in dorsiflexion and plantarflexion compared with NL rats. Peak dorsiflexion torque (but not plantarflexion torque) in CKD remained steady from 25 to 35 weeks, while in NL rats, peak torque increased. Mass, physiologic cross-sectional area (CSA) and fiber-type (myosin heavy chain isoform) proportions of EDL and SOL were not different between CKD and NL. However, the EDL of CKD rats showed reduced CSAs in all fiber types, while only MyHC-1 fibers were decreased in area in the SOL. CONCLUSIONS: The results of this study demonstrate that muscle function progressively declines in the Cy/+ rat model of CKD. Because whole muscle mass and architecture do not vary between CKD and NL, but CKD muscles show reduction in individual fiber CSA, our data suggest that the functional decline is related to increased muscle fiber atrophy.

In vivo reference point indentation measurement variability in skeletally mature inbred mice.

Reference point indentation (RPI) was developed to measure material-level mechanical properties of bone in vivo. Studies using RPI in vivo have discriminated between human subjects with previous skeletal fractures and those without and among dogs given different anti-remodeling drugs. Recently, this technology was extended to rats, providing the first in vivo data for rodents. The goal of the present study was to perform in vivo RPI measurements in mice, the most common animal model used to study bone. Twelve 16-week-old female C57BL/6 mice were subjected to RPI (three tests) on the anterior tibia, followed by a repeat test session on the contralateral limb 28 days later. A custom MATLAB program was used to derive several outcome parameters from RPI force-displacement curves: first cycle indentation distance (ID-1st), ID increase (IDI), total ID (TID), first cycle unloading slope (US-1st) and first cycle energy dissipation (ED-1st). Data within an individual were averaged across the three tests for each time point. Within-animal variation of all RPI parameters on day 1 ranged from 12.8 to 33.4% and from 14.1 to 22.4% on day 28. Between-animal variation on day 1 ranged from 11.4% to 22.8% and from 7.5% to 24.7% on day 28. At both time points, within- and between-animals, US-1st was the least variable parameter and IDI was most variable. All parameters were nonsignificantly lower at day 28 compared with day 1. These data are important to demonstrate the feasibility of collecting bone material property data longitudinally in mice and will inform the design of future studies in terms of statistical power and appropriate sample size considerations.