Cortical circuit dynamics underlying motor skill learning: from rodents to humans.

Motor learning is crucial for the survival of many animals. Acquiring a new motor skill involves complex alterations in both local neural circuits in many brain regions and long-range connections between them. Such changes can be observed anatomically and functionally. The primary motor cortex (M1) integrates information from diverse brain regions and plays a pivotal role in the acquisition and refinement of new motor skills. In this review, we discuss how motor learning affects the M1 at synaptic, cellular, and circuit levels. Wherever applicable, we attempt to relate and compare findings in humans, non-human primates, and rodents. Understanding the underlying principles shared by different species will deepen our understanding of the neurobiological and computational basis of motor learning.

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes? METHODS AND RESULTS: Adults with right heart catheterization or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012-2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients' 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2. Overall, 5016 PH and 19,454 patients without PH were used in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were female. The mean interval between ECG and right heart catheterization or echocardiogram was 3.66 and 2.23 days for patients with PH and patients without PH, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect pulmonary arterial hypertension, and 0.80, 0.73, and 0.76 to detect group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH; the area under the receiver operating characteristic curve was 0.79 or greater. CONCLUSIONS: A deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before right heart catheterization/echocardiogram diagnosis. This approach has the potential to decrease diagnostic delays in PH.

TWEAK-Fn14-RelB Signaling Cascade Promotes Stem Cell-like Features that Contribute to Post-Chemotherapy Ovarian Cancer Relapse.

Disease recurrence in high-grade serous ovarian cancer may be due to cancer stem-like cells (CSC) that are resistant to chemotherapy and capable of reestablishing heterogeneous tumors. The alternative NF-κB signaling pathway is implicated in this process; however, the mechanism is unknown. Here we show that TNF-like weak inducer of apoptosis (TWEAK) and its receptor, Fn14, are strong inducers of alternative NF-κB signaling and are enriched in ovarian tumors following chemotherapy treatment. We further show that TWEAK enhances spheroid formation ability, asymmetric division capacity, and expression of SOX2 and epithelial-to-mesenchymal transition genes VIM and ZEB1 in ovarian cancer cells, phenotypes that are enhanced when TWEAK is combined with carboplatin. Moreover, TWEAK in combination with chemotherapy induces expression of the CSC marker CD117 in CD117- cells. Blocking the TWEAK-Fn14-RelB signaling cascade with a small-molecule inhibitor of Fn14 prolongs survival following carboplatin chemotherapy in a mouse model of ovarian cancer. These data provide new insights into ovarian cancer CSC biology and highlight a signaling axis that should be explored for therapeutic development. IMPLICATIONS: This study identifies a unique mechanism for the induction of ovarian cancer stem cells that may serve as a novel therapeutic target for preventing relapse.

A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records.

BACKGROUND: This study aimed to develop a machine learning (ML) model to identify patients who are likely to have pulmonary hypertension (PH), using a large patient-level US-based electronic health record (EHR) database. METHODS: A gradient boosting model, XGBoost, was developed using data from Optum's US-based de-identified EHR dataset (2007-2019). PH and disease control adult patients were identified using diagnostic, treatment and procedure codes and were randomly split into the training (90%) or test set (10%). Model features included patient demographics, physician visits, diagnoses, procedures, prescriptions, and laboratory test results. SHapley Additive exPlanations values were used to determine feature importance. RESULTS: We identified 11,279,478 control and 115,822 PH patients (mean age, respectively: 62 and 68 years, both 53% female). The final model used 165 features, with the most important predictive features including diagnosis of heart failure, shortness of breath and atrial fibrillation. The model predicted PH with an area under the receiver operating characteristic curve (AUROC) of 0.92. AUROC remained above 0.80 for the prediction of PH up to and beyond 18 months before diagnosis. Among the PH patients, we also identified 955 pulmonary arterial hypertension (PAH) and 1432 chronic thromboembolic pulmonary hypertension (CTEPH) patients, and the range of AUROCs obtained for these cohorts was 0.79-0.90 and 0.87-0.96, respectively. CONCLUSIONS: This model to detect PH based on patients' EHR records is viable and performs well in subgroups of PAH and CTEPH patients. This approach has the potential to improve patient outcomes by reducing diagnostic delay in PH.

Risk of Stroke Outcomes in Atrial Fibrillation Patients Treated with Rivaroxaban and Warfarin.

OBJECTIVES: In a previous real-world study, rivaroxaban reduced the risk of stroke overall and severe stroke compared with warfarin in patients with nonvalvular atrial fibrillation (NVAF). The aim of this study was to assess the reproducibility in a different database of our previously observed results (Alberts M, et al. Stroke. 2020;51:549-555) on the risk of severe stroke among NVAF patients in a different population treated with rivaroxaban or warfarin. MATERIAL AND METHODS: This retrospective cohort study included patients from the IBM® MarketScan® Commercial and Medicare databases (2011-2019) who initiated rivaroxaban or warfarin after a diagnosis of NVAF, had ≥6 months of continuous health plan enrollment, had a CHA2DS2-VASc score ≥2, and had no history of stroke or anticoagulant use. Patient data were assessed until the earliest occurrence of a primary inpatient diagnosis of stroke, death, end of health plan enrollment, or end of study. Stroke severity was defined by National Institutes of Health Stroke Scale (NIHSS) score, imputed by random forest model. Cox proportional hazard regression was used to compare risk of stroke between cohorts, balanced by inverse probability of treatment weighting. RESULTS: The mean observation period from index date to either stroke, or end of eligibility or end of data was 28 months. Data from 13,599 rivaroxaban and 39,861 warfarin patients were included. Stroke occurred in 272 rivaroxaban-treated patients (0.97/100 person-years [PY]) and 1,303 warfarin-treated patients (1.32/100 PY). Rivaroxaban patients had lower risk for stroke overall (hazard ratio [HR], 0.82; 95% confidence interval [CI], 0.76-0.88) and for minor (NIHSS 1 to <5; HR, 0.83; 95% CI, 0.74-0.93), moderate (NIHSS 5 to <16; HR, 0.88; 95% CI, 0.78-0.99), and severe stroke (NIHSS 16 to 42; HR, 0.44; 95% CI, 0.22-0.91). CONCLUSIONS: The results of this study in a larger population of NVAF patients align with previous real-world findings and the ROCKET-AF trial by showing improved stroke prevention with rivaroxaban versus warfarin across all stroke severities.

Healthcare costs before and after stroke in patients with non-valvular atrial fibrillation who initiated treatment with rivaroxaban or warfarin.

AIMS: Rivaroxaban reduces stroke compared with warfarin in patients with non-valvular atrial fibrillation (NVAF). This study compared healthcare costs before and after stroke in NVAF patients treated with rivaroxaban or warfarin. MATERIALS AND METHODS: Using de-identified IBM MarketScan Commercial and Medicare databases, this retrospective cohort study (from 2011 to 2019) included patients with NVAF who initiated rivaroxaban or warfarin within 30 days after initial NVAF diagnosis. Patients who developed stroke were identified, and stroke severity was determined by the National Institutes of Health Stroke Scale (NIHSS) score, imputed by a random forest method. Total all-cause per-patient per-year (PPPY) costs of care were determined for patients: (1) who developed stroke during the pre- and post-stroke periods and (2) who remained stroke-free during the follow-up period. Treatment groups were balanced using inverse probability of treatment weighting. RESULTS: A total of 13,599 patients initiated rivaroxaban and 39,861 initiated warfarin, of which 272 (2.0%) and 1,303 (3.3%), respectively, developed stroke during a mean follow-up of 28 months. Among patients who developed stroke, PPPY costs increased from the pre-stroke to post-stroke period, with greater increases in the warfarin cohort relative to the rivaroxaban cohort. Overall, the costs increased by 1.78-fold for rivaroxaban vs 3.07-fold for warfarin; for less severe strokes (NIHSS < 5), costs increased 0.88-fold and 1.05-fold, respectively. Cost increases for more severe strokes (NIHSS ≥ 5) among rivaroxaban patients were half those for warfarin patients (3.19-fold vs 6.37-fold, respectively). Among patients without stroke, costs were similar during the follow-up period between the two treatment groups. CONCLUSIONS: Total all-cause costs of care increased in the post-stroke period, and particularly in the patients treated with warfarin relative to those treated with rivaroxaban. The lower rate of stroke in the rivaroxaban cohort suggests that greater pre- to post-stroke cost increases result from more strokes occurring in the warfarin cohort.

Risks of Stroke and Mortality in Atrial Fibrillation Patients Treated With Rivaroxaban and Warfarin.

Background and Purpose- Oral anticoagulation therapy is standard of care for patients with nonvalvular atrial fibrillation to prevent stroke. This study compared rivaroxaban and warfarin for stroke and all-cause mortality risk reduction in a real-world setting. Methods- This retrospective cohort study (2011-2017) included de-identified patients from the Optum Clinformatics Database who started treatment with rivaroxaban or warfarin within 30 days following initial diagnosis of nonvalvular atrial fibrillation. Before nonvalvular atrial fibrillation diagnosis, patients had 6 months of continuous health plan enrollment and CHA2DS2-VASc score ≥2. Stroke severity was determined by the National Institutes of Health Stroke Scale, imputed based on machine learning algorithms. Stroke and all-cause mortality risks were compared by treatment using Cox proportional hazard regression, with inverse probability of treatment weighting to balance cohorts for baseline risk factors. Stratified analysis by treatment duration was also performed. Results- During a mean follow-up of 27 months, 175 (1.33/100 patient-years [PY]) rivaroxaban-treated and 536 (1.66/100 PY) warfarin-treated patients developed stroke. The inverse probability of treatment weighting model showed that rivaroxaban reduced stroke risk by 19% (hazard ratio [HR], 0.81 [95% CI, 0.73-0.91]). Analysis by stroke severity revealed risk reductions by rivaroxaban of 48% for severe stroke (National Institutes of Health Stroke Scale score, 16-42; HR, 0.52 [95% CI, 0.33-0.82]) and 19% for minor stroke (National Institutes of Health Stroke Scale score, 1 to <5; HR, 0.81 [95% CI, 0.68-0.96]), but no difference for moderate stroke (National Institutes of Health Stroke Scale score, 5 to <16; HR, 0.93 [95% CI, 0.78-1.10]). A total of 41 (0.31/100 PY) rivaroxaban-treated and 147 (0.44/100 PY) warfarin-treated patients died poststroke, 12 (0.09/100 PY) and 67 (0.20/100 PY) of whom died within 30 days, representing mortality risk reductions by rivaroxaban of 24% (HR, 0.76 [95% CI, 0.61-0.95]) poststroke and 59% (HR, 0.41 [95% CI, 0.28-0.60]) within 30 days. Conclusions- After the initial diagnosis of atrial fibrillation, patients treated with rivaroxaban versus warfarin had significant risk reduction for stroke, especially severe stroke, and all-cause mortality after a stroke. Findings from this observational study may help inform anticoagulant choice for stroke prevention in patients with nonvalvular atrial fibrillation.

Assessing stroke severity using electronic health record data: a machine learning approach.

BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structured real-world evidence databases seldom include the severity. The aim of this study was to use machine learning models to impute NIHSS scores for all patients with newly diagnosed stroke from multi-institution electronic health record (EHR) data. METHODS: NIHSS scores available in the Optum© de-identified Integrated Claims-Clinical dataset were extracted from physician notes by applying natural language processing (NLP) methods. The cohort analyzed in the study consists of the 7149 patients with an inpatient or emergency room diagnosis of ischemic stroke, hemorrhagic stroke, or transient ischemic attack and a corresponding NLP-extracted NIHSS score. A subset of these patients (n = 1033, 14%) were held out for independent validation of model performance and the remaining patients (n = 6116, 86%) were used for training the model. Several machine learning models were evaluated, and parameters optimized using cross-validation on the training set. The model with optimal performance, a random forest model, was ultimately evaluated on the holdout set. RESULTS: Leveraging machine learning we identified the main factors in electronic health record data for assessing stroke severity, including death within the same month as stroke occurrence, length of hospital stay following stroke occurrence, aphagia/dysphagia diagnosis, hemiplegia diagnosis, and whether a patient was discharged to home or self-care. Comparing the imputed NIHSS scores to the NLP-extracted NIHSS scores on the holdout data set yielded an R2 (coefficient of determination) of 0.57, an R (Pearson correlation coefficient) of 0.76, and a root-mean-squared error of 4.5. CONCLUSIONS: Machine learning models built on EHR data can be used to determine proxies for stroke severity. This enables severity to be incorporated in studies of stroke patient outcomes using administrative and EHR databases.