Multi-morbidities are Not a Driving Factor for an Increase of COPD-Related 30-Day Readmission Risk

Background and Objective: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States. COPD is expensive to treat, whereas the quality of care is difficult to evaluate due to the high prevalence of multi-morbidity among COPD patients. In the US, the Hospital Readmissions Reduction Program (HRRP) was initiated by the Centers for Medicare and Medicaid Services to penalize hospitals for excessive 30-day readmission rates for six diseases, including COPD. This study examines the difference in 30-day readmission risk between COPD patients with and without comorbidities.Methods: In this retrospective cohort study, we used Cox regression to estimate the hazard ratio of 30-day readmission rates for COPD patients who had no comorbidity and those who had one, two or three, or four or more comorbidities. We controlled for individual, hospital and geographic factors. Data came from three sources: Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID), Area Health Resources Files (AHRF) and the American Hospital Association’s (AHA’s) annual survey database for the year of 2013.Results: COPD patients with comorbidities were less likely to be readmitted within 30 days relative to patients without comorbidities (aHR from 0.84 to 0.87, p \u3c 0.05). In a stratified analysis, female patients with one comorbidity had a lower risk of 30-day readmission compared to female patients without comorbidity (aHR = 0.80, p \u3c 0.05). Patients with public insurance who had comorbidities were less likely to be readmitted within 30 days in comparison with those who had no comorbidity (aHR from 0.79 to 0.84, p \u3c 0.05).Conclusion: COPD patients with comorbidities had a lower risk of 30-day readmission compared with patients without comorbidity. Future research could use a different study design to identify the effectiveness of the HRRP

Observation of long phase-coherence length in epitaxial La-doped CdO thin films

The search for long electron phase coherence length, which is the length that an electron can keep its quantum wave-like properties, has attracted considerable interest in the last several decades. Here, we report the long phase coherence length of ~ 3.7 micro meters in La-doped CdO thin films at 2 K. Systematical investigations of the La doping and the temperature dependences of the electron mobility and the electron phase coherence length reveal contrasting scattering mechanisms for these two physical properties. Furthermore, these results show that the oxygen vacancies could be the dominant scatters in CdO thin films that break the electron phase coherence, which would shed light on further investigation of phase coherence properties in oxide materials.Comment: 13 pages, 6 figure. SI: 8 pages. To appear in Phys. Rev.

A method for predicting postpartum depression via an ensemble neural network model

IntroductionPostpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers for PPD prediction, the requirements for high accuracy and the interpretability of models present new challenges.MethodsThis paper designs an ensemble neural network model for predicting PPD, which combines a Fully Connected Neural Network (FCNN) and a Neural Network with Dropout mechanism (DNN). The weights of FCNN and DNN in the proposed model are determined by their accuracies on the training set and respective Dropout values. The structure of the FCNN is simple and straightforward. The connection pattern among the neurons of the FCNN makes it easy to understand the relationship between the features and the target feature, endowing the proposed model with interpretability. Moreover, the proposed model does not directly rely on the Dropout mechanism to prevent overfitting. Its structure is more stable than that of the DNN, which weakens the negative impact of the Dropout mechanism on the interpretability of the proposed model. At the same time, the Dropout mechanism of the DNN reduces the overfitting risk of the proposed model and enhances its generalization ability, enabling the proposed model to better adapt to different clinical data.ResultsThe proposed model achieved the following performance metrics on the PPD dataset: accuracy of 0.933, precision of 0.958, recall of 0.939, F1-score of 0.948, Matthews Correlation Coefficient (MCC) of 0.855, specificity of 0.923, Negative Predictive Value (NPV) of 0.889, False Positive Rate (FPR) of 0.077, and False Negative Rate (FNR) of 0.061. Compared with 10 classic machine learning classifiers, under different dataset split ratios, the proposed model outperforms in terms of indicators such as accuracy, precision, recall, and F1-score, and also has high stability.DiscussionThe research results show that the proposed model effectively improves the prediction performance of PPD, which can provide guiding suggestions for relevant medical staff and postpartum women in clinical decision-making. In the future, plans include collecting more disease datasets, using the proposed model to predict these diseases, and constructing an online disease prediction platform to embed the proposed model, which will help with real-time disease prediction

SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation

Segment anything model (SAM), an eminent universal image segmentation model, has recently gathered considerable attention within the domain of medical image segmentation. Despite the remarkable performance of SAM on natural images, it grapples with significant performance degradation and limited generalization when confronted with medical images, particularly with those involving objects of low contrast, faint boundaries, intricate shapes, and diminutive sizes. In this paper, we propose SAMUS, a universal model tailored for ultrasound image segmentation. In contrast to previous SAM-based universal models, SAMUS pursues not only better generalization but also lower deployment cost, rendering it more suitable for clinical applications. Specifically, based on SAM, a parallel CNN branch is introduced to inject local features into the ViT encoder through cross-branch attention for better medical image segmentation. Then, a position adapter and a feature adapter are developed to adapt SAM from natural to medical domains and from requiring large-size inputs (1024x1024) to small-size inputs (256x256) for more clinical-friendly deployment. A comprehensive ultrasound dataset, comprising about 30k images and 69k masks and covering six object categories, is collected for verification. Extensive comparison experiments demonstrate SAMUS's superiority against the state-of-the-art task-specific models and universal foundation models under both task-specific evaluation and generalization evaluation. Moreover, SAMUS is deployable on entry-level GPUs, as it has been liberated from the constraints of long sequence encoding. The code, data, and models will be released at https://github.com/xianlin7/SAMUS