Effects of a collaborative health management model on people with congestive heart failure: A systematic review and meta-analysis.

AIM: To determine the effects of collaborative health management of congestive heart failure through the rigorous evaluation and extraction of evidence. BACKGROUND: Over the past two decades, cardiovascular disease has been the leading cause of death worldwide. Multidisciplinary team intervention for congestive heart failure has increased with population ageing and congestive heart failure incidence rate as well as cost of care. However, the effectiveness and feasibility of collaborative health management need to be explored. DESIGN: Systematic review and meta-analysis. METHODS: We conducted systematic literature searches in the Cochrane Library, PubMed, CINAHL and Medline for articles published between 2002 and 2022. After screening based on the inclusion and exclusion criteria, 13 articles were included in a rigorous review and evidence extraction process, evaluated methodological quality using the Jadad Quality Scale. Statistical heterogeneity was evaluated using Review Manager (RevMan Version 5.4) for the meta-analysis. RESULTS: In this study, a systematic review and meta-analysis were performed on 13 studies regarding the collaborative health management of people with congestive heart failure. The common result is that the collaborative health management model enables the enhancement of self-care and monitoring abilities, the strengthening of cardiac function, the alleviation of physiological and psychological symptoms and the improvement of readmission rates, mortality rate and quality of life. CONCLUSION: The congestive heart failure collaborative health management model could decrease the hospitalization rate related to congestive heart failure, all-cause mortality rate, and all-cause hospitalization rate, and improve the quality of life. IMPLICATIONS FOR PRACTICE: The collaborative health management model could effectively coordinate interdisciplinary team cooperation and provide information, which decreases hospitalization and mortality risks and improves their quality of life. NO PATIENT OR PUBLIC CONTRIBUTION: Our paper is a systematic review and meta-analysis, and such details do not apply to our work. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY?: The Collaborative Health Management Model provides in-depth insights, aiding in the design tailored to the specific circumstances of each country. Highlighting its critical role in the context of a global shortage of nursing staff, the model emphasizes the integration of multidisciplinary professional roles and the strengthening of collaboration as essential elements in addressing challenges posed by workforce shortages. Implementation of the Collaborative Health Management Model not only enhances patient care outcomes but also relieves pressure on healthcare systems, lowers medical costs, and addresses challenges arising from the shortage of nursing staff. Consequently, this model not only contributes to individual patient care improvement but also holds broader implications for enhancing the efficiency and sustainability of global healthcare systems. TRIAL AND PROTOCOL REGISTRATION: The detailed study protocol can be found on the PROSPERO website.

Anti-VEGF Therapy Possibly Extends Survival in Patients With Colorectal Brain Metastasis by Protecting Patients From Neurologic Disability.

BACKGROUND: Colorectal brain metastases (CBMs) are rare with poor prognosis. There is still no standard systemic treatment for multiple or unresectable CBM. our study aimed to explore the impact of anti-VEGF therapy on overall survival, brain-specific disease control, and neurologic symptom burden in patients with CBM. METHODS: A total of 65 patients with CBM under treatment were retrospectively enrolled and divided into anti-VEGF based systemic therapy or non-anti-VEGF based therapy. A total of 25 patients who received at least 3 cycles of anti-VEGF agent and 40 patients without anti-VEGF therapy were analyzed by endpoints of overall survival (OS), progression-free survival (PFS), intracranial PFS (iPFS) and neurogenic event-free survival (nEFS). Gene expression in paired primary metastatic colorectal cancer (mCRC), liver, lung and brain metastasis from NCBI data was analyzed using top Gene Ontology (GO) and cBioPortal. RESULTS: Patients who treated with anti-VEGF therapy had significantly longer OS (19.5 vs. 5.5 months, P = .009), iPFS (14.6 vs. 4.1 months, P < .001) and nEFS (17.6 vs. 4.4 months, P < .001). Patients who received anti-VEGF therapy beyond any disease progression presented with superior OS (19.7 vs. 9.4 months, P = .039). Top GO and cBioPortal analysis revealed a stronger molecular function of angiogenesis in intracranial metastasis. CONCLUSIONS: Anti-VEGF based systemic therapy showed favorable efficacy that was reflected in longer overall survival, iPFS and NEFS in patients with CBM.

Design, fabrication, and validation of an aberration-free integral imaging system for 3D reconstruction.

Optical design for reducing aberrations of the micro-lens-array-based integral imaging system is challenging. A design process combining the sequential and non-sequential modes of optical design software is proposed. The process is verified by a system assembled on a coordinate measuring machine with errors of several micrometers. Differences in the modulation transfer function, measured by the slanted-edge method, are less than 0.02 between the paraxial and off-axial regions. Reconstructed images of a U.S. quarter-dollar coin with different refocusing depths show the synthesized defocusing. The estimated depth map and depth-based reconstructed image show the rough shape of the coin.

Association rule mining for the ordered placement of traditional Chinese medicine containers: An experimental study.

In traditional Chinese medicine (TCM) clinics, the pharmacists responsible for dispensing the herbal medicine usually find the desired ingredients based on positions of the shelves (racks; frames; stands). Generally, these containers are arranged in an alphabetical order depending on the herbal medicine they contain. However, certain related ingredients tend to be used together in many prescriptions, even though the containers may be stored far away from each other. This can cause problems, especially when there are many patients and/or the limited number of pharmacists. If the dispensing time takes longer, it is likely to impact the satisfaction of the patients' experience. Moreover, the stamina of the pharmacists will be consumed quickly.In this study, we investigate on an association rule mining technology to improve efficiency in TCM dispensing based on the frequent pattern growth algorithm and try to identify which 2 or 3 herbal medicines will match together frequently in prescriptions. Furthermore, 3 experimental studies are conducted based on a dataset collected from a traditional Chinese medicine hospital. The dataset includes information for an entire year (2014), including 4 seasons and doctors. Afterward, a questionnaire on the usefulness of the extracted rules was administered to the pharmacists in the case hospital. The responses showed the mining results to be very valuable as a reference for the placement and ordering of the frames in the TCM pharmacies and drug stores.

SVM and SVM Ensembles in Breast Cancer Prediction.

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

Association between Aortic Remodeling and Stent Graft-Induced New Entry in Extensive Residual Type A Dissecting Aortic Aneurysm after Hybrid Arch Repair.

BACKGROUND: This study evaluated sequential aortic morphologic remodeling and influencing factors between distal stent graft-induced new entry (SINE) in chronic residual type A dissecting aortic aneurysm after extensive hybrid arch repair. METHODS: We retrospectively analyzed operative and follow-up data of 30 consecutive patients with chronic residual type A aortic dissection aneurysm treated by hybrid type III arch repair (ascending aortic and arch replacement combined with elephant trunk technique before stent-graft deployment) between November 2006 and October 2011. RESULTS: In 3 years, follow-up of 24 patients with successful 1-stage hybrid arch repair and stent grafting. The ratio of true lumen area increased at pulmonary artery level, but minimal change was seen in the thoracic segment distal to stent graft and abdominal aorta. Late distal SINE occurred in 14 patients (SINE group). Cross-sectional area showed significant differences in distal end of pre-stenting graft oversizing ratio (SINE group 4.32 vs. non-SINE group 2.23, P = 0.021(∗)). The thoracic segment thrombosis rate was 90% in SINE and 57% in non-SINE (P = 0.089) groups. CONCLUSIONS: In homogenous population of chronic residual type A dissection, noticeable false lumen thrombosis with true lumen progressive dilatation was only found at the proximal descending aortic segment extending to the middle of stent grafts in both groups. A smaller size selection of the distal stent graft by area measurement would be accompanied with poor aortic remodeling but might be beneficial for SINE prevention. On the other hand, a larger size selection of the distal stent graft area might be favorable for aortic remodeling but could potentially induce SINE.

On mining incomplete medical datasets: Ordering imputation and classification.

BACKGROUND: To collect medical datasets, it is usually the case that a number of data samples contain some missing values. Performing the data mining task over the incomplete datasets is a difficult problem. In general, missing value imputation can be approached, which aims at providing estimations for missing values by reasoning from the observed data. Consequently, the effectiveness of missing value imputation is heavily dependent on the observed data (or complete data) in the incomplete datasets. OBJECTIVE: In this paper, the research objective is to perform instance selection to filter out some noisy data (or outliers) from a given (complete) dataset to see its effect on the final imputation result. Specifically, four different processes of combining instance selection and missing value imputation are proposed and compared in terms of data classification. METHODS: Experiments are conducted based on 11 medical related datasets containing categorical, numerical, and mixed attribute types of data. In addition, missing values for each dataset are introduced into all attributes (the missing data rates are 10%, 20%, 30%, 40%, and 50%). For instance selection and missing value imputation, the DROP3 and k-nearest neighbor imputation methods are employed. On the other hand, the support vector machine (SVM) classifier is used to assess the final classification accuracy of the four different processes. RESULTS: The experimental results show that the second process by performing instance selection first and imputation second allows the SVM classifiers to outperform the other processes. CONCLUSIONS: For incomplete medical datasets containing some missing values, it is necessary to perform missing value imputation. In this paper, we demonstrate that instance selection can be used to filter out some noisy data or outliers before the imputation process. In other words, the observed data for missing value imputation may contain some noisy information, which can degrade the quality of the imputation result as well as the classification performance.

An efficient data preprocessing approach for large scale medical data mining.

BACKGROUND: The size of medical datasets is usually very large, which directly affects the computational cost of the data mining process. Instance selection is a data preprocessing step in the knowledge discovery process, which can be employed to reduce storage requirements while also maintaining the mining quality. This process aims to filter out outliers (or noisy data) from a given (training) dataset. However, when the dataset is very large in size, more time is required to accomplish the instance selection task. OBJECTIVE: In this paper, we introduce an efficient data preprocessing approach (EDP), which is composed of two steps. The first step is based on training a model over a small amount of training data after preforming instance selection. The model is then used to identify the rest of the large amount of training data. METHODS: Experiments are conducted based on two medical datasets for breast cancer and protein homology prediction problems that contain over 100000 data samples. In addition, three well-known instance selection algorithms are used, IB3, DROP3, and genetic algorithms. On the other hand, three popular classification techniques are used to construct the learning models for comparison, namely the CART decision tree, k-nearest neighbor (k-NN), and support vector machine (SVM). RESULTS: The results show that our proposed approach not only reduces the computational cost by nearly a factor of two or three over three other state-of-the-art algorithms, but also maintains the final classification accuracy. CONCLUSIONS: To perform instance selection over large scale medical datasets, it requires a large computational cost to directly execute existing instance selection algorithms. Our proposed EDP approach solves this problem by training a learning model to recognize good and noisy data. To consider both computational complexity and final classification accuracy, the proposed EDP has been demonstrated its efficiency and effectiveness in the large scale instance selection problem.

The impact of bird-beak configuration on aortic remodeling of distal arch pathology after thoracic endovascular aortic repair with the Zenith Pro-Form TX2 thoracic endograft.

OBJECTIVE: Structural changes and incomplete endograft apposition to the aortic arch (bird-beak configuration) after thoracic endovascular aortic repair are poorly understood. The aim of this study was to analyze the morphologic changes, conformability, and angulation factors in patients who underwent stainless steel-based stent graft repair of thoracic aortic pathology. METHODS: From March 2011 to March 2012, the study enrolled 19 patients with aortic pathology requiring proximal fixation in zones 2 and 3 who underwent stent graft repair using Zenith Pro-Form TX2 stent grafts (Cook Medical, Bloomington, Ind). For comparison, another 19 patients who received Zenith Z-Trak stent grafts were selected from December 2009 to February 2011. Chest computed tomography scans were analyzed at baseline, and then at 1, 6, and 12 months postoperatively. Arch angulation and bird-beak configuration were evaluated according to sealing zones of attachment by Aquarius iNtuition software (TeraRecon, San Mateo, Calif). RESULTS: The treated diseases included chronic type B aortic dissection in 17 patients and degenerative aneurysms in 21. Significant arch angle transformation was noted at the zone 2 level between the Pro-Form and Z-Trak treated groups (150° ± 11° vs 158° ± 6°; P = .033) and left subclavian artery level (152° ± 12° vs 160° ± 8°; P = .031) during 1 year of follow-up. The bird-beak configuration was detected in six patients (32%) in the Pro-Form group and in 11 (58%) in the Z-Trak group (P = .096) at 1 month, and in six (32%) in the Pro-Form group and in 14 (74%) in the Z-Trak group (P = .022) at 12 months. The mean bird-beak angle was significantly less in Pro-Form-treated patients at 1 month (5° ± 9° vs 15° ± 13°; P = .019) and at 1 year (6° ± 10° vs 18° ± 15°; P = .033). In the Pro-Form platform, a preoperative zone 2 angle <151.1° was a better estimation of the presence of a postoperative bird-beak configuration, with a sensitivity of 86% and specificity of 83%. CONCLUSIONS: Aortic remodeling after stainless steel stent graft repair of thoracic aortic pathology is a continuous process. Significant arch angle transformation was discovered over the zone 2 and left subclavian artery levels. TX2 Pro-Form stent grafts improved arch conformation after 1 year of follow-up. Furthermore, in the patients with dissection, a preoperative distal arch angle of zone 2 was predictive of postoperative bird-beak configuration, regardless of whether they were treated with a Pro-Form stent graft.

Elucidating the inhibitory mechanisms of magnolol on rat smooth muscle cell proliferation.

The pathological mechanism of percutaneous transluminal coronary angioplasty-induced restenosis has been attributed to outgrowth of vascular smooth muscle cells. Pretreatment with antioxidants has been shown to reduce restenosis. Magnolol, an active compound of Magnolia officinalis, has exhibited approximately 1,000 times more potent antioxidant effects than alpha-tocopherol. In this study, we demonstrate, using cytometric analysis, an approximate 61% reduction of smooth muscle cells progressing to the S-phase by 0.05 mg/ml of magnolol. A BrdU incorporation assay also showed a significant reduction (73%) of DNA synthesis using 0.05 mg/ml of magnolol. The protein level of the proliferating cell nuclear antigen was suppressed by approximately 48% using 0.05 mg/ml of magnolol. This was in agreement with the promoter activity of nuclear factor-kappa B, which was also attenuated by 0.05 mg/ml of magnolol. Since receptor interacting protein and caspase-3 protein expression levels were both increased by magnolol in a dose-dependent manner, the apoptotic pathway may mediate the inhibition of cell growth. Our finding that malondialdehyde formation was significantly inhibited by 0.05 mg/ml of magnolol further supported the antioxidant effect of magnolol. These studies suggest that magnolol might be a potential pharmacological reagent in preventing balloon injury-induced restenosis.