For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
10
views
33
references
 Top references
cited by
4
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      150
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Family nursing with the assistance of network improves clinical outcome and life quality in patients underwent coronary artery bypass grafting : A consolidated standards of reporting trials-compliant randomized controlled trial

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background:

          Family nursing with the assistance of network (FNAN) improves nurses’ practice and provides family/community-oriented nursing care. This study aimed to explore the effects of FNAN on the clinical outcome and life quality of coronary atherosclerotic heart disease (CHD) patients underwent coronary artery bypass grafting (CABG).

          Trial Design:

          This study is a randomized, placebo-controlled and double-blind trial.

          Methods:

          One-hundred and twelve patients underwent CABG were randomly divided into control group (CG, routine family nursing care) and experimental group (EG, FNAN) and the allocation ratio was 1:1. The situation of anxiety and depression were analyzed using the Hamilton Anxiety Scale (HAMA) scale and Hamilton Depression Scale (HAMD). Sleep quality was measured by using Pittsburgh Sleep Quality Index (PSQI). Lung function parameters were measured, including minute ventilation (MVV), partial pressure of oxygen (PaO 2), partial pressure of arterial carbon dioxide (PaCO 2), oxygen saturation measurement by pulse oximetry (SpO 2), forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC). Life quality was measured by using Chronic Obstructive Pulmonary Disease Assessment Test (CAT).

          Results:

          After a 3-month intervention, 10 and 6 patients were lost in the CG and EG groups, respectively. The scores of HAMA, HAMD, PSQI and CAT were reduced in the EG group when compared with the CG group ( P < .05). The values of MVV, PaO 2, SpO 2, FEV1 and FVC in the EG group was higher than those in the CG group whereas the levels of PaCO 2 in the EG group was lower than those in the CG group ( P < .05). PSQI score had a strong relationship with the values of MVV, PaO 2, PaCO 2, SpO 2, FEV1, and FVC.

          Conclusion:

          FNAN improves the clinical outcome and life quality in the patients underwent CABG.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

          Jeffrey J Nirschl, Andrew Janowczyk, Eliot Peyster … (2018)
          Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.
          Article 0 comments Cited 37 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The effectiveness of telehealth care on caregiver burden, mastery of stress, and family function among family caregivers of heart failure patients: a quasi-experimental study.

            Li-Chi Chiang, Wan-Chou Chen, Yu-Tzu Dai … (2012)
            Telehealth care was developed to provide home-based monitoring and support for patients with chronic disease. The positive effects on physical outcome have been reported; however, more evidence is required concerning the effects on family caregivers and family function for heart failure patients transitioning from the hospital to home.
            Article 0 comments Cited 37 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Predicting coronary artery disease: a comparison between two data mining algorithms

              Haleh Ayatollahi, Leila GHOLAMHOSSEINI, Masoud Salehi (2019)
              Background Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals. Methods The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing. Results The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test’s result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model. Conclusion According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.
              Article 0 comments Cited 23 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Medicine (Baltimore)
                Journal ID (iso-abbrev): Medicine (Baltimore)
                Journal ID (publisher-id): MEDI
                Title: Medicine
                Publisher: Lippincott Williams & Wilkins (Hagerstown, MD )
                ISSN (Print): 0025-7974
                ISSN (Electronic): 1536-5964
                Publication date Collection: 11 December 2020
                Publication date (Electronic): 11 December 2020
                Volume: 99
                Issue: 50
                Electronic Location Identifier: e23488
                Affiliations
                Department of Cardiac Surgery, The First Hospital of Jilin University, Changchun, China.
                Author notes
                [∗ ]Correspondence: Mi Jiang, Department of Cardiac Surgery, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun 130021, China (e-mail: jiangmidr@ 123456126.com ).
                Author information
                http://orcid.org/0000-0003-1362-6478
                Article
                Publisher ID: MD-D-20-04142 Accession ID: 23488
                DOI: 10.1097/MD.0000000000023488
                PMC ID: 7738076
                PubMed ID: 33327282
                SO-VID: b29c4965-e6c7-48e7-9614-d2ba3dbaf895
                Copyright © Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc.
                License:

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

                History
                Date received : 11 May 2020
                Date revision received : 17 September 2020
                Date accepted : 3 November 2020
                Categories
                Subject: 6300
                Subject: Research Article
                Subject: Clinical Trial/Experimental Study
                Custom metadata
                OPEN-ACCESS TRUE

                Keywords: coronary artery bypass grafting,family nursing through network,heart disease,life quality,outcome
                Data availability:
                Keywords: coronary artery bypass grafting, family nursing through network, heart disease, life quality, outcome

                Comments

                Comment on this article

                scite_

                Similar content150

                See all similar

                Cited by4

                See all cited by

                Most referenced authors293

                See all reference authors