scholarly journals Recommendations for a Clinical Decision Support for the Management of Individuals with Chronic Kidney Disease

2009 ◽  
Vol 4 (2) ◽  
pp. 273-283 ◽  
Author(s):  
Meenal B. Patwardhan ◽  
Kensaku Kawamoto ◽  
David Lobach ◽  
Uptal D. Patel ◽  
David B. Matchar
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Support ◽  
Kidney Disease ◽  
Clinical Decision Support ◽  
Clinical Decision

scholarly journals Effect of 2 Clinical Decision Support Strategies on Chronic Kidney Disease Outcomes in Primary Care

2018 ◽  
Vol 1 (6) ◽  
pp. e183377 ◽  
Author(s):  
Jennifer K. Carroll ◽  
Gerald Pulver ◽  
L. Miriam Dickinson ◽  
Wilson D. Pace ◽  
Joseph A. Vassalotti ◽  
...  
Keyword(s):  
Primary Care ◽  
Chronic Kidney Disease ◽  
Decision Support ◽  
Kidney Disease ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Disease Outcomes ◽  
Support Strategies

scholarly journals Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Suliman A. Alsuhibany ◽  
Sayed Abdel-Khalek ◽  
Ali Algarni ◽  
Aisha Fayomi ◽  
Deepak Gupta ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Deep Learning ◽  
Decision Support ◽  
Kidney Disease ◽  
Internet Of Things ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Healthcare Applications ◽  
Wide Range

Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches.

A Multicomponent Intervention to Improve Blood Pressure Management in Chronic Kidney Disease: A Protocol for a Pragmatic Clinical Trial

2021 ◽  
Author(s):  
John L. Kilgallon ◽  
Michael Gannon ◽  
Zoe Burns ◽  
Gearoid McMahon ◽  
Patricia Dykes ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Chronic Kidney Disease ◽  
Risk Factor ◽  
Decision Support ◽  
Kidney Disease ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Primary Care Providers ◽  
Behavioral Economic ◽  
Multicomponent Intervention

Abstract BackgroundChronic kidney disease is common, leads to end stage renal disease, and is a major risk factor for cardiovascular disease. Although both chronic kidney disease and hypertension, the main risk factor for disease progression, are not difficult to diagnose, both often go unrecognized by primary care providers. It has yet to be determined whether a multicomponent intervention that leverages electronic health records and behavioral economic principles can improve diagnosis, treatment, and control of hypertension in chronic kidney disease. MethodsThe aim of this pragmatic, cluster-randomized controlled trial is to evaluate a clinical decision support system based in behavioral economic and user-centered design principles that will: 1) synthesize existing laboratory tests, medication orders, and vital sign data; 2) increase recognition of chronic kidney disease, 3) increase recognition of uncontrolled hypertension in chronic kidney disease patients, and 4) deliver evidence-based chronic kidney disease and hypertension management recommendations. The intervention has been designed and piloted. The primary endpoint is the change in mean systolic blood pressure between baseline and 6 months compared across arms. We will use an effectiveness-implementation hybrid trial type 2 design and the RE-AIM framework to guide evaluation of process and outcome measures. Patients with two prior eGFR 16-59 mL/min/1.73m2 separated by 90 days or two prior UACR >30mg/g, one SBP >140 mmHg within the 2 years preceding the enrollment visit, and SBP >140 mmHg at enrollment will be included; patients with a most recent eGFR ≤ 20 or 2 previous eGFRs within 2 years separated by at least 90 days ≤ 15 will be excluded. Rao-Scott chi-square tests and GEE z-tests will be used. We calculated that 497 evaluable patients per arm and an average of 6 patients per provider would provide over 80% power to detect an average 3 mmHg SBP decrease in the intervention arm. Discussionhe proposed study, if successful, would be the first to improve hypertension in chronic kidney disease patients through a multicomponent intervention that incorporates clinical decision support and behavioral methods. Trial RegistrationClinicalTrials.gov identifier: NCT03679247. Registered September 20, 2018, https://clinicaltrials.gov/ct2/show/NCT03679247?term=Samal&draw=2&rank=1.

scholarly journals Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

2021 ◽  
Vol 19 (1) ◽  
pp. 226
Author(s):  
Syed Imran Ali ◽  
Su Woong Jung ◽  
Hafiz Syed Muhammad Bilal ◽  
Sang-Ho Lee ◽  
Jamil Hussain ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Support ◽  
Kidney Disease ◽  
User Experience ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Maintenance Hemodialysis ◽  
Mineral And Bone Disorder ◽  
Bone Disorder

Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched. In this regard, this study represents one of the first studies in which a CDSS is proposed for clinicians who manage patients with end-stage renal disease undergoing maintenance hemodialysis, almost all of whom have some manifestation of chronic kidney disease–mineral and bone disorder (CKD–MBD). The primary objective of the system is to aid clinicians in dosage prescription by levering medical domain knowledge as well existing practices. The proposed CDSS is evaluated with a real-world hemodialysis patient dataset acquired from Kyung Hee University Hospital, South Korea. Our evaluation demonstrates overall high compliance based on the concordance metric between the proposed CKD–MBD CDSS recommendations and the routine clinical practice. The concordance rate of overall medication dosing selection is 78.27%. Furthermore, the usability aspects of the system are also evaluated through the User Experience Questionnaire method to highlight the appealing aspects of the system for clinicians. The overall user experience dimension scores for pragmatic, hedonic, and attractiveness are 1.53, 1.48, and 1.41, respectively. A service reliability for the Cronbach’s alpha coefficient greater than 0.7 is achieved using the proposed system, whereas a dependability coefficient of the value 0.84 reveals a significant effect.

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