CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records

Abstract Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make t...

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Autores principales:	Rajagopalan Ramaswamy, Soon Nan Wee, Kavya George, Abhijit Ghosh, Joydeep Sarkar, Rolf Burghaus, Jörg Lippert
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/f854ddb615644dceb426e087b6f7d1c4
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spelling	oai:doaj.org-article:f854ddb615644dceb426e087b6f7d1c42021-11-15T18:41:53ZCKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records2163-830610.1002/psp4.12695https://doaj.org/article/f854ddb615644dceb426e087b6f7d1c42021-11-01T00:00:00Zhttps://doi.org/10.1002/psp4.12695https://doaj.org/toc/2163-8306Abstract Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make this a formidable task. We have developed a hybrid semimechanistic modeling methodology to investigate CKD progression. The model is represented as a system of ordinary differential equations with embedded neural networks and takes into account complex disease progression pathways, feedback loops, and effects of 53 medications to generate time trajectories of eight clinical biomarkers that capture CKD progression due to various risk factors. The model was applied to real world data of US patients with CKD to map the available longitudinal information onto a set of time‐invariant patient‐specific parameters with a clear biological interpretation. These parameters describing individual patients were used to segment the cohort using a clustering approach. Model‐based simulations were conducted to investigate cluster‐specific treatment strategies. The model was able to reliably reproduce the variability in biomarkers across the cohort. The clustering procedure segmented the cohort into five subpopulations – four with enhanced sensitivity to a specific risk factor (hypertension, hyperlipidemia, hyperglycemia, or impaired kidney) and one that is largely insensitive to any of the risk factors. Simulation studies were used to identify patient‐specific strategies to restrain or prevent CKD progression through management of specific risk factors. The semimechanistic model enables identification of disease progression phenotypes using longitudinal data that aid in prioritizing treatment strategies at individual patient level.Rajagopalan RamaswamySoon Nan WeeKavya GeorgeAbhijit GhoshJoydeep SarkarRolf BurghausJörg LippertWileyarticleTherapeutics. PharmacologyRM1-950ENCPT: Pharmacometrics & Systems Pharmacology, Vol 10, Iss 11, Pp 1343-1356 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Therapeutics. Pharmacology RM1-950
spellingShingle	Therapeutics. Pharmacology RM1-950 Rajagopalan Ramaswamy Soon Nan Wee Kavya George Abhijit Ghosh Joydeep Sarkar Rolf Burghaus Jörg Lippert CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records
description	Abstract Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make this a formidable task. We have developed a hybrid semimechanistic modeling methodology to investigate CKD progression. The model is represented as a system of ordinary differential equations with embedded neural networks and takes into account complex disease progression pathways, feedback loops, and effects of 53 medications to generate time trajectories of eight clinical biomarkers that capture CKD progression due to various risk factors. The model was applied to real world data of US patients with CKD to map the available longitudinal information onto a set of time‐invariant patient‐specific parameters with a clear biological interpretation. These parameters describing individual patients were used to segment the cohort using a clustering approach. Model‐based simulations were conducted to investigate cluster‐specific treatment strategies. The model was able to reliably reproduce the variability in biomarkers across the cohort. The clustering procedure segmented the cohort into five subpopulations – four with enhanced sensitivity to a specific risk factor (hypertension, hyperlipidemia, hyperglycemia, or impaired kidney) and one that is largely insensitive to any of the risk factors. Simulation studies were used to identify patient‐specific strategies to restrain or prevent CKD progression through management of specific risk factors. The semimechanistic model enables identification of disease progression phenotypes using longitudinal data that aid in prioritizing treatment strategies at individual patient level.
format	article
author	Rajagopalan Ramaswamy Soon Nan Wee Kavya George Abhijit Ghosh Joydeep Sarkar Rolf Burghaus Jörg Lippert
author_facet	Rajagopalan Ramaswamy Soon Nan Wee Kavya George Abhijit Ghosh Joydeep Sarkar Rolf Burghaus Jörg Lippert
author_sort	Rajagopalan Ramaswamy
title	CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records
title_short	CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records
title_full	CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records
title_fullStr	CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records
title_full_unstemmed	CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records
title_sort	ckd subpopulations defined by risk‐factors: a longitudinal analysis of electronic health records
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/f854ddb615644dceb426e087b6f7d1c4
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CKD subpopulations defined by risk‐factors: A longitudinal analysis of electronic health records

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