SwissPK<sup>cdw</sup> – A clinical data warehouse for the optimization of pediatric dosing regimens

Goers, Roland; Schmid, Diana Coman; Jäggi, Vera F.; Paioni, Paolo; Okoniewski, Michał; Parker, Althea; Bangerter, Beat; Georgakopoulou, Sofia; Sengstag, Thierry; Bielicki, Julia; Tilen, Romy; Vermeul, Swen; Krämer, Stefanie-Dorothea; Berger, Christoph; Rinn, Bernd; Schwabedissen, Henriette E. Meyer zu

doi:10.1002/psp4.12723

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…It will implement a harmonized paediatric health dataset across Switzerland, which is scalable to additional datasets and sites, and serves as a high-visibility example of how this infrastructure can be enriched with high-density data to deliver cutting-edge research on personalized health. SwissPedHealth also builds on previous successful national multi-centre initiatives such as SwissPedData, the Swiss Pharmacokinetics Clinical Data Warehouse project (SwissPKcdw), 43 the Swiss Paediatric Sepsis Study, 44,45 and the Genetic Study of Immunodeficiency (GSI). [46][47][48][49] The broad scope of SwissPedHealth also favours potential synergies with other current SPHN and PHRT funded projects (https://sphn.ch/network/projects/, such as LUCID, IICU, SPHN-SPAC, SwissPedDW, SwissPedHealth-PREPP, ADONIS, INFRA).…”

Section: Discussionmentioning

confidence: 99%

Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream

Mozun,

Belle,

Agostini

et al. 2024

Preprint

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Introduction: Children represent a large and vulnerable patient group. However, the evidence-base for most paediatric diagnostic and therapeutic procedures remains limited or is often inferred from adults. There is urgency to improve paediatric health care provision based on real-world evidence generation. The digital transformation is a unique opportunity to shape a data-driven, agile, learning health care system and deliver more efficient and personalized care to children and their families. The goal of SwissPedHealth is to build a sustainable and scalable infrastructure to make routine clinical data from paediatric hospitals in Switzerland interoperable, standardized, quality-controlled, and ready for observational research, quality assurance, trials, and health-policy creation. This paper describes the design, aims, and current achievements of SwissPedHealth. Methods and analysis: SwissPedHealth started in September 2022 as one of four National Data Streams co-funded by the Swiss Personalized Health Network (SPHN) and the Personalized Health and Related Technologies (PHRT). SwissPedHealth develops modular governance and regulatory strategies, and harnesses SPHN automatization procedures, in collaboration with clinical data warehouses, the Data Coordination Center, Biomedical Information Technology Network, and other SPHN institutions and funded projects. The SwissPedHealth consortium is led by a multi-site, multi-disciplinary Steering Committee incorporating patient and family representatives. The data stream contains work packages focusing on: 1) governance and implementation of standardized data collection, 2) nested projects to test the feasibility of the data stream, 3) a lighthouse project that enriches the data stream by integrating multi-omics data, aiming to improve diagnoses of rare diseases, and 4) engagement with families through patient and public involvement activities and bioethics interviews. Ethics and dissemination: The health database regulation of SwissPedHealth was approved by the ethics committee (AO_2022-00018). Research findings will be disseminated through national and international conferences, publications in peer-reviewed journals and in lay language via online media and podcasts.

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Section: Discussionmentioning

confidence: 99%

Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream

Mozun,

Belle,

Agostini

et al. 2024

Preprint

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“…Simulations may be easily adjusted and updated if new evidence emerges regarding exposure targets, such as increasing minimal inhibitory concentration (MIC), as has been observed for gentamicin [12,51]. Improved infrastructures allowing the collection and use of real-world data for scientific purposes [52] will further facilitate the development and evaluation of such model-informed dosing approaches and their implementation in a learn-and-confirm cycle (figure 1).…”

Section: Pharmacometric In Silico Studiesmentioning

confidence: 99%

Pharmacometric in silico studies used to facilitate a national dose standardisation process in neonatology – application to amikacin

Gotta,

Bielicki,

Paioni

et al. 2024

Swiss Med Wkly

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BACKGROUND AND AIMS: Pharmacometric in silico approaches are frequently applied to guide decisions concerning dosage regimes during the development of new medicines. We aimed to demonstrate how such pharmacometric modelling and simulation can provide a scientific rationale for optimising drug doses in the context of the Swiss national dose standardisation project in paediatrics using amikacin as a case study. METHODS: Amikacin neonatal dosage is stratified by post-menstrual age (PMA) and post-natal age (PNA) in Switzerland and many other countries. Clinical concerns have been raised for the subpopulation of neonates with a post-menstrual age of 30–35 weeks and a post-natal age of 0–14 days (“subpopulation of clinical concern”), as potentially oto-/nephrotoxic trough concentrations (Ctrough >5 mg/l) were observed with a once-daily dose of 15 mg/kg. We applied a two-compartmental population pharmacokinetic model (amikacin clearance depending on birth weight and post-natal age) to real-world demographic data from 1563 neonates receiving anti-infectives (median birth weight 2.3 kg, median post-natal age six days) and performed pharmacometric dose-exposure simulations to identify extended dosing intervals that would ensure non-toxic Ctrough (Ctrough <5 mg/l) dosages in most neonates. RESULTS: In the subpopulation of clinical concern, Ctrough <5 mg/l was predicted in 59% versus 79–99% of cases in all other subpopulations following the current recommendations. Elevated Ctrough values were associated with a post-natal age of less than seven days. Simulations showed that extending the dosing interval to ≥36 h in the subpopulation of clinical concern increased the frequency of a desirable Ctrough below 5 mg/l to >80%. CONCLUSION: Pharmacometric in silico studies using high-quality real-world demographic data can provide a scientific rationale for national paediatric dose optimisation. This may increase clinical acceptance of fine-tuned standardised dosing recommendations and support their implementation, including in vulnerable subpopulations.

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“…In many countries Clinical Data Warehouses (CDW) collect and reuse healthcare data from EMR for various applications covering all domains of medicine. [12][13][14][15][16][17][18][19][20] To integrate and analyse EMR data from patients treated at our university hospital for the purposes of research, education and institutional management, the hospital has established a CDW. 21 This data can be supplemented with publicly available data from the French National Institute of Statistics and Economic Studies (INSEE) (eg, data on social determinants of health (SDOH)).…”

Section: Open Accessmentioning

confidence: 99%

“…Moreover, the availability of EMR data facilitates the use of new approaches such as state-of-the-art machine-learning methods for risk prediction. In many countries Clinical Data Warehouses (CDW) collect and reuse healthcare data from EMR for various applications covering all domains of medicine 12–20. To integrate and analyse EMR data from patients treated at our university hospital for the purposes of research, education and institutional management, the hospital has established a CDW 21.…”

Section: Introductionmentioning

confidence: 99%

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Artemova,

von Schenck,

et al. 2023

BMJ Open

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PurposeIn-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to dateIn this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plansWe are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality.

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SwissPK^cdw – A clinical data warehouse for the optimization of pediatric dosing regimens

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 8 publications

References 30 publications

Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream

Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream

Pharmacometric in silico studies used to facilitate a national dose standardisation process in neonatology – application to amikacin

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

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SwissPKcdw – A clinical data warehouse for the optimization of pediatric dosing regimens

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 8 publications

References 30 publications

Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream

Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream

Pharmacometric in silico studies used to facilitate a national dose standardisation process in neonatology – application to amikacin

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

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SwissPK^cdw – A clinical data warehouse for the optimization of pediatric dosing regimens