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Quality Assessment in Paediatric Cardiology: Experiences from Leveraging a Clinical Data Warehouse
Wolfgang Wällisch1, Sven Dittrich1, Ariawan Purbojo2
1Department of Paediatric Cardiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany.
Insights
A new algorithm identifies mortality risk factors in pediatric cardiac surgery patients. The creatinine ratio emerged as a significant predictor, alongside extracorporeal membrane oxygenation and low body weight.
Area of Science:
- Pediatric Cardiology
- Cardiac Surgery Outcomes
- Health Informatics
Background:
- Quality metrics for pediatric heart centers often use registry data.
- This study introduces an algorithm to analyze raw clinical data for mortality risk factors post-pediatric cardiac surgery.
Purpose of the Study:
- To identify significant mortality risk factors in pediatric cardiac surgery patients.
- To develop a framework for real-time analysis of raw clinical data for quality assessment and risk stratification.
Main Methods:
- Retrospective monocentric study of patients under 18 years old from 2011-2020.
- Categorization of congenital heart disease (CHD) into four groups.
- Evaluation of preoperative, demographic, periprocedural, and postsurgical risk factors.
Main Results:
- 1700 patients with 2157 hospitalizations were analyzed.
- Extracorporeal membrane oxygenation (hazard ratio 13.97), weight < 2500 g, and the creatinine ratio were significant mortality predictors.
- Patients in the univentricular heart group I also showed elevated mortality risk.
Conclusions:
- The creatinine ratio is a strong laboratory-based predictor of mortality.
- Established predictors like ECMO and low body weight are confirmed.
- The framework supports privacy-preserving, real-time quality metric assessment and risk stratification.
Background:
The generation of quality metrics for paediatric heart centre programmes frequently relies on registry data, with all the known benefits and disadvantages. This retrospective monocentric study introduces an algorithm capable of processing unedited clinical data to identify mortality risk factors following paediatric cardiac surgery.
Methods:
Patients who had undergone cardiac surgery in the department during the period from 2011 to 2020 were included when aged < 18 years. Congenital heart disease (CHD) was categorised into four diagnosis groups through hierarchical integration of the index surgery and CHD diagnosis. We evaluated preoperative, demographic, periprocedural, and postsurgical risk factors.
Results:
A total of 1700 patients with 2157 hospitalization encounters were included. The risk factors for elevated mortality with the highest degree of significance were extracorporeal membrane oxygenation (hazard ratio 13.97, p < 0.001), weight < 2500 g, patients in the univentricular heart group I, and the creatinine ratio.
Conclusions:
Beyond confirming established predictors such as ECMO and low body weight < 2500 g, the present analysis highlights the creatinine ratio as a strong laboratory-based predictor of mortality. The applied framework serves as a foundational step towards enabling the real-time utilisation of raw datasets across multiple centres, thereby supporting privacy-preserving and efficient quality metric assessment as well as enhanced risk stratification.
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