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Title: Risk prediction following cardiac surgery
Author: Howitt, Samuel
ISNI:       0000 0004 7971 147X
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2019
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Objectives: Around 1000 patients die each year in the UK due to complications suffered following cardiac surgery. Early identification of those at risk of specific complications would allow targeted interventions aimed at reducing the harm caused by those complications. However, commonly used risk models only quantify overall mortality risk for groups of patients based on analyses of pre- and intra-operative data. These models do not predict specific complications and are unable to update risk estimates as postoperative events unfold. This thesis aims to advance understanding of postoperative risk prediction following cardiac surgery by assessing the performance of existing risk prediction tools in this population and developing novel risk models. Methods: Postoperative physiological monitoring data, blood test results, medication administration data and demographics for over 3000 patients were cleaned, analysed using computerised processing algorithms and entered into a comprehensive database. The database was used to validate three mortality models, the Sepsis-3 diagnostic criteria and the KDIGO AKI criteria. A novel dynamic Bayesian model which analyses an individual's urine output to predict their risk of severe oliguria was developed and validated. Finally, the potential usefulness of potassium and magnesium concentrations when predicting atrial fibrillation (AF) was assessed. Results: While the logistic Cardiac Surgery Score (logCASUS), Rapid Clinical Evaluation (RACE) and Sequential Organ Failure Assessment (SOFA) score all discriminated well between survivors and those who died, calibration of the models was inadequate. The Sepsis-3 criteria identified patients at increased risk of adverse outcomes. The KDIGO staging criteria were poorly calibrated, overestimating the risk associated with mild oliguria following cardiac surgery. The Bayesian urine output model discriminated excellently between those who did and did not go on to suffer severe oliguria and was well calibrated. Postoperative potassium and magnesium concentrations were similar for those who did and did not suffer AF. Conclusion: The clinical usefulness of existing risk stratification methods has been assessed and weaknesses identified. It has been demonstrated that serum potassium and magnesium concentrations are unlikely to be useful when predicting AF following cardiac surgery. A novel approach to modelling urine output has been described and validated. The model's performance should be assessed in other settings and then the clinical usefulness of the model could be assessed in clinical trials. The methodology described in this thesis should be replicated to improve postoperative prediction of other complications following cardiac surgery.
Supervisor: McCollum, Charles ; Malagon, Ignacio Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: risk prediction ; cardiac surgery