Defined as a platelet count <100x109/L with no known cause, immune thrombocytopenia (ITP) is a diagnosis of exclusion, meaning other thrombocytopenic conditions must be ruled out before establishing the ITP diagnosis. This can lead to errors, unnecessary exposures to expensive and harmful treatments, and increased patient anxiety and distress. In the absence of a standardized diagnostic test, a clinical prediction model, called the Predict-ITP tool, was developed to aid hematologists in establishing the ITP diagnosis among patients who present with thrombocytopenia. Based on a cohort of 839 patients referred to an academic hematology clinic and using penalized logistic regression, the following predictor variables for the ITP diagnosis were identified: 1) high platelet variability index; 2) lowest platelet count; 3) highest mean platelet volume; and 4) history of a major bleed. Internal validation was completed using bootstrap resampling, and showed good discrimination and excellent calibration. Following internal validation and prior to implementation, the Predict-ITP Tool must undergo external validation by evaluating the tool’s performance in a different cohort. A study protocol was developed with the objective of externally validating the Predict-ITP Tool by collecting data from 960 patients from 11 clinics across Canada. The tool will compute the probability of ITP using information available at the time of the initial consultation, and results will be compared with either the local hematologist’s diagnosis at the end of follow-up or the adjudicated diagnosis. Discrimination (the ability to differentiate between patients with and without ITP) and calibration (the agreement between predicted and actual classifications) of the tool will be assessed. The Predict-ITP Tool must demonstrate good discrimination (c-statistic ≥ 0.8) and excellent calibration (calibration-in-the-large close to 0; calibration slope close to 1) to achieve external validation. If implemented, this tool will improve diagnostic accuracy and reduce delays in diagnosis and unnecessary treatments and investigations. / Thesis / Master of Science (MSc) / There lack of a standardized test to diagnose immune thrombocytopenia (ITP) leads to delays in care, use of incorrect treatments, and increased patient anxiety. The Predict-ITP Tool was developed to classify patients as ITP or non-ITP using the following data: 1) platelet counts in the recent past; 2) the highest mean platelet volume; and 3) major bleeding at any time in the past. The preliminary internal validation study showed promise. I developed a study protocol to externally validate the Predict-ITP Tool that will collect data from 960 patients from 11 clinics across Canada to see how accurately the tool would have performed to classify patients as ITP or non-ITP at the first hematology visit compared with the gold standard clinical diagnosis by the hematologist or an independent expert committee. A successful external validation that demonstrates the tool’s predictive accuracy in an external population must be completed before widespread use.

京都大学 / 新制・課程博士 / 博士(社会健康医学) / 甲第24094号 / 社医博第125号 / 新制||社医||12(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 佐藤 俊哉, 教授 中山 健夫, 教授 松田 秀一 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

Objectives: Cardiovascular disease is the leading cause of mortality and morbidity in the developed world. Surgery can improve prognosis and relieve symptoms. Risk prediction models are increasingly being used to inform clinicians and patients about the risks of surgery, to facilitate clinical decision making and for the risk-adjustment of surgical outcome data. The importance of risk prediction models in cardiovascular surgery has been highlighted by the publication of cardiovascular surgery outcome data and the need for risk-adjustment. The overall objective of this thesis is to advance risk prediction modelling in cardiovascular surgery with a focus on the development of models for elective AAA repair and assessment of models for cardiac surgery. Methods: Three large clinical databases (two elective AAA repair and one cardiac surgery) were utilised. Each database was cleaned prior to analysis. Logistic regression was used to develop both regional and national risk prediction models for mortality following elective AAA repair. A regional model to identify the risk of developing renal failure following elective AAA repair was also developed. The performance of a widely used cardiac surgery risk prediction model (the logistic EuroSCORE) over time was evaluated using a national cardiac database. In addition an updated model version (EuroSCORE II) was validated and both models’ performance in emergency cardiac surgery was evaluated. Results: Regional risk models for mortality following elective AAA repair (VGNW model) and a model to predict post-operative renal failure were developed. Validation of the model for mortality using a national dataset demonstrated good performance compared to other available risk models. To improve generalisability a national model (the BAR score) with better discriminatory ability was developed. In a prospective validation of both models using regional data, the BAR score demonstrated excellent discrimination overall and good discrimination in procedural sub-groups. The EuroSCORE was found to have lost calibration over time due to a fall in observed mortality despite an increase in the predicted mortality of patients undergoing cardiac surgery. The EuroSCORE II demonstrated good performance for contemporary cardiac surgery. Both EuroSCORE models demonstrated inadequate performance for emergency cardiac surgery. Conclusions: Risk prediction models play an important role in cardiovascular surgery. Two accurate risk prediction models for mortality following elective AAA repair have been developed and can be used to risk-adjust surgical outcomes and facilitate clinical decision making. As surgical practice changes over time risk prediction models may lose accuracy which has implications for their application. Cardiac risk models may not be sufficiently accurate for high-risk patient groups such as those undergoing emergency surgery and specific emergency models may be required. Continuing research into new risk factors and model outcomes is needed and risk prediction models may play an increasing role in clinical decision making in the future.