Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction

Machine learning-based prediction of heart failure readmission or death: Implications of choosing the right model and the right metrics

Aims Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models. Methods and results We identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi‐layer perceptron (MLP)‐based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP‐based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity. Conclusions We show that for the medical data with class imbalance, the proposed MLP‐based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death

Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death

Background The prediction of readmission or death after a hospital discharge for heart failure (HF) remains a major challenge. Modern healthcare systems, electronic health records, and machine learning (ML) techniques allow us to mine data to select the most significant variables (allowing for reduction in the number of variables) without compromising the performance of models used for prediction of readmission and death. Moreover, ML methods based on transformation of variables may potentially further improve the performance. Objective To use ML techniques to determine the most relevant and also transform variables for the prediction of 30-day readmission or death in HF patients. Methods We identified all Western Australian patients aged 65 years and above admitted for HF between 2003–2008 in linked administrative data. We evaluated variables associated with HF readmission or death using standard statistical and ML based selection techniques. We also tested the new variables produced by transformation of the original variables. We developed multi-layer perceptron prediction models and compared their predictive performance using metrics such as Area Under the receiver operating characteristic Curve (AUC), sensitivity and specificity. Results Following hospital discharge, the proportion of 30-day readmissions or death was 23.7% in our cohort of 10,757 HF patients. The prediction model developed by us using a smaller set of variables (n = 8) had comparable performance (AUC 0.62) to the traditional model (n = 47, AUC 0.62). Transformation of the original 47 variables further improved (p<0.001) the performance of the predictive model (AUC 0.66). Conclusions A small set of variables selected using ML matched the performance of the model that used the full set of 47 variables for predicting 30-day readmission or death in HF patients. Model performance can be further significantly improved by transforming the original variables using ML methods

Contemporary Incidence and Prevalence of Rheumatic Fever and Rheumatic Heart Disease in Australia Using Linked Data: The Case for Policy Change

Background In 2018, the World Health Organization prioritized control of acute rheumatic fever (ARF) and rheumatic heart disease (RHD), including disease surveillance. We developed strategies for estimating contemporary ARF/RHD incidence and prevalence in Australia (2015-2017) by age group, sex, and region for Indigenous and non-Indigenous Australians based on innovative, direct methods. Methods and Results This population-based study used linked administrative data from 5 Australian jurisdictions. A cohort of ARF (age <45 years) and RHD cases (<55 years) were sourced from jurisdictional ARF/RHD registers, surgical registries, and inpatient data. We developed robust methods for epidemiologic case ascertainment for ARF/RHD. We calculated age-specific and age-standardized incidence and prevalence. Age-standardized rate and prevalence ratios compared disease burden between demographic subgroups. Of 1425 ARF episodes, 72.1% were first-ever, 88.8% in Indigenous people and 78.6% were aged <25 years. The age-standardized ARF first-ever rates were 71.9 and 0.60/100 000 for Indigenous and non-Indigenous populations, respectively (age-standardized rate ratio=124.1; 95% CI, 105.2-146.3). The 2017 Global Burden of Disease RHD prevalent counts for Australia (<55 years) underestimate the burden (1518 versus 6156 Australia-wide extrapolated from our study). The Indigenous age-standardized RHD prevalence (666.3/100 000) was 61.4 times higher (95% CI, 59.3-63.5) than non-Indigenous (10.9/100 000). Female RHD prevalence was double that in males. Regions in northern Australia had the highest rates. Conclusions This study provides the most accurate estimates to date of Australian ARF and RHD rates. The high Indigenous burden necessitates urgent government action. Findings suggest RHD may be underestimated in many high-resource settings. The linked data methods outlined here have potential for global applicability.Judith M. Katzenellenbogen, Daniela Bond-Smith, Rebecca J. Seth, Karen Dempsey, Jeffrey Cannon, Ingrid Stacey, Vicki Wade, Nicholas de Klerk, Melanie Greenland, Frank M. Sanfilippo, Alex Brown, Jonathan R. Carapetis, Rosemary Wyber, Lee Nedkoff, Joe Hung, Dawn Bessarab, Anna P. Ralp