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1. Excess Deaths Associated with Rheumatic Heart Disease, Australia, 2013-2017

Author

Stacey, Ingrid, Seth, Rebecca, Nedkoff, Lee, Wade, Vicki, Haynes, Emma, Carapetis, Jonathan, Hung, Joseph, Murray, Kevin, Bessarab, Dawn, and Katzenellenbogen, Judith

Subjects
Discrimination in medical care -- Analysis -- Health aspects -- Social aspects, Australian aborigines -- Health aspects -- Social aspects -- Analysis, Mortality -- Australia, Rheumatic heart disease -- Social aspects -- Health aspects -- Analysis, Health
Abstract

Rheumatic heart disease (RHD), caused by Streptococcus pyogenes infections, is driven by social determinants of health and disproportionately affects Aboriginal and Torres Strait Islanders in Australia (hereafter Indigenous Australians), causing [...]

Published
2024
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2. Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation

Author

Lu, Juan, Hutchens, Rebecca, Hung, Joseph, Bennamoun, Mohammed, McQuillan, Brendan, Briffa, Tom, Sohel, Ferdous, Murray, Kevin, Stewart, Jonathon, Chow, Benjamin, Sanfilippo, Frank, and Dwivedi, Girish

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Machine learning (ML) techniques may improve predictive performance and support decision-making for appropriate antithrombotic therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. Materials and Methods This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The primary outcome was ischemic stroke and major bleeding admission. The secondary outcomes were all-cause death and event-free survival. The discriminant power of ML models was compared with clinical risk scores by the area under the curve (AUC). Risk stratification was assessed using the net reclassification index. Results Multilabel gradient boosting machine provided the best discriminant power for stroke, major bleeding, and death (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models. It provided modest performance improvement for stroke compared to CHA2DS2-VASc (AUC = 0.652), but significantly improved major bleeding prediction compared to HAS-BLED (AUC = 0.522). It also had a much greater discriminant power for death compared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additional risk features (such as hemoglobin level, renal function, etc.) for each outcome. Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.

Published
2022

3. A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

Author

Wang, Tingting, Beyene, Habtamu B., Yi, Changyu, Cinel, Michelle, Mellett, Natalie A., Olshansky, Gavriel, Meikle, Thomas G., Wu, Jingqin, Dakic, Aleksandar, Watts, Gerald F., Hung, Joseph, Hui, Jennie, Beilby, John, Blangero, John, Kaddurah-Daouk, Rima, Salim, Agus, Moses, Eric K., Shaw, Jonathan E., Magliano, Dianna J., Huynh, Kevin, Giles, Corey, and Meikle, Peter J.

Published
2024
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4. Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study

Author

Beyene, Habtamu B., Huynh, Kevin, Wang, Tingting, Paul, Sudip, Cinel, Michelle, Mellett, Natalie A., Olshansky, Gavriel, Meikle, Thomas G., Watts, Gerald F., Hung, Joseph, Hui, Jennie, Beilby, John, Blangero, John, Moses, Eric K., Shaw, Jonathan E., Magliano, Dianna J., Giles, Corey, and Meikle, Peter J.

Published
2024
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7. Can we monitor heart attack in the troponin era: evidence from a population-based cohort study

Author

Rankin Jamie M, Finn Judith C, Bradshaw Pamela J, Ridout Stephen C, Knuiman Matthew W, Hobbs Michael ST, Sanfilippo Frank M, Sprivulis Peter C, and Hung Joseph

Subjects
AHA Medical/Scientific Statements, cardiac biomarkers, diagnosis, myocardial infarction, trends, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background Troponins (highly sensitive biomarkers of myocardial damage) increase counts of myocardial infarction (MI) in clinical practice, but their impact on trends in admission rates for MI in National statistics is uncertain. Methods Cases coded as MI or other cardiac diagnoses in the Hospital Morbidity Data Collection (MI-HMDC) in Western Australia in 1998 and 2003 were classified using revised criteria for MI developed by an International panel convened by the American Heart Association (AHA criteria) using information on symptoms, ECGs and cardiac biomarkers abstracted from samples of medical notes. Age-sex standardized rates of MI-HMDC were compared with rates of MI based on AHA criteria including troponins (MI-AHA) or traditional biomarkers only (MI-AHAck). Results Between 1998 and 2003, rates of MI-HMDC decreased by 3.5% whereas rates of MI-AHA increased by 17%, a difference largely due to increased false-negative cases in the HMDC associated with marked increased use of troponin tests in cardiac admissions generally, and progressively lower test thresholds. In contrast, rates of MI-AHAck declined by 18%. Conclusions Increasing misclassification of MI-AHA by the HMDC may be due to reluctance by clinicians to diagnose MI based on relatively small increases in troponin levels. These influences are likely to continue. Monitoring MI using AHA criteria will require calibration of commercially available troponin tests and agreement on lower diagnostic thresholds for epidemiological studies. Declining rates of MI-AHAck are consistent with long-standing trends in MI in Western Australia, suggesting that neither MI-HMDC nor MI-AHA reflect the true underlying population trends in MI.

Published
2011
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10. Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease

Author

Cadby, Gemma, Giles, Corey, Melton, Phillip E., Huynh, Kevin, Mellett, Natalie A., Duong, Thy, Nguyen, Anh, Cinel, Michelle, Smith, Alex, Olshansky, Gavriel, Wang, Tingting, Brozynska, Marta, Inouye, Mike, McCarthy, Nina S., Ariff, Amir, Hung, Joseph, Hui, Jennie, Beilby, John, Dubé, Marie-Pierre, Watts, Gerald F., Shah, Sonia, Wray, Naomi R., Lim, Wei Ling Florence, Chatterjee, Pratishtha, Martins, Ian, Laws, Simon M., Porter, Tenielle, Vacher, Michael, Bush, Ashley I., Rowe, Christopher C., Villemagne, Victor L., Ames, David, Masters, Colin L., Taddei, Kevin, Arnold, Matthias, Kastenmüller, Gabi, Nho, Kwangsik, Saykin, Andrew J., Han, Xianlin, Kaddurah-Daouk, Rima, Martins, Ralph N., Blangero, John, Meikle, Peter J., and Moses, Eric K.

Published
2022
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17. Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals

Author

Liu, Ching-Ti, Merino, Jordi, Rybin, Denis, DiCorpo, Daniel, Benke, Kelly S., Bragg-Gresham, Jennifer L., Canouil, Mickaël, Corre, Tanguy, Grallert, Harald, Isaacs, Aaron, Kutalik, Zoltan, Lahti, Jari, Marullo, Letizia, Marzi, Carola, Rasmussen-Torvik, Laura J., Rocheleau, Ghislain, Rueedi, Rico, Scapoli, Chiara, Verweij, Niek, Vogelzangs, Nicole, Willems, Sara M., Yengo, Loïc, Bakker, Stephan J. L., Beilby, John, Hui, Jennie, Kajantie, Eero, Müller-Nurasyid, Martina, Rathmann, Wolfgang, Balkau, Beverley, Bergmann, Sven, Eriksson, Johan G., Florez, Jose C., Froguel, Philippe, Harris, Tamara, Hung, Joseph, James, Alan L., Kavousi, Maryam, Miljkovic, Iva, Musk, Arthur W., Palmer, Lyle J., Peters, Annette, Roussel, Ronan, van der harst, Pim, van Duijn, Cornelia M., Vollenweider, Peter, Barroso, Inês, Prokopenko, Inga, Dupuis, Josée, Meigs, James B., and Bouatia-Naji, Nabila

Published
2019
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18. Additional file 1 of Unplanned 30-day readmissions, comorbidity and impact on one-year mortality following incident heart failure hospitalisation in Western Australia, 2001–2015

Author

Weber, Courtney, Hung, Joseph, Hickling, Siobhan, Li, Ian, Murray, Kevin, and Briffa, Tom

Abstract

Additional file 1. Supplementary Table 1. Definitions of ICD-9 and ICD-10-AM codes for identification of comorbid diseases and procedures. Supplementary Table 2. ICD-10-AM codes used to categorise the principal causes of 30-day unplanned readmissions in 30-day survivors. Supplementary Table 3. Clinical characteristics of 30-day survivors with an incident hospitalisation for heart failure between 2001-2015, stratified by sex. Supplementary Table 4. Clinical characteristics of patients with an incident hospitalisation for heart failure between 2001-2015, stratified by survival within 30 days post-discharge. Supplementary Table 5. The overall age-and sex-standardised rates and age-standardised rates by sex, per 1000 population, of 30-day unplanned readmission in patients who survived 30-days post-discharge after incident heart failure hospitalisation between 2001 and 2015.

Published
2023
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25. Long-term outcomes following rheumatic heart disease diagnosis in Australia

Author

Stacey, Ingrid, primary, Hung, Joseph, additional, Cannon, Jeff, additional, Seth, Rebecca J, additional, Remenyi, Bo, additional, Bond-Smith, Daniela, additional, Griffiths, Kalinda, additional, Sanfilippo, Frank, additional, Carapetis, Jonathan, additional, Murray, Kevin, additional, and Katzenellenbogen, Judith M, additional

Published
2021
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26. Additional file 6 of Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Author

Hunter, Michael L., Knuiman, Matthew W., Musk, Bill (A.W.), Hui, Jennie, Murray, Kevin, Beilby, John P., Hillman, David R., Hung, Joseph, Newton, Robert U., Bucks, Romola S., Straker, Leon, Walsh, John P., Zhu, Kun, Bruce, David G., Eikelboom, Robert H., Davis, Timothy M. E., Mackey, David A., and James, Alan L.

Subjects
mental disorders
Abstract

Additional file 6. Supplementary Table S4. Morbidity patterns and number of conditions. Number of participants with each number of conditions and percentage within each morbidity pattern.

Published
2021
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27. Additional file 5 of Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Author

Hunter, Michael L., Knuiman, Matthew W., Musk, Bill (A.W.), Hui, Jennie, Murray, Kevin, Beilby, John P., Hillman, David R., Hung, Joseph, Newton, Robert U., Bucks, Romola S., Straker, Leon, Walsh, John P., Zhu, Kun, Bruce, David G., Eikelboom, Robert H., Davis, Timothy M. E., Mackey, David A., and James, Alan L.

Abstract

Additional file 5 Supplementary Table S3. All triplets of conditions with prevalence > 1.5% and observed/expected (O/E) ratio > 1.5 (and p value

Published
2021
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28. Additional file 4 of Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Author

Hunter, Michael L., Knuiman, Matthew W., Musk, Bill (A.W.), Hui, Jennie, Murray, Kevin, Beilby, John P., Hillman, David R., Hung, Joseph, Newton, Robert U., Bucks, Romola S., Straker, Leon, Walsh, John P., Zhu, Kun, Bruce, David G., Eikelboom, Robert H., Davis, Timothy M. E., Mackey, David A., and James, Alan L.

Abstract

Additional file 4. Supplementary Table S2. Relationship between number of conditions and participant characteristics.

Published
2021
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29. Additional file 1 of Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Author

Hunter, Michael L., Knuiman, Matthew W., Musk, Bill (A.W.), Hui, Jennie, Murray, Kevin, Beilby, John P., Hillman, David R., Hung, Joseph, Newton, Robert U., Bucks, Romola S., Straker, Leon, Walsh, John P., Zhu, Kun, Bruce, David G., Eikelboom, Robert H., Davis, Timothy M. E., Mackey, David A., and James, Alan L.

Abstract

Additional file 1. Supplementary File 1. BHAS Phase 1 Questionnaire 2010–2015.

Published
2021
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30. Additional file 2 of Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Author

Hunter, Michael L., Knuiman, Matthew W., Musk, Bill (A.W.), Hui, Jennie, Murray, Kevin, Beilby, John P., Hillman, David R., Hung, Joseph, Newton, Robert U., Bucks, Romola S., Straker, Leon, Walsh, John P., Zhu, Kun, Bruce, David G., Eikelboom, Robert H., Davis, Timothy M. E., Mackey, David A., and James, Alan L.

Abstract

Additional file 2. Supplementary File 2. Statistical Supplement. Latent Class Analysis method.

Published
2021
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31. Additional file 3 of Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Author

Hunter, Michael L., Knuiman, Matthew W., Musk, Bill (A.W.), Hui, Jennie, Murray, Kevin, Beilby, John P., Hillman, David R., Hung, Joseph, Newton, Robert U., Bucks, Romola S., Straker, Leon, Walsh, John P., Zhu, Kun, Bruce, David G., Eikelboom, Robert H., Davis, Timothy M. E., Mackey, David A., and James, Alan L.

Abstract

Additional file 3. Supplementary Table S1. Mean, median and maximum number of conditions by gender and demographic groups.

Published
2021
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37. A randomised trial of the short-term effect of a minimal nutrition intervention in general practice

Author

Hickling, Siobhan, Knuiman, Matthew, Jamrozik, Konrad, and Hung, Joseph

Subjects
Folic acid -- Management, Diet therapy -- Methods, Company business management, Health
Abstract

Objective: To test the effectiveness of a minimal nutrition intervention to increase dietary intake of folate. Method: Randomised controlled trial in the general practice setting of personalised, face-to-face feedback with educational materials to increase dietary intake of folate. Results: One hundred and nine intervention and 94 control subjects completed baseline and follow-up assessments within the study period. At baseline (August-October 2004), there were no significant differences in folate intake scores between the intervention and control groups. At the eight-week follow-up, the mean folate intake score in the intervention group was significantly higher than in the control group, indicating that the nutrition intervention significantly increased reported dietary intake of folate. Conclusion: Presenting individual dietary feedback in conjunction with educational materials in the general practice setting is an effective way of changing dietary behaviour. Implications: The present study suggests a way in which health-related dietary change may be encouraged on a large scale without incurring a large cost. Keywords: Nutrition intervention; folate; general practice; dietary assessment.

Published
2007

39. Correction: High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies

Author

Beyene, Habtamu B., primary, Olshansky, Gavriel, additional, T. Smith, Adam Alexander, additional, Giles, Corey, additional, Huynh, Kevin, additional, Cinel, Michelle, additional, Mellett, Natalie A., additional, Cadby, Gemma, additional, Hung, Joseph, additional, Hui, Jennie, additional, Beilby, John, additional, Watts, Gerald F., additional, Shaw, Jonathan E., additional, Moses, Eric K., additional, Magliano, Dianna J., additional, and Meikle, Peter J., additional

Published
2020
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41. High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies

Author

Beyene, Habtamu B., primary, Olshansky, Gavriel, additional, T. Smith, Adam Alexander, additional, Giles, Corey, additional, Huynh, Kevin, additional, Cinel, Michelle, additional, Mellett, Natalie A., additional, Cadby, Gemma, additional, Hung, Joseph, additional, Hui, Jennie, additional, Beilby, John, additional, Watts, Gerald F., additional, Shaw, Jonathan S., additional, Moses, Eric K., additional, Magliano, Dianna J., additional, and Meikle, Peter J., additional

Published
2020
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45. Essentials of interventional cancer pain management

Author

Gulati, Amitabh, editor, Puttanniah, Vinay, editor, Bruel, Brian M., editor, Rosenberg, William S., editor, Hung, Joseph C., editor, Gulati, Amitabh, editor, Puttanniah, Vinay, editor, Bruel, Brian M., editor, Rosenberg, William S., editor, and Hung, Joseph C., editor

Published
2019

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