Relative contribution of trends in myocardial infarction event rates and case fatality to declines in mortality: an international comparative study of 1·95 million events in 80·4 million people in four countries

Camacho, Ximena; Nedkoff, Lee; Wright, Frederick J.; Nghiem, Nhung; Buajitti, Emmalin; Goldacre, Raphael; Rosella, Laura; Seminog, Olena; Tan, Eng Joo; Hayes, Alison; Hayen, Andrew; Wilson, Nick; Blakely, Tony; Clarke, Philip

doi:10.1016/s2468-2667(22)00006-8

Cited by 29 publications

(29 citation statements)

References 33 publications

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“…We also further restricted the population to people aged between 30 to 74 years old at the time of the census (in 2013). We selected this age range because of the relative rarity of CVD cases prior to 30 years of age and the presence of multiple comorbid conditions in people aged 75 years and older which were not fully captured in the Census and IDI [ 36 ]. Observations with missing age and sex information were very few and therefore excluded from the analysis.…”

Section: Methodsmentioning

confidence: 99%

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets

et al. 2023

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Objectives To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD). Methods We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-nearest neighbourhood (KNN) and random forest) with the traditional regression models. Results The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6–9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users. Conclusions This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.

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Section: Methodsmentioning

confidence: 99%

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets

et al. 2023

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“…It is with this appreciation that the study by Ximena Camacho and colleagues in The Lancet Public Health is of importance. 6 In one of the largest samples of any study of acute myocardial infarction to date, the authors analyse contemporary trends in event and case fatality rates for myocardial infarction across four jurisdictions with national healthcare systems from 2002-15. 6 This large sample size provides perhaps the most accurate estimation of event and case fatality rates of myocardial infarction to date, and allows for granularity when examining differences between demographic subgroups, which is often lacking from other studies.…”

Section: Reducing the Incidence And Mortality From Myocardial Infarctionmentioning

confidence: 99%

“…6 In one of the largest samples of any study of acute myocardial infarction to date, the authors analyse contemporary trends in event and case fatality rates for myocardial infarction across four jurisdictions with national healthcare systems from 2002-15. 6 This large sample size provides perhaps the most accurate estimation of event and case fatality rates of myocardial infarction to date, and allows for granularity when examining differences between demographic subgroups, which is often lacking from other studies. This adds meaningfully to our knowledge base and sets a high standard for evidence for epidemiological studies of cardiovascular disease moving forward.…”

Section: Reducing the Incidence And Mortality From Myocardial Infarctionmentioning

confidence: 99%

Reducing the incidence and mortality from myocardial infarction

Reed

Menon

2022

The Lancet Public Health

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“…Myocardial infarction (MI) is a severe disease that occurs globally; from 2002 to 2015, the incidence of MI was ~242/100,000 individuals per year ( 1 ). According to the universal definition of MI ( 2 ), it may be divided into five types and is primarily induced by acute myocardial ischemia resulting from several factors.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis identifies ferroptosis‑related genes in the regulatory mechanism of myocardial infarction

Jiang

Wang

et al. 2022

Exp Ther Med

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Since ferroptosis is considered to be a notable cause of cardiomyocyte death, inhibiting ferroptosis has become a novel strategy in reducing cardiac cell death and improving cardiopathic conditions. Therefore, the aim of the present study was to search for ferroptosis-related hub genes and determine their diagnostic value in myocardial infarction (MI) to aid in the diagnosis and treatment of the disease. A total of 10,286 DEGs were identified, including 6,822 upregulated and 3.464 downregulated genes in patients with MI compared with healthy controls. After overlapping with ferroptosis-related genes, 128 ferroptosis-related DEGs were obtained. WGCNA successfully identified a further eight functional modules, from which the blue module had the strongest correlation with MI. Blue module genes and ferroptosis-related differentially expressed genes were overlapped to obtain 20 ferroptosis-related genes associated with MI. Go and KEGG analysis showed that these genes were mainly enriched in cellular response to chemical stress, trans complex, transferring, phosphorus-containing groups, protein serine/threonine kinase activity, FoxO signaling pathway. Hub genes were obtained from 20 ferroptosis-related genes through the PPI network. The expression of hub genes was found to be down-regulated in the MI group. Finally, the miRNAs-hub genes and TFs-hub genes networks were constructed. The GSE141512 dataset and the use of RT-qPCR assays on patient blood samples were used to confirm these results. The results showed that ATM, PIK3CA, MAPK8, KRAS and SIRT1 may play key roles in the development of MI, and could therefore be novel markers or targets for the diagnosis or treatment of MI.

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Relative contribution of trends in myocardial infarction event rates and case fatality to declines in mortality: an international comparative study of 1·95 million events in 80·4 million people in four countries

Cited by 29 publications

References 33 publications

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets

Reducing the incidence and mortality from myocardial infarction

Bioinformatics analysis identifies ferroptosis‑related genes in the regulatory mechanism of myocardial infarction

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