Performance of cardiovascular disease risk prediction equations in more than 14 000 survivors of cancer in New Zealand primary care: a validation study

Tawfiq, Essa; Selak, Vanessa; Elwood, J Mark; Pylypchuk, Romana; Tin, Sandar Tin; Harwood, Matire; Grey, Corina; McKeage, Mark J.; Wells, Sue

doi:10.1016/s0140-6736(22)02405-9

Cited by 5 publications

(9 citation statements)

References 20 publications

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“…Participants were excluded if they already had CVD (or a CVD‐equivalent health condition such as diabetes or overt nephropathy, or diabetes with eGFR<45 mL/min/1.73m 2 , as defined according to the NZ CVD risk prediction equations 12 ) or if they were patients within 2 years of their first primary cancer diagnosis, at the index date. The latter group was excluded to minimize any patients still in the acute phase of their cancer treatment from the study, as had been done in a recent study assessing the validity of a CVD risk equation among cancer survivors 13 . Preceding primary cancer diagnosis (made between 3 January 1995 and 22 June 2016) was determined from the NZ Cancer Registry (NZCR), a legislatively‐mandated registry of all malignancies diagnosed in the country, excluding squamous and basal cell skin cancer 14 .…”

Section: Methodsmentioning

confidence: 99%

“…The latter group was excluded to minimize any patients still in the acute phase of their cancer treatment from the study, as had been done in a recent study assessing the validity of a CVD risk equation among cancer survivors. 13 Preceding primary cancer diagnosis (made between 3 January 1995 and 22 June 2016) was determined from the NZ Cancer Registry (NZCR), a legislatively-mandated registry of all malignancies diagnosed in the country, excluding squamous and basal cell skin cancer. 14 A primary cancer was defined as a cancer that originated in a body organ or tissue and was neither an extension, a recurrence nor a metastasis of an existing malignancy.…”

Section: Participantsmentioning

confidence: 99%

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Risk of cardiovascular disease in cancer survivors: A cohort study of 446,384 New Zealand primary care patients

Tawfiq,

Pylypchuk,

Elwood

et al. 2023

Cancer Medicine

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BackgroundGiven advances in the management of cancer, it is increasingly important for clinicians to appropriately manage the risk of cardiovascular disease (CVD) among cancer survivors. It is unclear whether CVD risk is increased among cancer survivors overall, and there is inconsistency in evidence to date about CVD incidence and mortality by cancer type.MethodsPatients aged 30‐74 years entered an open cohort study at the time of first CVD risk assessment, between 2004 and 2018, in primary care in New Zealand. Patients with established CVD or cancer within 2 years prior to study entry were excluded. Cancer diagnosis (1995–2016) was determined from a national cancer registry. Cause‐specific hazard models were used to examine the association between history of cancer and two outcomes: (1) CVD‐related hospitalization and/or death and (2) CVD death.ResultsThe study included 446,384 patients, of whom 14,263 (3.2%) were cancer survivors. Risk of CVD hospitalization and/or death was increased among cancer survivors compared with patients without cancer at cohort entry (multivariable‐adjusted hazard ratio, mHR, 1.11, 95% CI 1.05‐1.18), more so for CVD death (1.31, 1.14‐1.52). Risk of CVD hospitalization and/or death was increased in patients with myeloma (2.66, 1.60‐4.42), lung cancer (2.19, 1.48‐3.24) and non‐Hodgkin lymphoma (1.90, 1.42‐2.54), but not for some cancers (e.g., colorectal, 0.87, 0.71‐1.06). Risk of CVD death was increased in several cancer types including melanoma (1.73, 1.25‐2.38) and breast cancer (1.56, 1.16‐2.11).ConclusionCVD risk management needs to be prioritized among cancer survivors overall, and particularly in those with myeloma, lung cancer and non‐Hodgkin lymphoma given consistent evidence of increased risk.

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Section: Methodsmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

Risk of cardiovascular disease in cancer survivors: A cohort study of 446,384 New Zealand primary care patients

Tawfiq,

Pylypchuk,

Elwood

et al. 2023

Cancer Medicine

Self Cite

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“…Based on some existing relative work [1][2][3][4][5][37][38][39][40][41][42], the risk assessment indicators of CVD are selected initially as follows:…”

Section: Preliminary Selection Of Indicators For Risk Assessment Of Cvdmentioning

confidence: 99%

“…It has been reported that CVD has become the first cause of death of human beings, with more than 15 million people dying from CVD every year in the world. The number of deaths from CVD will increase by about 6 million in the next 20 years [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…The obtained results are consistent, which makes the investigation conclusions more real and reliable. (2) The factor analysis model is adopted to classify the main influencing factors of CVD, and the main influencing factors are divided into basic physical conditions, "three high" diseases and living habits, so as to establish a risk assessment system of CVD. (3) A new two-stage model combining K-means cluster analysis with RF is proposed to evaluate and predict patients' CVD risk.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

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Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established. Then, a two-stage model combining K-means cluster analysis and random forest (RF) is proposed to evaluate and predict the risk of CVD, and the predicted results are compared with the methods of Bayesian discrimination, K-means cluster analysis and RF. The results show that the prediction effect of the proposed two-stage model is better than that of the compared methods. Moreover, several suggestions for the government, the medical industry and the public are provided based on the research results.

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Chest Pain in Cancer Patients

Bima,

Lopez-Ayala,

Koechlin

et al. 2023

JACC: CardioOncology

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Performance of cardiovascular disease risk prediction equations in more than 14 000 survivors of cancer in New Zealand primary care: a validation study

Cited by 5 publications

References 20 publications

Risk of cardiovascular disease in cancer survivors: A cohort study of 446,384 New Zealand primary care patients

Risk of cardiovascular disease in cancer survivors: A cohort study of 446,384 New Zealand primary care patients

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Chest Pain in Cancer Patients

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