scholarly journals R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2139
Author(s):  
Yan Li ◽  
Matthew Sperrin ◽  
Tjeerd van Staa
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
R Package ◽  
Research Community ◽  
Risk Prediction Models ◽  
Risk Patients ◽  
Future Risk ◽  
The Uk

Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to better understand risk prediction scores and improve future risk prediction models. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.

scholarly journals R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 2139
Author(s):  
Yan Li ◽  
Matthew Sperrin ◽  
Tjeerd van Staa
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
R Package ◽  
Research Community ◽  
Risk Prediction Models ◽  
Risk Patients ◽  
Future Risk ◽  
The Uk

Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to better understand risk prediction scores and improve future risk prediction models. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.

scholarly journals R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2139
Author(s):  
Yan Li ◽  
Matthew Sperrin ◽  
Tjeerd van Staa
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
R Package ◽  
Research Community ◽  
Risk Prediction Models ◽  
Risk Patients ◽  
Future Risk ◽  
The Uk

Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to improve future risk prediction models based on a currently used risk prediction model. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.

scholarly journals Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment

2021 ◽  
Author(s):  
Zhe Xu ◽  
Matthew Arnold ◽  
David Stevens ◽  
Stephen Kaptoge ◽  
Lisa Pennells ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Predictive Performance ◽  
Statin Treatment ◽  
Clinical Practice Research Datalink ◽  
Cvd Risk ◽  
Risk Prediction Models

Abstract Cardiovascular disease (CVD) risk prediction models are used to identify high-risk individuals and guide statin-initiation. However, these models are usually derived from individuals who may initiate statins during follow-up. We present a simple approach to address statin-initiation to predict “statin-naïve” CVD risk. We analyzed primary care data (2004-2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (40-85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin-initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., numbers-needed-to-screen to prevent one case) against models ignoring statin-initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for versus ignoring statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in numbers-needed-to-screen to prevent one case. In conclusion, incorporating statin effects from trial results into risk prediction models enables statin-naïve CVD risk estimation, provides moderate gains in predictive ability, but had a limited impact on treatment decision-making under current guidelines in this population.

scholarly journals Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8232
Author(s):  
Amalia Hosein ◽  
Valerie Stoute ◽  
Samantha Chadee ◽  
Natasha Ramroop Singh
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Trinidad And Tobago ◽  
Risk Prediction ◽  
Prediction Models ◽  
Time Frame ◽  
Risk Scores ◽  
Cvd Risk ◽  
Biometric Data ◽  
Risk Prediction Models

Background Cardiovascular Disease (CVD) risk prediction models have been useful in estimating if individuals are at low, intermediate, or high risk, of experiencing a CVD event within some established time frame, usually 10 years. Central to this is the concern in Trinidad and Tobago of using pre-existing CVD risk prediction methods, based on populations in the developed world (e.g. ASSIGN, Framingham and QRISK®2), to establish risk for its multiracial/ethnic Caribbean population. The aim of this study was to determine which pre-existing CVD risk method is best suited for predicting CVD risk for individuals in this population. Method A survey was completed by 778 participants, 526 persons with no prior CVD, and 252 who previously reported a CVD event. Lifestyle and biometric data was collected from non-CVD participants, while for CVD participants, medical records were used to collect data at the first instance of CVD. The performances of three CVD risk prediction models (ASSIGN, Framingham and QRISK®2) were evaluated using their calculated risk scores. Results All three models (ASSIGN, Framingham and QRISK®2) identified less than 62% of cases (CVD participants) with a high proportion of false-positive predictions to true predictions as can be seen by positive predictabilities ranging from 78% (ASSIGN and Framingham) to 87% (QRISK®2). Further, for all three models, individuals whose scores fell into the misclassification range were 2X more likely to be individuals who had experienced a prior CVD event as opposed to healthy individuals. Conclusion The ASSIGN, Framingham and QRISK®2 models should be utilised with caution on a Trinidad and Tobago population of intermediate and high risk for CVD since these models were found to have underestimated the risk for individuals with CVD up to 2.5 times more often than they overestimated the risk for healthy persons.

Algorithms for Individual Prediction of COVID-19 infection and Hospitalization among Symptomatic patients based on health data (Preprint)

2021 ◽  
Author(s):  
Rossella Murtas ◽  
Nuccia Morici ◽  
Chiara Cogliati ◽  
Massimo Puoti ◽  
Barbara Omazzi ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Health Data ◽  
Low Risk ◽  
Risk Prediction Models ◽  
Predictive Algorithms ◽  
Medium Risk ◽  
Risk Patients ◽  
At Home

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has generated a huge strain on the health care system worldwide. The metropolitan area of Milan, Italy was one of the most hit area in the world. OBJECTIVE Robust risk prediction models are needed to stratify individual patient risk for public health purposes METHODS Two predictive algorithms were implemented in order to foresee the probability of being a COVID-19 patient and the risk of being hospitalized. The predictive model for COVID-19 positivity was developed in 61.956 symptomatic patients, whereas the model for COVID-19 hospitalization was developed in 36.834 COVID-19 positive patients. Exposures considered were age, gender, comorbidities and symptoms associated with COVID-19 (vomiting, cough, fever, diarrhoea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnoea). RESULTS The predictive models showed a good fit for predicting COVID-19 disease [AUC 72.6% (95% CI 71.6%-73.5%)] and hospitalization [AUC 79.8% (95% CI 78.6%-81%)]. Using these results, 118,804 patients with COVID-19 from October 25 to December 11, 2020 were stratified into low, medium and high risk for COVID-19 severity. Among the overall population, 67.030 (56%) were classified as low-risk, 43.886 (37%) medium-risk, and 7.888 (7%) high-risk, with 89% of the overall population being assisted at home, 9% hospitalized, and 2% dead. Among those assisted at home, most people (60%) were classified as low risk, whereas only 4% were classified at high risk. According to ordinal logistic regression, the OR of being hospitalised or dead was 5.0 (95% CI 4.6-5.4) in high-risk patients and 2.7 (95% CI 2.6-2.9) in medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care datasets with linkage to COVID-19 testing results, hospital admissions data and death records may assist in proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

scholarly journals Risk Prediction Models of Chronic Disease in Chinese Medicine: A Systematic Review

2021 ◽  
Author(s):  
Yi-tong Li ◽  
Yan Liu ◽  
Wei Yang ◽  
Xinlong Li ◽  
Deqiang Gao
Keyword(s):  
Diabetes Mellitus ◽  
Cardiovascular Disease ◽  
Chronic Disease ◽  
Chinese Medicine ◽  
Risk Prediction ◽  
Prediction Models ◽  
China National Knowledge Infrastructure ◽  
Laboratory Findings ◽  
Risk Prediction Models ◽  
Subjective Judgement

Abstract Objective: To summarize the risk prediction models of chronic disease in Chinese medicine, describe their performance, and assess suitability of clinical or administrative use. Methods: The China National Knowledge Infrastructure and Wanfang Data were searched through February 2021, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies of prediction models of chronic disease in Chinese medicine. Results: From 399 citations reviewed, 17 studies were included in the analysis. Most of the studies were from single-centers (50%) or did not external validated (81.25%). The sample sizes were smaller and the models’ discrimination were larger compared with studies in fully western medicine. All the models used both laboratory findings and subjective judgements from doctors or patients. 9 models concentrated on diabetes mellitus or cardiovascular disease, and showed better performance and clinical application. Conclusions: The prediction models of chronic disease in Chinese medicine have unique advantages due to their considerations of doctors’ and patients’ subjective judgement. Diabetes mellitus and cardiovascular disease prediction models were in higher quality and clinical usability. Efforts to improve their quality are needed as use becomes more widespread.

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