scholarly journals Can We and Should We Produce a model for Emergency Admissions derived from Primary Care Data?

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerry Bailey
Keyword(s):  
Primary Care ◽  
Statistical Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Structured Interview ◽  
Added Value ◽  
Emergency Admissions ◽  
Risk Prediction Models ◽  
Primary Care Data ◽  
Literature Reviews

ABSTRACT BackgroundSeveral Risk Prediction models exist for predicting emergency admissions. One exists derived solely from primary care data but this is only available in England. All the models currently available have limitations. The aim was to utilise the SAIL databank to develop a predictive model/tool using only primary care data that has comparable accuracy to the currently used risk stratification tools but overcomes the limitations of the current models available. ApproachA multidimensional approach was taken including literature reviews and qualitative interviews to the model to situate the statistical model. The need was identified to develop the contextual background in order to situate the statistical model development and understand the need for an improved model. Stakeholder groups were identified – patients, managers, General Practitioners and policy makers – and purposive sampling was used to obtain breadth and depth of representation into the subject area. Interviews were carried out in two stages initially to identify key themes and the breadth of the interest in stakeholder and influenced hypothesis generation. The second stage was to provide depth and clarity on specific themes with a more structured interview schedule. Results of the qualitave work triangulated with literature reviews influenced the statistical model build. DiscussionThe statistical challenge of building Risk Prediction models well documented. Understanding the shortcoming of current models as perceived by stakeholders is necessary to produce a model of added value which performs statistically similar however has a different functionality. The qualitative work that was carried out to situate this model added value to the models development but also raised some interesting ethical issues. The validity and performance of the models although important is only one aspect that influences whether GPs would use a risk prediction model and how acceptable it is to stakeholders. None of the patients interviewed in this thought that their data was, or would be without their knowledge, used in risk prediction models yet all were patients at practices where this was happening or had morbidities which would have necessitated the use of several in primary care.

scholarly journals Selection of eligible participants for screening for lung cancer using primary care data

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2021-217142
Author(s):  
Emma L O'Dowd ◽  
Kevin ten Haaf ◽  
Jaspreet Kaur ◽  
Stephen W Duffy ◽  
William Hamilton ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Primary Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Lung Cancers ◽  
Primary Care Data ◽  
C Statistic ◽  
The Cost ◽  
Predicted Risk

Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.MethodThe Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLPv2) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCOm2012) models. Lung cancer occurrence over 5–6 years was measured in ever-smokers aged 50–80 years and compared with 5-year (LLPv2) and 6-year (PLCOm2012) predicted risk.ResultsOver 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLPV2 produced a c-statistic of 0.700 (0.694–0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCOm2012 showed similar performance (c-statistic: 0.679 (0.673–0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLPv2) and 0.15% (PLCOm2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers.ConclusionRisk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.

P1547Circulating lipoprotein subfractions as new non-invasive biomarkers of coronary atherosclerosis

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Saether ◽  
E Madssen ◽  
E Vesterbekkmo ◽  
G Giskeodegaard ◽  
S Gjaere ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Coronary Atherosclerosis ◽  
Prediction Models ◽  
Statistical Tests ◽  
Added Value ◽  
Gensini Score ◽  
Lipoprotein Subfractions ◽  
Risk Prediction Models ◽  
Blood Samples ◽  
Ldl Particles

Abstract Objectives Coronary artery disease (CAD) is the most common cause of death globally. In the next decade, the number of people at risk is expected to increase, due to obesity, inactivity and diabetes. Therefore, precise risk-prediction models will be increasingly important for the healthcare system, to be able to initiate cost-efficient prevention strategies. One of the first steps in CAD-development is sub-clinical atherosclerosis. Biomarkers that could reflect the presence of coronary atherosclerosis would be extremely valuable for risk prediction of myocardial infarction (MI). Serum cholesterol levels are key variables in risk prediction; however, there is growing interest for exploring the potential of other lipid subclasses. The aim of this study is to identify specific lipoprotein subfractions that are associated with the extent of coronary atherosclerosis. Methods 60 patients with suspected CAD were enrolled. Blood samples were collected before the partiens underwent coronary angiography. The extent of coronary atherosclerosis were quantified using the Gensini score. The partients were classified into three groups based on their Gensini score (<20.5: normal, 20.6–30: non-significant CAD and >30.1: significant CAD). The blood samples were analyzed by nucelar magnetic resonance (NMR) lipidomics. Univariate and multivariate statistical tests were used to determine whether lipoprotein subfractions were associated with the extent of coronary atherosclerosis. Results and discussion Of the 117 lipoprotein subfractions quantified, 10 were different in patients with significant CAD compared to patients with normal vessels in non-statin users (p=0.005). Despite no difference in total cholesterol, LDL and HDL cholesterol between the three Gensini groups, NMR lipidomics revealed that patients with significant CAD had twice as many circulating LDL-5 and LDL-6 particles as patients with normal vessels. Furthermore, three types of small LDL-subfractions, called LDL-5-TG, LDL-5-ApoB and LDL-6-ApoB, were significantly increased in patients with significant CAD. Interestingly, previous studies have suggested that small LDL particles are more atherogenic than larger particles. In addition, patients with significant CAD had low levels of ApoA1 containing HDL particles, and high levels of two different small VLDL particles. Previous studies have indicated that small VLDLs are more atherogenic than larger VLDLs, and does to a greater extent penetrate the vessel intima. Conclusions This study reveals strong associations between serum lipoprotein subfractions and the degree of coronary atherosclerosis quantified by Gensini score. Especially, the high levels of certain types of small LDL-particles in patients with CAD, indicates that measuring lipoprotein subfractions may provide added value to risk prediction models for MI. However, these findings needs to be further explored and validated in large cohort studies. Acknowledgement/Funding Norwegian Health Association, the Liaison Committee between the Central Norway Regional Health Authority (RHA) and NTNU

scholarly journals Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort

2017 ◽  
Vol 23 (15) ◽  
pp. 4181-4189 ◽  
Author(s):  
Anika Hüsing ◽  
Renée T. Fortner ◽  
Tilman Kühn ◽  
Kim Overvad ◽  
Anne Tjønneland ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Added Value ◽  
Risk Prediction Models ◽  
Exogenous Hormones ◽  
Serum Hormone

scholarly journals Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e023070 ◽  
Author(s):  
Eric Yuk Fai Wan ◽  
Esther Yee Tak Yu ◽  
Weng Yee Chin ◽  
Colman Siu Cheung Fung ◽  
Ruby Lai Ping Kwok ◽  
...  
Keyword(s):  
Primary Care ◽  
Hong Kong ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cost Effective ◽  
Chinese Patients ◽  
Risk Prediction Models ◽  
Hospital Authority ◽  
All Cause Mortality ◽  
Patients With Diabetes

IntroductionDiabetes mellitus (DM) is a major disease burden worldwide because it is associated with disabling and lethal complications. DM complication risk assessment and stratification is key to cost-effective management and tertiary prevention for patients with diabetes in primary care. Existing risk prediction functions were found to be inaccurate in Chinese patients with diabetes in primary care. This study aims to develop 10-year risk prediction models for total cardiovascular diseases (CVD) and all-cause mortality among Chinese patients with DM in primary care.Methods and analysisA 10-year cohort study on a population-based primary care cohort of Chinese patients with diabetes, who were receiving care in the Hospital Authority General Outpatient Clinic on or before 1 January 2008, were identified from the clinical management system database of the Hospital Authority. All patients with complete baseline risk factors will be included and followed from 1 January 2008 to 31 December 2017 for the development and validation of prediction models. The analyses will be carried out separately for men and women. Two-thirds of subjects will be randomly selected as the training sample for model development. Cox regressions will be used to develop 10-year risk prediction models of total CVD and all-cause mortality. The validity of models will be tested on the remaining one-third of subjects by Harrell’s C-statistics and calibration plot. Risk prediction models for diabetic complications specific to Chinese patients in primary care will enable accurate risk stratification, prioritisation of resources and more cost-effective interventions for patients with DM in primary care.Ethics and disseminationThe study was approved by the Institutional Review Board of the University of Hong Kong—the Hospital Authority Hong Kong West Cluster (reference number: UW 15–258).Trial registration numberNCT03299010; Pre-results.

scholarly journals Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarega Gurudas ◽  
Manjula Nugawela ◽  
A. Toby Prevost ◽  
Thirunavukkarasu Sathish ◽  
Rohini Mathur ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Study Cohort ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Development And Validation

AbstractPrediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

scholarly journals Prediction models for prostate cancer to be used in the primary care setting: a systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e034661
Author(s):  
Mohammad Aladwani ◽  
Artitaya Lophatananon ◽  
William Ollier ◽  
Kenneth Muir
Keyword(s):  
Prostate Cancer ◽  
Systematic Review ◽  
Primary Care ◽  
Community Health ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Study Endpoint ◽  
Risk Prediction Models ◽  
Number Of Patients

ObjectiveTo identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.DesignSystematic review.Data sourcesMEDLINE and Embase databases combined from inception and up to the end of January 2019.EligibilityStudies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).Data extraction and synthesisRelevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.ResultsAn initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.ConclusionOnly a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

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