Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models

Abstract Objectives Following a maintenance course of rituximab (RTX) for ANCA-associated vasculitis (AAV), relapses occur on cessation of therapy, and further dosing is considered. This study aimed to develop relapse and infection risk prediction models to help guide decision making regarding extended RTX maintenance therapy. Methods Patients with a diagnosis of AAV who received 4–8 grams of RTX as maintenance treatment between 2002 and 2018 were included. Both induction and maintenance doses were included; most patients received standard departmental protocol consisting of 2× 1000 mg 2 weeks apart, followed by 1000 mg every 6 months for 2 years. Patients who continued on repeat RTX dosing long-term were excluded. Separate risk prediction models were derived for the outcomes of relapse and infection. Results A total of 147 patients were included in this study with a median follow-up of 63 months [interquartile range (IQR): 34–93]. Relapse: At time of last RTX, the model comprised seven predictors, with a corresponding C-index of 0.54. Discrimination between individuals using this model was not possible; however, discrimination could be achieved by grouping patients into low- and high-risk groups. When the model was applied 12 months post last RTX, the ability to discriminate relapse risk between individuals improved (C-index 0.65), and once again, clear discrimination was observed between patients from low- and high-risk groups. Infection: At time of last RTX, five predictors were retained in the model. The C-index was 0.64 allowing discrimination between low and high risk of infection groups. At 12 months post RTX, the C-index for the model was 0.63. Again, clear separation of patients from two risk groups was observed. Conclusion While our models had insufficient power to discriminate risk between individual patients they were able to assign patients into risk groups for both relapse and infection. The ability to identify risk groups may help in decisions regarding the potential benefit of ongoing RTX treatment. However, we caution the use of these prediction models until prospective multi-centre validation studies have been performed.

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Cardiovascular Disease Risk Prediction Models in Haiti: Implications for Primary Prevention in Low-Middle Income Countries

10.1101/2021.07.29.21261344 ◽

2021 ◽

Author(s):

Lily D Yan ◽

Jean Lookens Pierre ◽

Vanessa Rouzier ◽

Michel Theard ◽

Alexandra Apollon ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Risk Prediction ◽

Prediction Models ◽

Risk Groups ◽

Cvd Risk ◽

Middle Income ◽

Risk Prediction Models ◽

Middle Income Countries ◽

Low Middle Income Countries

Background Cardiovascular diseases (CVD) are rapidly increasing in low-middle income countries (LMICs). Accurate risk assessment is essential to reduce premature CVD by targeting primary prevention and risk factor treatment among high-risk groups. Available CVD risk prediction models are built on predominantly Caucasian, high-income country populations, and have not been evaluated in LMIC populations. Objective To compare the predicted 10-year risk of CVD and identify high-risk groups for targeted prevention and treatment in Haiti. Methods We used cross-sectional data within the Haiti CVD Cohort Study, including 653 adults ≥ 40 years without known history of CVD and with complete data. Six CVD risk prediction models were compared: pooled cohort equations (PCE), adjusted PCE with updated cohorts, Framingham CVD Lipids, Framingham CVD Body Mass Index (BMI), WHO Lipids, and WHO BMI. Risk factors were measured during clinical exams. Primary outcome was continuous and categorical predicted 10-year CVD risk. Secondary outcome was statin eligibility. Results Seventy percent were female, 65.5% lived on a daily income of ≤1 USD, 57.0% had hypertension, 14.5% had hypercholesterolemia, 9.3% had diabetes mellitus, 5.5% were current smokers, and 2.0% had HIV. Predicted 10-year CVD risk ranged from 3.9% in adjusted PCE (IQR 1.7-8.4) to 9.8% in Framingham-BMI (IQR 5.0-17.8), and Spearman rank correlation coefficients ranged from 0.87 to 0.98. The percent of the cohort categorized as high risk using the uniform threshold of 10-year CVD risk ≥ 7.5% ranged from 28.8% in the adjusted PCE model to 62.0% in the Framingham-BMI model (χ2 = 331, p value < 0.001). Statin eligibility also varied widely. Conclusions In the Haiti CVD Cohort, there was substantial variation in the proportion identified as high-risk and statin eligible using existing models, leading to very different treatment recommendations and public health implications depending on which prediction model is chosen. There is a need to design and validate CVD risk prediction tools for low-middle income countries that include locally relevant risk factors.

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

American Journal of Epidemiology ◽

10.1093/aje/kwab031 ◽

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.

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A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djz164 ◽

2019 ◽

Vol 112 (5) ◽

pp. 466-479 ◽

Cited By ~ 13

Author(s):

Kevin ten Haaf ◽

Mehrad Bastani ◽

Pianpian Cao ◽

Jihyoun Jeon ◽

Iakovos Toumazis ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Risk Prediction ◽

Prediction Models ◽

Lung Cancer Screening ◽

Comparative Modeling ◽

Risk Prediction Models ◽

Life Years ◽

Screening Strategies

Abstract Background Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations. Methods Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis. Results Risk-based screening strategies requiring similar screens among individuals ages 55–80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%. Conclusions Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages.

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Are neural networks the ultimate risk prediction models in patients at high risk of acute myocardial infarction?

European Journal of Preventive Cardiology ◽

10.1177/2047487319890972 ◽

2020 ◽

Vol 27 (19) ◽

pp. 2045-2046

Author(s):

Marius Roman

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Acute Myocardial Infarction ◽

High Risk ◽

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Models

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Serum Samples That Have Been Stored Long-Term (>10 Years) Can Be Used as a Suitable Data Source for Developing Cardiovascular Risk Prediction Models in Large Observational Rheumatoid Arthritis Cohorts

BioMed Research International ◽

10.1155/2014/930925 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Elke E. A. Arts ◽

Calin D. Popa ◽

Jacqueline P. Smith ◽

Onno J. Arntz ◽

Fons A. van de Loo ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Cardiovascular Risk ◽

Risk Prediction ◽

Correction Factor ◽

Prediction Models ◽

Risk Group ◽

Storage Effect ◽

Serum Samples ◽

Risk Prediction Models

Objective. There is an unmet need for a specific cardiovascular risk (CV) algorithm for rheumatoid arthritis (RA) patients. Lipoprotein data are often not available in RA cohorts but could be obtained from frozen blood samples. The objective of this study was to estimate the storage effect on lipoproteins in long-term (>10 years) frozen serum samples.Methods. Data were used from an inception RA cohort. Multiple serum samples from 152 patients were analyzed for lipoproteins, being frozen for 1–26 years at −20°C. Storage effect on lipoproteins was estimated using longitudinal regression analyses and a lipid decay correction factor was developed. Clinical impact of the storage effect on lipoproteins was assessed by calculating the number of patients reclassified to another CV risk group according to the SCORE risk calculator after applying the decay correction factor.Results. There was a significant effect of storage time on total cholesterol (TC) (P< 0.001) and high density lipoprotein cholesterol (HDL-c) levels (P< 0.001), not LDL-c (P= 0.83). The lipid decay correction factor was 0.03 mmol/L and 0.024 mmol/L per additional year of storage for TC and HDL-c, respectively. The TC : HDL ratio decreased after correction for storage effect. After correction, only 5% of patients were reclassified to another CV risk group.Conclusion. A modest storage decay effect on lipoproteins was found that is unlikely to significantly affect CV risk stratification. Serum samples that have been stored long-term (>10 years) can be used to obtain valid lipid levels for developing CV risk prediction models in RA cohorts, even without applying a decay correction factor.

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Long-term follow-up of women enrolled in a randomized trial of adjuvant chemotherapy for early stage ovarian cancer (ICON1)

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.5509 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 5509-5509 ◽

Cited By ~ 15

Author(s):

A. C. Swart

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Adjuvant Chemotherapy ◽

Adjuvant Treatment ◽

Early Stage ◽

Risk Groups ◽

P Value ◽

Long Term Follow Up

5509 Background: ICON1 and a meta-analysis of all relevant trials demonstrated an improvement in 5 year recurrence-free and overall survival (RFS and OS) for women with early-stage epithelial ovarian cancer (ES EOC) treated with adjuvant chemotherapy compared to no adjuvant chemotherapy. We aimed to determine if this initial benefit is maintained long-term and whether benefit is different with different risk groups of patients defined by stage, grade and histology. Method: 477 women with ES EOC were recruited from centres in Italy (271 women) UK (195) Switzerland (11) between August 1991 and January 2000. 5-year results were presented at ASCO 2001. Systematic long-term follow up was planned and completed in May 2006. Results: With a median follow-up of 9.2 years, 168 women have developed recurrent disease or died and 144 women have died. The Hazard Ratio (HR) for RFS of 0.70 in favour of adjuvant chemotherapy (95% CI 0.52–0.95 p= 0.023) translated into an improvement of 10-year absolute RFS of 10% from 57 to 67%. For OS, HR was 0.74 (95% CI 0.53–1.02 p= 0.066), a corresponding improvement in 10-year absolute OS of 8% from 64% to 72%. 26% of patients died from causes other than ovarian cancer. Stage I patients were grouped as low (Ia, grade 1), medium (Ia grade 2, Ib or Ic grade 1) and high risk (Ia, grade 3, Ib or IC grade 2 or 3, any clear cell). The test of interaction between risk groups and adjuvant treatment for RFS and OS was 0.055 and 0.13, respectively. The HR, 95%CI and p value are summarised in the table . Conclusions The long-term benefit of adjuvant treatment on RFS is confirmed. There is clear evidence that adjuvant chemotherapy reduces the risk of recurrence/death or death alone in high-risk patients but not in the low-risk group. [Table: see text] [Table: see text]

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Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

10.21203/rs.3.rs-138018/v1 ◽

2021 ◽

Author(s):

Maomao Cao ◽

He Li ◽

Dianqin Sun ◽

Siyi He ◽

Yadi Zheng ◽

...

Keyword(s):

Systematic Review ◽

High Risk ◽

Liver Cancer ◽

Risk Prediction ◽

Prediction Models ◽

Meta Analysis ◽

Risk Of Bias ◽

Risk Models ◽

Risk Prediction Models ◽

Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

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1227Colorectal cancer risk prediction models incorporating lifestyle and biomarker data: Results from the EPIC cohort

International Journal of Epidemiology ◽

10.1093/ije/dyab168.027 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Krasimira Aleksandrova ◽

Robin Reichmann ◽

Mazda Jenab ◽

Sabina Rinaldi ◽

Rudolf Kaaks ◽

...

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Risk Prediction ◽

Prediction Models ◽

Lifestyle Factors ◽

Health Concern ◽

Incidence Density ◽

Processed Meat ◽

Risk Prediction Models ◽

Biomarker Data

Abstract Background Colorectal cancer represents a major public health concern and there is a worrying tendency of increasing incidence rates among younger people in the last decades. Risk stratification of high-risk individuals may aid targeted disease prevention. We therefore aimed to evaluate the predictive value of a wide range of lifestyle and biomarker variables using data within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods A range of lifestyle, anthropometric and dietary variables in 329,885 participants in the EPIC cohort were evaluated as potential predictors for risk of colorectal cancer over 10 years. Biomarker measurements of 41 parameters were available for 1,320 CRC cases and 1,320 controls selected using incidence density matching. Best sets of predictors were selected using elastic net regularization with bootstrapping. Random survival forest was applied as a novel technique to validate the set of selected predictors taking variable interactions into account. Results The results suggested a set of lifestyle factors including age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary that showed good discrimination (Harrell's C-index: 0.710) and excellent calibration. The analyses further revealed a set of biomarkers that increased the predictive performance beyond age, sex and lifestyle factors. Conclusions Risk prediction models based on lifestyle and biomarker data may prove useful in the identification of individuals at high risk for colorectal cancer. Key messages Risk prediction models incorporating lifestyle and biomarker data could contribute to developing strategies for targeted colorectal cancer prevention.

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