Development of a multivariable prediction model to identify patients unlikely to complete a colonoscopy following an abnormal FIT test in community clinics

Abstract Background Colorectal cancer (CRC) is the 3rd leading cancer killer among men and women in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data available in the electronic health record to assess the probability of patients obtaining a colonoscopy following an abnormal FIT test. Methods To assess the probability of obtaining a colonoscopy, we used Cox regression to develop a risk prediction model among a retrospective cohort of patients with an abnormal FIT result. Results Of 1596 patients with an abnormal FIT result, 556 (34.8%) had a recorded colonoscopy within 6 months. The model shows an adequate separation of patients across risk levels for non-adherence to follow-up colonoscopy (bootstrap-corrected C-statistic > 0.63). The refined model included 8 variables: age, race, insurance, GINI income inequality, long-term anticoagulant use, receipt of a flu vaccine in the past year, frequency of missed clinic appointments, and clinic site. The probability of obtaining a follow-up colonoscopy within 6 months varied across quintiles; patients in the lowest quintile had an estimated 18% chance, whereas patients in the top quintile had a greater than 55% chance of obtaining a follow-up colonoscopy. Conclusions Knowing who is unlikely to follow-up on an abnormal FIT test could help identify patients who need an early intervention aimed at completing a follow-up colonoscopy. Trial registration This trial was registered at ClinicalTrials.gov (NCT01742065) on December 5, 2012. The protocol is available.

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Development of a Multivariable Prediction Model to Identify Patients Unlikely to Complete a Colonoscopy Following an Abnormal FIT Test in Community Clinics

10.21203/rs.3.rs-23641/v2 ◽

2020 ◽

Author(s):

Amanda Petrik ◽

Erin Keast ◽

Eric Johnson ◽

David H. Smith ◽

Gloria D. Coronado

Keyword(s):

Prediction Model ◽

Cox Regression ◽

Outreach Program ◽

Risk Levels ◽

C Statistic ◽

Clinic Site ◽

Flu Vaccine ◽

Using Data ◽

Priority Populations

Abstract Background: Colorectal cancer (CRC) is the 2nd leading cancer killer in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data available in the electronic health record to assess the probability of patients obtaining a colonoscopy following an abnormal FIT test. Methods: To assess the probability of obtaining a colonoscopy, we used Cox regression to develop a risk prediction model among a retrospective cohort of patients with an abnormal FIT result and a year of follow-up data. Results: Of 1596 patients with an abnormal FIT result, 556 (34.8%) had a recorded colonoscopy within 6 months. The model shows adequate separation of patients across risk levels for non-adherence to follow-up colonoscopy (bootstrap-corrected C-statistic > 0.63). The refined model included 8 variables: age, race, insurance, GINI income inequality, long term anticoagulant use, receipt of a flu vaccine in the past year, frequency of missed clinic appointments, and clinic site. Probability of obtaining a follow-up colonoscopy within 6 months varied across quintiles; patients in the lowest quintile had an estimated 18% chance, whereas patients in the top quintile had a greater than 55% chance of obtaining a follow-up colonoscopy. Conclusions: Knowing who is unlikely to follow-up on an abnormal FIT test could help identify patients who need an early intervention aimed at complete a follow-up colonoscopy. Trial registry: This trial was registered at ClinicalTrials.gov (NCT01742065) on December 5, 2012. The protocol is available.

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Development of a Multivariable Prediction Model to Identify Patients Unlikely to Complete a Colonoscopy Following an Abnormal FIT Test in Community Clinics

10.21203/rs.3.rs-23641/v1 ◽

2020 ◽

Author(s):

Amanda Petrik ◽

Erin Keast ◽

Eric Johnson ◽

David H. Smith ◽

Gloria D. Coronado

Keyword(s):

Prediction Model ◽

Cox Regression ◽

Outreach Program ◽

Risk Levels ◽

C Statistic ◽

Clinic Site ◽

Flu Vaccine ◽

Using Data ◽

Priority Populations

Abstract Background Colorectal cancer (CRC) is the 2nd leading cancer killer in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data available in the electronic health record to assess the probability of patients obtaining a colonoscopy following an abnormal FIT test.Methods We used Cox regression to develop a risk prediction model among a retrospective cohort of patients with an abnormal FIT result and a year of follow-up data.Results Of 1723 patients with an abnormal FIT result, 699 (40.6%) had a colonoscopy within 1 year and 597 (34.6%) had a recorded colonoscopy within 6 months. The model shows adequate separation of patients across risk levels for non-adherence to follow-up colonoscopy (bootstrap-corrected C-statistic > 0.63). The refined model included 8 variables: age, race, insurance, GINI income inequality, long term anticoagulant use, receipt of a flu vaccine in the past year, frequency of missed clinic appointments, and clinic site. Probability of obtaining a follow-up colonoscopy within 6 months varied across quintiles; patients in the lowest quintile had an estimated 18% chance, whereas patients in the top quintile had a greater than 55% chance of obtaining a follow-up colonoscopy.Conclusions Knowing who may be at risk for failing to follow-up on an abnormal FIT test could help identify patients in need of early interventions aimed at completing a colonoscopy.Trial registry This trial was registered at ClinicalTrials.gov (NCT01742065) on December 5, 2012.

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Development of a Multivariable Prediction Model to Identify Patients Unlikely to Complete a Colonoscopy Following an Abnormal FIT Test in Community Clinics

10.21203/rs.3.rs-23641/v3 ◽

2020 ◽

Author(s):

Amanda Petrik ◽

Erin Keast ◽

Eric Johnson ◽

David H. Smith ◽

Gloria D. Coronado

Keyword(s):

Prediction Model ◽

Cox Regression ◽

Outreach Program ◽

Risk Levels ◽

C Statistic ◽

Clinic Site ◽

Flu Vaccine ◽

Using Data ◽

Priority Populations

Abstract Background: Colorectal cancer (CRC) is the 2nd leading cancer killer in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data available in the electronic health record to assess the probability of patients obtaining a colonoscopy following an abnormal FIT test. Methods: To assess the probability of obtaining a colonoscopy, we used Cox regression to develop a risk prediction model among a retrospective cohort of patients with an abnormal FIT result. Results: Of 1596 patients with an abnormal FIT result, 556 (34.8%) had a recorded colonoscopy within 6 months. The model shows adequate separation of patients across risk levels for non-adherence to follow-up colonoscopy (bootstrap-corrected C-statistic > 0.63). The refined model included 8 variables: age, race, insurance, GINI income inequality, long term anticoagulant use, receipt of a flu vaccine in the past year, frequency of missed clinic appointments, and clinic site. Probability of obtaining a follow-up colonoscopy within 6 months varied across quintiles; patients in the lowest quintile had an estimated 18% chance, whereas patients in the top quintile had a greater than 55% chance of obtaining a follow-up colonoscopy. Conclusions: Knowing who is unlikely to follow-up on an abnormal FIT test could help identify patients who need an early intervention aimed at completing a follow-up colonoscopy. Trial registry: This trial was registered at ClinicalTrials.gov (NCT01742065) on December 5, 2012. The protocol is available.

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Development of a Multivariable Prediction Model to Identify Patients Unlikely to Complete a Colonoscopy Following an Abnormal FIT Test in Community Clinics

10.21203/rs.3.rs-23641/v4 ◽

2020 ◽

Author(s):

Amanda Petrik ◽

Erin Keast ◽

Eric Johnson ◽

David H. Smith ◽

Gloria D. Coronado

Keyword(s):

Prediction Model ◽

Cox Regression ◽

Test Methods ◽

Outreach Program ◽

Risk Levels ◽

C Statistic ◽

Clinic Site ◽

Flu Vaccine ◽

Using Data

Abstract Background: Colorectal cancer (CRC) is the 2 nd leading cancer killer in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data available in the electronic health record to assess the probability of patients obtaining a colonoscopy following an abnormal FIT test. Methods: To assess the probability of obtaining a colonoscopy, we used Cox regression to develop a risk prediction model among a retrospective cohort of patients with an abnormal FIT result. Results: Of 1596 patients with an abnormal FIT result, 556 (34.8%) had a recorded colonoscopy within 6 months. The model shows adequate separation of patients across risk levels for non-adherence to follow-up colonoscopy (bootstrap-corrected C-statistic > 0.63). The refined model included 8 variables: age, race, insurance, GINI income inequality, long term anticoagulant use, receipt of a flu vaccine in the past year, frequency of missed clinic appointments, and clinic site. Probability of obtaining a follow-up colonoscopy within 6 months varied across quintiles; patients in the lowest quintile had an estimated 18% chance, whereas patients in the top quintile had a greater than 55% chance of obtaining a follow-up colonoscopy. Conclusions: Knowing who is unlikely to follow-up on an abnormal FIT test could help identify patients who need an early intervention aimed at completing a follow-up colonoscopy. Trial registry: This trial was registered at ClinicalTrials.gov (NCT01742065) on December 5, 2012. The protocol is available.

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P5979Impact of disease stage on performance of strain markers for prediction of atrial fibrillation

European Heart Journal ◽

10.1093/eurheartj/ehz746.0700 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

S Ramkumar ◽

F Pathan ◽

H Kawakami ◽

A Ochi ◽

H Yang ◽

...

Keyword(s):

Atrial Fibrillation ◽

Predictive Value ◽

Regression Models ◽

Cox Regression ◽

Global Longitudinal Strain ◽

Disease Stage ◽

Risk Levels ◽

Lv Dysfunction ◽

Lv Mass

Abstract Background Efforts to predict incident atrial fibrillation (AF) may be associated with complications, and there is interest in AF prediction in primary prevention (PP; pts with risk factors) and secondary prevention (SP; pts with possible AF complications). These pts have different risk levels, we sought whether that influenced the predictive value of LV dysfunction (measured as global longitudinal strain, GLS) or LA dysfunction (LA reservoir strain). Methods The PP cohort comprised 351 community-based pts ≥65 years with ≥1 risk factor for AF (age 70±4y,43% male, median follow-up 22 months) and the SP cohort comprised 532 pts after transient ischaemic attack or stroke (age 68±12y, 51% male, median follow-up 36 months). GLS and LA strain were measured offline (Image Arena-Tomtec, Germany). AF was diagnosed by 12 lead ECG, Holter or by single lead monitor. The clinical and echocardiographic characteristics of those with AF were compared to those in sinus rhythm. Nested Cox-regression models were used to assess for independent and incremental predictive value of LA strain/GLS in both cohorts. Results Compared to SP, PP had higher clinical AF risk (CHARGE-AF 5.6±5.5% vs 4.7±12.1%, p=0.02) but a lower thromboembolic risk (CHA2DS2-VASC 3±2 vs. 4±2, p<0.001). AF developed in 42 PP pts (12%) and 61 SP (12%). AF patients were older, with higher CHARGE-AF score, LA volume and LV mass. Pts developing AF had reduced GLS (17±4% vs. 20±3%, p<0.001), reservoir (28±11% vs. 35±8%, p<0.001) and pump strain (13±7% vs. 17±5%, p<0.001). GLS and LA strain had greater AUC in SP (0.84 vs 0.58 for GLS and 0.85 vs 0.57 for reservoir strain, both p<0.001). Nested cox-regression models showed that LA reservoir strain was independently associated with AF in both cohorts (p<0.05). GLS was only independently associated with incident AF in SP (Figure). Conclusion LA reservoir strain is independently associated with AF in different risk cohorts and its effect is incremental to clinical parameters and LA volume. GLS may be more useful in AF risk assessment in those in SP. Acknowledgement/Funding This study was partially supported by the Tasmanian Community Fund and Siemens Healthcare Australia.

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Relationship between diabetic retinopathy stages and risk of major lower-extremity arterial disease in patients with type 2 diabetes

10.2337/figshare.12777407.v1 ◽

2020 ◽

Author(s):

Ninon Foussard ◽

Pierre-Jean Saulnier ◽

Louis Potier ◽

Stéphanie Ragot ◽

Fabrice Schneider ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Retinopathy ◽

Lower Extremity ◽

Incidence Rate ◽

Cox Regression ◽

Arterial Disease ◽

Lower Extremity Arterial Disease ◽

C Statistic

Objective. We evaluated the association between diabetic retinopathy stages and lower-extremity arterial disease (LEAD), its prognostic value, and the influence of potential contributors in this relationship in a prospective cohort of patients with type 2 diabetes. Research design and methods. Diabetic retinopathy was staged at baseline as absent, non-proliferative or proliferative. Cox regression model was fitted to compute HR (95% CI) for major LEAD (lower-limb amputation or revascularization) during follow-up by baseline retinopathy stages. Retinopathy-LEAD association was assessed in subgroups by age, gender, diabetes duration, HbA1c, systolic blood pressure, diabetic kidney disease, smoking, and macrovascular disease at baseline. The performance of retinopathy to stratify LEAD risk was assessed using c-statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Results. Among 1320 participants without a baseline history of LEAD, 94 (7.1%) patients developed a major LEAD during a 7.1-year median follow-up (incidence rate 9.6, 95%CI [7.8–11.7] per 1000 person-years). The LEAD incidence rate increased by worsening retinopathy: absent 5.5 (3.9–7.8), non-proliferative 14.6 (11.1–19.3), proliferative 20.1 (11.1–36.3) per 1000 person-years. Compared with absent retinopathy, non-proliferative (multi-adjusted HR 2.31, 95%CI [1.43–3.81], p=0.0006) and proliferative retinopathy (3.14 [1.40–6.15], p=0.007) remained associated with major LEAD. No heterogeneity was observed across subgroups. Retinopathy enhanced c-statistic (+0.023 [0.003–0.044], p=0.02), IDI (0.209 [0.130 – 0.321], p<0.001) and NRI (0.562 [0.382– 0.799], p<0.001) for LEAD risk, beyond traditional risk factors. Conclusions. An independent dose-response relationship was observed between diabetic retinopathy stages and major LEAD. Retinopathy yielded incremental prognostic information for LEAD risk stratification, suggesting its usefulness as LEAD predictor.

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COVID-19 vaccine effectiveness against laboratory confirmed symptomatic SARS-CoV-2 infection, COVID-19 related hospitalizations and deaths, among individuals aged 65 years or more in Portugal: a cohort study based on data-linkage of national registries February-September 2021

10.1101/2021.12.10.21267619 ◽

2021 ◽

Author(s):

Ausenda Machado ◽

Irina Kislaya ◽

Ana Paula Rodrigues ◽

Duarte Sequeira ◽

Joao Lima ◽

...

Keyword(s):

Cox Regression ◽

Viral Vector ◽

Vaccine Effectiveness ◽

Time Dependent ◽

Unique Identifier ◽

Symptomatic Infection ◽

Age Cohorts ◽

Electronic Health ◽

Using Data

Background: Using data from electronic health registries, this study intended to estimate the COVID-19 vaccine effectiveness in the population aged 65 years and more, against symptomatic infection, COVID-19 related hospitalizations and deaths, overall and by time since complete vaccination. Methods: We stablished a cohort of individuals aged 65 and more years old, resident in Portugal mainland, using the National Health Service unique identifier User number to link eight electronic health registries. Outcomes included were symptomatic SARS-CoV-2 infections, COVID-19 related hospitalizations or deaths. The exposures of interest were the mRNA vaccines (Cominarty or Spikevax) and the viral vector Vaxzevria vaccine. Complete scheme vaccine effectiveness (VE) was estimated as one minus the confounder adjusted hazard ratio, for each outcome, estimated by time-dependent Cox regression with time dependent vaccine exposure. Results: For the cohort of individuals aged 65-79 years, complete scheme VE against symptomatic infection varied between 43% (Vaxzevria) and 65% (mRNA vaccines). This estimate was slightly lower in the ≥80 year cohort (53% for mRNA vaccines. VE against COVID-19 hospitalization varied between 89% (95%CI: 52-94) for Vaxzevria and 95% (95%CI: 93-97) for mRNA vaccines for the cohort aged 65-79 years and was 76% (95%CI: 67-83) for mRNA vaccines in the ≥80 year cohort. High VE against COVID-19 related deaths were estimated, for both vaccine types, 95% and 81% for the 65-79 years and the ≥80 year cohort, respectively. We observed a significant waning of VE against symptomatic infection, with VE estimates reaching approximately 34% for both vaccine types and cohorts. Significant waning was observed for the COVID-19 hospitalizations in the ≥80 year cohort (decay from 83% 14-41 days to 63% 124 days after mRNA second dose). No significant waning effect was observed for COVID-19 related deaths in the period of follow-up of either cohorts. Conclusions: In a population with a high risk of SARS-CoV-2 complications, we observed higher overall VE estimates against more severe outcomes for both age cohorts when compared to symptomatic infections. Considering the analysis of VE according to time since complete vaccination, the results showed a waning effect for both age cohorts in symptomatic infection and COVID-19 hospitalization for the 80 and more yo cohort.

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751Investigation of the obesity paradox in kidney cancer: mystifying association or myth?

International Journal of Epidemiology ◽

10.1093/ije/dyab168.274 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Alicia Heath ◽

Joanna Clasen ◽

Elio Riboli ◽

Ghislaine Scelo ◽

David Muller

Keyword(s):

Kidney Cancer ◽

Cox Regression ◽

Obesity Paradox ◽

Hazard Ratios ◽

Increased Risk ◽

Incidence And Mortality ◽

All Cause Mortality ◽

Using Data ◽

Restricted Cubic Splines

Abstract Background An “obesity paradox” has been reported in kidney cancer, whereby obesity is a risk factor, yet appears to be associated with better survival. To evaluate this paradox, we investigated the association between pre-diagnostic adiposity and renal cell carcinoma (RCC) incidence and mortality. Methods Using data from 363,521 men and women in the European Prospective Investigation into Cancer and Nutrition (EPIC), Cox regression models yielded confounder-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for RCC incidence and mortality in relation to BMI modelled continuously and using restricted cubic splines. RCC-specific and all-cause mortality were evaluated among cases. Results During a mean follow-up of 14.9 years, 936 incident RCC cases were identified, 383 of whom died (278 due to RCC). Each 5 kg/m2 increment in BMI was associated with 27% and 46% higher RCC incidence and mortality (HRs=1.27, 95% CI 1.18-1.37 and 1.46, 95% CI 1.28-1.66, respectively). Comparing a BMI of 35 with 22 kg/m2, HRs for RCC incidence and mortality were 1.88 (95% CI 1.54-2.30) and 2.37 (95% CI 1.68-3.35), respectively. Among RCC cases, HRs per 5 kg/m2 increment in BMI were 1.22 (95% CI 1.07-1.41) for RCC-specific mortality and 1.18 (95% CI 1.04-1.34) for all-cause mortality. Similar, positive linear associations were evident for waist circumference and waist-to-hip ratio. Conclusions Obesity was associated with increased RCC incidence and mortality, and worse prognosis among cases. Key messages The kidney cancer-obesity paradox does not appear to be real. Higher adiposity is associated with an increased risk of incident and fatal RCC.

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Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

PLoS ONE ◽

10.1371/journal.pone.0254550 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254550

Author(s):

Matteo Luigi Giuseppe Leoni ◽

Luisa Lombardelli ◽

Davide Colombi ◽

Elena Giovanna Bignami ◽

Benedetta Pergolotti ◽

...

Keyword(s):

Prediction Model ◽

Critically Ill ◽

Critically Ill Patients ◽

Goodness Of Fit ◽

Cox Regression ◽

Prognostic Models ◽

Calibration Plot ◽

Internal Validation ◽

Icu Admission ◽

C Statistic

Background COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. Materials and methods We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot. Results 242 patients were included [median age, 64 years (56–71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6–18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). Conclusions We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

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