Dynamic Prediction of Advanced Colorectal Neoplasia in Inflammatory Bowel Disease.

BACKGROUND & AIMS: Colonoscopic surveillance is recommended in patients with colonic inflammatory bowel disease (IBD) given their increased risk of colorectal cancer (CRC). We aimed to develop and validate a dynamic prediction model for the occurrence of advanced colorectal neoplasia (aCRN, including high-grade dysplasia and CRC) in IBD. METHODS: We pooled data from 6 existing cohort studies from Canada, The Netherlands, the United Kingdom, and the United States. Patients with IBD and an indication for CRC surveillance were included if they underwent at least 1 follow-up procedure. Exclusion criteria included prior aCRN, prior colectomy, or an unclear indication for surveillance. Predictor variables were selected based on the literature. A dynamic prediction model was developed using a landmarking approach based on Cox proportional hazard modeling. Model performance was assessed with Harrell's concordance-statistic (discrimination) and by calibration curves. Generalizability across surveillance cohorts was evaluated by internal-external cross-validation. RESULTS: The surveillance cohorts comprised 3731 patients, enrolled and followed-up in the time period from 1973 to 2021, with a median follow-up period of 5.7 years (26,336 patient-years of follow-up evaluation); 146 individuals were diagnosed with aCRN. The model contained 8 predictors, with a cross-validation median concordance statistic of 0.74 and 0.75 for a 5- and 10-year prediction window, respectively. Calibration plots showed good calibration. Internal-external cross-validation results showed medium discrimination and reasonable to good calibration. CONCLUSIONS: The new prediction model showed good discrimination and calibration, however, generalizability results varied. Future research should focus on formal external validation and relate predicted aCRN risks to surveillance intervals before clinical application.

Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study.

AIMS: Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery. METHODS AND RESULTS: We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27). CONCLUSION: Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research.

Comprehensive comparison of stroke risk score performance: a systematic review and meta-analysis among 6 267 728 patients with atrial fibrillation.

AIMS: Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance. METHODS AND RESULTS: We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA2DS2-VASc and CHADS2. Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA2DS2-VASc and CHADS2, pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS2 demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA2DS2-VASc and CHADS2 only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies. CONCLUSION: Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA2DS2-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice. CLINICAL TRIAL REGISTRATION: ID CRD4202161247 (PROSPERO).

Bias of time-varying exposure effects due to time-varying covariate measurement strategies.

PURPOSE: In studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change. METHODS: To illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks. RESULTS: With monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching. CONCLUSION: Our simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals.

Cautionary note: propensity score matching does not account for bias due to censoring.

This article gives a review of the limitations of propensity score matching as a tool for confounding control in the presence of censoring. Using an illustrative simulation study, we emphasize the importance of explicit adjustment for selective loss to follow-up and explain how this may be achieved.

Smoking and colorectal neoplasia in patients with inflammatory bowel disease: Dose-effect relationship.

BACKGROUND AND AIMS: Prior studies on the effect of smoking on the risk of colitis-associated colorectal neoplasia (CRN) have reported conflicting results. We aimed to further elucidate the association between smoking, including possible dose-effects, and the development of colorectal neoplasia in patients with inflammatory bowel disease (IBD). METHODS: We performed a prospective multicenter cohort study including patients with colonic IBD enrolled in a surveillance program in four academic hospitals between 2011 and 2021. The effects of smoking status and pack-years at study entry on subsequent recurrent events of CRN (including indefinite, low- and high-grade dysplasia, and colorectal cancer [CRC]) were evaluated using uni- and multivariable Prentice, Williams, and Peterson total-time Cox proportional hazard models. Adjustment was performed for extensive disease, prior/index dysplasia, sex, age, first-degree relative with CRC, primary sclerosing cholangitis, and endoscopic inflammation. RESULTS: In 501 of the enrolled 576 patients, at least one follow-up surveillance was performed after the study index (median follow-up 5 years). CRN occurred at least once in 105 patients. Ever smoking was not associated with recurrent CRN risk (adjusted hazard ratio [aHR] 1.04, 95% confidence interval [CI] 0.75-1.44), but an increasing number of pack-years was associated with an increased risk of recurrent CRN (aHR per 10 pack-years 1.17, 95% CI 1.03-1.32; p < 0.05). Separate analyses per IBD type did not reveal differences. CONCLUSIONS: This study found that an increase in pack-years is associated with a higher risk of recurrent CRN in patients with IBD, independent of established CRN risk factors (NCT01464151).

Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies.

OBJECTIVES: Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously. STUDY DESIGN AND SETTING: We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study, we assess the performance of the four approaches compared with more commonly used approaches that do not account for measurement error, missing values, or confounding. RESULTS: The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well. CONCLUSION: There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure-outcome relations, even when the available sample size is relatively small.

Title, abstract, and keyword searching resulted in poor recovery of articles in systematic reviews of epidemiologic practice.

OBJECTIVE: Article full texts are often inaccessible via the standard search engines of biomedical literature, such as PubMed and Embase, which are commonly used for systematic reviews. Excluding the full-text bodies from a literature search may result in a small or selective subset of articles being included in the review because of the limited information that is available in only title, abstract, and keywords. This article describes a comparison of search strategies based on a systematic literature review of all articles published in 5 top-ranked epidemiology journals between 2000 and 2017. STUDY DESIGN AND SETTING: Based on a text-mining approach, we studied how nine different methodological topics were mentioned across text fields (title, abstract, keywords, and text body). The following methodological topics were studied: propensity score methods, inverse probability weighting, marginal structural modeling, multiple imputation, Kaplan-Meier estimation, number needed to treat, measurement error, randomized controlled trial, and latent class analysis. RESULTS: In total, 31,641 Hypertext Markup Language (HTML) files were downloaded from the journals' websites. For all methodological topics and journals, at most 50% of articles with a mention of a topic in the text body also mentioned the topic in the title, abstract, or keywords. For several topics, a gradual decrease over calendar time was observed of reporting in the title, abstract, or keywords. CONCLUSION: Literature searches based on title, abstract, and keywords alone may not be sufficiently sensitive for studies of epidemiological research practice. This study also illustrates the potential value of full-text literature searches, provided there is accessibility of full-text bodies for literature searches.

Atmospheric Pressure and Abdominal Aortic Aneurysm Rupture: Results From a Time Series Analysis and Case-Crossover Study.

BACKGROUND: Associations between atmospheric pressure and abdominal aortic aneurysm (AAA) rupture risk have been reported, but empirical evidence is inconclusive and largely derived from studies that did not account for possible nonlinearity, seasonality, and confounding by temperature. METHODS: Associations between atmospheric pressure and AAA rupture risk were investigated using local meteorological data and a case series of 358 patients admitted to hospital for ruptured AAA during the study period, January 2002 to December 2012. Two analyses were performed-a time series analysis and a case-crossover study. RESULTS: Results from the 2 analyses were similar; neither the time series analysis nor the case-crossover study showed a significant association between atmospheric pressure ( P = .627 and P = .625, respectively, for mean daily atmospheric pressure) or atmospheric pressure variation ( P = .464 and P = .816, respectively, for 24-hour change in mean daily atmospheric pressure) and AAA rupture risk. CONCLUSION: This study failed to support claims that atmospheric pressure causally affects AAA rupture risk. In interpreting our results, one should be aware that the range of atmospheric pressure observed in this study is not representative of the atmospheric pressure to which patients with AAA may be exposed, for example, during air travel or travel to high altitudes in the mountains. Making firm claims regarding these conditions in relation to AAA rupture risk is difficult at best. Furthermore, despite the fact that we used one of the largest case series to date to investigate the effect of atmospheric pressure on AAA rupture risk, it is possible that this study is simply too small to demonstrate a causal link.