Limited value of routine follow-up visits in chronic lymphocytic leukemia managed initially by watch and wait:A North Denmark population-based study

IntroductionThe majority of newly diagnosed chronic lymphocytic leukemia (CLL) patients are followed initially by watch and wait (WAW). Clinical practice varies and the value of frequent follow-up visits remains unclear. Thus, in this study we investigated the clinical value of follow-up visits for patients with CLL.MethodsWe collected data from diagnosis and follow-up visits for patients diagnosed with CLL and managed by WAW in the North Denmark Region between 2007–2014. High- and low-risk group patients were determined by Binet stage, IgVH status, and cytogenetics at diagnosis. The effect of risk group allocation on the probability of receiving CLL-directed treatment within two years was included in a multivariable logistic regression model adjusted for age and blood test results.Results273 patients were included in the study with a median follow-up of 3 years (IQR: 1.6–5.4). Overall, the median interval between follow-up visits was 98 days (95% CI: 96–100) (high-risk patients: 91 days [95% CI: 86–95] vs. low-risk patients: 105 days [95% CI: 100–110]). Among 2,312 follow-up visits, only 387 (17%) were associated with interventions. At the following time points: 6 months, 1 year, and 1.5 years, patients with low-risk CLL had significantly lower odds of initiating treatment compared to patients with high-risk CLL.ConclusionWAW plays an important role in managing CLL. Interventions at follow-up visits were infrequent and low-risk patients had significantly lower risk of treatment initiation. We question the value of routine follow-up in CLL in the absence of changes in symptoms and/or blood test results.</div

Brand-specific influenza vaccine effectiveness estimates during 2019/20 season in Europe – Results from the DRIVE EU study platform

DRIVE (Development of Robust and Innovative Vaccine Effectiveness) is an IMI funded public–private platform that aims to annually estimate brand-specific influenza vaccine effectiveness (IVE), for public health and regulatory purposes. IVE analyses and reporting are conducted by public partners in the con- sortium. In 2019/20, four primary care-based test-negative design (TND) studies (Austria, England, Italy (n = 2)), eight hospital-based TND studies (Finland, France, Italy, Romania, Spain (n = 4)), and one population- based cohort study (Finland) were conducted. The COVID-19 pandemic affected influenza surveillance in all participating study sites, therefore the study period was truncated on February 29, 2020. Age- stratified (6 m-17y, 18-64y, !65y), confounder-adjusted, site-specific adjusted IVE estimates were calcu- lated and pooled through meta-analysis. Parsimonious confounder-adjustment was performed, adjusting the estimates for age, sex and calendar time. TND studies included 3531 cases (351 vaccinated) and 5546 controls (1415 vaccinated) of all ages. IVE estimates were available for 8/11 brands marketed in Europe in 2019. Most children and adults &lt; 64y were captured in primary care setting and the most frequently observed vaccine brand was Vaxigrip Tetra. The estimate against any influenza for Vaxigrip Tetra in primary care setting was 61% (95%CI 38–77) in children and 32% (95%CI 13–59) in adults up to 64y. Most adults ! 65y were captured in hospital setting and the most frequently observed brand was Fluad, with an estimate of 52% (95%CI 27–68)

WHO global vaccine safety multi-country collaboration project on safety in pregnancy: Assessing the level of diagnostic certainty using standardized case definitions for perinatal and neonatal outcomes and maternal immunization.

Standardized case definitions strengthen post-marketing safety surveillance of new vaccines by improving generated data, interpretation and comparability across surveillance systems. The Global Alignment of Immunization Safety Assessment in Pregnancy (GAIA) project developed standardized case definitions for 21 key obstetric and neonatal terms following the Brighton Collaboration (BC) methodology. In this prospective cohort study, we assessed the applicability of GAIA definitions for maternal immunization exposure and for low birth weight (LBW), preterm birth, small for gestational age (SGA), stillbirth, neonatal death, neonatal infection, and congenital microcephaly. We identified the missing data elements that prevented identified cases and exposures from meeting the case definition (level 1-3 of BC diagnostic certainty). Over a one-year period (2019-2020), all births occurring in 21 sites (mostly secondary and tertiary hospitals) in 6 Low Middle Income Countries and 1 High Income Country were recorded and the 7 perinatal and neonatal outcome cases were identified from routine medical records. Up to 100 cases per outcome were recruited sequentially from each site. Most cases recruited for LBW, preterm birth and neonatal death met the GAIA case definitions. Birth weight, a key parameter for all three outcomes, was routinely recorded at all sites. The definitions for SGA, stillbirth, neonatal infection (particularly meningitis and respiratory infection) and congenital microcephaly were found to be less applicable. The main barrier to obtaining higher levels of diagnostic certainty was the lack of sonographic documentation of gestational age in first or second trimester. The definition for maternal immunization exposure was applicable, however, the highest level of diagnostic certainty was only reached at two sites. Improved documentation of maternal immunization will be important for vaccine safety studies. Following the field-testing of these 8 GAIA definitions, several improvements are suggested that may lead to their easier implementation, increased standardization and hence comparison across studies

Outlier robust modeling of survival curves in the presence of potentially time-varying coefficients

In time to event studies, censoring often occurs and models that take this into account are wide-spread. In the presence of outliers, standard estimators of model parameters may be affected such that results and conclusions are not reliable anymore. This in turn also hampers the detection of these outliers due to masking effects. To cope with outliers when using proportional hazard models, we propose to use the Brier score as a loss function. Since the coefficients often vary over time, we focus on the piecewise constant hazard model, which can flexibly model time-varying coefficients if a large number of cut-points is used. To prevent overfitting, we add a penalty term that potentially shrinks time-varying effects to constant effects. By fitting the coefficients of the piecewise constant hazard model using a penalized Brier score loss, we obtain a robust model that can handle time-varying coefficients. Its good performance is illustrated in a simulation study and using two datasets from practice.status: publishe

Estimating the loss of lifetime function using flexible parametric relative survival models

Abstract Background Within cancer care, dynamic evaluations of the loss in expectation of life provides useful information to patients as well as physicians. The loss of lifetime function yields the conditional loss in expectation of life given survival up to a specific time point. Due to the inevitable censoring in time-to-event data, loss of lifetime estimation requires extrapolation of both the patient and general population survival function. In this context, the accuracy of different extrapolation approaches has not previously been evaluated. Methods The loss of lifetime function was computed by decomposing the all-cause survival function using the relative and general population survival function. To allow extrapolation, the relative survival function was fitted using existing parametric relative survival models. In addition, we introduced a novel mixture cure model suitable for extrapolation. The accuracy of the estimated loss of lifetime function using various extrapolation approaches was assessed in a simulation study and by data from the Danish Cancer Registry where complete follow-up was available. In addition, we illustrated the proposed methodology by analyzing recent data from the Danish Lymphoma Registry. Results No uniformly superior extrapolation method was found, but flexible parametric mixture cure models and flexible parametric relative survival models seemed to be suitable in various scenarios. Conclusion Using extrapolation to estimate the loss of lifetime function requires careful consideration of the relative survival function outside the available follow-up period. We propose extensive sensitivity analyses when estimating the loss of lifetime function