Mortality after acute kidney injury and acute interstitial nephritis in patients prescribed immune checkpoint inhibitor therapy

BACKGROUND

In patients receiving immune checkpoint inhibitor (ICI) therapy, acute kidney injury (AKI) is common, and can occur either from kidney injury unrelated to ICI use or from immune activation resulting in acute interstitial nephritis (AIN). In this study, we test the hypothesis that occurrence of AIN indicates a favorable treatment response to ICI therapy and therefore among patients who develop AKI while on ICI therapy, those with AIN will demonstrate greater survival compared with others with AKI.

METHODS

In this observational cohort study, we included participants initiated on ICI therapy between 2013 and 2019. We tested the independent association of AKI and estimated AIN (eAIN) with mortality up to 1 year after therapy initiation as compared with those without AKI using time-varying Cox proportional hazard models controlling for demographics, comorbidities, cancer type, stage, and therapy, and baseline laboratory values. We defined eAIN as those with a predicted probability of AIN >90th percentile derived from a recently validated diagnostic model.

RESULTS

Of 2207 patients initiated on ICIs, 617 (28%) died at 1 year and 549 (25%) developed AKI. AKI was independently associated with higher mortality (adjusted HR, 2.28 (95% CI 1.90 to 2.72)). Those AKI patients with eAIN had more severe AKI as reflected by a higher peak serum creatinine (3.3 (IQR 2.1-6.1) vs 1.4 (1.2-1.9) mg/dL, p<0.001) but exhibited lower mortality than those without eAIN in univariable analysis (HR 0.43 (95% CI 0.21 to 0.89)) and after adjusting for demographics, comorbidities, and cancer type and severity (adjusted HR 0.44 (95% CI 0.21 to 0.93)).

CONCLUSION

In patients treated with ICI, mortality was higher in those with AKI unrelated to ICI but lower in those where the underlying etiology was AIN. Future studies could evaluate the association of biopsy-proven or biomarker-proven AIN with mortality in those receiving ICI therapy.

Quantitative methods for descriptive intersectional analysis with binary health outcomes

Intersectionality recognizes that in the context of sociohistorically shaped structural power relations, an individual's multiple social positions or identities (e.g., gender, ethnicity) can interact to affect health-related outcomes. Despite limited methodological guidance, intersectionality frameworks have increasingly been incorporated into epidemiological studies, both to describe health disparities and to examine their causes. This study aimed to advance methods in intersectional estimation of binary outcomes in descriptive health disparities research through evaluation of 7 potentially intersectional data analysis methods: cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision trees (CART, CTree, CHAID, random forest). Accuracy of estimated intersection-specific outcome prevalence was evaluated across 192 intersections using simulated data scenarios. For comparison we included a non-intersectional main effects regression. We additionally assessed variable selection performance amongst decision trees. Example analyses using National Health and Nutrition Examination Study data illustrated differences in results between methods. At larger sample sizes, all methods except for CART performed better than non-intersectional main effects regression. In smaller samples, MAIHDA was the most accurate method but showed no advantage over main effects regression, while random forest, cross-classification, and saturated regression were the least accurate, and CTree and CHAID performed moderately well. CART performed poorly for estimation and variable selection. Sensitivity analyses examining the bias-variance tradeoff suggest MAIHDA as the preferred unbiased method for accurate estimation of high-dimensional intersections at smaller sample sizes. Larger sample sizes are more imperative for other methods. Results support the adoption of an intersectional approach to descriptive epidemiology.

Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve

BACKGROUND

Advances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFR_CT).

PURPOSE

To assess the interoperator and intraoperator variability of coronary CT angiography-derived FFR_CT using a machine learning-based postprocessing prototype.

MATERIALS AND METHODS

We included 60 symptomatic patients who underwent coronary CT angiography. FFR_CT was calculated by two independent operators after training using a machine learning-based on-site prototype. FFR_CT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate interoperator variability effect in FFR_CT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.

RESULTS

A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFR_CT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 to 0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 to 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared with proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis.

CONCLUSION

A high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFR_CT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFR_CT.

Modelling the association between biomarkers and clinical outcome: an introduction to nonlinear joint models

Nonlinear joint models are a powerful tool to precisely analyze the association between a nonlinear biomarker and a time-to-event process, such as death. Here, we review the main methodological techniques required to build these models and to make inferences and predictions. We describe the main clinical applications and discuss the future developments of such models.

A Comparison of Confounder Selection and Adjustment Methods for Estimating Causal Effects Using Large Healthcare Databases

PURPOSE

Confounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challenges. The objective was to evaluate the capacity of such methods to select confounders and reduce unmeasured confounding bias.

METHODS

A simulation study with known true effects was conducted. Completely synthetic and partially synthetic data incorporating real large healthcare data were generated. We compared Bayesian Adjustment for Confounding, Generalized Bayesian Causal Effect Estimation, Group Lasso and Doubly Robust Estimation, high-dimensional propensity score, and scalable collaborative targeted maximum likelihood algorithms. For the high-dimensional propensity score, two adjustment approaches targeting the effect in the whole population were considered: full matching and inverse probability weighting.

RESULTS

In scenarios without hidden confounders, most methods were essentially unbiased. The bias and variance of the high-dimensional propensity score varied considerably according to the number of variables selected by the algorithm. In scenarios with hidden confounders, substantial bias reduction was achieved by using machine learning methods to identify proxies as compared to adjusting only by observed confounders. High-dimensional propensity score and Group Lasso performed poorly in the partially synthetic simulation. Bayesian Adjustment for Confounding, Generalized Bayesian Causal Effect Estimation, and scalable collaborative targeted maximum likelihood algorithms performed particularly well.

CONCLUSIONS

Machine learning can help to identify measured confounders in large healthcare databases. They can also capitalize on proxies of unmeasured confounders to substantially reduce residual confounding bias. This article is protected by copyright. All rights reserved.

Prediction of post-traumatic neuropathy following impacted mandibular third molar removal

OBJECTIVES

The extraction of impacted mandibular third molars is a common surgical procedure often associated with complications including post-traumatic neuropathy. Previous work has focused on identifying confounding factors, but a robust preoperative risk prediction model remains elusive.

METHODS

Using a dataset of 648 patients and 812 impacted mandibular third molars, we used least absolute shrinkage and selection operator (LASSO) to fit prediction models based on risk factors assessed at both the tooth and patient levels. In addition, we fitted multivariable logistic regression models with the Firth correction for generalized estimating equations (GEE).

RESULTS

The LASSO model for post-traumatic neuropathy identified distoangular impaction of ≥ 45° (odds ratio [OR] = 2.9), proximity to the inferior alveolar nerve of ≤ 3 mm (OR = 1.9), disadvantageous curving (OR = 1.4), and psychiatric conditions (OR = 2.1) as predictors [area under the receiving operator characteristic curve (AUC) = 0.75]. Among other complications analyzed, the LASSO model for bleeding identified deep embedding or full impaction (OR = 1.8), psychiatric conditions (OR = 1.3), and age (OR = 0.9) as predictors (AUC = 0.64). These associations between predictors and postoperative complications were fundamentally reinforced by the corresponding GEE models.

CONCLUSIONS

Our findings point to the predictability of post-traumatic neuropathy and bleeding based on tooth anatomy and patient characteristics, overall suggesting that preoperatively identifiable factors can predict the risk of adverse outcomes in the extraction of impacted mandibular third molars.

CLINICAL SIGNIFICANCE

Mandibular third molar extraction is both a routine procedure and a leading cause of trigeminal neuropathy. Prevention of post-traumatic neuropathy, aided by individualized preoperative risk prediction, is of high clinical relevance.

Measuring the ecological preference for growth of 150 of the most influential weeds in weed community structure associated with agronomic and horticultural crops

The phytosociological researches which intent for studying the performance of weeds and the structure of weed assemblages associated with different crops derives their importance mainly from the adverse effect of weeds on crop productivity. Consequently, it is worth questioning about the ecological preferences of the weed growth in response to three main drivers for weed community structure associated with agronomic, and horticultural crops: crop diversification, crop seasonality, and soil type. A study area was selected comprising farmland of Nile Delta and its adjoining east and west territories, Egypt. A total of 555 species were recorded in 30 agroecosystems monitored and depending on species frequency/abundance values, 150 species were designated as the most influential weeds in weed community structure associated with agronomic and horticultural crops. The ecological preference of species for crop seasonality was evident through the results of Agglomerative hierarchical clustering. Three weed assemblage groups (WAG) identified: WAG A associated with winter agronomic crops, WAG B associated with summer agronomic crops, and WAG C associated with perennial agronomic crops and horticultural crops (orchards). Their diversity evaluated at different levels. The growth preference of the 150 species which were assigned as most influential weeds was gauged in response to the three environmental variables. 61 species were faithful to WAG A, 45 to WAG B, and 44 to WAG C. Concerning crop diversification, 34-species were significantly affected and scored coefficient of variation ≥ 100%. As for soil type, indicator species analysis revealed that 66-species show growth preference in fine grained soil while 84-species prefer coarse grained soil. In the three vegetation units (WAG A - C), 12 within-group associations (alliances) were specified of less-common (differential) species. The record of these alliances match to a specific environmental condition (ecological niche) and in them 29 strong indicators are identified. Redundancy analysis was used to extract and summarize the variation in species records in the response matrix (species vs. sites) that can be explained by the three different types of growth preference (explanatory variables), and the partial linear effect of them was evaluated by variation partitioning.

Seeing the Forest for the Trees: Evaluating Population Data in Allergy-Immunology

A population-level study is essential for understanding treatment effects, epidemiologic phenomena, and health care best practices. Evaluating large populations and associated data requires an analytic framework, which is commonly used by statisticians, epidemiologists, and data scientists. This document will serve to provide an overview of these commonly employed methods in allergy and immunology research. We will draw upon recent examples from the allergy-immunology literature to contextualize discrete principles of relevance to population-level analysis that include statistical features of a study population, elements of statistical inference, regression analysis, and an overview of machine learning practices. Our intent is to guide the reader through a practical description of this important quantitative discipline and facilitate greater understanding about data and result display in the medical literature.

Methodology minute: A statistical test primer for infection prevention and control

Selecting the appropriate statistical tests for data analysis is a critical skill for the infection preventionist (IP), both for analyzing their own data as well as evaluating the scientific literature methodology. Obtaining results from data analyses has never been easier thanks to computational improvements, but the interpretation of results relies on a keen awareness that the approach was sound. The purpose of this primer is to introduce the infection preventionist to the ideas behind hypothesis testing with a focus on statistical test selection.

Applications of Doubled Haploids in Plant Breeding and Applied Research

Manifold and diverse applications of doubled haploid (DH) plants have emerged in academy and in the plant breeding industry since the first discovery of a haploid mutant in the Jimson Weed (Datura stramonium), followed by the first reports about anther culture in the same species, maternal haploids by wide crosses in tobacco (Nicotiana tabacum L.) and barley (Hordeum vulgare L.), interspecific hybridization, ovary culture (gynogenesis), isolated microspore culture, and more recently the CENH3 approach in thale cress (Arabidopsis thaliana L.) and other species. Research and development efforts were and are still significant in both user groups. Luckily, often academic and industrial partners cooperate in challenging and sometimes voluminous projects worldwide. Not only to develop innovative DH protocols and technologies per se, but also to exploit the advantages of DH plants in a huge variety of research and development experiments. This review concentrates not on the DH technologies per se, but on the application of DHs in plant-related research and development projects.

San Antonio Nathan Shock Center: your one-stop shop for aging research

With evolving cores, enrichment and training programs, and supported research projects, the San Antonio (SA) Nathan Shock Center has for 26 years provided critical support to investigators locally, nationally, and abroad. With its existing and growing intellectual capital, the SA Nathan Shock Center provides to local and external investigators an enhanced platform to conduct horizontally integrated (lifespan, healthspan, pathology, pharmacology) transformative research in the biology of aging, and serves as a springboard for advanced educational and training activities in aging research. The SA Nathan Shock Center consists of six cores: Administrative/Program Enrichment Core, Research Development Core, Aging Animal Models and Longevity Assessment Core, Pathology Core, Analytical Pharmacology and Drug Evaluation Core, and Integrated Physiology of Aging Core. The overarching goal of the SA Nathan Shock Center is to advance knowledge in the basic biology of aging and to identify molecular and cellular mechanisms that will facilitate the development of pharmacologic interventions and other strategies to extend healthy lifespan. In pursuit of this goal, we provide an innovative "one-stop shop" venue to accelerate transformative research in the biology of aging through our integrated research cores. Moreover, we aim to foster and promote career development of early-stage investigators in aging biology through our research development programs, to serve as a resource and partner to investigators from other Shock Centers, and to disseminate scientific knowledge and enhanced awareness about aging research. Overall, the SA Nathan Shock Center aims to be a leader in research that advances our understanding of the biology of aging and development of approaches to improve longevity and healthy aging.

Application of the Closure Principle Computational Approach Test to Assess Ecotoxicological Field Studies: Comparative Analysis Using Earthworm Field Test Abundance Data

Field studies to determine the effects of chemicals on earthworm communities are generally conducted according to International Organization for Standardization standard 11268-3 (and later comments). However, statistical test procedures suggested in the guideline are frequently criticized, mainly for 2 reasons: 1) Earthworm abundances are count data and often do not fulfill requirements for multiple t tests (normal distribution and homogeneity of variance), and 2) the resulting toxicity metrics of multiple testing procedures (no/lowest-observed-effect concentrations [NOEC/LOEC]) fail to adequately detect the actual level of effects. Recently, a new method to overcome these shortcomings was presented by the introduction of the closure principle computational approach test (CPCAT). We applied this statistical method to assess chemical effects on abundance in a large dataset of 26 earthworm field studies (with up to 3 test chemical application rates) and an additional extended study with 6 application rates. A comparative analysis was provided considering results of well-established multiple testing approaches (Dunnett's test) with particular consideration of the degree of overdispersion found in these data. It was shown that the CPCAT detects substantially more effects in earthworm field tests as statistically significant than standard t test approaches. This lowered the LOEC/NOEC for many chemical treatments to control comparisons. As a consequence, the statistically detected NOECs/LOECs were often set at lower percentage deviations between control and chemical treatment. This is the first time the performance of the CPCAT has been assessed within a comprehensive analysis of earthworm field study data. Environ Toxicol Chem 2021;40:1750-1760. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Decreased risk of non-infectious anterior uveitis with statin therapy in a large healthcare claims database

PURPOSE

The purpose of this study is to determine if statin therapy decreases the incidence of non-infectious uveitis (NIU) using a retrospective cohort study.

METHODS

Patients enrolled in a national insurance plan who initiated statin (n = 711,734, statin cohort) or other lipid-lowering therapy (n = 148,044, non-statin cohort) were observed for NIU development. Incident NIU in the primary analysis was defined as a new diagnosis code for NIU followed by a second instance of a NIU code within 120 days. For the secondary outcome definition, a corticosteroid prescription or code for an ocular corticosteroid injection within 120 days of the NIU diagnosis code was used instead of the second NIU diagnosis code. Estimation of NIU incidence used multivariable Cox proportional hazards regression. The proportional hazards assumption was satisfied by creating two time periods of analysis, ≤ 150 and > 150 days. Subanalyses were performed by anatomic subtype.

RESULTS

Overall, the primary outcome occurred 541 times over 690,465 person-years in the statin cohort and 103 times over 104,301 person-years in the non-statin cohort. No associations were seen in the ≤ 150-day analyses (p > 0.20 for all comparisons). However, after 150 days, the statin cohort was less likely to develop any uveitis [hazard ratio (HR) = 0.70, 95% confidence interval (CI): 0.51-0.97, P = 0.03] in the primary outcome analysis, but did not meet significance for the secondary outcome (HR = 0.85, 95% CI: 0.63-1.15, P = 0.30). Similarly, in the anatomic subtype analysis, after 150 days, the statin cohort was less likely to develop anterior uveitis (HR = 0.67, 95% CI: 0.47-0.97, P = 0.03) in the primary analysis, but the association did not reach significance for the secondary outcome (HR = 0.82, 95% CI: 0.56-1.20, P = 0.31).

CONCLUSION

Our results suggest that statin therapy for > 150 days decreases the incidence of NIU.

Calculation of the Paternity Index for STR with tri-allelic patterns in paternity testing

The calculation of the paternity index (PI) value of common bi-allelic genotypes at STR loci has been standardized in paternity cases. However, for tri-allelic patterns, a rare category of genotyping aberration in forensic practice, the statistical analysis in paternity testing remains disputed. The Type 1 tri-allelic pattern generally results from somatic mutation in the early stage of individual development. The Type 2 tri-allelic pattern is commonly generated by segmental duplication in the genome. In this study, practical and theoretical aspects of the evaluation of evidence concerning the Type 1 and Type 2 tri-allelic patterns in healthy individuals are discussed based on the likelihood ratio (LR) in different categories of kinship cases. The calculation of the PI value concerning tri-allelic genotypes is formulated according to the generation and genetic transmission of tri-allelic patterns. Meanwhile, a package tool named TriPI is developed to assist the calculation of the PI value in paternity testing concerning tri-allelic subjects, which could benefit the evaluation of the weight of evidence in the interpretation of tri-allelic pattern in forensic practice.

The role of epigenetic modifications for the pathogenesis of Crohn's disease

Epigenetics has become a promising field for finding new biomarkers and improving diagnosis, prognosis, and drug response in inflammatory bowel disease. The number of people suffering from inflammatory bowel diseases, especially Crohn's disease, has increased remarkably. Crohn's disease is assumed to be the result of a complex interplay between genetic susceptibility, environmental factors, and altered intestinal microbiota, leading to dysregulation of the innate and adaptive immune response. While many genetic variants have been identified to be associated with Crohn's disease, less is known about the influence of epigenetics in the pathogenesis of this disease. In this review, we provide an overview of current epigenetic studies in Crohn's disease. In particular, we enable a deeper insight into applied bioanalytical and computational tools, as well as a comprehensive update toward the cell-specific evaluation of DNA methylation and histone modifications.

Model-free estimates that complement information obtained from the hazard ratio

Understand the limitations of the modeling of survival data, especially as pertains to the Cox proportional hazards model.An introduction to model-free estimates of survival, namely, the restricted mean survival time/restricted mean lost time.Use R (The R Foundation for Statistical Computing, Austria) or STATA® (The STATACorp, College Station, TX, USA) to perform analyses and obtain these parameters from a dataset.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12055-021-01167-4.

Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways

INTRODUCTION

The study of Alzheimer's disease (AD) has revealed biological pathways with implications for disease neuropathology and pathophysiology. These pathway-level effects may also be mediated by individual characteristics or covariates such as age or sex. Evaluation of AD biological pathways in the context of interactions with these covariates is critical to the understanding of AD as well as the development of model systems used to study the disease.

METHODS

Gene set enrichment methods are powerful tools used to interpret gene-level statistics at the level of biological pathways. We introduce a method for quantifying gene set enrichment using likelihood ratio-derived test statistics (gsLRT), which accounts for sample covariates like age and sex. We then use our method to test for age and sex interactions with protein expression levels in AD and to compare the pathway results between human and mouse species.

RESULTS

Our method, based on nested logistic regressions is competitive with the existing standard for gene set testing in the context of linear models and complex experimental design. The gene sets we identify as having a significant association with AD-both with and without additional covariate interactions-are validated by previous studies. Differences between gsLRT results on mouse and human datasets are observed.

DISCUSSION

Characterizing biological pathways involved in AD builds on the important work involving single gene drivers. Our gene set enrichment method finds pathways that are significantly related to AD while accounting for covariates that may be relevant to disease development. The method highlights commonalities and differences between human AD and mouse models, which may inform the development of higher fidelity models for the study of AD.

Cultivating Statistical Literacy Among Health Professions Students: a Curricular Model

Delivery of optimal, evidence-based, patient-centered care requires health care providers with not only medical knowledge and clinical skills but also statistical literacy. It is incumbent upon health professions education programs to include this critical content within the curriculum. This mixed-method explored health professions learner's attitudes toward statistics and affirms an efficacious curricular model for cultivation of statistical literacy. Through demonstration of practical and statistical significance, this model offers a practical way to integrate essential statistical methods and application within a medical curriculum. This model demonstrated success in accomplishing learner competence and received high degrees of satisfaction via student evaluations.

Cord Serum Cytokines at Birth and Children's Anxiety-Depression Trajectories From 3 to 8 Years: The EDEN Mother-Child Cohort

BACKGROUND

Recent research suggests that immune dysregulation in pregnancy could be a risk factor for anxiety and depression symptoms in offspring. Whereas animal studies have demonstrated the importance of the link between perinatal cytokines and abnormal behaviors in offspring, human epidemiological studies in this area remain limited. The objectives of the study were to describe the network of cord serum cytokines at birth and test whether they are associated with subsequent anxiety and depression symptom trajectories in offspring.

METHODS

We used data and biological samples from 871 mother-child pairs followed up from pregnancy to 8 years of age and participating in the French mother-child cohort EDEN (a study on the pre- and early postnatal determinants of child health and development). Cord serum cytokines were measured at birth. Children's symptoms of anxiety and depression were assessed with the emotional difficulties subscore of the Strength and Difficulties Questionnaire at ages 3, 5, and 8 years, from which trajectories of anxiety-depression symptoms were derived.

RESULTS

Results showed a significant association between cord serum interleukin-7 at birth and the trajectories of children's anxiety-depression symptoms between ages 3 to 8 years (adjusted odds ratio, 0.73; 95% confidence interval, 0.57-0.93). The associations considered relevant confounders, including prenatal maternal depressive symptoms.

CONCLUSIONS

Early immune changes may contribute to subsequent anxiety and depression symptoms in childhood. Beyond the understanding of mechanisms underlying the occurrence of emotional difficulties in children, our findings open avenues for future research in human and animals.

Advancing Effective Clinical Trial Designs for Myelofibrosis

Design features of phase I, II, and III clinical trials of pharmaceutical interventions in myelofibrosis (MF) are discussed. Model-assisted and model-based designs for phase I trials are useful for maximizing therapeutic benefit and include novel approaches to dose escalation. Trials in MF have shifted to accommodate new challenges following approval of JAK inhibitor therapies. Standardized response criteria exist; however, alternative measures of response when evaluating newer agents may be needed. Noninferiority and other adaptive designs can be used to incorporate design changes over time. Patient-reported outcomes, including quality-of-life and symptom assessment, should be included as outcome measures.

A risk stratification scheme for synchronous oligometastatic non-small cell lung cancer developed by a multicentre analysis

BACKGROUNDS

Oligometastatic Non-Small Cell Lung Cancer (NSCLC) patients represent a category without a standard therapeutic approach. However, in selected oligometastatic NSCLC, radical surgery seems to offer a good prognosis. This retrospective study aimed to analyse the long-term outcomes of synchronous oligometastatic patients treated with curative intent and identify the factors associated with better results and the proposal of a risk stratification system for classifying the synchronous oligometastatic NSCLC.

METHODS

The medical records of patients from 18 centres with pathologically diagnosed synchronous oligometastatic NSCLC were retrospectively reviewed. The inclusion criteria were synchronous oligometastatic NSCLC, radical surgical treatment of the primary tumour with or without neoadjuvant/adjuvant therapy and radical treatment of all metastatic sites. The Kaplan - Meier method estimated survivals. A stratified backward stepwise Cox regression model was assessed for multivariable survival analyses.

RESULTS

281 patients were included. The most common site of metastasis was the brain, in 50.89 % patients. Median overall survival was 40 months (95 % CI: 29-53). Age ≤65 years (HR = 1.02, 95 % CI: 1.00-1.05; p = 0.019), single metastasis (HR = 0.71, 95 % CI: 0.45-1.13; p = 0.15) and presence of contralateral lung metastases (HR = 0.30, 95 % CI: 0.15 - 0.62; p = 0.001) were associated with a good prognosis. The presence of pathological N2 metastases negatively affected survival (HR = 2.00, 95 % CI: 1.21-3.32; p = 0.0065). These prognostic factors were used to build a simple risk classification scheme.

CONCLUSIONS

Treatment of selected synchronous oligometastatic NSCLC with curative purpose could be conducted safely and at acceptable 5-year survival levels, especially in younger patients with pN0 disease.

Learning to Critically Appraise Rheumatic Disease Literature: Educational Opportunities During Training and into Practice

To provide optimal patient care, rheumatologists must be equipped and motivated to critically appraise the literature. The conceptual frameworks Retrieval Enhanced Learning, Self-Determination Theory, and Communities of Practice can inform the design of educational approaches to promote critical appraisal in practice. HSS CLASS-Rheum® is a learning tool that can be used to help rheumatologists learn skills for critical appraisal through retrieval practice. Combining retrieval practice with opportunities for connection through Peer Instruction, journal clubs, and other forums can help support engagement and internalization of motivation, promoting persistence with critical appraisal in practice.