The Effect of Intrathoracic Lesion Location on Initial Tyrosine Kinase Inhibitor Response in Advanced Oncogene-Addicted Non-Small Cell Lung Cancer: A Comparison Between RECIST 1.1 and a Novel Method of Response Assessment (MAX).

INTRODUCTION: Different subtypes of non-small cell lung cancer (NSCLC) are associated with different patterns of metastatic spread. Anatomic location of lesions in the chest may influence patterns of cancer growth and the shrinkage to therapy. Consequently, lesion location could affect apparent response rates per RECIST. We sought to explore this and develop, as needed, treatment response assessments less affected by the location. METHODS: Cases of advanced oncogene-addicted NSCLC (EGFR, ALK, and ROS1) with pre- and on-therapy imaging during initial targeted therapy were identified. Lesions located in the lung parenchyma, pleural space or intra-thoracic lymph nodes were identified and analyzed separately from each other by RECIST 1.1 (unidimensional measurements) and by a novel MAX methodology (bidimensional measurements) which takes the axis with the greatest absolute percentage change on therapy in each location as the representative measurement. RESULTS: Three hundred three patients with 446 unidimensional measured lesions were included for RECIST analysis. Two hundred forty nine patients with 386 bidimensional measured lesions were included for MAX analysis, as well as the analysis comparing RECIST and MAX. Intrathoracic location significantly impacted percentage shrinkage and the response rate per RECIST. The response rates for pleural, intra-parenchymal and nodal lesions were 34.1%, 49.6%, and 68.3%, respectively (P = .0002). The MAX methodology both increased the apparent treatment effect and made it consistent between intrathoracic locations. For pleural, parenchymal and nodal lesions, the MAX calculated response rate were 83.7%, 72.2%, and 75.4%, respectively (P-value = .24). CONCLUSION: Intrathoracic lesion location affects RECIST-based treatment effectiveness estimations. The MAX methodology neutralizes location effect when examining impact of treatment and should be explored further.

Impact of a Hybrid Lung Cancer Screening Model on Patient Outcomes and Provider Behavior.

BACKGROUND: Lung cancer screening (LCS) implementation is complicated by the Centers for Medicare and Medicaid Services reimbursement requirements of shared decision-making and tobacco cessation counseling. LCS programs can utilize different structures to meet these requirements, but the impact of programmatic structure on provider behavior and screening outcomes is poorly described. PATIENTS AND METHODS: In a retrospective chart review of 624 patients in a hybrid structure, academic LCS program, we compared characteristics and outcomes of primary care provider (PCP)- and specialist-screened patients. We also assessed the impact of the availability of an LCS specialty clinic and best practice advisory (BPA) on PCP ordering patterns using electronic medical record generated reports. RESULTS: During the study period of July 1, 2014 through June 30, 2018, 48% of patients were specialist-screened and 52% were PCP-screened; there were no clinically relevant differences in patient characteristics or screening outcomes between these populations. PCPs demonstrate distinct practice patterns when offered the choice of specialist-driven or PCP-driven screening. Increased exposure to a LCS BPA is associated with increased PCP screening orders. The addition of a nurse navigator into the LCS program increased documentation of shared decision-making and tobacco cessation counseling to > 95% and virtually eliminated screening of ineligible patients. CONCLUSIONS: Systematic interventions including a BPA and nurse navigator are associated with increased screening and improved program quality, as evidenced by reduced screening of ineligible patients, increased lung cancer risk of the screened population, and improved compliance with LCS guidelines. Individual PCPs demonstrate clear preferences regarding LCS that should be considered in program design.

Same-level fall injuries in US workplaces by age group, gender, and industry.

BACKGROUND: As the workforce ages, occupational injuries from falls on the same level will increase. Some industries may be more affected than others. METHODS: We conducted a cross-sectional study using data from the Bureau of Labor Statistics to estimate same-level fall injury incidence rates by age group, gender, and industry for four sectors: 1) healthcare and social assistance; 2) manufacturing; 3) retail; and 4) transportation and warehousing. We calculated rate ratios and rate differences by age group and gender. RESULTS: Same-level fall injury incidence rates increase with age in all four sectors. However, patterns of rate ratios and rate differences vary by age group, gender, and industry. Younger workers, men, and manufacturing workers generally have lower rates. CONCLUSIONS: Variation in incidence rates suggests there are unrealized opportunities to prevent same-level fall injuries. Interventions should be evaluated for their effectiveness at reducing injuries, avoiding gender- or age-discrimination and improving work ability.

Facility-Level Percutaneous Coronary Intervention Readmission Rates Are Not Associated With Facility-Level Mortality: Insights From the VA Clinical Assessment, Reporting, and Tracking (CART) Program.

BACKGROUND: Thirty-day readmission after percutaneous coronary intervention (PCI) is common, costly, and linked to poor patient outcomes. Accordingly, facility-level 30-day readmission rates have been considered as a potential quality measure. However, it is unknown whether facility-level 30-day readmission rates are associated with facility-level mortality. We sought to determine the effect of 30-day readmissions after PCI on mortality at both the patient and facility level in the Veterans Administration hospital system. METHODS AND RESULTS: We included all patients who underwent PCI in the Veterans Administration hospital system nationally from October 2007 through August 2012, comparing all-cause mortality rates between patients with and without 30-day readmissions following PCI. Patients were then aggregated at the hospital level to evaluate the correlation between hospital-level readmission rates with hospital-level 1-year mortality rates. Among 41 069 patients undergoing PCI at 62 sites, 12.2% were readmitted within 30 days of discharge. Patients with 30-day readmission had higher risk-adjusted mortality (hazard ratio 1.53, 95% CI 1.44-1.63, P<0.0001). Facilities varied widely in 30-day readmission rates (systemwide range of 6.6-19.4%, median 11.8%, interquartile range 10.0-13.2%); however, adjusted facility-level readmission rates were not correlated with adjusted 1-year mortality rates. CONCLUSIONS: Thirty-day readmissions after PCI are common and are a significant risk factor for mortality for individual patients even after robust statistical adjustment for clinical confounding. However, lack of correlation between readmission and mortality at the facility level suggests that quality improvement based on facility-level readmission rates will not modify mortality in this high-risk group.

Adherence to dabigatran therapy and longitudinal patient outcomes: insights from the veterans health administration.

BACKGROUND: Dabigatran is a novel oral anti-coagulant (NOAC) that reduces risk of stroke in patients with non-valvular atrial fibrillation (NVAF). It does not require routine monitoring with laboratory testing which may have an adverse impact on adherence. We aimed to describe adherence to dabigatran in the first year after initiation and assess the association between non-adherence to dabigatran and clinical outcomes in a large integrated healthcare system. METHODS: We studied a national cohort of 5,376 patients with NVAF, initiated on dabigatran between October-2010 and September-2012 at all Veterans Affairs hospitals. Adherence to dabigatran was calculated as proportion of days covered (PDC) and association between PDC and outcomes was assessed using standard regression techniques. RESULTS: Mean age of the study cohort was 71.3 ± 9.7 years; 98.3% were men and mean CHADS2 score was 2.4 ± 1.2 (mean CHA2DS2VASc score 3.2 ± 1.4). Median PDC was 94% (IQR 76%-100%; mean PDC 84% ± 22%) over a median follow-up of 244 days (IQR 140-351). A total of 1,494 (27.8%) patients had a PDC <80% and were classified as non-adherent. After multivariable adjustment, lower adherence was associated with increased risk for combined all-cause mortality and stroke (HR 1.13, 95% CI 1.07-1.19 per 10% decrease in PDC). Adherence to dabigatran was not associated with non-fatal bleeding or myocardial infarction. CONCLUSIONS: In the year after initiation, adherence to dabigatran for a majority of patients is very good. However, 28% of patients in our cohort had poor adherence. Furthermore, lower adherence to dabigatran was associated with increased adverse outcomes. Concerted efforts are needed to optimize adherence to NOACs.

Cluster analysis using multivariate mixed effects models.

A common situation in the biological and social sciences is to have data on one or more variables measured longitudinally on a sample of individuals. A problem of growing interest in these areas is the grouping of individuals into one of two or more clusters according to their longitudinal behavior. Recently, methods have been proposed to deal with cases where individuals are classified into clusters through a linear model of mixed univariate effects deriving from a longitudinally measured variable. The method proposed in the current work deals with the case of clustering and then classification based on two or more variables measured longitudinally, through the fitting of non-linear multivariate mixed effect models, and with consideration given to parameter estimation for balanced and unbalanced data using an EM algorithm. The application of the method is illustrated with an example in which the clusters are identified and the classification into clusters is compared with the true membership of individuals in one of two groups, which is known at the end of the follow-up period.

Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data.

Multiple outcomes are often used to properly characterize an effect of interest. This article discusses model-based statistical methods for the classification of units into one of two or more groups where, for each unit, repeated measurements over time are obtained on each outcome. We relate the observed outcomes using multivariate nonlinear mixed-effects models to describe evolutions in different groups. Due to its flexibility, the random-effects approach for the joint modeling of multiple outcomes can be used to estimate population parameters for a discriminant model that classifies units into distinct predefined groups or populations. Parameter estimation is done via the expectation-maximization algorithm with a linear approximation step. We conduct a simulation study that sheds light on the effect that the linear approximation has on classification results. We present an example using data from a study in 161 pregnant women in Santiago, Chile, where the main interest is to predict normal versus abnormal pregnancy outcomes.

Non-linear random effects model for multivariate responses with missing data.

The use of random-effects models for the analysis of longitudinal data with missing responses has been discussed by several authors. In this paper, we extend the non-linear random-effects model for a single response to the case of multiple responses, allowing for arbitrary patterns of observed and missing data. Parameters for this model are estimated via the EM algorithm and by the first-order approximation available in SAS Proc NLMIXED. The set of equations for this estimation procedure is derived and these are appropriately modified to deal with missing data. The methodology is illustrated with an example using data coming from a study involving 161 pregnant women presenting to a private obstetrics clinic in Santiago, Chile.