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Overlap weighting, using weights defined as the probability of receiving the opposite treatment, is a relatively new, alternative propensity score-based weighting technique used to adjust for confounding when estimating causal treatment effects. It has preferable properties compared to inverse probability of treatment weighting, such as exact covariate balance, safeguards against extreme weights and emphasis on medical equipoise, where treatment decisions are most uncertain. In this article, we introduce the overlap weighting methodology, compare it to inverse probability of treatment weighting and provide some strategies for assessing weighting impact, through an applied example of hospital mortality. When the propensity score distributions have large separation, inverse probability of treatment weighting has been shown to produce biased and less efficient estimates of the treatment effect, making overlap weighting a preferred method in such cases. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) nodal staging does not account for tumor burden within the lymph nodes (LNs). In this retrospective single institution/single surgeon study, we assessed the significance of LN tumor burden in pN1 PCa patients after undergoing robotic-assisted radical prostatectomy with extended pelvic lymph node dissection. Consistent with prior reports, LN tumor burden was found to be significantly associated with biochemical recurrence-free survival (BRFS). This study was the first to report on the significance of the anatomical location of tumor deposits within the LN, as well as the quantified extent of extranodal extension (ENE). Likely due to sample size, the anatomical location within the LN and ENE did not show significant association with BRFS. We emphasize that PCa nodal staging and/or post-operative clinical nomograms should account for LN tumor burden. Background: Prostate cancer (PCa) nodal staging does not account for lymph node (LN) tumor burden. The LN anatomical compartment involved with the tumor or the quantified extent of extranodal extension (ENE) have not yet been studied in relation to biochemical recurrence-free survival (BRFS). Methods: Histopathological slides of 66 pN1 PCa patients who underwent extended pelvic lymph node dissection were reviewed. We recorded metrics to quantify LN tumor burden. We also characterized the LN anatomical compartments involved and quantified the extent of ENE. Results: The median follow-up time was 38 months. The median number of total LNs obtained per patient was 30 (IQR 23–37). In the risk-adjusted cox regression model, the following variables were associated with BRFS: mean size of the largest LN deposit per patient (log2: adjusted hazard ratio (aHR) = 1.91, p < 0.001), the mean total span of all LN deposits per patient (2.07, p < 0.001), and the mean percent surface area of the LN involved with the tumor (1.58, p < 0.001). There was no significant BRFS association for the LN anatomical compartment or the quantified extent of ENE. Conclusion: LN tumor burden is associated with BRFS. The LN anatomical compartments and the quantified extent of ENE did not show significant association with BRFS. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ahj.2005.07.009 Byline: YingXing Wu, Ruyun Jin, Gary L. Grunkemeier Abstract: The Clinical Outcomes Assessment Program (COAP) from the state of Washington recently published a risk model for hospital mortality after percutaneous coronary intervention (PCI), which was validated by a consortium of hospitals in 4 northeastern states. The Providence Health System (PHS) Cardiovascular Study Group data was used to further validate this COAP model using data from PHS hospitals in 3 western states. Author Affiliation: Providence Health System Cardiovascular Study Group, Providence Health System, Portland, OR Article History: Received 24 March 2005; Accepted 12 July 2005

Background The impact of remdesivir (RDV) on mortality rates in coronavirus disease 2019 (COVID-19) is controversial, and the mortality effect in subgroups of baseline disease severity has been incompletely explored. The purpose of this study was to assess the association of RDV with mortality rates in patients with COVID-19. Methods In this retrospective cohort study we compared persons receiving RDV with those receiving best supportive care (BSC). Patients hospitalized between 28 February and 28 May 2020 with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection were included with the development of COVID-19 pneumonia on chest radiography and hypoxia requiring supplemental oxygen or oxygen saturation ≤94% with room air. The primary outcome was overall survival, assessed with time-dependent Cox proportional hazards regression and multivariable adjustment, including calendar time, baseline patient characteristics, corticosteroid use, and random effects for hospital. Results A total of 1138 patients were enrolled, including 286 who received RDV and 852 treated with BSC, 400 of whom received hydroxychloroquine. Corticosteroids were used in 20.4% of the cohort (12.6% in RDV and 23% in BSC). Comparing persons receiving RDV with those receiving BSC, the hazard ratio (95% confidence interval) for death was 0.46 (.31–.69) in the univariate model (P <.001) and 0.60 (.40–.90) in the risk-adjusted model (P =.01). In the subgroup of persons with baseline use of low-flow oxygen, the hazard ratio (95% confidence interval) for death in RDV compared with BSC was 0.63 (.39–1.00; P =.049). Conclusion Treatment with RDV was associated with lower mortality rates than BSC. These findings remain the same in the subgroup with baseline use of low-flow oxygen. [ABSTRACT FROM AUTHOR]

The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database is the world's premier adult cardiac surgery outcomes registry. This tutorial explains the following: how STS updates the risk models that are used to calculate the predicted risks of adverse events in the registry; why STS on a quarterly basis adjusts or "calibrates" the observed-to-expected ratios to equal 1 (O/E = 1), thereby effectively making the annual number of adverse events predicted by the model match the annual number of adverse events observed in the entire registry; the differences between the calibrated and uncalibrated O/E ratios; and how and when to use each. [Display omitted] [ABSTRACT FROM AUTHOR]