单臂试验连续型数据的贝叶斯Meta分析方法及实现

目的 介绍单臂试验连续型数据的Meta分析模型、贝叶斯方法及实现。方法 阐述正态-正态层次模型,基于该模型框架,以贝叶斯方法拟合随机效应模型,对效应参数μ和异质性参数τ分别选择不同的先验,使用R软件的bayesmeta包对两个文献数据重新分析。结果 在正态-正态层次模型框架下,基于不同的先验信息,贝叶斯Meta分析结果为:数据1参数μ的点估计及95%CI分别为-4.26(-6.97, -1.92)和-4.50(-9.27, -0.53),参数τ点估计及95%CI分别为1.51(0.41, 2.75)和2.28(0.00, 6.57);数据2参数μ的点估计及95%CI分别为-4.07(-5.54, -2.71)和-4.12(-5.96,-2.46),参数τ点估计及95%CI分别为1.54(0.78, 2.48)和1.81(0.74, 3.51)。结论 不同的先验可能影响参数估计值。基于NNHM框架下的贝叶斯方法适用于单臂试验连续型数据的Meta分析。Bayesmeta包以其简单、快速、准确、可重量性算法等可以用于实现贝叶斯随机效应模型Meta分析。     

Medicare's hospital readmissions reduction program and the rise in observation stays

Objective

To evaluate whether Medicare's Hospital Readmissions Reduction Program (HRRP) is associated with increased observation stay use.

Data Sources and Study Setting

A nationally representative sample of fee-for-service Medicare claims, January 2009–September 2016.

Study Design

Using a difference-in-difference (DID) design, we modeled changes in observation stays as a proportion of total hospitalizations, separately comparing the initial (acute myocardial infarction, pneumonia, heart failure) and subsequent (chronic obstructive pulmonary disease) target conditions with a control group of nontarget conditions. Each model used 3 time periods: baseline (15 months before program announcement), an intervening period between announcement and implementation, and a 2-year post-implementation period, with specific dates defined by HRRP policies.

Data Collection/Extraction Methods

We derived a 20% random sample of all hospitalizations for beneficiaries continuously enrolled for 12 months before hospitalization (N = 7,162,189).

Principal Findings

Observation stays increased similarly for the initial HRRP target and nontarget conditions in the intervening period (0.01% points per month [95% CI −0.01, 0.3]). Post-implementation, observation stays increased significantly more for target versus nontarget conditions, but the difference is quite small (0.02% points per month [95% CI 0.002, 0.04]). Results for the COPD analysis were statistically insignificant in both policy periods.

Conclusions

The increase in observation stays is likely due to other factors, including audit activity and clinical advances.     

Autoimmune diseases as comorbidities for liver,gallbladder, and biliary duct cancers in Sweden

Background

Autoimmune diseases are associated with many cancers but there is a lack of population-based studies with different autoimmune diseases that have a long follow-up. This is also true of hepatobiliary cancers, which include hepatocellular cancer (HCC) and rarer entities of gallbladder cancer (GBC), intra- and extrahepatic cholangiocarcinoma (iCCA and eCCA), and ampullary cancer.

Methods

Diagnostic data on 43 autoimmune diseases were collected from the Swedish Inpatient Register from 1987 to 2018, and cancer data were derived from the national cancer registry from 1997 onward. Relative risks were expressed as standardized incidence ratios (SIRs).

Results

In a population of 13.6 million, 1.1 million autoimmune diseases were diagnosed and subsequent hepatobiliary cancer was diagnosed in 3191 patients (17.2% of cancers). SIRs for HCC were 2.73 (men) and 2.86 (women), 3.74/1.96 for iCCA, 2.65/1.37 for GBC, 2.38/1.64 for eCCA, and 1.80/1.85 for ampullary cancer. Significant associations between autoimmune disease and HCC were observed for 13 autoimmune diseases, with the highest risks being for autoimmune hepatitis (48.92/73.53, men/women) and primary biliary cirrhosis (38.03/54.48). GBC was increased after six autoimmune diseases, with high SIRs for ulcerative colitis (12.22/3.24) and men with Crohn disease (9.16). These autoimmune diseases were also associated with a high risk of iCCA, which had seven other associations, and eCCA, which had five other associations. Ampullary cancer occurrence was increased after four autoimmune diseases.

Conclusion

An autoimmune disease is a common precursor condition for hepatobiliary cancers. This calls for careful control of autoimmune disease symptoms in each patient and encouragement to practice a healthy lifestyle.     

Utility of Differential White Cell Count and Cell Population Data for Ruling Out COVID-19 Infection in Patients With Community-Acquired Pneumonia

IntroductionThe main aim of this study was to assess the utility of differential white cell count and cell population data (CPD) for the detection of COVID-19 in patients admitted for community-acquired pneumonia (CAP) of different etiologies.MethodsThis was a multicenter, observational, prospective study of adults aged ≥18 years admitted to three teaching hospitals in Spain from November 2019 to November 2021 with a diagnosis of CAP. At baseline, a Sysmex XN-20 analyzer was used to obtain detailed information related to the activation status and functional activity of white cells.ResultsThe sample was split into derivation and validation cohorts of 1065 and 717 patients, respectively. In the derivation cohort, COVID-19 was confirmed in 791 patients and ruled out in 274 patients, with mean ages of 62.13 (14.37) and 65.42 (16.62) years, respectively (p < 0.001). There were significant differences in all CPD parameters except MO-Y. The multivariate prediction model showed that lower NE-X, NE-WY, LY-Z, LY-WY, MO-WX, MO-WY, and MO-Z values and neutrophil-to-lymphocyte ratio were related to COVID-19 etiology with an AUC of 0.819 (0.790, 0.846). No significant differences were found comparing this model to another including biomarkers (p = 0.18).ConclusionsAbnormalities in white blood cell morphology based on a few cell population data values as well as NLR were able to accurately identify COVID-19 etiology. Moreover, systemic inflammation biomarkers currently used were unable to improve the predictive ability. We conclude that new peripheral blood biomarkers can help determine the etiology of CAP fast and inexpensively.