The Mortality Risk of Proton Pump Inhibitors in 1.9 Million US Seniors: An Extended Cox Survival Analysis.

BACKGROUND & AIMS: Observational studies have linked proton pump inhibitors (PPIs) with increased risk of mortality and other safety outcomes, in contradiction with a recent PPI randomized controlled trial (RCT). Observational studies may be prone to reverse causality, where deaths are attributed to the treatment rather than the conditions that are treated (protopathic bias). METHODS: We analyzed an incident drug user cohort of 1,930,728 elderly Medicare fee-for-service beneficiaries to evaluate the PPI-associated risk of death with a Cox regression analysis with time-varying covariates and propensity score adjustments. To correct for protopathic bias which occurs when a given drug is associated with prodromal signs of death, we implemented a lag-time approach by which any study drug taken during a 90-day look-back window before each death was disregarded. RESULTS: Among 1,930,728 study individuals, 80,972 (4.2%) died during a median 3.8 years of follow-up, yielding an overall unadjusted death rate/1000 person-years of 9.85; 14.31 for PPI users and 7.93 for non- users. With no lag-time, PPI use (vs no use) was associated with 10% increased mortality risk (adjusted HR=1.10; 95% CI 1.08-1.12). However, with a lag-time of 90 days, mortality risk associated with PPI use was near zero (adjusted HR=1.01; 95% CI 0.99-1.02). CONCLUSION: Given the usage patterns of PPIs in patients with conditions that may presage death, protopathic bias may explain the association of PPIs with increased risk of death reported in observational studies.

An improved method for the effect estimation of the intermediate event on the outcome based on the susceptible pre-identification.

BACKGROUND: In follow-up studies, the occurrence of the intermediate event may influence the risk of the outcome of interest. Existing methods estimate the effect of the intermediate event by including a time-varying covariate in the outcome model. However, the insusceptible fraction to the intermediate event in the study population has not been considered in the literature, leading to effect estimation bias due to the inaccurate dataset. METHODS: In this paper, we propose a new effect estimation method, in which the susceptible subpopulation is identified firstly so that the estimation could be conducted in the right population. Then, the effect is estimated via the extended Cox regression and landmark methods in the identified susceptible subpopulation. For susceptibility identification, patients with observed intermediate event time are classified as susceptible. Based on the mixture cure model fitted the incidence and time of the intermediate event, the susceptibility of the patient with censored intermediate event time is predicted by the residual intermediate event time imputation. The effect estimation performance of the new method was investigated in various scenarios via Monte-Carlo simulations with the performance of existing methods serving as the comparison. The application of the proposed method to mycosis fungoides data has been reported as an example. RESULTS: The simulation results show that the estimation bias of the proposed method is smaller than that of the existing methods, especially in the case of a large insusceptible fraction. The results hold for small sample sizes. Besides, the estimation bias of the new method decreases with the increase of the covariates, especially continuous covariates, in the mixture cure model. The heterogeneity of the effect of covariates on the outcome in the insusceptible and susceptible subpopulation, as well as the landmark time, does not affect the estimation performance of the new method. CONCLUSIONS: Based on the pre-identification of the susceptible, the proposed new method could improve the effect estimation accuracy of the intermediate event on the outcome when there is an insusceptible fraction to the intermediate event in the study population.

Association of Uncontrolled Hypertension or Diabetes Mellitus With Major Adverse Cardiovascular Events and Mortality in South Korea: Population-Based Cohort Study.

BACKGROUND: Managing hypertension (HT) and diabetes mellitus (DM) is crucial to preventing cardiovascular diseases. Few studies have investigated the incidence and risk of cardiovascular diseases or mortality in uncontrolled HT or DM in the Asian population. Epidemiological studies of cardiovascular disease should be conducted with continuous consideration of the changing disease risk profiles, lifestyles, and socioeconomic status over time. OBJECTIVE: We aimed to examine the association of uncontrolled HT or DM with the incidence of cardiovascular events or deaths from any cause. METHODS: This population-based retrospective study was conducted using data from the Korean National Health Insurance Service-National Health Screening Cohort, including patients aged 40-79 years who participated in national screening from 2002 to 2003 and were followed up until 2015. The health screening period from 2002 to 2013 was stratified into 6 index periods in 2-year cycles, and the follow-up period from 2004 to 2015 was stratified accordingly into 6 subsequent 2-year periods. The incidence rates and hazard ratio (HR) for major adverse cardiovascular events (MACE) and death from any cause were estimated according to HT or DM control status. Extended Cox models with time-dependent variables updated every 2 years, including sociodemographic characteristics, blood pressure (BP), fasting blood glucose (FBG), medication prescription, and adherence, were used. RESULTS: Among the total cohort of 440,249 patients, 155,765 (35.38%) were in the uncontrolled HT or DM group. More than 60% of the patients with HT or DM who were prescribed medications did not achieve the target BP or FBG. The incidence of MACE was 10.8-15.5 and 9.6-13.3 per 1000 person-years in the uncontrolled DM and uncontrolled HT groups, respectively, and increased with age. In the uncontrolled HT and DM group, the incidence of MACE was high (15.2-17.5 per 1000 person-years) at a relatively young age and showed no age-related trend. Adjusted HR for MACE were 1.28 (95% CI 1.23-1.32) for the uncontrolled DM group, 1.32 (95% CI 1.29-1.35) for the uncontrolled HT group, and 1.54 (95% CI 1.47-1.60) for the uncontrolled HT and DM group. Adjusted HR for death from any cause were 1.05 (95% CI 1.01-1.10) for the uncontrolled DM group, 1.13 (95% CI 1.10-1.16) for the uncontrolled HT group, and 1.17 (95% CI 1.12-1.23) for the uncontrolled HT and DM group. CONCLUSIONS: This up-to-date evidence of cardiovascular epidemiology in South Korea serves as the basis for planning public health policies to prevent cardiovascular diseases. The high uncontrolled rates of HT or DM, regardless of medication prescription, have led us to suggest the need for a novel system for effective BP or glycemic control, such as a community-wide management program using mobile health technology.

A randomized controlled trial of effectiveness of platelet-rich plasma gel and regular dressing on wound healing time in pilonidal sinus surgery: Role of different affecting factors.

BACKGROUND: The aim of this study was to assess the possible association between different factors such as age, sex, antibiotic consumption duration, angiogenesis and pain and "acceleration of wound healing" in pilonidal sinus patients after treating with platelet-rich plasma (PRP). METHODS: In this clinical trial, 110 patients were randomly divided into treatment arm and control group. After surgery, control group underwent classic wound dressing and the treatment arm experienced PRP gel therapy. Before achieving complete healing, wound incisional biopsy was performed in order to evaluate angiogenesis. During the study, other data such as pain and antibiotic consumption duration were also collected. Wound healing time of pilonidal sinus disease was analyzed using Extended and Stratify Cox model. Data were analyzed using R and STATA software. p<0.05 were considered statistically significant. RESULTS: The average wound volume was calculated 41.9 ± 8.01 cc in the controls and 42.35 ± 10.81 in the treatment arm group. The mean of healing time was 8.7 ± 1.18, 4.8 ± 0.87 weeks for control and treatment arm, respectively. There was a significant and strong negative association between healing time and wound volume (p<0.01). Moreover, a significant negative association was found between pain duration and angiogenesis (p<0.001), a strong positive significant association was found between healing time of the treatment arms (p<0.01), and the rate of wound healing for participants treated with PRP gel was 37.2 times more than that of controls. CONCLUSION: Authors hope for these finding to help the future researches to more thoroughly focus on the mentioned factors in order to find a suitable strategy for wound healing using PRP.

Survival analysis: part II - applied clinical data analysis.

As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.

Survival analysis: part II – applied clinical data analysis / 대한마취과학회지

As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodness-of-fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.