scholarly journals Association of lactate dehydrogenase with mortality in incident hemodialysis patients

2020 ◽  
Author(s):  
Soh Young Ryu ◽  
Carola-Ellen Kleine ◽  
Jui-Ting Hsiung ◽  
Christina Park ◽  
Connie M Rhee ◽  
...  
Keyword(s):  
Lactate Dehydrogenase ◽  
Cardiovascular Mortality ◽  
Survival Models ◽  
Time Varying ◽  
Dialysis Patients ◽  
Baseline Serum ◽  
Death Risk ◽  
Markers Of Inflammation ◽  
Time Varying Models ◽  
Related Mortality

Abstract Background Lactate dehydrogenase (LDH) plays a role in the glucose metabolism of the human body. Higher LDH levels have been linked to mortality in various cancer types; however, the relationship between LDH and survival in incident hemodialysis (HD) patients has not yet been examined. We hypothesized that higher LDH level is associated with higher death risk in these patients. Methods We examined the association of baseline and time-varying serum LDH with all-cause, cardiovascular and infection-related mortality among 109 632 adult incident HD patients receiving care from a large dialysis organization in the USA during January 2007 to December 2011. Baseline and time-varying survival models were adjusted for demographic variables and available clinical and laboratory surrogates of malnutrition–inflammation complex syndrome. Results There was a linear association between baseline serum LDH levels and all-cause, cardiovascular and infection-related mortality in both baseline and time-varying models, except for time-varying infection-related mortality. Adjustment for markers of inflammation and malnutrition attenuated the association in all models. In fully adjusted models, baseline LDH levels ≥360 U/L were associated with the highest risk of all-cause mortality (hazard ratios = 1.19, 95% confidence interval 1.14–1.25). In time-varying models, LDH >280 U/L was associated with higher death risk in all three hierarchical models for all-cause and cardiovascular mortality. Conclusions Higher LDH level >280 U/L was incrementally associated with higher all-cause and cardiovascular mortality in incident dialysis patients, whereas LDH <240 U/L was associated with better survival. These findings suggest that the assessment of metabolic functions and monitoring for comorbidities may confer survival benefit to dialysis patients.

scholarly journals Racial and Ethnic Differences in Mortality Associated with Serum Potassium in Incident Peritoneal Dialysis Patients

2019 ◽  
Vol 50 (5) ◽  
pp. 361-369 ◽  
Author(s):  
Rieko Eriguchi ◽  
Yoshitsugu Obi ◽  
Melissa Soohoo ◽  
Connie M. Rhee ◽  
Csaba P. Kovesdy ◽  
...  
Keyword(s):  
Peritoneal Dialysis ◽  
Serum Potassium ◽  
Dialysis Patients ◽  
Cox Models ◽  
Baseline Serum ◽  
Racial And Ethnic Differences ◽  
Hispanic Patients ◽  
Race Ethnicity ◽  
The Relationship ◽  
Related Mortality

Background: Abnormalities in serum potassium are risk factors for sudden cardiac death and arrhythmias among dialysis patients. Although a previous study in hemodialysis patients has shown that race/ethnicity may impact the relationship between serum potassium and mortality, the relationship remains unclear among peritoneal dialysis (PD) patients where the dynamics of serum potassium is more stable. Methods: Among 17,664 patients who started PD between January 1, 2007 and December 31, 2011 in a large US dialysis organization, we evaluated the association of serum potassium levels with all-cause and arrhythmia-related deaths across race/ethnicity using time-dependent Cox models with adjustments for demographics. We also used restricted cubic spline functions for serum potassium levels to explore non-linear associations. Results: Baseline serum potassium levels were the highest among Hispanics (4.2 ± 0.7 mEq/L) and lowest among non-Hispanic blacks (4.0 ± 0.7 mEq/L). Among 2,949 deaths during the follow-up of median 2.2 (interquartile ranges 1.3–3.2) years, 683 (23%) were arrhythmia-related deaths. Overall, both hyperkalemia and hypokalemia (i.e., serum potassium levels >5.0 and <3.5 mEq/L, respectively) were associated with higher all-cause and arrhythmia-related mortality. In a stratified analysis according to race/ethnicity, the association of hypokalemia with all-cause and arrhythmia-related mortality was consistent with an attenuation for arrhythmia-related mortality in non-Hispanic blacks. Hyperkalemia was associated with all-cause and arrhythmia-related mortality in non-Hispanic whites and non-Hispanic blacks, but no association was observed in Hispanics. Conclusion: Among incident PD patients, hypokalemia was consistently associated with all-cause and arrhythmia-related deaths irrespective of race/ethnicity. However, while hyperkalemia was associated with both death outcomes in non-Hispanic blacks and whites, it was not associated with either death outcome in Hispanic patients. Further studies are needed to demonstrate whether different strategies should be followed for the management of serum potassium levels according to race/ethnicity.

scholarly journals Simultaneous inference for time-varying models

2021 ◽  
Author(s):  
Sayar Karmakar ◽  
Stefan Richter ◽  
Wei Biao Wu
Keyword(s):  
Simultaneous Inference ◽  
Time Varying ◽  
Time Varying Models

Comparison of two recent ceramide-based coronary risk prediction scores: CERT and CERT-2

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Leiherer ◽  
A Muendlein ◽  
C.H Saely ◽  
R Laaksonen ◽  
M Laaperi ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Coronary Event ◽  
Cardiovascular Death ◽  
Survival Models ◽  
Risk Category ◽  
Net Reclassification Improvement ◽  
Event Risk ◽  
Risk Predictor ◽  
Risk Categories

Abstract   The Coronary Event Risk Test (CERT) is a validated cardiovascular risk predictor that uses circulating ceramide concentrations to allocate patients into one of four risk categories. This test has recently been updated (CERT-2), now additionally including phosphatidylcholine concentrations. The purpose of this study was to investigate the power of CERT and CERT-2 to predict cardiovascular mortality in patients with cardiovascular disease (CVD). We investigated a cohort of 999 patients with established CVD. Overall, comparing survival curves (figure) for over 12 years of follow up and the predictive power of survival models using net reclassification improvement (NRI), CERT-2 was the best predictor of cardiovascular mortality, surpassing CERT (NRI=0.456; p=0.01) and also the 2019 ESC-SCORE (NRI=0.163; p=0.04). Patients in the highest risk category of CERT as compared to the lowest category had a HR of 3.63 [2.09–6.30] for cardiovascular death; for CERT-2 the corresponding HR was 6.02 [2.47–14.64]. Among patients with T2DM (n=322), the HR for cardiovascular death was 3.00 [1.44–6.23] using CERT and 7.06 [1.64–30.50] using CERT-2; the corresponding HRs among non-diabetic subjects were 2.99 [1.20–7.46] and 3.43 [1.03–11.43], respectively. We conclude that both, CERT and CERT-2 scores are powerful predictors of cardiovascular mortality in CVD patients, especially in those patients with T2D. Performance is even higher with CERT-2. Funding Acknowledgement Type of funding source: None

scholarly journals Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lior Rennert ◽  
Moonseong Heo ◽  
Alain H. Litwin ◽  
Victor De Gruttola
Keyword(s):  
Mixed Effects ◽  
Effect Modification ◽  
External Factors ◽  
Mixed Effects Models ◽  
Time Varying ◽  
Intervention Effect ◽  
Type 1 Error ◽  
And Control ◽  
Related Mortality

Abstract Background Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification— in which external factors differentially impact intervention and control clusters. Results In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.

scholarly journals Cardiovascular and non-cardiovascular mortality in dialysis patients: where is the link?

2011 ◽  
Vol 1 (1) ◽  
pp. 21-23 ◽  
Author(s):  
Kitty J. Jager ◽  
Bengt Lindholm ◽  
David Goldsmith ◽  
Danilo Fliser ◽  
Andrzej Wiecek ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Dialysis Patients

Hospitalization Rates Among Dialysis Patients During Hurricane Katrina

2012 ◽  
Vol 27 (4) ◽  
pp. 325-329 ◽  
Author(s):  
David Howard ◽  
Rebecca Zhang ◽  
Yijian Huang ◽  
Nancy Kutner
Keyword(s):  
Hurricane Katrina ◽  
Large Scale ◽  
Service Use ◽  
Hospital Admissions ◽  
Gulf Coast ◽  
The United States ◽  
Time Varying ◽  
Dialysis Patients ◽  
Hospitalization Rates ◽  
The Impact

AbstractIntroductionDialysis centers struggled to maintain continuity of care for dialysis patients during and immediately following Hurricane Katrina's landfall on the US Gulf Coast in August 2005. However, the impact on patient health and service use is unclear.ProblemThe impact of Hurricane Katrina on hospitalization rates among dialysis patients was estimated.MethodsData from the United States Renal Data System were used to identify patients receiving dialysis from January 1, 2001 through August 29, 2005 at clinics that experienced service disruptions during Hurricane Katrina. A repeated events duration model was used with a time-varying Hurricane Katrina indicator to estimate trends in hospitalization rates. Trends were estimated separately by cause: surgical hospitalizations, medical, non-renal-related hospitalizations, and renal-related hospitalizations.ResultsThe rate ratio for all-cause hospitalization associated with the time-varying Hurricane Katrina indicator was 1.16 (95% CI, 1.05-1.29; P = .004). The ratios for cause-specific hospitalization were: surgery, 0.84 (95% CI, 0.68-1.04; P = .11); renal-related admissions, 2.53 (95% CI, 2.09-3.06); P < .001), and medical non-renal related, 1.04 (95% CI, 0.89-1.20; P = .63). The estimated number of excess renal-related hospital admissions attributable to Katrina was 140, representing approximately three percent of dialysis patients at the affected clinics.ConclusionsHospitalization rates among dialysis patients increased in the month following the Hurricane Katrina landfall, suggesting that providers and patients were not adequately prepared for large-scale disasters.Howard D, Zhang R, Huang Y, Kutner N. Hospitalization rates among dialysis patients during Hurricane Katrina. Prehosp Disaster Med. 2012;27(4):1-5.

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

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