Because chronic kidney disease (CKD) is often under-coded as a diagnosis in claims data, we aimed to develop claims-based prediction models for CKD phenotypes determined by laboratory results in ...electronic health records (EHRs).
We linked EHR from two networks (used as training and validation cohorts, respectively) with Medicare claims data. The study cohort included individuals ≥65 years with a valid serum creatinine result in the EHR from 2007 to 2017, excluding those with end-stage kidney disease or on dialysis. We used LASSO regression to select among 134 predictors for predicting continuous estimated glomerular filtration rate (eGFR). We assessed the model performance when predicting eGFR categories of <60, <45, <30 mL/min/1.73m
in terms of area under the receiver operating curves (AUC).
The model training cohort included 117,476 patients (mean age 74.8 years, female 58.2%) and the validation cohort included 56,744 patients (mean age 73.8 years, female 59.6%). In the validation cohort, the AUC of the primary model (with 113 predictors and an adjusted
of 0.35) for predicting eGFR <60, eGFR<45, and eGFR <30 mL/min/1.73m
categories was 0.81, 0.88, and 0.92, respectively, and the corresponding positive predictive values for these 3 phenotypes were 0.80 (95% confidence interval: 0.79, 0.81), 0.79 (0.75, 0.84), and 0.38 (0.30, 0.45), respectively.
We developed a claims-based model to determine clinical phenotypes of CKD stages defined by eGFR values. Researchers without access to laboratory results can use the model-predicted phenotypes as a proxy clinical endpoint or confounder and to enhance subgroup effect assessment.
The Model for End-Stage Liver Disease (MELD) score predicts disease severity and mortality in cirrhosis. To improve cirrhosis phenotyping in administrative databases lacking laboratory data, we aimed ...to develop and externally validate claims-based MELD prediction models, using claims data linked to electronic health records (EHR).
We included adults with established cirrhosis in two Medicare-linked EHR networks (training and internal validation; 2007-2017), and a Medicaid-linked EHR network (external validation; 2000-2014). Using least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation, we selected among 146 investigator-specified variables to develop models for predicting continuous MELD and relevant MELD categories (MELD<10, MELD≥15 and MELD≥20), with observed MELD calculated from laboratory data. Regression coefficients for each model were applied to the validation sets to predict patient-level MELD and assess model performance.
We identified 4501 patients in the Medicare training set (mean age 75.1 years, 18.5% female, mean MELD=13.0), and 2435 patients in the Medicare validation set (mean age: 74.3 years, 31.7% female, mean MELD=12.3). Our final model for predicting continuous MELD included 112 variables, explaining 58% of observed MELD variability; in the Medicare validation set, the area-under-the-receiver operating characteristic curves (AUC) for MELD<10 and MELD≥15 were 0.84 and 0.90, respectively; the AUC for the model predicting MELD≥20 (using 27 variables) was 0.93. Overall, these models correctly classified 77% of patients with MELD<10 (95% CI=0.75-0.78), 85% of patients with MELD≥15 (95% CI=0.84-0.87), and 87% of patients with MELD≥20 (95% CI=0.86-0.88). Results were consistent in the external validation set (n=2240).
Our MELD prediction tools can be used to improve cirrhosis phenotyping in administrative datasets lacking laboratory data.
Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS ...models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.
Purpose
The impact of metabolic syndrome (MetS) on recurrence of atrial fibrillation (AF) after catheter ablation remains uncertain. We conducted a meta-analysis to summarize the relative risks (RR) ...of AF recurrence after catheter ablation in patients with vs. without MetS and its components.
Methods
Among 839 articles identified from PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials, we included 23 studies with a total of 12,924 patients (7,594 with paroxysmal AF and 5,330 with nonparoxysmal AF) for analysis. Five of these had complete information on MetS components. Variables assessed comprised study design and population characteristics, AF ablation methods, use of anti-arrhythmic drugs, AF recurrence ascertainment methods, adjustment variables, and other quality indicators.
Results
Our meta-analysis found an elevated risk of AF recurrence after ablation in patients with vs. without MetS (pooled RR, 1.63; 95 % confidence interval (CI), 1.25–2.12). Among components of MetS, hypertension was a predictor of AF post-ablation recurrence in studies without adjustment for other MetS components (RR, 1.62; 95 % CI, 1.23–2.13) but not in those adjusting for two or more additional MetS components (RR, 1.03; 95 % CI, 0.88–1.20). There was a borderline association between overweight/obesity and AF recurrence after ablation (RR, 1.27; 95 % CI, 0.99–1.64).
Conclusions
MetS is associated with an increased risk of AF recurrence after catheter ablation. Further study of the MetS and its components as determinants of AF risk could help refine patient selection and improve procedural outcomes.
The benefit-risk profile of low-dose aspirin in primary prevention of cardiovascular disease is unclear. We sought to quantify upper gastrointestinal bleeding (UGIB) risk associated with low-dose ...aspirin in secondary versus primary prevention patients.
We performed a population-based nested case-control study using The Health Improvement Network (THIN) Database between 2000 and 2007. We identified 2049 cases of UGIB and 20,000 controls, frequency-matched to the cases on age, sex, and calendar year, who were subdivided into primary (without previous cardiovascular disease) and secondary (with previous cardiovascular disease) prevention populations. We estimated the relative risk of UGIB associated with the use of low-dose aspirin by multivariate logistic regression. The UGIB risk in patients taking low-dose aspirin relative to nonusers was significantly higher in the primary (adjusted relative risk, 1.90; 95% confidence interval, 1.59-2.26) than in the secondary (relative risk, 1.40; 95% confidence interval, 1.14-1.72; P value for the difference=0.0014) prevention cohort. However, as the baseline risk of UGIB was lower in the primary than in the secondary prevention cohort, numbers needed to harm per 1 year of low-dose aspirin use were 601 and 391 for primary and secondary prevention, respectively.
The relative risk of UGIB in patients taking low-dose aspirin is higher when used for primary than for secondary cardiovascular disease prevention, but this difference is more than compensated by the lower baseline risk in the primary prevention population. Such estimates are important for an assessment of the net clinical benefit in primary prevention.
Drug certainty-response in interview-based studies Yau, Wai-Ping; Lin, Kueiyu Joshua; Werler, Martha M. ...
Pharmacoepidemiology and drug safety,
November 2011, Letnik:
20, Številka:
11
Journal Article
Abstract Background Video laryngoscope has recently been introduced as an alternative for performing intubation; however, its validity in emergency settings has not been thoroughly evaluated. ...Therefore, we conducted a meta-analysis to assess its value compared with direct laryngoscope in emergency settings. Purpose We conducted a meta-analysis to assess its value compared with direct laryngoscope in emergency settings. Methods PubMed and EMBASE were searched for studies published through April 2011. Trials that reported data comparing video laryngoscope versus direct laryngoscope-assisted intubation in the emergency room or prehospital locations were included. Results Four trials reporting a total of 1305 participants were identified. During intubation, video laryngoscope failed to produce high rates of successful intubation (success rate: 0.70; 95% confidence interval CI: 0.49–1.01). Time to intubation was not different when using either video laryngoscope or direct laryngoscope (standardized mean difference: 0.19; 95% CI: -0.20—0.58). Furthermore, video laryngoscope seems to achieve a similar glottic view as direct laryngoscope (ratio of better glottic view: 0.96; 95% CI: 0.63–1.46). Conclusion In the reviewed studies, video laryngoscope was not superior to direct laryngoscope for performing intubation in emergency settings.
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