Power analysis for cluster randomized trials with continuous coprimary endpoints.

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K ( K ≥ 2 $K\ge 2$ ) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients among the observations in each cluster, we derive the closed-form joint distribution of K treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the K treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the MLMM are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

BACKGROUND: The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown. PURPOSE: To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions. MATERIAL AND METHODS: A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II-IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions. RESULTS: The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934-0.979) as well as in the test dataset (AUCs = 0.892-0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B. CONCLUSION: No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Propensity score weighting for causal subgroup analysis.

A common goal in comparative effectiveness research is to estimate treatment effects on prespecified subpopulations of patients. Though widely used in medical research, causal inference methods for such subgroup analysis (SGA) remain underdeveloped, particularly in observational studies. In this article, we develop a suite of analytical methods and visualization tools for causal SGA. First, we introduce the estimand of subgroup weighted average treatment effect and provide the corresponding propensity score weighting estimator. We show that balancing covariates within a subgroup bounds the bias of the estimator of subgroup causal effects. Second, we propose to use the overlap weighting (OW) method to achieve exact balance within subgroups. We further propose a method that combines OW and LASSO, to balance the bias-variance tradeoff in SGA. Finally, we design a new diagnostic graph-the Connect-S plot-for visualizing the subgroup covariate balance. Extensive simulation studies are presented to compare the proposed method with several existing methods. We apply the proposed methods to the patient-centered results for uterine fibroids (COMPARE-UF) registry data to evaluate alternative management options for uterine fibroids for relief of symptoms and quality of life.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach.

BACKGROUND: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. METHODS: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. RESULTS: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. CONCLUSION: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.

Radiological Society of North America/Quantitative Imaging Biomarker Alliance Shear Wave Speed Bias Quantification in Elastic and Viscoelastic Phantoms.

OBJECTIVES: To quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms. METHODS: Two elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz. RESULTS: The SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a - 0.27 ± 0.027-m/s (-12.2% ± 1.2%) bias when using the clinically implemented 60-Hz vibration source. CONCLUSIONS: Shear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140-Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

Matching with time-dependent treatments: A review and look forward.

Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data.

Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials.

Cluster randomized trials (CRTs) refer to experiments with randomization carried out at the cluster or the group level. While numerous statistical methods have been developed for the design and analysis of CRTs, most of the existing methods focused on testing the overall treatment effect across the population characteristics, with few discussions on the differential treatment effect among subpopulations. In addition, the sample size and power requirements for detecting differential treatment effect in CRTs remain unclear, but are helpful for studies planned with such an objective. In this article, we develop a new sample size formula for detecting treatment effect heterogeneity in two-level CRTs for continuous outcomes, continuous or binary covariates measured at cluster or individual level. We also investigate the roles of two intraclass correlation coefficients (ICCs): the adjusted ICC for the outcome of interest and the marginal ICC for the covariate of interest. We further derive a closed-form design effect formula to facilitate the application of the proposed method, and provide extensions to accommodate multiple covariates. Extensive simulations are carried out to validate the proposed formula in finite samples. We find that the empirical power agrees well with the prediction across a range of parameter constellations, when data are analyzed by a linear mixed effects model with a treatment-by-covariate interaction. Finally, we use data from the HF-ACTION study to illustrate the proposed sample size procedure for detecting heterogeneous treatment effects.

Enhanced Oral Bioavailability of Felodipine from Solid Lipid Nanoparticles Prepared Through Effervescent Dispersion Technique.

Felodipine (FLD), a dihydropyridine calcium channel blocker with excellent antihypertensive effect, is poorly soluble and undergoes extensive hepatic metabolism, which lead to poor oral bioavailability (about 15%) and limit its clinic application. The goal of this study was to develop solid lipid nanoparticles (SLNs) loading FLD to improve the oral bioavailability. The FLD loaded solid lipid nanoparticles (FLD-SLNs) were prepared by the effervescent dispersion technique developed by our laboratory, which might have some advantages over traditional methods. The FLD-SLNs showed desired particle characteristics with particle size (198.15 ± 1.82 nm), poly dispersity index (0.26 ± 0.02), zeta-potential (- 25.53 ± 0.60 mV), entrapment efficiency (95.65 ± 0.70%), drug loading (2.33 ± 0.10%), and a spherical appearance. Pharmacokinetic results showed that the FLD-SLNs presented 3.17-fold increase in area under the curve (AUC(0-t)) compared with free FLD after oral administration in beagle dogs, which indicated that SLNs prepared using the effervescent dispersion technique can improve the bioavailability of lipophilic drugs like felodipine by enhancement of absorption and reduction first-pass metabolism.

Association of CYP2C19 Polymorphism with Clopidogrel Resistance in Patients with Acute Coronary Syndrome in China.

BACKGROUND The relationship between clopidogrel-resistance (CR) and polymorphism located in genes encoding clopidogrel metabolism-related enzymes has not been fully explored. Thus far, few studies on CR-associated polymorphism have been conducted in the Chinese population. The purpose of this study was to identify CYP2C19 polymorphism associated with CR in patients with acute coronary syndrome in China. MATERIAL AND METHODS There were 125 patients with acute coronary syndromes (ACS) selected for this study. Of these, 27 patients (21.6%) showed CR (less than 10% reduction in platelet accumulation rate), while the remaining 98 patients (78.4%) were non-clopidogrel-resistant (NCR). RESULTS There were significant differences in the allele frequencies of CYP2C19 (rs4244285) (P=0.03) and CYP2C19 (rs4986893) (P=0.005) between the 2 groups; however, there was no significant difference in allele frequencies of ABCB1 (rs1045642) (P=0.661) and PON1 (rs662) (P=0.690) between the 2 groups. The null allele in the CYP2C19 (rs4244285) [odds ratio (OR)=5.317, 95% confidence interval (CI) 1.542-26.428, P=0.001] and CYP2C19 (rs4986893) (OR=4.295, 95%CI 1.312-17.517, P=0.013) is one of the causes of CR in patients with ACS in China. CONCLUSIONS The CYP2C19 polymorphism (rs4244285 and rs4986893) is the correlative factor of CR in patients with ACS in China. It was found that the null allele in the CYP2C19 polymorphism was related to the higher CR risk. According to the key role of CYP2C19 in the clopidogrel activation and the evaluated role of CYP2C19 in this study, further studies should be carried out to formulate therapeutic alternative methods for CR in patients with ACS.

Obesity's effect on asthma extends to diagnostic criteria.

BACKGROUND: The use of inflammatory biomarkers to delineate the type of lung inflammation present in asthmatic subjects is increasingly common. However, the effect of obesity on these markers is unknown. OBJECTIVES: We aimed to determine the effect of obesity on conventional markers of inflammation in asthmatic subjects. METHODS: We performed secondary analysis of data from 652 subjects previously enrolled in 2 Asthma Clinical Research Network trials. We performed linear correlations between biomarkers and logistic regression analysis to determine the predictive value of IgE levels, blood eosinophil counts, and fraction of exhaled nitric oxide values in relationship to sputum eosinophil counts (>2%), as well as to determine whether cut points existed that would maximize the sensitivity and specificity for predicting sputum eosinophilia in the 3 weight groups. RESULTS: Overall, statistically significant but relatively weak correlations were observed among all 4 markers of inflammation. Within obese subjects, the only significant correlation found was between IgE levels and blood eosinophil counts (r = 0.33, P < .001); furthermore, all other correlations between inflammatory markers were approximately 0, including correlations with sputum eosinophil counts. In addition, the predictive value of each biomarker alone or in combination was poor in obese subjects. In fact, in obese subjects none of the biomarkers of inflammation significantly predicted the presence of high sputum eosinophil counts. Obese asthmatic subjects have lower cut points for IgE levels (268 IU), fraction of exhaled nitric oxide values (14.5 ppb), and blood eosinophil counts (96 cells/µL) than all other groups. CONCLUSIONS: In obese asthmatic subjects conventional biomarkers of inflammation are poorly predictive of eosinophilic airway inflammation. As such, biomarkers currently used to delineate eosinophilic inflammation in asthmatic subjects should be approached with caution in these subjects.

Prevalence and Cost Analysis of Chronic Pain After Hernia Repair: A Potential Alternative Approach With Neurostimulation.

OBJECTIVES: Chronic pain (CP) affects a significant number of patients following hernia repair, ranging from 11 to 54% in the literature. The aim of this study was to assess the prevalence, overall costs, and health care utilization associated with CP after hernia repair. MATERIALS AND METHODS: A retrospective longitudinal study was performed using the Truven MarketScan® data base to identify patients who develop chronic neuropathic posthernia repair pain from 2001 to 2012. Patients were grouped into CP and No Chronic Pain (No CP) cohorts. Patients were excluded if they 1) were under 18 years of age; 2) had a previous pain diagnosis; 3) had CP diagnosed <90 days after the index hernia repair; 4) had less than one year of follow-up; or 5) had less than one-year baseline record before hernia repair. Patients were grouped into the CP cohort if their CP diagnosis was made within the two years following index hernia repair. Total, outpatient, and pain prescription costs were collected in the period of five years prehernia to nine years posthernia repair. A longitudinal multivariate analysis was used to model the effects of chronic neuropathic posthernia repair pain on total inpatient/outpatient and pain prescription costs. RESULTS: We identified 76,173 patients who underwent hernia repair and met inclusion criteria (CP: n = 14,919, No CP: n = 61,254). There was a trend for increased total inpatient/outpatient and pain prescription costs one-year posthernia repair, when compared to baseline costs for both cohorts. In both cohorts, total inpatient/outpatient costs remained elevated from baseline through nine years posthernia repair, with the CP cohort experiencing significantly higher cumulative median costs (CP: $51,334, No CP: $37,388). The CP diagnosis year was associated with a 1.75-fold increase (p < 0.001) in total inpatient/outpatient costs and a 2.26-fold increase (p < 0.001) in pain prescription costs versus all other years. In the longitudinal analysis, the CP cohort had a 1.14-fold increase (p < 0.001) in total inpatient/outpatient costs and 2.00-fold increase (p < 0.001) in pain prescription costs. CONCLUSIONS: Our study demonstrates the prevalence of CP after hernia surgery to be nearly 20%, with significantly increased costs and healthcare resource utilization. While current treatment paradigms are effective for many, there remains a large number of patients that could benefit from an overall approach that includes nonopioid treatments, such as potentially incorporating neurostimulation, for CP that presents posthernia repair.

Prevalence and Cost Analysis of Complex Regional Pain Syndrome (CRPS): A Role for Neuromodulation.

OBJECTIVE: The diagnosis and treatment of complex regional pain syndrome (CRPS) is challenging and there is a paucity of data describing its overall cost burden and quantifying its impact on the US healthcare system. The aim of this study was to assess the prevalence and healthcare utilization costs associated with CRPS. MATERIALS AND METHODS: A retrospective longitudinal study was performed using the Truven MarketScan® database to identify patients with a new indexed diagnosis of CRPS (Type I, II, or both) from 2001 to 2012. We collected total, outpatient, and pain prescription costs three years prior to CRPS diagnosis (baseline), at year of CRPS diagnosis, and eight-year post-CRPS diagnosis. A longitudinal multivariate analysis was used to model the estimated total and pain prescription cost ratios comparing patients diagnosed before and after CRPS. RESULTS: We included 35,316 patients with a newly indexed diagnosis of CRPS (Type I: n = 18,703, Type II: n = 14,599, Unspecified: n = 2014). Baseline characteristics were similar between the CRPS cohorts. Compared to two- and three-year baseline costs, one-year prior to diagnosis for all CRPS patients yielded the highest interquartile median [IQR] costs: total costs $7904[$3469, $16,084]; outpatient costs $6706[$3119, $12,715]; and pain prescription costs $1862[$147, $7649]. At the year of CRPS diagnosis, the median [IQR] costs were significantly higher than baseline costs: total costs $8508[$3943, $16,666]; outpatient costs $7251[$3527, $13,568]; and pain prescription costs $2077[$140, $8856]. Over the eight-year period after CRPS diagnosis, costs between all the years were similar, ranging from the highest (one-year) to lowest (seven-years), $4845 to $3888. The median total cumulative cost 8-years after CRPS diagnosis was $43,026 and $12,037 for pain prescription costs. [Correction added on 06 November 2017 after first online publication: the preceding sentence has been updated to demonstrate the median cumulative cost in replacement of the additive cumulative mean costs.]. During the CRPS diagnosis period, patients are expected to have a total cost 2.17-fold and prescription cost 2.56-fold of their baseline cost annually. CONCLUSIONS: Our study demonstrates that there is a significant increase in cost and healthcare resource utilization one-year prior to and around the time of CRPS diagnosis. Furthermore, there is an increased annual cost post-diagnosis compared to baseline costs prior to CRPS diagnosis.

Drivers and Risk Factors of Unplanned 30-Day Readmission Following Spinal Cord Stimulator Implantation.

OBJECTIVES: Unplanned 30-day readmission rates contribute significantly to growing national healthcare expenditures. Drivers of unplanned 30-day readmission after spinal cord stimulator (SCS) implantation are relatively unknown. The aim of this study was to determine drivers of 30-day unplanned readmission following SCS implantation. METHODS: The National Readmission Database was queried to identify all patients who underwent SCS implantation for the 2013 calendar year. Patients were grouped by readmission status, "No Readmission" and "Unplanned 30-day Readmission." Patient demographics and comorbidities were collected for each patient. The primary outcome of interest was the rate of unplanned 30-day readmissions and associated driving factors. A multivariate analysis was used to determine independent predictors of unplanned 30-day readmission after SCS implantation. RESULTS: We identified 1521 patients who underwent SCS implantation, with 113 (7.4%) experiencing an unplanned readmission within 30 days. Baseline patient demographics, comorbidities, and hospital characteristics were similar between both cohorts. The three main drivers for 30-day readmission after SCS implantation include: 1) infection (not related to SCS device), 2) infection due to device (limited to only hardware infection), and 3) mechanical complication of SCS device. Furthermore, obesity was found to be an independent predictor of 30-day readmission (OR: 1.86, p = 0.008). CONCLUSION: Our study suggests that infectious and mechanical complications are the primary drivers of unplanned 30-day readmission after SCS implantation, with obesity as an independent predictor of unplanned readmission. Given the technological advancements in SCS, repeated studies are necessary to identify factors associated with unplanned 30-day readmission rates after SCS implantation to improve patient outcomes and reduce associated costs.

The Volume-Outcome Effect: Impact on Trial-to-Permanent Conversion Rates in Spinal Cord Stimulation.

OBJECTIVES: Conversion rates from trial leads to permanent spinal cord stimulation (SCS) systems have important implications for healthcare resource utilization (HCRU) and pain management. We hypothesized that there is a volume-outcome effect, with chronic pain patients who visit high volume SCS implanters will have higher trial-to-permanent conversion rates. MATERIALS AND METHODS: We designed a large, retrospective analysis using the Truven MarketScan database analyzing adult SCS patients with provider information available, with or without IPG implantation from the years 2007 to 2012 was designed. Patients were divided into three provider-based groups: high (>25), medium (9-24), and low (3-8) volume providers. Univariate and multivariate models identified factors associated with successful conversion. RESULTS: A total of 17,850 unique trial implants were performed by 3028 providers. Of 13,879 patients with baseline data available, 8981 (64.7%) progressed to permanent SCS. Higher volume providers were associated with slightly higher conversion rates (65.9% vs. 63.3% low volume, p = 0.029), explant rates (9.2% vs. 7.7% medium volume, p = 0.026), younger age (52.0 ± 13.4 years vs. 53.0 ± 13.4 years, p = 0.0026), Medicare/Medicaid (47.8% vs. 35.0% low volume, p < 0.0001), Southern region (53.5% vs. 38.9% low volume, p < 0.0001), and higher Charlson comorbidity scores (1.0 [SD = 1.4], p = 0.0002). Multivariate regression results showed female gender (1.13 [95% CI: 1.05-1.22], p < 0.001) and high volume providers associated with higher odds of successful trial conversion (1.12 [95% CI: 1.02-1.22], p = 0.014). CONCLUSIONS: In this nationwide analysis, high volume providers achieved higher trial-to-permanent SCS conversion rates than lower volume providers. The study has implications for both training requirements and referral patterns to delineate minimum implant experience necessary for provider proficiency. Future studies may be useful to understand HCRU differences.

Explantation Rates and Healthcare Resource Utilization in Spinal Cord Stimulation.

OBJECTIVES: Certain patients ultimately undergo explantation of their spinal cord stimulation (SCS) devices. Understanding the predictors and rates of SCS explantation has important implications for healthcare resource utilization (HCRU) and pain management. The present study identifies explant predictors and discerns differences in HCRU for at-risk populations. METHODS: We designed a large, retrospective analysis using the Truven MarketScan Database. We included all adult patients who underwent a SCS trial from 2007 to 2012. Patients were grouped into cohorts that remained explant-free or underwent explantation over a three-year period, and multivariate models evaluated differences in healthcare resource utilization. RESULTS: A total of 8727 unique instances of trial implants between 2007 and 2012 were identified. Overall, 805 (9.2%) patients underwent device explantation. One year prior to SCS implantation, the explant cohort had significantly higher median baseline costs ($42,140.3 explant vs. $27,821.7 in non-explant groups; p < 0.0001), total number of pain encounters (180 vs. 103 p < 0.0001), and associated costs ($15,446.9 vs. $9,227.9; p < 0.0001). The explant cohort demonstrated increased use of procedures (19.0 vs. 9.0; p < 0.0001) compared to non-explanted patients. For each month after initial SCS implantation, explanted patients had a slower decrease in total costs (4% vs. 6% in non-explant; p < 0.01). At the month of explant, explant patients were expected to have incurred 2.65 times the total cost compared to the non-explant cohort (CR 2.65, 95% CI [1.83, 3.84]; p < 0.001). Medium volume providers had lower rates of explantation at one-year and three-years compared to low volume providers (p = 0.042). Increased age and Charlson index were independent predictors of explantation during the same periods. CONCLUSIONS: In this nationwide analysis, we identified that SCS device explantation is correlated with patients who have higher baseline costs, higher total cost post-SCS implantation, and increased use of procedures to control pain. The higher rates of explantation at three-years postimplant among low volume providers suggest that variations in provider experience and approach also contributes to differences in explantation rates.

Specialty-Based Variations in Spinal Cord Stimulation Success Rates for Treatment of Chronic Pain.

OBJECTIVES: Spinal cord stimulation (SCS) has emerged as an appropriate modality of treatment for intractable chronic pain. The present study examines variations in SCS trial-to-permanent conversion rates based on provider types performing the procedure. MATERIALS AND METHODS: We designed a large, retrospective analysis using the Truven MarketScan data base analyzing adult SCS patients with provider information available, with or without IPG implantation from the years 2007-2012. Patients were categorized based on provider type performing the implantation including anesthesiologists, neurosurgeons, orthopedic surgeons, and physical medicine and rehabilitation (PM&R). Univariate and multivariate models identified factors associated with successful conversion. RESULTS: A total of 7667 unique instances of SCS implants were identified across five providers. Overall, 4842 (63.2%) of those receiving trials underwent permanent SCS system implantation. Anesthesiology performed the majority of implants (62.8%), followed by neurosurgery (22.0%), orthopedic surgery (10.2%), and PM&R (5.3%). Compared to anesthesiologists, both neurosurgeons (OR 10.99, 95% CI [9.11, 13.25]; p < 0.001) and orthopedic surgeons (OR 4.64, 95% CI [3.81, 5.65]; p < 0.001) had significantly higher conversion rates, while PM&R (OR 0.71, 95% CI [0.58, 0.87]; p = 0.001) had significantly lower. Percutaneous implants comprised 5473 (71.4%) of all implants. Neurosurgeons and orthopedic surgeons performed a significantly greater number of paddle implants among the different providers (p < 0.0001). Explant rates were similar across all cohorts analyzed (average 11.6%; p = 0.546). CONCLUSIONS: In this nationwide analysis, our results suggest that over a recent five-year period, conversion rates are highest when SCS trials are performed by neurosurgeons and orthopedic surgeons. The study has important implications for establishing uniform guidelines for training, patient selection, and education of physicians across multiple disciplines.

Impact of Insurance Provider on Overall Costs in Failed Back Surgery Syndrome: A Cost Study of 122,827 Patients.

OBJECTIVES: Failed back surgery syndrome (FBSS) affects 40% of patients following spine surgery with estimated costs of $20 billion to the US health care system. The aim of this study was to assess the cost differences across the different insurance providers for FBSS patients. METHODS: A retrospective longitudinal study was performed using the Truven MarketScan® database to identify FBSS patients from 2001 to 2012. Patients were grouped into Commercial, Medicaid, or Medicare cohorts. We collected one-year prior to FBSS diagnosis (baseline), then at year of spinal cord stimulation (SCS)-implantation and nine-year post-SCS implantation cost outcomes. RESULTS: We identified 122,827 FBSS patients, with 117,499 patients who did not undergo an SCS-implantation (Commercial: n = 49,075, Medicaid: n = 23,180, Medicare: n = 45,244) and 5328 who did undergo an SCS implantation (Commercial: n = 2279, Medicaid: n = 1003, Medicare: n = 2046). Baseline characteristics were similar between the cohorts, with the Medicare-cohort being significantly older. Over the study period, there were significant differences in overall cost metrics between the cohorts who did not undergo SCS implantation with the Medicaid-cohort had the lowest annual median (interquartile range) total cost (Medicaid: $4530.4 [$1440.6, $11,973.5], Medicare: $7292.0 [$3371.4, $13,989.4], Commercial: $4944.3 [$363.8, $13,294.0], p < 0.0001). However, when comparing the patients who underwent SCS implantation, the commercial-cohort had the lowest annual median (interquartile range) total costs (Medicaid: $4045.6 [$1146.9, $11,533.9], Medicare: $7158.1 [$3160.4, $13,916.6], Commercial: $2098.1 [$0.0, $8919.6], p < 0.0001). CONCLUSIONS: Our study demonstrates a significant difference in overall costs between various insurance providers in the management of FBSS, with Medicaid-insured patients having lower overall costs compared to Commercial- and Medicare-patients. SCS is cost-effective across all insurance groups (Commercial > Medicaid > Medicare) beginning at two years and continuing through nine-year follow-up. Further studies are necessary to understand the cost differences between these insurance providers, in hopes of reducing unnecessary health care expenditures for patients with FBSS.

Development of a HPLC method to determine 5-fluorouracil in plasma: application in pharmacokinetics and steady-state concentration monitoring.

A simple, rapid and sensitive high-power liquid chromatographic (HPLC) method for analysis of 5-fluorouracil (5-FU) in patient plasma was developed and validated to study clinical pharmacokinetics (PK). Plasma sample preparation was processed with ammonium acetate buffer (pH 3.5; 0.01M) followed by liquid-liquid extraction with isopropanol/ethyl acetate (15 : 85, v/v). Extraction recovery ranged from 87.55 to 95.26%. Separation was performed using a C18 column at 25 °C with UV detection at 265 nm. The isocratic mobile phase composed of acetonitrile-ammonium acetate buffer (pH 3.5; 0.01M) (2.5 : 97.5 v/v) at a flow rate of 0.8 mL/min. Retention time was less than 7 minutes. Standard curve was linear between 0.01 - 10 µg/mL and 10 - 100 µg/mL for plasma sample. The limit of quantification was 10 ng/mL. The intra- and interday precision was below 10% (RSD). The accuracy ranged from 85.24 to 104.14%. The analysis method is rapid because it needs neither time-consuming extraction procedures nor complex chromatographic condition. The method was successfully applied to access pharmacokinetics and plasma concentration at steady state (SSC) of 5-FU. The results showed the PK and SSC of 5-FU characterized by a large interpatient variability. To increase therapeutic response and reduce toxicity, we should optimize 5-FU dose by investigating PK behavior to obtain ideal SSC..

Recent Advanced Methods for Extracting and Analyzing Cannabinoids from Cannabis-Infused Edibles and Detecting Hemp-Derived Contaminants in Food (2013-2023): A Comprehensive Review.

Cannabis-infused edibles are food products infused with a cannabis extract. These edibles include baked goods, candies, and beverages, offering an alternative way to consume cannabis instead of smoking or vaporizing it. Ensuring the accurate detection of cannabis-infused edibles and identification of any contaminants is crucial for public health and safety. This is particularly important for compliance with legal regulations as these substances can have significant psychoactive effects, especially on unsuspecting consumers such as children or individuals with certain medical conditions. Using efficient extraction methods can greatly improve detection accuracy, ensuring that the concentration of cannabinoids in edibles is measured correctly and adheres to dosage guidelines and legal limits. This review comprehensively examines the preparation and extraction techniques for cannabinoid edibles. It covers methods such as solid-phase extraction, enhanced matrix removal-lipid, QuEChERS, dissolution and dispersion techniques, liquid-phase extraction, and other emerging methodologies along with analytical techniques for cannabinoid analysis. The main analytical techniques employed for the determination of cannabinoids include liquid chromatography (LC), gas chromatography (GC), direct analysis in real time (DART), and mass spectrometry (MS). The application of these extraction and analytical techniques is further demonstrated through their use in analyzing specific edible samples, including oils, candies, beverages, solid coffee and tea, snacks, pet food, and contaminated products.

Protracted molecular dynamics and secondary structure introspection to identify dual-target inhibitors of Nipah virus exerting approved small molecules repurposing.

Nipah virus (NiV), with its significantly higher mortality rate compared to COVID-19, presents a looming threat as a potential next pandemic, particularly if constant mutations of NiV increase its transmissibility and transmission. Considering the importance of preventing the facilitation of the virus entry into host cells averting the process of assembly forming the viral envelope, and encapsulating the nucleocapsid, it is crucial to take the Nipah attachment glycoprotein-human ephrin-B2 and matrix protein as dual targets. Repurposing approved small molecules in drug development is a strategic choice, as it leverages molecules with known safety profiles, accelerating the path to finding effective treatments against NiV. The approved small molecules from DrugBank were used for repurposing and were subjected to extra precision docking followed by absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. The 4 best molecules were selected for 500 ns molecular dynamics (MD) simulation followed by Molecular mechanics with generalized Born and surface area solvation (MM-GBSA). Further, the free energy landscape, the principal component analysis followed by the defined secondary structure of proteins analysis were introspected. The inclusive analysis proposed that Iotrolan (DB09487) and Iodixanol (DB01249) are effective dual inhibitors, while Rutin (DB01698) and Lactitol (DB12942) were found to actively target the matrix protein only.