Benchmarking of Anesthesia and Surgical Control Times by Current Procedural Terminology (CPT®) Codes.

Over half of hospital revenue results from perioperative patient care, thus emphasizing the importance of efficient resource utilization within a hospital's suite of operating rooms (ORs). Predicting surgical case duration, including Anesthesia-controlled time (ACT) and Surgical-controlled time (SCT) has been significantly detailed throughout the literature as a means to help manage and predict OR scheduling. However, this information has previously been divided by surgical specialty, and only limited benchmarking data regarding ACT and SCT exists. We hypothesized that advancing the granularity of the ACT and SCT from surgical specialty to specific Current Procedural Terminology (CPT®) codes will produce data that is more accurate, less variable, and therefore more useful for OR schedule modeling and management. This single center study was conducted using times from surgeries performed at the University of Colorado Hospital (UCH) between September 2018 - September 2019. Individual cases were categorized by surgical specialty based on the specialty of the primary attending surgeon and CPT codes were compiled from billing data. Times were calculated as defined by the American Association of Clinical Directors. I2 values were calculated to assess heterogeneity of mean ACT and SCT times while Levene's test was utilized to assess heterogeneity of ACT and SCT variances. Statistical analyses for both ACT and SCT were calculated using JMP Statistical Discovery Software from SAS (Cary, NC) and R v3.6.3 (Vienna, Austria). All surgical cases (n = 87,537) performed at UCH from September 2018 to September 2019 were evaluated and 30,091 cases were included in the final analysis. All surgical subspecialties, with the exception of Podiatry, showed significant variability in ACT and SCT values between CPT codes within each surgical specialty. Furthermore, the variances of ACT and SCT values were also highly variable between CPT codes within each surgical specialty. Finally, benchmarking values of mean ACT and SCT with corresponding standard deviations are provided. Because each mean ACT and SCT value varies significantly between different CPT codes within a surgical specialty, using this granularity of data will likely enable improved accuracy in surgical schedule modeling compared to using mean ACT and SCT values for each surgical specialty as a whole. Furthermore, because there was significant variability of ACT and SCT variances between CPT codes, incorporating variance into surgical schedule modeling may also improve accuracy. Future investigations should include real-time simulations, logistical modeling, and labor utilization analyses as well as validation of benchmarking times in private practice settings.

Association of fat density with subclinical atherosclerosis.

BACKGROUND: Ectopic fat density is associated with cardiovascular disease (CVD) risk factors above and beyond fat volume. Volumetric measures of ectopic fat have been associated with CVD risk factors and subclinical atherosclerosis. The aim of this study was to investigate the association between fat density and subclinical atherosclerosis. METHODS AND RESULTS: Participants were drawn from the Multi-Detector Computed Tomography (MDCT) substudy of the Framingham Heart Study (n=3079; mean age, 50.1 years; 49.2% women). Fat density was indirectly estimated by computed tomography attenuation (Hounsfield Units [HU]) on abdominal scan slices. Visceral fat (VAT), subcutaneous fat (SAT), and pericardial fat HU and volumes were quantified using standard protocols; coronary and abdominal aortic calcium (CAC and AAC, respectively) were measured radiographically. Multivariable-adjusted logistic regression models were used to evaluate the association between adipose tissue HU and the presence of CAC and AAC. Overall, 17.1% of the participants had elevated CAC (Agatston score [AS]>100), and 23.3% had elevated AAC (AS>age-/sex-specific cutoffs). Per 5-unit decrement in VAT HU, the odds ratio (OR) for elevated CAC was 0.76 (95% confidence interval [CI], 0.65 to 0.89; P=0.0005), even after adjustment for body mass index or VAT volume. Results were similar for SAT HU. With decreasing VAT HU, we also observed an OR of 0.79 (95% CI, 0.67 to 0.92; P=0.004) for elevated AAC after multivariable adjustment. We found no significant associations between SAT HU and AAC. There was no significant association between pericardial fat HU and either CAC or AAC. CONCLUSIONS: Lower VAT and SAT HU, indirect estimates of fat quality, are associated with a lower risk of subclinical atherosclerosis.

Glucose oxidase-mediated polymerization as a platform for dual-mode signal amplification and biodetection.

We report the first use of a polymerization-based ELISA substrate solution employing enzymatically mediated radical polymerization as a dual-mode amplification strategy. Enzymes are selectively coupled to surfaces to generate radicals that subsequently lead to polymerization-based amplification (PBA) and biodetection. Sensitivity and amplification of the polymerization-based detection system were optimized in a microwell strip format using a biotinylated microwell surface with a glucose oxidase (GOx)-avidin conjugate. The immobilized GOx is used to initiate polymerization, enabling the detection of the biorecognition event visually or through the use of a plate reader. Assay response is compared to that of an enzymatic substrate utilizing nitroblue tetrazolium in a simplified assay using biotinylated wells. The polymerization substrate exhibits equivalent sensitivity (2 µg/mL of GOx-avidin) and over three times greater signal amplification than this traditional enzymatic substrate since each radical that is enzymatically generated leads to a large number of polymerization events. Enzyme-mediated polymerization proceeds in an ambient atmosphere without the need for external energy sources, which is an improvement upon previous PBA platforms. Substrate formulations are highly sensitive to both glucose and iron concentrations at the lowest enzyme concentrations. Increases in amplification time correspond to higher assay sensitivities with no increase in non-specific signal. Finally, the polymerization substrate generated a signal to noise ratio of 14 at the detection limit (156 ng/mL) in an assay of transforming growth factor-beta.