Curtis B. Storlie

Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Safety of Overlapping Surgery at a High-volume Referral Center

Objective: To compare safety profiles of overlapping and nonoverlapping surgical procedures at a large tertiary-referral center where overlapping surgery is performed. Background: Surgical procedures are frequently performed as overlapping, wherein one surgeon is responsible for 2 procedures occurring at the same time, but critical portions are not coincident. The safety of this practice has not been characterized. Methods: Primary analyses included elective, adult, inpatient surgical procedures from January 2013 to September 2015 available through University HealthSystem Consortium. Overlapping and nonoverlapping procedures were matched in an unbalanced manner (m:n) by procedure type. Confirmatory analyses from the American College of Surgeons-National Surgical Quality Improvement Program investigated elective surgical procedures from January 2011 to December 2014. We compared outcomes mortality and length of stay after adjustment for registry-predicted risk, case-mix, and surgeon using mixed models. Results: The University HealthSystem Consortium sample included 10,765 overlapping cases, of which 10,614 (98.6%) were matched to 16,111 nonoverlapping procedures. Adjusted odds ratio for inpatient mortality was greater for nonoverlapping procedures (adjusted odds ratio, OR = 2.14 vs overlapping procedures; 95% confidence interval, CI 1.23–3.73; P = 0.007) and length of stay was no different (+1% for nonoverlapping cases; 95% CI, −1% to +2%; P = 0.50). In confirmatory analyses, 93.7% (3712/3961) of overlapping procedures matched to 5,637 nonoverlapping procedures. The 30-day mortality (adjusted OR = 0.69 nonoverlapping vs overlapping procedures; 95% CI, 0.13–3.57; P = 0.65), morbidity (adjusted OR = 1.11; 95% CI, 0.92–1.35; P = 0.27) and length of stay (−4% for nonoverlapping; 95% CI, −4% to −3%; P < 0.001) were not clinically different. Conclusions: These findings from administrative and clinical registries support the safety of overlapping surgical procedures at this center but may not extrapolate to other centers.

Implications of CA19-9 elevation for survival, staging, and treatment sequencing in intrahepatic cholangiocarcinoma: A national cohort analysis

Optimal management of patients with intrahepatic cholangiocarcinoma (ICCA) and elevated CA19‐9 remains undefined. We hypothesized CA19‐9 elevation above normal indicates aggressive biology and that inclusion of CA19‐9 would improve staging discrimination.

The Fragility Index: a P -value in sheep’s clothing?

This editorial refers to ‘How robust are clinical trials in heart failure?’, by K.F. Docherty et al. , doi:10.1093/eurheartj/ehw427. Randomized controlled trials (RCTs) are designed to assess objectively the safety and efficacy of a specific intervention. While public reporting of the study design, conflict of interest declarations, and the peer review process strive to fortify study findings, the veracity of published research studies has been called into question.1 Recently, the Fragility Index (FI) has been introduced as an intuitive measure of the robustness of RCTs.2 This new statistic has been recently reported in the critical care setting,3 and studied and advocated for heart failure in the paper by Docherty et al .4 In this Editorial, we will provide a brief explanation of the FI and express why caution needs to be exercised when interpreting FI. The FI is a statistical summary of an RCT that utilizes 1:1 randomization and an outcome measure that can be categorized into two levels (e.g. 30-day mortality, yes or no). When a study reaches a statistically significant difference (and the sample size per group is fixed), one can show that the number of positive responses differs between the groups. Without loss of generality, assume that a study is comprised of a novel intervention and a control condition and that fewer events will be observed in the intervention group (i.e. the intervention provides a protective effect to the patients). Suppose that the study results suggest that the intervention is protective and that the result is statistically significant. Upon careful observation, it is noted that the number of events is reasonably small and that in absolute number there is not a large difference between the two study groups. One might naturally ask the question ‘how would the results be interpreted if one of …

Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

ABSTRACT Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger systems. Hence, multiscale modeling efforts must accurately quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian smoothing splines (BSS-ANOVA) framework. Our uncertainty quantification (UQ) approach could be considered intrusive as it includes the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here could be applicable to virtually all areas of science where multiscale modeling is used. Supplementary materials for this article are available online.

Incorporation of Treatment Response, Tumor Grade and Receptor Status Improves Staging Quality in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

BackgroundImproved staging systems that better predict survival for breast cancer patients who receive neoadjuvant chemotherapy (NAC) by accounting for clinical pathological stage plus estrogen receptor (ER) and grade (CPS+EG) and ERBB2 status (Neo-Bioscore) have been proposed. We sought to evaluate the generalizability and performance of these staging systems in a national cohort.MethodsThe National Cancer Database (2006–2012) was reviewed for patients with breast cancer who received NAC and survived ≥90 days after surgery. Four systems were evaluated: clinical/pathologic American Joint Committee on Cancer (AJCC) 7th edition, CPS+EG, and Neo-Bioscore. Unadjusted Kaplan–Meier analysis and adjusted Cox proportional hazards models quantified overall survival (OS). Systems were compared using area under the curve (AUC) and integrated discrimination improvement (IDI).ResultsOverall, 43,320 patients (5-year OS 76.0, 95% confidence interval [CI] 75.4–76.5%) were included, 12,002 of whom had evaluable Neo-Bioscore. AUC at 5 years for CPS+EG (0.720, 95% CI 0.714–0.726) and Neo-Bioscore (0.729, 95% CI 0.716–0.742) were improved relative to AJCC clinical (0.650, 95% CI 0.643–0.656) and pathologic (0.683, 95% CI 0.676–0.689) staging. Both CPS+EG (IDI 7.2, 95% CI 6.6–7.7%) and Neo-Bioscore (IDI 9.8, 95% CI 8.0–11.6%) demonstrated superior discrimination when compared with AJCC clinical staging at 5 years. Comparison of CPS+EG with Neo-Bioscore yielded an IDI of 2.6% (95% CI 0.9–4.5%), indicating that Neo-Bioscore is the best staging system.ConclusionsIn a heterogenous national cohort of breast cancer patients treated with NAC and surgery, the incorporation of chemotherapy response, tumor grade, ER status, and ERBB2 status into the staging system substantially improved on the AJCC TNM staging system in discrimination of OS. Neo-Bioscore provided the best staging discrimination.

Functional regression-based monitoring of quality of service in hospital emergency departments

Abstract This article focuses on building a statistical monitoring scheme for service systems that experience time-varying arrivals of customers and have time-varying service rates. There is lack of research in the systematic statistical monitoring of large-scale service systems, which is critical for maintaining a high quality of service. Motivated by the emergency department at a major academic medical center, this article intends to fill this research gap and provide a practical statistical monitoring scheme capable of detecting changes in service using readily available time stamp data. The proposed method is focused on building a functional regression model based on customer arrival and departure time instances from an in-control system. The model finds the expected departure intensity function for an observed arrival intensity on any given day of operation. The mean squared difference between the expected departure intensity function and the observed departure intensity functions is used to generate an alarm indicating a significant change in service. This methodology is validated using simulation and real data case studies. The proposed method can identify patterns of inefficiency or delay in service that are hard to detect using traditional statistical monitoring algorithms. The method offers a practical approach for monitoring service systems and determining when staffing levels need to be re-optimized.

Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes

OBJECTIVE Metformin is the preferred first-line medication for management of type 2 diabetes and prediabetes. However, over a third of patients experience primary or secondary therapeutic failure. We developed machine learning models to predict which patients initially prescribed metformin will achieve and maintain control of their blood glucose after one year of therapy. MATERIALS AND METHODS We performed a retrospective analysis of administrative claims data for 12,147 commercially-insured adults and Medicare Advantage beneficiaries with prediabetes or diabetes. Several machine learning models were trained using variables available at the time of metformin initiation to predict achievement and maintenance of hemoglobin A1c (HbA1c) < 7.0% after one year of therapy. RESULTS AUC performances based on five-fold cross-validation ranged from 0.58 to 0.75. The most influential variables driving the predictions were baseline HbA1c, starting metformin dosage, and presence of diabetes with complications. CONCLUSIONS Machine learning models can effectively predict primary or secondary metformin treatment failure within one year. This information can help identify effective individualized treatment strategies. Most of the implemented models outperformed traditional logistic regression, highlighting the potential for applying machine learning to problems in medicine.