Impact of Fit-for-Purpose Monitoring on EOR Planning

Challenges in the design of efficient EOR field pilots have been discussed and documented in the industry, particularly when it comes to optimization of monitoring plans for technical and economical perspectives. This paper explores the benefits of pilot planning where the monitoring/control strategies are included in the early stages of the design to reduce risk of measurements ambiguity and ensure good quality pilot results evaluation. It addresses the use of new and existing technology in monitoring by highlighting the advantages and challenges of each alternative including potential pairing of complementary options to achieve the pilot objectives including illustration of the use of continuous and sporadic measurements on the evaluation. The proposed approach starts with a review of reservoir performance, heterogeneity and pilot objectives to ascertain the plausible monitoring technologies/strategies to aid during the pilot de-risking, followed by the identification of adequate novel and mature monitoring options, which are specific to EOR type and measurement nature (permanent, time lapse, etc.). Advantages of incorporating the monitoring strategy as integral part of the pilot design, as well as evaluation of the effectiveness/viability in the presence of uncertainty of the selected monitoring alternatives are discussed providing a reference of suitable/plausible EOR specific technologies. The paper illustrates the importance of selecting monitoring alternatives that feed off each other and the importance of using fit-for-purpose evaluation algorithms and a digitally enabled, structured approach to analyze and democratize pilot results and enable actionable decisions in operations.

Characterizing and Quantifying Performance Heterogeneity in Cardiovascular Risk Prediction Models — A Step Towards Improved Disease Risk Assessment

Prediction models are commonly used to estimate risk for cardiovascular diseases; however, performance may vary substantially across relevant subgroups of the population. Here we investigated the variability of performance and fairness across a variety of subgroups for risk prediction of two common diseases, atherosclerotic cardiovascular disease (ASCVD) and atrial fibrillation (AF). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large data sets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity of established cardiovascular risk scores across subpopulations defined by age, sex, and presence of preexisting disease. For example, in CHARGE-AF, discrimination declined with increasing age, with concordance index of 0.72 [ 95% CI, 0.72–0.73 ] for the youngest (45–54y) subgroup to 0.57 [ 0.56–0.58 ], for the oldest (85–90y) subgroup in Explorys. The statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65–74y subgroup with a value of -0.33 [ 95% CI, -0.33–-0.33 ]. We observed also that large segments of the population suffered from both decreased discrimination (i.e., <0.7) and poor calibration (i.e., calibration slope outside of 0.7–1.3); for example, all individuals 75 or older in Explorys (17.4%). Our findings highlight the need to characterize and quantify how clinical risk models behave and perform within specific subpopulations so they can be used appropriately to facilitate more accurate and equitable assessment of disease risk.

Heterogeneity Analysis of Chest CT Predict Individual Prognosis of COVID-19 Patients

Background: Ground-glass opacity (GGO) and consolidation opacity (CLO) are the common CT lung opacities, and their heterogeneity may have potential for prognosis ofcoronavirus disease-19 (COVID-19) patients. Objective: This study aimed to estimate clinical outcomes in individual COVID-19 patients using histogram heterogeneity analysis based on CT opacities. Methods: 71 COVID-19 cases’ medical records were retrospectively reviewed from a designated hospital in Wuhan, China, from January 24th to February 28th at the early stage of the pandemic. Two characteristic lung abnormity opacities, GGO and CLO, were drawn on CT images to identify the heterogeneity using quantitative histogram analysis. The parameters (mean, mode, kurtosis, and skewness) were derived from histograms to evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. Results: A total of 57 COVID-19 cases were eligible for the study cohort after excluding 14 cases. The highest lung abnormalities were GGO mixed with CLO in both the survival populations (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as follows: GGO_skewness: specificity=66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity=76.92%, and AUC=0.746. Nomogram based on histogram parameters can predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively. Conclusion: Histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0–94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th–75th percentile [interquartile range (IQR)], 0.66–0.79), representing a median percent decrease in discrimination of −11.1% (IQR, −32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was −3.7% (IQR, −13.2 to 3.1) for closely related validations (n=123), −9.0 (IQR, −27.6 to 3.9) for related validations (n=862), and −17.2% (IQR, −42.3 to 0) for distantly related validations (n=717; P <0.001). Conclusions: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

Network Interconnectivity and Entry into Platform Markets

Digital technologies have led to the emergence of many platforms in our economy today. In certain platform networks, buyers in one market purchase services from providers in many other markets, whereas in others, buyers primarily purchase services from providers within the same market. Accordingly, network interconnectivity—which measures the degree to which consumers in one market purchase services from service providers in a different market—varies across different industries. We examine how network interconnectivity affects interactions between an incumbent platform serving multiple markets and an entrant platform seeking to enter one of these markets. Our model yields several interesting results. First, even if the entrant can advertise at no cost, it still may not want to make every user in a local market aware of its service, as doing so may trigger a competitive response from the incumbent. Second, having more mobile buyers, which increases interconnectivity between markets, can reduce the incumbent’s incentive to fight and, thus, increase the entrant’s incentive to expand. Third, stronger interconnectivity between markets may or may not make the incumbent more defensible: when advertising is not costly and mobile buyers consume in both their local markets and the markets they visit, a large number of mobile buyers will increase the entrant’s profitability, thereby making it difficult for the incumbent to deter entry. However, when advertising is costly or mobile buyers only consume in the markets they travel to, a large number of mobile buyers will help the incumbent deter entry. When advertising cost is at an intermediate level, the entrant prefers a market with moderate interconnectivity between markets. Fourth, we find that even if advanced targeting technologies can enable the entrant to also advertise to mobile buyers, the entrant may choose not to do so in order to avoid triggering the incumbent’s competitive response. Finally, we find that the presence of network effects is likely to decrease the entrant’s profit. Our results offer managerial implications for platform firms and help understand their performance heterogeneity.

Students’ attitudes towards performance heterogeneity and their relation to contextual factors

Previous studies examining attitudes towards performance heterogeneity have focused on attitudes among teachers. However, positive attitudes towards the school environment are also assumed to be conducive for students. The aim of this paper is to examine students’ attitudes towards performance heterogeneity with a sample of 784 5th-grade students. Based on the three-component theory of attitudes (Eagly & Chaiken, 1993), we investigated whether students’ attitudes towards performance heterogeneity are positive or negative. Furthermore, we analyzed contextual relations, focusing on whether students’ attitudes are linked to performance heterogeneity in the classroom and to a teachers’ behavior to manage performance heterogeneity. Descriptive statistics show that students’ attitudes towards performance heterogeneity are rather positive. Multi-level structural equation models reveal that contextual rather than individual characteristics relate to students’ attitudes towards performance heterogeneity. Accordingly, students in heterogeneous classes show a more positive attitude towards performance heterogeneity than students in less heterogeneous classes. In addition, a teachers’ capability of professionally managing heterogeneity is positively associated with students’ attitudes towards performance heterogeneity. Accordingly, students’ show more positive attitudes if teachers implement rules, effectively manage disruptions, orient themselves towards temporal reference norms, cultivate a positive error culture, and differentiate instruction in the classroom. We will discuss our results and consider implications for psychological aspects of education and learning.

External Validations of Cardiovascular Clinical Prediction Models: A Large-scale Review of the Literature

BackgroundThere are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested and how well they generally perform has not been broadly evaluated.MethodsA SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts PACE CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data.Results2030 external validations of 1382 CPMs were identified. 807 (58%) of the CPMs in the Registry have never been externally validated. On average there were 1.5 validations per CPM (range 0-94). The median external validation AUC was 0.73 (25th −75th percentile [IQR] 0.66, 0.79), representing a median percent decrease in discrimination of −11.1% (IQR −32.4%, +2.7%) compared to performance on derivation data. 81% (n = 1333) of validations reporting AUC showed discrimination below that reported in the derivation dataset. 53% (n = 983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was −3.7% (IQR −13.2, 3.1) for ‘closely related’ validations (n=123), −9.0 (IQR −27.6, 3.9) for ‘related validations’ (n=862) and −17.2% (IQR −42.3, 0) for ‘distantly related’ validations (n=717) (p<0.001).ConclusionMany published cardiovascular CPMs have never been externally validated and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

Prediction of COVID-19 Prognosis By Heterogeneity Analysis Based on Chest CT Scans

BackgroundGround-glass opacity (GGO) and consolidation opacity (CLO) are the common CT lung opacities, and their heterogeneity may have potential for prognosis in COVID-19 patients. This study aimed to estimate clinical outcome in individual COVID-19 patient by using histogram heterogeneity analysis based on CT opacities. Methods71 COVID-19 cases’ medical records were retrospectively reviewed from a designated hospital in Wuhan, China, from January 24th to February 28th at the early stage of pandemic. Two characteristic lung abnormity opacities, GGO and CLO were drawn on CT images to identify the heterogeneity by using quantitative histogram analysis. The parameters (mean, mode, kurtosis, skewness) were derived from histograms to evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. Results A total of 57 cases were eligible for the study cohort after exclusion 14 cases. The most highly frequency of lung abnormalities was GGO mixed with CLO in both survival population (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as following: GGO_skewness: specificity=66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity=76.92%, AUC=0.746. Nomogram based on histogram parameters has the ability to predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively.ConclusionsThe histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.