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OBJECTIVE To develop a multivariable model and online decision-support calculator to aid in preoperative discrimination of benign from malignant splenic masses in dogs. ANIMALS 522 dogs that underwent splenectomy because of splenic masses. PROCEDURES A multivariable model was developed with preoperative clinical data obtained retrospectively from the records of 422 dogs that underwent splenectomy. Inclusion criteria were the availability of complete abdominal ultrasonographic examination images and splenic histologic slides or histology reports for review. Variables considered potentially predictive of splenic malignancy were analyzed. A receiver operating characteristic curve was created for the final multivariable model, and area under the curve was calculated. The model was externally validated with data from 100 dogs that underwent splenectomy subsequent to model development and was used to create an online calculator to estimate probability of splenic malignancy in individual dogs. RESULTS The final multivariable model contained 8 clinical variables used to estimate splenic malignancy probability: serum total protein concentration, presence (vs absence) of ≥ 2 nRBCs/100 WBCs, ultrasonographically assessed splenic mass diameter, number of liver nodules (0, 1, or ≥ 2), presence (vs absence) of multiple splenic masses or nodules, moderate to marked splenic mass inhomogeneity, moderate to marked abdominal effusion, and mesenteric, omental, or peritoneal nodules. Areas under the receiver operating characteristic curves for the development and validation populations were 0.80 and 0.78, respectively. CONCLUSIONS AND CLINICAL RELEVANCE The online calculator (T-STAT.net or T-STAT.org) developed in this study can be used as an aid to estimate the probability of malignancy in dogs with splenic masses and has potential to facilitate owners' decisions regarding splenectomy.

Background:To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.Methods:To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.Results:Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.Conclusion:Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

Background:To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on “mathematical equipoise.”Objectives:As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis.Methods:With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making.Results:The KOMET predictive regression model for knee pain had four patient-specific variables, and an r2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received.Conclusions:Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.

The increasing use of electronic health records (EHRs) in veterinary medicine creates an opportunity to utilize the high volume of electronic patient data for mining and data-driven analytics with the goal of improving patient care and outcomes. A central focus of the Clinical and Translational Science Award One Health Alliance (COHA) is to integrate efforts across multiple disciplines to better understand shared diseases in animals and people. The ability to combine veterinary and human medical data provides a unique resource to study the interactions and relationships between animals, humans, and the environment. However, to effectively answer these questions, veterinary EHR data must first be prepared in the same way it is now commonly being done in human medicine to enable data mining and development of analytics to facilitate knowledge formation and solutions that advance our understanding of disease processes, with the ultimate goal of improving outcomes for veterinary patients and their owners. As a first step, COHA member institutions implemented a Common Data Model to standardize EHR data. Herein we present the approach executed within the COHA framework to prepare and optimize veterinary EHRs for data mining and knowledge formation based on the adoption of the Observational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model.

The formation of the CTSI One Health Alliance (COHA) network has generated the infrastructure necessary to support “Big Data” collaborative comparative and translational research in veterinary medicine. We describe the first step in the design, implementation, and dissemination of a collaborative information technology infrastructure that will serve the public and clinicians (COHA public/member based web site at https://ctsaonehealthalliance.org/) and its research focused COHA Research Workbench application. The core research infrastructure, TRANSLATOR (TRanslational ANimal Shared ColLAboraTive Observational Research), represents the foundation of a federated research-capable network to enable pooling large datasets from both electronic health records and publications. The public facing COHA website is a mechanism for both the dissemination of knowledge to the public and to foster collaborations amongst veterinary clinician scientists and the greater medical research community.

Background— Performance of prehospital ECGs expedites identification of ST-elevation myocardial infarction and reduces door-to-balloon times for patients receiving reperfusion therapy. To fully realize this benefit, emergency medical service performance must be measured and used in feedback reporting and quality improvement. Methods and Results— This quasi-experimental design trial tested an approach to improving emergency medical service prehospital ECGs using feedback reporting and quality improvement interventions in 2 cities’ emergency medical service agencies and receiving hospitals. All patients age ≥30 years, calling 9-1-1 with possible acute coronary syndrome, were included. In total, 6994 patients were included: 1589 patients in the baseline period without feedback and 5405 in the intervention period when there were feedback reports and quality improvement interventions. Mean age was 66±17 years, and women represented 51%. Feedback and quality improvement increased prehospital ECG performance for patients with acute coronary syndrome from 76% to 93% ( P =P =P =0.001), but the median total emergency medical service run time remained the same at 22 minutes. The proportion of patients with door-to-balloon times of ≤90 minutes increased from 27% to 67% ( P =0.006). Conclusions— Feedback reports and quality improvement improved prehospital ECG performance for patients with acute coronary syndrome and ST-elevation myocardial infarction and increased aspirin administration without prehospital transport delays. Improvements in door-to-balloon times were also seen.

The era of personalized medicine and targeted therapies is emerging in many areas of medicine. In ophthalmology, the disease age-related macular degeneration (AMD) stands alone in having several environmental and genetic risk factors with confirmed, combined high impact on its development and progression. The combination of demographic, behavioral, and genetic factors can achieve a high predictive value for progression to advanced stages of disease approaching 90%, unlike any other disease.1–,5 We developed genetic risk algorithms in 2006,6 subsequently adding nongenetic markers7 and more common genetic variants,2–,4,8 then recently validated these risk models in an independent study cohort.5 More genetic loci in a 9 gene model, including the strongly related rare variant in the gene CFH (R1210C),9 and common loci RAD51B and COL8A1,10,11 enhanced predictability for transition from early and intermediate stages to advanced disease.12 Knowledge about the genetic underpinnings of AMD continues to expand. New, rare genetic variants have been discovered in case-control association studies in the genes C313–,15 and C9,13 as well as a burden of several rare variants in CFI.13 While these variants are uncommon, they have a stronger impact on the development of the disease individually compared with the low to moderate impact of the 20 or more common variants associated with AMD. Given that previous analyses were based on cross-sectional analyses of prevalent cases, and that progression of disease over time can have different underlying predictors, we conducted longitudinal analyses to determine the impact of these new rare variants on transition to the late stages of AMD resulting in visual loss over time. We also assessed the independent association of these new variants on progression, controlling for other genetic and nongenetic predictors. Building upon our previous algorithms, we developed and assessed a new risk prediction model for progression of AMD (rPPMD) and evaluated the degree to which AMD progression could be attributed to its various components: environmental, ocular, and genetic. In order to lay the groundwork to disseminate these predictive models for clinical use, we developed a software framework and analytics components in the form of an online browser-based calculator (seddonamdriskscore.org, available in the public domain) to demonstrate its use with various subject risk profiles. The rPPMD software components are also designed to be adapted as a stand-alone application for a personal computer or mobile devices such as smartphones and tablets.