Ability of Caprini and Padua risk-assessment models to predict venous thromboembolism in a nationwide Veterans Affairs study.

OBJECTIVE: Venous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are two of the most commonly used risk-assessment models (RAMs) to quantify VTE risk. Both models perform well in select, high-risk cohorts. Although VTE RAMs were designed for use in all hospital admissions, they are mostly tested in select, high-risk cohorts. We aim to evaluate the two RAMs in a large, unselected cohort of patients. METHODS: We analyzed consecutive first hospital admissions of 1,252,460 unique surgical and non-surgical patients to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. Caprini and Padua scores were generated using the Veterans Affairs' national data repository. We determined the ability of the two RAMs to predict VTE within 90 days of admission. In secondary analyses, we evaluated prediction at 30 and 60 days, in surgical vs non-surgical patients, after excluding patients with upper extremity deep vein thrombosis, in patients hospitalized ≥72 hours, after including all-cause mortality in a composite outcome, and after accounting for prophylaxis in the predictive model. We used area under the receiver operating characteristic curves (AUCs) as the metric of prediction. RESULTS: A total of 330,388 (26.4%) surgical and 922,072 (73.6%) non-surgical consecutively hospitalized patients (total N = 1,252,460) were analyzed. Caprini scores ranged from 0 to 28 (median, 4; interquartile range [IQR], 3-6); Padua scores ranged from 0-13 (median, 1; IQR, 1-3). The RAMs showed good calibration and higher scores were associated with higher VTE rates. VTE developed in 35,557 patients (2.8%) within 90 days of admission. The ability of both models to predict 90-day VTE was low (AUCs: Caprini, 0.56; 95% confidence interval [CI], 0.56-0.56; Padua, 0.59; 95% CI, 0.58-0.59). Prediction remained low for surgical (Caprini, 0.54; 95% CI, 0.53-0.54; Padua, 0.56; 95% CI, 0.56-0.57) and non-surgical patients (Caprini, 0.59; 95% CI, 0.58-0.59; Padua, 0.59; 95% CI, 0.59-0.60). There was no clinically meaningful change in predictive performance in any of the sensitivity analyses. CONCLUSIONS: Caprini and Padua RAM scores have low ability to predict VTE events in a cohort of unselected consecutive hospitalizations. Improved VTE RAMs must be developed before they can be applied to a general hospital population.

Prediction of bleeding in patients being considered for venous thromboembolism prophylaxis.

BACKGROUND: Venous thromboembolism (pulmonary embolism and deep vein thrombosis) is an important preventable cause of in-hospital death. Prophylaxis with low doses of anticoagulants reduces the incidence of venous thromboembolism but can also cause bleeding. It is, therefore, important to stratify the risk of bleeding for hospitalized patients when considering pharmacologic prophylaxis. The IMPROVE (international medical prevention registry on venous thromboembolism) and Consensus risk assessment models (RAMs) are the two tools available for such patients. Few studies have evaluated their ability to predict bleeding in a large, unselected cohort of patients. We assessed the ability of the IMPROVE and Consensus bleeding RAMs to predict bleeding within 90 days of hospitalization in a comprehensive analysis encompassing all hospitalized patients, regardless of surgical vs nonsurgical status. METHODS: We analyzed consecutive first hospital admissions of 1,228,448 unique surgical and nonsurgical patients to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. IMPROVE and Consensus scores were generated using data from a repository of their common electronic medical records. We assessed the ability of the two RAMs to predict bleeding within 90 days of admission. We used area under the receiver operating characteristic curves to determine the prediction of bleeding by each RAM. RESULTS: Of 1,228,448 hospitalized patients, 324,959 (26.5%) were surgical and 903,489 (73.5%) were nonsurgical. Of these patients, 68,372 (5.6%) had a bleeding event within 90 days of admission. The Consensus RAM scores ranged from -5.60 to -1.21 (median, -4.93; interquartile range, -5.60 to -4.93). The IMPROVE RAM scores ranged from 0 to 22 (median, 3.5; interquartile range, 2.5-5). Both showed good calibration, with higher scores associated with higher bleeding rates. The ability of both RAMs to predict 90-day bleeding was low (area under the receiver operating characteristic curve 0.61 for the IMPROVE RAM and 0.59 for the Consensus RAM). The predictive ability was also low at 30 and 60 days for surgical and nonsurgical patients, patients receiving prophylactic, therapeutic, or no anticoagulation, and patients hospitalized for ≥72 hours. Prediction was also low across different bleeding outcomes (ie, any bleeding, gastrointestinal bleeding, nongastrointestinal bleeding, and bleeding or death). CONCLUSIONS: In this large, unselected, nationwide cohort of surgical and nonsurgical hospital admissions, increasing IMPROVE and Consensus bleeding RAM scores were associated with increasing bleeding rates. However, both RAMs had low ability to predict bleeding at 0 to 90 days after admission. Thus, the currently available RAMs require modification and rigorous reevaluation before they can be applied universally.

Natural Language Processing for Breast Imaging: A Systematic Review.

Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.

Building the foundation for a modern patient-partnered infrastructure to study temporomandibular disorders.

Background: Conflicting reports from varying stakeholders related to prognosis and outcomes following placement of temporomandibular joint (TMJ) implants gave rise to the development of the TMJ Patient-Led RoundTable initiative. Following an assessment of the current availability of data, the RoundTable concluded that a strategically Coordinated Registry Network (CRN) is needed to collect and generate accessible data on temporomandibular disorder (TMD) and its care. The aim of this study was therefore to advance the clinical understanding, usage, and adoption of a core minimum dataset for TMD patients as the first foundational step toward building the CRN. Methods: Candidate data elements were extracted from existing data sources and included in a Delphi survey administered to 92 participants. Data elements receiving less than 75% consensus were dropped. A purposive multi-stakeholder sub-group triangulated the items across patient and clinician-based experience to remove redundancies or duplicate items and reduce the response burden for both patients and clinicians. To reliably collect the identified data elements, the identified core minimum data elements were defined in the context of technical implementation within High-performance Integrated Virtual Environment (HIVE) web-application framework. HIVE was integrated with CHIOS™, an innovative permissioned blockchain platform, to strengthen the provenance of data captured in the registry and drive metadata to record all registry transaction and create a robust consent network. Results: A total of 59 multi-stakeholder participants responded to the Delphi survey. The completion of the Delphi surveys followed by the application of the required group consensus threshold resulted in the selection of 397 data elements (254 for patient-generated data elements and 143 for clinician generated data elements). The infrastructure development and integration of HIVE and CHIOS™ was completed showing the maintenance of all data transaction information in blockchain, flexible recording of patient consent, data cataloging, and consent validation through smart contracts. Conclusion: The identified data elements and development of the technological platform establishes a data infrastructure that facilitates the standardization and harmonization of data as well as perform high performance analytics needed to fully leverage the captured patient-generated data, clinical evidence, and other healthcare ecosystem data within the TMJ/TMD-CRN.

CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images.

We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generative algorithm produces images of sufficient accuracy to enable a DNN classifier to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic during the COVID-19 pandemic, because for quite some time it was difficult to obtain large volumes of COVID-19-positive images for training. Algorithms that can learn to screen for diseases using few examples are an important area of research. Furthermore, algorithms to produce deep synthetic images with smaller data volumes have the added benefit of reducing the barriers of data sharing between healthcare institutions. We present the cycle-consistent segmentation-generative adversarial network (CCS-GAN). CCS-GAN combines style transfer with pulmonary segmentation and relevant transfer learning from negative images in order to create a larger volume of synthetic positive images for the purposes of improving diagnostic classification performance. The performance of a VGG-19 classifier plus CCS-GAN was trained using a small sample of positive image slices ranging from at most 50 down to as few as 10 COVID-19-positive CT scan images. CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.

Ability of Caprini and Padua Risk-Assessment Models to Predict Venous Thromboembolism in a Nationwide Study.

Background: Venous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are the most commonly used risk-assessment models to quantify VTE risk. Both models perform well in select, high-risk cohorts. While VTE risk-stratification is recommended for all hospital admissions, few studies have evaluated the models in a large, unselected cohort of patients. Methods: We analyzed consecutive first hospital admissions of 1,252,460 unique surgical and non-surgical patients to 1,298 VA facilities nationwide between January 2016 and December 2021. Caprini and Padua scores were generated using the VA's national data repository. We first assessed the ability of the two RAMs to predict VTE within 90 days of admission. In secondary analyses, we evaluated prediction at 30 and 60 days, in surgical versus non-surgical patients, after excluding patients with upper extremity DVT, in patients hospitalized ≥72 hours, after including all-cause mortality in the composite outcome, and after accounting for prophylaxis in the predictive model. We used area under the receiver-operating characteristic curves (AUC) as the metric of prediction. Results: A total of 330,388 (26.4%) surgical and 922,072 (73.6%) non-surgical consecutively hospitalized patients (total n=1,252,460) were analyzed. Caprini scores ranged from 0-28 (median, interquartile range: 4, 3-6); Padua scores ranged from 0-13 (1, 1-3). The RAMs showed good calibration and higher scores were associated with higher VTE rates. VTE developed in 35,557 patients (2.8%) within 90 days of admission. The ability of both models to predict 90-day VTE was low (AUCs: Caprini 0.56 [95% CI 0.56-0.56], Padua 0.59 [0.58-0.59]). Prediction remained low for surgical (Caprini 0.54 [0.53-0.54], Padua 0.56 [0.56-0.57]) and non-surgical patients (Caprini 0.59 [0.58-0.59], Padua 0.59 [0.59-0.60]). There was no clinically meaningful change in predictive performance in patients admitted for ≥72 hours, after excluding upper extremity DVT from the outcome, after including all-cause mortality in the outcome, or after accounting for ongoing VTE prophylaxis. Conclusions: Caprini and Padua risk-assessment model scores have low ability to predict VTE events in a cohort of unselected consecutive hospitalizations. Improved VTE risk-assessment models must be developed before they can be applied to a general hospital population.

Systematic review of venous thromboembolism risk categories derived from Caprini score.

OBJECTIVE: Hospital-acquired venous thromboembolism (VTE, including pulmonary embolism [PE] and deep vein thrombosis [DVT]) is a preventable cause of hospital death. The Caprini risk assessment model (RAM) is one of the most commonly used tools to assess VTE risk. The RAM is operationalized in clinical practice by grouping several risk scores into VTE risk categories that drive decisions on prophylaxis. A correlation between increasing Caprini scores and rising VTE risk is well-established. We assessed whether the increasing VTE risk categories assigned on the basis of recommended score ranges also correlate with increasing VTE risk. METHODS: We conducted a systematic review of articles that used the Caprini RAM to assign VTE risk categories and that reported corresponding VTE rates. A Medline and EMBASE search retrieved 895 articles, of which 57 fulfilled inclusion criteria. RESULTS: Forty-eight (84%) of the articles were cohort studies, 7 (12%) were case-control studies, and 2 (4%) were cross-sectional studies. The populations varied from postsurgical to medical patients. There was variability in the number of VTE risk categories assigned by individual studies (6 used 5 risk categories, 37 used 4, 11 used 3, and 3 used 2), and in the cutoff scores defining the risk categories (scores from 0 alone to 0-10 for the low-risk category; from ≥5 to ≥10 for high risk). The VTE rates reported for similar risk categories also varied across studies (0%-12.3% in the low-risk category; 0%-40% for high risk). The Caprini RAM is designed to assess composite VTE risk; however, two studies reported PE or DVT rates alone, and many of the other studies did not specify the types of DVTs analyzed. The Caprini RAM predicts VTE at 30 days after assessment; however, only 17 studies measured outcomes at 30 days; the remaining studies had either shorter or longer follow-ups (0-180 days). CONCLUSIONS: The usefulness of the Caprini RAM is limited by heterogeneity in its implementation across centers. The score-derived VTE risk categorization has significant variability in the number of risk categories being used, the cutpoints used to define the risk categories, the outcome being measured, and the follow-up duration. This factor leads to similar risk categories being associated with different VTE rates, which impacts the clinical and research implications of the results. To enhance generalizability, there is a need for studies that validate the RAM in a broad population of medical and surgical patients, identify standardized risk categories, define risk of DVT and PE as distinct end points, and measure outcomes at standardized follow-up time points.

Using Big Data to Evaluate the Association between Periodontal Disease and Rheumatoid Arthritis.

An association between periodontal disease and rheumatoid arthritis is believed to exist. Most investigations into a possible relationship have been case-control studies with relatively low sample sizes. The advent of very large clinical repositories has created new opportunities for data-driven research. We conducted a retrospective cohort study to measure the association between periodontal disease and rheumatoid arthritis in a population of 25 million patients. We demonstrated that subjects with periodontal disease were roughly 1.4 times more likely to have rheumatoid arthritis. These results compare favorably with those of previous studies on smaller cohorts. Additional work is needed to identify the mechanisms behind this association and to determine if aggressive treatment of periodontal disease can alter the course of rheumatoid arthritis.

Metabolic profiling in personalized medicine: bridging the gap between knowledge and clinical practice in Type 2 diabetes.

Type 2 diabetes mellitus (DM2) is the most commonly diagnosed metabolic disease and its prevalence is expected to increase. Epidemiological studies clearly show excess mortality associated with DM2, as well as an increased risk of DM2-related complications. Advances in personalized medicine would greatly improve patient care in the field of diabetes and other metabolic diseases. Prediction of the disease in asymptomatic patients as well as its harsh complications in patients already diagnosed is becoming a necessity, with the considerable increase in the cost of the treatment. In the current article, we review the known clinical, molecular metabolic and genetic biomarkers that should be integrated in a future bioinformatic platform to be used at the point-of-care, and discuss the challenges we face in applying this vision of personalized medicine for diabetes into reality.