Category: Ankle; Other Introduction/Purpose: Finding factors that can exacerbate or ameliorate the incidence of Venous thromboembolism (VTE) can affect the process of making decision on whether to start prophylaxis or not, especially when on the verge of whether to-give or not-to-give prophylaxis. Among each patient's profile, medications are of the most important factors influencing surgeon's decision on the prophylactic methods in VTE-vulnerable patients. Among medications, Statins were shown to reduce the incidence of VTE in patients who were receiving them for hyperlipidemia and cardiovascular conditions. However, none of the current VTE prediction methods, particularly in orthopaedic practice, have considered statins protective. Herein we aimed to determine any correlations between statin consumption and the incidence of VTE in ankle fractures and whether to include statins in prediction models of VTE. Methods: In this case-control machine learning-based study, approved by the Institutional Review Board (IRB), the ICD and CPT codes were used to identify the patients who were diagnosed with ankle fracture in the Mass General Brigham database from 2004 to end of May 2021. After screening approximately 16,421 patients with ankle fractures, a total of 1,176 patients who were suspect VTE according to their signs and symptoms were recruited, 239 had confirmed VTE (case group) and 937 did not have VTE (controls). Forty-nine cases and 396 controls were statin users. Using a semi-automated machine learning-based algorithm, patients' demographics, past medical and surgical history, fracture characteristics and weber classification, and statin consumption status were obtained, and values were organized in a numerical analyzable manner in the dataset. We used chi-squared and Pearson correlation tests where applicable, and outcomes were displayed and interpreted using p-value (p<0.05 considered significant) and odds ratio (OR). Results: The mean age and BMI in our case group were 55.1+-17.0 y/o and 30.0+-6.0, respectively; age and BMI in the controls were 69.4+-13.2 (p=0.09 vs. cases) and 29.2+-6.6 (p=0.12 vs. cases), respectively. Gender distribution is depicted in table 1. In addition, we found that in our population, a total of 239 patients had VTE, from which 49 (21%) were taking Statins and 190 (79%) were not. Out of the 937 patients who did not develop VTE, 396 (42%) were taking Statins whereas 541 (58%) were not. We found that patients taking statins had lower incidence of VTE after their ankle fracture, compared with patients not taking statins (OR=0.36, p <0.001). The distribution of statin users/non-users among cases and controls is shown in table 2. Moreover, using our machine learning algorithm, conditions that would necessitate the use of statins including cardiovascular diseases and hyperlipidemia showed negative significant correlation with VTE (p<=0.02). Conclusion: Several studies have suggested that hyperlipidemic blood is prone to a greater generation of thrombin, endothelial dysfunction, and higher platelet activity. By disturbing these mechanisms, statins play a protective role against VTE. Herein, using machine learning algorithms together with statistical analysis, we found that Statins were significantly associated with a lower rate of VTE in patients with an ankle fracture. These findings can be considered in future prediction models that are built based on patient-specific factors. Knowing the protective effect of statins can also help clinicians with deciding on prophylaxis administration in at VTE-risk patients.fig
Open and closed rhinoplasty are two main approaches to perform nasal modifications. According to current literature, there is no current consensus among plastic surgeons and otolaryngologists on which technique is preferred in terms of aesthetic result, complications, and patient satisfaction. This study uses published research to determine whether open or closed rhinoplasty leads to superior patient outcomes. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews were followed and a literature search was conducted in four databases based on our search strategy. Articles were then imported into COVIDENCE where they underwent primary screening and full-text review. Twenty articles were selected in this study after 243 articles were screened. There were 4 case series, 12 retrospective cohort studies, 1 prospective cohort study, 1 case–control, and 2 outcomes research. There were three cosmetic studies, eight functional studies, and nine studies that included both cosmetic and functional components. Sixteen studies utilized both open and closed rhinoplasty and four utilized open rhinoplasty. Both techniques demonstrated high patient and provider satisfaction and no advantage was found between techniques. Based on available studies, we cannot conclude if there is a preference between open or closed rhinoplasty in terms of which technique leads to better patient outcomes. Several studies determined that open rhinoplasty and closed rhinoplasty leads to comparative patient satisfaction. To make outcome reporting more reliable and uniform among studies, authors should look to utilize the Nasal Obstruction and Septoplasty Effectiveness scale and the Rhinoplasty Outcome Evaluation.
Category: Ankle; Other Introduction/Purpose: The risk of venous thromboembolism (VTE) after foot and ankle surgery is significantly lower than rates after hip/knee arthroplasty, but it isn't zero. Specific subgroups of patients may be at higher risk, forcing patients and clinicians to navigate the risks and benefits of chemoprophylaxis with insufficient data. Efforts have been made to add clarity to such decision making using conventional data-analysis and risk-scoring methods, but none of these methods were patient-specific or built on robust models of a given patient's individual characteristics. In this study we used machine-learning to determine the potential risk factors for VTE after ankle fracture. We aimed to develop a patient-specific predictive model to assist clinicians and patients in deciding upon the use of postoperative chemoprophylaxis after foot and ankle surgery. Methods: In this preliminary machine-learning-based case-control study, 16,421 patients with ankle fractures were recruited retrospectively. We used an automated-string search method to find patients who were clinically suspected to have developed VTE. Among 1176 such patients, 239 had confirmed VTE within 180 days of sustaining an ankle fracture (cases) and 937 did not (controls). Groups were further sub-divided in patients who had been receiving chemoprophylaxis and those who hadn't. Over 110 factors and variables including patient demographics, past-medical and surgical history, fracture characteristics, treatment, medications, and laboratory values were included in our machine-learning dataset. Three analytical algorithms were used in our machine-learning methods including backward-logistic-regression, decision-tree-classifier (depth=5), and neural networks (two dense layers (n=16 and 4), two drop-out layers, and a sigmoid classifier). Conventional statistics were also used to compare the case and control groups (chi-squared, t-test, p<0.05 considered significant), and the odds-ratio (OR) was calculated for significant parameters. Results: Based on overall performance scores including specificity, sensitivity, area under the ROC curve, accuracy, PPV, NPV, F- 1 score, among the 3 machine-learning methods, the Backward-Logistic-Regression model was superior in predicting VTE post ankle fracture and in determining whether administering prophylaxis can be beneficial for the patient or not (Tables 1 and 2). Other than the previously suggested risk factors, our algorithms showed a positive correlation between the incidence of VTE and smoking (OR=2.09, p<0.001), age <55 y/o (p=0.001), open fracture (OR=2.49, p<0.001), male sex (OR=1.98, p<0.001), surgical versus nonoperative treatment (OR=1.88, p=0.001), and multiple fractures at the time of trauma (OR=1.9, p=0.001). Factors that showed negative correlation with VTE include statins use (OR=0.36, p<0.001), hypertension (OR=0.53, p=0.001), vitamin D (OR=0.43, 0.002), calcium supplementation (OR= 0.43, p= 0.01), hyperlipidemia (OR=0.55, p=0.006), cataract (OR=0.19, p=0.01), osteoporosis (OR=0.36, p=0.02), cardiovascular diseases (OR=0.54, p=0.02), hypokalemia (OR=0.26, p=0.03), and proton pump inhibitor use (OR=0.5, p=0.03). Conclusion: Our machine learning algorithms showed that factors such as tobacco use, younger age, open fracture, multi-trauma, operative treatment, as well as male sex heightened the risk of VTE. In contrast, certain factors such as vitamin D supplementation had negative correlation with VTE. Machine learning algorithms acted in a more complex manner and incorporated more factors in decision-making compared to conventional methods. External validation using larger and more granular datasets as well as using the algorithms in trial modes (shadow modes) are needed to build trust in this algorithm to assist clinicians in predicting/preventing VTE after foot and ankle surgeries.
Introduction: More than 200 treatments have been tested for COVID-19 in over 7000 clinical trials. Most of these treatments are repurposed generic drugs, many of which have been studied extensively for the treatment of cancer. As cancer patients are particularly vulnerable, there is a need to understand how COVID-19 treatments might affect a patient’s cancer. As part of the Reboot: COVID-Cancer Project, a living and freely available resource of clinical studies that report outcomes for cancer patients, we have developed a semi-automated pipeline to identify all relevant published clinical studies and registered clinical trials where COVID-19 drugs were tested for the treatment of cancer. Methods: Published clinical studies were assembled using targeted search queries in PubMed, rule-based approaches, and machine learning models. Machine learning models applied to natural language processing tasks were used to predict the drug, cancer type, study type, and therapeutic association. We used domain-specific rules and post-processing steps to further refine results, including determining whether a drug was used alone or in combination. Registered clinical trials were compiled from clinicaltrials.gov using targeted search queries, automated mapping, and rule-based screening. We extracted key information about each trial, such as the drug, cancer type, phase, location, trial status, age, gender, and availability of results. We applied our pipeline to a curated set of 202 drugs being tested for the treatment of COVID-19 in at least two interventional clinical trials worldwide, of which 27 are FDA-approved drugs that are standard of care for cancer, and 115 are FDA-approved drugs primarily used for non-cancer indications. Results: We found 28,138 published clinical studies and 9,118 registered clinical trials where the 202 drugs were tested for cancer. The published clinical studies include 5,286 case studies, 2,559 randomized controlled trials (RCTs), and 20,294 non-RCT clinical trials or observational studies. In 37% of the cases, the drug was used alone and not in combination. Lymphoid cancers were the most commonly tested, comprising 30% of studies. Possible benefit of the drug was found in 64% of publications. Of the 115 FDA-approved non-cancer drugs being tested for COVID-19, there is at least one published clinical study for 84 (73%) drugs. An additional 12 FDA-approved non-cancer drugs have been tested for the treatment of cancer in clinical trials, but have no results reported. Of the registered clinical trials, 39% are currently active, 66% are Phase 2 or later, and lymphoid cancers are again the most common, representing 29% of the trials. Discussion: Given the interconnection between COVID-19 and cancer, it is essential to understand how drugs used for COVID-19 might impact a patient’s cancer. We have created a living resource for rapid review of information. The datasets are updated monthly and are freely available via an interactive dashboard. Citation Format: Allison Britt, Emily Yang, Devon Crittenden, Thomas Bhangdia, Ben Nye, Emily-Claire Duffy, Saradha Miriyala, Emily van der Veen, Sam Marchant, Kelly Fan, Ellie Strauss, Katherine McKinley, Kriti Sharda, Murshea Tuor, Ishita Mahajan, Noopur Ranganathan, Lailoo Perriello, Byron Wallace, Pradeep Mangalath, Laura B. Kleiman, Catherine Del Vecchio Fitz. Effects of COVID-19 treatments on cancer: A machine learning approach to synthesize clinical evidence at scale [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr P04.
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