RESUMO
BACKGROUND: As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted "At Risk" CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of "At Risk" CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates. RESULTS: A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score. Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.
RESUMO
PURPOSE: To apply and monitor a single institution's adherence to internally established guidelines for the preoperative administration of platelets and/or fresh frozen plasma (FFP) before a specified subset of minimally invasive interventional radiology (IR) procedures. MATERIALS AND METHODS: Beginning in December 2008, we implemented a set of restrictive guidelines for preoperative platelet and/or FFP administration before IR procedures at a single academic hospital. Basing our program on the methodology of Lean Six Sigma, we compared the number and appropriateness of transfusions between the months of January and October in 2008 (prepolicy), again in 2010 (postpolicy), and finally in 2015 (follow-up). Patients with a platelet count less than or equal to 50,000 or an international normalized ratio greater than or equal to 1.7 met criteria for receiving platelets or FFP, respectively, before their IR procedure. For all three periods, we compared the rates of transfusion, hemorrhagic complications, and proportion of appropriate versus inappropriate blood product administration (BPA) per our guidelines. RESULTS: There was a significant increase in the number of appropriate BPAs between 2008 and 2010 from 58% to 76% (P = .021). Between 2010 and 2015, the rate trended up further, from 76% to 88% (P = .051). Overall, between 2008 and 2015, the improvement from 58% to 88% was significant (P < .001). The rate of hemorrhagic complications was extremely low in all three groups. CONCLUSION: Restrictive guidelines for receiving platelets and FFP administrations before IR procedures can sustainably decrease the rate of overall BPA while increasing the proportion of appropriate BPA without impacting the rate of hemorrhagic complications.