Background Chemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. Objective This study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. Methods Data were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. Results The decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. Conclusions This study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs.
BACKGROUND Chemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. OBJECTIVE This study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. METHODS Data were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. RESULTS The decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. CONCLUSIONS This study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs.
Introduction: Acute myocardial infarction (AMI) is the most important form of ischemic heart disease (IHD). Coronary artery disease (CAD) is an increasingly important medical and public health problem and is the leading cause of mortality in Bangladesh. AMI is the rapid development of myocardial necrosis caused by a critical imbalance between the oxygen supply and demand of the myocardium. Total occlusion of the coronary arteries for more than 4-6 hrs results in irreversible myocardial necrosis, but reperfusion within this period can salvage the myocardium and reduce morbidity and mortality. Objectives: To assess the role of carvedilol in prevention of adrenaline induced cardiac damage in experimental animal. Materials and Methods: This experimental study was carried out in the department of pharmacology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka for a period of One year spanning from July 2004 to June 2005. Fifty two healthy rats of Long Evan Norwegian strains, 3-4 months of ages of both sexes, weight between 180-220g were used. The experiment was divided into two parts: Part-I and Part-II. Thirty two rats were selected for Part-I experiment and subdivided into Group-I and Group-II. In Part-II experiment, 20 rats were selected and placed as Group-III. Group-I (12 rats) of control group was treated with 02 doses of inj distilled water (D/W) subcutaneously (S.C.) 24 hrs apart and serum creatine kinase-MB (CK-MB) level and hepatic and cardiac reduced glutathione (GSH) contents were estimated from 06 (Group-Ia) rats after 12 hrs and serum aspartate aminotransferase (AST) level and hepatic and cardiac reduced GSH contents were estimated from 06 (Group-Ib) rats after 24 hrs of 2nd inj of D/W. Group-II (20 rats) was treated with 02 doses of inj adrenaline (2mg/kg) S.C. in 24 hrs interval and in above mentioned way serum CK-MB level, GSH (hepatic and cardiac) contents and serum AST and GSH (hepatic and cardiac) contents were estimated 12 hrs and 24 hrs after the 2nd inj of adrenaline respectively. In experimental group (Group-III) all the rats (20) were treated with carvedilol (1 mg/kg) orally for 14 consecutive days and then were given 02 doses of inj adrenaline with the interval of 24 hrs and again serum CK-MB level and GSH (hepatic and cardiac) contents were estimated from half of the rats (10) after 12 hrs of injection and serum AST level and GSH (hepatic and cardiac) contents were measured from half of the rats (10) after 24 hrs of 2nd injection of adrenaline. Results: Adrenaline (2mg/kg) induced myocardial damage was evaluated biochemically by significant (P˂0.001) increase in CK-MB and AST levels. Free radical production following adrenaline induced myocardial damage was reflected by significant (P˂0.001) depletion in hepatic and cardiac reduced glutathione (GSH) contents. Cardioprotection provided by carvedilol pretreatment in adrenaline induced myocardial infarction was assessed by significant prevention of increase in serum CK-MB and AST levels. Antioxidant property of carvedilol was evaluated by significant (P<0.001) prevention of depletion in hepatic and cardiac GSH contents. The results of the study indicated that carvedilol pretreatment provided effective prevention in adrenaline induced myocardial damage and also provided effective antioxidative action. Conclusion: This study indicated that adrenaline administration induced myocardial damage as evidenced by increase in serum CK-MB and AST levels which was associated with free radical production as reflected by depletion in hepatic and cardiac GSH contents. It was observed that carvedilol through their antioxidant property in addition to their β-blocking effect prevents free radical mediated injury of catecholamine assault following MI. Journal of Armed Forces Medical College Bangladesh Vol.12(2) 2016: 127-134
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