A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology.

Squamous cell carcinoma represents the most common cancer affecting the oral cavity. At the University of Naples "Federico II", two different antibiotic protocols were used in patients undergoing oral mucosa cancer surgery from 2006 to 2018. From 2011, there was a shift; the combination of Cefazolin plus Clindamycin as a postoperative prophylactic protocol was chosen. In this paper, a health technology assessment (HTA) is performed by using the Six Sigma and DMAIC (Define, Measure, Analyse, Improve, Control) cycle in order to compare the performance of the antibiotic protocols according to the length of hospital stay (LOS). The data (13 variables) of two groups were collected and analysed; overall, 136 patients were involved. The American Society of Anaesthesiologist score, use of lymphadenectomy or tracheotomy and the presence of infections influenced LOS significantly (p-value < 0.05) in both groups. Then, the groups were compared: the overall difference between LOS of the groups was not statistically significant, but some insights were provided by comparing the LOS of the groups according to each variable. In conclusion, in light of the insights provided by this study regarding the comparison of two antibiotic protocols, the utilization of DMAIC cycle and Six Sigma tools to perform HTA studies could be considered in future research.

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals.

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I MS ). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for I MS ). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.

Linear discriminant analysis and principal component analysis to predict coronary artery disease.

Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

Laboratory markers in ulcerative colitis: Current insights and future advances.

Ulcerative colitis (UC) and Crohn's disease (CD) are the major forms of inflammatory bowel diseases (IBD) in man. Despite some common features, these forms can be distinguished by different genetic predisposition, risk factors and clinical, endoscopic and histological characteristics. The aetiology of both CD and UC remains unknown, but several evidences suggest that CD and perhaps UC are due to an excessive immune response directed against normal constituents of the intestinal bacterial flora. Tests sometimes invasive are routine for the diagnosis and care of patients with IBD. Diagnosis of UC is based on clinical symptoms combined with radiological and endoscopic investigations. The employment of non-invasive biomarkers is needed. These biomarkers have the potential to avoid invasive diagnostic tests that may result in discomfort and potential complications. The ability to determine the type, severity, prognosis and response to therapy of UC, using biomarkers has long been a goal of clinical researchers. We describe the biomarkers assessed in UC, with special reference to acute-phase proteins and serologic markers and thereafter, we describe the new biological markers and the biological markers could be developed in the future: (1) serum markers of acute phase response: The laboratory tests most used to measure the acute-phase proteins in clinical practice are the serum concentration of C-reactive protein and the erythrocyte sedimentation rate. Other biomarkers of inflammation in UC include platelet count, leukocyte count, and serum albumin and serum orosomucoid concentrations; (2) serologic markers/antibodies: In the last decades serological and immunologic biomarkers have been studied extensively in immunology and have been used in clinical practice to detect specific pathologies. In UC, the presence of these antibodies can aid as surrogate markers for the aberrant host immune response; and (3) future biomarkers: The development of biomarkers in UC will be very important in the future. The progress of molecular biology tools (microarrays, proteomics and nanotechnology) have revolutionised the field of the biomarker discovery. The advances in bioinformatics coupled with cross-disciplinary collaborations have greatly enhanced our ability to retrieve, characterize and analyse large amounts of data generated by the technological advances. The techniques available for biomarkers development are genomics (single nucleotide polymorphism genotyping, pharmacogenetics and gene expression analyses) and proteomics. In the future, the addition of new serological markers will add significant benefit. Correlating serologic markers with genotypes and clinical phenotypes should enhance our understanding of pathophysiology of UC.