Is multidimensional scaling suitable for mapping the input respiratory impedance in subjects and patients?

This paper presents the application of multidimensional scaling (MDS) analysis to data emerging from noninvasive lung function tests, namely the input respiratory impedance. The aim is to obtain a geometrical mapping of the diseases in a 3D space representation, allowing analysis of (dis)similarities between subjects within the same pathology groups, as well as between the various groups. The adult patient groups investigated were healthy, diagnosed chronic obstructive pulmonary disease (COPD) and diagnosed kyphoscoliosis, respectively. The children patient groups were healthy, asthma and cystic fibrosis. The results suggest that MDS can be successfully employed for mapping purposes of restrictive (kyphoscoliosis) and obstructive (COPD) pathologies. Hence, MDS tools can be further examined to define clear limits between pools of patients for clinical classification, and used as a training aid for medical traineeship.

Reliability of nasal auscultation and their relation to middle ear condition – a clinical interest to pediatrics respiratory physiotherapy

Background: Acute respiratory infections are usual in children under three years old occurring in upper respiratory tract, having an impact on child and caregiver’s quality of life predisposing to otitis media or bronchiolitis. There are few valid and reliable measures to determine the child’s respiratory condition and to guide the physiotherapy intervention. Aim: To assess the intra and inter rater reliability of nasal auscultation, to analyze the relation between sounds’ classification and middle ear’s pressure and compliance as well as with the Clinical Severity Score. Methods: A cross-sectional observational study was composed by 125 nursery children aged up to three years old. Tympanometry, pulmonary and nasal auscultation and application of Clinical Severity Score were performed to each child. Nasal auscultation sounds’ were recorded and sent to 3 blinded experts, that classified, as “obstructed” and “unobstructed”, with a 48 hours interval, in order to analyze inter and intra rater reliability. Results: Nasal auscultation revealed a substantial inter and intra rater reliability (=0,749 and evaluator A - K= 0,691; evaluator B - K= 0,605 and evaluator C - K= 0,724, respectively). Both ears’ pressure was significantly lower in children with an "unobstructed" nasal sound when compared with an “obstructed” nasal sound (t=-3,599, p<0,001 in left ear; t=-2,258, p=0,026 in right ear). Compliance in both ears was significantly lower in children with an "obstructed" nasal sound when compared with “unobstructed” nasal sound (t=-2,728, p=0,007 in left ear; t=-3,830, p<0,001 in right ear). There was a statistically significant association between sounds’ classification and tympanograms types in both ear’s (=11,437, p=0,003 in left ear; =13,535, p=0,001 in right ear). There was a trend to children with an "unobstructed" nasal sound that had a lower clinical severity score when compared with “obstructed” children. Conclusion: It was observed a good intra and substantial inter reliability for nasal auscultation. Nasal auscultation sounds’ classification was related to middle ears’ pressure and compliance.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.