Selected recommendations from international guidelines on obstructive sleep apnoea / Nurul Yaqeen Mohd Esa and Ahmad Izuanuddin Ismail.

Obstructive sleep apnoea (OSA) is increasingly seen as a major health threat globally. However, it is still underdiagnosed mainly among Asian population partly due to lack of understanding on the pathophysiology, and limited access to the diagnostic and management aspect of the disease. Recurring complete and/or partial collapses of the upper airways define OSA. Based on the number of apnoeas and/or hypopnoeas per hour of sleep, OSA is categorized as mild, moderate and severe. Both the American Association of Sleep Medicine (AASM) and American College of Physicians (ACP) has published guidelines regarding the management of OSA in adults. Three recommendations have been suggested by the guidelines which can be used to tailor the management of OSA. The aim of this article is to select relevant recommendations from these guidelines in epidemiology, pathophysiology, diagnostic procedures and treatment for proper management of OSA, while considering specific patient populations, such as hypertensive, diabetic, obese and Asian patients

Improved Boosted Decision Tree Algorithms by Adaptive Apriori and Post-Pruning for Predicting Obstructive Sleep Apnea

The improved version of Boosted Decision Tree algorithm, named as Boosted Adaptive Apriori post-Pruned Decision Tree (Boosted AApoP-DT), was developed by referring to Adaptive Apriori (AA) properties and by using post-pruning technique. The post-pruning technique used is mainly the error-complexity pruning for the decision trees categorized under Classification and Regression Trees. This technique estimates the re-substitution, cross-validation and generalization error rates before and after the post-pruning. The novelty of the post-pruning technique applied is that it is augmented by AA properties and these depend on the data characteristics in the dataset(s) being accessed. This algorithm is then boosted by using AdaBoost ensemble method. After comparing and contrasting this developed algorithm with the algorithm without being augmented by AA, i.e., Boosted post-Pruned Decision Tree (Boosted poP-DT), and the classical boosted decision tree algorithm, i.e., Boosted DT, there is a stepwise improvement shown when comparison proceeds from Boosted DT to Boosted poP-DT and to Boosted AApoP-DT

Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study

Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why incorporation of newly proposed and formulated regularization on feature selections based on correlation studies are necessary to achieve a better prediction or classification. Feature selections based on correlation studies are incorporated into the proposed formulations for the weighting portions of the objective functions for SVM. Proposed cfsw-SVM algorithms are then developed. Proposed formulations on SVM regularization parameter provides synergistic adjustments between prediction or classification accuracy and the level of correlations among features in the SVM implemented. Prediction and/or classification accuracies of cfsw-SVM algorithms are significantly improved

Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea

An improved Boosting algorithm, named as Boosted PARM-DT, was developed by pre-pruning techniques and Associative Rule Mining (ARM) on decision trees built from the clinical datasets** collected for Obstructive Sleep Apnea (OSA). The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed by adopting pre-pruning techniques on tree depth, minimum leaf and/or parent node size observations and maximum number of tree splits, based on Apriori and/or Adaptive Apriori (AA) frameworks, is boosted to achieve better predictive accuracies. The improved algorithms were implemented in OSA dataset and UCI online databases for comparisons. Better predictive accuracies were achieved in all the applied datasets/databases when comparing the classical algorithm, i.e. Boosted DT, with the improved one, i.e. Boosted PARM-DT

T-BACCO SCORE: A predictive scoring tool for tuberculosis (TB) loss to follow-up among TB smokers.

IntroductionLoss to follow-up (LTFU) and smoking during TB treatment are major challenges for TB control programs. Smoking increases the severity and prolongs TB treatment duration, which lead to a higher rate of LTFU. We aim to develop a prognostic scoring tool to predict LTFU among TB patients who smoke to improve successful TB treatment outcomes.Materials and methodsThe development of the prognostic model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between 2013 until 2017, which were obtained from the Malaysian Tuberculosis Information System (MyTB) database. Data were randomly split into development and internal validation cohorts. A simple prognostic score (T-BACCO SCORE) was constructed based on the regression coefficients of predictors in the final logistic model of the development cohort. Estimated missing data was 2.8% from the development cohort and was completely at random. Model discrimination was determined using c-statistics (AUCs), and calibration was based on the Hosmer and Lemeshow goodness of fit test and calibration plot.ResultsThe model highlights several variables with different T-BACCO SCORE values as predictors for LTFU among TB patients who smoke (e.g., age group, ethnicity, locality, nationality, educational level, monthly income level, employment status, TB case category, TB detection methods, X-ray categories, HIV status, and sputum status). The prognostic scores were categorized into three groups that predict the risk for LTFU: low-risk ( 25 points). The model exhibited fair discrimination with a c-statistic of 0.681 (95% CI 0.627-0.710) and good calibration with a nonsignificant chi-square Hosmer‒Lemeshow's goodness of fit test χ2 = 4.893 and accompanying p value of 0.769.ConclusionPredicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The applicability of the tool in clinical settings helps health care professionals manage TB smokers based on their risk scores. Further external validation should be carried out prior to use

Fatal pneumonia following search and rescue operation

Introduction On 26th June 2010, a young man was suspected to have drowned at Lubuk Yu, a natural recreational forest with river and waterfall in Pahang. A rescue team was formed, comprising of 150 members from police offi cers, army offi cers, divers, fi remen and volunteers from a nearby village. His body was recovered fi ve days later. Following this rescue operation, at least 22 people presented with an acute febrile illness. Objective To describe ten patients with melioidotic pneumonia. Results Six were culture-confi rmed for melioidosis only while four were positive for leptospirosis (based on polymerase chain reaction) and melioidosis. All except one had diabetes mellitus. Among these 10 patients, all had fever and cough, 8 had shortness of breath. Surprisingly, symptoms of myalgia, diarrhoea and vomiting were the presenting complaints in some of these patients. Chest radiograph showed bilateral consolidation in 6 and 7 patients died. Conclusions Melioidotic pneumonia in this series was associated with high mortality

Bronchoscopic Features and Morphology of Endobronchial Tuberculosis: A Malaysian Tertiary Hospital Experience

The diagnosis of endobronchial tuberculosis (EBTB) is difficult as it is not well visualized radiologically, and bronchoscopy is not routinely performed for tuberculosis (TB) patients. Bronchoscopic characterization via endoscopic macroscopic features can speed up the diagnosis of EBTB and prompt immediate treatment. In this study, we identified the clinical and bronchoscopic morphology of 17 patients who were diagnosed with EBTB from 2018 to 2020. Demographics, radiological, microbiological and histopathological data were recorded. Endobronchial lesions were classified according to Chung classification. The diagnosis was made based on a histopathological examination (HPE) of endobronchial biopsy, and/or positive ‘Acid-fast bacilli’ (AFB) microscopy/Mycobacterium tuberculosis (MTB) culture on microbiological examination of bronchial alveolar lavage (BAL) and/or positive MTB culture on endobronchial biopsy specimens. Furthermore, EBTB was predominant in young women, age 20 to 49 years old, with a male to female ratio of 1 to 2. Underlying comorbidities were found in 53% of the patients. Cough, fever and weight loss were the main symptoms (23.5%). The indications for bronchoscopy are smear-negative TB and persistent consolidation on chest radiographs. Consolidation was the main radiological finding (53%). An active caseating lesion was the main EBTB endobronchial subtype (53%). The leading HPE finding was caseating granulomatous inflammation (47%). All patients showed good clinical response to TB treatment. Repeated bronchoscopy in six patients post TB treatment showed a complete resolution of the endobronchial lesion. EBTB bronchoscopic characterization is paramount to ensure correct diagnosis, immediate treatment and to prevent complication