Background The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Objective The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. Methods This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. Results The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. Conclusions Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.
Nosocomial infections caused by Acinetobacter baumannii have increased in recent years. Isolates of multidrug-resistant A. baumannii (MDRAB) have been recovered in Taiwan since 1999. The characteristics of 55 patients with MDRAB bacteraemia infections occurring between January 2003 and February 2005 were analysed retrospectively. The overall 30-day mortality rate was 49%. The portal of entry was identified in 80% of patients, with the respiratory tract being implicated most frequently. Among the different antimicrobial regimens prescribed, the combination of a carbapenem and ampicillin-sulbactam was associated with a better outcome than the combination of a carbapenem and amikacin, or a carbapenem alone.
, 15 isolates of pandrug-resistant unidentified Acinetobacter species were recovered from seven patients treated on different wards or intensive care units. Both 16S-23S rRNA intergenic spacer PCR-restriction fragment length polymorphism profiles and sequence analysis of these isolates identified them as Acinetobacter baumannii. This pandrug-resistant A. baumannii strain with an unusual phenotype could persist in humans for long periods and was widely disseminated throughout the hospital.
e Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) (Bruker Biotyper) was able to accurately identify 98.6% (142/144) of Acinetobacter baumannii isolates, 72.4% (63/87) of A. nosocomialis isolates, and 97.6% (41/42) of A. pittii isolates. All Acinetobacter junii, A. ursingii, A. johnsonii, and A. radioresistens isolates (n ؍ 28) could also be identified correctly by Bruker Biotyper. Isolates of Acinetobacter species causing human infections predominantly belong to Acinetobacter calcoaceticus (genospecies 1), A. baumannii (genospecies 2), A. pittii (genospecies 3), and A. nosocomialis (genospecies 13TU) (1-4). Because these isolates are phenotypically similar and difficult to distinguish using traditional microbiological and biochemical tests, they are frequently reported as A. calcoaceticus-A. baumannii complex (ACB complex) (1-4). Although several commercially available matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) systems are rapidly emerging novel technology widely used in clinical microbiology laboratories for rapid identification of commonly encountered bacteria and fungi (5-8), few studies have compared the accuracy of different MALDI-TOF MS systems in identifying Acinetobacter species to the genospecies level (9-12).A total of 286 blood isolates of ACB complex and 39 isolates of non-ACB complex that had been recovered from patients treated at the National Taiwan University Hospital from 2010 to 2012 were obtained. Conventional biochemical identification methods as well as the Phoenix bacterial identification system (NMIC/ ID-72 cards; Becton, Dickinson Diagnostic Instrument Systems, Sparks, MD) and Vitek 2 (GN cards; bioMérieux, Marcy l'Etoile, France) were used for the identification of the isolates (4). We sequenced the 16S-23S rRNA gene intergenic spacer (ITS) region and a 350-bp highly variable zone of the rpoB gene to identify the isolates to the genospecies level (2, 4). The sequences obtained were compared with published sequences in the GenBank database using the BLASTN algorithm (http://www.ncbi.nlm.nih.gov /blast).Bacteria were prepared for analysis by the MALDI Bruker Biotyper system as previously described (8, 10). Identification scores of Ն2.000 indicated species-level identification, scores of 1.700 to 1.999 indicated genus-level identification, and scores of Ͻ1.700 indicated no reliable identification (8, 10). Clustering analysis of
QOL assessments from family caregivers agreed more closely with patients than did those from nurses using EQ-5D evaluations for patients with clear cognition, but either proxy was acceptable for rating PMV patients with poor cognition.
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