Timing of tracheostomy as a determinant of weaning success in critically ill patients: a retrospective study

INTRODUCTION: Tracheostomy is frequently performed in critically ill patients for prolonged intubation. However, the optimal timing of tracheostomy, and its impact on weaning from mechanical ventilation and outcomes in critically ill patients who require mechanical ventilation remain controversial. METHODS: The medical records of patients who underwent tracheostomy in the medical intensive care unit (ICU) of a tertiary medical centre from July 1998 to June 2001 were reviewed. Clinical characteristics, length of stay in the ICU, rates of post-tracheostomy pneumonia, weaning from mechanical ventilation and mortality rates were analyzed. RESULTS: A total of 163 patients (93 men and 70 women) were included; their mean age was 70 years. Patients were classified into two groups: successful weaning (n = 78) and failure to wean (n = 85). Shorter intubation periods (P = 0.02), length of ICU stay (P = 0.001) and post-tracheostomy ICU stay (P = 0.005) were noted in patients in the successful weaning group. Patients who underwent tracheostomy more than 3 weeks after intubation had higher ICU mortality rates and rates of weaning failure. The length of intubation correlated with the length of ICU stay in the successful weaning group (r = 0.70; P < 0.001). Multivariate analysis revealed that tracheostomy after 3 weeks of intubation, poor oxygenation before tracheostomy (arterial oxygen tension/fractional inspired oxygen ratio <250) and occurrence of nosocomial pneumonia after tracheostomy were independent predictors of weaning failure. CONCLUSION: The study suggests that tracheostomy after 21 days of intubation is associated with a higher rate of failure to wean from mechanical ventilation, longer ICU stay and higher ICU mortality

Primary Liver Abscess Caused by One Clone of Klebsiella pneumoniae with Two Colonial Morphotypes and Resistotypes

Two diabetic patients with primary liver abscess, who initially responded unsatisfactorily to intravenous ceftriaxone or cefoxitin treatment and had abscess drainage, were found to be infected with a single clone of Klebsiella pneumoniae with two different colonial morphotypes and resistotypes. Primary liver abscess caused by second-generation cephalosporin-resistant K. pneumoniae strains may be an emerging problem in Taiwan

Design of microarray probes for virus identification and detection of emerging viruses at the genus level

BACKGROUND: Most virus detection methods are geared towards the detection of specific single viruses or just a few known targets, and lack the capability to uncover the novel viruses that cause emerging viral infections. To address this issue, we developed a computational method that identifies the conserved viral sequences at the genus level for all viral genomes available in GenBank, and established a virus probe library. The virus probes are used not only to identify known viruses but also for discerning the genera of emerging or uncharacterized ones. RESULTS: Using the microarray approach, the identity of the virus in a test sample is determined by the signals of both genus and species-specific probes. The genera of emerging and uncharacterized viruses are determined based on hybridization of the viral sequences to the conserved probes for the existing viral genera. A detection and classification procedure to determine the identity of a virus directly from detection signals results in the rapid identification of the virus. CONCLUSION: We have demonstrated the validity and feasibility of the above strategy with a small number of viral samples. The probe design algorithm can be applied to any publicly available viral sequence database. The strategy of using separate genus and species probe sets enables the use of a straightforward virus identity calculation directly based on the hybridization signals. Our virus identification strategy has great potential in the diagnosis of viral infections. The virus genus and specific probe database and the associated summary tables are available a

Outcome and prognostic factors in critically ill patients with systemic lupus erythematosus: a retrospective study

INTRODUCTION: Systemic lupus erythematosus (SLE) is an archetypal autoimmune disease, involving multiple organ systems with varying course and prognosis. However, there is a paucity of clinical data regarding prognostic factors in SLE patients admitted to the intensive care unit (ICU). METHODS: From January 1992 to December 2000, all patients admitted to the ICU with a diagnosis of SLE were included. Patients were excluded if the diagnosis of SLE was established at or after ICU admission. A multivariate logistic regression model was applied using Acute Physiology and Chronic Health Evaluation II scores and variables that were at least moderately associated (P < 0.2) with survival in the univariate analysis. RESULTS: A total of 51 patients meeting the criteria were included. The mortality rate was 47%. The most common cause of admission was pneumonia with acute respiratory distress syndrome. Multivariate logistic regression analysis showed that intracranial haemorrhage occurring while the patient was in the ICU (relative risk = 18.68), complicating gastrointestinal bleeding (relative risk = 6.97) and concurrent septic shock (relative risk = 77.06) were associated with greater risk of dying, whereas causes of ICU admission and Acute Physiology and Chronic Health Evaluation II score were not significantly associated with death. CONCLUSION: The mortality rate in critically ill SLE patients was high. Gastrointestinal bleeding, intracranial haemorrhage and septic shock were significant prognostic factors in SLE patients admitted to the ICU

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Trends and predictors of changes in pulmonary function after treatment for pulmonary tuberculosis

OBJECTIVES: The present study aimed to investigate the trends in changes in pulmonary function and the risk factors for pulmonary function deterioration in patients with pulmonary tuberculosis after completing treatment. INTRODUCTION: Patients usually have pulmonary function abnormalities after completing treatment for pulmonary tuberculosis. The time course for changes in pulmonary function and the risk factors for deterioration have not been well studied. METHODS: A total of 115 patients with 162 pulmonary function results were analyzed. We retrieved demographic and clinical data, radiographic scores, bacteriological data, and pulmonary function data. A generalized additive model with a locally weighted scatterplot smoothing technique was used to evaluate the trends in changes in pulmonary function. A generalized estimating equation model was used to determine the risk factors associated with deterioration of pulmonary function. RESULTS: The median interval between the end of anti-tuberculosis treatment and the pulmonary function test was 16 months (range: 0 to 112 months). The nadir of pulmonary function occurred approximately 18 months after the completion of the treatment. The risk factors associated with pulmonary function deterioration included smear-positive disease, extensive pulmonary involvement prior to anti-tuberculosis treatment, prolonged anti-tuberculosis treatment, and reduced radiographic improvement after treatment. CONCLUSIONS: After the completion of anti-tuberculosis TB treatment, several risk factors predicted pulmonary function deterioration. For patients with significant respiratory symptoms and multiple risk factors, the pulmonary function test should be followed up to monitor the progression of functional impairment, especially within the first 18 months after the completion of anti-tuberculosis treatment