Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Patient-controlled iv delivery of opioids for postoperative pain management is a popular alternative to the traditional im route of administration. However, occasional patients receiving opioids in this manner develop severe respiratory depression. The purpose of this paper is to determine the incidence of, and factors contributing to, the development of this complication. To do this, the Office of Medical Quality Improvement retrospectively searched for reports of respiratory depression in a database compiled from the charts of approximately 1600 patients who had received PCA at the University of Alberta Hospitals in 1992. Eight cases of serious respiratory depression were detected. Factors associated with the occurrence of respiratory depression included the concurrent use of a background infusion, advanced age, concomitant administration of sedative/hypnotic medications, and pre-existing sleep apnoea syndrome. No cases were attributed to operator error or equipment malfunction. In conclusion, the risk of respiratory depression with patient-controlled opioid administration is similar to that observed when opioids are delivered by the traditional im or spinal routes. The safe and effective use of patient-controlled analgesia depends upon knowledgeable medical and nursing staff, clearly defined nursing policy and procedures, and frequent patient follow-up.
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PMID:Respiratory depression associated with patient-controlled analgesia: a review of eight cases. 790 32

A Pressure Bed Sensor (PBS) can offer an unobtrusive method for sleep monitoring. This study focuses on the detection of the sleep related breathing disorders using a PBS in comparison to the methods used in a sleep laboratory. A newly developed PCA modeling approach for the eight sensor signals of the PBS is evaluated using the Reduced Respiratory Amplitude Index (RRAI) as a central measure. The method computes the respiration amplitude with the Hilbert transform, and then detects the events based on a 20% amplitude reduction from the baseline signal. A similar calculation was used for the sleep laboratory RIP measurements, and both PBS and RIP were compared against the reference based on the nasal flow signal. In the reference RRAI method, the respiratory-disordered events were obtained using RemLogic respiration analyzer to detect over 50% amplitude reduction in the nasal respiratory flow, but removing the RemLogic standard hypopnea event associations on the oxygen desaturation events and the sleep arousals. The movement artifacts were automatically detected based on the movement activity signal of the PBS. Twenty-five (25) out of 28 patients were finally analysed. On average 87% of a night measurement has been covered by the system. The correlation coefficient was 0.92 between the PBS and the reference RRAI, and the performance of the PBS was similar with the RIP belts. Classifying the severity of the sleep related breathing by dividing RRAI in groups according to the severity criteria, the sensitivity was 92% and the specificity was 70% for the PBS. The results suggest that PBS recording can provide an easy and un-obstructive alternative method for the detection of the sleep disordered breathing and thus has a great promise for the home monitoring.
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PMID:Detection of sleep-disordered breating with Pressure Bed Sensor. 2410 44

In this paper, we extracted hand crafted features from the ECG signals and evaluated the performance of different combination of features for sleep apnoea detection. We calculated the ECG derived respiratory (EDR) signal using three methods (QRS area, amplitude demodulation and fast PCA methods) and then calculated the cardiopulmonary coupling (CPC) spectrum using each EDR method. We then extracted features from the CPC spectrums and the time and frequency representations of the heart rate variability (HRV) and EDR signals Then, we compared the performance results of different combinations of the features used for automated sleep apnoea detection. We also applied a temporal optimisation method by averaging the features of every three adjacent epochs. Two classifiers were used to detect sleep apnoea: the extreme learning machine (ELM), and linear discriminant analysis. The features were evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. The PCA CPC features obtained the highest accuracy of 86.5% and AUC of 0.94 using LDA classifier. The performance results of the combined features (of PCA method) obtained the same results. We conclude that for this study, the CPC features using fast PCA method are our best feature set for sleep apnoea detection.
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PMID:Comparing Different Methods of Hand-crafted HRV, EDR and CPC Features for Sleep Apnoea Detection. 3194 18