From March 1978, 196 Carpentier-Edwards standard bioprostheses (stCE) were implanted in 194 patients. There were 154 isolated mitral valve replacements (MVR) and 42 aortic plus mitral valve replacements (AVR/MVR) with a mean follow-up of 7.05 (range 0-15.2) years and 7.15 (range 0-13.8) years, respectively. Freedom from structural valve failure at 10 years was 70.8% +/- 4.9% (MVR) and 59.6% +/- 11.1% (AVR/MVR). The incidence of structural valve failure increased sharply after 7 years. Freedom from thromboembolism was 83.0% +/- 3.8% (MVR) and 89.0 +/- 6.0% (AVR/MVR). Thromboembolic events were related to the presence of atrial fibrillation in patients not receiving anticoagulation. Anticoagulant-related haemorrhage was rare. Freedom from mitral valve prosthetic endocarditis at 10 years was 90.9% +/- 3.1% (MVR) and 86.1% +/- 8.4% (AVR/MVR). Prosthetic valve endocarditis was associated with more than 60% mortality. The probability of event-free survival at 10 years follow-up was 43.6% +/- 4.6% (MVR) and 33.3% +/- 8.6% (AVR/MVR). The performance of the stCE in the mitral position shows a low rate of thromboembolic events and anticoagulant-related haemorrhage, but the long-term performance of the prosthesis is unsatisfactory due to a high rate of structural valve failure. This confirms earlier reports.
An automatic method of detection of transients, step changes and slope changes in time series is developed based on a modified form of the EWMA forecasting algorithm. A backward CUSUM scheme is used to detect changes in the behaviour of the series and criteria are developed to assess automatically whether the change is a transient, step change, slope change or a false alarm. The forecasting algorithm is then automatically amended in the light of the determined change.
The performance of the resulting system is compared with straightforward Box–Jenkins modelling and forecasting and also with the multistate Bayesian forecasting approach.
For a selected group of 17 patients following cardiac surgery, 33 discrete elements of nursing workload have been defined. It was possible to identify 3 broad types of activity undertaken by the bedside nurse; technical nursing care (Type 1), intermittent nursing care (Type 2), and learning activities (Type 3). The latter is the balance of the nurses time which is largely concerned with observation and liaison with other staff. The 17 patients comprised 4 groups of pathologies, 2 each representative of the adult and paediatric workload. The primary bedside nurse performs virtually all of the Type 1 activities, recruiting the assistance of a secondary nurse principally to change the patients' posture (a Type 2 activity). The only practically significant period of time spent by the secondary nurse is in clinical discussion with the bedside nurse, largely at change of shift. Statistically significant differences in bedside nursing activities between age groups can be explained by the relative physical size of the patients and equipment. However the nurse maintains a higher level of awareness with shorter periods of inattention when nursing children. A greater number of significant differences between pathologies were found within the paediatric group of patients; this may indicate that a broader spectrum of such differences is to be found in this age group. An estimate was made of the impact of computer technology on the nurse's bedside workload.
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