Cardiovascular reflexes were studied in 22 healthy women before they were pregnant, once during each pregnancy trimester and after delivery to evaluate the effect of pregnancy on autonomic control of haemodynamics. The Valsalva manoeuvre, the deep breathing test, the orthostatic test and the isometric handgrip test were used to assess changes in autonomic nervous function. We found that pregnancy altered the heart rate response in the Valsalva manoeuvre, the deep breathing test and the orthostatic tests. The deep breathing difference (p = 0.03) and max/min ratio (p = 0.03) decreased in pregnancy, whereas standing heart rate increased (p < 0.0001). Both the systolic and diastolic blood pressure increased after standing up during pregnancy. The circulatory responses to isometric exercise were not affected by pregnancy. The results show that parasympathetic responsiveness is decreased in pregnancy and that it returns to normal after delivery.
Objective: Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). Methods: The value of ML and extensive clinical data was analyzed in a retrospective registry study of 9066 consecutive ACS patients (January 2007 to October 2017). Main outcome was sixmonth mortality. Prediction models were developed using two ML methods, logistic regression and extreme gradient boosting (xgboost). The models were fitted in training set of patients treated in 2007-2014 and 2017 (81%, n ¼ 7344) and validated in a separate validation set of patients treated in 2015-2016 with full GRACE score data available for comparison of model accuracy (19%, n ¼ 1722). Results: Overall, six-month mortality was 7.3% (n ¼ 660). Several variables were found to be significantly associated with six-month mortality by both ML methods. The xgboost scored the best performance: AUC 0.890 (0.864-0.916). The AUC values for logistic regression and GRACE score were 0.867(0.837-0.897) and 0.822 (0.785-0.859), respectively. The AUC value of xgboost was better when compared to logistic regression (p ¼ .012) and GRACE score (p < .00001). Conclusions: The use of extensive phenotype data and novel machine learning improves prediction of mortality in ACS over traditional GRACE score. KEY MESSAGES The collection of extensive cardiovascular phenotype data from electronic health records as well as from data recorded by physicians can be used highly effectively in prediction of mortality after acute coronary syndrome. Supervised machine learning methods such as logistic regression and extreme gradient boosting using extensive phenotype data significantly outperform conventional risk assessment by the current golden standard GRACE score. Integration of electronic health records and the use of supervised machine learning methods can be easily applied in a single centre level to model the risk of mortality.
We developed a system consisting of both wearable and ambient technologies designed to monitor personal wellbeing for several months during daily life. The variables monitored included bodyweight, blood pressure, heart-rate variability and air temperature. Two different user groups were studied: there were 17 working-age subjects participating in a vocational rehabilitation programme and 19 elderly people living in an assisted living facility. The working-age subjects collected data for a total of 1406 days; the average participation period was 83 days (range 43-99). The elderly subjects collected data for a total of 1593 days; the average participation period was 84 days (range 19-107). Usage, technical feasibility and usability of the system were also studied. Some technical and practical problems appeared which we had not expected such as thunder storm damage to equipment in homes and scheduling differences between staff and the subjects. The users gave positive feedback in almost all their responses in a questionnaire. The study suggests that the data-collection rate is likely be 70-90% for typical health monitoring data.
Variability of heart rate (HRV) and transthoracic electric impedance respirogram (TEZ) were examined by spectral analysis in three groups of neonates: healthy term babies (22), healthy preterm babies (21), and preterm babies with respiratory distress syndrome (RDS) (11). Heart rate, TEZ, PtcO2, and PtcCO2 were monitored during quiet sleep on the 1st, 3rd, and 5th day of postnatal life. Autospectra for trend-corrected segments of heart rate and TEZ as well as their cross-spectral density was in less than 0.2 Hz [low frequency (LF)] area (less than 12 cycles/min) in all the neonates. Intergroup comparisons of average band-integrated spectra revealed that the LF spectral density of HRV was greater in the term babies than in the preterm babies on day 3. In the babies with RDS, both LF and high-frequency (HF, greater than 0.2 Hz) were abnormally low throughout the study. In the term infants, the TEZ amplitude spectrum was flat on day 1. On later days, a peak corresponding to the average respiratory rate emerged. In the healthy preterm babies, there was a LF peak in TEZ autospectrum on all days. In the babies with RDS, the peak of ventilator frequency was initially present; finally, the respiratory activity accumulated in the LF area. In the cross-spectra of term babies, there was a LF peak on all days. On day 5, an additional HF peak appeared, representing respiratory sinus arrhythmia. In the healthy preterm babies, only a LF peak was present.(ABSTRACT TRUNCATED AT 250 WORDS)
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