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WOS: 000468732700018, PubMed ID: 31054483, Background and aims: One of the objectives of the ESC-EORP EUROASPIRE V survey is to determine how well European guidelines on the management of dyslipidaemias are implemented in coronary patients. Methods: Standardized methods were used by trained technicians to collect information on 7824 patients from 130 centers in 27 countries, from the medical records and at a visit at least 6 months after hospitalization for a coronary event. All lipid measurements were performed in one central laboratory. Patients were divided into three groups: on high-intensity LDL-C-lowering-drug therapy (LLT), on low or moderate-intensity LLT and on no LLT. Results: At the time of the visit, almost half of the patients were on a high-intensity LLT. Between hospital discharge and the visit, LLT had been reduced in intensity or interrupted in 20.8% of the patients and had been started or increased in intensity in 11.7%. In those who had interrupted LLT or had reduced the intensity, intolerance to LLT and the advice of their physician were reported as the reason why in 15.8 and 36.8% of the cases, respectively. LDL-C control was better in those on a high-intensity LLT compared to those on low or moderate intensity LLT. LDL-C control was better in men than women and in patients with self-reported diabetes. Conclusions: The results of the EUROASPIRE V survey show that most coronary patients have a less than optimal management of LDL-C. More professional strategies are needed, aiming at lifestyle changes and LLT adapted to the need of the individual patient., ESC - EORP; AmgenAmgen; Eli LillyEli Lilly; PfizerPfizer; SanofiSanofi-Aventis; Ferrer; Novo NordiskNovo Nordisk, The EUROASPIRE V survey was carried out under the auspices of the ESC - EORP. Since the start of EORP, the following companies have supported the programme: Amgen, Eli Lilly, Pfizer, Sanofi, Ferrer and Novo Nordisk. The sponsors of the EUROASPIRE surveys had no role in the design, data collection, data analysis, data interpretation, decision to publish, or writing the manuscript.

There may be a relationship between sleep and pain in patients with chronic back pain. We collected day-time pain and nighttime activity data from 18 patients diagnosed with chronic back pain. The patients were followed for 6 days and 5 nights. Pain levels were collected every 90 min between 0800 hours and 2,200 hours using a computerized electronic diary. Activity levels were collected using a wrist accelerometer (Actiwatch AW-64). The Actiwatch sampled activity counts every 1 min. Patients were asked to wear the Actiwatch on their non-dominant arm. The pain level measurements were interpolated using cubic splines. A mean pain level was calculated for each period 0800 hours to 2,200 hours as well as for the 6-day period. The difference between the mean pain levels for the 6-day period and each 0800 hours to 2,200 hours period was calculated for each patient. Nighttime activity data were analyzed using the Actiwatch Sleep Analysis software. Correlations were calculated between the Actiwatch Sleep Analysis variables and the mean pain level differences for each patient and period. The correlation analysis was performed with SPSS 7.5. We were unable to show any significant relationships.A different approach to analyze the data was used. A Self-Organizing Map (SOM) Neural Network was trained using the original nighttime activity level time series from 10 randomly selected patients. Recall was then performed on all the activity level data. Correlations were calculated between the pain level variance for the 6-day period for each patient and the corresponding difference in the SOM output coordinates. The correlation was found to be r = 0.73, p0.01). We conclude that daytime pain levels are not directly correlated with sleep in the following night and that sleep is not directly correlated with daytime pain levels on the following day in this group of patients. There appears to be a correlation between the difference in nighttime activity levels and patterns and the daytime pain variance. Patients who experience large fluctuations in daytime pain levels also show a higher variability in their nighttime activity levels and patterns. Even though we were unable to show a direct relationship between daytime pain and sleep, it may be reasonable to assume that better pain control resulting in less daytime pain fluctuations can provide more stable nighttime activity levels and patterns in this limited group of patients. By using a neural network model, we were able to extract information from the nighttime activity levels even though a traditional statistical analysis was unsuccessful.

Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. In this paper, we have explored new techniques of patient assessment that may prospectively identify this minority of patients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over 4 month's time. Patients monitored their pain and activity levels continuously over the first 3 weeks. Pain and functional status were assessed at baseline and at 3 weeks following enrollment. Follow-up assessment of functional status and progress were performed at 2 and 4 months. The pain and activity levels were categorized using a self-organizing-map neural network. A back-propagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r e 0.96, p < 0.01) and mental health (r e0.80, p < 0.01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain.

Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.

We studied the temporal relationship between pain and activity in patients with acute or chronic low back pain. We studied 15 patients with acute low back pain and 15 patients with chronic low back pain over 3 wk. The activity levels were collected automatically using a wrist accelerometer and were sampled every minute. The pain levels were recorded at least every 90 min using a pocket-sized electronic diary. The time series from each patient were then analyzed using the cross-correlation function at various time offsets. We found that during the first 7 days of acute low back pain, there was a significant (P < 0.01) degree of cross-correlation between activity and pain. On average, pain followed activity by approximately 30 min. As these patients improved and reported less pain, the relationship between activity and pain disappeared. There was no such relationship at any point among the patients with chronic low back pain.