An epidemiological study determining blood pressure in a Portuguese cohort: the Guimaraes/Vizela study

Surveying the evolution of blood pressure (BP) levels and hypertension (HTN) prevalence is important. A stringent strategy was utilized in a population cohort study. The BP was measured at two visits at least 3 months apart, and the results were analyzed using the following two methods: the Surveillance method (three BP measurements were performed in one visit, and the results were compared with those published previously for the identical method) and the Clinical method (three measurements per visit for two visits, and the concordant results in both visits were used to determine the BP classification). A total of 2542 subjects completed the evaluation. Using the Clinical method, an average systolic/diastolic BP value of 129.8/76.8?mm?Hg was obtained, and the prevalence of HTN was 31.6%. Of the hypertensive patients, 74.3% were aware of his/her condition; 69.1% were treated and 40.8% of those treated had adequate BP control. A total of 24.7% of subjects changed his/her BP classification between visits, and 13.7% misreported HTN. Using the Surveillance method, we determined that the average global SBP has been maintained, with HTN prevalence increasing in this region, drifting from reported trends nationally and worldwide. There has been improvement in the proportion of treated and controlled subjects; however, the Surveillance method overestimated the HTN prevalence and underestimated the proportion of treated and controlled subjects. The BP levels were higher than observed worldwide in high-cardiovascular (CV) risk countries as well as higher than the minimum risk exposure level for developing CV disease.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.