Data obtained in a study in primary care in collaboration with Dr. Ch. Merlo and Prof. B. Martina were recently reevaluated for the communality scores. This method allowed to demonstrate the importance of variables in a correlation analysis. With the outcomes "primary" and "secondary" cardiovascular events, the importance of systolic blood pressure in the correlation and follow-up time is confirmed, whereas the diastolic blood pressure was diminished in the difference of communalities (see above, B below shows the initial correlation (OVERALS); exact same variables were used for the "Comparison of Communalities").
The data (see Seminar) were further analyzed for the putative progress in the severity of disease within the follow-up time. Surprisingly, neural network models predict outcomes in a nearly reliable way and, seemingly, a decrease in the importance of "age" as a variable provides increased accuracy in the prediction. Could this be related to the increase of "birefringence" that may be captured by variables such as the one provided by the known paradoxon of the adiposity that may grow See Neural Network? Whereas some have renamed the obesity paradoxon as "paradigmatic" for variables, others have found that obesity can only substitute for a variable newly determined, i.e. muscle mass that instead provides the causal explanation.
Hypertensive Emergency in Primary Care. 164 patients were triaged in hypertensive emergency. Patients that arrived with blood pressures at or above 180 mmHg/110 mmHg systolic/diastolic at their primary care physician were included in this study (age above 20 years). Categorical regression and other statistical methods led to the illustration of the decision taken by the experienced practitioners. The emergency (E), urgency (U) and no symptoms (S) classification that was carried through was modeled (initial model) and outcome in deciding the respective treatment or hospitalisation was incorporated in a model: The decision for optimal treatment did likely reflect the final resource allocation and suggests economical use (final model). In the completed follow-up of 140 patients (14 months), altered auto-regulation of blood vasculature was evident in the first days with diastolic blood pressure elevation seen as a protective factor in avoidance of the ischemia that would ensue with rapid drug-mediated lowering, problematic systolic blood pressure values, however, did lead to primary cardiovascular events (A). The neural network prediction allowed to classify 94.9 % in total of all patients for the first time, yet suggested that future work should include a minimum of 200 patients in follow-up for a controlled and reliable neural network prediction (B). The SPRINT study and its reanalysis of study data has recently shown the lowered risk of cardiovascular disease with diastolic blood pressures in the range of >55 mmHg to <90 mmHg which conforms to a rule that had been described with previous diseased study groups, but has here been demonstrated for both, patients with a history or without a history of cardiovascular events (Khan et al. Hypertension. 71:840-847 (2018)). The data that we had collected in the follow-up after the hypertensive emergency or urgency in 2009-2011 (according to the familiar definition), had suggested a treatment path of keeping the diastolic blood pressure elevated to 90 mmHg or higher for 6 days following the visit to the primary care doctor consistent with data of the SPRINT study and the pathophysiological assumptions. This advice resulted from a non-linear model of the study data. See Sobieraj et al. (European Heart Journal (2019)) for further discussion concerning the benefit or harm of lowered diastolic blood pressures in a Cox proportional hazard model (surrogate marker instead of causal role). Drug technology for select treatment of diastolic or systolic blood pressure is currently not available. Hypertension data (C) on the elderly had not been widely catalogued. The trend in health expenditures (including private, insurance/gov. sources) is compared with blood pressure trends as determined from WHO measurement campaigns and data (D).