Diagnosing hypertension in primary care: a retrospective cohort study to investigate the importance of night-time blood pressure assessment.

BACKGROUND: Ambulatory blood-pressure monitoring (ABPM) has become less frequent in primary care since the COVID-19 pandemic, with home blood-pressure monitoring (HBPM) often the preferred alternative; however, HBPM cannot measure night-time blood pressure (BP), and patients whose night-time BP does not dip, or rises (reverse dipping), have poorer cardiovascular outcomes. AIM: To investigate the importance of measuring night-time BP when assessing individuals for hypertension. DESIGN AND SETTING: Retrospective cohort study of two patient populations - namely, hospital patients admitted to four UK acute hospitals located in Oxfordshire, and participants of the BP in different ethnic groups (BP-Eth) study, who were recruited from 28 UK general practices in the West Midlands. METHOD: Using BP data collected for the two cohorts, three systolic BP phenotypes (dipper, non-dipper, and reverse dipper) were studied. RESULTS: Among the hospital cohort, 48.9% (n = 10 610/21 716) patients were 'reverse dippers', with an average day-night systolic BP difference of +8.0 mmHg. Among the community (BP-Eth) cohort, 10.8% (n = 63/585) of patients were reverse dippers, with an average day-night systolic BP difference of +8.5 mmHg. Non-dipper and reverse-dipper phenotypes both had lower daytime systolic BP and higher night-time systolic BP than the dipper phenotype. Average daytime systolic BP was lowest in the reverse-dipping phenotype (this was 6.5 mmHg and 6.8 mmHg lower than for the dipper phenotype in the hospital and community cohorts, respectively), thereby placing them at risk of undiagnosed, or masked, hypertension. CONCLUSION: Not measuring night-time BP puts all groups, other than dippers, at risk of failure to identify hypertension. As a result of this study, it is recommended that GPs should offer ABPM to all patients aged ≥60 years as a minimum when assessing for hypertension.

Non-Contact Assessment of Peripheral Artery Haemodynamics Using Infrared Video Thermography.

Skin temperature has long been used as a natural indicator of vascular diseases in the extremities. Considerable correlation between oscillations in skin surface temperature and oscillations of skin blood flow has previously been demonstrated. We hypothesised that the impairment of blood flow in stenotic (subcutaneous) peripheral arteries would influence cutaneous temperature such that, by measuring gradients in the temperature distribution over skin surfaces, one may be able to diagnose or quantify the progression of vascular conditions in whose pathogenesis a reduction in subcutaneous blood perfusion plays a critical role (e.g. peripheral artery disease). As proof of principle, this study investigates the local changes in the skin temperature of healthy humans (15 male, [Formula: see text] years old, BMI [Formula: see text] kg/m 2) undergoing two physical challenges designed to vary their haemodynamic status. Skin temperature was measured in four central regions (forehead, neck, chest, and left shoulder) and four peripheral regions (left upper arm, forearm, wrist, and hand) using an infrared thermal camera. We compare inter-region patterns. Median temperature over the peripheral regions decreased from baseline after both challenges (maximum decrease: [Formula: see text] °C at 60 s after exercise; [Formula: see text] and [Formula: see text] °C at 180 s of cold-water immersion; [Formula: see text]). Median temperature over the central regions showed no significant changes. Our results show that the non-contact measurement of perfusion-related changes in peripheral temperature from infrared video data is feasible. Further research will be directed towards the thermographic study of patients with symptomatic peripheral vascular disease.

The dimensional reduction method for identification of parameters that trade-off due to similar model roles.

Parameter identification is an important and widely used process across the field of biomedical engineering. However, it is susceptible to a number of potential difficulties, such as parameter trade-off, causing premature convergence at non-optimal parameter values. The proposed Dimensional Reduction Method (DRM) addresses this issue by iteratively reducing the dimension of hyperplanes where trade off occurs, and running subsequent identification processes within these hyperplanes. The DRM was validated using clinical data to optimize 4 parameters of the widely used Bergman Minimal Model of glucose and insulin kinetics, as well as in-silico data to optimize 5 parameters of the Pulmonary Recruitment (PR) Model. Results were compared with the popular Levenberg-Marquardt (LMQ) Algorithm using a Monte-Carlo methodology, with both methods afforded equivalent computational resources. The DRM converged to a lower or equal residual value in all tests run using the Bergman Minimal Model and actual patient data. For the PR model, the DRM attained significantly lower overall median parameter error values and lower residuals in the vast majority of tests. This shows the DRM has potential to provide better resolution of optimum parameter values for the variety of biomedical models in which significant levels of parameter trade-off occur.

An Assessment of the relationship between dicrotic notch timing and cardiac preload.

Cardiovascular disease (CVD) patient outcomes can be improved by extracting and synthesizing as much useful information as possible from a limited number of available measurements. An important metric in assessing the pathological state of CVD patients is cardiac preload. Left ventricular preload can be estimated through the surrogate measurement of left ventricular end diastolic volume (LVEDV). However, cardiac volumes are difficult to measure, clinically. This study develops a 3 parameter model relating the location of the dicrotic notch in the aortic waveform to LVEDV. This model was constructed using data from porcine experiments (N = 5 pietrain pigs, weights 20-28kg). The median difference between the observed LVEDV and modelled LVEDV was 5.4%, with a 100% range of 3.0% to 15.1%. Model parameters varied between individuals as well as contractile states. The median correlation was ρ = 0.77, with a minimum of 0.58 and maximum of 0.95. This model could be used to estimate prseload from the commonly measured aortic pressure waveform.

Use of the DISST model to estimate the HOMA and Matsuda indexes using only a basal insulin assay.

It is hypothesized that early detection of reduced insulin sensitivity (SI) could prompt intervention that may reduce the considerable financial strain type 2 diabetes mellitus (T2DM) places on global health care. Reduction of the cost of already inexpensive SI metrics such as the Matsuda and HOMA indexes would enable more widespread, economically feasible use of these metrics for screening. The goal of this research was to determine a means of reducing the number of insulin samples and therefore the cost required to provide an accurate Matsuda Index value. The Dynamic Insulin Sensitivity and Secretion Test (DISST) model was used with the glucose and basal insulin measurements from an Oral Glucose Tolerance Test (OGTT) to predict patient insulin responses. The insulin response to the OGTT was determined via population based regression analysis that incorporated the 60-minute glucose and basal insulin values. The proposed method derived accurate and precise Matsuda Indices as compared to the fully sampled Matsuda (R = .95) using only the basal assay insulin-level data and 4 glucose measurements. Using a model employing the basal insulin also allows for determination of the 1-day HOMA value. The DISST model was successfully modified to allow for the accurate prediction an individual's insulin response to the OGTT. In turn, this enabled highly accurate and precise estimation of a Matsuda Index using only the glucose and basal insulin assays. As insulin assays account for the majority of the cost of the Matsuda Index, this model offers a significant reduction in assay cost.