Rupert Payne

Arterial Stiffness and Hypertension: Emerging Concepts

Arterial stiffness is increasingly recognized as an important prognostic index and potential therapeutic target in patients with hypertension. It is closely linked to, but by no means synonymous with, raised blood pressure, and its physiopathology is still not fully understood. Aortic stiffness and arterial pulse wave reflections are key determinants of elevated central systolic pressure and are associated with adverse cardiovascular outcomes, independent of blood pressure. Indeed, the 2003 European Society of Hypertension guidelines on the management of hypertension acknowledge the potential role of arterial stiffness measurement in clinical management1 and have prompted the publication of a consensus document on the measurement of central blood pressure and hemodynamics.2 A detailed expert consensus document has also been published on the methodologic and clinical issues around arterial stiffness.3 Broader implementation of these techniques into routine care seems inevitable. In this review, we have examined recent research in this field published in Hypertension , focusing on mechanistic work, methods for measuring stiffness, important clinical associations, and effects of treatment. ### Mechanisms and Causes of Arterial Stiffness Hypertension and arterial stiffness are closely associated with age.4 Degeneration of compliant elastin fibers, and deposition of stiffer collagen, is considered a key cause of age-related arterial stiffening. Moreover, blood pressure plays a significant role in determining vessel wall structure, with remodeling occurring to compensate for changes in wall stress. One potential mechanism is through matrix metalloproteinases, which modulate extracellular matrix proteins. When angiotensin II is given to mice, matrix metalloproteinase 9 activity is induced, resulting in enhanced collagen degradation. This improves the intrinsic distensibility of elastic arteries and, thus, blunts any blood pressure rise.5 Impairment of this compensatory mechanism may, therefore, contribute to increased stiffness. The organization of elastic fibers is also important. Inhibition of the vascular adhesion protein semicarbazide-sensitive amine oxidase in a rat model results in …

The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study

Background: Multimorbidity, the presence of more than 1 long-term disorder, is associated with increased use of health services, but unplanned admissions to hospital may often be undesirable. Furthermore, socioeconomic deprivation and mental health comorbidity may lead to additional unplanned admissions. We examined the association between unplanned admission to hospital and physical multimorbidity, mental health and socioeconomic deprivation. Methods: We conducted a retrospective cohort study using data from 180 815 patients aged 20 years and older who were registered with 40 general practices in Scotland. Details of 32 physical and 8 mental health morbidities were extracted from the patients’ electronic health records (as of Apr. 1, 2006) and linked to hospital admission data. We then recorded the occurrence of unplanned or potentially preventable unplanned acute (nonpsychiatric) admissions to hospital in the subsequent 12 months. We used logistic regression models, adjusting for age and sex, to determine associations between unplanned or potentially preventable unplanned admissions to hospital and physical multimorbidity, mental health and socioeconomic deprivation. Results: We identified 10 828 (6.0%) patients who had at least 1 unplanned admission to hospital and 2037 (1.1%) patients who had at least 1 potentially preventable unplanned admission to hospital. Both unplanned and potentially preventable unplanned admissions were independently associated with increasing physical multimorbidity (for ≥ 4 v. 0 conditions, odds ratio [OR] 5.87 [95% confidence interval (CI) 5.45–6.32] for unplanned admissions, OR 14.38 [95% CI 11.87–17.43] for potentially preventable unplanned admissions), mental health conditions (for ≥ 1 v. 0 conditions, OR 2.01 [95% CI 1.92–2.09] for unplanned admissions, OR 1.80 [95% CI 1.64–1.97] for potentially preventable unplanned admissions) and socioeconomic deprivation (for most v. least deprived quintile, OR 1.56 [95% CI 1.43–1.70] for unplanned admissions, OR 1.98 [95% CI 1.63–2.41] for potentially preventable unplanned admissions). Interpretation: Physical multimorbidity was strongly associated with unplanned admission to hospital, including admissions that were potentially preventable. The risk of admission to hospital was exacerbated by the coexistence of mental health conditions and socioeconomic deprivation.

Is polypharmacy always hazardous?: A retrospective cohort analysis using linked electronic health records from primary and secondary care

AIMS Prescribing multiple medications is associated with various adverse outcomes, and polypharmacy is commonly considered suggestive of poor prescribing. Polypharmacy might thus be associated with unplanned hospitalization. We sought to test this assumption. METHODS Scottish primary care data for 180 815 adults with long-term clinical conditions and numbers of regular medications were linked to national hospital admissions data for the following year. Using logistic regression (age, gender and deprivation adjusted), we modelled the association of prescribing with unplanned admission for patients with different numbers of long-term conditions. RESULTS Admissions were more common in patients on multiple medications, but admission risk varied with the number of conditions. For patients with one condition, the odds ratio for unplanned admission for four to six medications was 1.25 (95% confidence interval 1.11-1.42) vs. one to three medications, and 3.42 (95% confidence interval 2.72-4.28) for ≥10 medications vs. one to three medications. However, this effect was greatly reduced for patients with multiple conditions; amongst patients with six or more conditions, those on four to six medications were no more likely to have unplanned admissions than those taking one to three medications (odds ratio 1.00; 95% confidence interval 0.88-1.14), and those taking ≥10 medications had a modestly increased risk of admission (odds ratio 1.50; 95% confidence interval 1.31-1.71). CONCLUSIONS Unplanned hospitalization is strongly associated with the number of regular medications. However, the effect is reduced in patients with multiple conditions, in whom only the most extreme levels of polypharmacy are associated with increased admissions. Assumptions that polypharmacy is always hazardous and represents poor care should be tempered by clinical assessment of the conditions for which those drugs are being prescribed.

Is polypharmacy always hazardous

AIMS Prescribing multiple medications is associated with various adverse outcomes, and polypharmacy is commonly considered suggestive of poor prescribing. Polypharmacy might thus be associated with unplanned hospitalization. We sought to test this assumption. METHODS Scottish primary care data for 180 815 adults with long-term clinical conditions and numbers of regular medications were linked to national hospital admissions data for the following year. Using logistic regression (age, gender and deprivation adjusted), we modelled the association of prescribing with unplanned admission for patients with different numbers of long-term conditions. RESULTS Admissions were more common in patients on multiple medications, but admission risk varied with the number of conditions. For patients with one condition, the odds ratio for unplanned admission for four to six medications was 1.25 (95% confidence interval 1.11-1.42) vs. one to three medications, and 3.42 (95% confidence interval 2.72-4.28) for ≥10 medications vs. one to three medications. However, this effect was greatly reduced for patients with multiple conditions; amongst patients with six or more conditions, those on four to six medications were no more likely to have unplanned admissions than those taking one to three medications (odds ratio 1.00; 95% confidence interval 0.88-1.14), and those taking ≥10 medications had a modestly increased risk of admission (odds ratio 1.50; 95% confidence interval 1.31-1.71). CONCLUSIONS Unplanned hospitalization is strongly associated with the number of regular medications. However, the effect is reduced in patients with multiple conditions, in whom only the most extreme levels of polypharmacy are associated with increased admissions. Assumptions that polypharmacy is always hazardous and represents poor care should be tempered by clinical assessment of the conditions for which those drugs are being prescribed.

Polypharmacy: one of the greatest prescribing challenges in general practice

Pharmaceutical interventions are addressed by a number of papers in this months issue of the journal. Drug therapy is the major intervention offered by the NHS to enhance and sustain the health of the population. Medicines cost the NHS in excess of £10 billion annually, with the total cost and number of prescriptions steadily rising; the majority of prescribing occurs in general practice. Given this investment, together with the shift of chronic disease management to primary care, GPs need to ensure their prescribing is effective in maximising health gains while minimising risks to patients. Polypharmacy is arguably one of the most pressing prescribing issues. There is no formally accepted definition, but it is usually considered as concurrent prescribing of at least four or five drugs. In a German study, over a quarter of older patients in primary care were found to be on five or more medicines,1 and a recent Italian study found that over 35% of older patients were exposed to polypharmacy.2 Data from the Swedish Prescribed Drug Register also show a prevalence of over 5% of patients in their 40s, and around 12% of those in …

ACE inhibitor and angiotensin receptor-II antagonist prescribing and hospital admissions with acute kidney injury: a longitudinal ecological study.

Background ACE Inhibitors (ACE-I) and Angiotensin-Receptor Antagonists (ARAs) are commonly prescribed but can cause acute kidney injury (AKI) during intercurrent illness. Rates of hospitalization with AKI are increasing. We aimed to determine whether hospital AKI admission rates are associated with increased ACE-I/ARA prescribing. Methods and Findings English NHS prescribing data for ACE-I/ARA prescriptions were matched at the level of the general practice to numbers of hospital admissions with a primary diagnosis of AKI. Numbers of prescriptions were weighted for the demographic characteristics of general practices by expressing prescribing as rates where the denominator is Age, Sex, and Temporary Resident Originated Prescribing Units (ASTRO-PUs). We performed a mixed-effect Poisson regression to model the number of admissions for AKI occurring in each practice for each of 4 years from 1/4/2007. From 2007/8-2010/11, crude AKI admission rates increased from 0.38 to 0.57 per 1000 patients (51.6% increase), and national annual ACE-I/ARA prescribing rates increased by 0.032 from 0.202 to 0.234 (15.8% increase). There was strong evidence (p<0.001) that increases in practice-level prescribing of ACE-I/ARA over the study period were associated with an increase in AKI admission rates. The increase in prescribing seen in a typical practice corresponded to an increase in admissions of approximately 5.1% (rate ratio = 1.051 for a 0.03 per ASTRO-PU increase in annual prescribing rate, 95%CI 1.047-1.055). Using the regression model we predict that 1,636 (95%CI 1,540-1,780) AKI admissions would have been avoided if prescribing rates were at the 2007/8 level, equivalent to 14.8% of the total increase in AKI admissions. Conclusion In this ecological analysis, up to 15% of the increase in AKI admissions in England over a 4-year time period is potentially attributable to increased prescribing of ACE-I and ARAs. However, these findings are limited by the lack of patient level data such as indication for prescribing and patient characteristics.

Metabolic parameters associated with arterial stiffness in older adults with Type 2 diabetes: the Edinburgh Type 2 Diabetes Study

Objective: Increased arterial stiffness, as measured by pulse wave velocity (PWV), is associated with increased cardiovascular risk in the general population. Few studies have examined factors associated with increased PWV in people with Type 2 diabetes. The aim of this study was to determine whether there was a link between PWV and clinical variables associated with central obesity, in men and women with Type 2 diabetes. Research designs and methods: Eight hundred and sixty individuals [mean age (±SD) 69 (±4) years] from the Edinburgh Type 2 Diabetes Study, underwent applanation tonometry using a high-fidelity micromanometer. PWV was measured by sequentially recording electrocardiogram-gated carotid and femoral artery waveforms. Results: Waist circumference (&bgr; = 0.10, P < 0.01) and waist : hip ratio (&bgr; = 0.10, P < 0.01) were independently associated with PWV, but not with BMI. In a stepwise multiple regression model, mean arterial pressure (&bgr; = 0.26, P < 0.01) and age (&bgr; = 0.23, P < 0.01) were strongly associated with PWV. The associations between the central obesity measures and PWV were independent of age, sex, duration of diabetes and metabolic factors associated with central obesity. Duration of diabetes (&bgr; = 0.10, P < 0.01) and glycated hemoglobin (&bgr; = 0.09, P < 0.01) were also found to be independent predictors of arterial stiffness. Obesity biomarkers such as C-reactive protein, leptin, tumour necrosis factor-&agr; and interleukin-6 were not associated with arterial stiffness. Conclusion: Central obesity in people with Type 2 diabetes was associated with increased arterial stiffness. This association was independent of the conventional factors associated with central obesity and further studies are required to identify the mechanisms involved.

The accuracy of diagnostic coding for acute kidney injury in England – a single centre study

BackgroundAcute kidney injury (AKI) is an independent risk factor for mortality and is responsible for a significant burden of healthcare expenditure, so accurate measurement of its incidence is important. Administrative coding data has been used for assessing AKI incidence, and shows an increasing proportion of hospital bed days attributable to AKI. However, the accuracy of coding for AKI and changes in coding over time have not been studied in England.MethodsWe studied a random sample of admissions from 2005 and 2010 where ICD-10 code N17 (acute renal failure) was recorded in the administrative coding data at one acute NHS Foundation Trust in England. Using the medical notes and computerised records we examined the demographic and clinical details of these admissions.ResultsAgainst a 6.3% (95% CI 4.8-7.9%) increase in all non-elective admissions, we found a 64% increase in acute renal failure admissions (95% CI 41%-92%, p < 0.001) in 2010 compared to 2005. Median age was 78 years (IQR 72–87), 11-25% had a relevant pre-admission co-morbidity and 64% (55-73%) were taking drugs known to be associated with AKI. Over both years, 95% (91-99%) of cases examined met the Kidney Disease: Improving Global Outcomes criteria for AKI.ConclusionsPatients with hospital admissions where AKI has been coded are elderly with multiple co-morbidities. Our results demonstrate a high positive predictive value of coding data for a clinical diagnosis of AKI, with no suggestion of marked changes in coding of AKI between 2005 and 2010.

A generalized arterial transfer function derived at rest underestimates augmentation of central pressure after exercise.

Objectives Peripheral exercise blood pressure and resting central blood pressure are considered more relevant to cardiovascular health than resting peripheral blood pressure. Central exercise blood pressure may well be an even more useful measure, but there is no simple non-invasive means of determining it. The aim of the present study was to establish whether the estimation of central blood pressure from peripheral blood pressure using a transfer function derived at rest, would hold after aerobic exercise. Methods Thirty healthy young men were studied before and immediately (< 1 min) and 10 min after 15 min bicycle exercise at 65–70% of maximum heart rate. Simultaneous carotid and radial artery waveforms were recorded, and radial-to-carotid generalized transfer functions (GTF) were calculated using Fourier analysis for rest and immediately postexercise. Central systolic blood pressure (SBP) and augmentation index (AIx) were calculated for measured and derived waves. Results The resting GTF underestimated central SBP and AIx immediately (−5.8 ± 2.1 mmHg, P = 0.01; −8.3 ± 2.9%, P = 0.008) and 10 min after (−2.0 ± 0.7 mmHg, P = 0.008; −7.0 ± 2.1%, P = 0.003) exercise. No significant bias was found between measured and derived (using resting GTF) carotid values at rest. The use of an exercise-specific GTF resulted in no specific bias immediately or 10 min after exercise, although it overestimated blood pressure and AIx at rest (2.5 ± 1.0 mmHg, P = 0.02; 11.3 ± 3.0%, P = 0.001). Conclusion A peripheral-to-central arterial GTF derived at rest significantly underestimates key measures of central arterial pressure immediately after exercise, and pressure estimations may be improved by the use of an exercise-specific GTF.

Cardiovascular risk: Cardiovascular risk

Cardiovascular disease is a major, growing, worldwide problem. It is important that individuals at risk of developing cardiovascular disease can be effectively identified and appropriately stratified according to risk. This review examines what we understand by the term risk, traditional and novel risk factors, clinical scoring systems, and the use of risk for informing prescribing decisions. Many different cardiovascular risk factors have been identified. Established, traditional factors such as ageing are powerful predictors of adverse outcome, and in the case of hypertension and dyslipidaemia are the major targets for therapeutic intervention. Numerous novel biomarkers have also been described, such as inflammatory and genetic markers. These have yet to be shown to be of value in improving risk prediction, but may represent potential therapeutic targets and facilitate more targeted use of existing therapies. Risk factors have been incorporated into several cardiovascular disease prediction algorithms, such as the Framingham equation, SCORE and QRISK. These have relatively poor predictive power, and uncertainties remain with regards to aspects such as choice of equation, different risk thresholds and the roles of relative risk, lifetime risk and reversible factors in identifying and treating at-risk individuals. Nonetheless, such scores provide objective and transparent means of quantifying risk and their integration into therapeutic guidelines enables equitable and cost-effective distribution of health service resources and improves the consistency and quality of clinical decision making.