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BACKGROUND: Health services have failed to respond to the pressures of multimorbidity. Improved measures of multimorbidity are needed for conducting research, planning services and allocating resources. METHODS: We modelled the association between 37 morbidities and 3 key outcomes (primary care consultations, unplanned hospital admission, death) at 1 and 5 years. We extracted development (n = 300000) and validation (n = 150000) samples from the UK Clinical Practice Research Datalink. We constructed a general-outcome multimorbidity score by averaging the standardized weights of the separate outcome scores. We compared performance with the Charlson Comorbidity Index. RESULTS: Models that included all 37 conditions were acceptable predictors of general practitioner consultations (C-index 0.732, 95% confidence interval [CI] 0.731-0.734), unplanned hospital admission (C-index 0.742, 95% CI 0.7370.747) and death at 1 year (C-index 0.912, 95% CI 0.905-0.918). Models reduced to the 20 conditions with the greatest combined prevalence/weight showed similar predictive ability (C-indices 0.727, 95% CI 0.725-0.728; 0.738, 95% CI 0.732-0.743; and 0.910, 95% CI 0.904-0.917, respectively). They also predicted 5-year outcomes similarly for consultations and death (C-indices 0.735, 95% CI 0.7340.736, and 0.889, 95% CI 0.885-0.892, respectively) but performed less well for admissions (C-index 0.708, 95% CI 0.705- 0.712). The performance of the general-outcome score was similar to that of the outcome-specific models. These models performed significantly better than those based on the Charlson Comorbidity Index for consultations (C-index 0.691, 95% CI 0.690-0.693) and admissions (C-index 0.703, 95% CI 0.697-0.709) and similarly for mortality (C-index 0.907, 95% CI 0.900-0.914). INTERPRETATION: The Cambridge Multimorbidity Score is robust and can be either tailored or not tailored to specific health outcomes. It will be valuable to those planning clinical services, policymakers allocating resources and researchers seeking to account for the effect of multimorbidity., Patients with multiple long-term health conditions are commonly seen by clinicians in generalist and specialist settings. (1,2) Services and policies have failed to respond to the pressures that multimorbidity places [...]

Polypharmacy is a common issue in clinical practice, with 20% of adults given ≥5 regular medications. It particularly impacts elderly individuals and those with multiple morbidities, and is worsened by single-disease, guideline-driven prescribing and service pressures. Although sometimes appropriate, it can also be problematic, associated with a range of adverse outcomes including hazardous prescribing, excess treatment burden, poor quality of life, higher health service use, and increased morbidity and mortality. Polypharmacy management is complex because of the presence of multiple problems, competing priorities and clinical uncertainty. The mainstay of management is currently medication review, which should be holistic and patient-centred, and employ shared decision-making. Key elements include addressing unnecessary prescribing, ineffective prescribing, drug safety and cost-effectiveness. Deprescribing is a core means of reducing polypharmacy, although there are several important barriers to its delivery, including a weak evidence base. In the longer term, preventing inappropriate polypharmacy requires a significant shift in prescribing culture, deployment of decision-support tools, and is likely to take considerable time to achieve.

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 1 1.87-17.43] for potentially preventable unplanned admissions), mental health conditions (for [greater than or equal to] 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., Multimorbidity--usually defined as the presence of more than 1 long-term disorder--is becoming the norm rather than the exception as populations age. (12) A recent study found that most people older [...]

Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.

Polypharmacy is a common issue in clinical practice, with 20% of adults given ≥5 regular medications. It particularly impacts elderly individuals and those with multiple morbidities, and is worsened by single-disease, guideline-driven prescribing and service pressures. Although sometimes appropriate, it can also be problematic, associated with a range of adverse outcomes including hazardous prescribing, excess treatment burden, poor quality of life, higher health service use, and increased morbidity and mortality. Polypharmacy management is complex because of the presence of multiple problems, competing priorities and clinical uncertainty. The mainstay of management is currently medication review, which should be holistic and patient centred, and employ shared decision-making. Key elements include addressing unnecessary prescribing, ineffective prescribing, drug safety and cost-effectiveness. Deprescribing is a core means of reducing polypharmacy, although there are several important barriers to its delivery, including a weak evidence base. In the longer term, preventing inappropriate polypharmacy requires a significant shift in prescribing culture, and is likely to take considerable time to achieve.

ObjectiveTo determine whether provision of web-based lifestyle advice and coronary heart disease risk information either based on phenotypic characteristics or phenotypic plus genetic characteristics affects changes in objectively measured health behaviours.MethodsA parallel-group, open randomised trial including 956 male and female blood donors with no history of cardiovascular disease (mean [SD] age=56.7 [8.8] years) randomised to four study groups: control group (no information provided); web-based lifestyle advice only (lifestyle group); lifestyle advice plus information on estimated 10-year coronary heart disease risk based on phenotypic characteristics (phenotypic risk estimate) (phenotypic group) and lifestyle advice plus information on estimated 10-year coronary heart disease risk based on phenotypic (phenotypic risk estimate) and genetic characteristics (genetic risk estimate) (genetic group). The primary outcome was change in physical activity from baseline to 12 weeks assessed by wrist-worn accelerometer.Results928 (97.1%) participants completed the trial. There was no evidence of intervention effects on physical activity (difference in adjusted mean change from baseline): lifestyle group vs control group 0.09 milligravity (mg) (95% CI −1.15 to 1.33); genetic group vs phenotypic group −0.33 mg (95% CI −1.55 to 0.90); phenotypic group and genetic group vs control group −0.52 mg (95% CI −1.59 to 0.55) and vs lifestyle group −0.61 mg (95% CI −1.67 to 0.46). There was no evidence of intervention effects on secondary biological, emotional and health-related behavioural outcomes except self-reported fruit and vegetable intake.ConclusionsProvision of risk information, whether based on phenotypic or genotypic characteristics, alongside web-based lifestyle advice did not importantly affect objectively measured levels of physical activity, other health-related behaviours, biological risk factors or emotional well-being.Trial registration numberISRCTN17721237; Pre-results.

Polypharmacy is an increasing and global issue affecting primary care. Although sometimes appropriate, polypharmacy can also be problematic, leading to a range of adverse consequences. Deprescribing is the process of supervised withdrawal of an inappropriate medication and has the potential to reduce some of the problems associated with polypharmacy. It is a complex and sensitive process. We examine the issue of deprescribing from the perspective of primary care. Key steps in the deprescribing process are a review of medications and corresponding indications, consideration of harms, assessment of eligibility for discontinuation, prioritisation of medications and implementation of a stopping plan with appropriate monitoring. Patient involvement is a key feature of this process. Deprescribing should be considered in the context of end-of-life care and medication safety, but approaches are also required to identify other situations where deprescribing is appropriate. General practitioners are well positioned to facilitate deprescribing, usually through formal medication review, with decisions informed by a range of other healthcare professionals. Guidelines are available that help guide these processes. A range of studies have explored attitudes towards deprescribing; patients are generally supportive of the concept, although clinician views are varied. The successful implementation of deprescribing strategies still requires important patient and clinician barriers to be overcome, and clinical trial evidence of effectiveness and safety is essential.

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.

ObjectiveTo develop and externally validate the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify the risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment.DesignRetrospective cohort study.SettingPrimary care data from electronic health records contained within the UK Clinical Practice Research Datalink (CPRD).ParticipantsPatients aged 40 years or older with at least one blood pressure measurement between 130 mm Hg and 179 mm Hg.Main outcome measureFirst serious fall, defined as hospital admission or death with a primary diagnosis of a fall within 10 years of the index date (12 months after cohort entry). Model development was conducted using a Fine-Gray approach in data from CPRD GOLD, accounting for the competing risk of death from other causes, with subsequent recalibration at one, five, and 10 years using pseudo values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves and the observed to expected ratio, C statistic, and D statistic, pooled across general practices, and clinical utility using decision curve analysis at thresholds around 10%.ResultsAnalysis included 1 772 600 patients (experiencing 62 691 serious falls) from CPRD GOLD used in model development, and 3 805 366 (experiencing 206 956 serious falls) from CPRD Aurum in the external validation. The final model consisted of 24 predictors, including age, sex, ethnicity, alcohol consumption, living in an area of high social deprivation, a history of falls, multiple sclerosis, and prescriptions of antihypertensives, antidepressants, hypnotics, and anxiolytics. Upon external validation, the recalibrated model showed good discrimination, with pooled C statistics of 0.833 (95% confidence interval 0.831 to 0.835) and 0.843 (0.841 to 0.844) at five and 10 years, respectively. Original model calibration was poor on visual inspection and although this was improved with recalibration, under-prediction of risk remained (observed to expected ratio at 10 years 1.839, 95% confidence interval 1.811 to 1.865). Nevertheless, decision curve analysis suggests potential clinical utility, with net benefit larger than other strategies.ConclusionsThis prediction model uses commonly recorded clinical characteristics and distinguishes well between patients at high and low risk of falls in the next 1-10 years. Although miscalibration was evident on external validation, the model still had potential clinical utility around risk thresholds of 10% and so could be useful in routine clinical practice to help identify those at high risk of falls who might benefit from closer monitoring or early intervention to prevent future falls. Further studies are needed to explore the appropriate thresholds that maximise the model’s clinical utility and cost effectiveness.

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.

The article presents case studies related to polypharmacy and details that even if clinically justified, it can be inappropriate. It also presents three case studies which include a 69-year-old man with epilepsy and hypertension, a 55-year-old woman with left-shoulder pain, and a 76-year-old lady with falls and atypical chest pains.

ObjectiveTo examine the association between antihypertensive treatment and specific adverse events.DesignSystematic review and meta-analysis.Eligibility criteriaRandomised controlled trials of adults receiving antihypertensives compared with placebo or no treatment, more antihypertensive drugs compared with fewer antihypertensive drugs, or higher blood pressure targets compared with lower targets. To avoid small early phase trials, studies were required to have at least 650 patient years of follow-up.Information sourcesSearches were conducted in Embase, Medline, CENTRAL, and the Science Citation Index databases from inception until 14 April 2020.Main outcome measuresThe primary outcome was falls during trial follow-up. Secondary outcomes were acute kidney injury, fractures, gout, hyperkalaemia, hypokalaemia, hypotension, and syncope. Additional outcomes related to death and major cardiovascular events were extracted. Risk of bias was assessed using the Cochrane risk of bias tool, and random effects meta-analysis was used to pool rate ratios, odds ratios, and hazard ratios across studies, allowing for between study heterogeneity (τ2).ResultsOf 15 023 articles screened for inclusion, 58 randomised controlled trials were identified, including 280 638 participants followed up for a median of 3 (interquartile range 2-4) years. Most of the trials (n=40, 69%) had a low risk of bias. Among seven trials reporting data for falls, no evidence was found of an association with antihypertensive treatment (summary risk ratio 1.05, 95% confidence interval 0.89 to 1.24, τ2=0.009). Antihypertensives were associated with an increased risk of acute kidney injury (1.18, 95% confidence interval 1.01 to 1.39, τ2=0.037, n=15), hyperkalaemia (1.89, 1.56 to 2.30, τ2=0.122, n=26), hypotension (1.97, 1.67 to 2.32, τ2=0.132, n=35), and syncope (1.28, 1.03 to 1.59, τ2=0.050, n=16). The heterogeneity between studies assessing acute kidney injury and hyperkalaemia events was reduced when focusing on drugs that affect the renin angiotensin-aldosterone system. Results were robust to sensitivity analyses focusing on adverse events leading to withdrawal from each trial. Antihypertensive treatment was associated with a reduced risk of all cause mortality, cardiovascular death, and stroke, but not of myocardial infarction.ConclusionsThis meta-analysis found no evidence to suggest that antihypertensive treatment is associated with falls but found evidence of an association with mild (hyperkalaemia, hypotension) and severe adverse events (acute kidney injury, syncope). These data could be used to inform shared decision making between doctors and patients about initiation and continuation of antihypertensive treatment, especially in patients at high risk of harm because of previous adverse events or poor renal function.RegistrationPROSPERO CRD42018116860.

ObjectiveTo examine the concordance between dates of death recorded in UK primary care and national mortality records.MethodsUK primary care data from the Clinical Practice Research Datalink were linked to Office for National Statistics (ONS) data, for 118 571 patients who died between September 2010 and September 2015. Logistic regression was used to examine factors associated with discrepancy in death dates between data sets.ResultsDeath dates matched in 76.8% of cases with primary care dates preceding ONS date in 2.9%, and following in 20.3% of cases; 92.2% of cases differed by <2 weeks. Primary care date was >4 weeks later than ONS in 1.5% of cases and occurred more frequently with deaths categorised as ‘external’ (15.8% vs 0.8% for cancer), and in younger patients (15.9% vs 1% for 18–29 and 80–89 years, respectively). General practices with the greatest discrepancies (97.5th percentile) had around 200 times higher odds of recording substantially discordant dates than practices with the lowest discrepancies (2.5th percentile).ConclusionDates of death in primary care records often disagree with national records and should be treated with caution. There is marked variation between practices, and studies involving young patients, unexplained deaths and where precise date of death is important are particularly vulnerable to these issues.

Multimorbidity is a major challenge facing health systems. To help understand the changes to services and policies that are required, we investigated which diseases co-occur and which combinations lead to the highest mortality and service use.

BackgroundLittle is known about patterns of prescribing in the last year of life in primary care.MethodsRecently completed analysis of UK Clinical Practice Research Datalink (CPRD), a large database extracted from GP records that is representative of the UK population. Prescribing data for the last 12 months of life were extracted from 1 18 571 patients‘ GP records who died between September 2010 and August 2015. Two clinicians used British National Formulary codes to identify eight palliative care drug categories: antiemetics, anti-secretory, benzodiazepines, steroids, strong injectable opioids, strong patch opioids, strong enteral opioids, and weak oral opioids. The percentage of patients prescribed each medications was calculated at weekly intervals during the year before death, further stratified by cause of death and age at death. Random effects logistic regression models were run adjusted for age-group, sex, and cause of death.ResultsLevels of prescribing significantly increased for all drugs at time of death compared with one year before death (with the exception of weak oral opioids and steroids where results were inconsistent across ages, genders, and causes of death). The largest increase was for 90+year old male patients prescribed strong injectable opioids (OR: 21032, 95% CI: 3499 to 12641) and the greatest decrease for females prescribed steroids who died of external causes (OR: 0.52, 95% CI: 0.34 to 0.70). Patients who died of cancer generally showed the most marked increase in prescribing towards the end of life compared with other causes of death.ConclusionsPrimary care patterns of prescribing in the last year of life has not been previously investigated. The findings of this recently completed study of a large nationally-representative dataset reveal increasing GP prescribing in response to the rising symptom burden of all patient groups as death approached. The implications for clinical practice and future research will be discussed.

BackgroundGuidelines advise prescription of angiotensin-converting enzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs) for several disorders, such as chronic kidney disease. Yet, treatment with these drugs is associated with acute kidney injury (AKI) during episodes of intercurrent illness. Frequency of AKI admission is increasing. Whether increased AKI admissions are associated with increased prescription of ACE inhibitors and ARBs is unknown.