Deprescribing in cardiometabolic conditions in older patients: a systematic review.

We conduct a systematic review to investigate current deprescribing practices and evaluate outcomes and adverse events with deprescribing of preventive medications in older patients with either an end-of-life designation or residing in long-term care facilities with cardiometabolic conditions. Studies were identified using a literature search of MEDLINE, EMBASE, Web of Science, clinicaltrials.gov.uk, CINAHLS, and the Cochrane Register from inception to March 2022. Studies reviewed included observational studies and randomised control trials (RCTs). Data was extracted on baseline characteristics, deprescribing rates, adverse events and outcomes, and quality of life indicators, and was discussed using a narrative approach. Thirteen studies were identified for inclusion. Deprescribing approaches included complete withdrawal, dose reduction or tapering, or switching to an alternative medication, for at least one preventive medication. Deprescribing success rates ranged from 27 to 94.7%. The studies reported no significant changes in laboratory values or adverse outcomes but did find mixed outcomes for hospitalisations and a slight increase in mortality rates when comparing the intervention and control groups. Lack of good-quality randomised control trials suggests that deprescribing in the older population residing in long-term care facilities with cardiometabolic conditions and multimorbidity is feasible when controlled and regularly monitored by an appropriate healthcare clinician, and that the benefits outweigh the potential harm in this cohort of patients. Due to the limited evidence and the heterogeneity of studies, a meta-analysis was not performed and as such further research is required to assess the benefits of deprescribing in this patient population. Systematic review registration: PROSPERO CRD42021291061.

Life expectancy following a cardiovascular event in individuals with and without type 2 diabetes: A UK multi-ethnic population-based observational study.

BACKGROUND AND AIMS: We aimed to evaluate the life expectancy following the first cardiovascular disease (CVD) event by type 2 diabetes (T2D) status and ethnicity. METHODS AND RESULTS: We used the Clinical Practice Research Datalink database in England (UK), linked to the Hospital Episode Statistics information, to identify individuals with and without T2D who survived a first CVD event between 1st Jan 2007 and 31st Dec 2017; subsequent death events were extracted from the Office for National Statistics database. Ethnicity was categorised as White, South Asian (SA), Black, or other. Flexible parametric survival models were used to estimate survival and predict life expectancy. 59,939 individuals with first CVD event were included: 7596 (12.7%) with T2D (60.9% men; mean age at event: 69.7 years [63.2 years in SA, 65.9 in Black, 70.2 in White]) and 52,343 without T2D (56.7% men; 65.9 years [54.7 in Black, 58.2 in SA, 66.3 in White]). Accounting for potential confounders (sex, deprivation, lipid-lowering medication, current smoking, and pre-existing hypertension), comparing individuals with vs without T2D the mortality rate was 53% higher in White (hazard ratio [HR]: 1.53 [95% CI: 1.44, 1.62]), corresponding to a potential loss of 3.87 (3.30, 4.44) life years at the age of 50 years in individuals with T2D. No evidence of a difference in life expectancy was observed in individuals of SA (HR: 0.82 [0.52, 1.29]; -1.36 [-4.58, 1.86] life years), Black (HR: 1.26 [0.59, 2.70]; 1.21 [-2.99, 5.41] life years); and other (HR: 1.64 [0.80, 3.39]; 3.89 [-2.28, 9.99] life years) ethnic group. CONCLUSION: Following a CVD event, T2D is associated with a different prognosis and life years lost among ethnic groups.

Are major lower extremity amputations well recorded in primary care electronic health records?: Insights from primary care electronic health records in England.

AIMS: Major lower extremity amputations (MLEAs) are understood to be well recorded in secondary care in England in the Hospital Episode Statistics (HES) database. It is unclear how well MLEAs are recorded in primary care databases. BACKGROUND: This study compared MLEA event case ascertainment in Clinical Practice Research Datalink (CPRD) to that in HES. METHODS: MLEA events were ascertained in CPRD and in HES linkage between 1 January 2010 and 31 December 2019. The number of MLEA events and the number of patients with at least one MLEA in each database were recorded and compared. Individual events were matched between the databases using varying date-matching windows. Reasons for differences in case ascertainment were explored. FINDINGS: In total 23 262 patients had at least one MLEA record, 8716 (37.5%) had an MLEA record in HES only, 5393 (23.2%) in CPRD only and 9153 (39.4%) in both. Out of a total of 75 221 events, 13 071 (62.4%) were recorded in HES only and 44 151 (81.3%) in CPRD only. 7874 (37.6%) of HES events were recorded in CPRD and 10 125 (18.6%) of CPRD events were recorded in HES when using the maximum date matching window of 28 days plus the time between admission and procedure. The main reasons for differences in case ascertainment included, re-recordings and miscoding in CPRD.Compared to HES, MLEAs are poorly recorded in CPRD predominantly due to re-recordings of events and miscoding procedures. CPRD data cannot solely be relied upon to ascertain cases of MLEA; however, HES linkage to CPRD may be useful to obtain medical history of diagnoses, medication and diagnostic tests.

Attitudes of older adults and their carers towards de-prescribing: A systematic review.

AIM: The aim of this systematic review is to explore the attitudes of older adults (≥65 years old) and their carers towards de-prescribing. METHODS: We identified relevant studies from three databases; MEDLINE, CINAHL and Web of Science. Two reviewers (MS, SS) independently extracted data from each selected study using a standardised self-developed data extraction form. Main findings of the studies were summarised descriptively. RESULTS: A total of 35 studies were included in the review. Of them, 19 were questionnaire studies, 11 semi-structured interviews, 4 focus groups and 1 study used the nominal group technique approach. Most older adults and their carers were willing to have medication de-prescribed if told to do so by a healthcare professional (HCP). Other factors that increased willingness to de-prescribing included; trust in the HCP, side effects and inconvenience from medications as well as the prospect of follow-up and monitoring during de-prescribing. In contrast, perceived effectiveness, unawareness of lack of benefit, negative expectations of ageing and fear were factors preventing de-prescribing. CONCLUSION: De-prescribing is an important concept in older people given the harm associated with polypharmacy in this age group. Overall, older adults and their carers are willing to have medication de-prescribed if facilitated by their HCP. However, there remain a few barriers to de-prescribing which may need to be addressed in certain patients, through discussions between older adults/their carers and a HCP, to allow de-prescribing to be more effective.

Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K.

BACKGROUND: Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection. METHODS: We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25-hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein. RESULTS: Among 360,283 participants, the median age was 68 [range 48-85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]). CONCLUSION: The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment.

Obesity, chronic disease, age, and in-hospital mortality in patients with covid-19: analysis of ISARIC clinical characterisation protocol UK cohort.

BACKGROUND: Although age, obesity and pre-existing chronic diseases are established risk factors for COVID-19 outcomes, their interactions have not been well researched. METHODS: We used data from the Clinical Characterisation Protocol UK (CCP-UK) for Severe Emerging Infection developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC). Patients admitted to hospital with COVID-19 from 6th February to 12th October 2020 were included where there was a coded outcome following hospital admission. Obesity was determined by an assessment from a clinician and chronic disease by medical records. Chronic diseases included: chronic cardiac disease, hypertension, chronic kidney disease, chronic pulmonary disease, diabetes and cancer. Mutually exclusive categories of obesity, with or without chronic disease, were created. Associations with in-hospital mortality were examined across sex and age categories. RESULTS: The analysis included 27,624 women with 6407 (23.2%) in-hospital deaths and 35,065 men with 10,001 (28.5%) in-hospital deaths. The prevalence of chronic disease in women and men was 66.3 and 68.5%, respectively, while that of obesity was 12.9 and 11.1%, respectively. Association of obesity and chronic disease status varied by age (p < 0.001). Under 50 years of age, obesity and chronic disease were associated with in-hospital mortality within 28 days of admission in a dose-response manner, such that patients with both obesity and chronic disease had the highest risk with a hazard ratio (HR) of in-hospital mortality of 2.99 (95% CI: 2.12, 4.21) in men and 2.16 (1.42, 3.26) in women compared to patients without obesity or chronic disease. Between the ages of 50-69 years, obesity and chronic disease remained associated with in-hospital COVID-19 mortality, but survival in those with obesity was similar to those with and without prevalent chronic disease. Beyond the age of 70 years in men and 80 years in women there was no meaningful difference between those with and without obesity and/or chronic disease. CONCLUSION: Obesity and chronic disease are important risk factors for in-hospital mortality in younger age groups, with the combination of chronic disease and obesity being particularly important in those under 50 years of age. These findings have implications for targeted public health interventions, vaccination strategies and in-hospital clinical decision making.

Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: A longitudinal cohort study.

BACKGROUND: Whether a healthy lifestyle impacts longevity in the presence of multimorbidity is unclear. We investigated the associations between healthy lifestyle and life expectancy in people with and without multimorbidity. METHODS AND FINDINGS: A total of 480,940 middle-aged adults (median age of 58 years [range 38-73], 46% male, 95% white) were analysed in the UK Biobank; this longitudinal study collected data between 2006 and 2010, and participants were followed up until 2016. We extracted 36 chronic conditions and defined multimorbidity as 2 or more conditions. Four lifestyle factors, based on national guidelines, were used: leisure-time physical activity, smoking, diet, and alcohol consumption. A combined weighted score was developed and grouped participants into 4 categories: very unhealthy, unhealthy, healthy, and very healthy. Survival models were applied to predict life expectancy, adjusting for ethnicity, working status, deprivation, body mass index, and sedentary time. A total of 93,746 (19.5%) participants had multimorbidity. During a mean follow-up of 7 (range 2-9) years, 11,006 deaths occurred. At 45 years, in men with multimorbidity an unhealthy score was associated with a gain of 1.5 (95% confidence interval [CI] -0.3 to 3.3; P = 0.102) additional life years compared to very unhealthy score, though the association was not significant, whilst a healthy score was significantly associated with a gain of 4.5 (3.3 to 5.7; P < 0.001) life years and a very healthy score with 6.3 (5.0 to 7.7; P < 0.001) years. Corresponding estimates in women were 3.5 (95% CI 0.7 to 6.3; P = 0.016), 6.4 (4.8 to 7.9; P < 0.001), and 7.6 (6.0 to 9.2; P < 0.001) years. Results were consistent in those without multimorbidity and in several sensitivity analyses. For individual lifestyle factors, no current smoking was associated with the largest survival benefit. The main limitations were that we could not explore the consistency of our results using a more restrictive definition of multimorbidity including only cardiometabolic conditions, and participants were not representative of the UK as a whole. CONCLUSIONS: In this analysis of data from the UK Biobank, we found that regardless of the presence of multimorbidity, engaging in a healthier lifestyle was associated with up to 6.3 years longer life for men and 7.6 years for women; however, not all lifestyle risk factors equally correlated with life expectancy, with smoking being significantly worse than others.

Prevalence of co-morbidities and their association with mortality in patients with COVID-19: A systematic review and meta-analysis.

AIM: To estimate the prevalence of both cardiometabolic and other co-morbidities in patients with COVID-19, and to estimate the increased risk of severity of disease and mortality in people with co-morbidities. MATERIALS AND METHODS: Medline, Scopus and the World Health Organization website were searched for global research on COVID-19 conducted from January 2019 up to 23 April 2020. Study inclusion was restricted to English language publications, original articles that reported the prevalence of co-morbidities in individuals with COVID-19, and case series including more than 10 patients. Eighteen studies were selected for inclusion. Data were analysed using random effects meta-analysis models. RESULTS: Eighteen studies with a total of 14 558 individuals were identified. The pooled prevalence for co-morbidities in patients with COVID-19 disease was 22.9% (95% CI: 15.8 to 29.9) for hypertension, 11.5% (9.7 to 13.4) for diabetes, and 9.7% (6.8 to 12.6) for cardiovascular disease (CVD). For chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), cerebrovascular disease and cancer, the pooled prevalences were all less than 4%. With the exception of cerebrovascular disease, all the other co-morbidities presented a significantly increased risk for having severe COVID-19. In addition, the risk of mortality was significantly increased in individuals with CVD, COPD, CKD, cerebrovascular disease and cancer. CONCLUSIONS: In individuals with COVID-19, the presence of co-morbidities (both cardiometabolic and other) is associated with a higher risk of severe COVID-19 and mortality. These findings have important implications for public health with regard to risk stratification and future planning.

Risk of esophageal cancer in achalasia cardia: A meta-analysis.

INTRODUCTION: The association between cancer of the esophagus and achalasia has long been recognized. However, it has also been recognized that cancers themselves can give rise to achalasia-like syndromes. The risk of developing cancer is also a factor in assessing whether there is a potential role for surveillance in this disease. This paper uses published work to form the basis for a meta-analysis of the risk of developing esophageal cancer among patients with pre-existing achalasia. METHODS: This paper considered cancer risk reported in a range of studies of achalasia published over a 50-year period. Twenty-seven potential studies were identified. In 16 reports, it was possible to extract information on both length of follow-up and duration of achalasia so that person-years duration (PYD) could be calculated. The analysis was stratified between cancers identified in the first year after diagnosis of achalasia and cancers identified in subsequent years. RESULTS: From pooling the results of 16 studies, the incidence rate of esophageal cancer in achalasia patients was estimated to be 1.36 (95% CI: 0.56, 2.51) per 1000 person years. This is over 10 times higher than the general population incidence rates as reported by the lARC. CONCLUSIONS: Therefore, our meta-analysis shows that achalasia is a major risk factor for the development of esophageal cancer. This is supported by the results from the time-stratified analysis. Incidence of esophageal cancer per 1000 person years was lower in the first year after diagnosis of achalasia than in subsequent years. This is strong evidence against the idea that achalasia may be induced by esophageal cancer instead of vice versa.

Association of smoking and cardiometabolic parameters with albuminuria in people with type 2 diabetes mellitus: a systematic review and meta-analysis.

AIMS: Smoking is a strong risk factor for albuminuria in people with type 2 diabetes mellitus (T2DM). However, it is unclear whether this sequela of smoking is brought about by its action on cardiometabolic parameters or the relationship is independent. The aim of this systematic review is to explore this relationship. METHODS: Electronic databases on cross-sectional and prospective studies in Medline and Embase were searched from January 1946 to May 2018. Adult smokers with T2DM were included, and other types of diabetes were excluded. RESULTS: A random effects meta-analysis of 20,056 participants from 13 studies found that the odds ratio (OR) of smokers developing albuminuria compared to non-smokers was 2.13 (95% CI 1.32, 3.45). Apart from smoking, the odds ratio of other risk factors associated with albuminuria were: age 1.24 (95% CI 0.84, 1.64), male sex 1.39 (95% CI 1.16, 1.67), duration of diabetes 1.78 (95% CI 1.32, 2.23), HbA1c 0.63 (95% CI 0.45, 0.81), SBP 6.03 (95% CI 4.10, 7.97), DBP 1.85 (95% CI 1.08, 2.62), total cholesterol 0.06 (95% CI - 0.05, 0.17) and HDL cholesterol - 0.01 (95% CI - 0.04, 0.02), triglyceride 0.22 (95% CI 0.12, 0.33) and BMI 0.40 (95% CI 0.00-0.80). When the smoking status was adjusted in a mixed effect meta-regression model, the duration of diabetes was the only statistically significant factor that influenced the prevalence of albuminuria. In smokers, each year's increase in the duration of T2DM was associated with an increased risk of albuminuria of 0.19 units (95% CI 0.07, 0.31) on the log odds scale or increased the odds approximately by 23%, compared to non-smokers. Prediction from the meta-regression model also suggested that the odds ratios of albuminuria in smokers after a diabetes duration of 9 years and 16 years were 1.53 (95% CI 1.10, 2.13) and 5.94 (95% CI 2.53, 13.95), respectively. CONCLUSIONS: Continuing to smoke and the duration of diabetes are two strong predictors of albuminuria in smokers with T2DM. With a global surge in younger smokers developing T2DM, smoking cessation interventions at an early stage of disease trajectory should be promoted.

Relationship of cardiometabolic parameters in non-smokers, current smokers, and quitters in diabetes: a systematic review and meta-analysis.

BACKGROUND: Smoking is associated with increased macrovascular and microvascular complications in people with diabetes. In addition to other concomitant vascular perturbations, it also seems to influence the cardiometabolic parameters, which may partly explain the accelerated rate of vascular complications in smokers with diabetes. While smoking cessation is advocated as a universal component of the management of diabetes, there is some anecdotal evidence that HbA1c could increase following smoking cessation. The aim of this review is to explore the relationship between smoking and its cessation on cardiometabolic parameters in diabetes. METHODS: Searches were conducted on Medline, EMBASE and CINAHL up to March 2016. After screening 6866 studies (Additional file 1), 14 observational studies with a total of 98,978 participants' with either type 1 or type 2 diabetes were selected for review. Narrative synthesis and meta-analyses were carried out to explore the relationship between smoking and its cessation. RESULTS: Meta-analysis showed that the pooled mean difference of HbA1c between non-smokers and smokers was -0.61% (95% CI -0.88 to -0.33, p < 0.0001). The difference in LDL cholesterol between non-smokers and smokers was -0.11 mmol/l (95% CI -0.21 to -0.01, p = 0.04). The difference in HDL cholesterol between non-smokers and smokers was 0.12 mmol/l (95% CI 0.08-0.15, p < 0.001). However, there was no statistically significant difference in blood pressure between the two groups. The difference in HbA1c between quitters and continued smokers was not statistically significant -0.10% (95% CI -0.42 to 0.21, p = 0.53). However, a narrative synthesis revealed that over a period of 10 years, the HbA1c was comparable between non-smokers and quitters. CONCLUSION: Non-smokers have a statistically significant lower HbA1c and more favourable lipid profile compared to smokers. Smoking cessation does not lead to an increase in HbA1c in long-term and may reduce vascular complications in diabetes by its favourable impact on lipid profile.

A comparison of cost per case detected of screening strategies for Type 2 diabetes and impaired glucose regulation: modelling study.

BACKGROUND: To determine a cost per case detected for different screening strategies for both Type 2 diabetes alone and in combination with impaired glucose regulation. METHODS: Bayesian framework modelling study using data from the ADDITION-Leicester screening study in UK multi-ethnic primary care setting. There were 5794 people aged 40-75 years (77.4% white European; 22.6% south Asian) without previously known diabetes. We compared 212 screening strategies including blood tests, a computer practice data score and a risk score, as part of a multi-stage process that all used an oral glucose tolerance test as the diagnostic test. Simulation models were created using sensitivity estimates for the expected cost per case. RESULTS: The estimated costs per case identified for the 18 most sensitive strategies varied from £457 to £1639 (€526-1886, for £1=€1.15) for diabetes and £148-913 (€170-1050) for both diabetes and impaired glucose regulation. The lowest costing diabetes strategies ranged from £457 to £523 (€526-601) involving a two-stage screening strategy, a non-invasive risk stratifying tool followed by a blood test, producing sensitivities ranging from 67.1 to 82.4%. CONCLUSION: Screening a population using a non-invasive risk stratification tool followed by a screening blood test is the most cost-effective method of screening for diabetes and abnormal glucose tolerance.

Randomised controlled trial to compare GP-run orthopaedic clinics based in hospital outpatient departments and general practices.

BACKGROUND: To reduce outpatient waiting times, a growing number of outpatient clinics for selected groups of patients are being provided by GPs with special interests (GPwSIs). AIM: To determine whether there are differences in patient satisfaction or clinical outcome among patients attending orthopaedic clinics provided by GPwSIs in hospital or community settings. DESIGN OF STUDY: Randomised controlled trial. SETTING: Hospital outpatient departments or general practices. METHOD: Three hundred and twenty-one patients with minor orthopaedic problems were referred by GPs to the orthopaedic surgery department of the University Hospitals of Leicester NHS Trust; 168 patients were randomised to care by GPwSIs in practices, and 153 were randomised to care by the same GPwSIs in clinics held at hospital outpatient departments. Patients completed the SF-36v2 and satisfaction questionnaires at their first appointment, and again 3 months later. RESULTS: There was no significant difference between the sites in changes in health. After the first clinic attendance, patients attending practice-based clinics were more satisfied with access to appointments and information received. CONCLUSION: For selected orthopaedic referrals seen by GPwSIs, there were no significant differences in clinical outcomes between practice-based and hospital-based clinics, but some features of practice-based clinics tend to be preferred by patients.