Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies: an individual-level participant data analysis

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

Background

It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use.

Methods

Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n = 1,825,289), UK Biobank (n = 502,363), and the UK Household Longitudinal Study (UKHLS) (n = 49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL).

Findings

Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR = 4.47, hospitalisation IRR = 1.84; GP use IRR = 2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR = 4.52, hospitalisation IRR = 1.53, GP use IRR = 2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR = 2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years.

Interpretation

Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs.

Funding

This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).

Description of subgroup reporting in clinical trials of chronic diseases: a meta-epidemiological study

INTRODUCTION

In trials, subgroup analyses are used to examine whether treatment effects differ by important patient characteristics. However, which subgroups are most commonly reported has not been comprehensively described.

DESIGN AND SETTINGS

Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes.

METHODS

We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup_reporting_app/).

RESULTS

Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare.

CONCLUSION

Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse.

PROSPERO REGISTRATION NUMBER

CRD42018048202.

The value of cardiopulmonary comorbidity in patients with acute large vessel occlusion stroke undergoing endovascular thrombectomy: a retrospective, observational cohort study

BACKGROUND

Chronic lung and heart diseases are more likely to lead an intensive end point after stroke onset. We aimed to investigate characteristics and outcomes of endovascular thrombectomy (EVT) in patients with acute large vessel occlusion stroke (ALVOS) and identify the role of comorbid chronic cardiopulmonary diseases in ALVOS pathogenesis.

METHODS

In this single-center retrospective study, 191 consecutive patients who underwent EVT due to large vessel occlusion stroke in neurological intensive care unit were included. The chronic cardiopulmonary comorbidities and several conventional stroke risk factors were assessed. The primary efficacy outcome was functional independence (defined as a mRS of 0 to 2) at day 90. The primary safety outcomes were death within 90 days and the occurrence of symptomatic intracranial hemorrhage(sICH). Univariate analysis was applied to evaluate the relationship between factors and clinical outcomes, and logistic regression model were developed to predict the prognosis of ALVOS.

RESULTS

Endovascular therapy in ALVOS patients with chronic cardiopulmonary diseases, as compared with those without comorbidity, was associated with an unfavorable shift in the NHISS 24 h after EVT [8(4,15.25) versus 12(7.5,18.5), P = 0.005] and the lower percentage of patients who were functionally independent at 90 days, defined as a score on the modified Rankin scale of 0 to 2 (51.6% versus 25.4%, P = 0.000). There was no significant between-group difference in the frequency of mortality (12.1% versus 14.9%, P = 0.580) and symptomatic intracranial hemorrhage (13.7% versus 19.4%, P = 0.302) or of serious adverse events. Moreover, a prediction model showed that existence of cardiopulmonary comorbidities (OR = 0.456, 95%CI 0.209 to 0.992, P = 0.048) was independently associated with functional independence at day 90.

CONCLUSIONS

EVT was safe in ALVOS patients with chronic cardiopulmonary diseases, whereas the unfavorable outcomes were achieved in such patients. Moreover, cardiopulmonary comorbidity had certain clinical predictive value for worse stroke prognosis.

Participant characteristics and exclusion from phase 3/4 industry funded trials of chronic medical conditions: meta-analysis of individual participant level data

Objectives

To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure).

Design

Bayesian meta-analysis of individual participant level data.

Setting

Industry funded phase 3/4 trials of chronic medical conditions.

Participants

Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage.

Main outcome measures

For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male v female), comorbidity count (per one additional comorbidity), and race or ethnic group. Trial level analyses were combined in Bayesian hierarchical models with pooling across condition.

Results

In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13).

Conclusions

Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials.

Trial registration number

PROSPERO CRD42018048202.

The Benefits and Harms of Lung Cancer Screening in Individuals With Comorbidities

Introduction

Individuals with a history of smoking and a high risk of lung cancer often have a high prevalence of smoking-related comorbidities. The presence of these comorbidities might alter the benefit-to-harm ratio of lung cancer screening by influencing the risk of complications, quality of life, and competing risks of death. Nevertheless, individuals with chronic diseases are underrepresented in screening clinical trials. In this study, we use microsimulation modeling to determine the impact of chronic diseases on lung cancer benefits and harms.

Methods

We extended a validated lung cancer screening microsimulation model that comprehensively recapitulates an individual's lung cancer development, progression, detection, follow-up, treatment, and survival. We parameterized the model to reflect the impact of chronic diseases on complications from invasive testing, quality of life, and mortality in individuals in five-year age categories between the ages of 50 and 80 years. Outcomes included life-years (LY) gained per 100,000 in patients with chronic obstructive pulmonary disease, diabetes mellitus, heart disease, and history of stroke compared with screening-eligible individuals without comorbidities.

Results

Among individuals between the ages of 50 and 54 years, we found that the presence of a comorbidity altered the LY gained from screening per 100,000 individuals depending on the comorbidity: 4296 LY with no comorbidities; 3462 LY, 3260 LY, 3031 LY, and 3257 LY with chronic obstructive pulmonary disease, heart disease, diabetes mellitus, and stroke, respectively. We observed greater reductions in LY gained in individuals with two comorbidities; we observed similar patterns for individuals between the ages of 55 and 59 years, 60 and 64 years, 65 and 69 years, 70 and 74 years, and 75 and 80 years.

Conclusions

Comorbidities reduce LY gained from screening per 100,000 compared with no comorbidities, and our results can be used by clinicians when discussing the benefits and harms of screening in their patients with comorbidities.

Eligibility of patients with chronic obstructive pulmonary disease for inclusion in randomised control trials investigating triple therapy: a study using routinely collected data

BACKGROUND

Randomised control trials (RCTs) with strict eligibility criteria can lead to trial populations not commonly seen in clinical practice. We described the proportion of people with chronic obstructive pulmonary disease (COPD) in England eligible for RCTs investigating treatment with triple therapy.

METHODS

MEDLINE and Clinicaltrials.gov were searched for RCTs investigating triple therapy and eligibility criteria for each trial were extracted. Using routinely collected primary care data from Clinical Practice Research Datalink Aurum linked with Hospital Episode Statistics, we defined a population of COPD patients registered at a general practice in England, who were ≥ 40 years old, and had a history of smoking. Inclusion date was January 1, 2020. Patients who died earlier or left the general practice were excluded. Eligibility criteria for each RCT was applied to the population of COPD patients and the proportion of patients meeting each trial eligibility criteria were described.

RESULTS

26 RCTs investigating triple therapy were identified from the literature. The most common eligibility criteria were post-bronchodilator FEV1% predicted 30-80%, ≥ 2 moderate/≥ 1 severe exacerbations 12-months prior, no moderate exacerbations one-month prior and no severe exacerbations three-months prior, and the use of maintenance therapy or ICS use prior to inclusion. After applying each RCT eligibility criteria to our population of 79,810 COPD patients, a median of 11.2% [interquartile range (IQR) 1.8-17.4] of patients met eligibility criteria. The most discriminatory criteria included the presence exacerbations of COPD and previous COPD related medication use with a median of 67.6% (IQR 8.5-73.4) and 63% (IQR 69.3-38.4) of COPD patients not meeting these criteria, respectively.

CONCLUSION

Data from these RCTs may not be generalisable to the wider population of people with COPD seen in everyday clinical practice and real-world evidence studies are needed to supplement trials to understand effectiveness in all people with COPD.

Personalised lung cancer risk stratification and lung cancer screening: do general practice electronic medical records have a role?

BACKGROUND

In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility.

METHODS

The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination.

RESULTS

Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots).

DISCUSSION

A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.

Associations of Multimorbidity With Stroke Severity, Subtype, Premorbid Disability, and Early Mortality: Oxford Vascular Study

BACKGROUND AND OBJECTIVES

Patients with multimorbidity are underrepresented in clinical trials. Inclusion in stroke trials is often limited by exclusion based on premorbid disability, concerns about worse poststroke outcomes in acute treatment trials, and a possibly increased proportion of hemorrhagic vs ischemic stroke in prevention trials. Multimorbidity is associated with an increased mortality after stroke, but it is unclear whether this is driven by an increased stroke severity or is confounded by particular stroke subtypes or premorbid disability. We aimed to determine the independent association of multimorbidity with stroke severity taking account of these main potential confounders.

METHODS

In a population-based incidence study (Oxford Vascular Study; 2002-2017), prestroke multimorbidity (Charlson Comorbidity Index [CCI]; unweighted/weighted) in all first-in-study strokes was related to postacute severity (≈24 hours; NIH Stroke Scale [NIHSS]), stroke subtype (hemorrhagic vs ischemic; Trial of Org 10172 in Acute Stroke Treatment [TOAST]), and premorbid disability (modified Rankin scale [mRS] score ≥2) using age-adjusted/sex-adjusted logistic and linear regression models and to 90-day mortality using Cox proportional hazard models.

RESULTS

Among 2,492 patients (mean/SD age = 74.5/13.9 years; 1,216/48.8% male; 2,160/86.7% ischemic strokes; mean/SD NIHSS = 5.7/7.1), 1,402 (56.2%) had at least 1 CCI comorbidity, and 700 (28.1%) had multimorbidity. Although multimorbidity was strongly related to premorbid mRS ≥2 (adjusted odds ratio [aOR] per CCI comorbidity 1.42, 1.31-1.54, p < 0.001), and comorbidity burden was crudely associated with an increased severity of ischemic stroke (OR per comorbidity 1.12, 1.01-1.23 for NIHSS 5-9, p = 0.027; 1.15, 1.06-1.26 for NIHSS ≥10; p = 0.001), no association with severity remained after stratification by TOAST subtype (aOR 1.02, 0.90-1.14, p = 0.78 for NIHSS 5-9 vs 0-4; 0.99, 0.91-1.07, p = 0.75 for NIHSS ≥10 vs 0-4), or within any individual subtype. The proportion of intracerebral hemorrhage vs ischemic stroke was lower in patients with multimorbidity (aOR per comorbidity 0.80, 0.70-0.92, p < 0.001), and multimorbidity was only weakly associated with 90-day mortality after adjustment for age, sex, severity, and premorbid disability (adjusted hazard ratio per comorbidity 1.09, 1.04-1.14, p < 0.001). Results were unchanged using the weighted CCI.

DISCUSSION

Multimorbidity is common in patients with stroke and is strongly related to premorbid disability but is not independently associated with an increased ischemic stroke severity. Greater inclusion of patients with multimorbidity is unlikely therefore to undermine the effectiveness of interventions in clinical trials but would increase external validity.

Frailty in randomised controlled trials for dementia or mild cognitive impairment measured via the frailty index: prevalence and prediction of serious adverse events and attrition

BACKGROUND

Frailty and dementia have a bidirectional relationship. However, frailty is rarely reported in clinical trials for dementia and mild cognitive impairment (MCI) which limits assessment of trial applicability. This study aimed to use a frailty index (FI)-a cumulative deficit model of frailty-to measure frailty using individual participant data (IPD) from clinical trials for MCI and dementia. Moreover, the study aimed to quantify the prevalence of frailty and its association with serious adverse events (SAEs) and trial attrition.

METHODS

We analysed IPD from dementia (n = 1) and MCI (n = 2) trials. An FI comprising physical deficits was created for each trial using baseline IPD. Poisson and logistic regression were used to examine associations with SAEs and attrition, respectively. Estimates were pooled in random effects meta-analysis. Analyses were repeated using an FI incorporating cognitive as well as physical deficits, and results compared.

RESULTS

Frailty could be estimated in all trial participants. The mean physical FI was 0.14 (SD 0.06) and 0.14 (SD 0.06) in the MCI trials and 0.24 (SD 0.08) in the dementia trial. Frailty prevalence (FI > 0.24) was 6.9%/7.6% in MCI trials and 48.6% in the dementia trial. After including cognitive deficits, the prevalence was similar in MCI (6.1% and 6.7%) but higher in dementia (75.4%). The 99th percentile of FI (0.31 and 0.30 in MCI, 0.44 in dementia) was lower than in most general population studies. Frailty was associated with SAEs: physical FI IRR = 1.60 [1.40, 1.82]; physical/cognitive FI IRR = 1.64 [1.42, 1.88]. In a meta-analysis of all three trials, the estimated association between frailty and trial attrition included the null (physical FI OR = 1.17 [0.92, 1.48]; physical/cognitive FI OR = 1.16 [0.92, 1.46]), although higher frailty index values were associated with attrition in the dementia trial.

CONCLUSION

Measuring frailty from baseline IPD in dementia and MCI trials is feasible. Those living with more severe frailty may be under-represented. Frailty is associated with SAEs. Including only physical deficits may underestimate frailty in dementia. Frailty can and should be measured in future and existing trials for dementia and MCI, and efforts should be made to facilitate inclusion of people living with frailty.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials

BACKGROUND

People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD).

METHODS AND FINDINGS

We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities.

CONCLUSIONS

Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Utilization and factors precluding receipt of checkpoint inhibitor consolidation for stage III NSCLC in a large U.S. academic health system

OBJECTIVES

We sought to determine the proportion of patients with stage III non-small cell lung cancer (NSCLC) who initiate consolidation durvalumab or other immune checkpoint inhibitors (ICIs) after concurrent chemoradiotherapy (cCRT), as well as reasons for nonreceipt and prognostic implications.

MATERIALS AND METHODS

We retrospectively identified consecutive patients with unresectable stage III NSCLC treated with definitive cCRT between October 2017 and December 2021 within a large US academic health system. Patients either received consolidation ICIs (ICI group) or did not (no-ICI group). Baseline characteristics and overall survival (OS) of the groups were assessed. Factors predictive of ICI nonreceipt were evaluated using logistic regression.

RESULTS

Of 333 patients who completed cCRT, 229 (69%) initiated consolidation ICIs; 104 (31%) did not. Reasons for ICI nonreceipt included progressive disease post-cCRT (N = 31, 9%), comorbidity or intercurrent illness (N = 25, 8%), cCRT toxicity (N = 23, 7%; 19/23 pneumonitis), and EGFR/ALK alteration (N = 14, 4%). The no-ICI group had worse performance status and a higher rate of baseline pulmonary comorbidity. Larger planning target volume was associated with post-cCRT progressive disease, and higher lung radiation dose with cCRT toxicity. Median OS was 16 months in the no-ICI group and 34.4 months in the ICI group. In the no-ICI group, OS was superior among those with EGFR/ALK alterations (median 44.5 months) and worst among those with progressive disease (median 5.9 months, P < 0.001).

CONCLUSION

31% of patients who completed cCRT for stage III NSCLC did not receive consolidation ICIs. Survival amongst these patients is poor, especially for those with progressive disease post-cCRT.

Correlations between comorbidities in trials and the community: An individual-level participant data meta-analysis

Background

People with comorbidities are under-represented in randomised controlled trials, and it is unknown whether patterns of comorbidity are similar in trials and the community.

Methods

Individual-level participant data were obtained for 83 clinical trials (54,688 participants) for 16 index conditions from two trial repositories: Yale University Open Data Access (YODA) and the Centre for Global Clinical Research Data (Vivli). Community data (860,177 individuals) were extracted from the Secure Anonymised Information Linkage (SAIL) databank for the same index conditions. Comorbidities were defined using concomitant medications. For each index condition, we estimated correlations between comorbidities separately in trials and community data. For the six commonest comorbidities we estimated all pairwise correlations using Bayesian multivariate probit models, conditioning on age and sex. Correlation estimates from trials with the same index condition were combined into a single estimate. We then compared the trial and community estimates for each index condition.

Results

Despite a higher prevalence of comorbidities in the community than in trials, the correlations between comorbidities were mostly similar in both settings. On comparing correlations between the community and trials, 21% of correlations were stronger in the community, 10% were stronger in the trials and 68% were similar in both. In the community, 5% of correlations were negative, 21% were null, 56% were weakly positive and 18% were strongly positive. Equivalent results for the trials were 11%, 33%, 45% and 10% respectively.

Conclusions

Comorbidity correlations are generally similar in both the trials and community, providing some evidence for the reporting of comorbidity-specific findings from clinical trials.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis

BACKGROUND

Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life.

METHODS AND FINDINGS

Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631.

CONCLUSIONS

Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity.

TRIAL REGISTRATION

A prespecified protocol was registered on PROSPERO (CRD42018048202).

Lipid-Lowering Trials Are Not Representative of Patients Managed in Clinical Practice: A Systematic Review and Meta-Analysis of Exclusion Criteria

Background Randomized clinical trials (RCTs) might not be representative of the real-world population because of unreasonable exclusion criteria. We sought to determine which groups of patients are excluded from RCTs that included lipid-lowering therapy. Methods and Results We retrieved all trials from the Cholesterol Treatment Trialists Collaboration and systematically searched for large (≥1000 participants) lipid-lowering therapy RCTs, defined as statins, ezetimibe, and PCSK9 inhibitors. We predefined groups: older adults (>70 or >75 years), women, non-Whites, chronic kidney failure, heart failure, immunosuppression, cancer, dementia, treated thyroid disease, chronic obstructive pulmonary disease, mental illness, atrial fibrillation, multimorbidity (≥2 chronic diseases), and polypharmacy. We counted the number of RCTs excluding patients of the predefined groups and meta-analyzed the prevalence of included patients to obtain pooled estimates with a random-effects model. We included 42 RCTs (298 605 patients). Eighty-one percent of trials excluded patients with severe and 76% those with moderate kidney failure. Seventy-one percent of trials excluded groups of women, 64% excluded patients with moderate to severe heart failure, 64% those with immunosuppressant conditions, 48% those with cancer, 29% those with dementia, and 29% of trials excluded older adults. The pooled prevalence for patients >70 years of age was 25% (95% CI, 0%-49%), 11% (3%-18%) for >75 years of age, and 51% (38%-63%) for multimorbidity. Conclusions The majority of lipid-lowering therapy trials excluded patients with common diseases, such as moderate-to-severe kidney disease or heart failure or with immunosuppression. Underrepresenting certain populations, including women and older adults, might lead to limited transportability of study results and uncertainty on possible side-effects and efficacy in these groups. Future trials should promote diversity in the recruitment strategies and improve equity in cardiovascular research. Registration URL: ClinicalTrials.gov; Unique Identifier: CRD42021253909.

Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates

The populational heterogeneity of a disease, in part due to comorbidity, poses several complexities. Individual comorbidity profiles, on the other hand, contain useful information to refine phenotyping, prognostication, and risk assessment, and they provide clues to underlying biology. Nevertheless, the spectrum and the implications of the diagnosis profiles remain largely uncharted. Here we mapped comorbidity patterns in 100 common diseases using 4-year retrospective data from 526,779 patients and developed an online tool to visualize the results. Our analysis exposed disease-specific patient subgroups with distinctive diagnosis patterns, survival functions, and laboratory correlates. Computational modeling and real-world data shed light on the structure, variation, and relevance of populational comorbidity patterns, paving the way for improved diagnostics, risk assessment, and individualization of care. Variation in outcomes and biological correlates of a disease emphasizes the importance of evaluating the generalizability of current treatment strategies, as well as considering the limitations that selective inclusion criteria pose on clinical trials.

A systematic review and meta-analysis of the effect of intravitreal VEGF inhibitors on cardiorenal outcomes

BACKGROUND

Vascular endothelial growth factor inhibitors (VEGFi) have transformed the treatment of many retinal diseases, including diabetic maculopathy. Increasing evidence supports systemic absorption of intravitreal VEGFi and development of significant cardiorenal side effects.

METHODS

Systematic review and meta-analysis (PROSPERO: CRD42020189037) of randomised controlled trials of intravitreal VEGFi treatments (bevacizumab, ranibizumab and aflibercept) for any eye disease. Outcomes of interest were cardiorenal side effects (hypertension, proteinuria, kidney function decline and heart failure). Fixed-effects meta-analyses were conducted where possible.

RESULTS

There were 78 trials (81 comparisons; 13 175 participants) that met criteria for inclusion: 47% were trials in diabetic eye disease. Hypertension (29 trials; 8570 participants) was equally common in VEGFi and control groups (7.3 versus 5.4%; RR 1.08 [0.91; 1.28]). New or worsening heart failure (10 trials; 3384 participants) had similar incidence in VEGFi and control groups (RR 1.03 [0.70; 1.51]). Proteinuria (5 trials; 1902 participants) was detectable in some VEGFi-treated participants (0.2%) but not controls (0.0%; RR 4.43 [0.49; 40.0]). Kidney function decline (9 trials; 3471 participants) was similar in VEGFi and control groups. In participants with diabetic eye disease, risk of all-cause mortality was higher in VEGFi-treated participants (RR 1.62 [1.04; 2.46]).

CONCLUSION

In trials of intravitreal VEGFi, we did not identify an increased risk of cardiorenal outcomes, though these outcomes were reported in only a minority of cases. There was an increased risk of death in VEGFi-treated participants with diabetic eye disease. Additional scrutiny of post-licensing observational data may improve recognition of safety concerns in VEGFi-treated patients.

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data

BACKGROUND

The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care.

METHODS

This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov . Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity.

RESULTS

For 12/21 index conditions, the pooled observed/expected SAE ratio was <1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates <1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55-0.64; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.87 (0.58-1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most.

CONCLUSIONS

Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common.

Effect of age, sex, and morbidity count on trial attrition: meta-analysis of individual participant level data from phase 3/4 industry funded clinical trials

Objectives

To estimate the association between individual participant characteristics and attrition from randomised controlled trials.

Design

Meta-analysis of individual participant level data (IPD).

Data sources

Clinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).

Eligibility criteria for selecting studies

Eligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).

Main outcome measures

Association between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.

Results

In 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.

Conclusions

Increased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

Cardiovascular Safety Reporting in Contemporary Breast Cancer Clinical Trials

Background

Several cancer therapies have been associated with cardiovascular harm in early‐phase clinical trials. However, some cardiovascular harms do not manifest until later‐phase trials. To limit interdisease variability, we focused on breast cancer. Thus, we assessed the reporting of cardiovascular safety monitoring and outcomes in phase 2 and 3 contemporary breast cancer clinical trials.

Methods and Results

We searched Embase and Medline records for phase 2 and 3 breast cancer pharmacotherapy trials. We examined exclusion criterion as a result of cardiovascular conditions, adverse cardiovascular event reporting, and cardiovascular safety assessment through cardiovascular imaging, ECG, troponin, or natriuretic peptides. Fisher's exact test was utilized to compare reporting. Fifty clinical trials were included in our study. Patients were excluded because of cardiovascular conditions in 42 (84%) trials. Heart failure was a frequent exclusion criterion (n=31; 62% trials). Adverse cardiovascular events were reported in 43 (86%) trials. Cardiovascular safety assessments were not reported in 23 (46%) trials, whereas natriuretic peptide and troponin assessments were not reported in any trial. Cardiovascular safety assessments were more frequently reported in industry‐funded trials (69.2% versus 0.0%; P<0.001), and in trials administering targeted/immunotherapy agents compared with only hormonal/conventional chemotherapy (78.6% versus 22.7%, P<0.001).

Conclusions

Our findings demonstrate significant under‐representation of patients with cardiovascular conditions or prevalent cardiovascular disease in contemporary later‐phase breast cancer trials. Additionally, cardiovascular safety is not routinely monitored in these trials. Therefore, contemporary breast cancer clinical trials may possibly underestimate the cardiovascular risks of cancer pharmacotherapy agents for use in clinical practice.

Projections of dependency and associated social care expenditure for the older population in England to 2038: effect of varying disability progression

Objectives

to assess the effect of recent stalling of life expectancy and various scenarios for disability progression on projections of social care expenditure between 2018 and 2038, and the likelihood of reaching the Ageing Society Grand Challenge mission of five extra healthy, independent years at birth.

Design

two linked projections models: the Population Ageing and Care Simulation (PACSim) model and the Care Policy and Evaluation Centre long-term care projections model, updated to include 2018-based population projections.

Population

PACSim: about 303,589 individuals aged 35 years and over (a 1% random sample of the England population in 2014) created from three nationally representative longitudinal ageing studies.

Main outcome measures

Total social care expenditure (public and private) for older people, and men and women’s independent life expectancy at age 65 (IndLE65) under five scenarios of changing disability progression and recovery with and without lower life expectancy.

Results

between 2018 and 2038, total care expenditure was projected to increase by 94.1%–1.25% of GDP; men’s IndLE65 increasing by 14.7% (range 11.3–16.5%), exceeding the 8% equivalent of the increase in five healthy, independent years at birth, although women’s IndLE65 increased by only 4.7% (range 3.2–5.8%). A 10% reduction in disability progression and increase in recovery resulted in the lowest increase in total care expenditure and increases in both men’s and women’s IndLE65 exceeding 8%.

Conclusions

interventions that slow down disability progression, and improve recovery, could significantly reduce social care expenditure and meet government targets for increases in healthy, independent years.

Pharmacological treatment of major depressive disorder according to severity in psychiatric inpatients: results from the AMSP pharmacovigilance program from 2001-2017

The International Classification of Diseases (10^th Version) categorizes major depressive disorder (MDD) according to severity. Guidelines provide recommendations for the treatment of MDD according to severity. Aim of this study was to assess real-life utilization of psychotropic drugs based on severity of MDD in psychiatric inpatients. Drug utilization data from the program “Drug Safety in Psychiatry” (German: Arzneimittelsicherheit in der Psychiatrie, AMSP) were analyzed according to the severity of MDD. From 2001 to 2017, 43,868 psychiatric inpatients with MDD were treated in participating hospitals. Most patients were treated with ≥ 1 antidepressant drug (ADD; 85.8% of patients with moderate MDD, 89.8% of patients with severe MDD, and 87.9% of patients with psychotic MDD). More severely depressed patients were more often treated with selective serotonin–norepinephrine reuptake inhibitors and mirtazapine and less often with selective serotonin reuptake inhibitors (p < 0.001 each). Use of antipsychotic drugs (APDs), especially second-generation APDs, increased significantly with severity (37.0%, 47.9%, 84.1%; p < 0.001 each). APD + ADD was the most used combination (32.8%, 43.6%, 74.4%), followed by two ADDs (26.3%, 29.3%, 24.9%). Use of lithium was minimal (3.3%, 6.1% ,7.1%). The number of psychotropic drugs increased with severity of MDD—patients with psychotic MDD had the highest utilization of psychotropic drugs (93.4%, 96.5%, 98.7%; p < 0.001). ADD monotherapy was observed to a lesser extent, even in patients with non-severe MDD (23.2%, 17.1%, 4.4%). Findings reveal substantial discrepancies between guideline recommendations and real-life drug utilization, indicating that guidelines may insufficiently consider clinical needs within the psychiatric inpatient setting.

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

BACKGROUND

Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample.

METHODS AND FINDINGS

These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included.

CONCLUSIONS

In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.

Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability

BACKGROUND

SELFBACK, an artificial intelligence (AI)-based app delivering evidence-based tailored self-management support to people with low back pain (LBP), has been shown to reduce LBP-related disability when added to usual care. LBP commonly co-occurs with multimorbidity (≥ 2 long-term conditions) or pain at other musculoskeletal sites, so this study explores if these factors modify the effect of the SELFBACK app or influence outcome trajectories over time.

METHODS

Secondary analysis of a randomized controlled trial with 9-month follow-up. Primary outcome is as follows: LBP-related disability (Roland Morris Disability Questionnaire, RMDQ). Secondary outcomes are as follows: stress/depression/illness perception/self-efficacy/general health/quality of life/physical activity/global perceived effect. We used linear mixed models for continuous outcomes and logistic generalized estimating equation for binary outcomes. Analyses were stratified to assess effect modification, whereas control (n = 229) and intervention (n = 232) groups were pooled in analyses of outcome trajectories.

RESULTS

Baseline multimorbidity and co-occurring musculoskeletal pain sites did not modify the effect of the SELFBACK app. The effect was somewhat stronger in people with multimorbidity than among those with LBP only (difference in RMDQ due to interaction, - 0.9[95 % CI - 2.5 to 0.6]). Participants with a greater number of long-term conditions and more co-occurring musculoskeletal pain had higher levels of baseline disability (RMDQ 11.3 for ≥ 2 long-term conditions vs 9.5 for LBP only; 11.3 for ≥ 4 musculoskeletal pain sites vs 10.2 for ≤ 1 additional musculoskeletal pain site); along with higher baseline scores for stress/depression/illness perception and poorer pain self-efficacy/general health ratings. In the pooled sample, LBP-related disability improved slightly less over time for people with ≥ 2 long-term conditions additional to LBP compared to no multimorbidity and for those with ≥4 co-occurring musculoskeletal pain sites compared to ≤ 1 additional musculoskeletal pain site (difference in mean change at 9 months = 1.5 and 2.2, respectively). All groups reported little improvement in secondary outcomes over time.

CONCLUSIONS

Multimorbidity or co-occurring musculoskeletal pain does not modify the effect of the selfBACK app on LBP-related disability or other secondary outcomes. Although people with these health problems have worse scores both at baseline and 9 months, the AI-based selfBACK app appears to be helpful for those with multimorbidity or co-occurring musculoskeletal pain.

TRIAL REGISTRATION

NCT03798288 . Date of registration: 9 January 2019.

SUBCLINICAL INFLAMMATION IN NON/ALCOHOLIC FATTY LIVER DISEASE AT THE REMOTE PERIOD AFTER THE CHORNOBYL ACCIDENT

OBJECTIVE

to evaluate the parameters of inflammatory reaction and oxidative stress in patients with non-alcoholicfatty liver disease (NAFLD) in the remote period after the influence of the Chornobyl accident factors.

MATERIALS AND METHODS

Eighty two patients with NAFLD who had been exposed to ionizing radiation as a result ofthe Chornobyl accident and have concomitant cardiovascular pathology were examined. Hematological parametersand the level of highly sensitive C-reactive protein (hsCRP) were determined, and the content of products of oxida-tive modification of lipids and proteins was evaluated.

RESULTS

Activation of the processes of oxidative modification of lipids and proteins was observed in most patientswith NAFLD. According to the level of hsCRP, the presence of subclinical inflammation and the risk of developingcomplicated cardiovascular pathology was found in 58 % of patients with NAFLD. The neutrophil / lymphocyte ratiocorrelates positively with hsCRP and can be used as an available routine clinical marker for selection among patientswith NAFLD persons with increased risk of cardiovascular complications.

CONCLUSIONS

HsCRP, oxidative modification products of lipids and proteins, ESR, and leukograms should be used toassess the degree of systemic inflammation in people affected by the Chornobyl accident, suffering NAFLD with con-comitant cardiovascular disease.

Digital Technology Application for Improved Responses to Health Care Challenges: Lessons Learned From COVID-19

While COVID-19 is still ongoing and associated with more than 5 million deaths, the scope and speed of advances over the past year in terms of scientific discovery, data dissemination, and technology have been staggering. It is not a matter of “if” but “when” we will face the next pandemic, and how we leverage technology and data management effectively to create flexible ecosystems that facilitate collaboration, equitable care, and innovation will determine its severity and scale. The aim of this review is to address emerging challenges that came to light during the pandemic in health care and innovations that enabled us to adapt and continue to care for patients. The pandemic highlighted the need for seismic shifts in care paradigms and technology with considerations related to the digital divide and health literacy for digital health interventions to reach full potential and improve health outcomes. We discuss advances in telemedicine, remote patient monitoring, and emerging wearable technologies. Despite the promise of digital health, we emphasise the importance of addressing its limitations, including interpretation challenges, accuracy of findings, and artificial intelligence–driven algorithms. We summarise the most recent recommendation of the Virtual Care Task Force to scaling virtual medical services in Canada. Finally, we propose a model for optimal implementation of health digital innovations with 5 tenets including data management, data security, digital biomarkers, useful artificial intelligence, and clinical integration.

Hospitalisation events in people with chronic kidney disease as a component of multimorbidity: parallel cohort studies in research and routine care settings

BACKGROUND

Chronic kidney disease (CKD) typically co-exists with multimorbidity (presence of 2 or more long-term conditions: LTCs). The associations between CKD, multimorbidity and hospitalisation rates are not known. The aim of this study was to examine hospitalisation rates in people with multimorbidity with and without CKD. Amongst people with CKD, the aim was to identify risk factors for hospitalisation.

METHODS

Two cohorts were studied in parallel: UK Biobank (a prospective research study: 2006-2020) and Secure Anonymised Information Linkage Databank (SAIL: a routine care database, Wales, UK: 2011-2018). Adults were included if their kidney function was measured at baseline. Nine categories of participants were used: zero LTCs; one, two, three and four or more LTCs excluding CKD; and one, two, three and four or more LTCs including CKD. Emergency hospitalisation events were obtained from linked hospital records.

RESULTS

Amongst 469,339 UK Biobank participants, those without CKD had a median of 1 LTC and those with CKD had a median of 3 LTCs. Amongst 1,620,490 SAIL participants, those without CKD had a median of 1 LTC and those with CKD had a median of 5 LTCs. Compared to those with zero LTCs, participants with four or more LTCs (excluding CKD) had high event rates (rate ratios UK Biobank 4.95 (95% confidence interval 4.82-5.08)/SAIL 3.77 (3.71-3.82)) with higher rates if CKD was one of the LTCs (rate ratios UK Biobank 7.83 (7.42-8.25)/SAIL 9.92 (9.75-10.09)). Amongst people with CKD, risk factors for hospitalisation were advanced CKD, age over 60, multiple cardiometabolic LTCs, combined physical and mental LTCs and complex patterns of multimorbidity (LTCs in three or more body systems).

CONCLUSIONS

People with multimorbidity have high rates of hospitalisation. Importantly, the rates are two to three times higher when CKD is one of the multimorbid conditions. Further research is needed into the mechanism underpinning this to inform strategies to prevent hospitalisation in this very high-risk group.

Generalisability of Randomised Controlled Trials in Heart Failure with Reduced Ejection Fraction

Background

Heart failure (HF) trials have stringent inclusion and exclusion criteria, but limited data exist regarding generalizability of trials. We compared patient characteristics and outcomes between patients with HF and reduced ejection fraction (HFrEF) in trials and observational registries.

Methods and Results

Individual patient data for 16 922 patients from five randomized clinical trials and 46 914 patients from two HF registries were included. The registry patients were categorized into trial-eligible and non-eligible groups using the most commonly used inclusion and exclusion criteria. A total of 26 104 (56%) registry patients fulfilled the eligibility criteria. Unadjusted all-cause mortality rates at 1 year were lowest in the trial population (7%), followed by trial-eligible patients (12%) and trial-non-eligible registry patients (26%). After adjustment for age and sex, all-cause mortality rates were similar between trial participants and trial-eligible registry patients [standardized mortality ratio (SMR) 0.97; 95% confidence interval (CI) 0.92–1.03] but cardiovascular mortality was higher in trial participants (SMR 1.19; 1.12–1.27). After full case-mix adjustment, the SMR for cardiovascular mortality remained higher in the trials at 1.28 (1.20–1.37) compared to RCT-eligible registry patients.

Conclusion

In contemporary HF registries, over half of HFrEF patients would have been eligible for trial enrolment. Crude clinical event rates were lower in the trials, but, after adjustment for case-mix, trial participants had similar rates of survival as registries. Despite this, they had about 30% higher cardiovascular mortality rates. Age and sex were the main drivers of differences in clinical outcomes between HF trials and observational HF registries.

Improving the Estimation of Subgroup Effects for Clinical Trial Participants with Multimorbidity by Incorporating Drug Class-Level Information in Bayesian Hierarchical Models: A Simulation Study

Background

There is limited guidance for using common drug therapies in the context of multimorbidity. In part, this is because their effectiveness for patients with specific comorbidities cannot easily be established using subgroup analyses in clinical trials. Here, we use simulations to explore the feasibility and implications of concurrently estimating effects of related drug treatments in patients with multimorbidity by partially pooling subgroup efficacy estimates across trials.

Methods

We performed simulations based on the characteristics of 161 real clinical trials of noninsulin glucose-lowering drugs for diabetes, estimating subgroup effects for patients with a hypothetical comorbidity across related trials in different scenarios using Bayesian hierarchical generalized linear models. We structured models according to an established ontology—the World Health Organization Anatomic Chemical Therapeutic Classifications—allowing us to nest all trials within drugs and all drugs within anatomic chemical therapeutic classes, with effects partially pooled at each level of the hierarchy. In a range of scenarios, we compared the performance of this model to random effects meta-analyses of all drugs individually.

Results

Hierarchical, ontology-based Bayesian models were unbiased and accurately recovered simulated comorbidity-drug interactions. Compared with single-drug meta-analyses, they offered a relative increase in precision of up to 250% in some scenarios because of information sharing across the hierarchy. Because of the relative precision of the approaches, a large proportion of small subgroup effects was detectable only using the hierarchical model.

Conclusions

By assuming that similar drugs may have similar subgroup effects, Bayesian hierarchical models based on structures defined by existing ontologies can be used to improve the precision of treatment efficacy estimates in patients with multimorbidity, with potential implications for clinical decision making.

An Update on Medication Use in Older Adults: a Narrative Review

Purpose of Review

The global phenomenon of population aging is impacting the health and care needs of society. The use of medications by older adults is acknowledged to be the most common form of medical intervention for many acute and chronic conditions and prescribing in this population continues to increase. In this narrative review, we summarise the age-related factors that should be considered when prescribing for older adults, address some of the perennial challenges related to medicine use in older people, and highlight important emerging research in this area.

Recent Findings

A range of age-related factors should be considered when prescribing for older adults. However, the evidence base still lacks data pertaining to older adults due to their continued under-representation in clinical trials. Multimorbidity, polypharmacy, and inappropriate prescribing continue to remain prevalent among older adults, although recent research has been focused on the development and evaluation of complex interventions to address these challenges.

Summary

Further high-quality studies of interventions to improve and support medication use in older adults are needed, ensuring that older adults are well represented in such trials and consideration is given to the measurement of patient- and provider-focused outcomes.

Observed and expected serious adverse event rates in randomised clinical trials for hypertension: an observational study comparing trials that do and do not focus on older people

BACKGROUND

Representativeness of antihypertensive drug trials is uncertain, as many trials recruit few or no older people. Some trials specifically recruit older participants to address this. Here, we assess the representativeness of trials focusing on older people by comparing the rates of serious adverse events in these trials with the rates in trials of a general adult population (ie, standard trials), and comparing these findings to the rate of hospitalisations and deaths in people with hypertension starting a similar treatment in routine clinical practice.

METHODS

For this observational study, we identified randomised controlled trials (phase 2/3, 3, or 4) of renin-angiotensin-aldosterone system (RAAS) drugs for hypertension registered from 1999 onwards with ClinicalTrials.gov. Serious adverse events are routinely included in trial reports and are predominantly accounted for by all-cause hospitalisations and deaths. We compared serious adverse event rates in older-people trials (minimum inclusion age ≥60 years) and standard trials (minimum inclusion age <60 years) using Poisson regression models adjusted for trial characteristics (drug type, comparison type, phase, and outcome type). We identified a community cohort of 56 036 adults with hypertension commencing similar drugs to obtain an expected rate of emergency or urgent hospitalisations or deaths, and compared this rate to observed serious adverse event rates in each trial, adjusted for age and sex. For standard trials and for older-people trials, we calculated the standardised ratio of the expected to the observed rate of serious adverse events using Poisson regression models.

FINDINGS

We included 110 trials, of which 11 (10%) were older-people trials and 99 (90%) were standard trials. Older-people trials had a higher rate of serious adverse events than did standard trials (median events per person per year 0·18 [IQR 0·12-0·29] vs 0·11 [0·08-0·18]; adjusted incidence rate ratio 1·76 [95% CI 1·01-3·03]). The hospitalisation and death rate in the community for those taking RAAS antihypertensives was much greater than the rate of serious adverse events reported in standard trials (standardised ratio [SR] 4·23, 95% CI 3·51-5·09) and older-people trials (4·76, 2·89-7·86), adjusting for age and sex. The magnitude of risk increase for serious adverse events in community patients taking RAAS did not differ when comparing older-people and standard trials (ratio of SRs 1·13, 95% CI 0·66-1·92).

INTERPRETATION

Trials report substantially fewer serious adverse events than expected from rates of hospitalisations and deaths among similar-aged people receiving equivalent treatments in the community. Serious adverse event rates might be a useful metric to assess trial representativeness. Clinicians should be cautious when applying trial recommendations to older people, even when trials focus on older participants.

FUNDING

Wellcome Trust, Medical Research Council.

External validity of type 2 diabetes clinical trials on cardiovascular outcomes for a multimorbid population

AIM

To investigate the external validity of recent antihyperglycaemic trials evaluating cardiovascular outcomes in a multimorbid population.

MATERIALS AND METHODS

Selection criteria of 15 randomized controlled trials from the 2020 American Diabetes Association Standard of Care statement were applied in a stepwise manner to tertiary care patients with type 2 diabetes. Primary outcomes were the number of patients eligible per individual trial and for the aggregate of trials. Secondary outcomes included patient predictors of trial eligibility.

RESULTS

Of 1059 patients, the mean (SD) age was 66 (10.74) years, the median (IQR) Charlson index was 2 (2, 3) and 458 (43%) had documented cardiovascular disease. The median (IQR) number of patients included in individual trials was 263 (174.25-308.75) and 795 (75.1%) of them were eligible for at least one trial. Among those 264 ineligible, 127 (48.1%) had an HbA1c level of 7% or less and no cardiovascular disease; 53.5% and 34.4% of the patients were eligible for two and three different classes of drugs, respectively. The strongest predictor of trial eligibility was cardiovascular disease (risk ratio 2.17, 95% CI 2.01-2.35).

CONCLUSIONS

A considerable proportion of multimorbid patients would be eligible for recent antihyperglycaemic trials. This positive finding can be attributed to development guidance in diabetes trials and the different approach we took, in which we evaluated inclusion by trials as an aggregate.

The association of comorbidities with administration of adjuvant chemotherapy in stage III colon cancer patients: a systematic review and meta-analysis

Background:

Chemotherapy is an established treatment for stage III colon cancer cases. Older age is known to be associated with less chemotherapy use in these patients, but there might be other relevant factors besides age that influence treatment administration. We summarized evidence on associations between comorbidity and adjuvant chemotherapy administration in stage III colon cancer patients in a systematic review and meta-analysis.

Methods:

We searched the PubMed and Web of Science databases up to 2 June 2020 for studies on comorbidities and chemotherapy use in patients with stage III colon cancer. Summary odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using random-effects models. Subgroup analyses according to year of colon cancer diagnosis, timing of comorbidity assessment, and geographical region were also conducted.

Results:

Thirty-three studies were included in this review, including 219,406 stage III colon cancer patients overall. Chemotherapy administration was 60.9% (95% CI: 56.9% to 64.9%), increasing from 57.1% before 2001 to 66.3% after 2010. There were inverse associations between comorbidities and chemotherapy administration. Compared with patients with Charlson comorbidity score 0, those with scores 1 (OR = 0.79, 95% CI = 0.72–0.87) and 2+ (OR = 0.49, 95% CI = 0.42–0.56) received chemotherapy less often. Among comorbidities, the strongest predictors of chemotherapy non-use were dementia (OR = 0.37, 95% CI = 0.33–0.54), followed by heart failure (OR = 0.44, 95% CI = 0.28–0.70) and stroke (OR = 0.56, 95% CI = 0.38–0.81).

Conclusions:

Merely 60% of stage III colon cancer patients receive chemotherapy. Comorbidities are strong predictors of chemotherapy non-use, but the association differs by comorbid condition and is strongest with dementia. Given the survival disadvantage of colon cancer patients with comorbidities, further evidence on the risk–benefit ratio of chemotherapy according to the type and severity of comorbidity and on the extent to which the survival disadvantage of comorbidity is explained by less use or lower tolerability of chemotherapy is needed to foster personalized medical care in these patients.

The use of geroprotectors to prevent multimorbidity: Opportunities and challenges

Over 60 % of people over the age of 65 will suffer from multiple diseases concomitantly but the common approach is to treat each disease separately. As age-associated diseases have common underlying mechanisms there is potential to tackle many diseases with the same pharmacological intervention. These are known as geroprotectors and could overcome the problems related to polypharmacy seen with the use of the single disease model. With some geroprotectors now reaching the end stage of preclinical studies and early clinical trials, there is a need to review the evidence and assess how they can be translated practically and effectively into routine practice. Despite promising evidence, there are many gaps and challenges in our understanding that must be addressed to make geroprotective medicine effective in the treatment of age-associated multimorbidity. Here we highlight the key barriers to clinical translation and discuss whether geroprotectors such as metformin, rapamycin and senolytics can tackle all age-associated diseases at the same dose, or whether a more nuanced approach is required. The evidence suggests that geroprotectors' mode of action may differ in different tissues or in response to different inducers of accelerating ageing, suggesting that a blunt 'one drug for many diseases' approach may not work. We make the case for the use of artificial intelligence to better understand multimorbidity, allowing identification of clusters and networks of diseases that are significantly associated beyond chance and the underpinning molecular pathway of ageing causal to each cluster. This will allow us to better understand the development of multimorbidity, select a more homogenous group of patients for intervention, match them with the appropriate geroprotector and identify biomarkers specific to the cluster.

Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions

BACKGROUND

Frailty is common in clinical practice, but trials rarely report on participant frailty. Consequently, clinicians and guideline-developers assume frailty is largely absent from trials and have questioned the relevance of trial findings to frail people. Therefore, we examined frailty in phase 3/4 industry-sponsored clinical trials of pharmacological interventions for three exemplar conditions: type 2 diabetes mellitus (T2DM), rheumatoid arthritis (RA), and chronic obstructive pulmonary disease (COPD).

METHODS

We constructed a 40-item frailty index (FI) in 19 clinical trials (7 T2DM, 8 RA, 4 COPD, mean age 42-65 years) using individual-level participant data. Participants with a FI > 0.24 were considered 'frail'. Baseline disease severity was assessed using HbA1c for T2DM, Disease Activity Score-28 (DAS28) for RA, and % predicted FEV1 for COPD. Using generalised gamma regression, we modelled FI on age, sex, and disease severity. In negative binomial regression, we modelled serious adverse event rates on FI and combined results for each index condition in a random-effects meta-analysis.

RESULTS

All trials included frail participants: prevalence 7-21% in T2DM trials, 33-73% in RA trials, and 15-22% in COPD trials. The 99th centile of the FI ranged between 0.35 and 0.45. Female sex was associated with higher FI in all trials. Increased disease severity was associated with higher FI in RA and COPD, but not T2DM. Frailty was associated with age in T2DM and RA trials, but not in COPD. Across all trials, and after adjusting for age, sex, and disease severity, higher FI predicted increased risk of serious adverse events; the pooled incidence rate ratios (per 0.1-point increase in FI scale) were 1.46 (95% CI 1.21-1.75), 1.45 (1.13-1.87), and 1.99 (1.43-2.76) for T2DM, RA, and COPD, respectively.

CONCLUSION

The upper limit of frailty in trials is lower than has been described in the general population. However, mild to moderate frailty was common, suggesting trial data may be harnessed to inform disease management in people living with frailty. Participants with higher FI experienced more serious adverse events, suggesting screening for frailty in trial participants would enable identification of those that merit closer monitoring. Frailty is identifiable and prevalent among middle-aged and older participants in phase 3/4 drug trials and has clinically important safety implications.

[State of the coronary arteries and assessment of the role of hormone replacement therapy in patients with coronary heart disease against the background of primary manifest hypothyroidism]

Aim To study features of coronary damage and incidence of different types of acute coronary syndrome (ACS) in history associated with primary symptomatic hypothyroidism in patients with ischemic heart disease (IHD) and possible associations of replacement hormonal therapy with lipidogram indexes.Material and methods This retrospective study included 344 patients with IHD and functional class I-III stable angina (ССS, 1976). Of them 100 patients had primary symptomatic hypothyroidism and 244 had no hypothyroidism. Coronary angiography was performed for all patients included into this study. Routine laboratory, instrumental and clinical indexes were analyzed. Hypothyroidism was confirmed by levels of thyrotropic hormone, free triiodothyronine, and thyroxine. Comparative analysis was performed for the incidence of ACS types in history, types of coronary injury, and laboratory, instrumental and clinical indexes with assessment of potential interrelations. Statistically significant results were reported. Type of data distribution was evaluated with the Kolmogorov-Smirnov test. Quantitative data with normal (Gaussian) distribution were presented as mean (M) and standard deviation (SD). Data with attributes of non-normal distribution were presented as median (Me) with maximum and minimum values (min; max). Statistical significance of differences between means was assessed with the Mann-Whitney test. Logistic regression analysis was used in parallel for evaluating dependence of a quantitative variable on values of two or more quantitative or qualitative variables (factors). Significance level for testing of statistical hypotheses was р&lt;0.05.Results Incidence of ST segment elevation ACS (STEACS) was significantly higher in IHD patients with hypothyroidism than in the group without hypothyroidism (61.6 and 35.6 %, р=0.03) and also with three-vessel coronary artery disease (60.6 and 30.6 %, р=0.001). In the IHD group with hypothyroidism, levels of total cholesterol, triglycerides, and low- and very low-density lipoproteins were significantly increased compared to the respective values in patients without hypothyroidism (р&lt;0.0001). An inverse correlation was found between lipidogram indexes and L-thyroxine (р&lt;0.0001).Conclusion The incidence of STEACS associated with primary symptomatic hypothyroidism in history was significantly higher in the patient group with IHD on the background of primary symptomatic hypothyroidism compared to the comparison group. Also, the incidence of three-vessel coronary disease was significantly greater than in the IHD patient group without hypothyroidism. A significant association was found between the replacement hormonal therapy and the best lipidogram indexes. The authors suggested that the key factor for prevention of adverse cardiovascular events in IHD with hypothyroidism is achieving control of clinical manifestations of hypothyroidism with replacement hormonal therapy.

Medical cannabis use in the United States: a retrospective database study

Introduction

Growing interest in the medicinal properties of cannabis has led to an increase in its use to treat medical conditions, and the establishment of state-specific medical cannabis programs. Despite medical cannabis being legal in 33 states and the District of Colombia, there remains a paucity of data characterizing the patients accessing medical cannabis programs.

Methods

We retrospectively reviewed a registry with data from 33 medical cannabis evaluation clinics in the United States, owned and operated by CB2 Insights. Data were collected primarily by face-to-face interviews for patients seeking medical cannabis certification between November 18, 2018 and March 18, 2020. Patients were removed from the analysis if they did not have a valid date of birth, were less than 18, or did not have a primary medical condition reported; a total of 61,379 patients were included in the analysis. Data were summarized using descriptive statistics expressed as a mean (standard deviation (SD)) or median (interquartile range (IQR)) as appropriate for continuous variables, and number (percent) for categorical variables. Statistical tests performed across groups included t-tests, chi-squared tests and regression.

Results

The average age of patients was 45.5, 54.8% were male and the majority were Caucasian (87.5%). Female patients were significantly older than males (47.0 compared to 44.6). Most patients reported cannabis experience prior to seeking medical certification (66.9%). The top three mutually exclusive primary medical conditions reported were unspecified chronic pain (38.8%), anxiety (13.5%) and post-traumatic stress disorder (PTSD) (8.4%). The average number of comorbid conditions reported was 2.7, of which anxiety was the most common (28.3%). Females reported significantly more comorbid conditions than males (3.1 compared to 2.3).

Conclusion

This retrospective study highlighted the range and number of conditions for which patients in the US seek medical cannabis. Rigorous clinical trials investigating the use of medical cannabis to treat pain conditions, anxiety, insomnia, depression and PTSD would benefit a large number of patients, many of whom use medical cannabis to treat multiple conditions.

Identification and prevalence of frailty in diabetes mellitus and association with clinical outcomes: a systematic review protocol

INTRODUCTION

Diabetes mellitus is common and growing in prevalence, and an increasing proportion of people with diabetes are living to older age. Frailty is, therefore, becoming an important concept in diabetes. Frailty is associated with older age and describes a state of increased susceptibility to decompensation in response to physiological stress. A range of measures have been used to quantify frailty. This systematic review aims to identify measures used to quantify frailty in people with diabetes (any type); to summarise the prevalence of frailty in diabetes; and to describe the relationship between frailty and adverse clinical outcomes in people with diabetes.

METHODS AND ANALYSIS

Three electronic databases (Medline, Embase and Web of Science) will be searched from 2000 to November 2019 and supplemented by citation searching of relevant articles and hand searching of reference lists. Two reviewers will independently review titles, abstracts and full texts. Inclusion criteria include: (1) adults with any type of diabetes mellitus; (2) quantify frailty using any validated frailty measure; (3) report the prevalence of frailty and/or the association between frailty and clinical outcomes in people with diabetes; (4) studies that assess generic (eg, mortality, hospital admission and falls) or diabetes-specific outcomes (eg, hypoglycaemic episodes, cardiovascular events, diabetic nephropathy and diabetic retinopathy); (5) cross-sectional and longitudinal observational studies. Study quality will be assessed using the Newcastle-Ottawa Scale for observational studies. Clinical and methodological heterogeneity will be assessed, and a random effects meta-analysis performed if appropriate. Otherwise, a narrative synthesis will be performed.

ETHICS AND DISSEMINATION

This manuscript describes the protocol for a systematic review of observational studies and does not require ethical approval.

PROSPERO REGISTRATION NUMBER

CRD42020163109.

Associations between multimorbidity and adverse clinical outcomes in patients with chronic kidney disease: a systematic review and meta-analysis

OBJECTIVE

To systematically review the literature exploring the associations between multimorbidity (the presence of two or more long-term conditions (LTCs)) and adverse clinical outcomes in patients with chronic kidney disease (CKD).

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, EMBASE, CINAHL, Cochrane Library and SCOPUS (1946-2019). The main search terms were 'Chronic Kidney Failure' and 'Multimorbid*'.

ELIGIBILITY CRITERIA

Observational studies of adults over the age of 18 with CKD stages 3-5, that is, estimated glomerular filtration rate less than 60 mL/min/1.73 m2. The exposure was multimorbidity quantified by measures and the outcomes were all-cause mortality, renal progression, hospitalisation and cardiovascular events. We did not consider CKD as a comorbid LTC.

DATA EXTRACTION AND SYNTHESIS

Newcastle-Ottawa Scale for quality appraisal and risk of bias assessment and fixed effects meta-analysis for data synthesis.

RESULTS

Of 1852 papers identified, 26 met the inclusion criteria. 21 papers involved patients with advanced CKD and no studies were from low or middle-income countries. All-cause mortality was an outcome in all studies. Patients with multimorbidity were at higher risk of mortality compared with patients without multimorbidity (total risk ratio 2.28 (95% CI 1.81 to 2.88)). The risk of mortality was higher with increasing multimorbidity (total HR 1.31 (95% CI 1.27 to 1.36)) and both concordant and discordant LTCs were associated with heightened risk. Multimorbidity was associated with renal progression in four studies, hospitalisation in five studies and cardiovascular events in two studies.

LIMITATIONS

Meta-analysis could only include 10 of 26 papers as the methodologies of studies were heterogeneous.

CONCLUSIONS

There are associations between multimorbidity and adverse clinical outcomes in patients with CKD. However, most data relate to mortality risk in patients with advanced CKD. There is limited evidence regarding patients with mild to moderate CKD, outcomes such as cardiovascular events, types of LTCs and regarding patients from low or middle-income countries.

PROSPERO REGISTRATION NUMBER

CRD42019147424.