A Robotic Clamped-Kinematic System to Study Knee Ligament Injury.

Knee ligament injury is among the most common sports injuries and is associated with long recovery periods and low return-to-sport rates. Unfortunately, the mechanics of ligament injury are difficult to study in vivo, and computational studies provide limited insight. The objective of this study was to implement and validate a robotic system capable of reproducing natural six degree-of-freedom clamped-kinematic trajectories on human cadaver knees (meaning that positions and orientations are rigidly controlled and resultant loads are measured). To accomplish this, we leveraged the field's recent access to high-fidelity bone kinematics from dynamic biplanar radiography (DBR), and implemented these kinematics in a coordinate frame built around the knee's natural flexion-extension axis. We assessed our system's capabilities in the context of ACL injury, by moving seven cadaveric knee specimens through kinematics derived from walking, running, drop jump, and ACL injury. We then used robotically simulated clinical stability tests to evaluate the hypothesis that knee stability would be only reduced by the motions intended to injure the knee. Our results show that the structural integrity of the knee was not compromised by non-injurious motions, while the injury motion produced a clinically relevant ACL injury with characteristic anterior and valgus instability. We also demonstrated that our robotic system can provide direct measurements of reaction loads during a variety of motions, and facilitate gross evaluation of ligament failure mechanisms. Clamped-kinematic robotic evaluation of cadaver knees has the potential to deepen understanding of the mechanics of knee ligament injury.

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).

Loop diuretic therapy with or without heart failure: impact on prognosis.

BACKGROUND AND AIMS: Many patients are prescribed loop diuretics without a diagnostic record of heart failure. Little is known about their characteristics and prognosis. METHODS: Glasgow regional health records (2009-16) were obtained for adults with cardiovascular disease or taking loop diuretics. Outcomes were investigated using Cox models with hazard ratios adjusted for age, sex, socioeconomic deprivation, and comorbid disease (adjHR). RESULTS: Of 198 898 patients (median age 65 years; 55% women), 161 935 (81%) neither took loop diuretics nor had a diagnostic record of heart failure (reference group), 23 963 (12%) were taking loop diuretics but had no heart failure recorded, 7844 (4%) had heart failure recorded and took loop diuretics, and 5156 (3%) had heart failure recorded but were not receiving loop diuretics. Compared to the reference group, five-year mortality was only slightly higher for heart failure in the absence of loop diuretics [22%; adjHR 1.2 (95% CI 1.1-1.3)], substantially higher for those taking loop diuretics with no record of heart failure [40%; adjHR 1.8 (95% CI 1.7-1.8)], and highest for heart failure treated with loop diuretics [52%; adjHR 2.2 (95% CI 2.0-2.2)]. CONCLUSIONS: For patients with cardiovascular disease, many are prescribed loop diuretics without a recorded diagnosis of heart failure. Mortality is more strongly associated with loop diuretic use than with a record of heart failure. The diagnosis of heart failure may be often missed, or loop diuretic use is associated with other conditions with a prognosis similar to heart failure, or inappropriate loop diuretic use increases mortality; all might be true.

Impact of COVID-19 and Non-COVID-19 Hospitalized Pneumonia on Longer-Term Cardiovascular Mortality in People With Type 2 Diabetes: A Nationwide Prospective Cohort Study From Scotland.

OBJECTIVE: In this study we examine whether hospitalized coronavirus disease 2019 (COVID-19) pneumonia increases long-term cardiovascular mortality more than other hospitalized pneumonias in people with type 2 diabetes and aim to quantify the relative cardiovascular disease (CVD) mortality risks associated with COVID-19 versus non-COVID-19 pneumonia. RESEARCH DESIGN AND METHODS: With use of the SCI-Diabetes register, two cohorts were identified: individuals with type 2 diabetes in 2016 and at the 2020 pandemic onset. Hospital and death records were linked for determination of pneumonia exposure and CVD deaths. Poisson regression estimated rate ratios (RRs) for CVD death associated with both pneumonia types, with adjustment for confounders. Median follow-up durations were 1,461 days (2016 cohort) and 700 days (2020 cohort). RESULTS: The adjusted RR for CVD death following non-COVID-19 pneumonia was 5.51 (95% CI 5.31-5.71) prepandemic and 7.3 (6.86-7.76) during the pandemic. For COVID-19 pneumonia, the RR was 9.13 (8.55-9.75). Beyond 30 days post pneumonia, the RRs converged, to 4.24 (3.90-4.60) for non-COVID-19 and 3.35 (3.00-3.74) for COVID-19 pneumonia, consistent even with exclusion of prior CVD cases. CONCLUSIONS: Hospitalized pneumonia, irrespective of causal agent, marks an increased risk for CVD death immediately and over the long-term. COVID-19 pneumonia poses a higher CVD death risk than other pneumonias in the short-term, but this distinction diminishes over time. These insights underscore the need for including pneumonia in CVD risk assessments, with particular attention to the acute impact of COVID-19 pneumonia.

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.

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.

Assessing the performance of physician's prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study.

Aim: This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials & methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.

Sex differences in cancer outcomes across the range of eGFR.

BACKGROUND AND HYPOTHESIS: People with chronic kidney disease (CKD) have increased incidence and mortality from most cancer types. We hypothesised that odds of presenting with advanced cancer may vary according to differences in eGFR, that this could contribute to increased all-cause mortality and that sex differences may exist. METHODS: Data were from Secure Anonymised Information Linkage Databank, including people with de-novo cancer diagnosis (2011-2017) and two kidney function tests within two years prior to diagnosis to determine baseline eGFR (mL/min/1.73m2). Logistic regression models determined odds of presenting with advanced cancer by baseline eGFR. Cox proportional hazards models tested associations between baseline eGFRcr and all-cause mortality. RESULTS: eGFR < 30 was associated with higher odds of presenting with advanced cancer of prostate, breast and female genital organs, but not other cancer sites. Compared to eGFR > 75-90, eGFR < 30 was associated with greater hazards of all-cause mortality in both sexes, but the association was stronger in females (female: HR 1.71, 95%CI 1.56-1.88; male versus female comparison HR 0.88, 95%CI 0.78-0.90). CONCLUSIONS: Lower or higher eGFR was not associated with substantially higher odds of presenting with advanced cancer across most cancer sites, but was associated with reduced survival. A stronger assocation with all-cause mortality in females compared to males with eGFR < 30 is concerning and warrants further scrutiny.

An observational analysis of frailty in combination with loneliness or social isolation and their association with socioeconomic deprivation, hospitalisation and mortality among UK Biobank participants.

Frailty, social isolation, and loneliness have individually been associated with adverse health outcomes. This study examines how frailty in combination with loneliness or social isolation is associated with socioeconomic deprivation and with all-cause mortality and hospitalisation rate in a middle-aged and older population. Baseline data from 461,047 UK Biobank participants (aged 37-73) were used to assess frailty (frailty phenotype), social isolation, and loneliness. Weibull models assessed the association between frailty in combination with loneliness or social isolation and all-cause mortality adjusted for age/sex/smoking/alcohol/socioeconomic-status and number of long-term conditions. Negative binomial regression models assessed hospitalisation rate. Frailty prevalence was 3.38%, loneliness 4.75% and social isolation 9.04%. Frailty was present across all ages and increased with age. Loneliness and social isolation were more common in younger participants compared to older. Co-occurrence of frailty and loneliness or social isolation was most common in participants with high socioeconomic deprivation. Frailty was associated with increased mortality and hospitalisation regardless of social isolation/loneliness. Hazard ratios for mortality were 2.47 (2.27-2.69) with social isolation and 2.17 (2.05-2.29) without social isolation, 2.14 (1.92-2.38) with loneliness and 2.16 (2.05-2.27) without loneliness. Loneliness and social isolation were associated with mortality and hospitalisation in robust participants, but this was attenuated in the context of frailty. Frailty and loneliness/social isolation affect individuals across a wide age spectrum and disproportionately co-occur in areas of high deprivation. All were associated with adverse outcomes, but the association between loneliness and social isolation and adverse outcomes was attenuated in the context of frailty. Future interventions should target people living with frailty or loneliness/social isolation, regardless of age.

Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data.

BACKGROUND: Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI). METHODS: We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D'Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor. RESULTS: In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801-27 in SDI groups I; 623-23 in SDI group V). CONCLUSIONS: Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models.

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.

Validating the SMART2 Score in a Racially Diverse High-Risk Nationwide Cohort of Patients Receiving Coronary Artery Bypass Grafting.

Background We tested the potential of the Secondary Manifestations of Arterial Disease (SMART2) risk score for use in patients undergoing coronary artery bypass grafting. Methods and Results We conducted an external validation of the SMART2 score in a racially diverse high-risk national cohort (2010-2019) that underwent isolated coronary artery bypass grafting. We calculated the preoperative SMART2 score and modeled the 5-year major adverse cardiovascular event (cardiovascular mortality+myocardial infarction+stroke) incidence. We evaluated SMART2 score discrimination at 5 years using c-statistic and calibration with observed/expected ratio and calibration plots. We analyzed the potential clinical benefit using decision curves. We repeated these analyses in clinical subgroups, diabetes, chronic kidney disease, and polyvascular disease, and separately in White and Black patients. In 27 443 (mean age, 65 years; 10% Black individuals) US veterans undergoing coronary artery bypass grafting (2010-2019) nationwide, the 5-year major adverse cardiovascular event rate was 25%; 27% patients were in high predicted risk (>30% 5-year major adverse cardiovascular events). SMART2 score discrimination (c-statistic: 64) was comparable to the original study (c-statistic: 67) and was best in patients with chronic kidney disease (c-statistic: 66). However, it underpredicted major adverse cardiovascular event rates in the whole cohort (observed/expected ratio, 1.45) as well as in all studied subgroups. The SMART2 score performed better in White than Black patients. On decision curve analysis, the SMART2 score provides a net benefit over a wide range of risk thresholds. Conclusions The SMART2 model performs well in a racially diverse coronary artery bypass grafting cohort, with better predictive capabilities at the upper range of baseline risk, and can therefore be used to guide secondary preventive pharmacotherapy.

Transportability of two heart failure trials to a disease registry using individual patient data.

OBJECTIVES: Randomized controlled trials are the gold-standard for determining therapeutic efficacy, but are often unrepresentative of real-world settings. Statistical transportation methods (hereafter transportation) can partially account for these differences, improving trial applicability without breaking randomization. We transported treatment effects from two heart failure (HF) trials to a HF registry. STUDY DESIGN AND SETTING: Individual-patient-level data from two trials (Carvedilol or Metoprolol European Trial (COMET), comparing carvedilol and metoprolol, and digitalis investigation group trial (DIG), comparing digoxin and placebo) and a Scottish HF registry were obtained. The primary end point for both trials was all-cause mortality; composite outcomes were all-cause mortality or hospitalization for COMET and HF-related death or hospitalization for DIG. We performed transportation using regression-based and inverse odds of sampling weights (IOSW) approaches. RESULTS: Registry patients were older, had poorer renal function and received higher-doses of loop-diuretics than trial participants. For each trial, point estimates were similar for the original and IOSW (e.g., DIG composite outcome: OR 0.75 (0.69, 0.82) vs. 0.73 (0.64, 0.83)). Treatment effect estimates were also similar when examining high-risk (0.64 (0.46, 0.89)) and low-risk registry patients (0.73 (0.61, 0.86)). Similar results were obtained using regression-based transportation. CONCLUSION: Regression-based or IOSW approaches can be used to transport trial effect estimates to patients administrative/registry data, with only moderate reductions in precision.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service.

BACKGROUND: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. METHODS: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. RESULTS: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). CONCLUSIONS: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Bariatric surgery, novel glucose-lowering agents, and insulin for type 2 diabetes and obesity: Bayesian network meta-analysis of randomized controlled trials.

BACKGROUND: This network meta-analysis aimed to compare the effects of bariatric surgery, novel glucose-lowering agents (SGLT2i, GLP1RA, DPP4i), and insulin for patients with type 2 diabetes mellitus (T2DM) and obesity. METHODS: Four databases were searched from inception to April 2023 to identify randomized controlled trials (RCTs) comparing bariatric surgery, SGLT2i, GLP1RA, DPP4i, insulin, and/or placebo/usual care among patients with T2DM and obesity in the achievement of HbA1c < 7.0 per cent within one year, and 12-month changes in HbA1c and body weight. RESULTS: A total of 376 eligible RCTs (149 824 patients) were analysed. Bariatric surgery had significantly higher rates of achieving HbA1c < 7.0 per cent than SGLT2i (RR = 2.46, 95 per cent c.i. = 1.28, 4.92), DPP4i (RR = 2.59, 95 per cent c.i. = 1.36, 5.13), insulin (RR = 2.27, 95 per cent c.i. = 1.18, 4.58) and placebo/usual care (RR = 4.02, 95 per cent c.i. = 2.13, 7.93), but had no statistically significant difference from GLP1RA (RR = 1.73, 95 per cent c.i. = 0.91, 3.44), regardless of oral (RR = 1.33, 95 per cent c.i. = 0.66, 2.79) or injectable (RR = 1.75, 95 per cent c.i. = 0.92, 3.45) administration. Significantly more GLP1RA patients achieved HbA1c < 7.0 per cent than other non-surgical treatments. Bariatric surgery had the greatest reductions in HbA1c (∼1 per cent more) and body weight (∼15 kg more) at 12 months. Among novel glucose-lowering medications, GLP1RA was associated with greater reductions in HbA1c than SGLT2i (-0.39 per cent, 95 per cent c.i. = -0.55, -0.22) and DPP4i (-0.51 per cent, 95 per cent c.i. = -0.64, -0.39) at 12 months, while GLP1RA (-1.74 kg, 95 per cent c.i. = -2.48, -1.01) and SGLT2i (-2.23 kg, 95 per cent c.i. = -3.07, -1.39) showed greater reductions in body weight than DPP4i at 12 months. CONCLUSION: Bariatric surgery showed superiority in glycaemic control and weight management compared to non-surgical approaches. GLP1RA administered by oral or injectable form demonstrated reduced HbA1c and body weight at 12 months, and was preferable over other non-surgical treatments among patients with T2DM and obesity. PROSPERO REGISTRATION NO: CRD42020201507.

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.

The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.

BACKGROUND: Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. METHODS AND FINDINGS: We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. CONCLUSIONS: In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.

Assessing Representativeness of Randomised Controlled Trials using Serious Adverse Events.

Context: 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. Objectives: We explore an approach assessing trial representativeness by comparing rates of trial Serious Adverse Events (SAEs: most of which reflect hospitalisations/deaths) to rates of hospitalisation/death in routine care (which, in a trial setting, would be SAEs be definition). Study design: Secondary analysis of trial and routine healthcare data. Dataset and population: 483 trials (n=636,267) from clinicaltrials.gov across 21 index conditions. A routine care comparison was identified from SAIL databank (n=2.3M). Instrument: SAIL data were used to derive the expected rate of hospitalisations/deaths by age, sex and index condition. Outcomes: We calculated the expected number of SAEs for each trial compared to the observed number of SAEs (observed/expected SAE ratio). We then re-calculated the observed/expected SAE ratio additionally accounting for comorbidity count in 125 trials for which we accessed individual participant data. Results: For 12/21 index conditions the observed/expected SAE ratio was <1, indicating fewer SAEs in trials than expected given community rates of hospitalisations and deaths. A further 6/21 had point estimates <1 but the 95% CI included the null. The median observed/expected SAE ratio was 0.60 (95% CI 0.56-0.65; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.88 (0.59-1.33; IBD). Higher comorbidity count was associated with SAEs/hospitalisations and deaths across index conditions. For most trials, the observed/expected ratio was attenuated but remained <1 after additionally accounting for comorbidity count. Conclusion: 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 applicability of trial findings to older populations in whom multimorbidity and frailty are common.