Current diagnostics for HIV-associated tuberculosis are suboptimal, with missed diagnoses contributing to high hospital mortality and approximately 374 000 annual HIV-positive deaths globally. Urine-based assays have a good diagnostic yield; therefore, we aimed to assess whether urine-based screening in HIV-positive inpatients for tuberculosis improved outcomes.We did a pragmatic, multicentre, double-blind, randomised controlled trial in two hospitals in Malawi and South Africa. We included HIV-positive medical inpatients aged 18 years or more who were not taking tuberculosis treatment. We randomly assigned patients (1:1), using a computer-generated list of random block size stratified by site, to either the standard-of-care or the intervention screening group, irrespective of symptoms or clinical presentation. Attending clinicians made decisions about care; and patients, clinicians, and the study team were masked to the group allocation. In both groups, sputum was tested using the Xpert MTB/RIF assay . In the standard-of-care group, urine samples were not tested for tuberculosis. In the intervention group, urine was tested with the Alere Determine TB-LAM Ag , and Xpert assays. The primary outcome was all-cause 56-day mortality. Subgroup analyses for the primary outcome were prespecified based on baseline CD4 count, haemoglobin, clinical suspicion for tuberculosis; and by study site and calendar time. We used an intention-to-treat principle for our analyses. This trial is registered with the ISRCTN registry, number ISRCTN71603869.Between Oct 26, 2015, and Sept 19, 2017, we screened 4788 HIV-positive adults, of which 2600 (54%) were randomly assigned to the study groups (n=1300 for each group). 13 patients were excluded after randomisation from analysis in each group, leaving 2574 in the final intention-to-treat analysis (n=1287 in each group). At admission, 1861 patients were taking antiretroviral therapy and median CD4 count was 227 cells per μL (IQR 79–436). Mortality at 56 days was reported for 272 (21%) of 1287 patients in the standard-of-care group and 235 (18%) of 1287 in the intervention group . In three of the 12 prespecified, but underpowered subgroups, mortality was lower in the intervention group than in the standard-of-care group for CD4 counts less than 100 cells per μL , severe anaemia , and patients with clinically suspected tuberculosis ; with no difference by site or calendar period. Adverse events were similar in both groups.Urine-based tuberculosis screening did not reduce overall mortality in all HIV-positive inpatients, but might benefit some high-risk subgroups. Implementation could contribute towards global targets to reduce tuberculosis mortality.Joint Global Health Trials Scheme of the Medical Research Council, the UK Department for International Development, and the Wellcome Trust. Tuberculosis remains the single major cause of mortality in patients with HIV globally, accounting for an estimated 374 000 deaths in 2016.3Mycobacterium tuberculosis nucleic acids from sputum and has been widely scaled-up and decentralised, but patients with HIV-associated tuberculosis tend to have relatively low mycobacterial concentrations in pulmonary secretions and difficulty expectorating.8Suboptimal diagnostics are an important contributor to poor outcomes from HIV-associated tuberculosis. Tuberculosis is commonly disseminated, presents with non-specific clinical features, and is only diagnosed before death in half of cases with a fatal outcome.Evidence before the studyWe searched MEDLINE for studies that investigated the effect of urine lipoarabinomannan assay (LAM) or Xpert MTB/RIF assay (Xpert) on mortality or tuberculosis diagnosis in HIV-positive patients published from Jan 1, 2000, to Sept 30, 2016. We combined search terms for LAM  or Xpert (“urine Xpert” or “urinary Xpert”) with HIV  and mortality , or tuberculosis diagnosis or screening . We identified 14 observational studies, mostly done in antiretroviral therapy naive outpatients or hospital inpatients, which assessed the diagnostic accuracy of urine LAM or Xpert for tuberculosis or their association with mortality, or both. These studies showed moderate-to-good diagnostic yield of urinary assays in patients with advanced immunosuppression and in hospital inpatients, and an association with higher disease severity, poor prognosis, and mortality. Since undertaking our trial, one randomised trial has assessed adjunctive urine LAM testing in HIV-positive inpatients suspected of tuberculosis and found a reduction in 8-week mortality. However, whether systematic urine-based screening for tuberculosis (using urine LAM and Xpert) for all HIV-positive hospital inpatients (irrespective of tuberculosis symptoms) could reduce mortality compared with sputum tuberculosis testing remained unclear.Added value of this studyThe findings from this randomised trial suggest that urine-based tuberculosis screening of HIV-positive hospital inpatients might reduce 56-day mortality in defined clinical subgroups . Moreover, wider application  could substantially reduce the risk of being discharged from hospital with undiagnosed tuberculosis in all patient groups. The major incremental diagnostic benefit was from urine LAM.Implications of all the available evidenceThese data support implementation of urine LAM-based screening of all HIV-positive inpatients for tuberculosis in hospitals in high HIV and tuberculosis burden settings, because the reliance on a combination of sputum-based diagnosis and clinically guided empirical treatment left patients at unacceptably high risk of discharge and death from undiagnosed tuberculosis. Collectively, current evidence supports international policy change to scale-up and broaden the use of urine-LAM testing in patients admitted to hospital . Incremental gain was too limited to support urine Xpert. Because screening efficiency is dependent on prevalence, these results cannot be extrapolated to outpatients. Urine LAM screening could contribute towards reducing mortality and morbidity from HIV-associated tuberculosis and meeting global targets for tuberculosis mortality reduction.Urine can be readily obtained from patients admitted to hospital and is suitable for rapid tuberculosis diagnosis using either a lateral flow assay for lipoarabinomannan  or Xpert MTB/RIF. Although urine is not a sample recommended by WHO for Xpert, studies report high specificity for tuberculosis in HIV-positive patients.16Urine-based screening might provide more complete, timely, and potentially life-saving diagnosis of tuberculosis among HIV-positive hospital inpatients.We did a pragmatic, multicentre, parallel-group, double-blind, randomised controlled trial. We enrolled patients who were admitted to medical wards at Zomba Central Hospital in Malawi  and Edendale Hospital in South Africa , irrespective of tuberculosis symptoms or admitting presentation. The study design has been previously described in detail,All admissions to the medical wards were screened for eligibility by study nurses or clinicians. Screening occurred during office hours on weekdays, with patients enrolled as close to admission as possible. All patients with an unknown HIV status were offered point-of-care rapid HIV testing as per local guidelines . We inclWe randomly assigned eligible patients on enrolment (1:1) to either the standard-of-care tuberculosis screening group or the intervention screening group. Randomisation was stratified by site and a randomisation list of unique patient identifiers was generated by the study statistician using a computer-generated random block size. On enrolment, study nurses or clinicians took a consecutive sealed opaque envelope containing the unique patient identifier but not the study group, to which they remained masked. A paired set of sealed envelopes were kept in a locked cabinet in the study laboratory, labelled with the unique patient identifier and containing the study group allocation. These were opened by the laboratory technician on receipt of study tuberculosis screening specimens. Investigators, all study staff (other than the laboratory technician and statistician), hospital attending clinical teams, and patients were masked to the study group allocation.Following enrolment, 50 mL of urine and a single, spontaneously expectorated sputum sample were collected by the study team for tuberculosis screening. Failure to produce a specimen was not an exclusion criterion. The patient's attending clinical team had the option of sending additional samples for routine tuberculosis investigations available at the study hospital; the Tuberculosis screening samples  were processed according to study group allocation by the study laboratory technician, and assays were run during office hours and processed as soon as possible after arrival of a specimen in the laboratory. In both groups, sputum was tested using the Xpert MTB/RIF assay . In the standard-of-care group, urine samples were not tested for tuberculosis. In the intervention group, 60 μL of unconcentrated urine was tested with the Alere Determine TB-LAM Ag assay  as per the manufacturer's instructions, and 40–50 mL of urine was concentrated by centrifugation for testing with Xpert. Urine Xpert and TB-LAM were processed simultaneously. We deemed TB-LAM positive using the grade 1 cutoff on the manufacturer's post-2014 reference card, which was referred to as the grade 2 cutoff before 2014. The Once all the tuberculosis specimens received had been processed, tuberculosis screening results were reported to the attending clinical team as positive, negative, or not done to maintain masking, with neither study group nor individual test results communicated to attending clinical or study teams. Rifampicin resistance results, if available, were also reported . ClinicaThe study team documented patients' clinical events during hospital admission, including but not limited to tuberculosis investigations and diagnosis, commencement of tuberculosis treatment and any side-effects, management of HIV (including stopping or starting ART), and discharge or death. Follow-up at 56 days for those discharged from hospital alive was done in person by outpatient attendance. Those who did not attend were contacted by telephone or a home visit, or both, with interview of next of kin to establish vital status if required.The primary outcome was the cumulative risk of all-cause mortality at 56 days from enrolment. Subgroup analysis for the primary outcome was prespecified in populations with higher risk of tuberculosis, mortality, or both ; and by study site and calendar time. Secondary outcomes were time to mortality, proportions of patients with microbiologically confirmed tuberculosis and clinically diagnosed tuberculosis, time from randomisation to tuberculosis diagnosis and to start of tuberculosis treatment, time from tuberculosis diagnosis to treatment initiation, prescription of antimicrobials, ART initiation , duration of hospitalisation, and hospital readmission events.Microbiologically confirmed tuberculosis was defined as one or more positive specimens for acid fast bacilli, Xpert, mycobacterial culture, or TB-LAM. Clinically diagnosed tuberculosis was defined by the decision to treat for tuberculosis in the absence of microbiological confirmation. Patients with any tuberculosis diagnosis  were also reported. We recorded for all patients whether tuberculosis was included in the admitting differential diagnoses by attending clinicians, referred to as clinically suspected tuberculosis.The sample size calculation was based on the assumption that 56-day mortality risk would be 25% in the standard-of-care group, and loss to follow-up would be 10% or less. We therefore calculated that enrolling 1300 patients per group would provide at least 80% power to detect a 5% absolute mortality reduction in the intervention group, with a two-sided type 1 error of 5% .We used an intention-to-treat principle for all our analyses, including all eligible patients that were randomly assigned. For the primary outcome, we calculated a risk difference with 95% CIs for mortality at 56 days comparing the standard-of-care group with the intervention group with the following: using a generalised linear model with identity link function and binomial family, adjusting for study site, using a fixed effect, and assuming participants lost to follow-up had not died. An odds ratio adjusted for site with 95% CIs was also calculated using logistic regression. Prespecified subgroup analyses were done for the primary outcome. These subgroups were study site , baseline CD4 counts (<100 cells per μL or ≥100 cells per μL), severe anaemia (haemoglobin <8 g/dL or ≥8 g/dL), whether tuberculosis was clinically suspected at admission, and calendar time . A sensitivity analysis was also done assuming all losses to follow-up had died.Secondary outcomes were compared between the study groups using adjusted risk difference and adjusted odds ratio (aOR) for binary outcomes, Cox proportional hazards regression for time-to-event outcomes, and Kaplan-Meier curves for time to mortality. 95% CI were calculated for all analyses. In exploratory post-hoc analyses, tuberculosis diagnoses were also compared between study groups using the same subgroups as the primary outcome  to investigate whether the absence of mortality benefit was accompanied by a lack of difference in tuberculosis diagnosis. Diagnostic yields of urine-based tuberculosis tests were calculated post hoc as a proportion of all microbiologically confirmed tuberculosis to better understand the respective contributions of TB-LAM and urine Xpert.We did all the analyses using SAS (version 9.4). This study is registered with the ISRCTN registry, number ISRCTN71603869.The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.Between Oct 26, 2015, and Sept 19, 2017, we screened 4788 HIV-positive adult medical admissions. Of these admissions, 1928 (40%) patients were ineligible, 260 (5%) did not provide consent to participate, and 2600 (54%) were randomly assigned to the study groups  of 2574 patients had died: 272 (21%) of 1287 in the standard-of-care group and 235 (18%) of 1287 in the intervention group, giving an adjusted risk difference of −2·8%  to prevent one death . Median vs 85 [7%] of 1287; adjusted risk difference 9·9% [95% CI 7·5–12·4]; p<0·0001) and fewer clinically diagnosed tuberculosis  of 2574 patients, whereas only 1464 (57%) produced sputum . Chest radiographs as part of routine care were taken in 1231 (48%) of 2574 patients during inpatient stay  in tuberculosis diagnoses in patients with CD4 counts of 100 cells per μL or more, and 8·0% (5·0–11·1) in those not clinically suspected of tuberculosis at admission . IncreasAlthough the 56-day all-cause mortality showed no significant differences between the standard-of-care and intervention groups, the addition of urine-based tuberculosis screening using TB-LAM and Xpert to sputum-based screening in all HIV-positive medical inpatients significantly reduced mortality at 56 days in prespecified high-risk subgroups, and substantially increased tuberculosis diagnoses and treatment across all patients. Fewer patients were on tuberculosis treatment at discharge in the standard-of-care group than in the intervention group, and more patients died after discharge, suggesting discharge with undiagnosed and untreated active tuberculosis as the main underlying mechanism. For every ten extra patients with confirmed tuberculosis discharged on treatment in the intervention group, there were 3·7 fewer deaths after discharge, supporting high individual risk of rapid progression to death in undiagnosed HIV-associated tuberculosis that could have been detected and treated through urine-based screening.Morbidity and mortality from HIV-associated tuberculosis are slowly decreasing in Africa, mainly reflecting the expansion of HIV diagnosis and ART programmes rather than tuberculosis-specific diagnostic and prevention interventions.vs 227 cells per μL), and a greater proportion of participants had tuberculosis (intervention groups: 51·6% vs 21·8%), reflecting different inclusion criteria , as well as underlying population trends in ART coverage. Early survival benefit from these two trials underscores the fulminant course of undiagnosed tuberculosis in highly immunosuppressed patients, and the higher yield of urinary diagnostics and difficulty diagnosing tuberculosis among groups of hospitalised HIV-positive patients by other means.15Our findings are consistent with the 4% (95% CI 1–7) mortality reduction in HIV-positive inpatients with clinically suspected tuberculosis reported from a diagnostic (not screening) randomised trial of urine LAM testing.Uniquely, STAMP recruited considerable numbers of less immunosuppressed or critically ill patients who fall outside current recommendations for urinary tuberculosis diagnostic assays.23Better clinical acumen and alternative investigations such as radiology leading to early empirical tuberculosis treatment can mitigate the effect of new diagnostic tests, as observed in relatively well resourced inpatient and outpatient settings, for example in South Africa.The least costly and easiest urine test (TB-LAM) had major incremental diagnostic benefit in this trial, with urine Xpert (which is more complex because of urine centrifugation) contributing few additional diagnoses. This finding argues for the use of LAM alone as the urinary diagnostic for screening, an approach supported by STAMP's cost-effectiveness projections being reported separately.Despite the qualified mortality benefits, we consider our results to be supportive of routine implementation of systematic screening with urinary LAM, in addition to sputum Xpert, for all HIV-positive inpatients, given that LAM screening provides a substantial increase in diagnosis of a treatable but frequently fatal condition (disseminated tuberculosis). Systematic screening on admission to hospital is a simple strategy that avoids expense and delay from identifying high-risk groups .There are limitations to our study. In sample size calculations, we assumed a higher mortality and burden of tuberculosis than we observed, possibly because of the success of ART scale-up. We did not include a culture reference standard, as this reference is neither standard of care nor routinely available in Malawi, and would have presented ethical dilemmas or affected generalisability. Relatively few participants in Malawi produced sputum. This difficulty in expectorating sputum is, however, typical of unselected HIV-positive outpatient and inpatient cohorts in resource-constrained settings, and is a major barrier to implementation of sputum-based tuberculosis screening.In conclusion, we report a survival benefit from urine-based tuberculosis screening of HIV-positive hospital admissions in high-risk subgroups, and a broader benefit through substantially increased predischarge tuberculosis diagnosis and treatment in all patients. Tuberculosis screening with urine-LAM lateral flow assays is inexpensive and easily implementable, requiring minimal infrastructure and training. Tuberculosis diagnosed through urine-based screening was complementary to tuberculosis diagnosed through standard clinical investigations in both countries, with inferred higher risk of discharge with undiagnosed tuberculosis in the standard-of-care group than in the intervention group. Anticipated improvements in analytical performance of next-generation LAM assays might add to the diagnostic yield from systematic urine screening.


The impact of risk factors on poor outcome after ischemic stroke is well known, but estimating the amount of poor outcome attributable to single factors is challenging in presence of multimorbidity. We aim to compare population attributable risk estimates obtained from different statistical approaches regarding their consistency. We use a real-life data set from the PROSCIS study to identify predictors for mortality and functional impairment one year after first-ever ischemic stroke and quantify their contribution to poor outcome using population attributable risks.The PROSpective Cohort with Incident Stroke (PROSCIS) is a prospective observational hospital-based cohort study of patients after first-ever stroke conducted independently in Berlin (PROSCIS-B) and Munich (PROSCIS-M). The association of baseline factors with poor outcome one year after stroke in PROSCIS-B was analysed using multiple logistic regression analysis and population attributable risks were calculated, which were estimated using sequential population attributable risk based on a multiple generalized additive regression model, doubly robust estimation, as well as using average sequential population attributable risk. Findings were reproduced in an independent validation sample from PROSCIS-M.Out of 507 patients with available outcome information after 12 months in PROSCIS-B, 20.5% suffered from poor outcome. Factors associated with poor outcome were age, pre-stroke physical disability, stroke severity (NIHSS), education, and diabetes mellitus. The order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but population attributable risk estimates varied markedly between the methods. In PROSCIS-M, incidence of poor outcome and distribution of baseline parameters were comparable. The multiple logistic regression model could be reproduced for all predictors, except pre-stroke physical disability. Similar to PROSCIS-B, the order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but magnitudes of population attributable risk differed markedly between the methods.Ranking of risk factors by population impact is not affected by the different statistical approaches. Thus, for a rational decision on which risk factor to target in disease interventions, population attributable risk is a supportive tool. However, population attributable risk estimates are difficult to interpret and are not comparable when they origin from studies applying different methodology. The predictors for poor outcome identified in PROSCIS-B have a relevant impact on mortality and functional impairment one year after first-ever ischemic stroke. A number of studies identify prognostic factors for death or poor functional outcome after stroke –5. Most Population attributable risk (PAR), as a function of risk factor prevalence and relative risk, is used to calculate the amount of poor outcome that is attributable to a prognostic factor , 9. The In this study, we aim to compare PAR estimates obtained from different statistical approaches regarding their consistency to evaluate the impact of the underlying statistical model on the PAR estimates. This is the first study that simultaneously assesses and directly compares PAR values obtained by different methodologies. We use a real-life data set from the PROSCIS study to identify baseline prognostic factors for death or functional impairment one year after first-ever ischemic stroke and assess the extent to which each prognostic factor contributes to poor outcome. We validate the findings within an independent data set.https://doi.org/10.7910/DVN/REBNRX).The data that support the findings of this study are available to all interested researchers at Harvard Dataverse (The Prospective Cohort with Incident Stroke (PROSCIS) is a prospective, observational, hospital-based cohort study of patients with first-ever stroke conducted independently at two centers in Germany . Details have been published previously . BrieflyPatients 18 years or older who suffered from stroke according to the WHO criteria were included into the PROSCIS-B cohort. Exclusion criteria were prior stroke and participation in an intervention study. We restrict our analyses to patients with ischemic stroke and known survival status and functional outcome one year after stroke. A subsample of 200 patients randomly selected from the PROSCIS-M cohort serves as an external validation data set.Factors that are already known to have an impact on poor outcome after stroke and that were collected at baseline in the PROSCIS-B study were analyzed: sociodemographic parameters , stroke related risk factors , etiologic subtype of ischemic stroke according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification  and stroPoor outcome was defined as death or functional impairment >2 or Barthel Index (BI)<60) one year after ischemic stroke –17. Bartstay = 0.05) to select predictors for further analysis in order to identify the strongest and mutually independent risk factors while keeping the number of covariables to the minimum. Firth’s likelihood penalization method [logistf [Univariable binary logistic regression analysis was conducted to model the chance of poor outcome comparing exposed to non-exposed individuals. Parameters with univariable p-values <0.1 were transferred to multiple binary logistic regression analysis with Firth’s maximum likelihood penalization method and backward selection  and the ethics committee of the Ludwig-Maximilians-University Munich . Trained physicians of the trial teams carried out recruitment of patients. Patients or their legal representative gave written informed consent for study participation. Patients had to be awake and responsive to be enrolled into the trial.Consent for publication of raw data was not obtained from the participants. We have followed guidelines on preparing clinical data for publication . ResultaBetween March 2010 and May 2013, 690 patients were recruited for PROSCIS-B. Of these, 627 patients were included in the study due to ischemic stroke. Thereof, 17 (2.7%) patients were lost to follow-up and 103 (16.4%) were alive, but had incomplete data on mRS or BI at one year. Overall, 507 (80.9%) patients were available for our analyses. Complete patient flow is shown in Patient characteristics at baseline are shown in In unadjusted logistic regression, age, sex, graduation, education, etiologic subtype of stroke, NIHSS, and pre-stroke regular alcohol consumption, degree of physical activity, physical disability, hypertension, DM, AF, MI/AP, TIA and PAD were associated with poor outcome (p<0.1) . IndepenResults from PAR estimation for PROSCIS-B are presented in Validation of the analysis was conducted in a randomly selected subsample of 200 patients from the PROSCIS-M cohort. Thirty-nine (19.5%) patients had poor outcome one year after first-ever ischemic stroke. PROSCIS-M patients were older (69.9±13.4 vs. 66.7±13.2 years), more often female (43.0% vs. 38.9%), had more severe strokes (median NIHSS (IQR): 3 (1–5) vs. 2 (1–4)), had more often dyslipidaemia (36.0% vs. 25.7%) and TIA (6.5% vs. 2.9%), and less often DM (13.0% vs. 21.5%), AF (15.5% vs. 22.1%) and MI/AP (11.0% vs 16.0%) compared to patients from PROSCIS-B . ParametResults from PAR estimation are shown in However, the impact of risk factors on poor outcome differs between PROSCIS-B and PROSCIS-M, attributing the highest amount of poor outcome to physical disability and age≥75 years in PROSCIS-B and to NIHSS>4 and age≥75 years in PROSCIS-M.The planning of an intervention study requires knowledge of the approximate amount of poor outcome attributable to the risk factor at target. PAR is a methodological tool to prioritize targets for modification according to their assumed contribution to reduce the outcome of interest, in our case patient’s mortality and morbidity. While different estimation methods were used in previous studies , 5–7, toFurthermore, PAR values depend on the definition of exposure categories, since the application of software for PAR estimation required dichotomization of the prognostic factors. Consideration of continuous factors or factors with multiple categories was not possible. In addition, elimination of all expositions from the population is unrealistic and hence, the PAR can only be interpreted as the in theory maximum avoidable amount of poor outcome that might be preventable, if in the best scenario all expositions could be eliminated from the population.In the present study, we estimated the population impact of known risk factors for death or functional impairment one year after ischemic stroke in a large prospective cohort (PROSCIS-B) and validated the findings in an independent cohort (PROSCIS-M). Both cohorts were designed identically, applied the same study core protocol, and the same statistical approaches were used for analysis. The associations determined from the validation cohort PROSCIS-M were similar to those from PROSCIS-B, except pre-stroke physical disability. Although we observed nominal higher probabilities in disabled compared to non-disabled patients for death , but not for dependency , no statistically significant associations could be observed in univariable or in multivariable analysis. This fact is probably caused by the small sample size of PROSCIS-M. The effects of age, education and NIHSS were on the same range as observed in PROSCIS-B. The effect of diabetes mellitus pointed in the same direction, but was estimated higher with less precision. The PAR magnitudes for age≥75 years (17.29% vs. 20.89%), and DM (7.61% vs. 10.03%) were comparable between both cohorts. The size of the estimated PAR values was different for physical disability pre-stroke (average PAR 18.48% vs. 2.89%), NIHSS>4 (10.90% vs. 26.13%) and education≤10 years (10.39% vs. 18.86%) for PROSCIS-B and PROSCIS-M. This might be a result of the heterogeneity of both cohorts: in PROSCIS-M, pre-stroke physical disability was less prevalent compared to PROSCIS-B and no significant association with poor outcome was found. In addition, patients in PROSCIS-B had less severe strokes and were younger, which possibly explains the notably smaller amount of poor outcome attributable to NIHSS. Moreover, patients from PROSCIS-M were better educated, which at least partly explains the higher impact of education on poor outcome. All prognostic factors for death or functional impairment after first-ever ischemic stroke in our cohort are in line with those found in previous studies –32. FurtSome limitations of our study have to be considered. First, the study represents a secondary analysis of a prospective study originally designed to develop prediction models for recurrent vascular events in ischemic stroke patients. Thus, we only considered factors on hospital admission for analyses and no in-depth information on pre-stroke patient conditions, in-hospital treatment or comorbidities beyond cardiovascular diseases were available. Hence, we cannot exclude that residual confounding might be present in this study, possibly leading to an over-estimation of PAR. Nevertheless, the comprehensive and well-documented data collection process as well as the standardized conduction of all examinations helped countering information bias for the collected data. Second, the recruitment was regionally restricted to patients treated only in stroke units in Berlin and regional conditions might have had influence on the study collective, which may impede the generalizability the results. Third, we have observed a selection of study patients to less severe strokes within PROSCIS-B (median baseline NIHSS of 2 points). This might be caused by several factors: (1) recruitment took place in stroke units only, excluding more severe strokes treated at neurological intensive care units, (2) only patients able to consent or with a legal guardian could be enrolled into the study, (3) patients with available outcome after 12 months had lower values of NIHSS compared to patients with unavailable outcome information. This selection probably comes along with selective clinical characteristics such as young age, less comorbidity, and lower rates of cardio-embolic strokes as well as rather good outcomes one year after stroke and according to this, the PAR estimates from this population can only be extrapolated to similar minor stroke populations. Fourth, due to the low incidence of poor outcome of 104 out of 507 patients we used a covariable selection procedure to reduce the number of variables in the model. However, this could potentially have led to residual confounding, as we might not have enough power to detect an association of the classical risk factors like atrial fibrillation, hypertension or transient ischemic attack with poor outcome, possibly leading to an over-estimation of PAR.To conclude, PAR is a supportive tool to make a rational decision on which risk factor to target in intervention studies. Ranking of risk factors regarding their population impact by magnitudes of PAR was independent of the estimation method. However, PAR values are difficult to interpret and are not comparable when they origin from studies applying different methodology.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Four inhaler adherence clusters have been identified using the INCA audio device in COPD patients: (1) regular use/good technique, (2) regular use/frequent technique errors, (3) irregular use/good technique, and (4) irregular use/frequent technique errors. Their relationship with healthcare utilization and mortality was established, but the cost-effectiveness of adherence-enhancing interventions is unknown. In this exploratory study, we aimed to estimate the potential cost-effectiveness of reaching optimal adherence in the three suboptimal adherence clusters, i.e., a theoretical shift of clusters 2, 3, and 4 to cluster 1. Cost-effectiveness was estimated over a 5-year time horizon using the Irish healthcare payer perspective. We used a previously developed COPD health-economic model that was updated with INCA trial data and Irish national economic and epidemiological data. For each cluster, interventions would result in additional quality-adjusted life years gained at reasonable investment. Cost-effectiveness was most favorable in cluster 3, with possible cost savings of €845/annum/person. Optimal implementation of inhaled therapy involves both regular use as well as good inhaler technique.6 In our previous work, we identified four distinct clusters of inhaler adherence that were associated with differential clinical outcomes.7 Economic evaluations of adherence-enhancing interventions in each of those clusters have not been performed, but could help prioritizing specific target populations for tailored interventions. The aim of this follow-up study is to estimate the potential cost-effectiveness of reaching optimal adherence in each suboptimal adherence cluster.In chronic obstructive pulmonary disease (COPD), real-world adherence to maintenance therapy can be as low as 20%.n = 226) have been described elsewhere.7 In short, cluster 1 had the lowest mortality during follow-up. Cluster 2 had the highest rate of antibiotic and/or oral steroids community prescriptions. The highest overall healthcare use was attributable to patients from cluster 3. Cluster 4 had the highest mortality .Cost-effectiveness analysis results are shown in Table We performed exploratory cost-effectiveness modeling to assess the potential economic benefits of improved adherence in three clusters of patients with suboptimal adherence. In all clusters, interventions seem cost-effective. Moreover, in patients with irregular use but good inhaler technique, a possible cost-saving of €845/annum/person could be yielded despite the higher cost of medication arising from better adherence.9 They could include education and training on inhaler technique for those with frequent technique errors, patient reminders for those with irregular use due to forgetfulness, and shared decision-making and motivational interviewing for those with irregular use due to a conscious, intentional decision.9Remote monitoring of adherence on a longitudinal basis provides an accurate evaluation of regularity of use as well as inhaler technique. The use of a technology, such as the one described in this report, identified four patterns of adherence and may give guidance as to how we might approach this challenge in a personalized manner. Hence, in the future, by providing personalized interventions to the highest risk groups we may both improve adherence and have a positive impact on both clinical and economic outcomes in those most in need, such as those in cluster 3. The need for personalized adherence-enhancing interventions has recently been highlighted.This study is a first attempt to estimate the cost-effectiveness of interventions in different adherence clusters, but was limited by the use of post hoc and short-term effectiveness data for the different clusters. Regarding generalizability, absolute costs may differ per country or setting, but we expect relative cluster results to be comparable. Our exploratory cost-effectiveness model estimates should be confirmed when long-term clinical intervention trial data become available, including extensive sensitivity and scenario analyses as well as analyses in a non-admitted primary care COPD population.Personalized adherence interventions targeting patient-specific regularity of inhaler use and inhaler technique could result in clinical and economic benefits for COPD patients.This was an exploratory cost-effectiveness analysis. Ethical approval for the clinical study was obtained from the Beaumont Hospital Ethics Committee.1 Briefly, hospitalized patients with COPD, prescribed salmeterol/fluticasone propionate , were included and written informed consent was obtained from all participants.Patient selection and data collection has been described previously.TM device that can track both timing and quality of inhaler use.1 The INCA acoustic recordings allowed for identification of the following four adherence clusters:7 (1) regular use/good technique, (2) regular use/frequent technique errors, (3) irregular use/good technique, and (4) irregular use/frequent technique errors. Cluster-specific healthcare utilization and mortality was studied previously7 and is summarized in Fig. Adherence was assessed using the INCA5 was used and populated with Irish and INCA-specific cost  and effect  data  in terms of costs  per quality-adjusted life-year (QALY) gained from the Irish healthcare payer’s perspective assuming fixed per-patient intervention costs of €200 (authors’ estimation). Three ICERs were calculated using cluster 1 as intervention and clusters 2, 3, and 4, respectively, as usual care scenarios. In line with recommendations,11 a policy-relevant time horizon of 5 years was used, taking into account a 5% discount rate for both costs and effects as per Irish Health Information and Quality Authority guidelines (www.hiqa.ie).The cost-effectiveness of enhanced adherence, i.e., the theoretical shift of patients from poor adherence  to good adherence , was estimated. Therefore, a previously developed, described, and validated health-economic model1 and other data that support the findings of this study are available from the corresponding author on reasonable request.Detailed data and characteristics of the study population are available in ref. Appendix 1: Input parameters for cost-effectiveness model


Left ventricular ejection fraction (LVEF) is the most frequently used parameter in the assessment of heart failure (HF). Cardiac index (CI) is considered a potential alternative to LVEF despite limited evidence. We aimed to assess and compare the predictive accuracy of LVEF and echocardiographically-assessed CI in HF patients.A single-centre, retrospective cohort study was conducted in patients hospitalized for acute HF from 2010–2016. Cox proportional hazard models including either LVEF or CI were created to predict all cause death, cardiovascular (CV) death, or first HF-readmission. Of 334 patients included in the analysis, 58.7% exhibited HF with reduced LVEF (HFrEF). Left ventricular ejection fraction did not show correlation with any endpoint, while CI was predictive of HF-readmission in the entire cohort. Both the LVEF-based and CI-based models demonstrated moderate discriminative accuracy when predicting all-cause death, CV death, or HF-readmission. Left ventricular ejection fraction proved to be an independent predictor of CV mortality in HFrEF-patients, while CI was predictive of HF-readmission in the non-HFrEF group.Left ventricular ejection fraction seemed to be associated more closely with disease severity in HFrEF, and CI in the non-HFrEF group, in this real-life cohort of elderly HF patients. The LVEF-based and CI-based predictive models have clinically similar predictive accuracy for mortality and HF-readmission, thus CI may be a potential alternative to LVEF in the assessment of left ventricular function. Cardiac index may be an important new tool in the assessment of HF patients with midrange or preserved LVEF. Heart failure (HF) affects a large segment of the population worldwide, and has a mortality risk comparable to malignancies., 2 The dEarly studies identified left ventricular ejection fraction (LVEF) as a useful, easily obtainable, prognostic echocardiographic parameter. Low LVEF is correlated to reduced cardiac output (CO), cardiac index (CI), and poor tissue perfusion and has been shown correlated with increased risk of mortality and morbidity., 6 TheseCardiac output and cardiac index have also been shown to correlate with mortality, but theiThe current research aims to compare the predictive value of LVEF to that of CI-based models for mortality and re-hospitalization in an unselected HF population in a retrospective study.We conducted a retrospective cohort study comprising patients hospitalized for HF from 2010–2016 at the Heart-Lung Clinic of Örebro University Hospital, Sweden. Patients were eligible for inclusion if the primary reason for hospital admission was acute HF, if they had an echocardiography after admission with stored images available for post-hoc measurement of CI, and were at least 18 years old at the time of echocardiography. If a patient had more than one admission matching the inclusion criteria during the study period, only the first such event was taken into account. Patients were excluded if they presented any cardiac condition that would affect the measurement of CI, for example moderate or severe aortic valve disease, hypertrophic obstructive cardiomyopathy or congenital heart disease, or if they did not fulfil the diagnostic criteria of HF according to the 2016 guidelines of the European Society of Cardiology (ESC). No criteCI = LVOT − VTI * HR * LVOTd2 * 0.7854/BSA. Left ventricular outflow tract diameter (LVOTd) was measured from parasternal long axis images, as close to the aortic valve as possible. Left ventricular outflow tract velocity-time integral (LVOT-VTI) was measured on pulsatile doppler LVOT flow pattern images obtained from an apical 5-chamber view. LVOT-VTI was assessed on as many cardiac cycles as possible (maximum of 5) and the average of these measurements was used in the calculation of CI, in order to reduce possible inaccuracies caused by arrhythmias. Measurement bias of LVOTd and LVOT-VTI due to potentially suboptimal imaging plane and angle deviation could however not be eliminated due to the retrospective setting of the study. Mean cardiac cycle length (CCL) was assessed by measuring and averaging the duration of the cardiac cycle of LVOT-VTI measurements from the end of the ejection period of the previous systole to the end of the index ejection period. Heart rate (HR) was then calculated as HR = 1/CCL. Body surface area (BSA) was calculated using the DuBois-formula.All echocardiographic examinations were performed and analysed by experienced echocardiographers. Left ventricular ejection fraction was assessed during initial examination, and a different echocardiographer assessed CI values retrospectively, blinded to outcomes. Cardiac index was calculated as Endpoints were defined as all-cause death, cardiovascular (CV) death, or first HF-related hospital readmission. Information on HF-related readmission was obtained from electronic patient journals. Mortality and date of death were obtained from the Swedish Death Registry. Cause of death was assessed by two independent investigators based on available patient journals and, in cases of discrepancy, adjudicated by a third.Prognostic models were created using the Cox proportional hazard method including either LVEF or CI together with available traditional risk factors for HF: age, sex, diabetes, ischemic aetiology, chronic obstructive pulmonary disease (COPD), glomerular filtration rate (GFR), body mass index (BMI), atrial fibrillation, and heart rate. Data of traditional risk factors for morbidity and mortality with HF were collected by manual search in patient electronic journals. The diagnosis of heart failure was confirmed using the diagnostic criteria for heart failure with reduced ejection fraction , mid-range ejection fraction  and preserved ejection fraction  as described in the 2016 guidelines of the ESC. ClinicalCategorical variables were compared using chi-square test and unpaired two-tailed t-test was used for the comparison of continuous variables. Discriminative accuracy of the prediction models was assessed by Harrell’s c-index, with standard deviation obtained by the jack-knife method and compared by unpaired t-test. The P-value of 0.05 was considered significant.The investigation conforms to the principles outlined in the Declaration of Helsinki and was approved by the Regional Ethical Review Board in Uppsala, Sweden. Data on patient demographics and outcomes were collected from the patients’ electronic medical records and the Swedish Death Registry, and linked to echocardiographic data by personal identification numbers. All data was anonymized before further processing. The Regional Ethical Review Board did not waive any requirement for informed consent.2. The majority of subjects were >70 years old and had multiple comorbidities. All patients received guideline directed medical treatment. Medications and devices applied after the index hospital admission and after treatment optimization are shown in We found 1823 acute HF-related hospital admissions of 1274 individuals during the study period. Lack of echocardiography after hospital admission excluded 456 patients from the study, and 139 were excluded due to the presence of moderate to severe aortic valve disease or hypertrophic obstructive cardiomyopathy. Of the remaining 679 patients, echocardiography images necessary for CO measurement and documented height and weight needed for BSA calculation at the time of echocardiography were available for 339. Five patients exhibited LVEF≥40% and elevated natriuretic peptides but presented no echocardiographic evidence of diastolic dysfunction or structural heart disease, so did not fulfil the diagnostic criteria of HF. The final subject group of 334 patients was a heterogeneous population comprising patients with reduced ejection fraction (HFrEF) (n = 196), mid-range ejection faction (HFmrEF) (n = 65), or preserved ejection fraction (HFpEF) (n = 73) and included those with newly diagnosed HF before treatment optimization as well as patients with a long history of HF, already on optimal medical treatment at the time of admission. Patient baseline characteristics are shown in 2 vs. 30.5±11.3 ml/ m2, p = 0.0005) and higher heart rate , resulting in lower CI ; however this difference did not reach statistical significance. As irregular heart rhythm leads to beat-to-beat changes in SVI and CI, intra-observer variability assessment was limited to the subgroup of 179 patients with sinus rhythm and at least 2 cardiac cycles. The absolute intra-observer variability for SVI was 0.2±3.9ml/m2 (relative variability 0.5±13.0%). For single beat estimates of CI, intra-observer variability was 0.01±0.24 l/min/m2 (relative variability 0.1±10.7%).A total of 1133 cardiac cycles were analysed. Patients with atrial fibrillation had more cardiac cycles available than those with sinus rhythm . They had significantly lower stroke volume index . Cardiac index was not associated with all-cause death (P = 0.271) or CV death (P = 0.209) but showed significant association with HF-readmission (P = 0.020).One-hundred-seventy-three patients died , and 133 underwent HF-related hospital readmission during 2.3±1.9 years of follow-up. On univariate Cox proportional hazards analysis, LVEF was not associated with all-cause death, CV death, or HF-readmission . Cardiac index showed no correlation with all-cause or CV death but remained an independent predictor of HF-readmission . The risk ratios of the final prediction models in the overall cohort are presented in When adjusted for traditional risk factors, LVEF showed no significant correlation with the endpoints (P < 0.001), CV death , and HF-readmission , with slight but significant difference in all cases.Both the LVEF-based and CI-based models had moderate discriminative accuracy when predicting all-cause death . Cardiac index showed a strong association with all-cause death but did not demonstrate correlation with CV death or HF-readmission. After adjustment for other risk factors, neither LVEF nor CI showed correlation with all-cause death or HF-readmission, but LVEF became an independent predictor of CV death .In the combined group of 65 HFmrEF and 73 HFpEF patients, neither LVEF nor CI was individually predictive of all-cause death or CV death. LVEF had no correlation with HF-readmission; however CI showed a significant association with HF-readmission. On adjusted analysis, CI proved to be an independent predictor of HF-readmission but not of all-cause or CV death, while LVEF had no independent predictive value for any endpoint .Our study fills a gap in information on echocardiographic measures of cardiac function. Left ventricular ejection fraction is used routinely as standard for the diagnosis and management of HF, however the moderate accuracy of the LVEF measurement raises concerns when using cut-off values as indication for potentially life-saving treatment. Earlier In contrast to earlier invasive studies of younger patients showing a strong association among LVEF, CI, and prognosis, our results show that both LVEF and CI measured by transthoracic echocardiography are poor predictors of all-cause death in an elderly HF cohort, which may be explained by a high proportion of deaths from causes other than HF in an aging population., 14 CardThe major limitation of this study was the timing of the echocardiography. A significant proportion of patients were examined early after admission for new onset HF, before initiation of therapy. As therapy may lead to an improvement in hemodynamic status and significant increase in both LVEF and CI, assessment of these parameters early in the disease course may have had limited value as predictors of later events. Further It is well known that approximately 30–40% of patients with guideline-based indication for cardiac resynchronization therapy do not experience improvement in left ventricular function or in clinical symptoms., 17 ApprThe group of HF patients with an LVEF ≥40% has been the focus of research in recent decades. Results of all large mortality trials in this patient population have been neutral despite efforts to refine the diagnostic criteria for HF. Our analS1 File(XLSX)Click here for additional data file.


Supplemental Digital Content is available in the text. Total cholesterol and high-density lipoprotein cholesterol (HDL-C) measurements are central to cardiovascular disease (CVD) risk assessment, but there is continuing debate around the utility of other lipids for risk prediction.Participants from UK Biobank without baseline CVD and not taking statins, with relevant lipid measurements (n=346 686), were included in the primary analysis. An incident fatal or nonfatal CVD event occurred in 6216 participants  over a median of 8.9 years. Associations of nonfasting lipid measurements  with CVD were compared using Cox models adjusting for classical risk factors, and predictive utility was determined by the C-index and net reclassification index. Prediction was also tested in 68 649 participants taking a statin with or without baseline CVD (3515 CVD events).r>0.90), while HDL-C was strongly correlated with ApoA1 (r=0.92). After adjustment for classical risk factors, 1 SD increase in ApoB, direct LDL-C, and non–HDL-C had similar associations with composite fatal/nonfatal CVD events . Associations for 1 SD increase in HDL-C and ApoA1 were also similar . Adding either total cholesterol and HDL-C, or ApoB and ApoA, to a CVD risk prediction model  yielded similar improvement in discrimination . Once total and HDL-C were in the model, no further substantive improvement was achieved with the addition of ApoB  or any measure of LDL-C. Results for predictive utility were similar for a fatal CVD outcome, and in a discordance analysis. In participants taking a statin, classical risk factors  were improved by non–HDL-C  or ApoB . However, adding ApoB or LDL-C to a model already containing non–HDL-C did not further improve discrimination.ApoB, LDL-C, and non–HDL-C were highly correlated (Measurement of total cholesterol and HDL-C in the nonfasted state is sufficient to capture the lipid-associated risk in CVD prediction, with no meaningful improvement from addition of apolipoproteins, direct or calculated LDL-C. Once total cholesterol and high-density lipoprotein cholesterol are included in a primary cardiovascular disease risk prediction score in a cohort at low risk, there is no evidence that addition of other lipids or lipoproteins  appreciably improve prediction.Similar findings were reproduced in those taking a statin at baseline.Calls for widespread use of apolipoproteins are not warranted given the negligible difference in risk prediction beyond total cholesterol and high-density lipoprotein cholesterol.Direct low-density lipoprotein cholesterol is also not required for risk prediction; non–high-density lipoprotein cholesterol is a cheaper equivalent predictor of risk on and off statins, without requirement for a fasting sample.553Editorial, see p 1 and pharmacological reduction of circulating non–high-density lipoprotein cholesterol (HDL-C) is beneficial in primary and secondary CVD prevention.3 Widely-used clinical CVD risk calculators frequently include classical biochemistry measures of total cholesterol, HDL-C, or a combination of these.7Hyperlipidemia has been widely documented to be associated with higher cardiovascular disease (CVD) risk,8Total cholesterol is an estimate of the cholesterol in all very-low-density lipoproteins, intermediate-density lipoproteins, low-density lipoproteins (LDL), high-density lipoprotein, and lipoprotein(a) in the fasted state, as well as chylomicrons and their remnant particles in the unfasted state. Notably, the majority of total cholesterol resides in LDL particles, and the additional clinical utility of recently developed direct LDL-cholesterol (LDL-C) measurements are not entirely clear. Non–HDL-C has been suggested as a pragmatic and cost-effective alternative to direct LDL-C measurement, requiring only total cholesterol and HDL-C to be performed.10 This is partly because of the proteins being key components of lipoprotein function, and their measurement facilitating quantification of objective particle numbers.8 ApoB is thought to be of similar use in CVD prediction as non–HDL-C and LDL-C, but in some studies has shown improvements in prediction in scenarios where treated LDL-C levels are within the normal range.11 However, an Emerging Risk Factor Collaboration individual participant meta-analysis from 37 prospective cohorts (165 544 participants) suggested that replacement of traditional lipid parameters with ApoA1 and ApoB measurements only offered very modest improvement in CVD prediction.13 Despite this, enthusiasm for clinical use of apolipoprotein measurements beyond LDL-C or total cholesterol remains high in many places.14Apolipoproteins (Apo) A1 and B are the principle protein components of HDL and non-HDL particles, respectively, and have also been of interest for their potential in improving CVD risk prediction.Therefore, despite considerable research, identification of the most informative measures of blood lipids in terms of CVD risk is still a matter of some contention. UK Biobank is a large, prospective, population-based cohort study that has recently released data on baseline biochemistry measurements including routine lipids and apolipoproteins in participants who consented to blood sample collection. Participants were generally of an age range where CVD risk scoring is conducted in clinical practice. All blood tests were conducted in a single dedicated ISO17025 accredited biochemistry laboratory using robust methodologies. These data therefore might be more robust than data from some constituent studies in previous meta-analyses and may robustly address controversies around biomarker utility in risk scoring. The aim of this study was to investigate whether additional lipid/lipoprotein measurements improve prediction of CVD events over established risk scores.The data reported in this article are available via application to the UK Biobank to other researchers for purposes of reproducing the results or replicating the procedure.16 UK Biobank received ethical approval from the North West Multi-Center Research Ethics Committee (11/NW/03820). All participants gave written informed consent before enrollment in the study, which was conducted in accordance with the principles of the Declaration of Helsinki.UK Biobank recruited 502 639 participants (aged 37–73 years) from 22 assessment centers across the UK between 2007 and 2010. Baseline biological measurements were recorded, and touch-screen questionnaires were administered, as described elsewhere.17 Smoking status was categorized into never or former/current smoking for this analysis. Ethnicity was coded as white, black, South Asian, or other. Nonfasting venous blood sampling was conducted, and collection procedures for the study were validated.18 Biochemistry measures were performed at a dedicated central laboratory between 2014 and 2017, including serum total cholesterol, HDL-C, triglycerides, direct LDL-C, and ApoA1 and ApoB. Non–HDL-C was calculated as total minus HDL-C.19 LDL-C was additionally calculated using 2 formulae/schema: Friedewald and Martin/Hopkins. Friedewald LDL is not calculated in 10 884 patients with triglycerides >400 mg/dL (2.2%). In this group, a direct LDL measurement was used as a substitute measure to minimize loss of data and mimics a potential clinical scenario where patients with a high triglycerides result may automatically have LDL directly measured. Remnant cholesterol was also calculated (Methods in the online-only Data Supplement). Further details of these measurements and assay performances can be found in the UK Biobank online showcase and protocol,20 and further information is listed in the supplement (Methods in the online-only Data Supplement).The first (of 2) measurements of systolic and diastolic blood pressure were used for these analyses, preferentially using an automated measurement. Repeated blood pressure measurements were very similar and highly correlated.The definition of known baseline diabetes included self-reported type 1 or type 2 diabetes  or those who reported using insulin. Statin and antihypertensive medication use were self-reported. Baseline cardiovascular disease was defined as self-reported historic angina, myocardial infarction, stroke, or transient ischemic attack.International Classification of Diseases, Tenth Revision:[ICD-10]:I20–25, I60–64) or hospitalization for cardiovascular disease 7 from Hospital Episode Statistics. A secondary outcome was death from cardiovascular disease as the underlying cause specifically  (Methods in the online-only Data Supplement), reflecting outcomes used in the European Systematic Coronary Risk Evaluation (SCORE) clinical guidelines.21Date and cause of death were obtained from death certificates held by the National Health Service Information Center for participants from England and Wales and the National Health Service Central Register Scotland for participants from Scotland. There were 2 outcomes of interest in the current study. The primary outcome was incident fatal and nonfatal cardiovascular disease that reflects the American College of Cardiology/American Heart Association guidelines prediction score including death from cardiovascular disease as the underlying cause , or the first date of hospitalization for CVD (in outcome 1), whichever came first.Participants with a baseline history of CVD or on statins were excluded from the primary analyses, but associations of lipids with CVD events were analyzed in this group as a secondary aim. All analyses were performed on those with complete data for exposures and covariates of interest.Classical risk factors were expressed as mean (SD) as all were approximately normally distributed, and number (%) for categorical variables. Each lipid/protein variable was also categorized into quintiles. The distribution of classical risk factors by categories of each lipid/protein variable  were assessed using tests for trends across the variable using ANOVA, or a chi-squared test, as appropriate. Associations between classical risk factors and lipid/protein variables with CVD events were also tabulated using these methods.Correlations between log-transformed total cholesterol, HDL-C, LDL-C, non–HDL-C, ApoA1, and ApoB were assessed using Pearson correlation coefficient, both without adjustment, then accounting for sex and age.7 Models of fatal CVD were adjusted for nonlipid variables used in the European SCORE clinical guidelines: age, sex, systolic blood pressure, and smoking.21 Cox proportional hazards models conducted in people who had baseline CVD or were taking a statin were also additionally adjusted for baseline CVD and statin use. Follow-up time was calculated as days from assessment to first CVD hospital diagnosis or CVD death, the end of follow-up, or loss to follow-up, whichever occurred first. We made qualitative comparisons between the hazard ratios (HR) for the lipid and lipoprotein variables; fixing the β coefficients for covariates in the models made no substantial difference to overall conclusions.Event-free survival was initially explored for each quintile of lipid/protein using univariable Kaplan-Meier models for both incident and fatal CVD. The association of each lipid/protein variable with incident nonfatal and fatal CVD was tested using Cox proportional hazard models. Models for the incident fatal/nonfatal CVD event were adjusted for nonlipid variables broadly comparable to those used in the American College of Cardiology/American Heart Association guidelines: age, sex, ethnicity, systolic blood pressure, diastolic blood pressure, antihypertensive medication, diabetes, and smoking.22) were constructed for each lipid/protein variable, and the fully adjusted relationship (as above) with CVD events was plotted. Changing knots did not substantially change the shape of the associations (data not shown).To visualize the relationship between each lipid/protein variable and outcome, restricted cubic splines with 4 knots . Potential pairwise interactions between classical risk factors and lipid/protein variables were considered. The proportional hazard assumption was checked by visual inspection of Schöenfeld residuals. Utility of lipids to discriminate composite and fatal CVD events were then tested using Harrell C-statistics using an either/or model  or an additive model . We also investigated model fit using changes in Akaike Information Criteria and Bayesian Information Criteria. We used a categorical net reclassification index, with 1000 bootstraps to estimate 95% CI, to investigate the changes in predicted risk classification across the 7.5% 10-year risk boundary used to allocate statins in the American College of Cardiology/American Heart Association model.24 where utility of lipid and lipoprotein measurements were checked.A discordance sensitivity analysis was also performed to check utility of LDL-C and apolipoprotein measurement in a group where ApoB and direct LDL-C were not in “agreement.” The absolute difference in the percentile of ApoB and direct LDL for each participant was used to stratify the population into a discordant group (those with an absolute difference of >10 percentage points between their ApoB and LDL percentiles)25). A P value of <0.05 was considered statistically significant.All analyses were performed using STATA 14 (StataCorp LP) and R3.5.1 . Data were also available in 68 649 participants taking a statin with or without baseline CVD .Tables I through IV in the online-only Data Supplement). Women were more likely to have high total cholesterol levels (Table I in the online-only Data Supplement), whereas men were more likely to have high ApoB levels (Table II in the online-only Data Supplement). Both HDL-C and ApoA1 were associated with older age and were higher in women (Tables V and VI in the online-only Data Supplement). ApoB, direct and calculated LDL-C, and non–HDL-C were all highly correlated with each other (r>0.90), while HDL-C was strongly associated with ApoA1 (r=0.924) . The incident fatal/nonfatal CVD outcome occurred in 6216 participants (1.8%), and fatal CVD occurred in 1656 participants (0.5%).Table VII in the online-only Data Supplement). In Kaplan-Meier curves, lipids and lipoproteins by quintiles had strong univariable associations with the incident CVD event . Similar associations were seen for fatal CVD, although the separation of the survival curves for total cholesterol was less clear .Incident CVD events occurred more frequently in those participants with more adverse risk profiles, including more frequently being older, male, smokers, and with higher systolic blood pressure, diastolic blood pressure, total cholesterol, lower HDL-C and ApoA1, and higher LDL-C and ApoB Table . SimilarFigure III in the online-only Data Supplement), and the shape of the association of total cholesterol with risk of fatal CVD was “U-shaped” . Exclusion of the first 2 years of follow-up did not substantially attenuate these results .In Cox models adjusting for nonlipid classical risk factors, the shapes of the association of total cholesterol, non–HDL-C, and ApoB with risk (HR) of the composite CVD outcome were positive, with increases in the gradient of risk at around 220 mg/dL for total cholesterol and 1.1g/L for ApoB . Specifically, 1 SD increments in ApoB, LDL-C, and non–HDL-C were broadly equally associated with subsequent CVD risk . In the discordance sensitivity analysis, ApoB had stronger associations with CVD than LDL-C and non–HDL-C (Tables X and XI in the online-only Data Supplement). There was evidence that ApoB and LDL-C measurements were more strongly associated with risk in men, at younger age, in people who smoke, and in those with high triglycerides, although the association was still positive in all subgroups (Table XII in the online-only Data Supplement). Generally, interactions were less pronounced for HDL-C and ApoA1 (Table XII in the online-only Data Supplement).After adjustment for classical risk factors, 1 SD increase in ApoB 0.23 g/L), LDL-C (31.2 mg/dL), and non–HDL-C (39.2 mg/dL) had similar associations with incident CVD events , as did measures of calculated LDL-C . In analysis of the categorical net reclassification index across the 7.5% 10-year risk threshold, there was some evidence that addition of either total cholesterol and HDL-C or ApoB and ApoA1 improved classification, particularly among cases (Table XIV in the online-only Data Supplement). However once total cholesterol and HDL-C were in the model, addition of any measurement of LDL-C or apolipoprotein did not improve classification among cases or controls, and the upper end of the 95% CI did not exceed 0.51% improvement in any overall model (Table XIV in the online-only Data Supplement).In a model of CVD prediction, classical risk factors without lipids yielded reasonable discrimination  Figure . DiscrimFigure V in the online-only Data Supplement). Adding other lipids or proteins to a model already containing total and HDL-C generally offered no discriminative benefit for fatal CVD .Similarly, in a model of fatal CVD prediction based on European SCORE risk factors plus current treatments, classical risk factors without lipids yielded a C-index of 0.7813 in the cohort without baseline CVD and not taking a statin . Discrimination was improved on the addition of total and HDL-C . There was a similar improvement on addition of ApoA1 and ApoB  , which was not improved on the addition of ApoA1 and ApoB , direct LDL , Friedewald LDL-C , or Martins-Hopkins LDL .Figure VI in the online-only Data Supplement). However, adding ApoB or LDL to a model already containing non-HDL did not further improve discrimination .In participants taking a statin, classical risk factors (including baseline CVD status) also yielded reasonable discrimination of the fatal/nonfatal CVD outcome . Adding non-HDL improved discrimination , as did adding ApoB or any lipid measure , there was no improvement in discrimination when apolipoproteins were added to the risk score model.We acknowledge that risk prediction metrics in the current study do not necessarily inform us about the clinical implications of reductions in different lipid parameters on treatment.35 The use of Hospital Episode Statistics data to identify hospitalization for CVD shows reasonable accuracy compared to active biomedical examinations; for instance, the sensitivity and specificity for coronary heart disease have been reported at 72% and 96%.37 Further potential limitations include the relatively low average CVD risk of participants. Even so, baseline risk factors were predictive of CVD in the expected manner, and risk prediction models performed broadly in line with expectations. We were unable to update participant risk during follow-up for new initiation of statin therapy, although we did consider those taking statins as a separate group and report similar findings, suggesting the influence of statins and changes in statin status during follow-up are likely to be minimal. Separate studies would be required to investigate the utility of different measures of cholesterol and apolipoproteins in investigating statin efficacy on CVD events.38 UK Biobank is not representative of the whole UK population, but this is generally not a concern in investigating risk associations.39 Incident events were not adjudicated, as they would be in a clinical trial, but ascertainment of such events has been in line with other cohort studies used to generate risk scores.41 The C-statistic has been criticized for a lack of sensitivity to potentially important clinical changes in predictive utility, but the results of our analysis using the net reclassification index are consistent with increments in the C-statistic. Finally, as most of the participants were white, separate work is needed to validate these findings in different races.The strengths of our study include the large cohort size at an age relevant to CVD risk scoring, biochemistry assays performed consistent with the internationally recognized standard for testing and calibration laboratories, and using models with comprehensive adjustment for all usual cardiovascular risk factors. We were also able to separately analyze participants already on statins as well as those with previous CVD. The numbers of events accrued in the study are also considerable in the context of existing literature. Passive collection of Hospital Episode Statistics data has the advantage over active cohort studies that loss to follow-up is minimized. The primary reason for loss to follow-up here is emigration. Estimates suggest that emigration in the UK Biobank cohort is minimal (≈0.3%).In conclusion, data from UK Biobank show that the predictive ability of total cholesterol and HDL-C, in the context of other classical risk factors, are not improved by the addition or replacement with apolipoproteins in the assessment of CVD risk. Similarly, non–HDL-C appears adequate to assess on-treatment lipid lowering therapy in this setting. Although lipoprotein(a) measurements may have a role in some settings, these large scale data support the measurement of standard total cholesterol and HDL-C in the nonfasting setting as being perfectly adequate in their capture of lipid-associated CVD risk and for determining non–HDL-C as a treatment target for those already on statins.This research was conducted using the UK Biobank resource. We thank the participants of the UK Biobank. The work was performed under UK Biobank project number 9310.The work in this study was supported by a grant from Chest, Heart, and Stroke Association Scotland [Res16/A165]. R. Brown was supported by a Medical Research Council Doctoral Training Partnership MR/N013166/1.Dr Paul Welsh has received grant support from Roche, AstraZeneca, and Boehringer Ingelheim. Dr Sattar has received fees for consulting, speaking, and/or honoraria from Roche Diagnostics, AstraZeneca, Boehringer Ingelheim, NovoNordisk, Amgen, Sanofi/Regeneron, and Janssen.


Background Daytime sleepiness can be assessed by the Epworth Sleepiness Scale (ESS), which is widely used in the field of sleep medicine as a subjective measure of a patient’s sleepiness. Also, health utility assessed by the mean Short-Form Six-Dimension (SF-6D) score, one of several preference-based utility measures, is an important measure in health care. We aimed to examine age-related differences in daytime sleepiness and health utility and their relationship in patients 5 months after cardiac surgery. Methods; This cross-sectional study assessed 51 consecutive cardiac surgery patients who were divided into a middle-aged  and older-age group . The mean ESS and SF-6D utility scores were measured at 5 months after cardiac surgery and compared. In addition, the relationship between ESS and SF-6D utility scores were assessed. Results; There were no significant differences between the middle-aged and older-aged groups in either the mean ESS  or SF-6D utility  scores. However, there was a negative correlation between both values in all of the patients after cardiac surgery . Conclusions; Although there were no age-related differences in the ESS and SF-6D utility values between the two groups, there was a negative correlation between these values in all patients at 5 months after cardiac surgery. This suggested that sleepiness is associated with decreased utility scores in patients at 5 months after cardiac surgery. Poor sleep quality, sleeplessness, and sleep disorder are common among patients recovering from cardiac surgery such as coronary artery bypass grafting (CABG) ,2. PreviCardiac rehabilitation (CR) in cardiac patients helps to reduce risk factors, improve exercise capacity and health-related QOL, lower the number of subsequent cardiac events, and reduce all-cause mortality and hospitalization costs ,14,15,16Therefore, we hypothesized that there might be age-related differences in daytime sleepiness and health utility, and daytime sleepiness might be related to health utility as evaluated by self-reported questionnaire. The purpose of the present study was to determine both age-related differences in daytime sleepiness and health utility and the relationship between daytime sleepiness and health utility in patients at 5 months after cardiac surgery as assessed by self-reported questionnaires.n = 36) and older-age group . We reviewed the patients’ medical records to evaluate the following patient characteristics of age, sex, body mass index (BMI), etiology, left ventricular ejection fraction (LVEF), and medications. These characteristics and daytime sleepiness and health utility were assessed at 5 months after cardiac surgery and compared between the two groups. Exclusion criteria included patients with neurological, peripheral vascular, orthopedic, pulmonary, and advanced renal disease and those on dialysis. The present study complied with the principles of the Declaration of Helsinki regarding human investigations and was approved by the local institutional review board. All patients provided their informed consent.This cross-sectional study comprised 67 consecutive cardiac patients with coronary artery bypass grafting or valve replacement and valvuloplasty who visited hospital as outpatients 5 months after cardiac surgery and were referred to the department of rehabilitation medicine for the evaluation of daytime sleepiness and health utility. The patients were initially divided into a middle-aged group .Results are expressed as the mean ± standard deviation (SD). We used the unpaired Of the 67 patients, 16 were excluded from the study because of lack of data on clinical characteristics, the ESS, and the SF-6D utility score at 5 months after cardiac surgery. Thus, of the remaining 51 patients, 29 comprised the middle-aged group, and 22 comprised the older-age group. Also, it appeared that both groups experienced practically no decompensations during the initial 5 months post discharge. Except for age, the clinical characteristics were not significantly different between the two groups .r = −4.10, p = 0.003).The mean ESS and SF-6D utility scores collected from the two groups are presented in To our knowledge, this is first time that the mean ESS as an index of daytime sleepiness and the mean SF-6D utility score as an index of health utility have been evaluated in relation to age in cardiac surgery patients at 5 month after surgery. Moreover, the relationship between these two values was also evaluated. The main finding of this study was that there were no significant differences in the mean ESS and SF-6D utility score values at 5 months after cardiac surgery between the middle-aged and the older-aged groups. However, there was a negative correlation between two these values in all patients.With the exception of age, all other clinical characteristics of the patients were almost identical between the middle- and older-aged groups. Therefore, clinical characteristics of the patients other than age likely had little effect on any differences in the mean ESS and SF-6D utility scores found in the two groups. We evaluated daytime sleepiness status with the ESS because it is well validated, and scores from several subject groups have been published ,8,23,24.r = 0.41, with r < 0.4 indicating a weak, r ≥ 0.4 but <0.6 indicating a moderate, and r ≥ 0.6 but <0.8 a strong correlation [In a cost-utility analysis carried out in tandem with a randomized controlled trial using the SF-6D in learning and coping strategies in CR that comprised 825 patients over 18 years of age admitted with ischemic heart disease or heart failure, Dehbarez et al. reported mean SF-6D scores of 0.739 at baseline, 0.798 at 2 months after intervention, and 0.794 after 5 months of follow-up . Althougrelation ). Lopes relation  showed tESS scores of 11 or higher indicate excessive daytime sleepiness. In some reports, cardiac patients such as those who underwent CABG complained of sleeplessness , and sleThere are several limitations in the present study. The sample size is very small and is comprised almost entirely of male cardiac surgery patients. Additional analysis of sex-related differences in daytime sleepiness and health utility in female cardiac patients is also required. We did not evaluate the effects of postoperative complications or the start day of an earlier in-hospital rehabilitation program on selection bias, or of longer hospital stays. We also did not examine the presence or absence of drugs and patient age in relation to daytime sleepiness.In addition, the analyses reported in A recent study reported that cardiac surgery is correlated with obstructive sleep apnea (OSA) in cardiovascular outcomes after a period of 4.5 years, where it was shown that OSA is independently associated with a higher rate of long-term cardiovascular events after CABG and may have prognostic and economic significance in CABG surgery . We did This study attempted to identify both age-related differences in sleep quality and health utility and the relationship between these values in patients in the chronic phase at 5 months after cardiac surgery. We found no significant age-related differences between sleep quality and health utility at 5 months after cardiac surgery. However, there was a significant negative correlation between these values in all patients. Because this study lacks long-term follow-up data on sleep quality and health utility, additional study will be required to evaluate outcomes over longer periods of time in these patients.