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BACKGROUND: Studies across the globe generally reported increased mortality risks associated with particulate matter with aerodynamic diameter [less than or equal to]2.5 [micro]m (P[M.sub.2.5]) exposure with large heterogeneity in the magnitude of reported associations and the shape of concentration-response functions (CRFs). We aimed to evaluate the impact of key study design factors (including confounders, applied exposure model, population age, and outcome definition) on P[M.sub.2.5] effect estimates by harmonizing analyses on three previously published large studies in Canada [Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE), 1991-2016], the United States (Medicare, 2000-2016), and Europe [Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), 2000-2016] as much as possible. METHODS: We harmonized the study populations to individuals 65+ years of age, applied the same satellite-derived P[M.sub.2.5] exposure estimates, and selected the same sets of potential confounders and the same outcome. We evaluated whether differences in previously published effect estimates across cohorts were reduced after harmonization among these factors. Additional analyses were conducted to assess the influence of key design features on estimated risks, including adjusted covariates and exposure assessment method. A combined CRF was assessed with meta-analysis based on the extended shape-constrained health impact function (eSCHIF). RESULTS: More than 81 million participants were included, contributing 692 million person-years of follow-up. Hazard ratios and 95% confidence intervals (CIs) for all-cause mortality associated with a 5-[micro]g/[m.sup.3] increase in P[M.sub.2.5] were 1.039 (1.032, 1.046) in MAPLE, 1.025 (1.021, 1.029) in Medicare, and 1.041 (1.014, 1.069) in ELAPSE. Applying a harmonized analytical approach marginally reduced difference in the observed associations across the three studies. Magnitude of the association was affected by the adjusted covariates, exposure assessment methodology, age of the population, and marginally by outcome definition. Shape of the CRFs differed across cohorts but generally showed associations down to the lowest observed P[M.sub.2.5] levels. A common CRF suggested a monotonically increased risk down to the lowest exposure level. https://doi.org/10.1289/EHP12141, Introduction Ambient particulate matter (PM) pollution has been identified as a leading risk factor for global disease burden. (1) Evaluating the risks attributable to PM with aerodynamic diameter [less than [...]

BACKGROUND: Studies across the globe generally reported increased mortality risks associated with particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) exposure with large heterogeneity in the magnitude of reported associations and the shape of concentration-response functions (CRFs). We aimed to evaluate the impact of key study design factors (including confounders, applied exposure model, population age, and outcome definition) on PM2.5 effect estimates by harmonizing analyses on three previously published large studies in Canada [Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE), 1991-2016], the United States (Medicare, 2000-2016), and Europe [Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), 2000-2016] as much as possible. METHODS: We harmonized the study populations to individuals 65+ years of age, applied the same satellite-derived PM2.5 exposure estimates, and selected the same sets of potential confounders and the same outcome. We evaluated whether differences in previously published effect estimates across cohorts were reduced after harmonization among these factors. Additional analyses were conducted to assess the influence of key design features on estimated risks, including adjusted covariates and exposure assessment method. A combined CRF was assessed with meta-analysis based on the extended shape-constrained health impact function (eSCHIF). RESULTS: More than 81 million participants were included, contributing 692 million person-years of follow-up. Hazard ratios and 95% confidence intervals (CIs) for all-cause mortality associated with a 5-μg/m³ increase in PM2.5 were 1.039 (1.032, 1.046) in MAPLE, 1.025 (1.021, 1.029) in Medicare, and 1.041 (1.014, 1.069) in ELAPSE. Applying a harmonized analytical approach marginally reduced difference in the observed associations across the three studies. Magnitude of the association was affected by the adjusted covariates, exposure assessment methodology, age of the population, and marginally by outcome definition. Shape of the CRFs differed across cohorts but generally showed associations down to the lowest observed PM2.5 levels. A common CRF suggested a monotonically increased risk down to the lowest exposure level. [ABSTRACT FROM AUTHOR]

aimed to investigate the relationship between PPV and registry-based 30-day mortality after AMI admission and between-hospital variation in PPV. Study Design and Setting: An electronic record review was performed in a nationwide sample of Norwegian hospitals. Clinical signs and cardiac troponin measurements were abstracted and analyzed using a mixture model for likelihood ratios and parametric bootstrapping. Results: The overall PPV was estimated to be 97%. We found no statistically significant association between hospital PPV and the classification of hospitals into low, intermediate, and high registry-based 30-day mortality. There was significant variation between hospitals, with a PPV range of 91– 100%. Conclusion: We found no evidence that variation in PPV of AMI diagnosis can explain variation between hospitals in registry-based 30-day mortality after admission. However, PPV varied significantly between hospitals. We were able to use a very efficient statistical approach to the analysis and handling of various sources of uncertainty. [ABSTRACT FROM AUTHOR]

Background The purpose of our study was to evaluate the effects of a new reimbursement rule for anti hypertensive medication that made thiazides mandatory first-line drugs for newly treated, uncomplicated [...]

Aims: A prolonged corrected QT interval (QTc) ≥500 ms is associated with high all-cause mortality in hospitalized patients. We aimed to explore any difference in short- and long-term mortality in patients with QTc ≥500 ms compared with patients with QTc <500 ms after adjustment for comorbidity and main diagnosis.Methods and Results: Patients with QTc ≥500 ms who were hospitalized at Telemark Hospital Trust, Norway between January 2007 and April 2014 were identified. Thirty-day and 3-year all-cause mortality in 980 patients with QTc ≥500 ms were compared with 980 patients with QTc <500 ms, matched for age and sex and adjusting for Charlson comorbidity index (CCI), previous admissions, and main diagnoses. QTc ≥500 ms was associated with increased 30-day all-cause mortality [hazard ratio (HR) 1.90, 95% confidence interval (CI) 1.38-2.62; P < 0.001]. There was no significant difference in mortality between patients with QTc ≥500 ms and patients with QTc <500 ms who died between 30 days and 3 years; 32% vs. 29%, P = 0.20. Graded CCI was associated with increased 3-year all-cause mortality (CCI 1-2: HR 1.62, 95% CI 1.34-1.96; P < 0.001; CCI 3-4: HR 2.50, 95% CI 1.95-3.21; P < 0.001; CCI ≥5: HR 3.76, 95% CI 2.85-4.96; P < 0.001) but was not associated with 30-day all-cause mortality.Conclusion: QTc ≥500 ms is a powerful predictor of short-term mortality overruling comorbidities. QTc ≥500 ms also predicted long-term mortality, but this effect was mainly caused by the increased short-term mortality. For long-term mortality, comorbidity was more important. [ABSTRACT FROM AUTHOR]

Objectives Postoperative wound dehiscence (PWD) is a serious complication to laparotomy, leading to higher mortality, readmissions and cost. The aims of the present study are to investigate whether risk adjusted PWD rates could reliably differentiate between Norwegian hospitals, and whether PWD rates were associated with hospital characteristics such as hospital type and laparotomy volume. Design Observational study using patient administrative data from all Norwegian hospitals, obtained from the Norwegian Patient Registry, for the period 2011-2015, and linked using the unique person identification number. Participants All patients undergoing laparotomy, aged at least 15 years, with length of stay at least 2 days and no diagnosis code for immunocompromised state or relating to pregnancy, childbirth and puerperium. The final data set comprised 66 925 patients with 78 086 laparotomy episodes from 47 hospitals. Outcomes The outcome was wound dehiscence, identified by the presence of a wound reclosure code, risk adjusted for patient characteristics and operation type. Results The final data set comprised 1477 wound dehiscences. Crude PWD rates varied from 0% to 5.1% among hospitals, with an overall rate of 1.89%. Three hospitals with statistically significantly higher PWD than average were identified, after case mix adjustment and correction for multiple comparisons. Hospital volume was not associated with PWD rate, except that hospitals with very few laparotomies had lower PWD rates. Conclusions Among Norwegian hospitals, there is considerable variation in PWD rate that cannot be explained by operation type, age or comorbidity. This warrants further investigation into possible causes, such as surgical technique, perioperative procedures or handling of complications. The risk adjusted PWD rate after laparotomy is a candidate quality indicator for Norwegian hospitals. [ABSTRACT FROM AUTHOR]

Introduction: A common quality indicator for monitoring and comparing hospitals is based on death within 30 days of admission. An important use is to determine whether a hospital has higher or lower mortality than other hospitals. Thus, the ability to identify such outliers correctly is essential. Two approaches for detection are: 1) calculating the ratio of observed to expected number of deaths (OE) per hospital and 2) including all hospitals in a logistic regression (LR) comparing each hospital to a form of average over all hospitals. The aim of this study was to compare OE and LR with respect to correctly identifying 30-day mortality outliers. Modifications of the methods, i.e., variance corrected approach of OE (OE-Faris), bias corrected LR (LR-Firth), and trimmed mean variants of LR and LR-Firth were also studied. Materials and methods: To study the properties of OE and LR and their variants, we performed a simulation study by generating patient data from hospitals with known outlier status (low mortality, high mortality, non-outlier). Data from simulated scenarios with varying number of hospitals, hospital volume, and mortality outlier status, were analysed by the different methods and compared by level of significance (ability to falsely claim an outlier) and power (ability to reveal an outlier). Moreover, administrative data for patients with acute myocardial infarction (AMI), stroke, and hip fracture from Norwegian hospitals for 2012–2014 were analysed. Results: None of the methods achieved the nominal (test) level of significance for both low and high mortality outliers. For low mortality outliers, the levels of significance were increased four- to fivefold for OE and OE-Faris. For high mortality outliers, OE and OE-Faris, LR 25% trimmed and LR-Firth 10% and 25% trimmed maintained approximately the nominal level. The methods agreed with respect to outlier status for 94.1% of the AMI hospitals, 98.0% of the stroke, and 97.8% of the hip fracture hospitals. Conclusion: We recommend, on the balance, LR-Firth 10% or 25% trimmed for detection of both low and high mortality outliers. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records. Methods: We used PAS data from all Norwegian hospitals (2005–2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed. Results: The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes. Conclusions: This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway. [ABSTRACT FROM AUTHOR]

Background: The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator. Methods and Findings: Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013. Conclusions: Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate survival curves (Kaplan-Meier) as a means of identifying areas in the clinical pathway amenable to quality improvement. Design: Observational before-after study. Setting: In Norway, annual public reporting of nationwide 30-day in-and-out-of-hospital mortality (30D) for three medical conditions started in 2011: first time acute myocardial infarction (AMI), stroke and hip fracture; reported for 2009. 12 of 61 hospitals had statistically significant lower/higher mortality compared with the hospital mean. Participants: Three hospitals with significantly higher mortality requested detailed analyses for quality improvement purposes: Telemark Hospital Trust Skien (AMI and stroke), Østfold Hospital Trust Fredrikstad (stroke), Innlandet Hospital Trust Gjøvik (hip fracture). Outcome measures: Survival curves, crude and risk-adjusted 30D before (2008-2009) and after (2012-2013). Interventions: Unadjusted survival curves for the outlier hospitals were compared to curves based on pooled data from the other hospitals for the 30-day period 2008-2009. For patients admitted with AMI (Skien), stroke (Fredrikstad) and hip fracture (Gjøvik), the curves suggested increased mortality from the initial part of the clinical pathway. For stroke (Skien), increased mortality appeared after about 8 days. The curve profiles were thought to reflect suboptimal care in various phases in the clinical pathway. This informed improvement efforts. Results: For 2008-2009, hospital-specific curves differed from other hospitals: borderline significant for AMI (p=0.064), highly significant (p<0.005) for the remainder. After intervention, no difference was found (p>0.188). Before-after comparison of the curves within each hospital revealed a significant change for Fredrikstad (p=0.006). For the three hospitals, crude 30D declined and they were non-outliers for risk-adjusted 30D for 2013. [ABSTRACT FROM AUTHOR]

Background: We conducted an Internet-based randomized trial comparing three valence framing presentations of the benefits of antihypertensive medication in preventing cardiovascular disease (CVD) for people with newly diagnosed hypertension to determine which framing presentation resulted in choices most consistent with participants' values. Methods and Findings: In this second in a series of televised trials in cooperation with the Norwegian Broadcasting Company, adult volunteers rated the relative importance of the consequences of taking antihypertensive medication using visual analogue scales (VAS). Participants viewed information (or no information) to which they were randomized and decided whether or not to take medication. We compared positive framing over 10 years (the number escaping CVD per 1000); negative framing over 10 years (the number that will have CVD) and negative framing per year over 10 years of the effects of antihypertensive medication on the 10-year risk for CVD for a 40 year-old man with newly diagnosed hypertension without other risk factors. Finally, all participants were shown all presentations and detailed patient information about hypertension and were asked to decide again. We calculated a relative importance score (RIS) by subtracting the VAS-scores for the undesirable consequences of antihypertensive medication from the VAS-score for the benefit of CVD risk reduction. We used logistic regression to determine the association between participants' RIS and their choice. 1,528 participants completed the study. The statistically significant differences between the groups in the likelihood of choosing to take antihypertensive medication in relation to different values (RIS) increased as the RIS increased. Positively framed information lead to decisions most consistent with those made by everyone for the second, more fully informed decision. There was a statistically significant decrease in deciding to take antihypertensives on the second decision, both within groups and overall. Conclusions: For decisions about taking antihypertensive medication for people with a relatively low baseline risk of CVD (70 per 1000 over 10 years), both positive and negative framing resulted in significantly more people deciding to take medication compared to what participants decided after being shown all three of the presentations. Trial Registration: International Standard Randomised Controlled Trial Number Register ISRCTN 33771631 [ABSTRACT FROM AUTHOR]

Background: We conducted an Internet-based randomized trial comparing four graphical displays of the benefits of antibiotics for people with sore throat who must decide whether to go to the doctor to seek treatment. Our objective was to determine which display resulted in choices most consistent with participants' values. Methods and Findings: This was the first of a series of televised trials undertaken in cooperation with the Norwegian Broadcasting Company. We recruited adult volunteers in Norway through a nationally televised weekly health program. Participants went to our Web site and rated the relative importance of the consequences of treatment using visual analogue scales (VAS). They viewed the graphical display (or no information) to which they were randomized and were asked to decide whether to go to the doctor for an antibiotic prescription. We compared four presentations: face icons (happy/sad) or a bar graph showing the proportion of people with symptoms on day three with and without treatment, a bar graph of the average duration of symptoms, and a bar graph of proportion with symptoms on both days three and seven. Before completing the study, all participants were shown all the displays and detailed patient information about the treatment of sore throat and were asked to decide again. We calculated a relative importance score (RIS) by subtracting the VAS scores for the undesirable consequences of antibiotics from the VAS score for the benefit of symptom relief. We used logistic regression to determine the association between participants' RIS and their choice. 1,760 participants completed the study. There were statistically significant differences in the likelihood of choosing to go to the doctor in relation to different values (RIS). Of the four presentations, the bar graph of duration of symptoms resulted in decisions that were most consistent with the more fully informed second decision. Most participants also preferred this presentation (38%) and found it easiest to understand (37%). Participants shown the other three presentations were more likely to decide to go to the doctor based on their first decision than everyone based on the second decision. Participants preferred the graph using faces the least (14.4%). Conclusions: For decisions about going to the doctor to get antibiotics for sore throat, treatment effects presented by a bar graph showing the duration of symptoms helped people make decisions more consistent with their values than treatment effects presented as graphical displays of proportions of people with sore throat following treatment. [ABSTRACT FROM AUTHOR]

Background: Different presentations of treatment effects can affect decisions. However, previous studies have not evaluated which presentations best help people make decisions that are consistent with their own values. We undertook a pilot study to compare different methods for doing this. Methods and Findings: We conducted an Internet-based randomized trial comparing summary statistics for communicating the effects of statins on the risk of coronary heart disease (CHD). Participants rated the relative importance of treatment consequences using visual analogue scales (VAS) and category rating scales (CRS) with five response options. We randomized participants to either VAS or CRS first and to one of six summary statistics: relative risk reduction (RRR) and five absolute measures of effect: absolute risk reduction, number needed to treat, event rates, tablets needed to take, and natural frequencies (whole numbers). We used logistic regression to determine the association between participants' elicited values and treatment choices. 770 participants age 18 or over and literate in English completed the study. In all, 13% in the VAS-first group failed to complete their VAS rating, while 9% of the CRS-first group failed to complete their scoring (p = 0.03). Different ways of weighting the elicited values had little impact on the analyses comparing the different presentations. Most (51%) preferred the RRR compared to the other five summary statistics (1% to 25%, p = 0.074). However, decisions in the group presented the RRR deviated substantially from those made in the other five groups. The odds of participants in the RRR group deciding to take statins were 3.1 to 5.8 times that of those in the other groups across a wide range of values (p = 0.0007). Participants with a scientific background, who were more numerate or had more years of education were more likely to decide not to take statins. Conclusions: Internet-based trials comparing different presentations of treatment effects are feasible, but recruiting participants is a major challenge. Despite a slightly higher response rate for CRS, VAS is preferable to avoid approximation of a continuous variable. Although most participants preferred the RRR, participants shown the RRR were more likely to decide to take statins regardless of their values compared with participants who were shown any of the five other summary statistics. Trial Registration: Controlled-Trials.com ISRCTN85194921 [ABSTRACT FROM AUTHOR]

Background: Long-term exposure to ambient air pollution has been linked to childhood-onset asthma, although evidence is still insufficient. Within the multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), we examined the associations of long-term exposures to particulate matter with a diameter <2.5 µm (PM 2.5 ), nitrogen dioxide (NO 2 ) and black carbon (BC) with asthma incidence in adults., Methods: We pooled data from three cohorts in Denmark and Sweden with information on asthma hospital diagnoses. The average concentrations of air pollutants in 2010 were modelled by hybrid land-use regression models at participants' baseline residential addresses. Associations of air pollution exposures with asthma incidence were explored with Cox proportional hazard models, adjusting for potential confounders., Results: Of 98 326 participants, 1965 developed asthma during a mean follow-up of 16.6 years. We observed associations in fully adjusted models with hazard ratios of 1.22 (95% CI 1.04-1.43) per 5 μg·m -3 for PM 2.5 , 1.17 (95% CI 1.10-1.25) per 10 µg·m -3 for NO 2 and 1.15 (95% CI 1.08-1.23) per 0.5×10 -5  m -1 for BC. Hazard ratios were larger in cohort subsets with exposure levels below the European Union and US limit values and possibly World Health Organization guidelines for PM 2.5 and NO 2 . NO 2 and BC estimates remained unchanged in two-pollutant models with PM 2.5 , whereas PM 2.5 estimates were attenuated to unity. The concentration-response curves showed no evidence of a threshold., Conclusions: Long-term exposure to air pollution, especially from fossil fuel combustion sources such as motorised traffic, was associated with adult-onset asthma, even at levels below the current limit values., Competing Interests: Conflict of interest: S. Liu has nothing to disclose. Conflict of interest: J.T. Jørgensen has nothing to disclose. Conflict of interest: P. Ljungman has nothing to disclose. Conflict of interest: G. Pershagen has nothing to disclose. Conflict of interest: T. Bellander has nothing to disclose. Conflict of interest: K. Leander has nothing to disclose. Conflict of interest: P.K.E. Magnusson has nothing to disclose. Conflict of interest: D. Rizzuto has nothing to disclose. Conflict of interest: U.A. Hvidtfeldt has nothing to disclose. Conflict of interest: O. Raaschou-Nielsen has nothing to disclose. Conflict of interest: K. Wolf has nothing to disclose. Conflict of interest: B. Hoffmann has nothing to disclose. Conflict of interest: B. Brunekreef has nothing to disclose. Conflict of interest: M. Strak has nothing to disclose. Conflict of interest: J. Chen has nothing to disclose. Conflict of interest: A. Mehta has nothing to disclose. Conflict of interest: R.W. Atkinson has nothing to disclose. Conflict of interest: M. Bauwelinck has nothing to disclose. Conflict of interest: R. Varraso has nothing to disclose. Conflict of interest: M-C. Boutron-Ruault has nothing to disclose. Conflict of interest: J. Brandt has nothing to disclose. Conflict of interest: G. Cesaroni has nothing to disclose. Conflict of interest: F. Forastiere has nothing to disclose. Conflict of interest: D. Fecht has nothing to disclose. Conflict of interest: J. Gulliver has nothing to disclose. Conflict of interest: O. Hertel has nothing to disclose. Conflict of interest: K. de Hoogh has nothing to disclose. Conflict of interest: N.A.H. Janssen has nothing to disclose. Conflict of interest: K. Katsouyanni has nothing to disclose. Conflict of interest: M. Ketzel has nothing to disclose. Conflict of interest: J.O. Klompmaker has nothing to disclose. Conflict of interest: G. Nagel has nothing to disclose. Conflict of interest: B. Oftedal has nothing to disclose. Conflict of interest: A. Peters has nothing to disclose. Conflict of interest: A. Tjønneland has nothing to disclose. Conflict of interest: S.P. Rodopoulou has nothing to disclose. Conflict of interest: E Samoli has nothing to disclose. Conflict of interest: D.T. Kristoffersen has nothing to disclose. Conflict of interest: T. Sigsgaard has nothing to disclose. Conflict of interest: M. Stafoggia has nothing to disclose. Conflict of interest: D. Vienneau has nothing to disclose. Conflict of interest: G. Weinmayr has nothing to disclose. Conflict of interest: G. Hoek has nothing to disclose. Conflict of interest: Z.J. Andersen has nothing to disclose., (Copyright ©ERS 2021.)

Background: In Norway, almost 8 500 patients received mechanical ventilation in 2019. We have investigated the effect of early activity in mechanically ventilated adults in intensive care units on the duration of ventilation, weaning time from mechanical ventilation, mortality, length of stay, and adverse events., Method: We conducted a systematic literature search for randomised controlled trials in nine databases. Two authors selected studies, extracted data and then rated the certainty of evidence according to the GRADE framework., Results: A total of 3 270 titles and abstracts were read, and 17 studies with 1 805 patients and a low-to-moderate risk of bias were included. The analyses showed that early mobilisation reduced both the mean duration of ventilation (-1.43 days; 95 % CI -2.68 to -0.18, p = 0.02) and the ICU length of stay (-1.08 days; 95 % CI -1.95 to -0.21, p = 0.02), with the certainty of evidence rated as moderate. Inspiratory muscle training had no effect on the duration of mechanical ventilation (-0.11; 95 % CI -1.76 to 1.53, p = 0.89) or on the weaning time from mechanical ventilation (-0.33; 95 % CI -1.31 to 0.65, p = 0.51), with the certainty of evidence rated as low. Neither early mobilisation nor inspiratory muscle training affected mortality. Few adverse events were reported., Interpretation: The analyses show that early mobilisation is safe and can shorten the duration of mechanical ventilation and the ICU length of stay, but has no effect on mortality. Inspiratory muscle training had no effect on any of the outcome measures.

Aims: A prolonged corrected QT interval (QTc) ≥500 ms is associated with high all-cause mortality in hospitalized patients. We aimed to explore any difference in short- and long-term mortality in patients with QTc ≥500 ms compared with patients with QTc <500 ms after adjustment for comorbidity and main diagnosis., Methods and Results: Patients with QTc ≥500 ms who were hospitalized at Telemark Hospital Trust, Norway between January 2007 and April 2014 were identified. Thirty-day and 3-year all-cause mortality in 980 patients with QTc ≥500 ms were compared with 980 patients with QTc <500 ms, matched for age and sex and adjusting for Charlson comorbidity index (CCI), previous admissions, and main diagnoses. QTc ≥500 ms was associated with increased 30-day all-cause mortality [hazard ratio (HR) 1.90, 95% confidence interval (CI) 1.38-2.62; P < 0.001]. There was no significant difference in mortality between patients with QTc ≥500 ms and patients with QTc <500 ms who died between 30 days and 3 years; 32% vs. 29%, P = 0.20. Graded CCI was associated with increased 3-year all-cause mortality (CCI 1-2: HR 1.62, 95% CI 1.34-1.96; P < 0.001; CCI 3-4: HR 2.50, 95% CI 1.95-3.21; P < 0.001; CCI ≥5: HR 3.76, 95% CI 2.85-4.96; P < 0.001) but was not associated with 30-day all-cause mortality., Conclusion: QTc ≥500 ms is a powerful predictor of short-term mortality overruling comorbidities. QTc ≥500 ms also predicted long-term mortality, but this effect was mainly caused by the increased short-term mortality. For long-term mortality, comorbidity was more important., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Objective: To systematically compare the outcomes of participants in randomised controlled trials (RCTs) with those in comparable non-participants who received the same or similar treatment., Data Sources: Bibliographic databases, reference lists from eligible articles, medical journals, and study authors., Review Methods: RCTs and cohort studies that evaluated the clinical outcomes of participants in RCTs and comparable non-participants who received the same or similar treatment., Results: Five RCTs (six comparisons) and 50 cohort studies (85 comparisons) provided data on 31,140 patients treated in RCTs and 20,380 comparable patients treated outside RCTs. In the five RCTs, in which patients were given the option of participating or not, the comparisons provided limited information because of small sample sizes (a total of 412 patients) and the nature of the questions considered. 73 dichotomous outcomes were compared, of which 59 reported no statistically significant differences. For patients treated within RCTs, 10 comparisons reported significantly better outcomes and four reported significantly worse outcomes. Significantly heterogeneity was found (I2 = 89%) among the comparisons of 73 dichotomous outcomes; none of our a priori explanatory factors helped explain this heterogeneity. The 18 comparisons of continuous outcomes showed no significant differences in heterogeneity (I2 = 0%). The overall pooled estimate for continuous outcomes of the effect of participating in an RCT was not significant (standardised mean difference 0.01, 95% confidence interval -0.10 to 0.12)., Conclusion: No strong evidence was found of a harmful or beneficial effect of participating in RCTs compared with receiving the same or similar treatment outside such trials.