METHODS: In an open-label, randomized trial, we enrolled critically ill adults who had been undergoing ventilation for less than 12 hours in the ICU and were expected to continue to receive ventilatory support for longer than the next calendar day to receive dexmedetomidine as the sole or primary sedative or to receive usual care (propofol, midazolam, or other sedatives). The target range of sedation-scores on the Richmond Agitation and Sedation Scale (which is scored from -5 [unresponsive] to +4 [combative]) was -2 to +1 (lightly sedated to restless). The primary outcome was the rate of death from any cause at 90 days.

RESULTS: We enrolled 4000 patients at a median interval of 4.6 hours between eligibility and randomization. In a modified intention-to-treat analysis involving 3904 patients, the primary outcome event occurred in 566 of 1948 (29.1%) in the dexmedetomidine group and in 569 of 1956 (29.1%) in the usual-care group (adjusted risk difference, 0.0 percentage points; 95% confidence interval, -2.9 to 2.8). An ancillary finding was that to achieve the prescribed level of sedation, patients in the dexmedetomidine group received supplemental propofol (64% of patients), midazolam (3%), or both (7%) during the first 2 days after randomization; in the usual-care group, these drugs were administered as primary sedatives in 60%, 12%, and 20% of the patients, respectively. Bradycardia and hypotension were more common in the dexmedetomidine group.

METHODS: Patients with AIH-ACLF without baseline infection/hepatic encephalopathy were identified from APASL ACLF research consortium (AARC) database. Diagnosis of AIH-ACLF was based mainly on histology. Those treated with steroids were assessed for non-response (defined as death or liver transplant at 90 days for present study). Laboratory parameters, AARC, and model for end-stage liver disease (MELD) scores were assessed at baseline and day 3 to identify early non-response. Utility of dynamic SURFASA score [- 6.80 + 1.92*(D0-INR) + 1.94*(∆%3-INR) + 1.64*(∆%3-bilirubin)] was also evaluated. The performance of early predictors was compared with changes in MELD score at 2 weeks.

METHODS: Consecutive ACLF patients were monitored for the development of SIRS/sepsis and associated complications and followed till 90 days, liver transplant or death.

METHODS: We identified drugs as precipitants of ACLF among prospective cohort of patients with ACLF from the Asian Pacific Association of Study of Liver (APASL) ACLF Research Consortium (AARC) database. Drugs were considered precipitants after exclusion of known causes together with a temporal association between exposure and decompensation. Outcome was defined as death from decompensation.

RESULTS: Of the 3,132 patients with ACLF, drugs were implicated as a cause in 329 (10.5%, mean age 47 years, 65% men) and other nondrug causes in 2,803 (89.5%) (group B). Complementary and alternative medications (71.7%) were the commonest insult, followed by combination antituberculosis therapy drugs (27.3%). Alcoholic liver disease (28.6%), cryptogenic liver disease (25.5%), and non-alcoholic steatohepatitis (NASH) (16.7%) were common causes of underlying liver diseases. Patients with drug-induced ACLF had jaundice (100%), ascites (88%), encephalopathy (46.5%), high Model for End-Stage Liver Disease (MELD) (30.2), and Child-Turcotte-Pugh score (12.1). The overall 90-day mortality was higher in drug-induced (46.5%) than in non-drug-induced ACLF (38.8%) (P = 0.007). The Cox regression model identified arterial lactate (P < 0.001) and total bilirubin (P = 0.008) as predictors of mortality.

METHODS: Data was collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19.

METHODS AND FINDINGS: Utilising the Asian Sudden Cardiac Death in Heart Failure (ASIAN-HF) registry (11 Asian regions including Taiwan, Hong Kong, China, India, Malaysia, Thailand, Singapore, Indonesia, Philippines, Japan, and Korea; 46 centres with enrolment between 1 October 2012 and 6 October 2016), we prospectively examined 5,964 patients with symptomatic HF (mean age 61.3 ± 13.3 years, 26% women, mean BMI 25.3 ± 5.3 kg/m2, 16% with HF with preserved ejection fraction [HFpEF; ejection fraction ≥ 50%]), among whom 2,051 also had waist-to-height ratio (WHtR) measurements (mean age 60.8 ± 12.9 years, 24% women, mean BMI 25.0 ± 5.2 kg/m2, 7% HFpEF). Patients were categorised by BMI quartiles or WHtR quartiles or 4 combined groups of BMI (low, <24.5 kg/m2 [lean], or high, ≥24.5 kg/m2 [obese]) and WHtR (low, <0.55 [thin], or high, ≥0.55 [fat]). Cox proportional hazards models were used to examine a 1-year composite outcome (HF hospitalisation or mortality). Across BMI quartiles, higher BMI was associated with lower risk of the composite outcome (ptrend < 0.001). Contrastingly, higher WHtR was associated with higher risk of the composite outcome. Individuals in the lean-fat group, with low BMI and high WHtR (13.9%), were more likely to be women (35.4%) and to be from low-income countries (47.7%) (predominantly in South/Southeast Asia), and had higher prevalence of diabetes (46%), worse quality of life scores (63.3 ± 24.2), and a higher rate of the composite outcome (51/232; 22%), compared to the other groups (p < 0.05 for all). Following multivariable adjustment, the lean-fat group had higher adjusted risk of the composite outcome (hazard ratio 1.93, 95% CI 1.17-3.18, p = 0.01), compared to the obese-thin group, with high BMI and low WHtR. Results were consistent across both HF subtypes (HFpEF and HF with reduced ejection fraction [HFrEF]; pinteraction = 0.355). Selection bias and residual confounding are potential limitations of such multinational observational registries.

CONCLUSIONS: In this cohort of Asian patients with HF, the 'obesity paradox' is observed only when defined using BMI, with WHtR showing the opposite association with the composite outcome. Lean-fat patients, with high WHtR and low BMI, have the worst outcomes. A direct correlation between high WHtR and the composite outcome is apparent in both HFpEF and HFrEF.

DESIGN: Retrospective observational analysis.

SETTING: 56 acute stroke hospitals in eight countries.

PARTICIPANTS: 1074 trial physiotherapists, nurses, and other clinicians.

OUTCOME MEASURES: Number of babies born during trial recruitment per trial participant recruited.

RESULTS: With 198 site recruitment years and 2104 patients recruited during AVERT, 120 babies were born to trial staff. Births led to an estimated 10% loss in time to achieve recruitment. Parental leave was linked to six trial site closures. The number of participants needed to recruit per baby born was 17.5 (95% confidence interval 14.7 to 21.0); additional trial costs associated with each birth were estimated at 5736 Australian dollars on average.

CONCLUSION: The staff absences registered in AVERT owing to parental leave led to delayed trial recruitment and increased costs, and should be considered by trial investigators when planning research and estimating budgets. However, the celebration of new life became a highlight of the annual AVERT collaborators' meetings and helped maintain a cohesive collaborative group.

TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry no 12606000185561.

DESIGN: AVERT is a prospective, parallel group, assessor-blinded randomised clinical trial. This paper presents data assessing the generalisability of AVERT.

SETTING: Acute stroke units at 44 hospitals in 8 countries.

PARTICIPANTS: The first 20,000 patients screened for AVERT, of whom 1158 were recruited and randomised.

MODEL: We use the Proximal Similarity Model, which considers the person, place, and setting and practice, as a framework for considering generalisability. As well as comparing the recruited patients with the target population, we also performed an exploratory analysis of the demographic, clinical, site and process factors associated with recruitment.

RESULTS: The demographics and stroke characteristics of the included patients in the trial were broadly similar to population-based norms, with the exception that AVERT had a greater proportion of men. The most common reason for non-recruitment was late arrival to hospital (ie, >24 h). Overall, being older and female reduced the odds of recruitment to the trial. More women than men were excluded for most of the reasons, including refusal. The odds of exclusion due to early deterioration were particularly high for those with severe stroke (OR=10.4, p<0.001, 95% CI 9.27 to 11.65).

CONCLUSIONS: A model which explores person, place, and setting and practice factors can provide important information about the external validity of a trial, and could be applied to other clinical trials.