Defining phenotypes and treatment effect heterogeneity to inform acute respiratory distress syndrome and sepsis trials: secondary analyses of three RCTs

Background Sepsis and acute respiratory distress syndrome are two heterogeneous acute illnesses with high risk of death and for which there are many ‘statistically negative’ randomised controlled trials. We hypothesised that negative randomised controlled trials occur because of between-participant differences in response to treatment, illness manifestation (phenotype) and risk of outcomes (heterogeneity). Objectives To assess (1) heterogeneity of treatment effect, which tests whether or not treatment effect varies with a patient’s pre-randomisation risk of outcome; and (2) whether or not subphenotypes explain the treatment response differences in sepsis and acute respiratory distress syndrome demonstrated in randomised controlled trials. Study population We performed secondary analysis of two randomised controlled trials in patients with sepsis [i.e. the Vasopressin vs Noradrenaline as Initial Therapy in Septic Shock (VANISH) trial and the Levosimendan for the Prevention of Acute oRgan Dysfunction in Sepsis (LeoPARDS) trial] and one acute respiratory distress syndrome multicentre randomised controlled trial [i.e. the Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute lung injury to Reduce Pulmonary dysfunction (HARP-2) trial], conducted in the UK. The VANISH trial is a 2 × 2 factorial randomised controlled trial of vasopressin (Pressyn AR®; Ferring Pharmaceuticals, Saint-Prex, Switzerland) and hydrocortisone sodium phosphate (hereafter referred to as hydrocortisone) (EfcortesolTM; Amdipharm plc, St Helier, Jersey) compared with placebo. The LeoPARDS trial is a two-arm-parallel-group randomised controlled trial of levosimendan (Simdax®; Orion Pharma, Espoo, Finland) compared with placebo. The HARP-2 trial is a parallel-group randomised controlled trial of simvastatin compared with placebo. Methods To test for heterogeneity of the effect on 28-day mortality of vasopressin, hydrocortisone and levosimendan in patients with sepsis and of simvastatin in patients with acute respiratory distress syndrome. We used the total Acute Physiology And Chronic Health Evaluation II (APACHE II) score as the baseline risk measurement, comparing treatment effects in patients with baseline APACHE II scores above (high) and below (low) the median using regression models with an interaction between treatment and baseline risk. To identify subphenotypes, we performed latent class analysis using only baseline clinical and biomarker data, and compared clinical outcomes across subphenotypes and treatment groups. Results The odds of death in the highest APACHE II quartile compared with the lowest quartile ranged from 4.9 to 7.4, across the three trials. We did not observe heterogeneity of treatment effect for vasopressin, hydrocortisone and levosimendan. In the HARP-2 trial, simvastatin reduced mortality in the low-APACHE II group and increased mortality in the high-APACHE II group. In the VANISH trial, a two-subphenotype model provided the best fit for the data. Subphenotype 2 individuals had more inflammation and shorter survival. There were no treatment effect differences between the two subphenotypes. In the LeoPARDS trial, a three-subphenotype model provided the best fit for the data. Subphenotype 3 individuals had the greatest inflammation and lowest survival. There were no treatment effect differences between the three subphenotypes, although survival was lowest in the levosimendan group for all subphenotypes. In the HARP-2 trial, a two-subphenotype model provided the best fit for the data. The inflammatory subphenotype was associated with fewer ventilator-free days and higher 28-day mortality. Limitations The lack of heterogeneity of treatment effect and any treatment effect differences between sepsis subphenotypes may be secondary to the lack of statistical power to detect such effects, if they truly exist. Conclusions We highlight lack of heterogeneity of treatment effect in all three trial populations. We report three subphenotypes in sepsis and two subphenotypes in acute respiratory distress syndrome, with an inflammatory phenotype with greater risk of death as a consistent finding in both sepsis and acute respiratory distress syndrome. Future work Our analysis highlights the need to identify key discriminant markers to characterise subphenotypes in sepsis and acute respiratory distress syndrome with an observational cohort study. Funding This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 10. See the NIHR Journals Library website for further project information.

Time course and heterogeneity of treatment effect of the collaborative chronic care model on psychiatric hospitalization rates: A survival analysis using routinely collected electronic medical records

Background Health systems are undergoing widespread adoption of the collaborative chronic care model (CCM). Care structured around the CCM may reduce costly psychiatric hospitalizations. Little is known, however, about the time course or heterogeneity of treatment effects (HTE) for CCM on psychiatric hospitalization. Rationale Assessment of CCM implementation support on psychiatric hospitalization might be more efficient if the timing were informed by an expected time course. Further, understanding HTE could help determine who should be referred for intervention. Objectives (i) Estimate the trajectory of CCM effect on psychiatric hospitalization rates. (ii) Explore HTE for CCM across demographic and clinical characteristics. Methods Data from a stepped wedge CCM implementation trial were reanalyzed using 5 570 patients in CCM treatment and 46 443 patients receiving usual care. Time-to-event data was constructed from routine medical records. Effect trajectory of CCM on psychiatric hospitalization was simulated from an extended Cox model over one year of implementation support. Covariate risk contributions were estimated from subset stratified Cox models without using simulation. Ratios of hazard ratios (RHR) allowed comparison by trial arm for HTE analysis, also without simulation. No standard Cox proportional hazards models were used for either estimating the time-course or heterogeneity of treatment effect. Results The effect of CCM implementation support increased most rapidly immediately after implementation start and grew more gradually throughout the rest of the study. On the final study day, psychiatric hospitalization rates in the treatment arm were 17% to 49% times lower than controls, with adjustment for all model covariates (HR 0.66; 95% CI 0.51–0.83). Our analysis of HTE favored usual care for those with a history of prior psychiatric hospitalization (RHR 4.92; 95% CI 3.15–7.7) but favored CCM for those with depression (RHR 0.61; 95% CI: 0.41–0.91). Having a single medical diagnosis, compared to having none, favored CCM (RHR 0.52; 95% CI 0.31–0.86). Conclusion Reduction of psychiatric hospitalization is evident immediately after start of CCM implementation support, but assessments may be better timed once the effect size begins to stabilize, which may be as early as six months. HTE findings for CCM can guide future research on utility of CCM in specific populations.