Tighiouart, Hocine

Abstract Background: Despite recent therapeutic advances in AS cHL, PET-adaptive chemotherapy-based regimens remain a first-line treatment option, especially for patients (pts) without access to novel agents. Most PET-adaptive regiments start with either 2 cycles of escBEACOPP (escBEACOPP2) or ABVD (ABVD2) prior to interim PET assessment (iPET), followed by treatment de-escalation or intensification by iPET result. There have been no direct comparisons of escBEACOPP2 vs ABVD2 PET-adaptive regimens. Additionally, the AS-Hodgkin Lymphoma International Prognostication Index (A-HIPI) score (Rodday JCO 2023) has not been fully explored in escBEACOPP2 regimens. Therefore, we utilized multiple analytic approaches to compare outcomes of PET-adaptive escBEACOPP2 and ABVD2 regimens, while adjusting for and assessing the effect of the baseline A-HIPI score. Methods: Through the global HoLISTIC Consortium (www.hodgkinconsortium.com), we obtained individual patient data from 4 AS clinical trials of newly diagnosed cHL pts treated with PET-adaptive escBEACOPP2 (AHL2011) or ABVD2 (SWOG0816, RATHL, HD0607) regimens. Pts were restricted to age 18 to 65 years (y), stage IIB-IV, and PET-adapted treatment arms. Positive iPET was based on Deauville score >3. Treatment effects of escBEACOPP2 vs ABVD2 (reference) on 5y progression-free survival (PFS) and overall survival (OS) were first evaluated using Cox models. Importantly, baseline disease risk was adjusted for using the 5y PFS or OS A-HIPI score (including stage, sex, age, bulk, lymphocyte count, albumin, white blood cell count), where higher scores indicate higher predicted risk (scale 1-100). The A-HIPI was modeled per 1 standard deviation (SD) increase. Our multistate model (MSM) comprised 4 health states: diagnosis, sustained remission at 1yr, treatment failure, and death from any cause. Pts all started in the diagnosis state and transitioned to other states without return to prior states. We assessed the impact of treatment on transitions (except binary sustained remission at 1y) that involved ≥5 pts per treatment group, adjusting for A-HIPI for all transitions. All effects are reported as adjusted hazard ratios (aHRs) with 95% confidence intervals (CI). Results: 2,381 AS cHL pts were included (n=372 for escBEACOPP2, n=2009 for ABVD2). For escBEACOPP2 and ABVD2 groups (median follow-up 60 and 56 months), respectively, the mean baseline A-HIPI scores (i.e., predicted 5y PFS event rate) were 25.1 (SD=6.3) and 22.9 (SD=6.5), the rates of positive iPET were 11.3% and 17.0%, 5y PFS was 87.0% and 79.5%, and 5y OS was 96.3% and 94.6%. In Cox models, escBEACOPP2 was associated with significantly improved PFS compared to ABVD2 (aHR=0.54, 95% CI=0.39-0.73); the aHR for OS was 0.67 (95% CI=0.37-1.19). Notably, higher risk A-HIPI score was associated with significantly worse PFS (aHR=1.42, 95% CI=1.31-1.54) and OS (aHR=1.60, 95% CI=1.47-1.76), independent of treatment. In the MSM, the effect of escBEACOPP2 compared to ABVD2 regimens was modeled in 3 transitions with adjustment for A-HIPI score: those treated with escBEACOPP2 had lower likelihood of treatment failure with (aHR=0.38, 95% CI=0.22-0.66) or without (aHR=0.61, 95% CI=0.40-0.92) sustained remission at 1y; the aHR for treatment failure to death was 0.55 (95% CI=0.25-1.22). Higher risk baseline A-HIPI score was associated with higher likelihood of treatment failure and death without sustained remission at 1y (aHR=1.43, 95% CI=1.28-1.61 and aHR=2.50, 95% CI=1.80-3.49, respectively). Furthermore, higher risk baseline A-HIPI score was associated with higher likelihood of treatment failure after achieving sustained remission at 1y (aHR=1.29, 95% CI=1.12-1.48) as well as death after treatment failure (aHR=1.54, 95% CI=1.27-1.86). Conclusions: Comparing newly diagnosed adult AS cHL PET-adaptive regimens with adjustment for baseline A-HIPI score, we found pts treated with escBEACOPP2 on AHL2011 had improved PFS versus pooled pt data from 3 ABVD2 trials. Using MSM, we further demonstrated that escBEACOPP2 was associated with a lower likelihood of treatment failure with or without achieving sustained remission at 1yr. In addition, baseline A-HIPI score was associated with PFS and OS, which was independent of treatment. Finally, higher baseline A-HIPI scores maintained prognostic impact throughout the disease course, including increased risk of treatment failure for pts in remission at 1yr and death after treatment failure.

Abstract Background: Although progression-free survival (PFS) in early-stage (ES) classic Hodgkin Lymphoma (cHL) is high, there remains a subset of patients (pts) at higher risk of worse outcomes, particularly in those with unfavorable risk disease. Leveraging pt-level data from within the HoLISTIC (Hodgkin Lymphoma International STudy for Individual Care) Consortium, the E-HIPI was developed in clinical trial pts and validated in real-world registry pts to predict 2-year (y) PFS (Rodday. NEJM Evidence 2025). Continuous, objective, and readily measurable pre-treatment variables were considered, with sex, maximum tumor diameter, albumin, and hemoglobin included as significant predictors. Using the E-HIPI, interactive calculators for pt risk, comparison, and stratification were created (https://rtools.mayo.edu/holistic_ehipi/). Although the E-HIPI was developed and validated in both unfavorable and favorable ES cHL pts, the clinical predictors are primarily markers of unfavorable disease. In the development and initial validation cohorts, 2y PFS was 93.7% and 90.3%, respectively, the C-statistic was 0.63 in both cohorts, calibration was strong with slight underprediction of events in the validation cohort, and the E-HIPI outperformed the historic binary classification of patients as favorable or unfavorable using EORTC criteria. However, since pts in the original E-HIPI development and validation were primarily treated with ABVD chemotherapy protocols, additional validation is needed, particularly among pts with unfavorable ES cHL who were treated initially with more intensive chemotherapeutic platforms (e.g., BEACOPP-based). Thus, we conducted an external validation of the E-HIPI in pts treated on recent unfavorable ES cHL GHSG trials. Methods: The validation cohort included 2367 adults (ages 18 to 60y) with newly-diagnosed unfavorable cHL from randomized phase 3 trials conducted by the GHSG (HD14 von Tresckow. JCO 2012; and HD17 Borchmann. Lancet 2021). We calculated the predicted probability of 2y PFS events based on the E-HIPI model equation (scale 1-100%). Model performance was assessed using discrimination and calibration. Discrimination, which is the model's ability to separate high and low risk pts, was assessed using Harrell's C-statistic. Calibration, which is the model's ability to accurately predict absolute risk, was assessed by comparing observed and predicted probabilities of 2y PFS events within quintiles of predicted probabilities and estimating calibration-in-the-large (predicted minus observed event rate) and the calibration slope. Results: For pts treated on unfavorable GHSG trials, mean age was 33y, 95% had stage II disease, mean baseline E-HIPI score (i.e., predicted 2y PFS event rate) was 6.0% (SD=2.3%), and the observed 2y PFS rate was 96.2% (95%CI: 95.4-97.0%). The C-statistic was 0.65 (95% CI: 0.59-0.70), calibration-in-the-large was 0.02, and the calibration slope was 1.08 (95% CI: 0.52, 1.64). Within the lowest risk E-HIPI quintile, the observed 2y PFS event rate was 1.6%, compared with 6.1% in the highest risk quintile. Conclusions: In external validation of the recently published E-HIPI in pts with cHL treated on unfavorable GHSG trials using more intensive BEACOPP-based protocols, discrimination was similar to the original development and validation cohorts. Collectively, the E-HIPI is a treatment-agnostic prediction tool across varied treatment regimens for unfavorable ES cHL pts. Better 2y PFS in the GHSG trials than in the E-HIPI development and initial validation cohorts may have contributed to mild misclassification, likely reflecting differences in treatment regimens. This may be resolved with recalibration or incorporating granular treatment variables into future models. Additional research is needed to understand E-HIPI performance in pts with favorable disease who have even lower event rates as well as to more fully predict individualized pt outcomes across varied chemotherapy platforms and radiation received for all ES cHL pts.

Significance Statement Changes in albuminuria and GFR slope are individually used as surrogate end points in clinical trials of CKD progression, and studies have demonstrated that each is associated with treatment effects on clinical end points. In this study, the authors sought to develop a conceptual framework that combines both surrogate end points to better predict treatment effects on clinical end points in Phase 2 trials. The results demonstrate that information from the combined treatment effects on albuminuria and GFR slope improves the prediction of treatment effects on the clinical end point for Phase 2 trials with sample sizes between 100 and 200 patients and duration of follow-up ranging from 1 to 2 years. These findings may help inform design of clinical trials for interventions aimed at slowing CKD progression. Background Changes in log urinary albumin-to-creatinine ratio (UACR) and GFR slope are individually used as surrogate end points in clinical trials of CKD progression. Whether combining these surrogate end points might strengthen inferences about clinical benefit is unknown. Methods Using Bayesian meta-regressions across 41 randomized trials of CKD progression, we characterized the combined relationship between the treatment effects on the clinical end point (sustained doubling of serum creatinine, GFR <15 ml/min per 1.73 m 2 , or kidney failure) and treatment effects on UACR change and chronic GFR slope after 3 months. We applied the results to the design of Phase 2 trials on the basis of UACR change and chronic GFR slope in combination. Results Treatment effects on the clinical end point were strongly associated with the combination of treatment effects on UACR change and chronic slope. The posterior median meta-regression coefficients for treatment effects were −0.41 (95% Bayesian Credible Interval, −0.64 to −0.17) per 1 ml/min per 1.73 m 2 per year for the treatment effect on GFR slope and −0.06 (95% Bayesian Credible Interval, −0.90 to 0.77) for the treatment effect on UACR change. The predicted probability of clinical benefit when considering both surrogates was determined primarily by estimated treatment effects on UACR when sample size was small (approximately 60 patients per treatment arm) and follow-up brief (approximately 1 year), with the importance of GFR slope increasing for larger sample sizes and longer follow-up. Conclusions In Phase 2 trials of CKD with sample sizes of 100–200 patients per arm and follow-up between 1 and 2 years, combining information from treatment effects on UACR change and GFR slope improved the prediction of treatment effects on clinical end points.

Background Acute changes in GFR can occur after initiation of interventions targeting progression of CKD. These acute changes complicate the interpretation of long-term treatment effects. Methods To assess the magnitude and consistency of acute effects in randomized clinical trials and explore factors that might affect them, we performed a meta-analysis of 53 randomized clinical trials for CKD progression, enrolling 56,413 participants with at least one estimated GFR measurement by 6 months after randomization. We defined acute treatment effects as the mean difference in GFR slope from baseline to 3 months between randomized groups. We performed univariable and multivariable metaregression to assess the effect of intervention type, disease state, baseline GFR, and albuminuria on the magnitude of acute effects. Results The mean acute effect across all studies was −0.21 ml/min per 1.73 m 2 (95% confidence interval, −0.63 to 0.22) over 3 months, with substantial heterogeneity across interventions (95% coverage interval across studies, −2.50 to +2.08 ml/min per 1.73 m 2 ). We observed negative average acute effects in renin angiotensin system blockade, BP lowering, and sodium-glucose cotransporter 2 inhibitor trials, and positive acute effects in trials of immunosuppressive agents. Larger negative acute effects were observed in trials with a higher mean baseline GFR. Conclusion The magnitude and consistency of acute GFR effects vary across different interventions, and are larger at higher baseline GFR. Understanding the nature and magnitude of acute effects can help inform the optimal design of randomized clinical trials evaluating disease progression in CKD.

Abstract Rationale Portopulmonary hypertension (PPHTN) occurs in 6% of liver transplant candidates. The pathogenesis of this complication of portal hypertension is poorly understood. Objectives To identify genetic risk factors for PPHTN in patients with advanced liver disease. Methods We performed a multicenter case-control study of patients with portal hypertension. Cases had a mean pulmonary artery pressure &gt;25 mm Hg, pulmonary vascular resistance &gt;240 dynes·s−1·cm−5, and pulmonary capillary wedge pressure ≤15 mm Hg. Controls had a right ventricular systolic pressure &lt; 40 mm Hg (if estimated) and normal right-sided cardiac morphology by transthoracic echocardiography. We genotyped 1,079 common single nucleotide polymorphisms (SNPs) in 93 candidate genes in each patient. Measurements and Main Results The study sample included 31 cases and 104 controls. Twenty-nine SNPs in 15 candidate genes were associated with the risk of PPHTN (P &lt; 0.05). Multiple SNPs in the genes coding for estrogen receptor 1, aromatase, phosphodiesterase 5, angiopoietin 1, and calcium binding protein A4 were associated with the risk of PPHTN. The biological relevance of one of the aromatase SNPs was supported by an association with plasma estradiol levels. Conclusions Genetic variation in estrogen signaling and cell growth regulators is associated with the risk of PPHTN. These biologic pathways may elucidate the mechanism for the development of PPHTN in certain patients with severe liver disease.