Clinical Trial Design Issues in Systemic Sclerosis: an Update

CONQUER Scleroderma: association of gastrointestinal tract symptoms in early disease with resource utilization

OBJECTIVES

SSc is associated with increased health-care resource utilization and economic burden. The Collaborative National Quality and Efficacy Registry (CONQUER) is a US-based collaborative that collects longitudinal follow-up data on SSc patients with <5 years of disease duration enrolled at scleroderma centres in the USA. The objective of this study was to investigate the relationship between gastrointestinal tract symptoms and self-reported resource utilization in CONQUER participants.

METHODS

CONQUER participants who had completed a baseline and 12-month Gastrointestinal Tract Questionnaire (GIT 2.0) and a Resource Utilization Questionnaire (RUQ) were included in this analysis. Patients were categorized by total GIT 2.0 severity: none-to-mild (0-0.49); moderate (0.50-1.00), and severe-to-very severe (1.01-3.00). Clinical features and medication exposures were examined in each of these categories. The 12-month RUQ responses were summarized by GIT 2.0 score categories at 12 months.

RESULTS

Among the 211 CONQUER participants who met the inclusion criteria, most (64%) had mild GIT symptoms, 26% had moderate symptoms, and 10% severe GIT symptoms at 12 months. The categorization of GIT total severity score by RUQ showed that more upper endoscopy procedures and inpatient hospitalization occurred in the CONQUER participants with severe GIT symptoms. These patients with severe GIT symptoms also reported the use of more adaptive equipment.

CONCLUSION

This report from the CONQUER cohort suggests that severe GIT symptoms result in more resource utilization. It is especially important to understand resource utilization in early disease cohorts when disease activity, rather than damage, primarily contributes to health-related costs of SSc.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

BACKGROUND

Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes.

METHODS

Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context.

RESULTS

Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response.

CONCLUSION

Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Assessment of disease outcome measures in systemic sclerosis

The assessment of disease activity in systemic sclerosis (SSc) is challenging owing to its heterogeneous manifestations across multiple organ systems, the variable rate of disease progression and regression, and the relative paucity of patients in early-phase therapeutic trials. Despite some recent successes, most clinical trials have failed to show efficacy, underscoring the need for improved outcome measures linked directly to disease pathogenesis, particularly applicable for biomarker studies focused on skin disease. Current outcome measures in SSc-associated interstitial lung disease and SSc skin disease are largely adequate, although advancing imaging technology and the incorporation of skin mRNA biomarkers might provide opportunities for earlier detection of the therapeutic effect. Biomarkers can further inform pathogenesis, enabling early phase trials to act as reverse translational studies through the incorporation of routine high-throughput sequencing.

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement

OBJECTIVE

We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).

METHODS

Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).

RESULTS

aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.

CONCLUSION

CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

The prognostic value of cardiopulmonary exercise testing in interstitial lung disease: a systematic review

The heterogeneity of interstitial lung disease (ILD) results in prognostic uncertainty concerning end-of-life discussions and optimal timing for transplantation. Effective prognostic markers and prediction models are needed. Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of the physiological changes in the respiratory, cardiovascular and musculoskeletal systems in a controlled laboratory environment. It has shown promise as a prognostic factor for other chronic respiratory conditions. We sought to evaluate the prognostic value of CPET in predicting outcomes in longitudinal studies of ILD.

MEDLINE, Embase and the Cochrane Database of Systematic Reviews were used to identify studies reporting the prognostic value of CPET in predicting outcomes in longitudinal studies of ILD. Study quality was assessed using the Quality in Prognosis Study risk of bias tool.

Thirteen studies were included that reported the prognostic value of CPET in ILD. All studies reported at least one CPET parameter predicting clinical outcomes in ILD, with survival being the principal outcome assessed. Maximum oxygen consumption, reduced ventilatory efficiency and exercise-induced hypoxaemia were all reported to have prognostic value in ILD. Issues with study design (primarily due to inherent problems of retrospective studies, patient selection and presentation of numerous CPET parameters), insufficient adjustment for important confounders and inadequate statistical analyses limit the strength of the conclusions that can be drawn at this stage.

There is insufficient evidence to confirm the value of CPET in facilitating “real-world” clinical decisions in ILD. Additional prospective studies are required to validate the putative prognostic associations reported in previous studies in carefully phenotyped patient populations.

There is presently insufficient evidence to confirm the value of CPET in facilitating “real-world” clinical decisions in ILD. Additional prospective studies are required to validate the putative prognostic associations reported in previous studies.https://bit.ly/3dfp5kq

Novel Imaging Strategies in Systemic Sclerosis

PURPOSE OF REVIEW

Imaging modalities such as computed tomography, ultrasound, magnetic resonance imaging, and molecular imaging are being used to evaluate for disease in systemic sclerosis (SSc) patients. Here, we review novel imaging strategies to detect organ and vascular complications of SSc and novel imaging techniques for assessing interstitial lung disease and pulmonary hypertension in other conditions that may have further applicability to SSc.

RECENT FINDINGS

Imaging techniques can be used to identify disease in the lungs, pulmonary vascular system, heart, skin, vascular tissue, and gastrointestinal tract of SSc patients. These show promise in detecting early disease, many without the use of ionizing radiation. Novel imaging techniques in patients with SSc can be used to detect disease in multiple susceptible organs. These imaging strategies have potential for early disease detection, as well as potential for incorporation into clinical trials to accelerate the development of SSc therapies.

Treatment Algorithms for Systemic Sclerosis According to Experts

OBJECTIVE

There is a lack of agreement regarding treatment for many aspects of systemic sclerosis (SSc). We undertook this study to generate SSc treatment algorithms endorsed by a high percentage of SSc experts.

METHODS

Experts from the Scleroderma Clinical Trials Consortium and the Canadian Scleroderma Research group (n = 170) were asked whether they agreed with SSc algorithms from 2012. Two consensus rounds refined agreement; 62, 54, and 48 experts (36%, 32%, and 28%, respectively) completed the first, second, and third surveys, respectively.

RESULTS

For treatment of scleroderma renal crisis, 81% of experts agreed (first-, second-, and third-line treatments were angiotensin-converting enzyme inhibitors, then adding calcium-channel blockers [CCBs], then adding angiotensin receptor blockers [ARBs], respectively). For pulmonary arterial hypertension (PAH), 81% of experts agreed (for mild PAH, treatments were phosphodiesterase 5 [PDE5] inhibitors, then endothelin receptor antagonists plus PDE5 inhibitors, then prostanoids, respectively; for severe PAH, prostanoids were first-line treatment). For mild Raynaud's phenomenon (RP), 79% of experts agreed (treatments were CCBs, then adding PDE5 inhibitors, then ARBs or switching to another CCB, respectively; after the third line of treatment, mild RP was deemed severe). For severe RP, the first- through fourth-line treatments were CCBs, then adding PDE5 inhibitors or prostanoids, then adding PDE5 inhibitors (if not added as second-line treatment) or prostanoids (if not added as second-line treatment), then switching to another CCB, respectively. For active treatment of digital ulcers, 66% of experts agreed (first- and second-line treatments were CCBs and PDE5 inhibitors, respectively). For interstitial lung disease, 69% of experts agreed (for induction therapy, treatments were mycophenolate mofetil [MMF], intravenous cyclophosphamide [IV CYC], and rituximab, respectively; for maintenance, first-line treatment was MMF). For skin involvement, 71% of experts agreed (for a modified Rodnan skin thickness score [MRSS] of 24, first- and second-line treatments were methotrexate [MTX] and MMF, respectively; for an MRSS of 32, first- through fourth-line treatments were MMF, MTX, IV CYC, and hematopoietic stem cell transplantation, respectively). For inflammatory arthritis, 79% of experts agreed (first- through fourth-line treatments were MTX, low-dose glucocorticoids, hydroxychloroquine, and rituximab or tocilizumab, respectively). Algorithms for cardiac and gastrointestinal involvement had ≥75% agreement.

CONCLUSION

Total agreement for SSc algorithms was considerable. These algorithms may guide treatment.