In an era of considerable advances in many inflammatory diseases, progress in the diagnosis and therapy of autoimmune connective tissue diseases (CTDs) such as systemic sclerosis, mixed CTD, and Sjogren’s syndrome has been less pronounced. The reasons for this are myriad but likely reflect the rarity, complex pathogenesis, multisystem nature, and clinical heterogeneity of these diseases. Evidence for many of the commonly used therapies from randomized controlled trials is lacking or uninterpretable, in part due to difficulties in recruiting adequate sample sizes, dearth of robust outcome measures, and lack of generalizability of results to all patients due to inclusion of highly selected patients in randomized trials.

Nonetheless, some of the barriers to improving care are being overcome by the creation of large longitudinal cohorts of patients through collaborations between researchers across nations and even across the world. By combining meticulous and systematic collection of data and biospecimens from patients attending dedicated clinics serving defined populations, the opportunities to better understand CTDs in a real-world setting have never been greater. For some CTDs, simply defining in detail the clinical and serological manifestations, genetic predispositions, and risk factors for future disease complications in larger numbers of patients than can ever be recruited in one center, can ultimately lead to improvements in care for each patient. The development of screening algorithms for disease complications such as pulmonary arterial hypertension is an example. For others, patient cohorts have permitted outcome measures of disease activity and damage that are requisite for successful randomized controlled trials of new therapies, to be derived and validated. Should randomized controlled trials of new therapies become available, such cohorts allow patients to gain access to new therapies and provide a ready source of well-characterized patients for recruitment.

In the absence of randomized trials of new therapies, current best practice approaches to therapy can be embedded in longitudinal cohort studies, providing the opportunity for pragmatic studies of structured care protocols of existing therapies to optimize patient outcomes, for example, for Raynaud’s phenomenon and interstitial lung disease. Subsequent audits of outcomes and evidence-based modifications of protocols have the potential to lead to further incremental improvements in patient care.

As complications of individual autoimmune CTDs do not develop in all patients, study of organ-based therapies is even more challenging than disease-specific therapies and highlights the value of large longitudinal cohorts. However, many of the more complex issues such as pregnancy, antiphospholipid syndrome, increased cardiovascular risk, and screening for malignancy are common to many CTDs, and cohort studies can provide further insight into these issues.

Even if the purpose of a longitudinal cohort is predominantly comprehensive data collection for research, a successful cohort study is not without its challenges. Standardization of classification criteria, definitions of disease manifestations, and how variables such as medication exposure are collected as well as variability in serological and histopathological investigations all require special consideration. Ensuring the completeness and accuracy of the data collected and minimizing losses to follow-up are time consuming but essential to ensure the quality of the data sets. Statistical analyses may be complex and require the skills of a clinical epidemiologist and a biostatistician. Nonetheless, cohort studies avoid some of the disadvantages of population-based administrative databases such as coding error and inadequate collection of clinical data such as patient-reported outcomes. However, data linkage of longitudinal cohorts with administrative databases can validate data such as history of malignancy and facilitate studies of health-care usage and burden of disease to the community. In addition, data harmonization, allowing merging of data exported from several clinical databases, can increase sample size and the number of outcomes of interest, thereby increasing the power to evaluate important clinical associations.

In this issue, some chapters focus on individual CTDs and others are dedicated to specific disease manifestations common to more than one CTD, demonstrating the advances in diagnosis and treatment that have been achieved through longitudinal cohort studies, and offering insights into the impact these cohorts could have on improving patient care in the future.