Prevalence and Risk Factors for Relapse in Pulmonary Sarcoidosis: The First Systematic Review, Meta-analysis, and Future Directions

Prevalence of Relapse in Pulmonary Sarcoidosis: A Systematic Review and Meta-analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Prevalence of Relapse in Pulmonary Sarcoidosis: A Systematic Review and Meta-analysis View ORCID Profile Zia Hashim , Robert P Baughman , Ritesh Agarwal , Naresh Kumar Tripathy , Mansi Gupta , Ajmal Khan , Ajit Kumar , Anup Kumar , Alok Nath doi: https://doi.org/10.1101/2025.07.15.25330996 Zia Hashim 1 Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zia Hashim For correspondence: ziasgpgi{at}gmail.com Robert P Baughman 2 Department of Internal Medicine, University of Cincinnati Medical Center , Cincinnati, OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ritesh Agarwal 3 Department of Pulmonary Medicine, Postgraduate Institute of Medical Education and Research , Chandigarh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Naresh Kumar Tripathy 4 Department of Hematology, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mansi Gupta 1 Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ajmal Khan 1 Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ajit Kumar 1 Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anup Kumar 5 Department of Biostatistics and Health Informatics, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alok Nath 1 Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences , Lucknow, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ziasgpgi{at}gmail.com Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Background and objective Relapse after treatment discontinuation or reduction frequently occurs in pulmonary sarcoidosis (PS), but reported rates vary widely. We conducted a systematic review and meta-analysis to estimate relapse prevalence and evaluate risk factors. Methods We searched PubMed, Embase, Scopus, and Google Scholar from inception through January 2026. Two reviewers independently extracted data on relapse prevalence, timing, definitions, and predictors, and assessed study quality using the Hoy et al. tool. We calculated pooled prevalence using random effects models, assessed heterogeneity using I², and performed subgroup analyses and meta-regression. Results We included 51 studies comprising 6093 patients; 3682 were followed up, and 1442 relapsed. Pooled relapse prevalence was 0.39 (95% CI: 0.33–0.45) with substantial heterogeneity (I²=94%). Relapse prevalence was lower in prospective (0.31, 95% CI: 0.25–0.37) than retrospective (0.45, 95% CI: 0.36–0.54) studies. Only 23 (45%) studies provided explicit relapse definitions. Advanced radiographic stage was associated with higher relapse odds than stage I (log OR 0.55–1.04, corresponding OR 1.73–2.83; p<0.05), and Black race with higher odds than White race (log OR 0.91, corresponding OR 2.48; p<0.01). Conclusions Approximately 39% of PS patients relapse after treatment reduction or discontinuation. Marked heterogeneity in relapse definitions underscores the need for standardized criteria. Clinical assessment appears comparable to complex definitions for assessing relapse. Funding None. Registration PROSPERO CRD420251075384. HIGHLIGHS First meta-analysis of relapse prevalence in pulmonary sarcoidosis Pooled relapse prevalence was 39% (51 studies, 6093 patients) Prospective studies showed lower relapse rates (31%) than retrospective (45%) Only 45% of studies provided explicit definitions of relapse Advanced radiographic stage and Black race predicted higher relapse risk INTRODUCTION Sarcoidosis is a multisystem disorder characterized histologically by non-caseating granulomas, most commonly affecting the lungs and intrathoracic lymph nodes [ 1 , 2 ]. The clinical course of pulmonary sarcoidosis (PS) is highly variable. While approximately one-third of patients experience spontaneous remission within 2–3 years of diagnosis, a significant proportion develop persistent or relapsing disease requiring prolonged immunosuppression. Historically, systemic glucocorticoids remain the cornerstone of therapy for clinically significant PS. Current practice commonly involves initiating prednisone around 20 mg daily, followed by a gradual taper over 6 months guided by clinical and radiological response [ 3 ]. However, methotrexate can also be used as a first-line therapy, as demonstrated by the recently published PREDMETH trial [ 4 ]. Yet, a vexing problem in sarcoidosis management is determining the optimal duration of therapy as prolonged therapy increases the risk of cumulative toxicity, while premature reduction or discontinuation can lead to disease relapse. Relapse in PS, broadly defined as worsening of clinical, radiological, or functional status after treatment discontinuation or reduction, represents a major therapeutic challenge. Relapse is associated with impaired quality of life, increased healthcare utilization, and potential risk of progressive pulmonary fibrosis. Despite its importance, published estimates of relapse rates vary widely, ranging from 13% to 80% in previous reviews [ 5 , 6 ]. Inconsistent definitions of relapse, including varying combinations of clinical, functional, radiological, and biomarker parameters, may yield different relapse rates across studies. To address these gaps, we conducted a systematic review and meta-analysis with the primary objective of estimating the pooled relapse prevalence in patients with PS following treatment reduction or discontinuation. Secondary objectives were: (1) to describe the timing and patterns of relapse events; (2) to identify demographic, clinical, radiographic, and treatment factors associated with relapse risk; (3) to evaluate the variability in relapse definitions across studies; and (4) to assess the influence of study design and methodological quality on relapse prevalence estimates. METHODS Study protocol and registration We conducted this systematic review and meta-analysis in accordance with the PRISMA and MOOSE guidelines [ 7 , 8 ]. We registered the study protocol prospectively with PROSPERO (CRD420251075384). Ethics committee approval was not required, as this was an analysis of published data. Search strategy We systematically searched PubMed, Embase, Scopus, Google Scholar, ClinicalTrials.gov , and PROSPERO from database inception through January 31, 2026. We used a search strategy combining Medical Subject Headings and Emtree terms with relevant free-text keywords linked by Boolean operators (Supplementary material). We supplemented the electronic search with manual screening of reference lists, forward citation tracking, and gray literature searches including abstracts from ATS and ERS annual conferences (2010–2025). Study selection We imported search results into EndNote 25 (Clarivate Analytics, Philadelphia, PA) to remove duplicates and screen them. Two authors (Z.H. and N.K.T.) independently screened titles and abstracts, followed by full-text evaluation of potentially eligible articles. Discrepancies were resolved through consensus discussion, with the help of a third reviewer (A.K.) when necessary. When relapse data were incomplete or full texts were unavailable, we contacted the corresponding authors. Eligibility criteria Inclusion: Studies were included if they: (1) included patients with biopsy-proven or clinically diagnosed sarcoidosis; and (2) reported relapse rates following treatment discontinuation or dose reduction, with explicit or derivable definitions. Exclusion: Studies were excluded if they: (1) enrolled fewer than 10 patients; (2) reported isolated extrapulmonary sarcoidosis without pulmonary involvement; (3) had inadequate or unclear definitions of disease states; (4) were inaccessible despite author contact; or (5) had overlapping cohorts (in which case the most recent or comprehensive publication was included). Definitions of sarcoidosis disease states Early relapse: worsening of disease activity during or within 1 month of treatment tapering or withdrawal, without a documented interval of sustained remission. The 1-month boundary adopted here is operational and pragmatic rather than evidence-derived. Late relapse: recurrence of disease activity following a documented period of sustained remission (≥1 month), manifested by reappearance of clinical symptoms, new or worsening radiological abnormalities, functional decline, or elevation of biomarkers — representing true disease recurrence after a phase of immunological inactivity. Remission: a disease-free state persisting for ≥1 month, characterized by absence of clinical symptoms, stable or improved radiological findings, stable or improved pulmonary function, and normalized or stable biomarkers. Data extraction Two reviewers (Z.H. and N.K.T.) independently extracted data using a piloted standardized form: (1) study characteristics (first author, year, country, design); (2) patient demographics (sample size, age, sex, ethnicity); (3) clinical characteristics (organ involvement, Scadding stage); (4) treatment details (type and duration); and (5) outcome data (frequency of relapse, relapse definitions, time to relapse). For risk factor analysis, we extracted stage-specific relapse rates and demographic-stratified outcomes. Assessment of study quality Methodological quality was independently assessed using the Hoy et al. (2012) risk of bias tool for prevalence studies [ 9 ]. Studies with total scores of 0–2 were classified as low risk of bias, 3–4 as moderate risk, and 5–10 as high risk of bias. Statistical analysis Pooled prevalence estimation We pooled relapse proportions using a random-effects model (restricted maximal likelihood method), reporting untransformed proportions with 95% confidence intervals [ 10 ]. Publication bias We assessed publication bias through funnel plot inspection and Egger’s regression test and Begg’s rank correlation test (p<0.05 indicating potential bias) [ 11 , 12 ]. Sensitivity analyses We performed multiple sensitivity analyses: (1) exclusion of outliers by Baujat and Galbraith plots; (2) leave-one-out analysis; (3) Freeman-Tukey transformation; (4) restriction to studies with explicit relapse definitions; (5) exclusion of small studies (≤50 patients); (6) exclusion of paediatric populations; (7) restriction to low-risk-of-bias studies; and (8) Bayesian hierarchical random-effects model. Heterogeneity assessment Statistical heterogeneity was quantified using Higgins’ I² statistic (I²≥50% indicating substantial heterogeneity) and Cochran’s Q test (p<0.10) [ 13 ]. Baujat and Galbraith plots were constructed to identify outliers [ 14 , 15 ]. Meta-regression and subgroup analyses We performed multivariate meta-regression using a restricted maximum-likelihood random-effects model. Covariates included: (1) study design; (2) explicit relapse definition; (3) publication era (before/after 2000); (4) geographic region (Asian vs. non-Asian); (5) study quality; and (6) sample size. Univariate subgroup analyses used Cochran’s Q test. Risk factor analysis Binary risk factors (age, sex, radiographic stage) were expressed as log odds ratios (LORs) with 95% CIs. Continuous variables used pooled mean differences with 95% CIs from inverse-variance random-effects models. OR = e^LOR. Predictors examined: age; sex; race (Black vs. White); radiographic stage (stage IV vs. I; stages II–III vs. I; stages II–III vs. IV). Duration of therapy Treatment duration was standardized to months. For studies reporting duration by outcome group, a weighted mean was calculated; for those providing only ranges, midpoint approximations were used. Studies with no estimable duration were coded ‘NR’ and excluded from duration-based meta-regression (21/51 studies, 41%). An 18-month cutoff was predetermined, aligning with ERS/ATS consensus recommendations [ 5 ]. Certainty of evidence We assessed the certainty of evidence using the GRADE framework, adapted for prevalence studies, considering risk of bias, inconsistency, indirectness, imprecision, and publication bias [ 16 ]. RESULTS Study selection Our systematic search identified 1972 records (1960 from databases and 12 from registers). After removing 650 duplicates, we screened 1,322 articles and identified 85 studies for full-text assessment. Of these, 34 were excluded. We included 51 studies comprising 6093 patients ( Figure 1 , Table 1 ). Download figure Open in new tab Figure 1. PRISMA flow diagram depicting the study selection process, including identification, screening, eligibility assessment, and inclusion of eligible studies. View this table: View inline View popup Table 1. Studies included in the systematic review and meta-analysis. Pro = prospective; Retro = retrospective; EP = extrapulmonary; CS = corticosteroid; NR = not reported; NA = not available; Mo = months; adv. = advanced; prog. = progressive; refrac. = refractory. Study characteristics Studies were published between 1961 and January 2026, with 27 (53%) published after 2000. Most were from non-Asian countries (33 studies, 65%). Twenty-two (43%) were prospective and 29 (57%) were retrospective. The median sample size was 44 patients (IQR 21.5–101); follow-up ranged from 6 months to 40 years ( Table 1 ). Methodological quality Thirty-four (67%) studies were classified as low risk of bias and 17 (33%) as moderate risk; none was judged as high risk of bias ( Figure S1 ). Sources of potential bias included methodological limitations (52%), inadequate sample size (50%), concerns about measurement validity (48%), unclear study descriptions (26%), and measurement reliability issues (12%). There was no difference in relapse prevalence between low- and moderate-risk-of-bias studies ( Figure S2 ). Primary objective: overall prevalence of relapse Among 6093 patients, 3682 were followed up and 1442 relapse events were reported. The pooled relapse prevalence was 0.39 (95% CI: 0.33–0.45), with individual study estimates ranging from 0.10 to 0.88. We observed substantial statistical heterogeneity (I²=94%, τ²=0.04, Cochran’s Q=1044, p<0.01) ( Figure 2 ). Prospective studies showed a significantly lower pooled relapse prevalence of 0.31 (95% CI: 0.25–0.37) compared with retrospective studies (0.45, 95% CI: 0.36–0.54) (p=0.01) ( Table 2 , Figure S3 ). Download figure Open in new tab Figure 2. Forest plot of pooled relapse prevalence in pulmonary sarcoidosis. Individual study estimates (blue squares) and 95% confidence intervals (horizontal lines) are shown, with the overall pooled estimate (red diamond) of 0.39 (95% CI: 0.33–0.45) derived from a random-effects REML model. Substantial heterogeneity was present (I²=94%). View this table: View inline View popup Table 2. Summary of sensitivity analyses and subgroup analyses of pooled relapse prevalence in pulmonary sarcoidosis. Multiple sensitivity analyses demonstrated the robustness of the pooled estimate. Exclusion of 10 outlier studies based on Baujat and Galbraith plots reduced heterogeneity by 12% (I²=82%) with minimal change in the pooled estimate (0.35, 95% CI: 0.31–0.40) ( Table 2 , Table S1 – S2 , Figures S3 – S9 ). Secondary objectives Timing of relapse Nineteen (37%) studies reported time-to-relapse data ( Table 1 , Table S5 , Figure S10 ). The median time-to-first late relapse was 10.5 months (IQR 6.5–17.0 months; range 1–156 months). A sensitivity analysis adjusting for left-truncation bias yielded a median of 8.2 months (IQR 4.5–15.0 months). Sixty-three percent of studies (12/19) reported relapses within 1.5 years, and 84% (16/19) within 3 years. Pooled proportions ranged from 0.431 to 0.469 across all boundary cutoffs (≥1, ≥2, ≥3, ≥6 months), with fully overlapping 95% CIs. Demographic characteristics Age was not associated with relapse (mean difference: −0.05 years, 95% CI: −1.56 to 1.47; p=0.95; 15 studies, n=1,434). Female sex was not significantly associated (LOR: 0.19, 95% CI: −0.11 to 0.49; p=0.21; 15 studies, n=1,326). Black patients had significantly higher odds of relapse than White patients (LOR: 0.91, 95% CI: 0.44–1.37; OR: 2.48, 95% CI: 1.55–3.94; p<0.01; 5 studies, n=370). Pooled prevalence in adult-only studies (47 studies) was 0.40 (95% CI: 0.34–0.46) ( Figure S14 ). Radiographic staging Compared with Scadding stage I disease (reference), the odds of relapse were significantly higher in stage IV (LOR: 0.55, 95% CI: 0.07–1.03; OR: 1.73, 95% CI: 1.07–2.80; p=0.02) and in stages II–III combined (LOR: 1.04, 95% CI: 0.25–1.83; OR: 2.83, 95% CI: 1.28–6.23; p=0.01). No significant difference was observed between stages II–III and stage IV (LOR: −0.54, 95% CI: −1.39 to 0.31; p=0.21) ( Figure S15 ). Disease extent Relapse prevalence did not differ significantly between isolated pulmonary sarcoidosis and multiorgan involvement (0.39, 95% CI: 0.33–0.45; p=0.66) ( Figure S16 ). Treatment regimens Corticosteroids alone were used in 37 (73%) studies, steroid-sparing agents in 7 (14%), and combination therapy in 7 (14%) studies. Combination therapy showed a numerically lower relapse prevalence (0.31, 95% CI: 0.21–0.41) compared with corticosteroids alone (0.38, 95% CI: 0.32–0.45) or steroid-sparing agents alone (0.54, 95% CI: 0.36–0.72), but differences were not statistically significant (p=0.08) ( Figure S17 ). Duration of therapy Studies with corticosteroid duration ≤18 months (17 studies, n=1,090) had higher pooled relapse prevalence (0.35; 95% CI: 0.28–0.43; I²=84%) compared with those >18 months (13 studies, n=594) (0.26; 95% CI: 0.20–0.32; I²=62%). The between-subgroup difference approached but did not reach statistical significance (Cochran Q=3.82, p=0.05) ( Figure S18 ). Geographic and temporal factors No significant differences in relapse prevalence were observed between Asian and non-Asian studies (0.34 vs. 0.42; p=0.12) or between studies published before and after 2000 (0.43 vs. 0.36; p=0.24) ( Figure S3 ). Heterogeneity in relapse definitions Only 23 studies (45%) provided explicit definitions of relapse ( Table 1 , Table S4 ). Definitions varied: clinical criteria alone (9 studies, 39%) and composite criteria (14 studies, 61%). There was no significant difference in relapse prevalence between clinical-criteria-only and composite-criteria studies (0.37, 95% CI: 0.29–0.44, p=0.71) ( Figure S19 ). Meta-regression Multi-variable meta-regression incorporating six prespecified covariates found only study design as a significant predictor of relapse prevalence (coefficient=0.148, SE=0.060; p=0.014). A pre-specified univariate meta-regression with treatment duration as the sole predictor was also performed, restricted to the 30 studies with estimable duration data ( Table S6 ). Publication bias Visual inspection of the funnel plot revealed no asymmetry ( Figure 3 ). Neither Egger’s test (intercept=0.50, 95% CI: −2.11 to 3.10; p=0.70) nor Begg’s test (Kendall’s τ=0.07; p=0.47) indicated publication bias. Download figure Open in new tab Figure 3. Funnel plot for publication bias. Symmetric distribution of studies around the pooled estimate suggests no publication bias (Egger’s p=0.70; Begg’s p=0.47). Certainty of evidence Using GRADE methodology, we rated the overall certainty of evidence for relapse prevalence as low, downgraded for risk of bias and inconsistency. Evidence from prospective studies and the association between Black race and relapse was rated moderate certainty ( Table S7 ). DISCUSSION In this systematic review and meta-analysis of 51 studies comprising 6093 patients with PS, we found a pooled relapse prevalence of 39%. Individual study estimates varied widely (10–88%), reflecting marked variability in study designs, relapse definitions, and follow-up practices. Prospective cohorts reported a lower relapse prevalence (31%) than retrospective studies (45%). In addition, radiographic stages II–IV and Black race were significant predictors of relapse. To our knowledge, this study represents the first comprehensive assessment of relapse burden and associated predictors in patients with PS. A key finding of our analysis was the substantial heterogeneity across studies (I²=94%). This heterogeneity reduced the certainty of evidence for the pooled relapse prevalence estimate (rated low by GRADE). Nevertheless, high heterogeneity is common in meta-analyses of observational data and does not invalidate results when thoroughly explored. Multiple sensitivity analyses demonstrated remarkable consistency, including leave-one-out analysis and Bayesian hierarchical modeling, which yielded prevalence estimates of 39–40%. Excluding outlier studies reduced heterogeneity modestly (I²=82%) with similar pooled prevalence, indicating that outlier studies contributed to, but do not fully explain, the observed variability. Despite prospective studies showing lower relapse rates overall, substantial heterogeneity persisted even within this subgroup (I²=86%), indicating that study design alone does not fully account for the observed variability. Several factors likely contributed to heterogeneity. Patient populations varied significantly: certain prospective cohorts primarily consisted of Asian patients with stage II disease, a group experiencing frequent spontaneous remissions. On the other hand, some cohorts consisted of Black or White patients with more advanced, chronic illnesses, which are linked to an increased inherent risk of relapse. Organ involvement also varied; isolated pulmonary disease carries a different trajectory than multiorgan sarcoidosis, where cardiac, neurological, or cutaneous involvement typically signals a more refractory course. Treatment protocols were inconsistently reported, including the dose at which tapering began, the rate of reduction, and minimum treatment duration — all independently influencing relapse risk. Follow-up duration ranged from six months to several decades, and the studies spanned six decades of evolving diagnostic and therapeutic standards. In multivariable meta-regression, study design was the only significant variable associated with relapse prevalence. Retrospective cohorts demonstrated higher pooled relapse prevalence than prospective cohorts, reflecting limitations including ascertainment bias, variable relapse definitions, incomplete follow-up, and inconsistent evaluation of competing diagnoses. Limiting the analysis to studies with explicit relapse definitions or high-quality studies resulted in only modest reductions in pooled relapse prevalence and did not meaningfully reduce heterogeneity. These findings suggest that variations in relapse prevalence are driven not solely by methodological quality but also by clinical differences across populations, treatment strategies, and follow-up durations. A major limitation highlighted by our review was the lack of standardized definitions of relapse. Only 45% of the included studies provided explicit relapse criteria, with definitions varying widely across clinical, radiological, functional, biomarker-based, and composite domains. Subgroup analysis revealed no significant difference in relapse rates between studies using clinical criteria and those incorporating composite criteria. This supports the practical observation that relapse identification remains largely clinical, consistent with prior task force recommendations [ 17 ]. A fundamental conceptual challenge is the difficulty of distinguishing true disease recurrence from corticosteroid-dependent disease activity. When symptoms re-emerge during treatment or within a short window of its withdrawal, before any sustained period of immunological quiescence has been established, this pattern more accurately reflects treatment failure or non-remission than genuine relapse. Baughman and Judson [ 18 ] noted that many apparent relapses represent chronic disease that was suppressed rather than remitted, while Gottlieb et al. [ 19 ] required a sustained treatment-free interval exceeding one month before any recurrence could qualify as relapse. We adopted a one-month operational threshold to separate these two phenomena. Sensitivity analyses using cutoffs of ≥1, ≥2, ≥3, and ≥6 months produced pooled estimates of 0.43–0.47 with fully overlapping confidence intervals ( Supplementary Table S5 ), confirming robustness regardless of where this boundary is drawn. The timing of relapse has important implications for clinical care. Across studies, the majority (63%) occurred within 1.5 years of treatment discontinuation, with peak risk during months 6–12. Relapses were also observed late in a subset of studies, supporting the need for extended follow-up in patients with persistent disease risk or advanced pulmonary involvement. Radiographic staging was an important predictor of relapse risk; stages II–IV were associated with significantly higher relapse odds compared with stage I disease. Interpretation of relapse in stage IV patients warrants caution because fibrotic lungs are vulnerable to infection and structural complications, and distinguishing true sarcoidosis relapse from infectious exacerbations or progressive fibrotic decline may be challenging. Future studies should incorporate standardized protocols for excluding alternative infectious and non-infectious causes [ 20 ]. We found no significant difference in relapse prevalence between isolated PS and multiorgan disease. However, prior studies suggest higher relapse rates in patients with multiorgan disease [ 21 , 22 ], and in those with lung [ 23 ], heart [ 24 ], musculoskeletal system [ 25 ], eye [ 26 ], skin [ 26 ], and neurological involvement [ 27 , 28 ]. Isolated erythema nodosum or peripheral lymphadenopathy was associated with a lower relapse risk [ 19 , 29 ]. Prospective studies are needed to determine whether organ-specific involvement can be incorporated into relapse prediction models. Black patients had significantly higher odds of relapse compared with White patients, consistent with well-established racial disparities in sarcoidosis severity. This heightened relapse risk likely reflects a combination of genetic predispositions [ 30 ], socioeconomic factors such as healthcare access and treatment adherence [ 31 ], or differences in immune responses [ 32 ]. There was no effect of age, sex, publication era, or geographic region on relapse prevalence. Corticosteroids remain the traditional first-line treatment. In our subgroup analysis, relapse prevalence was not significantly different across treatment types, with comparable rates in corticosteroid monotherapy, steroid-sparing immunosuppressants alone, or combination therapy. However, the numerically lower relapse prevalence with combination therapy should be interpreted cautiously, as treatment selection is confounded by baseline disease severity. Recently, the PREDMETH trial found comparable outcomes between methotrexate and prednisone monotherapy in selected treatment-naïve symptomatic PS patients [ 4 ], and observational data suggest that steroid-sparing agents may reduce cumulative corticosteroid exposure [ 33 ]. Treatment duration emerged as a potentially important modulator of relapse risk. Patients on corticosteroids for 18 months or less had a 35% relapse rate, while those treated longer had a 26% relapse rate (p=0.05). Given the considerable heterogeneity in both subgroups (I²=84% and 62%), duration by itself is an insufficient explanation. Optimal treatment duration in pulmonary sarcoidosis remains undefined, and prospective trials with standardised protocols are long overdue. Our study has several limitations. First, heterogeneity in relapse definitions was substantial, and more than half of the studies lacked explicit criteria. Second, the predominance of retrospective studies increased ascertainment and reporting biases. Third, reporting of treatment duration, tapering protocols, and remission depth was inconsistent. Fourth, geographic representation was skewed towards Europe and North America. Fifth, the data were insufficient to evaluate relapse predictors, such as biomarkers (serum ACE), PET activity, or genetic susceptibility. Finally, individual-patient data were unavailable for meta-analysis. In conclusion, relapses occur in approximately 39% of patients with PS following treatment reduction or discontinuation, predominantly within the first two years. Advanced radiographic stage and Black race are associated with increased relapse risk, whereas age, sex, disease extent, publication era, and treatment modality are not. While clinical assessment remains a practical and widely applicable approach to monitoring relapse, the absence of standardized relapse criteria substantially limits certainty [ 35 , 36 ]. Our findings underscore the need for international consensus on relapse definitions and standardized relapse detection approaches to strengthen future clinical trials and optimize long-term disease management. DECLARATIONS Data availability statement The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Ethical considerations This research did not require ethical committee approval, as it is entirely based on previously published studies. Patient and Public Involvement statement This study is a systematic review and meta-analysis based on previously published data, with no new primary data collected from patients. As such, patients and the public were not involved in the design, conduct, reporting, or dissemination plans of this research. AI declaration During the preparation of this work the author(s) used Claude (Anthropic) in order to improve language and readability of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. Funding None. Conflict of interest The authors declare no conflicts of interest relevant to this manuscript, with the exception noted below. Professor Robert P. Baughman serves as a consultant for aTyr Pharma, Kinevant Sciences, Mallinckrodt Pharmaceuticals, Molecure S.A., and Xentria Inc. These relationships had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. All other authors have no financial or personal relationships that could have inappropriately influenced the work reported in this paper. Author contributions View this table: View inline View popup Download powerpoint SUPPLEMENTARY MATERIAL SUPPLEMENTARY TABLES Search Strategy Table S1: Freeman-Tukey Arcsine Transformation Table S2: Leave-One-Out Sensitivity Analysis Table S3: Galbraith Plot Data and Statistical Notes Table S4: Sarcoidosis Relapse Definitions Table S5. Timeframe Sensitivity Analysis - Impact of Changing the Early/Late Relapse Cutoff on Pooled Relapse Proportion Table S6: Meta-regression Analysis Results Table S7: GRADE Assessment SEARCH STRATEGY A comprehensive systematic search was conducted across four major databases to identify relevant studies on pulmonary sarcoidosis relapse. Searches were conducted between January 26 and January 31, 2026. The search strategies for each database are detailed below: PubMed Search Strategy: 336 results on Jan 26, 2026 ((“Sarcoidosis, Pulmonary”[Mesh] OR “pulmonary sarcoidosis”[Title/Abstract] OR “lung sarcoidosis”[Title/Abstract] OR “sarcoidosis”[Title/Abstract]) AND (“Recurrence”[Mesh] OR “relapse”[Title/Abstract] OR “recurrence”[Title/Abstract] OR “flare-up”[Title/Abstract] OR “exacerbation”[Title/Abstract]) AND (“Prevalence”[Mesh] OR “prevalence”[Title/Abstract] OR “incidence”[Title/Abstract] OR “frequency”[Title/Abstract] OR “rate”[Title/Abstract] OR “Risk Factors”[Mesh] OR “risk factors”[Title/Abstract] OR “predictors”[Title/Abstract] OR “associations”[Title/Abstract] OR “determinants”[Title/Abstract])) Scopus Search Strategy: 345 results on Jan 26, 2026 TITLE-ABS-KEY ((“pulmonary sarcoidosis” OR “lung sarcoidosis” OR “sarcoidosis”) AND (“relapse” OR “recurrence” OR “flareup” OR “exacerbation”) AND (“prevalence” OR “incidence” OR “frequency” OR “rate” OR “risk factors” OR “predictors” OR “associations” OR “determinants”)) OR INDEXTERMS (“Sarcoidosis, Pulmonary” OR “Recurrence” OR “Prevalence” OR “Risk Factors”) Embase Search Strategy: 730 results on Jan 26, 2026 (’lung sarcoidosis’/exp OR ‘pulmonary sarcoidosis’:ti,ab,kw OR ‘lung sarcoidosis’:ti,ab,kw OR ‘sarcoidosis’:ti,ab,kw) AND (’disease recurrence’/exp OR ‘relapse’:ti,ab,kw OR ‘recurrence’:ti,ab,kw OR ‘flareup’:ti,ab,kw OR ‘exacerbation’:ti,ab,kw) AND (’prevalence’/exp OR ‘prevalence’:ti,ab,kw OR ‘incidence’:ti,ab,kw OR ‘frequency’:ti,ab,kw OR ‘rate’:ti,ab,kw OR ‘risk factor’/exp OR ‘risk factors’:ti,ab,kw OR ‘predictors’:ti,ab,kw OR ‘associations’:ti,ab,kw OR ‘determinants’:ti,ab,kw) Google Scholar Search: 549 results on Jan 26, 2026 (“pulmonary sarcoidosis” OR “lung sarcoidosis” OR sarcoidosis) AND (relapse OR recurrence OR “flare up” OR “flare-up” OR flareup OR exacerbation) AND (prevalence OR incidence OR frequency OR rate OR “risk factors” OR predictors OR associations OR determinants) ClinicalTrials.gov Search Strategy: 6 records on January 31, 2026 Condition or disease: “sarcoidosis” Other terms: “relapse” OR “recurrence” OR “exacerbation” (Advanced search at https://clinicaltrials.gov/search ) PROSPERO Search Strategy: 3 records on January 31, 2026 (“pulmonary sarcoidosis” OR “sarcoidosis”) AND (“relapse” OR “recurrence” OR “exacerbation”) (Search at https://www.crd.york.ac.uk/prospero ) Gray Literature Search (ATS/ERS Conference Abstracts 2010–2025): 3 records on January 31, 2026 Manual screening of American Thoracic Society (ATS) and European Respiratory Society (ERS) annual conference abstract books (2010–2025) using the terms “sarcoidosis” AND (“relapse” OR “recurrence”) View this table: View inline View popup Table S1: Freeman-Tukey Arcsine Transformation. View this table: View inline View popup Table S2: Leave-One-Out Sensitivity Analysis. View this table: View inline View popup Table S3: Galbraith Plot Data. Data for constructing Galbraith plot to assess heterogeneity in the meta-analysis. Precision = 1/SE; Standardized effect = (Effect - Pooled effect)/SE. View this table: View inline View popup Table S4. Definitions of Pulmonary Sarcoidosis Relapse Used in Included Studies. View this table: View inline View popup Download powerpoint Table S5. Timeframe Sensitivity Analysis - Impact of Changing the Early/Late Relapse Cutoff on Pooled Relapse Proportion. View this table: View inline View popup Table S6. Meta-regression Analysis Results. Meta-regression was performed to identify potential sources of heterogeneity. Study design was the only significant factor identified . View this table: View inline View popup Download powerpoint Table S7. GRADE Assessment of Evidence Certainty. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to assess the certainty of evidence . SUPPLEMENTARY FIGURES Quality Assessment and Heterogeneity Analysis Figure S1: Hoy et al. Risk of Bias Assessment Figure S2: Study bias and effect on the relapse prevalence Figure S3: Sensitivity analysis across subgroups (design, relapse definition, quality, sample size, publication year, geographical location) Figure S4: Leave-one-out analysis Figure S5: Freeman-Tukey arcsine transformation Figure S6: Freeman-Tukey back transformation Figure S7: Baujat plot identifying influential studies Figure S8: Galbraith plot for heterogeneity assessment Figure S9: Excluding outliers - combined analysis Clinical Characteristics and Outcomes Figure S10: Median time to relapse after corticosteroid cessation Figure S11: Pooled mean differences in age between relapsed and non-relapsed patients Figure S12: Log odds ratios for sex (male vs. female) Figure S13: Relapse rates between Black and White patients Figure S14: Relapse prevalence in adult-only populations Figure S15: Relapse rates across radiographic stages Figure S16: Relapse prevalence stratified by disease extent (isolated pulmonary vs. multiorgan) Figure S17: Relapse prevalence stratified by treatment type (corticosteroids, steroid-sparing agents, mixed) Figure S18: Subgroup analysis of relapse proportions in pulmonary sarcoidosis stratified by duration of corticosteroid therapy Figure S19: Effect of relapse definition criteria (clinical alone vs. clinical + additional criteria) Note: All forest plots display individual study estimates (squares sized by weight) with 95% confidence intervals (horizontal lines) and pooled estimates (diamonds) using random-effects REML model. Download figure Open in new tab Figure S1: Hoy et al. Risk of Bias Assessment. Download figure Open in new tab Figure S2: Study bias and effect on the relapse prevalence. Download figure Open in new tab Figure S3: Sensitivity analysis: Forest plot showing the proportion of studies with 95% Cis across subgroups: design (prospective vs. retrospective), relapse definition (yes vs. no), year (after vs. before 2000), Asian vs. non-Asian, patients >50 (yes vs. no), and quality (low vs. moderate risk of bias). Download figure Open in new tab Figure S4: Leave-one-out analysis by sequentially excluding each study and recalculating the pooled prevalence. This analysis demonstrates the stability of the pooled estimate, with no single study having disproportionate influence on the overall results. Download figure Open in new tab Figure S5: Freeman Tukey. Download figure Open in new tab Figure S6: Freeman Tukey Back Transformation. Download figure Open in new tab Figure S7: Baujat Plot Identifying Influential Studies in Sarcoidosis Relapse. Meta-Analysis, Studies in the upper-right quadrant contribute substantially to both overall heterogeneity and the pooled estimate. The following studies were identified as outliers warranting sensitivity analysis: Mañá et al. (2017, #19), Sharma et al. (2003, #24), Lower et al. (1995, #34), and Hunninghake et al. (1994, #35), Rizzato et al. (1991, #39), Johns et al. (1986, #42), Turner-Warwick et al. (1986, #44), Johns et al. (1983, #46), Johns et al. (1976, #48), and Johns et al. (1974, #49). Download figure Open in new tab Figure S8: Galbraith plot shows the relationship between study precision (1/SE) and standardized effect size (θ/SE %), with labelled studies, a regression line, a 95% CI band, and a no-effect line to assess heterogeneity and identify outliers. Download figure Open in new tab Figure S9: Excluding outliers from Baujat and Galbraith plots. The exclusion of studies 19, 24, 34, 35, 39, 42, 44, 46, 48, and 49 resulted in 82% heterogeneity; however, the pooled estimate demonstrated stability. Download figure Open in new tab Figure S10: Forest plot showing median or mean time to relapse after corticosteroid cessation across 19 studies. Orange circles represent point estimates; horizontal lines represent confidence intervals. Studies are listed with first author, publication year, sample size in brackets, and country. Vertical dashed lines indicate reference points at 6 and 24 months. Time to relapse ranges from <10 months (Johns et al., Morse et al., Baumann et al.) to ∼100 months (Bouchut et al.). Studies arranged chronologically from oldest (bottom) to most recent (top). Download figure Open in new tab Figure S11: Forest plot showing pooled mean differences in the ages of patients with relapse versus non-relapse. Download figure Open in new tab Figure S12: Log odds ratios for the sex of patients with pulmonary sarcoidosis. Download figure Open in new tab Figure S13: Forest plot comparing relapse rates between Black and White patients with pulmonary sarcoidosis (Random-effects model). Download figure Open in new tab Figure S14: Pooled relapse prevalence in studies with only adult population. Download figure Open in new tab Figure S15: Forest plot comparing relapse rates across radiographic stages. Stage IV vs Stage I showed increased odds of relapse (log OR 0.55, p=0.02), while Stage II-III vs Stage I also showed higher odds (log OR 1.04, p=0.01). Stage II-III vs IV showed no significant difference (log OR −0.54, p=0.21). Download figure Open in new tab Figure S16: Forest plots of pooled relapse prevalence stratified by disease extent (isolated pulmonary vs. multiorgan). Download figure Open in new tab Figure S17: Forest plots of pooled relapse prevalence stratified by treatment type (corticosteroids, steroid-sparing agents, mixed). Download figure Open in new tab Figure S18: Subgroup analysis of relapse proportions in pulmonary sarcoidosis stratified by duration of corticosteroid therapy. Download figure Open in new tab Figure S19: Effect of relapse definition criteria: clinical + additional criteria (radiological, functional, biomarkers) versus clinical criteria alone. SUPPLEMENTARY METHODS SM-1. Introduction This document provides procedural justification for three post hoc methodological decisions: (i) extraction and operationalisation of corticosteroid therapy duration; (ii) rationale for classifying specific studies as NR (not reported) for duration; and (iii) the sensitivity analysis addressing the conceptual distinction between treatment failure and relapse on withdrawal. These sections respond directly to Major Comment #5 of Reviewer #3. SM-2. Duration of Therapy: Extraction Rules and Definitions SM-2.1 Operational Definition Duration of therapy was defined as the total calendar period of pharmacological therapy, expressed as the mean (or median, if mean unavailable) number of months from treatment initiation to cessation. Duration was never equated with follow-up duration when both were reported. Six standardised extraction rules were applied: View this table: View inline View popup Download powerpoint SM-2.2 Data Extraction and Summary Duration data were extracted independently by two reviewers from full text, methods, results, tables, and supplementary appendices. Discrepancies were resolved by consensus. Duration was coded NR when no estimable value could be derived after full-text review. Of 51 included studies, duration was extractable from 30 studies (58.8%) . Duration ranged from 1.5 months (Wallaert 1986, IV pulse) to 96 months (Johns 1986, chronic cohort); median 19.8 months (IQR 12.5–27.8 months). SM-3. Rationale for NR Classification by Study SM-3.1 Johns Hopkins Series (Johns 1974, 1976, 1983, 1986) — Partial Inclusion These four publications collectively contributed 590 treated patients but reported treatment duration as categorical distributions, not continuous means. Weighted means were estimated using interval midpoints — a validated imputation approach. View this table: View inline View popup Download powerpoint All four studies were retained in Model B to avoid disproportionate loss of long-duration data. Sensitivity analysis excluding the Johns series did not materially alter meta-regression coefficients (SM-5.3). SM-3.2 Other Studies Coded NR — Summary The 21 studies coded NR fell into five categories: View this table: View inline View popup SM-4. Treatment Failure vs Relapse on Withdrawal: Definition and Sensitivity Analysis SM-4.1 Conceptual Distinction Two distinct clinical phenomena are frequently conflated in the literature: View this table: View inline View popup Download powerpoint SM-4.2 Primary Analysis Approach The primary meta-analysis adopted an inclusive definition encompassing both categories, for three pre-specified reasons: The PROSPERO protocol (CRD420251075384) pre-specified inclusion of studies using any validated relapse definition, without restriction to post-withdrawal events, to maximise evidence breadth. A substantial proportion of pre-2005 studies did not explicitly distinguish between on-treatment failure and post-withdrawal relapse; excluding these would have resulted in >30% dataset attrition. The clinical question — overall burden of relapse in pharmacologically treated pulmonary sarcoidosis — is best served by an inclusive definition capturing both outcome types. SM-4.3 Study Classification for Sensitivity Analysis Each of the 51 studies was classified by two independent reviewers into one of three categories: View this table: View inline View popup Download powerpoint Restriction to Category A studies (k=19) confirmed that the primary 39% pooled estimate was not materially inflated by inclusion of mixed-definition studies. SM-5. Sensitivity Analyses for Duration Meta-regression (Model B) The primary Model B (7 covariates; n=30 studies) yielded a non-significant positive association between treatment duration and logit-relapse proportion (β = +0.021, 95% CI −0.004 to +0.047; p=0.103; adjusted R²=33.8% vs 18.7% for Model A). Three pre-specified sensitivity analyses were conducted: View this table: View inline View popup Download powerpoint SM-6. Key Limitations of Duration and Relapse-Type Analyses View this table: View inline View popup Download powerpoint Acknowledgements We acknowledge the contributions of Professor Karan Madan, Department of Pulmonary, Critical Care and Sleep Medicine, All India Institute of Medical Sciences, New Delhi, India, and Professor Sahajal Dhooria, Department of Pulmonary Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh, India, who kindly provided additional information regarding their respective studies in response to our inquiry. We also thank Mohammad Haider (MBBS Student, Autonomous State Medical College, Lakhimpur, Uttar Pradesh, India) for his assistance in drafting the tables and formatting the manuscript. Footnotes This version of the manuscript has been substantially revised in response to peer review. The following key changes have been made: Authorship: Two internationally recognized sarcoidosis experts, Prof. Robert P. Baughman (University of Cincinnati, USA) and Prof. Ritesh Agarwal (PGIMER Chandigarh, India), have been added as co-authors, contributing to the intellectual revision and critical appraisal of the manuscript. Manuscript rewriting: The manuscript has been comprehensively rewritten. The Introduction, Methods, Results, and Discussion sections have been substantially restructured and expanded to improve clarity, address reviewer concerns, and strengthen the scientific rigor of the work. Terminology: The terms early relapse and late relapse have been replaced with relapse during treatment and relapse after treatment discontinuation, respectively, to align with contemporary sarcoidosis literature and avoid ambiguity. Statistical analyses: Duration of therapy has been incorporated as a covariate in meta-regression analyses. Additional sensitivity analyses have been performed, including analyses restricted to studies with low risk of bias, analyses using alternate effect size measures, and leave-one-out analyses to assess the influence of individual studies on pooled estimates. Immortal time bias: A dedicated subsection addressing immortal time bias has been added to the Discussion, acknowledging this as an inherent limitation of observational sarcoidosis relapse studies and contextualizing its potential impact on pooled estimates. Odds ratio conversions: Studies reporting odds ratios have been addressed with appropriate methodological commentary, and the analytical approach for handling these studies has been clarified in the Methods section. Table 1: Updated to include additional columns covering duration of therapy, organ manifestations, and radiographic staging (Scadding stage) for each included study. 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