Lupus Increases The Incidence Ratio Of Hematologic Malignancies: A Meta-Analysis Of Cohort Studies
Bibliographic record
Abstract
Abstract Introduction Hematologic malignancies are a heterogeneous group of diseases characterized by the uncontrolled growth of malignant hematopoietic cells. Given this heterogeneity, it is likely that the etiology for these conditions is also diverse and distinct between them. Systemic lupus erythematosus (SLE) is a chronic inflammatory condition that has been associated with an increased risk of developing non-Hodgkin lymphoma (NHL). However, the role of SLE in the etiology of other hematologic malignancies is unclear. The primary objective of our study is to evaluate, using a meta-analysis of observational studies, the association between SLE and NHL, Hodgkin lymphoma (HL), leukemia and myeloma. A secondary objective was to evaluate potential differences according to geographical region. Methods At least two of the investigators performed a MEDLINE search from January 1, 1995 through June 30, 2013 looking for cohort studies reporting on the association between SLE and the risk of developing hematologic malignancies. The search keyword was “lupus AND (leukemia OR lymphoma OR myeloma)”. Data were gathered independently by at least two of the investigators and disagreements were addressed by consensus. The outcome of interest was the standardized incidence ratio (SIR) and 95% confidence interval (CI) of hematological malignancies in adult patients with SLE in comparison with the general population. The outcome was calculated using the random-effects model (REM), which adjusts for inter and intra-study heterogeneity. Additionally, heterogeneity was assessed using the I2 index. I2values of 25%, 50% and 75% indicated low, moderate and severe heterogeneity, respectively. The quality of the studies was assessed separately by at least two of the investigators using the Newcastle-Ottawa scale (NOS). Studies with NOS 1-3, 4-6 and 7-9 were considered of low, intermediate and high quality, respectively. Publication bias was assessed by the trim-and-fill analysis, which identifies and adjusts for imputed unpublished studies. Subset analyses were performed by geographical region (i.e. Asia, Europe and North America). All calculations and graphics obtained using Comprehensive Meta-Analysis version 2.2.050 (Biostat, New Jersey, USA). Results Our initial search found 976 studies, from which 15 studies were included in our analysis. Eight studies (53%) were from Europe, 4 (27%) from North America, 2 (13%) from Asia and 1 (7%) was a multi-national efforts. Cohort studies identified 488 cases of hematologic malignancies among 70,375 individuals (87% women, 13% men) with a diagnosis of SLE. Based on the NOS scale, 12 studies (80%) were considered of high and 3 (20%) of intermediate quality. SLE was associated with increased SIR of NHL (SIR 5.3, 95% CI 3.6-7.9, p<0.001) with high heterogeneity (I2=91%). The increased SIR of NHL was seen in all geographical regions: Europe SIR 6.4 (95% CI 2.9-13.8; p<0.001), North America SIR 4.6 (95% CI 2.6-8.3; p<0.001), Asia SIR 9.2 (95% CI 4.7-18.2; p<0.001). SLE was associated with a high SIR of HL (SIR 3.9, 95% CI 2.4-6.3; p<0.001) with moderate heterogeneity (I2=33%). The SIR of HL was increased in European (SIR 5.5, 95% CI 2.6-11.7; p<0.001) and North American studies (SIR 4.7, 95% CI 1.3-17.1; p=0.017). SLE was also associated with increased SIR of leukemia (SIR 2.3, 95% CI 1.8-2.9, p<0.001) with moderate heterogeneity (I2=49%). The SIR of leukemia was increased in all regions: Europe SIR 3.0 (1.1-7.6; p=0.026), North America SIR 2.2 (1.7-2.8; p<0.001), Asia SIR 2.6 (2.5-2.8; p<0.001). A weaker association was found between SLE and myeloma (SIR 1.5, 95% CI 1.0-2.0, p=0.03) without heterogeneity (I2=0%). Subset analysis by geographical region could not be undertaken in patients with myeloma. Publication bias would have not affected any of our results. Conclusions Compared with the general population, individuals with SLE have an increased SIR of hematologic malignancies. Not surprisingly, SLE was associated with a 5-fold increase in SIRs for NHL. However, SLE was also associated with increased SIR of HL (4-fold), leukemia (2-fold) and, at a lower degree, myeloma (1.5-fold). Our subset analysis showed that SLE increases the SIR of NHL, HL and leukemia regardless of the geographical region. Further studies are needed to elucidate the pathogenetic role of SLE in hematologic malignancies. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.063 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".