Limits...
Predicting the risk for lymphoma development in Sjogren syndrome

View Article: PubMed Central - PubMed

ABSTRACT

The heightened risk of non-Hodgkin lymphoma (NHL) development in primary Sjogren syndrome (SS) is well established. Several adverse clinical and laboratory predictors have been described. In the current work, we aimed to formulate a predictive score for NHL development, based on clinical, serological, and histopathological findings at the time of SS diagnosis. In the present case–control study of 381 primary SS patients and 92 primary SS patients with concomitant NHL, clinical, serological, and histopathological variables at the time of SS diagnosis were retrospectively recorded. For the identification of predictors for NHL development univariate and multivariate models were constructed. Salivary gland enlargement (SGE), lymphadenopathy, Raynaud phenomenon, anti-Ro/SSA or/and anti-La/SSB autoantibodies, rheumatoid factor (RF) positivity, monoclonal gammopathy, and C4 hypocomplementemia were shown to be independent predictors for NHL development. On the basis of the number of independent risk factors identified, a predictive risk score for NHL development was formulated. Thus, patients presenting with ≤2 risk factors had a 3.8% probability of NHL development, those with 3 to 6 risk factors 39.9% (OR (95%CI): 16.6 [6.5–42.5], P < 0.05), while in the presence of all 7 risk factors the corresponding probability reached 100% (OR [95%CI]: 210.0 [10.0–4412.9], P < 0.0001). In conclusion, an easy to use diagnostic scoring tool for NHL development in the context of SS is presented. This model is highly significant for the design of early therapeutic interventions in high risk SS patients for NHL development.

No MeSH data available.


Related in: MedlinePlus

The performance evaluation of the predictive model for NHL development with the formation of ROC curves. The AUC was 0.9 (95%CI: 0.8–0.9, P < 0.001). AUC = area under the curve, CI = confidence interval, NHL = non-Hodgkin lymphoma, ROC = receiver operating characteristic.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4998301&req=5

Figure 2: The performance evaluation of the predictive model for NHL development with the formation of ROC curves. The AUC was 0.9 (95%CI: 0.8–0.9, P < 0.001). AUC = area under the curve, CI = confidence interval, NHL = non-Hodgkin lymphoma, ROC = receiver operating characteristic.

Mentions: In these formulas, binary variables were coded as follows—SGE: presence = 1, absence = 0; Raynaud phenomenon: presence = 1, absence = 0; lymphadenopathy: presence = 1, absence = 0; monoclonal gammopathy: presence = 1, absence = 0; RF positivity: presence = 1, absence = 0; C4 hypocomplementemia: presence = 1, absence = 0; and anti-Ro/SSA and/or La/SSB positivity: presence = 1, absence = 0. When receiver operating characteristic curves for the predictive model were fitted, the area under the curve was 0.9, 95%CI: 0.8 to 0.9, P < 0.001 (Fig. 2). Hosmer–Lemeshow goodness-of-fit statistics were 4.8, P = 0.78.


Predicting the risk for lymphoma development in Sjogren syndrome
The performance evaluation of the predictive model for NHL development with the formation of ROC curves. The AUC was 0.9 (95%CI: 0.8–0.9, P < 0.001). AUC = area under the curve, CI = confidence interval, NHL = non-Hodgkin lymphoma, ROC = receiver operating characteristic.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4998301&req=5

Figure 2: The performance evaluation of the predictive model for NHL development with the formation of ROC curves. The AUC was 0.9 (95%CI: 0.8–0.9, P < 0.001). AUC = area under the curve, CI = confidence interval, NHL = non-Hodgkin lymphoma, ROC = receiver operating characteristic.
Mentions: In these formulas, binary variables were coded as follows—SGE: presence = 1, absence = 0; Raynaud phenomenon: presence = 1, absence = 0; lymphadenopathy: presence = 1, absence = 0; monoclonal gammopathy: presence = 1, absence = 0; RF positivity: presence = 1, absence = 0; C4 hypocomplementemia: presence = 1, absence = 0; and anti-Ro/SSA and/or La/SSB positivity: presence = 1, absence = 0. When receiver operating characteristic curves for the predictive model were fitted, the area under the curve was 0.9, 95%CI: 0.8 to 0.9, P < 0.001 (Fig. 2). Hosmer–Lemeshow goodness-of-fit statistics were 4.8, P = 0.78.

View Article: PubMed Central - PubMed

ABSTRACT

The heightened risk of non-Hodgkin lymphoma (NHL) development in primary Sjogren syndrome (SS) is well established. Several adverse clinical and laboratory predictors have been described. In the current work, we aimed to formulate a predictive score for NHL development, based on clinical, serological, and histopathological findings at the time of SS diagnosis. In the present case&ndash;control study of 381 primary SS patients and 92 primary SS patients with concomitant NHL, clinical, serological, and histopathological variables at the time of SS diagnosis were retrospectively recorded. For the identification of predictors for NHL development univariate and multivariate models were constructed. Salivary gland enlargement (SGE), lymphadenopathy, Raynaud phenomenon, anti-Ro/SSA or/and anti-La/SSB autoantibodies, rheumatoid factor (RF) positivity, monoclonal gammopathy, and C4 hypocomplementemia were shown to be independent predictors for NHL development. On the basis of the number of independent risk factors identified, a predictive risk score for NHL development was formulated. Thus, patients presenting with &le;2 risk factors had a 3.8% probability of NHL development, those with 3 to 6 risk factors 39.9% (OR (95%CI): 16.6 [6.5&ndash;42.5], P&#8202;&lt;&#8202;0.05), while in the presence of all 7 risk factors the corresponding probability reached 100% (OR [95%CI]: 210.0 [10.0&ndash;4412.9], P&#8202;&lt;&#8202;0.0001). In conclusion, an easy to use diagnostic scoring tool for NHL development in the context of SS is presented. This model is highly significant for the design of early therapeutic interventions in high risk SS patients for NHL development.

No MeSH data available.


Related in: MedlinePlus