Аuthors: A. V. Stasyshyn
Replacement therapy in hemophilia patients is complicated by the formation of inhibitory antibodies against factor VIII (IX) (inhibitors) among 30 % of hemophilia A patients and 3–5 % of hemophilia B patients. The treatment of bleeding and elimination of inhibitors is complicated, costly and not always successful. The goal of this study is to develop a simple score that stratifies patients with hemophilia according to their risk of developing inhibitory antibodies.
Data and methods. The data consists of 135 patients with hemophilia A and B divided into two groups: Group I – patients with stable inhibitor (74 persons) and group II – patients without inhibitor (61 people). We analyzed 16 factors affecting the tendency to develop inhibitor: type of hemophilia (A or B), severity, age, age at diagnosis, hereditary or sporadic, family history of inhibitors, intensive treatment at initial treatment (ED/1 episode), age at first exposure to FVIII/IX, reason for first treatment with FVIII, FVIII/IX product type, prophylaxis or “on demand” treatment, clotting factor concentrate switching, significant and life-threatening bleeding localization; surgery (classified according to two features): I – the urgency (urgent and selective); II – the type (large and small), and the presence of purulent complications. To examine the relationship between risk factors and the likelihood of inhibitor development, we used regression models of discrete choice. Specifically, we estimate the range of one factor binomial choice models to study the individual effects of each factor on the probability of inhibitor development. In addition, we analyze the joint impact of risk factors using multiple logit regression that allows exploring the effects and importance of each factor, controlling for the presence of other factors. Adequacy of logit models was conducted using chi-square test, and the significance of regression coefficients was based on Student Wald statistics. Finally, we calculated the theoretical values of probability of an inhibitor development for each patient and ranges (95 % confidence interval) of predicted risk of inhibitor development in patients with the value of the dependent variable “yes” and “no”.
Results.All factors except age are categorical variables and are included in the regression as dummy variables. Estimated coefficients represent the marginal effects of each factor on the probability of inhibitor development. To build a multivatiate logit model we used stewise forward regression with 1 %, 5 % and 10 % significance levels. The first model includes the following factors: age clotting factor concentrate switching, FVIII/IX product type purulent complications and the number of days of exposure (ED)/1 episode of bleeding. At 5 % significance level, the type of surgery (large and small) as well as positive "inhibitory" history are added. At the 10 % significance level, the model additionally contains a variable characterizing the urgency of surgery (urgent or planned). In this approach, the factors that determine the type of hemophilia, age, diagnosis and life-threatening bleeding are not significant and therefore, we do not include them in the final multivariate regression. The number ED/1 episode has the highest impact on the probability of inhibitor development: if it increases, the likelihood of inhibitor development increases by 27 %. For patients who changed the type of concentrates, the likelihood of inhibitor development increases by 23 %. The effect of the FVIII/IX product type, the age and the type of the surgery - is negative and significant, while purulent complications and burdened "inhibitory" history result in the increase in the likelihood of inhibitor development by 21 % and 12 %, respectively. This multivariate logit model allows predicting the likelihood of developing an inhibitor for a patient based on the information about the values of each of these factors.
Conclusion: The prediction based on the multiple logit regression developed in this paper allows the identification of patients at high risk of inhibitor development. According to our model, the factors associated with treatment have the highest impact on the probability of inhibitor development. Based on the results, reducing the frequency of inhibitor development can be achieved by changing the approaches to the treatment of patients with hemophilia.
Keywords: hemophilia, inhibitor, risk factors, prognosis, logit regression.