Fig 1.
Clinical states and transitions in SDHB-mutation carriers.
Transition probabilies are provided in the Tsdhb matrix, with 4 rows and 4 columns, upper right. Numerical values for steady states and transition probabilities are displayed here and also given in Table 1 and Table 2. For example, p12 is the probability of moving from state 1 to state 2 and p11 is the probability to stay in this state 1.
Fig 2.
Clinical states and transitions in SDHD-mutation carriers.
Only the situation where the pathogenic variant is inherited from the father is considered. Transition probabilies are provided in the Tsdhd matrix. Transition probabilities are represented in a 4–4 matrix (with 4 rows and 4 columns, upper right). Numerical values for steady states and transition probabilities are displayed here and also given in Table 1 and Table 2.
Table 1.
Estimated percentages at different clinically-defined steady states in SDHB and SDHD-related PPGL.
Table 2.
Differences in estimated transition probabilities into the different clinically-defined steady states in SDHB and SDHD-related PPGL.
Fig 3.
Disease scenarios and rules of disease dynamics.
Four clinical states and 16 transition probabilities between states are represented (using Markov chains). Population-level transition probabilities are represented in the 4–4 matrix T (i.e with 4 rows and 4 columns, upper right). The transition probabilities are denoted by pij and pii, where pij is the probability of moving from state i to state j and pii is the probability to stay in this state i. Numerical values for transition probabilities are given in Table 1. According to international nomenclature of stochastic matrix, the row vector of the Markov chains has been written in order to meet the following criteria: non negative coefficients and the sum of each row equal to 1.