Patient trajectories after diagnosis of diffuse large B-cell lymphoma-a multistate modelling approach to estimate the chance of lasting remission

Abstract

Background: Achieving lasting remission for at least 2 years is a good indicator for favourable prognosis long term after Diffuse large B-cell lymphoma (DLBCL). The aim of this study was to provide real-world probabilities, useful in risk communication and clinical decision-making, of the chance for lasting remissions by clinical characteristics.

Methods: DLBCL patients in remission after primary treatment recorded in the Swedish Lymphoma register 2007-2014 (n = 2941) were followed for relapse and death using multistate models to study patient trajectories. Flexible parametric models were used to estimate transition rates.

Results: At 2 years, 80.7% (95% CI: 79.0-82.2) of the patients were predicted to remain in remission and 13.2% (95% CI: 11.9-14.6) to have relapsed. The relapse risk peaked at 7 months, and the annual decline of patients in remission stabilised after 2 years. The majority of patients in the second remission transitioned into a new relapse. The probability of a lasting remission was reduced by 20.4% units for patients with IPI 4-5 compared to patients with IPI 0-1, and time in remission was shortened by 3.5 months.

Conclusion: The long-term prognosis was overall favourable with 80% achieving durable first remissions. However, prognosis varied by clinical subgroups and relapsing patients seldom achieved durable second remissions.

Fig. 1. Illustration of the multistate model used to define the transitions (arrow) between different states (boxes).

The flow of individuals through the various patient trajectories is indicated by the numbers on each transition. (1) In all, 471 (88%) of 538 relapsing patients had follow-up data beyond the first relapse and could contribute to later transitions. (2) In total, 41 patients responded to later line treatment (following the second relapse) and 14 patients had a third relapse. No patients responded to later line treatment following the third relapse.

Fig. 2. Probabilities of being in a given state by time since first remission (in years) plotted together with the probability of having transitioned to a subsequent state (i.e., where did the patients leave to).

Panel a shows the probability of being in remission and the probability of subsequently having transitioned into either first relapse or death. Panel b shows the probability of being in the state “first relapse” and the probability of subsequently having transitioned into either second remission or death or first relapse. Panel c shows the probability of being in “second remission” after relapse treatment and the probability of subsequently having transitioned into a second relapse or death. Panel d shows the probability of being in a second relapse and subsequently having transitioned into death (the only possible transition). Note: in order to provide greater detail to the figure, the scale of the y axis differs between panels.

Fig. 3. Probabilities of being in a given state at different times since first remission (in years) by age group and age-adjusted international prognostic index (aaIPI).

a Age ≤60 years, aaIPI <2; b age ≤60 years, aaIPI ≥2; c age 61–70 years, aaIPI <2; d age 61–70 years, aaIPI ≥2; e age 71–80 years, aaIPI <2; f age 71–80 years, aaIPI ≥2; g age >80 years, aaIPI <2, and h age >80 years, aaIPI ≥2.

Fig. 4. Impact of clinical patient and DLBCL factors on the probability of being in remission and length of stay.

The clinical characteristics are contrasted in terms of the difference in the 2 years remission transition probability and length of stay in days. All contrasts are adjusted for age at diagnosis, calendar year of diagnosis and sex at (a) difference in transition probability at 2 years (b) difference in length of stay at 2 years.