Clinical impact of treatment delay in pancreatic cancer patients revisited

Dear Editor, We have read with great interest the study by Jooste et al. that investigated patient delay, treatment delay and pancreatic cancer survival in two French population-based registries, and found little evidence to suggest that timely care was associated with improved patient survival. At the first glance, this is a clashing finding if we consider that high mortality in pancreatic cancer is mostly attributed to late diagnosis in locally advanced or metastatic stage of the disease, when treatment options are limited. Computed tomography findings suggest that large (>2 cm) pancreatic cancers develop metastases within a mean CT interval of 3.5 months (range 1.2–8.4 months); and in a previous multivariate analysis of pancreas ductal adenocarcinoma patients in the USA, progression time from T1 to T4 stage was estimated to be just over 1 year. Assuming exponential tumor growth, the speed of tumor growing is highest in the last weeks of natural course, in the diagnostic phase and just before treatment initiation. We argue that the conclusion by Jooste et al. (that the timeliness of pancreatic cancer care is not significantly associated with patient survival) cannot be generalized beyond the study settings, and the analysis needs to be carefully assessed for methodology limitations before building public health policies upon the findings. In this letter, we point out a few methodologic aspects that might substantially shape the conclusions drawn from this study. First, treatment delay (time from first consultation to start of treatment) was captured as a continuous variable in days, but analyzed only as a categoric parameter: patients with less than the median 29 days of treatment delay were compared to patients with treatment delay of 29 days. Rationale for this simplification was not provided, and the reader has no hint on how long the treatment delays actually were in the two groups. Therefore, it is unclear whether the analysis was oversimplified and a more graded analysis (e.g., comparing treatment delay quintiles, or using the delay as a continuous variable) would have shown a different picture. We kindly ask the authors to provide the medians and interquartile ranges of treatment delay for all patient groups analyzed; and either to consider preparing more graded analyses of their impressive dataset (e.g., along treatment delay quintiles or continuous data) or to justify the selected simplified approach. Another important issue is the careful selection of explanatory variables in multivariate regression models. The selection of covariates might not be solely based on their significance in univariate models, but shall also consider their independence and causative links. To explain the importance of this problem, let us give an illustration from another medical field. Imagine a study that evaluates the impact of preterm birth on perinatal mortality. Since both preterm birth and low birthweight are strong predictors of perinatal mortality in univariate models, one could include both explanatory variables in a multivariate model. However, preterm birth and low birthweight are closely interconnected, as low birthweight is in the causal pathway between preterm birth and perinatal mortality, therefore there is a high chance to find limited evidence on the association of preterm birth with perinatal mortality when the analysis is adjusted to low birthweight. There is no strong technical objection against the inclusion of low birthweight to the multivariate model (if the model converges, and coefficients can be calculated); nevertheless, the proper wording of the conclusion would read as “preterm birth was not associated with perinatal mortality when low birthweight status was kept unchanged. ” This conclusion is biased, since preterm babies typically have low birthweight, with immature organs which lead to perinatal mortality. Similarly, in the study by Jooste et al. the impact of patient and treatment delays on 3-year excess mortality rate was adjusted to the presence of metastasis at diagnosis, and to the feasibility of curative surgery (beyond other covariates). This method neglects the most important promise of shorter patient and treatment delays, i.e. to detect patients in earlier stage when curative surgery is still an option. Accordingly, we believe that the conclusion from this model is misleading as excess mortality rate was not associated with patient and treatment delays when cancer stage at diagnosis and radicality of surgery was kept unchanged (also adjusting for patient age, sex and socioeconomic status quintiles). This finding may be correct from the statistical point of view, but it does not answer the clinically important question: is excess mortality rate associated with delays in care, when adjusted only to independent predictors e.g. age, sex and socioeconomic status? The paper did not describe a corresponding analysis to answer this question. However, highest rate of Le tt er to th e E di to r