Predictors of prolonged treatment time intervals in oral cavity cancer

OBJECTIVES

Delays in treatment time intervals have been associated with overall survival in oral cavity squamous cell carcinoma (OCSCC). The aim of this study was to identify bottlenecks leading to prolonged treatment intervals.

MATERIAL AND METHODS

A retrospective analysis was conducted using a cohort of OCSCC patients who underwent surgery and adjuvant radiation therapy. The endpoints of interest were prolonged treatment intervals. Multivariable logistic regression was used to adjust for patient and tumour characteristics.

RESULTS

Median diagnosis-to-treatment interval (DTI) and surgery to initiation of postoperative radiation therapy interval (S-PORT) were 39 days (IQR 30-54) and 64 days (IQR 54-66), respectively. Prolonged DTI was associated with older age, worse Charlson Comorbidity index scores and worse T stages. Patients with prolonged DTI had longer times to preoperative imaging reports (25 vs 9 days; P < 0.01). Time to preoperative pathology did not differ. Prolonged S-PORT was associated with longer times to pathology report (28 vs 18 days; P < 0.01), to maxillofacial consult (38 vs 15 days; P < 0.01) and to maxillofacial approval of radiation (50 vs 28 days; P < 0.01). In patients requiring medical oncology consults, those with prolonged S-PORT had longer waiting times until consultation (58 vs 38 days; P = 0.02). Multivariate analysis showed independent predictors of prolonged DTI: time to preoperative imaging; and prolonged S-PORT: time to pathology report, time to maxillofacial consult, and time to medical oncology consult.

CONCLUSIONS

Strategies targeting these organizational bottlenecks may be effective for shortening treatment time intervals, hence representing potential opportunities for improving oncological outcomes in OCSCC patients.

Predicting morbidity by local similarities in multi-scale patient trajectories

Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.