Disparity in the Overall Survival Improvement over Time and the Effect Time-to-Treatment on the Outcome of Acute Promyelocytic Leukemia: A US National Cancer Data Base Study from 1998-2011
Authors: Al-Kali Aref , Tibes Raoul , Foran James M. , Patnaik Mrinal M , Gangat Naseema , Bartley Adam C , Elliott Michelle , Litzow Mark R , Go Ronald S. 
Published: October 2019

Background: Approximately 1,000 acute promyelocytic leukemia (APL) patients are diagnosed in the US annually. APL has the highest cure and early mortality rates of all acute leukemias. The availability of all trans-retinoic acid (ATRA) in 1995 and arsenic trioxide in 2000 has further improved the overall survival (OS). However, whether there is uniform improvement in OS across demographic subgroups and if sooner time-to-treatment (TTT) reduces early mortality are unknown. We used the National Cancer Data Base (NCDB), a nationwide oncology registry covering 70% of the US cancer population to answer these questions.

Methods: We identified patients with APL diagnosed from 1998-2011 using ICD-O-3 code 9866. Survival functions were estimated using the Kaplan-Meier method and 30-day mortality was estimated as 1-survival at 30 days. We compared OS according to demographic subgroups (gender, age [18-40, 41-64, and >65 years], race [White, Black, Native American, Asian/Pacific Islander, and other], and ethnicity) across years of diagnosis (Period [P] 1: 1998-2001, P2: 2002-2006, and P3: 2007-2011). For 30-day mortality, we compared the rates according to demographic subgroups and time periods and determined the effect of TTT (days from APL diagnosis to ATRA and/or chemotherapy). For this analysis we included only patients treated within 30 days of diagnosis and those diagnosed from 2003 to 2011. TTT was grouped as 0, 1, 2, or >3 days. We used mixed-effects logistic regression model to obtain odds ratio comparing treatment delays. Our model accounted for multiple subjects per facility with a random intercept and was adjusted for gender, Charlson-Deyo score, and year of diagnosis.

Results: Characteristics: There were 8,084 APL patients in our study. The median age at diagnosis was 51 (24% were older than 65) years and 50% were females. White race (85%) was the most common, followed by black (11%) and Asian (3%). Twelve percent were Hispanic in origin. Slightly more patients (54.8%) were treated in academic centers. Thirty seven percent had either Medicaid or Medicare insurance. OS: Race had no impact on OS, although Asians had a trend towards improved OS (P=0.06). Both females and young age affected OS favorably (P <0.0001). In general, OS improved over the three time periods (P=0.002). Across Periods 1-3, we observed significant improvement in OS among the males (P=0.03), 18-40 (P=0.035) and 41-64 (P=0.005) age groups, Whites (P=0.012), and non-Spanish/Hispanics (P=0.002), but not among their counterparts. 30-day mortality rate: Age above 65 years was associated with highest 30-day mortality (33%). Similarly male gender had statistically significant higher 30-day mortality (shown in the Figure). There was no statistically significant difference among Periods 1-3. As expected, untreated patients had an increased risk of death versus treated (HR=1.9, confidence interval 1.7-2.1). TTT: A third (33.5%) of the patients was treated within two days of APL diagnosis. TTT was not independently associated with lower 30-day mortality rate (shown in the Table). Presence of disseminated intravascular coagulopathy (DIC) data was not available to analyze.

Conclusions: From 1998-2011, the OS of APL continued to improve but not uniformly across demographic subgroups. In contrast, the 30-day mortality did not improve in the same time period. Both Age above 65 and male gender predicted worse 30-day mortality and OS. Our study did not find a favorable association between shorter TTT and lower 30-day mortality rate. Our study was not able to study these findings among different risk groups.

Disclosures

Al-Kali: Celgene: Research Funding. Tibes:TetraLogic Pharmaceuticals: Research Funding.