RESEARCH ARTICLE
A JH
Intensive chemotherapy, azacitidine, or supportive care in older acute myeloid leukemia patients: An analysis from a regional healthcare network Pierre Bories,1,2 Sarah Bertoli,1 Emilie Berard,3,4 Julie Laurent,3 Eliane Duchayne,5 Audrey Sarry,1 Eric Delabesse,5,6 Odile Beyne-Rauzy,1,6 Franc¸oise Huguet,1 and Christian Recher1,6* We assessed in a French regional healthcare network the distribution of treatments, prognostic factors, and outcome of 334 newly diagnosed acute myeloid leukemia patients aged 60 years or older over a 4-year period of time (2007–2010). Patients were selected in daily practice for intensive chemotherapy (n 5 115), azacitidine (n 5 95), or best supportive care (n 5 124). In these three groups, median overall survival was 18.9, 11.3, and 1.8 months, respectively. In the azacitidine group, multivariate analysis showed that overall survival was negatively impacted by higher age (P 5 0.010 for one unit increase), unfavorable cytogenetics (P 5 0.001), lymphocyte count 1.5 (for azacitidine versus ICT) with two-sided type 1 error rate of 5% (a 5 0.05) for the comparison of two exponential survival distributions [22]. Statistical analysis was performed on STATA statistical software, release 11.2 (STATA Corp., College Station, TX). We described patients’ characteristics using number and frequency for qualitative data, and median, inter-quartile range (IQR), and range (minimum-maximum) for quantitative data. Comparisons of patient characteristics were performed using Student’s t-test (or Mann-Whitney’s test when distribution departed from normality or when homoscedasticity was rejected) for continuous variable and the v2 test (or Fisher’s exact test in case of small expected numbers) for categorical variable. In case of multiple comparisons (azacitidine versus ICT and azacitidine versus BSC), P values were corrected using Bonferroni method. In each subgroup of patients (azacitidine, ICT, and BSC) differences in response rate and early death were compared between groups using the v2 test (or Fisher’s exact test in case of small expected numbers); differences in survival functions were tested using the Log-Rank test. Independent prognostic factors for response in ICT and azacitidine groups were assessed using a logistic regression model. Independent prognostic factors for OS in azacitidine, ICT, and BSC groups were assessed using a Cox model. Variables initially introduced in the model were all variables associated with response or OS in univariate analysis with a P value
A JH
Intensive chemotherapy, azacitidine, or supportive care in older acute myeloid leukemia patients: An analysis from a regional healthcare network Pierre Bories,1,2 Sarah Bertoli,1 Emilie Berard,3,4 Julie Laurent,3 Eliane Duchayne,5 Audrey Sarry,1 Eric Delabesse,5,6 Odile Beyne-Rauzy,1,6 Franc¸oise Huguet,1 and Christian Recher1,6* We assessed in a French regional healthcare network the distribution of treatments, prognostic factors, and outcome of 334 newly diagnosed acute myeloid leukemia patients aged 60 years or older over a 4-year period of time (2007–2010). Patients were selected in daily practice for intensive chemotherapy (n 5 115), azacitidine (n 5 95), or best supportive care (n 5 124). In these three groups, median overall survival was 18.9, 11.3, and 1.8 months, respectively. In the azacitidine group, multivariate analysis showed that overall survival was negatively impacted by higher age (P 5 0.010 for one unit increase), unfavorable cytogenetics (P 5 0.001), lymphocyte count 1.5 (for azacitidine versus ICT) with two-sided type 1 error rate of 5% (a 5 0.05) for the comparison of two exponential survival distributions [22]. Statistical analysis was performed on STATA statistical software, release 11.2 (STATA Corp., College Station, TX). We described patients’ characteristics using number and frequency for qualitative data, and median, inter-quartile range (IQR), and range (minimum-maximum) for quantitative data. Comparisons of patient characteristics were performed using Student’s t-test (or Mann-Whitney’s test when distribution departed from normality or when homoscedasticity was rejected) for continuous variable and the v2 test (or Fisher’s exact test in case of small expected numbers) for categorical variable. In case of multiple comparisons (azacitidine versus ICT and azacitidine versus BSC), P values were corrected using Bonferroni method. In each subgroup of patients (azacitidine, ICT, and BSC) differences in response rate and early death were compared between groups using the v2 test (or Fisher’s exact test in case of small expected numbers); differences in survival functions were tested using the Log-Rank test. Independent prognostic factors for response in ICT and azacitidine groups were assessed using a logistic regression model. Independent prognostic factors for OS in azacitidine, ICT, and BSC groups were assessed using a Cox model. Variables initially introduced in the model were all variables associated with response or OS in univariate analysis with a P value
