scholarly journals Treatment-related mortality in newly diagnosed pediatric cancer: a population-based analysis

2018 ◽  
Vol 7 (3) ◽  
pp. 707-715 ◽  
Author(s):  
Paul Gibson ◽  
Jason D. Pole ◽  
Tanya Lazor ◽  
Donna Johnston ◽  
Carol Portwine ◽  
...  
Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4317-4317 ◽  
Author(s):  
Anand P. Jillella ◽  
Farrukh Awan ◽  
Ravindra B. Kolhe ◽  
Jeremy M Pantin ◽  
Devi D Morrison ◽  
...  

Abstract Abstract 4317 Background: APL is widely accepted as a curable leukemia with most multi-institutional studies showing very low treatment related mortality. This is in contrast to treatment in clinical practice outside the study population where the treatment related mortality is higher. A few recent population based studies show that mortality maybe as high as 30% in APL patients during induction. A recent analysis of SEER data from 13 population-based cancer registries with 1400 APL patients in the US showed that 17% of all patients and 24% of patients greater than 55 years of age die within one month of diagnosis. Swedish registry data and Brazilian data also show this high mortality during induction. The most common causes of death are bleeding, infection, differentiation syndrome and multi-organ failure. Patients who survive induction have an excellent cure rate with few late relapses. Hence, decreasing early deaths is a high priority both at experienced as well as smaller centers with limited leukemia treatment experience in this highly curable disease. Methods: At Georgia Health Sciences University, between 7/2005 and 6/2009, 19 patients were diagnosed with APL. Seven patients (5 high-risk and 2 low-risk) died during induction resulting in an unusually high mortality rate of 37%. All patients who survived induction are still in remission at present. The high early death rate prompted us to develop a simple, 2 page treatment algorithm that focuses on quick diagnosis, prompt initiation of therapy, and proactive and aggressive management of all the major causes of death during induction. We also developed a network of physicians in smaller community based treatment centers and gave them access to our protocol and helped them manage these patients in the induction period with the hypothesis that this standardized treatment approach will result in decreasing induction mortality. Results: From 11/2010 to 7/2012, we treated 5 patients at GHSU and helped manage 4 patients at 2 outreach sites. The age range was 30 to 60; two patients were high-risk, 6 intermediate- and one low-risk. In the pre-algorithm cohort the cumulative survival was 63.1% at 1 year with all deaths happening within 31 days. In contrast, after the implementation of a standardized algorithm the cumulative survival was 100% with no deaths during the induction or subsequent follow-up period, log rank p-value=0.05, with a median follow-up of more than 4-years in surviving patients. Conclusions: While we recognize that this is a small cohort, our own experience and a similar approach pioneered by investigators in Brazil clearly shows that this centralized, algorithm-based management under the direct supervision of a leukemia expert can be an effective intervention to decrease early deaths in APL. Based on the Brazilian experience an international consortium was formed to reduce the mortality and interim data show a reduction in early mortality to 7.5% with this networking of treatment centers. We believe our experience warrants large scale implementation with development of a network of physicians and standardization of treatment in the United States to improve early outcomes in this highly curable leukemia. Disclosures: Awan: Allos Therapeutics: Speakers Bureau.


2017 ◽  
Vol 116 (4) ◽  
pp. 540-545 ◽  
Author(s):  
Jason D Pole ◽  
◽  
Paul Gibson ◽  
Marie-Chantal Ethier ◽  
Tanya Lazor ◽  
...  

2013 ◽  
Vol 31 (11) ◽  
pp. 1435-1441 ◽  
Author(s):  
Sandra Eloranta ◽  
Paul C. Lambert ◽  
Jan Sjöberg ◽  
Therese M.L. Andersson ◽  
Magnus Björkholm ◽  
...  

PurposeHodgkin lymphoma (HL) survival in Sweden has improved dramatically over the last 40 years, but little is known about the extent to which efforts aimed at reducing long-term treatment-related mortality have contributed to the improved prognosis.MethodsWe used population-based data from Sweden to estimate the contribution of treatment-related mortality caused by diseases of the circulatory system (DCS) to temporal trends in excess HL mortality among 5,462 patients diagnosed at ages 19 to 80 between 1973 and 2006. Flexible parametric survival models were used to estimate excess mortality. In addition, we used recent advances in statistical methodology to estimate excess mortality in the presence of competing causes of death.ResultsExcess DCS mortality within 20 years after diagnosis has decreased continually since the mid-1980s and is expected to further decrease among patients diagnosed in the modern era. Age at diagnosis and sex were important predictors for excess DCS mortality, with advanced age and male sex being associated with higher excess DCS mortality. However, when accounting for competing causes of death, we found that excess DCS mortality constitutes a relatively small proportion of the overall mortality among patients with HL in Sweden.ConclusionExcess DCS mortality is no longer a common source of mortality among Swedish patients with HL. The main causes of death among long-term survivors today are causes other than HL, although other (non-DCS) excess mortality also persists for as long as 20 years after diagnosis, particularly among older patients.


Leukemia ◽  
2013 ◽  
Vol 28 (2) ◽  
pp. 289-292 ◽  
Author(s):  
M Othus ◽  
H Kantarjian ◽  
S Petersdorf ◽  
F Ravandi ◽  
J Godwin ◽  
...  

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2277-2277
Author(s):  
Colin Godwin ◽  
Megan Othus ◽  
Vicky Sandhu ◽  
Elihu H. Estey ◽  
Roland B. Walter

Abstract Introduction Despite improvements in supportive care, treatment-related mortality (TRM) remains a significant problem for patients with acute myeloid leukemia (AML). In order to quantify fitness for intensive AML therapies, we recently developed a multivariate model for predicting TRM, defined as death within 28 days of treatment initiation in patients undergoing intensive induction chemotherapy for newly-diagnosed AML. Here, we examine the performance of this TRM model in patients with relapsed or refractory AML and assess whether the model can be improved for this population. Methods Using a database of patients treated for AML at our institution since 2003, we identified patients with relapsed or refractory disease treated with intensive chemotherapy, defined as regimens that were at least as intense as typical “7+3” regimens; patients who received treatment protocols with cytarabine <100 mg/m2/day or demethylating agents alone were excluded. We collected the following variables required to calculate the TRM score: ECOG performance status, age, platelet count, albumin, presence or absence of secondary AML, WBC, peripheral blood blast percentage, and creatinine. TRM scores were then calculated as described in J Clin Oncol 2011;29:4417. For a subset of these patients, the following additional variables were collected: duration of prior complete remission, number of prior chemotherapy regimens, number of prior hematopoietic cell transplants, and the number of antibiotics prescribed for treatment of presumed or documented infections at the time of chemotherapy initiation. For this smaller subset, we used a logistic regression model to create a preliminary updated TRM model that included these clinical variables in addition to the variables in the original TRM score. Finally, we used the area under the receiver operator characteristic curve (AUC) to quantify the ability of a model to predict TRM; in this approach, an AUC of 1 indicates perfect prediction of TRM while an AUC of 0.5 indicates no prediction; AUC values of 0.6-0.7, 0.7-0.8, and 0.8-0.9 are commonly considered as poor, fair, and good, respectively. Results A total of 270 patients met our study inclusion criteria. Fifteen (5.6%) died within 28 days of starting chemotherapy, i.e. experienced TRM. The AUC for the previously published TRM score in predicting early death in this population was 0.66. For comparison, in our original study of 2,238 patients with newly-diagnosed AML, we obtained an AUC of 0.82. Expanding the definition of TRM to include those patients who died within 60 (N = 47, 17.4%) or 90 days (N = 75, 27.8%) of starting chemotherapy did not improve the original model’s performance in our cohort (AUCs of 0.62 and 0.59, respectively). The additional variables described above were available for 133 of the 270 patients in our cohort. Within this subset, the AUC for the original TRM score was 0.65 in predicting death within 28 days. Incorporation of these new covariates in a preliminary TRM model yielded an improved AUC of 0.81 for this smaller subset. Conclusions Our data indicate that the TRM score, originally developed for patients with newly-diagnosed AML, only has a fair ability to predict TRM in patients with relapsed and refractory AML. However, with inclusion of additional covariates, such as prior CR duration and number of prior chemotherapies, TRM can be predicted quite well, to a degree similar to the accuracy demonstrated by the original model in newly-diagnosed patients. While our findings need to be confirmed in larger, independent cohorts of patients, they suggest the possibility of developing an objective measure to determine fitness for intensive salvage chemotherapy in AML. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2132-2132 ◽  
Author(s):  
Anna Maria Testi ◽  
Robin Foa ◽  
Mazin Faisal Al-Jadiry ◽  
Maria Luisa Moleti ◽  
Salma Abbas Al-Hadad ◽  
...  

Abstract Abstract 2132 The overall cure rates for children with acute lymphoblastic leukemia (ALL) treated and managed in high-income countries have reached approximately 80% over the last two decades. Unfortunately, these advances in survival have not fully translated into low-income countries where the survival rates remain significantly lower (<35%). Potential reasons for these different results include higher rates of relapse, a high degree of treatment abandonment, insufficient diagnostic work-up procedures, limited availability of effective drugs and supportive measures, and, consequently, high rates of treatment-related mortality (TRM). We examined the incidence, causes and risk factors for early (<60 days) TRM in pediatric patients (≤15 years) with newly diagnosed ALL managed at the oncology unit of the Children Welfare Teaching Hospital in Baghdad (Iraq), over a 3-year period (2007 – 2009). Data were prospectively collected in Baghdad and analyzed at the GIMEMA Data Center in Rome. From January 2007 to December 2009, a total of 319 children (median age 5.2 years, range 0.3–13.9; 171 males and 148 females) with newly diagnosed ALL were registered; the diagnosis of ALL was confirmed by BM aspirate, according to the FAB classification; patients with L3 morphology (6 cases) were included. The median duration of symptoms prior to diagnosis was 4 weeks, ranging from 1 to 76 weeks. At disease onset, 179 children (56%) presented fever and 30 (9.4%) had hemorrhages; liver and renal functions were impaired in 9/290 (3%) and 21/289 (7.3%) patients with available data. The median Hb level was 7.0 g/dl (range 2.4–14.9), the median WBC count was 16.9 × 109/l (range 0.2–900) and the median platelet count was 35.0 × 109/l (range 0.1–598). CSF was positive in 14/290 children (4.8%). Patients were defined as low (153, 48%), intermediate (127, 40%) or high (33, 10%) risk according to clinical and laboratory parameters (age, hepatosplenomegaly, mediastinal mass, WBC, Hb level, platelet count, CNS and testicular infiltration). Sixteen children were discharged following the parents’ decision (14 before any treatment and 2 after 2 days); 303 children are evaluable for early TRM. Treatment consisted of a modified BFM-95 protocol in the first 31, a modified MRC UKALL-2003 protocol in 266 and the LMB/FAB-96 protocol in the 6 children with ALL-L3 morphology. Up to September 25, 2008 all trials did not include the 7-day steroid pre-phase that was introduced thereafter. A total of 249 children (82%) achieved a complete response in the first 60 days of treatment. The cumulative incidence of early TRM was 16% (48/303); it significantly decreased throughout the study period (2007: 21/88, 24%; 2008: 15/98, 15%; 2009: 12/117, 10% p 0.009). Several variables (sex, age, symptoms duration, hepatosplenomegaly, Hb level, WBC count, platelet count, bleeding, fever, impaired liver and renal function, CNS positivity, risk group, steroid pre-phase and induction complications) were examined as potential predictors of TRM. In univariate analysis, the occurrence of induction complications significantly increased the early TRM: hemorrhage 26% vs 11% p 0.001; infection 18% vs 2% p 0.005. A highly significant favorable impact on early TRM was represented by the 7-day steroid pre-phase; 36/169 children (21%) who did not receive the pre-phase died within the first 60 days of treatment compared to 12/134 children (9%) who underwent the steroid pre-phase (p 0.003). When the steroid pre-phase was placed in multivariable models with each of induction complications or other clinical parameters, it remained an independent predictor of TRM. Our experience confirms that a protocol-based care of children with ALL has to include the prednisone pre-phase that in low-income countries may contribute to a better risk definition and also to a significant reduction of early TRM. Disclosures: Foa: Roche: Consultancy, Speakers Bureau.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2191-2191 ◽  
Author(s):  
Roland B. Walter ◽  
Megan Othus ◽  
Gautam Borthakur ◽  
Farhad Ravandi ◽  
Jorge E. Cortes ◽  
...  

Abstract Abstract 2191 Background: Treatment protocols for newly diagnosed AML typically use age (often 60 years) alone to restrict eligibility to either younger or older patients. Implied in this practice is the assumption that age is the principal predictor of therapeutic failure in AML due to either early treatment-related mortality (TRM) or resistance to therapy in patients who do not incur TRM. Yet, clinical observation and previous studies indicate that other prognostic factors modulate the effect of age on TRM and resistance, suggesting that age as sole or primary criterion for treatment allocation may be suboptimal. Methods: To test this hypothesis in newly-diagnosed non-APL AML, we quantified the relative effects of age and other covariates using 1,127 patients (median age: 57 years) treated on Southwest Oncology Group (SWOG) trials from 1986–2009 and 1,604 patients (median age: 61 years) treated on various protocols at M.D. Anderson Cancer Center (MDA) from 2000–2008. We calculated weekly hazard rates (the probability of death in week × given that the patient was alive at the beginning of the week) for both cohorts overall and in various age subgroups. We used the area under the receiver operator characteristic curve (AUC) to quantify the effects of covariates for prediction of TRM and resistance (no TRM but patient does not enter CR or relapses within 1 year of CR), where an AUC of 1 indicates that a covariate is perfect at prediction while an AUC of 0.5 indicates no prediction (i.e. it is no better than flipping a coin). Results: Despite the use of different treatment protocols, survival in the SWOG and MDA cohorts was virtually superimposable. In both cohorts, the maximum weekly hazard occurred at weeks 3 and 4 from start of treatment, after which it decreased. The maximum hazard was relatively independent of age and remained between weeks 3 and 5 in patients age <60 years, age 60–70 years, and age >70 years, respectively. The existence of such a discrete cut-point suggested that patients who die early are qualitatively distinct and prompted us to examine the relative effect of age and other covariates in patients who (a) died within the first 30 days of treatment (our empirically-based definition of TRM, 9% of MDA and 12 % of SWOG patients, respectively) and (b) survived at least 30 days but did not enter complete remission or relapsed within 1 year (“resistant”, 43% of MDA and 59% of SWOG patients, respectively). A model including age alone to predict early mortality had an AUC of 0.67, while a model including performance status (PS) alone had an AUC of 0.72. By comparison, a more refined model hat included PS, age, platelet count, cytogenetics, secondary AML, albumin, white blood cell count, peripheral blood blast count, and LDH yielded an AUC of 0.86. Elimination of age resulted in a model with an AUC that was only minimally lower (0.85). Prediction of resistance was more difficult with a model including age, secondary AML, cytogenetics, peripheral blood blasts, race, hemoglobin, and marrow neutrophils giving an AUC of only 0.70. Elimination of age had little effect (AUC 0.67) while age alone gave an AUC of 0.64. Conclusion: Age alone appears inadequate in predicting resistance and particularly TRM. The use of models based on several covariates improves predictive ability, but the ability to predict resistance is still limited, suggesting the value of randomized trials to assess treatment designed to reduce resistance. The observation that elimination of age has little effect on the predictive ability of such models, suggests that age is primarily a surrogate for other covariates. Disclosures: No relevant conflicts of interest to declare.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 129-129 ◽  
Author(s):  
Megan Othus ◽  
Hagop M. Kantarjian ◽  
Stephen Petersdorf ◽  
Farhad Ravandi ◽  
Jorge E. Cortes ◽  
...  

Abstract Abstract 129 Background Recent emphasis has been placed on administration of induction regimens less intense than standard 3+7 for patients with newly diagnosed AML. A primary goal is to reduce the presumed average treatment related mortality (TRM) rate of 10% occurring within the first 28 days after start of 3+7 or higher intensity therapies; TRM rates have been > 30% in patients who are older and/or have poor performance status (PS]. (Walter et al. JCO 2012). This practice presupposes that TRM rates with higher intensity induction regimens are static, a notion seemingly difficult to reconcile with advances in supportive care (e.g. newer anti-aspergillosis drugs) that have sharply reduced rates of non-relapse mortality after allogeneic hematopoietic cell transplant (Gooley et al. NEJM 2010). Methods We thus addressed rates of TRM from 1991–2009 in 1,409 patients given 3+7 induction regimens on SWOG protocols (cytarabine dose 100 mg/m2 daily × 7) and 1,933 patients given induction regimens containing higher cytarabine doses (at least 1.0 g/m2 daily × 4–5 days) at MDA, variably combined with idarubicin, fludarabine or other agents. Multivariate analyses were used to account for confounding factors. Results TRM rates declined both in SWOG and at MDA. However this reduction must account for the declining ages of patient given 3+7 or more intense induction (p<0.001 in both SWOG and at MDA) and their improved PS (p<0.001 SWOG and MDA);the considerably younger nature of SWOG patients during 2006–2009 reflects the switch to less intense induction regimens for many older patients; such regimens were not included in this analysis. Additionally other covariates associated with TRM (more blood blasts, lower platelets, secondary AML) by Walter et al. (JCO 2012)were unevenly distributed in the various time periods(for example no secondary AML in SWOG 2006–2009). Multivariate logistic regression was thus performed to account for the effect of age, PS, and these other covariates in the reduction in TRM. After such accounting, odds ratios (ORs) for TRM at MDA were (relative to 1995) 0.89, 0.7, and 0.36 for 1996–2000,2001–2005, and 2006–2009 respectively with the null hypothesis of no change over time rejected at p = 0.006. For SWOG, not including secondary AML as a covariate ORs were (relative to 1991– 1995) 0.75,0.78, and 0.42 for 1996–2000,2001–2005,and 2006–2009 respectively; again the hypothesis of no change with time was rejected (p = 0.037). There were no interactions between reduced TRM and age, WBC or performance status suggesting the reduction in TRM was a general phenomenon. Conclusion There has been a reduction over time in TRM after “intensive” induction possibly due to better supportive care. Although various selection biases cannot be excluded, this decline is not due to younger age or better performance status and needs to be considered when choosing AML induction therapy. Disclosures: No relevant conflicts of interest to declare.


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