University of Groningen Perfect pitstops Loeffen, Erik

Perfect pitstops

Loeffen, Erik

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Loeffen, E. (2019). Perfect pitstops: Towards evidence-based supportive care in children with cancer.

Rijksuniversiteit Groningen.

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TREATMENT-RELATED MORTALITY IN

CHILDREN WITH CANCER: PREVALENCE

AND RISK-FACTORS

CHAPTER 11

11.1 ABSTRACT

PURPOSE

Intensive treatment regimens have contributed to a marked increase in childhood cancer survival rates. Death due to treatment-related adverse effects becomes an increasingly important area to further improve overall survival. In this study we aimed to examine five-year survival in a cohort of children with cancer, and to identify risk factors for treatment-related mortality (TRM).

PATIENTS AND METHODS

All children (aged <18 years at diagnosis) diagnosed with cancer in two Dutch university hospitals between 2003 and 2012 were included, survival status was determined, and causes of death were analyzed. TRM was defined as death occurring in the absence of progressive cancer. Various demographic and treatment factors were evaluated, for which a multivariate competing risks analysis was performed.

RESULTS

1,764 patients were included; overall five-year survival was 78.6%. Of all deaths, 81 (21.4%) were treatment-related with infection being responsible for more than half of these deaths. Forty percent of TRM deaths occurred in the first three months after initial diagnosis. Factors associated with TRM were diagnosis of a hematological malignancy, age at diagnosis <1 year, and receipt of allogeneic hematopoietic stem cell transplantation. In children suffering from hematological malignancies, TRM accounted for 58.6% of deaths.

CONCLUSION

Over one in five deaths in children with cancer death was related to treatment, mostly due to infection. In children suffering from a hematological malignancy more children died due to their treatment than due to progression of their disease. To further increase overall survival, clinical as well as a research focus should be put on lowering TRM rates. The findings presented in this study might help identifying areas for improvement.

11.2 INTRODUCTION

Cure rates of children with cancer have increased greatly in the past decades, largely due to more intensive, multimodal treatment regimens.[1] These treatment regimens, mostly encompassing chemotherapy, radiotherapy and/or surgery, are however associated with several adverse effects, such as pain, febrile neutropenia, and nausea. These adverse effects diminish quality of life and can have serious treatment implications, such as delay or reduction of treatment. In addition, a portion of children with cancer dies as a result of these intensive treatments. As the cure rates keep improving and fewer children die of cancer, death due to treatment-related adverse effects, so-called treatment-related mortality (TRM), becomes an increasingly important area to further improve overall survival.[2]

Various specific causes for TRM exist. A well-known cause is febrile neutropenia, with reported rates of one in 40 children with acute lymphoblastic leukemia (ALL) that die due to infection.[3] But the list of other (less frequently occurring) causes of TRM is long, with amongst others hemorrhage, graft-versus-host disease, tumor lysis syndrome, and encephalopathy.[4] The area of supportive care focuses on identifying these adverse effects as early as possible and treating (or even preventing) them as effectively as possible. Defining when death is caused by treatment and when it is caused by cancer (progressive disease death; PD) is not as straightforward as it might seem. One might say, all deaths are due to cancer as the child would not have had cancer treatment without the disease. In addition, specific situations can be interpreted in multiple ways. For example, chemotherapy-related severe venous occlusive disease is a direct consequence of treatment, but occurs more frequently in specific situations related to the disease (e.g. high tumor load). In addition, there are causes that do not seem to fit either category (TRM or PD), for instance an accident.

This complexity was acknowledged by International Pediatric Oncology Mortality Classification Group (IPOMCG), and in 2015 this group introduced a consensus-based definition of TRM: death occurring in the absence of progressive cancer.[5,6] In addition a cause-of-death attribution system was introduced, in which death is classified as treatment-related or due to progressive disease (PD). This classification system was piloted in a Canadian population and proved to have good reliability and validity, and was later validated in a childhood cancer cohort in the United Kingdom.[7] The classification system was subsequently used in two Canadian studies focusing on differences between TRM and PD, and on identifying univariate risk factors for TRM.[8,9]

In this study we aimed to examine causes of death in a Dutch cohort of children with cancer, and to explore known and novel risk factors for TRM in a multivariate manner. We also aimed to describe the specific causes of TRM, overall and in the initial phase of treatment (first three months).

11.3 METHODS

This study was conducted in the University Medical Center Groningen (UMCG) and the Academic Medical Center (AMC) Amsterdam, the Netherlands. All children (aged <18 years at diagnosis) diagnosed with cancer between January 1st _{2003 and December 31}st

2012, and primarily treated at the UMCG or AMC were eligible for inclusion. CAUSES OF DEATH

TRM was defined in accordance with the aforementioned IPOMCG definition: death occurring in the absence of progressive cancer.[6] Using the IPOMCG cause of death attribution system, a probable or possible cause of death for TRM was chosen from 11 possible categories, such as infection, metabolic or external causes. In addition, we assigned the relevant ICD-10 codes for cause of death.[10] We also registered which patients died in the first three months after initial diagnosis, to describe causes for early TRM.

RISK FACTORS

Several factors were evaluated to determine their potential association with TRM: sex, diagnosis, age at diagnosis, nutritional status at diagnosis, hematopoietic stem cell transplantation (HSCT, including type), relapse, treatment era, and travel time to nearest shared care hospital (travel times were only available for patients of the UMCG). Age at diagnosis was categorized in four groups: infant (<1 year), toddler/preschooler (1-5 years), child (5-12 years), or adolescent (12-18 years). Nutritional status was assessed using BMI z-score at diagnosis, which is an age and sex adjusted standard deviation score of BMI. BMI z-scores were calculated using a purpose-build BMI z-score module of the Netherlands Organisation for Applied Scientific Research (TNO), with “The Netherlands 2010, BMI for age” serving as reference. Cutoff values for undernourished and overnourished were set at −2.0 and 2.0, respectively.

DATA COLLECTION

A local data manager provided lists with eligible patients and relevant outcomes from the local childhood cancer registries. The individual electronic patient records were hand searched for missing and additional data (e.g. length and weight at diagnosis). In March 2018, for all patients the survival status was verified in the Dutch population register, to check correctness of our critical outcome (survival status).

Data regarding cause of death was extracted from the electronic patient records using a data extraction form that underwent a two-phase pilot. For the first pilot, that focused on usability and consistency, 20 patients were randomly selected for which two researchers (JB, EL) independently extracted data. Inter-rater reliability (IRR) had to be >90% to continue to the second pilot, if this was not the case the pilot was repeated with 20 other random patients. The second pilot served to evaluate if the extracted data was sufficient to unambiguously determine TRM or PD, and main cause of death. For this purpose, in each center 20 patients were randomly selected and the data extraction form was completed by one researcher (JB), who also served as the first rater to designate cause of death. Subsequently a second independent rater (AMC patients: RK, UMCG patients: EL) designated the cause of death using the completed form. The form was finalized when the IRR for cause of death was ≥95% (if below, the pilot was repeated with 40 other random patients).

Further data extraction was performed by one researcher (JB). After data extraction was completed, two independent researchers (RK, EL) classified the cause of death of all patients based upon the information in the data extraction form. These results were compared and all discrepancies were discussed in detail and resolved by consensus. If consensus was not reached a third reviewer (WT) would have been consulted.

STATISTICAL ANALYSIS

Survival was defined as time from diagnosis till death, patients who were still alive five years after diagnosis were censored. As TRM and PD are competing risks (i.e. when one has occurred, the other cannot occur anymore), a competing risks analysis was necessary. In this analysis the person experiencing the competing event (in this study PD) remains in the risk set for the event of interest (in this study TRM). The Fine and Gray proportional hazards model was used for these analyses, yielding subhazard ratios (SHRs) and 95% confidence intervals (CIs).[11] These analyses where performed univariate and multivariate, in the latter only variables with a significant association with TRM in univariate analysis were included. For categorical variables the group in which the TRM

was expected to be the lowest was chosen as reference group. After competing risks analyses, cumulative incidence functions (CIFs) were plotted to visualize findings.

The significance level of all tests was determined at p<0.05. Statistical analyses were performed using Stata Statistical Software: Release 15 (StataCorp LLC, College Station, TX, USA) and R version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria).[12] SENSITIVITY ANALYSIS

In case of missing data concerning cause of death, we planned to perform two-sided extreme scenario testing. This means running all competitive risks analyses again twice, first with the cases with an unknown cause of death assigned as TRM, second with these cases assigned as PD. Results were compared to the original findings. if other variables had missing data, we ran the analyses again with the missing data imputed using multiple imputation (number of imputations dependent upon percentage of missing data according to Graham et al. with the lowest threshold (<1%) for tolerated power falloff).[13] ETHICAL APPROVAL

This study was approved by the Medical Ethical Committee of the UMCG. As no patients were being subjected to actions or have been imposed to rules of conduct, it was not obligatory to seek informed consent.

Table 11.1. Patient characteristics Total

(n=1,764) Hematological (n=659) (n=717)Solid (n=388)Brain

n % n % n % n %

Sex

Female 792 44.9 275 41.7 332 46.3 185 47.7

Male 972 55.1 384 58.3 385 53.7 203 52.3

Age at diagnosis (years)

median (interquartile range)

7.1 (3.1-12.6) 7.7 (3.7-13.0) 6.2 (2.0-12.6) 7.5 (3.8-11.8) Age at diagnosis (categories)

Under 1 year 156 8.8 28 4.2 103 14.4 25 6.4

1 to 5 years 523 29.6 205 3.1 213 29.7 105 27.1

5 to 12 years 588 33.3 225 34.1 199 27.8 164 42.3

12 to 18 years 497 28.2 201 30.5 202 28.2 94 24.2

BMI-z score at diagnosis

Between -2 and 2 1,051 84.7 488 86.4 422 86.1 141 75.8 Under -2 96 7.7 36 6.4 36 7.3 24 12.9 Above 2 94 7.6 41 7.3 32 6.5 21 11.3 Relapse Yes 434 24.9 132 20.2 177 25.2 125 32.4 No 1,307 75.1 422 79.8 524 74.8 261 67.7 HSCT Allogeneic 105 6.0 102 15.6 3 0.4 0 0.0 Autologous 108 6.2 19 2.9 65 9.3 24 6.2 No 1,530 87.8 534 81.5 634 90.3 362 93.8 Deceased Yes 378 21.4 103 15.6 154 21.5 121 31.2 No 1,386 78.6 556 84.4 563 78.5 267 68.8

Classification cause of death

PD 286 16.2 41 6.2 134 18.7 111 28.6

TRM 81 4.6 58 8.8 15 2.1 8 2.1

Unknown/unclassifiable 11 0.6 4 0.6 5 0.7 2 0.5

11.4 RESULTS

PATIENT CHARACTERISTICS

A total of 1,764 children diagnosed with cancer were included, with a median age at diagnosis of 7.1 years (interquartile range 3.1 to 12.6 years). In total, 378 children (21.4%) died within five years of diagnosis, with a median survival of 364 days (interquartile range 171 to 642 days). All patient characteristics are shown in Table 11.1.

Figure 11.1. Five-year survival status curves, displaying occurrence of treatment-related mortality (TRM) and death due to progressive disease (PD) within a) all diagnoses combined, b) children with a hematological malignancy, c) children with a solid tumor, and d) children with a brain tumor.

CAUSES OF DEATH

For both phases of the extraction pilot, one round was sufficient to reach the IRR cut-off (see Supplemental material 11/S1 for final data extraction form). In our cohort, three in every four deaths were due to PD (n=286, 75.7%). TRM was the cause of death in 81 children (21.4%), corresponding to a five-year cumulative incidence of TRM of 4.59%

no information in patient record) or not classifiable (n=1, cause fit neither category). Within diagnosis groups the distribution of causes of deaths differed (see Figure 11.1). In children with a solid tumor or brain tumor the major cause of death was PD, accounting for 89.9% and 93.3% of deaths, respectively. In children with a hematological malignancy however, TRM was the major cause of death with 58 out of 103 deaths (56.3%) due to TRM, and 41 (39.8%) due to PD.

The predominant cause of TRM was infection (n=43, 53.1%), accounting for more than half of TRM deaths. Other frequently occurring causes were central nervous system (CNS) related causes (n=15, 18.5%) and immunomediated (n=8, 9.9%). A large portion of deaths due to TRM occurred in the first three months after initial diagnosis (n=32, 39.5% of TRM), of which nearly half (n=15, 46.9%) were due to infection. In fact, a subgroup analysis including only patients who did not relapse and did not receive a HSCT, showed that nearly two out of three (65.2%) deaths due to infection occurred in the first three months after initial diagnosis. The majority (n=13, 86.7%) of these early infection deaths occurred in children with a hematological malignancy (see Table 11.2), with the associated pathogen being bacteria in six cases and candida or aspergillus in seven cases. See Supplemental material 11/S2 for an overview of the assigned ICD-10 codes, per TRM subgroup.

COMPETITIVE RISKS ANALYSIS

In univariate competitive risks analyses variables significantly associated with occurrence of TRM were diagnosis, age at diagnosis, and HSCT status (see Table 11.3, for CIFs see Figure 11.2). In a subsequent multivariate analysis including these variables the following factors remained significantly associated with TRM: diagnosis of hematological malignancy (SHR 4.22, 95% CI 2.31-7.70, p<.001), age at diagnosis <1 year (SHR 2.83, 95% CI 1.38-5.79, p=0.004), and receipt of allogeneic HSCT (SHR 2.57, 95% CI 1.50-4.40, p=0.001). See Figure 11.3 for a graphical representation of the analysis.

A.

B.

Figure 11.2. Cumulative incidence functions (CIF) of treatment-related mortality in the presence of competing risks (death due to progressive disease) stratified by a) type of malignancy, and b) age at diagnosis.

Table 11.2. Causes of treatment-related mortality, attributed according to the classification system by Alexander et al.[6] Numbers are presented for the complete follow-up period (first five years after initial diagnosis) and for the first three months after initial diagnosis.

Total

(n=1,764) Hematological(n=659) (n=717)Solid (n=388)Brain

n % n % n % n %

Total number of TRM cases

During complete follow-up 81 100 58 100 15 100 8 100

In first 3 months 32 39,5 22 37,9 6 40,0 4 50,0

Infection

During complete follow-up 43 53,1 30 51,7 6 40,0 2 25,0

In first 3 months 15 18,5 13 22,4 1 6,7 1 12,5

Haemorrhage

During complete follow-up 6 7,4 5 8,6 1 6,7 0 0,0

In first 3 months 4 4,9 3 5,2 1 6,7 0 0,0

Cardiac System

During complete follow-up 3 3,7 2 3,4 1 6,7 0 0,0

In first 3 months 0 0,0 0 0,0 0 0,0 0 0,0

Immunomediated

During complete follow-up 8 11,1 8 13,8 0 0,0 0 0,0

In first 3 months 0 0,0 0 0,0 0 0,0 0 0,0

CNS-related

During complete follow-up 15 17,3 6 10,3 3 20,0 6 75,0

In first 3 months 9 11,1 4 6,9 2 13,3 3 37,5

Respiratory System

During complete follow-up 5 9,9 4 6,9 1 6,7 0 0,0

In first 3 months 2 2,5 1 1,7 1 6,7 0 0,0

Gastrointestinal System

During complete follow-up 1 1,2 1 1,7 0 0,0 0 0,0

In first 3 months 0 0,0 0 0,0 0 0,0 0 0,0

External Causes

During complete follow-up 2 2,5 0 0,0 2 13,3 0 0,0

In first 3 months 0 0,0 0 0,0 0 0,0 0 0,0

Classification not possible

During complete follow-up 3 3,7 2 3,4 1 6,7 0 0,0

In first 3 months 2 2,5 1 1,7 1 6,7 0 0,0

Table 11.3. Results of univariate and multivariate competing risks regression analyses (Fine and Gray proportional subhazards model), TRM is event of interest, PD is competing event.

Survival status (5 years after initial diagnosis) Alive (n=1,386) TRM (n=81) PD (n=286) Univariate n % n % n % SHR‡ _{95% CI}‡ _p‡ Sex Female 634 80.3 39 4.9 117 14.8 1 Male 752 78.1 42 4.4 169 17.5 0.88 0.57-1.35 .547 Diagnosis Solid tumor 563 79.1 15 2.1 134 18.8 1 Hematological tumor 556 84.9 58 8.9 41 6.3 4.33 2.45-7.64 <.001 Brain tumor 267 69.2 8 2.1 111 28.8 0.99 0.42-2.33 .976 Age at diagnosis < 1 yrs 116 74.4 27 17.3 13 8.3 2.83 1.38-5.79 .004 1 - 5 yrs 412 79.4 85 16.4 22 4.2 1.39 0.75-2.59 .301 5 - 12 yrs 473 80.7 95 16.2 18 3.1 1.86 1.03-3.37 .039 12 - 18 yrs 385 78.3 79 16.1 28 5.7 HSCT No 1,246 81.8 58 3.8 219 14.4 1 Yes, allogeneic 65 63.7 19 18.6 18 17.6 4.86 2.95-8.03 <.001 Yes, autologous 54 50.5 4 3.7 49 45.8 0.97 0.35-2.68 .957 Relapse No 1,231 87.8 61 4.4 110 7.8 1 Yes 133 40.4 20 6.1 176 53.5 1.36 0.83-2.25 .224 BMI z-score at diagnosis

-2.0 – 2.0 875 62.8 41 2.9 478 34.3 1 < -2.0 63 64.3 5 5.1 30 30.6 1.37 0.54-3.47 .501 > 2.0 35 81.4 3 7.0 5 11.6 1.92 0.59-6.28 .279 Treatment era Jan 2003 - Dec 2007 652 79.9 42 5.1 122 15.0 1 Jan 2008 - Dec 2012 734 78.3 39 4.2 164 17.5 0.81 0.52-1.25 .335 Travel time shared care

hospital <15 minutes 139 69.2 18 9.0 44 21.9 1 ≥15 minutes 455 80.8 27 4.8 81 14.4 0.67 0.37-1.22 .186 50.5 4 3.7 49 45.8 1.37 0.50-3.74 .540

Table 11.3. Continued

Survival status (5 years after initial diagnosis) Alive (n=1,386) TRM (n=81) PD (n=286) Multivariate n % n % n % SHR† _{95% CI}† _p† Diagnosis Solid tumor 563 79.1 15 2.1 134 18.8 1 Hematological tumor 556 84.9 58 8.9 41 6.3 4.22 2.31-7.70 <.001 Brain tumor 267 69.2 8 2.1 111 28.8 1.18 0.49-2.83 .711 Age at diagnosis < 1 yrs 116 74.4 27 17.3 13 8.3 4.31 2.09-8.87 <.001 1 - 5 yrs 412 79.4 85 16.4 22 4.2 1.47 0.79-2.74 .228 5 - 12 yrs 473 80.7 95 16.2 18 3.1 1 12 - 18 yrs 385 78.3 79 16.1 28 5.7 1.79 1.00-3.20 .051 HSCT No 1,246 81.8 58 3.8 219 14.4 1 Yes, allogeneic 65 63.7 19 18.6 18 17.6 2.57 1.50-4.40 .001 Yes, autologous 54 50,5 4 3.7 49 45.8 1.37 0.50-3.74 .540 * 11 patients who died were either classified as unknown (n=10) or not classifiable (n=1)

‡ Univariate competing risks regression analyses (Fine and Gray proportional subhazards model) † Multivariate competing risks regression analyses (Fine and Gray proportional subhazards model) including diagnosis, age at diagnosis, and HSCT.

1. 3. Ri sk t ab le d ep ic tin g t he F in e & G ra y s ub d is tr ib ut io n h az ar d s m o d el . A s in g le s ilh ou et te d ep ic ts 1 0 c hi ld re n. I n a c om p et iti ve r is k a na ly si s, p le w ho h av e s uf fe re d t he c om p et in g e ve nt ( in t hi s c as e P D ) r em ai n i n t he r is k s et ( w hi te s ilh ou et te s) . B la ck s ilh ou et te s d ep ic t c hi ld re n s til l a liv e, ou et te s d ep ic t c hi ld re n w ho h av e d ie d d ue t o T RM , a nd y el lo w s ilh ou et te s d ep ic t c hi ld re n w ho h av e d ie d d ue t o P D .

11.5 DISCUSSION

In our cohort of 1,764 children who were diagnosed with cancer, overall five-year survival was 78.6%. Over one in five deaths (n=81, 21.4%) were treatment-related, and thus one in every 22 children treated for cancer died due to their treatment. In children with a hematological malignancy the majority of deaths (56.3%) was due to TRM. Notably, 40% of all TRM deaths occurred in the first three months after initial diagnosis. Factors that were related to TRM in a multivariate competing risks model were: diagnosis of hematological malignancy, age at diagnosis <1 year, and receipt of allogeneic HSCT. More than half of TRM deaths were due to infection. Particular attention should be paid to early infections in children with a hematological malignancy, as this accounted for over 40% of TRM in the first three months after initial diagnosis. With a complete follow-up for our critical outcome (survival status), the use of a clear definition of TRM, the detailed description of designated causes of death for TRM, and the use of multivariate competing risks analyses, this study provides a new insight in the occurrence and etiology of TRM. Previous studies exploring TRM often suffered from the lack of a uniform definition of TRM, and generally focused on identifying risk factors for TRM in a single type of malignancy.[14,15]

Regarding prevalence of TRM, our findings are in line with previous studies that report on TRM rates. For instance, in a heterogeneous childhood cancer sample five-year cumulative incidence of TRM was 3.9% (in our study 4.6%), with higher numbers being reported in studies that focused specifically on hematological malignancies (e.g. 8% in children with acute myeloid leukemia).[9,16] Little is known regarding the distribution of causes of TRM, although one study that focused specifically on infection related mortality in children with ALL also found this to be the predominant cause of TRM deaths.[3] In the latter study, infection related mortality occurred mainly during induction treatment, which is in line with our findings regarding early infectious deaths.

During the present study, we identified an important limitation of the classification system as proposed by the IPOMCG. Children who die due to a medical condition unrelated to their cancer (e.g. hereditary kidney disease) do not fit any of the categories. In addition, we believe classifying children who die due to accidents or homicide as TRM (none in our cohort) is questionable. For all the aforementioned cases the addition of a “unknown / unclassifiable” category would be valuable. In competing risks analyses these could either be treated as added competitive event or have their influence explored using two-sided

system could too benefit from a ‘Classification not possible” category, e.g. for patients who die in the absence of progressive disease, but for whom no information is available to designate a subgroup classification.

To our knowledge this is the first study to combine the use of the IPOMCG definition for TRM with a multivariate competing risks model to explore risk factors for TRM in a heterogeneous childhood cancer population. In a study including 896 Austrian ALL patients, age <1 years was also associated with higher rates of TRM.[17] However, in this study female gender was also associated with higher rates of TRM, while in our study it was not. One other study used the IPOMCG definition and explored risk factors for TRM in a heterogeneous childhood cancer population, in Canada.[9] Although this study provided important insights, multivariate analyses were lacking. Identified univariate risk factors for TRM in this study were leukemia/lymphoma diagnosis, age <1 years, metastatic disease, diagnosis before January 1st _{2008 (data collection was also from}

2003 to 2012), HSCT, and relapse. Importantly, survival status in this study was checked for on December 31st _{2012, thus some patients would still be in treatment. In our study}

we collected data after December 31st _{2017, so all patients had a complete follow-up of}

at least five years. This difference might explain why in the Canadian study ‘diagnosis before January 1st _{2008’ was associated with a significantly higher rate of TRM while it}

was not in our study.

Although the use of a pilot-tested, standardized data extraction form in a dual independent manner increased the rigor of our findings, we did have some missing data as is often the case in retrospective studies. In our study this was very limited, except for nutritional status at diagnosis (missing data for almost 30% of patients). In addition, for some patients who had died, no information about cause of death was available in the electronic patient records. We chose not to exclude patients with missing data as this might lead to a biased sample. Instead, we performed multiple sensitivity analyses to explore the influence this had on our findings and found no differences in conclusions between the sensitivity analyses and the main analyses.

More studies to evaluate causes of death and risk factors for TRM in children with cancer are needed. In these studies data should be collected in a prospective, standardized (and ideally automated) manner using the electronic patient records, as this would both increase completeness and accuracy, and decrease workload. Also, it would be worthwhile to collect more detailed information about treatment and supportive care received, e.g. prophylaxis for infections. This study also has implications for clinical care, most notably the focus on early infectious complications in children with hematological

be given to infants, as these appear to be more susceptible to TRM. These findings, and the notion that with increasing cure rates the portion of children that die due to TRM might continue to grow. further emphasize the importance of seeking the right balance between desirable and undesirable consequences of treatment. Naturally one cannot simply decrease treatment intensity to lower TRM, as this on its turn might increase deaths due to progressive disease. Therefore clinical as well as a research focus should be put on improving supportive care and identifying treatment modifications that decrease TRM rates without affecting anti-cancer efficacy.

11.6 CONCLUSION

We found that 4.6% of all children with cancer in our cohort died due to TRM, with 40% of these deaths occurring in the first three months after diagnosis. Overall TRM accounted for one in five deaths. In children with a hematological malignancy, more children died due to TRM than due to progressive disease. Infection was the major cause of TRM, both overall and in the first three months after diagnosis. Clinical and research effort should be focused on lowering TRM rates by improving supportive care and lowering treatment intensity without compromising efficacy.

ACKNOWLEDGEMENTS

The project “Towards evidence-based guidelines for supportive care in childhood oncology” is supported by the Alpe d’HuZes foundation / Dutch Cancer Society (RUG 2013-6345). We thank Nynke Zwart and Ellen Kilsdonk for providing the lists with eligible patients.

SUPPLEMENTAL MATERIALS

The following supplemental materials are available, 11/S2 is included below, 11/S1 is available upon request:

11/S1 Final Data Extraction Form (translated) (2 pages)

11/S2 Codes of the ICD-10 used in coding TRM cases, including definitions (1 page)

11.7 REFERENCES

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[3] O’Connor D, Bate J, Wade R. Infection-related mortality in children with acute lymphoblastic leukemia: a retrospective analysis of infectious deaths on UKALL 2003. Blood. 2014;124(7):1056–62.

[4] Christensen MS, Heyman M, Möttönen M, Zeller B, Jonmundsson G, Hasle H. Treatment-related death in childhood acute lymphoblastic leukaemia in the Nordic countries: 1992-2001. Br J Haematol. 2005;131(1):50–8.

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[6] Alexander S, Pole JD, Gibson P, Lee M, Hesser T, Chi SN, et al. Classification of treatment-related mortality in children with cancer: A systematic assessment. Lancet Oncol. 2015;16(16):e604-610.

[7] Hassan H, Rompola M, Glaser AW, Kinsey SE, Phillips RS. Validation of a classification system for treatment-related mortality in children with cancer. BMJ Paediatr Open. 2017;1(1):e000082.

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[9] Gibson P, Pole JD, Lazor T, Johnston D, Portwine C, Silva M, et al. Treatment-related mortality in newly diagnosed pediatric cancer: a population-based analysis. Cancer Med. 2018;7(3):707–15.

[10] World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Geneva; 2016.

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[12] Cleves M, Gould WW, Marhcenko Y V. An Introduction to Survival Analysis Using Stata. Revised Th. Stata Press; 2016. 386-408 p.

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11.9 SUPPLEMENTAL MATERIALS

Codes of the ICD-10 used in coding TRM cases, including definitions.

TRM subgroup ICD code Definition

Infection A40.0 Sepsis due to streptococcus, group A A41.5 Sepsis due to other Gram-negative organisms A41.8 Other specified sepsis

A41.9 Sepsis, unspecified B37.7 Candidal sepsis

B44.0 Invasive pulmonary aspergillosis B44.8 Other forms of aspergillosis B44.9 Aspergillosis, unspecified B48.7 Opportunistic mycoses

B96.1 Klebsiella pneumoniae [K. pneumoniae] as the cause of diseases classified to other chapters (linked to: K63.1 - Perforation of intestine (nontraumatic))

B97.0 Adenovirus as the cause of diseases classified to other chapters (linked to: T86.0 - Bone-marrow transplant rejection)

Haemorrhage K92.2 Gastrointestinal haemorrhage, unspecified

R04.9 Haemorrhage from respiratory passages, unspecified Cardiac System I42.0 Dilated cardiomyopathy

I46.9 Cardiac arrest, unspecified Immunomediated T86.0 Bone-marrow transplant rejection Neurological (CNS) G93.6 Cerebral oedema

I61.9 Intracerebral haemorrhage, intraventricular I61.9 Intracerebral haemorrhage, unspecified

I62.9 Intracranial haemorrhage (nontraumatic), unspecified I63.9 Cerebral infarction, unspecified

Respiratory System J96.9 Respiratory failure, unspecified Gastrointestinal

System

K72.9 Hepatic failure, unspecified

External Causes T88.9 Complication of surgical and medical care, unspecified Classification not

possible

R96.1 Death occurring less than 24 hours from onset of symptoms, not otherwise explained