Skip to main content

Prevalence of symptoms in children with acute lymphoblastic leukaemia: a systematic review and meta-analysis

Abstract

Background

Children with acute lymphoblastic leukaemia (ALL) experience multiple symptoms that occur in complicated patterns and negatively affect patient outcomes. To date, no systematic review has been performed on the prevalence of symptoms in children with ALL.

Objective

The study aimed to report and analyse the prevalence of symptoms in children with ALL during treatment.

Methods

A systematic search was conducted in eight databases (PubMed, Ovid Embase, Web of Science, CINAHL, PsycINFO, China WanFang Database, China Science and Technology Journal Database, and China National Knowledge Infrastructure) for studies published between January 1, 2000, and August 12, 2023. The methodological quality of the included studies was evaluated and a meta-analysis was performed to pool the prevalence of symptoms.

Results

In total, 17 studies were included, from which 34 symptoms were identified. The symptom prevalence ranged between 1.5 and 91.0% and the most frequent symptoms observed were fatigue, lack of energy, dry mouth, lack of appetite, sweating, and feeling irritable, which occurred in at least 60% of the patients.

Conclusions

Symptoms remain highly prevalent in paediatric patients with ALL, which provides support for the need for symptom assessment in the clinical setting. Specific intervention is urgently needed to mitigate the symptoms in children with ALL and help them cope with the symptom burden.

Peer Review reports

Introduction

Acute lymphoblastic leukaemia (ALL) is the most common malignancy in children worldwide [1]. The overall age-standardised incidence rate of leukaemia is 48.4 per million person-years in children aged 0–14 years [2]. Due to the improvements in the treatment of paediatric ALL over the past several decades, the 5-year survival rate now exceeds 90% in most developed countries [3].

Previous studies have suggested that paediatric patients with ALL often experience various symptoms, such as lack of energy, sweating, lack of appetite, nausea, and vomiting [4, 5], which in turn affected the patient’s outcome and the quality of life (QoL) [6]. To optimise the QOL in children with ALL, a comprehensive symptom assessment is needed to achieve symptom control. However, evidence-based criteria on how often symptoms should be assessed and which symptoms should be prioritised for assessment among children with ALL remains unknown [7].

Several reviews on the multiple symptoms experienced by children with cancer have been published [8, 9]. Although inferences can be made from these studies, paediatric ALL might present a different set of symptoms. Indeed, the findings of the Children’s Oncology Group State of the Science Symposium on Symptom Distress (2018) suggested that the most commonly reported symptoms were inconsistent among different cancer types [10]. This suggests that identifying the symptom profile for specific diseases and treatment groups is important for developing targeted interventions and preventive guidance to minimise symptom-related distress. To date, no systematic review has been performed on the prevalence of symptoms in children with ALL. Therefore, a systematic review and meta-analysis were performed to identify and analyse the prevalence of the symptoms in children with ALL that has been reported in clinical settings.

Methods

Protocol and registration

This systematic review and meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines [11] and registered on PROSPERO (CRD42021269421) (https://www.crd.york.ac.uk/PROSPERO/).

Search strategies and data sources

A systematic electronic search was conducted across eight databases (PubMed, Ovid Embase, Web of Science, CINAHL, PsycINFO, China WanFang Database, China Science and Technology Journal Database, and China National Knowledge Infrastructure) between January 1, 2000, and August 12, 2023. The bibliographies of relevant reviews and articles were hand-searched for potential studies for inclusion. The search terms were developed using free and subject terms and were combined with the Boolean operator OR/AND. The keywords included ‘child’, ‘paediatric’, ‘leukaemia’, and ‘symptom’. The search was restricted to peer-reviewed journal articles published in English and Chinese. Appropriate methodological filters were used for specific databases, where applicable. Other types of grey literature (e.g., conference abstracts) were excluded owing to the lack of details on the study methodology or findings. All retrieval strategies are shown in Supplemental Table S1.

Eligibility criteria

The following inclusion criteria were used to select full-text articles: (1) quantitative design; (2) reporting on the prevalence of symptoms in children aged ≤ 18 years diagnosed with ALL; and (3) published in English or Chinese peer-reviewed journals. The minimum sample size was limited to 30 in observational studies to avoid selection bias from small studies. Studies with heterogeneous populations of patients with cancer were also included if the results for the patients with ALL were analysed separately. Studies with participants aged > 18 years were included if a separate analysis was performed for patients aged ≤ 18 years.

The exclusion criteria were: (1) the study design did not report empirical data (e.g. opinions, case reports, reviews, or editorials); (2) studies that focused on children receiving palliative care or consisting childhood cancer long-term survivors; (3) measuring symptoms using single symptom items drawn from the QoL or health status measures (e.g. Paediatric Quality of Life Inventory-4.0 Generic Core or Paediatric Quality of Life Inventory-Cancer Module; studies using such scoring systems were excluded because they focused on health-related QoL); and (4) measuring symptoms using an unvalidated scale.

Study selection

Citations were imported to EndNote X9 (www.myendnoteweb.com/), and duplicates were removed. The study selection process was carried out by two investigators. After duplicate studies were excluded, the investigators independently assessed records based on the titles, abstracts, and full texts. If there was any disagreement between researchers, a third researcher settled the issue.

Data extraction and synthesis

The data were collected from the included studies by two independent researchers using a standardised data sheet comprising the following items: authors, country of the study, publication year, study design, sample characteristics, and main findings. Any disagreement was resolved by a third author. The mean prevalence was computed by averaging the reported values across the available time points in longitudinal studies. When studies used different terminology to describe the same symptoms, the terms used by the original authors were retained to preserve the intended meaning. We synthesised the extracted data and presented the findings as narrative descriptions and descriptive statistics.

Quality appraisal

The data quality was critically appraised using the Joanna Briggs Institute (JBI) Meta-Analysis tool for cross-sectional,and case-control studies [12]. The revised JBI tool for cohort studies was used for longitudinal studies because items 1, 2, and 6 were not applicable. Two investigators independently performed bias assessments. Any discrepancies in judgement regarding the risk of bias were resolved by discussion with a third review author acting as an arbiter, if necessary.

Data analysis

We conducted meta-analysis when at least 2 studies reported comparable data measuring the same outcomes. The pooled frequency of symptoms was computed with weighted mean and standard errors, using a 95% confidence interval (CI) [13]. Heterogeneity among the studies was assessed using Cochran’s Q statistic, and heterogeneity was considered to be present when P < .05. The magnitude of heterogeneity was measured using I-square (I 2) statistic. I 2 values of 25%, 50%, and 75% were considered to indicate low, moderate, and high heterogeneity, respectively [14]. Random-effects model results were presented for data with high heterogeneity. Otherwise, fixed-effect model results were reported. A two-sided P < .05 indicated statistical significance. Other analyses (i.e., subgroup analysis, sensitivity analyses, and publication bias) were not performed because the included studies were insufficient for analysis. All statistical analyses were performed using Comprehensive Meta-Analysis version 3 (http//www.meta-analysis.com).

Results

Search results

The search yielded 7270 studies, of which 2588 were duplicates. After screening the titles and abstracts, 166 studies were included for full-text evaluation, and 17, [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31] met the inclusion criteria. Furthermore, 85 studies were identified via a reference search but none met the inclusion criteria. Details of the screening process are shown in the Fig. 1.

Fig. 1
figure 1

PRISMA 2020 flow diagram

Characteristics of studies

In total, 17 studies published between 2010 and 2023 were included, and their characteristics are summarised in Table 1. Of the 17 included studies, 5 were longitudinal studies, and 12 were cross-sectional studies. There were a total of 1719 participants from seven countries: China (n = 6), Canada (n = 4), the United States (n = 3), the Netherlands (n = 2), Indonesia (n = 1), Australia (n = 1), and Dutch (n = 1). The sample size ranged between 34 and 216, and most of the included studies (n = 9, 52.94%) recruited children with a wide age range (2–18 years), but three studies enrolled children aged < 9 years [15,16,17].

Table 1 Summary of the included studies

Of the included studies, eight focused on multiple symptoms and nine on a single symptom. Of the nine studies that focussed on a single symptom, four discussed fatigue [21, 23, 25, 27], followed by sleep disturbance (n = 3) [16, 20, 26], taste alteration (n = 1) [22], and feeling scared or worried (n = 1) [24]. The tools to measure these symptoms, including those measuring single symptoms, and multi-symptom inventories, are listed in Table 2. Fatigue is most often assessed by the PedsQLTMMFS (n = 6). The MSAS 10–18 was the most commonly used scale to measure multiple symptoms (n = 5).

Table 2 Instrument List (N = 10)

Three categories of symptom reporters existed: only children reporters (n = 5), children and parent reporters (n = 6), and only parent reporters (n = 6). In longitudinal studies, the length of follow-up time ranged from 7 days to 18 months. One study included 3 months follow-up after treatment completion [17]. Of the 17 studies, most explored symptom experiences during ALL therapy without distinguishing between treatment stages. Three studies focused on the maintenance chemotherapy period [16, 18, 20].

Methodological quality of included studies

The details of the evaluation process are presented in Supplemental Tables S2, S3 and S4. The lack of identification of confounding factors and strategies to deal with confounding factors (n = 5) and lack of strategies to address incomplete follow-up (n = 3) were the main reasons for the risk of bias.

Symptom prevalence

Thirty-four symptoms were identified across the seventeen studies. The symptom frequency ranged from 1.5% (urinary problems) to 91.0% (fatigue). A meta-analysis was performed (if available) to combine the symptom data from multiple studies (Table 3). Among the physical symptoms, the pooled prevalence for fatigue (6 studies; 796 patients) was 91.0% (95% CI 57.4–98.7%). The pooled prevalence for lack of energy (2 studies; 164 patients) was 79.7% (95% CI 17.4–98.7%). The pooled prevalence for dry mouth (1 studies; 89 patients) was 68.5% (95% CI 60.0–75.9%). The pooled prevalence for lack of appetite (2 studies; 164 patients) was 67.3% (95% CI 24.5–92.9%). And the pooled prevalence for sweating (2 studies; 164 patients) was 61.3% (95% CI 10.6–95.5%). Among the psychological symptoms, the pooled prevalence for feeling irritable (2 studies; 164 patients) was 60.0% (95% CI 36.9–79.4%). The pooled prevalence for feeling nervous (2 studies; 164 patients) was 51.7% (95% CI 23.5–78.9%). The pooled prevalence for worrying (3 studies; 451 patients) was 42.5% (95% CI 23.6–63.9%). And the pooled prevalence for feeling sad (2 studies; 164 patients) was 42.4% (95% CI 21.2–66.7%). Forest plots for the most common symptoms with a pooled prevalence estimate of more than 60% are shown in Fig. 2.

Table 3 Meta-analyses of symptom frequency (Pooled Estimates and Heterogeneity of Included Articles per Symptom)
Fig. 2
figure 2

Forest plot of the most common symptoms in childhood acute lymphoblastic leukaemia with a pooled prevalence estimate of ≥ 60%

Discussion

This systematic review and meta-analysis was the first to estimate the aggregate prevalence of symptoms during treatment in children with ALL. In children with ALL, 34 symptoms were identified. Symptoms remained highly prevalent in paediatric patients with ALL. The most prevalent symptoms were fatigue, lack of energy, dry mouth, lack of appetite, sweating, and feeling irritable, and these occurred in at least 60% of the children with ALL. We could not find additional meta-analysis studies on the incidence of symptoms during treatment in children with cancer; thus, comparisons could not be made. While compared to the systematic reviews of childhood cancer survivors, the prevalence rates of these symptoms were higher. Hong et al. reported a pooled prevalence of 39% for fatigue, 40% for dry mouth, 31% for lack of appetite, and 14% for sweating [32]. This result may be explained by the fact that some symptoms were resolved by treatment. It also suggested symptoms may persist for months to years after the completion of treatment. However, due to the patients analyzed in their study with different cancer diagnoses, we cannot draw definitive conclusions. Further research should focus on examining more homogeneous patient groups, which is a strength of the present review.

The current systematic review indicates that although children with ALL experienced multiple persistent adverse symptoms during therapy, they received disproportionate attention. Similar to previous research [32], the most studied physical symptoms were fatigue and sleep disturbance, and their biological mechanism [33,34,35], influence factor [19], and trajectories [36] had been explored. However, little is known about the biological mechanisms and management strategies of dry mouth, lack of appetite, and sweating, yet the prevalence of some of these symptoms was greater than the more commonly assessed symptoms. Similarly, psychological symptoms, including irritability (60.0%), worrying (42.5%), depression (27.6%), and anxiety (24.8%) have not received sufficient attention. The limited understanding of these symptoms may also partly explain the lack of effective management strategies to address them. Given the negative impact of symptom burden on the QOL, increased attention needs to be paid to these symptoms.

Since various tools with preselected lists of symptoms were used to collect data, symptoms not included in the lists were not measured. The symptoms identified in this study do not represent the complete symptom experience of children with ALL. Moreover, the MSAS 10–18 was the most commonly used scale to measure multiple symptoms. Although MSAS 10–18 has proven to be reliable and valid [37], it was originally developed for adults and might miss some essential dimensions of the symptom experiences in children. Therefore, concept elicitation interview [38] with ALL is recommended in future studies to enable researchers to develop an age-appropriate and accurately representative tool for symptom assessment.

Limitations

This review had some limitations. First, only studies published in English or Chinese were included, causing a potential language bias. Second, inconsistencies existed in the assessment and reporting of symptoms across the included studies. Such inconsistencies and gaps led to variability between the studies. Computing the pooled mean frequency for several symptoms from disparate and incompatible data for conducting a meta-analysis is challenging. The evidence in this review is, therefore, weak, and the exact prevalence of symptoms in children with ALL during treatment remains to be determined.

Clinical application

Results of this systematic review show that despite the development of new guidelines for symptom assessment and management [39, 40], the prevalence of symptoms is still high during ALL therapy. That might probably be because, in the past decades, most paediatric oncology research has focused on improving the cure rates, which led to the remarkable increase of the 5-year survival rate to 90% [3]. However, efforts to manage symptoms in children with cancer have not kept pace with new advances in the cure for childhood cancer. Nurses have a critical role in symptom assessment and management. Specific intervention is urgently needed to mitigate the symptoms in children with ALL and help them cope with the symptom burden.

Conclusion

Our review provides a comprehensive overview of the existing literature with respect to the symptoms that children with ALL experience during treatment. Future research needs to explore interventions to improve the symptom burden, especially for symptoms that receive less attention at present, to minimize the symptom distress and improve the QoL of children.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. GBD 2017 Childhood Cancer Collaborators. The global burden of childhood and adolescent cancer in 2017: an analysis of the global burden of Disease Study 2017. Lancet Oncol. 2019;20(9):1211–25.

    Article  Google Scholar 

  2. Steliarova-Foucher E, Colombet M, Ries LAG, et al. International incidence of childhood cancer, 2001-10: a population-based registry study. Lancet Oncol. 2017;18(6):719–31.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bonaventure A, Harewood R, Stiller CA, et al. Worldwide comparison of survival from childhood Leukaemia for 1995–2009, by subtype, age, and sex (CONCORD-2): a population-based study of individual data for 89 828 children from 198 registries in 53 countries. Lancet Haematol. 2017;4(5):e202-217.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Schetelig J, Baldauf H, Koster L, et al. Haplotype motif-based models for KIR-Genotype informed selection of hematopoietic cell donors fail to predict outcome of patients with myelodysplastic syndromes or secondary Acute Myeloid Leukemia. Front Immunol. 2020;11:584520.

    Article  CAS  PubMed  Google Scholar 

  5. Tremolada M, Taverna L, Chiavetta IT, Bonichini S, Putti MC, Biffi A. Psychological wellbeing in adolescents with Leukaemia: a comparative study with typical development peers. Int J Environ Res Public Health. 2020;17(2): 567.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rodgers CC, Hooke MC, Taylor OA, et al. Childhood Cancer Symptom Cluster: Leukemia and Health-Related Quality of Life. Oncol Nurs Forum. 2019;46(2):228–37.

    PubMed  PubMed Central  Google Scholar 

  7. Withycombe JS, Haugen M, Zupanec S, Macpherson CF, Landier W. Consensus recommendations from the children’s Oncology Group nursing Discipline’s state of the Science Symposium: Symptom Assessment during Childhood Cancer Treatment. J Pediatr Oncol Nurs. 2019;36(4):294–9.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Erickson JM, Macpherson CF, Ameringer S, Baggott C, Linder L, Stegenga K. Symptoms and symptom clusters in adolescents receiving cancer treatment: a review of the literature. Int J Nurs Stud. 2013;50(6):847–69.

    Article  PubMed  Google Scholar 

  9. Cheng L, Liu F, Feng S, Wang Y, Gu Y, Kang Q. Symptom experience of children with Cancer younger than eight years of age: an integrative review. J Pain Symptom Manage. 2019;58(1):157–66.

    Article  PubMed  Google Scholar 

  10. Skeens MA, Cullen P, Stanek J, Hockenberry M. Perspectives of Childhood Cancer Symptom-related distress: results of the state of the Science Survey. J Pediatr Oncol Nurs. 2019;36(4):287–93.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Moola SMZ, Tufanaru C, Aromataris E et al. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z, eds. JBI Manual for Evidence Synthesis. JBI. 2020. https://synthesismanual.jbi.global.

  13. George BJ, Aban IB. An application of meta-analysis based on DerSimonian and Laird method. J Nucl Cardiol. 2016;23(4):690–2.

    Article  PubMed  Google Scholar 

  14. Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11(2):193–206.

    Article  PubMed  Google Scholar 

  15. Steur LMH, Grootenhuis MA, Van Someren EJW, et al. High prevalence of parent-reported sleep problems in pediatric patients with acute lymphoblastic Leukemia after induction therapy. Pediatr Blood Cancer. 2020;67(4): e28165.

    Article  PubMed  Google Scholar 

  16. McCarthy MC, Bastiani J, Williams LK. Are parenting behaviors associated with child sleep problems during treatment for acute lymphoblastic Leukemia? Cancer Med. 2016;5(7):1473–80.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kunin-Batson AS, Lu X, Balsamo L, et al. Prevalence and predictors of anxiety and depression after completion of chemotherapy for childhood acute lymphoblastic Leukemia: a prospective longitudinal study. Cancer. 2016;122(10):1608–17.

    Article  PubMed  Google Scholar 

  18. Zupanec S, Jones H, Stremler R. Sleep habits and fatigue of children receiving maintenance chemotherapy for ALL and their parents. J Pediatr Oncol Nurs. 2010;27(4):217–28.

    Article  PubMed  Google Scholar 

  19. Ren W, Shi C, Zhao X, et al. Fatigue of children with acute lymphoblastic Leukemia during chemotherapy and influencing factors (Chinese version). Chin J Mod Nurs. 2017;23(07):922–5.

    Google Scholar 

  20. Daniel LC, Walsh CM, Meltzer LJ, Barakat LP, Kloss JD. The relationship between child and caregiver sleep in acute lymphoblastic Leukemia maintenance. Support Care Cancer. 2018;26(4):1123–32.

    Article  PubMed  Google Scholar 

  21. Fadhilah A, Allenidekania A. The relationship between activity level and fatigue in Indonesian children with Acute lymphocytic Leukemia in the home setting. Compr Child Adolesc Nurs. 2019;42(sup1):47–55.

    Article  PubMed  Google Scholar 

  22. Loves R, Tomlinson D, Baggott C, et al. Taste changes in children with cancer and hematopoietic stem cell transplant recipients. Support Care Cancer. 2019;27(6):2247–54.

    Article  PubMed  Google Scholar 

  23. Tomlinson D, Baggott C, Dix D, et al. Severely bothersome fatigue in children and adolescents with cancer and hematopoietic stem cell transplant recipients. Support Care Cancer. 2019;27(7):2665–71.

    Article  PubMed  Google Scholar 

  24. Hyslop S, Tomlinson D, Baggott C, et al. Feeling scared or worried self-report in children receiving cancer treatments using the Symptom Screening in Pediatrics Tool (SSPedi). Support Care Cancer. 2021;29(6):3137–44.

    Article  PubMed  Google Scholar 

  25. Zhou H, Zhang C, shen X, Zheng X, Wu Y, Ying J. Cancer-related fatigue status and influencing factors in children with acute lymphoblastic Leukemia (Chinese version). Chin Nurs Manage. 2021;21(04):508–11.

    Google Scholar 

  26. Xi L, Wu G, Du X. Analyzing sleep status in children with acute Leukemia. Ital J Pediatr. 2023;49(1):7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Irestorm E, Steur LMH, Kaspers GJL, et al. Fatigue trajectories during pediatric ALL therapy are associated with fatigue after treatment: a national longitudinal cohort study. Support Care Cancer. 2022;31(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  28. BU X, You L, Liu K, YE Q, et al. Study of fatigue and related factors among children with acute lymphoblastic leukemia receiving chemotherapy in hospital(Chinese version). Chin J Pract Nurs. 2015;31(13):973–6.

  29. Hockenberry MJ, Taylor OA, Pasvogel A, Rodgers C, McCarthy K, Gundy P, Montgomery DW, Ribbeck P, Scheurer ME, Moore IM. The influence of oxidative stress on symptom occurrence, severity, and distress during childhood leukemia treatment. Oncol Nurs Forum. 2014;41(4):E238–47. https://doi.org/10.1188/14.ONF.E238-E247.

  30. MA F, GE Y, SHI Y, et al. Analysis of correlation between fatigue and discomfort symptom of in children with acute lymphoblastic leukemia and its influencing factors(Chinese version). Today Nurse. 2019;26(6):25–8.

  31. Li R, Ma J, Chan Y, Yan Q. Symptom clusters among children with acute lymphocytic leukemia during chemotherapy: a longitudinal study (Chinese version). J Nurs Sci. 2019,34(16):5–9. https://doi.org/10.3870/j.issn.1001-4152.2019.16.005.

  32. Hong HC, Min A, Kim YM. A systematic review and pooled prevalence of symptoms among childhood and adolescent and young adult cancer survivors. J Clin Nurs. 2023;32(9–10):1768–94.

    Article  PubMed  Google Scholar 

  33. Hockenberry MJ, Moore IMK, Scheurer ME, et al. Influence of nitrosative stress on fatigue during Childhood Leukemia Treatment. Biol Res Nurs. 2018;20(4):403–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Vallance K, Liu W, Mandrell BN, et al. Mechanisms of dexamethasone-induced disturbed sleep and fatigue in paediatric patients receiving treatment for ALL. Eur J Cancer. 2010;46(10):1848–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Vallance K, Yang J, Li J, Crabtree VM, Hinds PS, Mandrell BN. Disturbed sleep in pediatric patients with Leukemia: the potential role of interleukin-6 (-174GC) and Tumor necrosis factor (-308GA) polymorphism. Oncol Nurs Forum. 2011;38(5):E365-372.

    Article  PubMed  Google Scholar 

  36. Hockenberry MJ, Hooke MC, Rodgers C, et al. Symptom trajectories in children receiving treatment for Leukemia: a latent class growth analysis with Multitrajectory modeling. J Pain Symptom Manage. 2017;54(1):1–8.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Collins JJ, Byrnes ME, Dunkel IJ, et al. The measurement of symptoms in children with cancer. J Pain Symptom Manage. 2000;19(5):363–77.

    Article  CAS  PubMed  Google Scholar 

  38. Weaver MS, Reeve BB, Baker JN, et al. Concept-elicitation phase for the development of the pediatric patient-reported outcome version of the common terminology criteria for adverse events. Cancer. 2016;122(1):141–8.

    Article  PubMed  Google Scholar 

  39. Loeffen EAH, Kremer LCM, Mulder RL, et al. The importance of evidence-based supportive care practice guidelines in childhood cancer-a plea for their development and implementation. Support Care Cancer. 2017;25(4):1121–5.

    Article  CAS  PubMed  Google Scholar 

  40. Linder LA, Ameringer S, Stegenga K, Macpherson CF, Erickson J. A person-centered Approach to Symptom Assessment and Management for Children and adolescents receiving Cancer Treatment. Semin Oncol Nurs. 2021;37(3): 151164.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Wenkui Xu for his contribution to the meta-analysis.

Funding

This study was supported by the Science and Technology Innovation Joint Fund Project of Fujian Province (Grant No. 2019Y9029). The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the funder. The funders peer-reviewed and approved the project but had no role in the design or conduct of the study, analysis or interpretation of the data, or in the preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

Xiaoyan Lan: Conceptualization, Data extraction, Study selection, Study risk of bias assessment, Formal analysis, Writing-Original draft preparation. Junjun Wu: Conceptualization, Data extraction, Formal analysis, Writing-Original draft preparation. Zhenling Liao: Study selection, Study risk of bias assessment. Yong Wu: Supervision, Writing-Reviewing, and Editing.Rong Hu: Supervision, Funding acquisition, Writing-Reviewing, and Editing.All authors approved the final manuscript and have participated sufficiently in the work to take public responsibility for appropriate portions of the content.

Corresponding authors

Correspondence to Yong Wu or Rong Hu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplemental Table S1

. Search strategy. Supplemental Table S2. Quality assessment of the included cross-sectional studies. Supplemental Table S3. Quality assessment of the included case-control study. Supplemental Table S4. Quality assessment of the included longitudinal study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lan, X., Wu, J., Liao, Z. et al. Prevalence of symptoms in children with acute lymphoblastic leukaemia: a systematic review and meta-analysis. BMC Cancer 23, 1113 (2023). https://doi.org/10.1186/s12885-023-11581-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-023-11581-z

Keywords