Growth after hematopoietic stem cell transplantation in children with acute myeloid leukemia
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Previous studies have shown that hematopoietic stem cell transplantation (HSCT) may result in growth impairment. Disease type, differences in treatment before HSCT and treatment duration before HSCT can affect growth after HSCT and act as confounding variables. By contrast, acute myeloid leukemia (AML) patients receive HSCT during their first remission and are not treated by steroids during their relatively short period of induction therapy time. The purpose of this study was to evaluate 5 years growth after HSCT and to find factors influencing final adult height (FAH) in childhood AML patients.
Among 97 AML patients whom received HSCT in Seoul National University Hospital, we report 24 patients whose puberty began at least 3 years after HSCT and 19 patients who reached FAH without relapse. Medical records were retrospectively reviewed. Summary measure analysis was used to evaluate for 5 year growth after HSCT and to find statistical differences between factors. Univariate and multivariate regression analysis was performed to find factors influencing FAH.
Five years growth after HSCT: Patients received HSCT at 4.2 years of age. Six patients received radiotherapy (RT) and chronic GVHD (cGVHD) were in 4 patients. History of RT and cGVHD significantly impaired the first 5 years growth after HSCT. But cGVHD seems to influence to only the first 2 years growth after HSCT. Age at HSCT, gender and history of steroid use were not significantly affected 5 years growth after HSCT.
Final adult height after HSCT: Patients received HSCT at 10.1 years of age. Four patients received RT. In patients reached FAH without relapse after HSCT, only history of RT significantly reduced FAH. Age at HSCT, gender and history of steroid use were not significantly affected FAH.
Growth impairment after HSCT in AML patients might be occurred. But without RT history, growth impairment seems to be temporary and improve by catch-up growth. HSCT with conditioning regimen consists of only chemotherapy thought to be not to significantly decrease FAH. So the growth hormone treatment is seems to be not needed in non-RT patients. But in patients who received RT, catch-up growth will not be shown and eventually attain reduced FAH.Keywords:
Univariate analysis
Medical record
Few studies have investigated the association between severity of lymphopenia and clinical outcome during chemotherapy or hematopoietic stem cell transplantation (HSCT). We investigated this issue by retrospectively analyzing pediatric patients who received allogeneic-HSCT (allo-HSCT) using a newly developed parameter called the LD-index that combines both the duration and the intensity of lymphopenia. A total of 92 patients underwent allo-HSCT in our hospital from April 2007 to August 2019. The median age at HSCT was 10.3 years (range 0.4 – 28.1). The median LD-index was 9,285 (range 2,217 – 36,064). A significantly high association was observed between the LD-index and the incidence of chronic graft-versus-host disease (GvHD) (p = 0.0045). Analysis of predictive factors for chronic GvHD was carried out using univariate analysis. Lower LD-index, donor source and duration (days) of lymphopenia were found to be significant factors associated with chronic GvHD. Multivariate analysis, however, only identified an association between lower LD-index and increased incidence of chronic GvHD (p = 0.004). In conclusion, the duration and the intensity of lymphopenia after allo-HSCT have an effect on the development of chronic GvHD.
Univariate analysis
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Abstract This chapter begins with a discussion of definition and theoretical background of confounding. It then focuses on the quantification of potential confounding, evaluation of confounding, and integrated assessment of potential confounding.
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Effect modification
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Confounding may be present in nonrandomized etiological research involving human populations. It can result in erroneous conclusions about the effect of exposure on a disease outcome or about any form of causality between predictors and outcomes. Confounding can wholly or partially account for the apparent effect of the risk factor under consideration or mask the underlying, true association. Not controlling for the effects of confounding can lead to biased results, thus compromising the validity of study conclusions. The three goals of this article are: (1) to define a confounder or a confounding variable, (2) to discuss strategies for controlling the effects of confounding, and (3) to illustrate the perverse effects of confounding with the help of an example.
Causality
Etiology
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Abstract The first part of this chapter discusses the conditions under which a factor can confound the association between exposure and disease, and the conditions under which this cannot occur. It also differentiates confounders from antecedents or mediators. The next part discusses methods devised to neutralize the effects of confounders. Two standard methods are presented: matching to prevent confounding in the data by equalizing the exposed and the unexposed on a potential confounder, and statistical adjustment to compensate for confounding in the data by separating the effects of the exposure from the effects of the confounder.
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Abstract Confounding biases study results when the effect of the exposure on the outcome mixes with the effects of other risk and protective factors for the outcome that are present differentially by exposure status. However, not all differences between the exposed and unexposed group cause confounding. Thus, sources of confounding must be identified before they can be addressed. Confounding is absent in an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe. In an actual study, an observed unexposed population represents the unobserved parallel universe. Thinking about differences between this substitute population and the unexposed parallel universe helps identify sources of confounding. These differences can then be represented in a diagram that shows how risk and protective factors for the outcome are related to the exposure. Sources of confounding identified in the diagram should be addressed analytically and through study design. However, treating all factors that differ by exposure status as confounders without considering the structure of their relation to the exposure can introduce bias. For example, conditions affected by the exposure are not confounders. There are also special types of confounding, such as time‐varying confounding and unfixable confounding. It is important to evaluate carefully whether factors of interest contribute to confounding because bias can be introduced both by ignoring potential confounders and by adjusting for factors that are not confounders. The resulting bias can result in misleading conclusions about the effect of the exposure of interest on the outcome.
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Abstract Misclassification of exposure in epidemiologic investigations has been extensively studied and is now well understood. In contrast, misclassification of confounding factors has been much less investigated. First, we consider a situation with confounding by age, in which misclassification is introduced through stratification of this inherently continuous variable. This misclassification turns out to be benign: 75% of the original confounding is removed by stratification into two age classes and more than 90% by using three age classes. Second, we consider a situation with serious confounding and serious misclassification of the confounding factor but no misclassification of the exposure. In this situation, the misclassification turns out to be of importance. After stratification for the misclassified confounding factor, it appears as though the exposure has a stronger effect on the incidence than the confounder, which is the reverse of the true situation.
Stratification (seeds)
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