Abstract
Reference intervals are essential for the interpretation of laboratory test results in medicine. We propose a novel indirect approach to estimate reference intervals from real-world data as ...an alternative to direct methods, which require samples from healthy individuals. The presented
refineR
algorithm separates the non-pathological distribution from the pathological distribution of observed test results using an inverse approach and identifies the model that best explains the non-pathological distribution. To evaluate its performance, we simulated test results from six common laboratory analytes with a varying location and fraction of pathological test results. Estimated reference intervals were compared to the ground truth, an alternative indirect method (
kosmic
), and the direct method (N = 120 and N = 400 samples). Overall,
refineR
achieved the lowest mean percentage error of all methods (2.77%). Analyzing the amount of reference intervals within ± 1 total error deviation from the ground truth,
refineR
(82.5%) was inferior to the direct method with N = 400 samples (90.1%), but outperformed
kosmic
(70.8%) and the direct method with N = 120 (67.4%). Additionally, reference intervals estimated from pediatric data were comparable to published direct method studies. In conclusion, the
refineR
algorithm enables precise estimation of reference intervals from real-world data and represents a viable complement to the direct method.
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Pediatric laboratory test results must be interpreted in the context of interindividual variation and age- and sex-dependent dynamics. Reference intervals as presently defined for separate age groups ...can only approximate the age-related dynamics encountered in pediatrics. Continuous reference intervals from birth to adulthood are not available for most laboratory analytes because of the ethical and practical constraints of defining reference intervals using a population of healthy community children. We applied an indirect method to generate continuous reference intervals for 22 hematologic and biochemical analytes by analyzing clinical laboratory data from blood samples taken during clinical care of patients.
We included samples from 32 000 different inpatients and outpatients (167 000 samples per analyte) from a German pediatric tertiary care center. Measurements were performed on a Sysmex-XE 2100 and a Cobas Integra 800 during clinical care over a 6-year period. The distribution of samples considered normal was estimated with an established indirect statistical approach and used for the calculation of reference intervals.
We provide continuous reference intervals from birth to adulthood for 9 hematology analytes (hemoglobin, hematocrit, red cell indices, red cell count, red cell distribution width, white cell count, and platelet count) and 13 biochemical analytes (sodium, chloride, potassium, calcium, magnesium, phosphate, creatinine, aspartate transaminase, alanine transaminase, γ-glutamyltransferase, alkaline phosphatase, lactate dehydrogenase, and total protein).
Continuous reference intervals capture the population changes in laboratory analytes during pediatric development more accurately than age groups. After local validation, the reference intervals provided should allow a more precise consideration of these dynamics in clinical decision making.
Reference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric ...reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of "healthy" samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a single step. Estimated reference intervals from varying settings with simulated data demonstrate the ability to accurately estimate latent distributions from unlabeled data using different implementations of MDNs. Comparing the performance with alternative estimation approaches further highlights the importance of modeling the mixture component weights as a function of the input in order to avoid biased estimates for all other parameters and the resulting reference intervals. We also provide a strategy to generate partially customized starting weights to improve proper identification of the latent components. Finally, the application on real-world hemoglobin samples provides results in line with current gold standard approaches, but also suggests further investigations with respect to adequate regularization strategies in order to prevent overfitting the data. Mixture density networks provide a promising approach capable of extracting the distribution of healthy samples from unlabeled laboratory databases while simultaneously and explicitly estimating all parameters and component weights as non-linear functions of the covariate(s), thereby allowing the estimation of age-dependent reference intervals in a single step. Further studies on model regularization and asymmetric component distributions are warranted to consolidate our findings and expand the scope of applications.
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Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods ...for the estimation of reference limits focus either on modelling the age-dependent dynamics of different analytes directly in a prospective setting or the extraction of independent distributions from contaminated data sources, e.g. data with latent heterogeneity due to unlabeled pathologic cases. In this article, we propose a new method to estimate indirect reference limits with non-linear dependencies on covariates from contaminated datasets by combining the framework of mixture models and distributional regression.
Simulation results based on mixtures of Gaussian and gamma distributions suggest accurate approximation of the true quantiles that improves with increasing sample size and decreasing overlap between the mixture components. Due to the high flexibility of the framework, initialization of the algorithm requires careful considerations regarding appropriate starting weights. Estimated quantiles from the extracted distribution of healthy hemoglobin concentration in boys and girls provide clinically useful pediatric reference limits similar to solutions obtained using different approaches which require more samples and are computationally more expensive.
Latent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity.
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Appropriate reference intervals are essential when using laboratory test results to guide medical decisions. Conventional approaches for the establishment of reference intervals rely on large samples ...from healthy and homogenous reference populations. However, this approach is associated with substantial financial and logistic challenges, subject to ethical restrictions in children, and limited in older individuals due to the high prevalence of chronic morbidities and medication. We implemented an indirect method for reference interval estimation, which uses mixed physiological and abnormal test results from clinical information systems, to overcome these restrictions. The algorithm minimizes the difference between an estimated parametrical distribution and a truncated part of the observed distribution, specifically, the Kolmogorov-Smirnov-distance between a hypothetical Gaussian distribution and the observed distribution of test results after Box-Cox-transformation. Simulations of common laboratory tests with increasing proportions of abnormal test results show reliable reference interval estimations even in challenging simulation scenarios, when <20% test results are abnormal. Additionally, reference intervals generated using samples from a university hospital's laboratory information system, with a gradually increasing proportion of abnormal test results remained stable, even if samples from units with a substantial prevalence of pathologies were included. A high-performance open-source C++ implementation is available at https://gitlab.miracum.org/kosmic.
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Abstract
Reference intervals are essential for interpreting laboratory test results. Continuous reference intervals precisely capture physiological age-specific dynamics that occur throughout life, ...and thus have the potential to improve clinical decision-making. However, established approaches for estimating continuous reference intervals require samples from healthy individuals, and are therefore substantially restricted. Indirect methods operating on routine measurements enable the estimation of one-dimensional reference intervals, however, no automated approach exists that integrates the dependency on a continuous covariate like age. We propose an integrated pipeline for the fully automated estimation of continuous reference intervals expressed as a generalized additive model for location, scale and shape based on discrete model estimates using an indirect method (refineR). The results are free of subjective user-input, enable conversion of test results into z-scores and can be integrated into laboratory information systems. Comparison of our results to established and validated reference intervals from the CALIPER and PEDREF studies and manufacturers’ package inserts shows good agreement of reference limits, indicating that the proposed pipeline generates high-quality results. In conclusion, the developed pipeline enables the generation of high-precision percentile charts and continuous reference intervals. It represents the first parameter-less and fully automated solution for the indirect estimation of continuous reference intervals.
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Introduction
Neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and lymphocyte‐to‐monocyte ratio (LMR) are emerging biomarkers for systemic inflammation and have been shown to ...predict morbidity and mortality for several diseases. However, lack of pediatric reference intervals (RIs) prevents their comprehensive use in patient care and medical research.
Material and Methods
We calculated reference intervals and corresponding confidence intervals for NLR, PLR, and LMR from birth to 18 years using a data‐mining approach: We analyzed 232 746 blood counts from 60 685 patients performed during patient care and excluded patients with elevated C‐reactive protein and procalcitonin. Test results were separated according to age and sex, and the distribution of physiological ratios was estimated using an indirect approach (refineR). Additionally, we investigated the ratios’ diagnostic benefit for different inflammatory diseases (acute appendicitis, asthma, Bell's palsy, Henoch‐Schonlein purpura, and cystic fibrosis) using the newly obtained reference intervals.
Results
We estimated age‐ and sex‐specific reference intervals from birth to adulthood for NLR, PLR, and LMR. Analyses in pediatric inflammatory diseases showed that PLR and LMR were poor markers to detect the examined inflammatory diseases, while NLR was significantly increased in patients with appendicitis and asthma.
Conclusion
We provide pediatric reference intervals for NLR, PLR, and LMR to improve the interpretation of these biomarkers in children.
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In the treatment of childhood acute lymphoblastic leukemia (ALL), current protocols combine initial high-dose multiagent chemotherapy with prolonged oral therapy with 6-mercaptopurine (6MP) and ...low-dose methotrexate (MTX) maintenance therapy. Decades of research on ALL treatment have resulted in survival rates of approximately 90%. However, dose-response relationships vary widely between patients and insight into the influencing factors, that would allow for improved personalized treatment management, is insufficient. We use a detailed data set with measurements of thioguanine nucleotides and MTX in red blood cells and absolute neutrophil count (ANC) to develop pharmacokinetic models for 6MP and MTX, as well as a pharmacokinetic-pharmacodynamic (PKPD) model capable of predicting individual ANC levels and thus contributing to the development of personalized treatment strategies. Here, we show that integrating metabolite measurements in red blood cells into the full PKPD model improves results when less data is available, but that model predictions are comparable to those of a fixed pharmacokinetic model when data availability is not limited, providing further evidence of the quality of existing models. With this comprehensive model development leading to dynamics similar to simpler models, we validate the suitability of this model structure and provide a foundation for further exploration of maintenance therapy strategies through simulation and optimization.
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The cellular reconstitution after childhood cancer therapy is associated with the risk of infection and efficacy of revaccination. Many studies have described the reconstitution after stem cell ...transplantation (SCT). The recovery after cancer treatment in children who have not undergone SCT has mainly been investigated in acute lymphoblastic leukemia (ALL), less for solid tumors. Here, we have examined the temporal evolution of total leukocyte, neutrophil and lymphocyte counts as surrogate parameters for the post-therapeutic immune recovery in a cohort of n = 52 patients with ALL in comparison to n = 58 patients with Hodgkin's disease (HD) and n = 22 patients with Ewing sarcoma (ES). Patients with ALL showed an efficient increase in blood counts reaching the age-adjusted lower limits of normal between 4 and 5 months after the end of maintenance therapy. The two groups of patients with HD and ES exhibited a comparably delayed recovery of total leukocytes due to a protracted post-therapeutic lymphopenia which was most pronounced in patients with HD after irradiation. Overall, we observed a clearly more efficient resurgence of total lymphocyte counts in patients aged below 12 years compared to patients aged 12 to 18 years. Our results underline that the kinetics of cellular reconstitution after therapy for HD and ES differ significantly from ALL and depend on treatment regimens and modalities as well as on patient age. This suggests a need for disease, treatment, and age specific recommendations concerning the duration of infection prophylaxis and the timing of revaccination.
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Reference intervals for laboratory test results have to be appropriate for the population in which they are used to be clinically useful. While sex and age are established partitioning criteria, ...patients' origin also influences laboratory test results, but is not commonly considered when creating or applying reference intervals. In the German population, stratification for ethnicity is rarely performed, and no ethnicity-specific hematology reference intervals have been reported yet. In this retrospective study, we investigated whether specific reference intervals are warranted for the numerically largest group of non-German descent, individuals originating from Turkey. To this end, we analyzed 1,314,754 test results from 167,294 patients from six German centers. Using a name-based algorithm, 1.9% of patients were identified as originating from Turkey, in line with census data and the algorithm's sensitivity. Reference intervals and their confidence intervals were calculated using an indirect data mining approach, and Turkish and non-Turkish reference limits overlapped completely or partially in nearly all analytes, regardless of age and sex, and only 5/144 (3.5%) subgroups' reference limits showed no overlap. We therefore conclude that the current practice of using common reference intervals is appropriate and allows correct clinical decision-making in patients originating from Turkey.
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