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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.

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.

Background 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. Results 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. Conclusions 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.

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 .

Background 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. Results 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. Conclusions 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.

Natriuretic regulation of extracellular fluid volume homeostasis includes suppression of the renin-angiotensin-aldosterone system, pressure natriuresis, and reduced renal nerve activity, actions that concomitantly increase urinary Na+ excretion and lead to increased urine volume. The resulting natriuresis-driven diuretic water loss is assumed to control the extracellular volume. Here, we have demonstrated that urine concentration, and therefore regulation of water conservation, is an important control system for urine formation and extracellular volume homeostasis in mice and humans across various levels of salt intake. We observed that the renal concentration mechanism couples natriuresis with correspondent renal water reabsorption, limits natriuretic osmotic diuresis, and results in concurrent extracellular volume conservation and concentration of salt excreted into urine. This water-conserving mechanism of dietary salt excretion relies on urea transporter-driven urea recycling by the kidneys and on urea production by liver and skeletal muscle. The energy-intense nature of hepatic and extrahepatic urea osmolyte production for renal water conservation requires reprioritization of energy and substrate metabolism in liver and skeletal muscle, resulting in hepatic ketogenesis and glucocorticoid-driven muscle catabolism, which are prevented by increasing food intake. This natriuretic-ureotelic, water-conserving principle relies on metabolism-driven extracellular volume control and is regulated by concerted liver, muscle, and renal actions.

Arginase 1 (Arg1), which converts l-arginine into ornithine and urea, exerts pleiotropic immunoregulatory effects. However, the function of Arg1 in inflammatory bowel disease (IBD) remains poorly characterized. Here, we found that Arg1 expression correlated with the degree of inflammation in intestinal tissues from IBD patients. In mice, Arg1 was upregulated in an IL-4/IL-13- and intestinal microbiota-dependent manner. Tie2-Cre Arg1fl/fl mice lacking Arg1 in hematopoietic and endothelial cells recovered faster from colitis than Arg1-expressing (Arg1fl/fl) littermates. This correlated with decreased vessel density, compositional changes in intestinal microbiota, diminished infiltration by myeloid cells, and an accumulation of intraluminal polyamines that promote epithelial healing. The proresolving effect of Arg1 deletion was reduced by an l-arginine-free diet, but rescued by simultaneous deletion of other l-arginine-metabolizing enzymes, such as Arg2 or Nos2, demonstrating that protection from colitis requires l-arginine. Fecal microbiota transfers from Tie2-Cre Arg1fl/fl mice into WT recipients ameliorated intestinal inflammation, while transfers from WT littermates into Arg1-deficient mice prevented an advanced recovery from colitis. Thus, an increased availability of l-arginine as well as altered intestinal microbiota and metabolic products accounts for the accelerated resolution from colitis in the absence of Arg1. Consequently, l-arginine metabolism may serve as a target for clinical intervention in IBD patients.

The idea that increasing salt intake increases drinking and urine volume is widely accepted. We tested the hypothesis that an increase in salt intake of 6 g/d would change fluid balance in men living under ultra-long-term controlled conditions. Over the course of 2 separate space flight simulation studies of 105 and 205 days' duration, we exposed 10 healthy men to 3 salt intake levels (12, 9, or 6 g/d). All other nutrients were maintained constant. We studied the effect of salt-driven changes in mineralocorticoid and glucocorticoid urinary excretion on day-to-day osmolyte and water balance. A 6-g/d increase in salt intake increased urine osmolyte excretion, but reduced free-water clearance, indicating endogenous free water accrual by urine concentration. The resulting endogenous water surplus reduced fluid intake at the 12-g/d salt intake level. Across all 3 levels of salt intake, half-weekly and weekly rhythmical mineralocorticoid release promoted free water reabsorption via the renal concentration mechanism. Mineralocorticoid-coupled increases in free water reabsorption were counterbalanced by rhythmical glucocorticoid release, with excretion of endogenous osmolyte and water surplus by relative urine dilution. A 6-g/d increase in salt intake decreased the level of rhythmical mineralocorticoid release and elevated rhythmical glucocorticoid release. The projected effect of salt-driven hormone rhythm modulation corresponded well with the measured decrease in water intake and an increase in urine volume with surplus osmolyte excretion. Humans regulate osmolyte and water balance by rhythmical mineralocorticoid and glucocorticoid release, endogenous accrual of surplus body water, and precise surplus excretion. Federal Ministry for Economics and Technology/DLR; the Interdisciplinary Centre for Clinical Research; the NIH; the American Heart Association (AHA); the Renal Research Institute; and the TOYOBO Biotechnology Foundation. Food products were donated by APETITO, Coppenrath und Wiese, ENERVIT, HIPP, Katadyn, Kellogg, Molda, and Unilever.

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.

Reliable automated blood cell characterization and quantification remain challenging in pathologic samples, whereas slide reviews due to unnecessary flagging should be avoided. We compared 4 modern hematology analyzers—Abbott Sapphire, Siemens Advia 120, Sysmex XE-2100, and Beckman Coulter DxH 800—regarding complete blood cell count (CBC), leukocyte differential count, and flagging efficacy in a total of 202 samples from hematology patients and normal controls. Manual differential count was used as reference. The analyzers exhibited very good correlation for CBC parameters. Neutrophils and eosinophils also showed very good correlations, whereas lymphocytes and monocytes correlated fairly. The Advia 120 displayed notably lower measurements for both parameters, which is attributable to classification of some events as large unstained cells. Basophil counts were unreliable with all analyzers. Flagging for blasts and immature granulocytes showed moderate sensitivity and specificity. Operators must not rely on blast flagging alone to detect leukemic samples with any analyzer.