Indirect determination of serum creatinine reference intervals in a Pakistani pediatric population using big data analytics

Background: The indirect methods of reference intervals (RI) establishment based on data mining are utilized to overcome the ethical, practical challenges and the cost associated with the conventional direct approach.Aim: To generate RIs for serum creatinine in children and adolescents using an indirect statistical tool.Methods: Data mining of the laboratory information system was performed for serum creatinine analyzed from birth to 17 years for both genders. The timeline was set at six years from January 2013 to December 2018. Microsoft Excel 2010 and an indirect algorithm developed by the German Society of Clinical Chemistry and Laboratory Medicine\u27s Working Group on Guide Limits were used for the data analysis.Results: Data were extracted from 96104 samples and after excluding multiple samples for the same individual, we calculated RIs for 21920 males and 14846 females, with stratification into six discrete age groups.Conclusion: Serum creatinine dynamics varied significantly across gender and age groups

Reference Interval Estimation from Mixed Distributions using Truncation Points and the Kolmogorov-Smirnov Distance (kosmic)

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

KETOS: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services

Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR)

Establishment of reference intervals for alkaline phosphatase in Pakistani children using a data mining approach

Objective: To establish reference intervals (RIs) for alkaline phosphatase (ALP) levels in Pakistani children using an indirect data mining approach.Methods: ALP levels analyzed on a Siemens Advia 1800 analyzer using the International Federation of Clinical Chemistry\u27s photometric method for both inpatients and outpatients aged 1 to 17 years between January 2013 and December 2017, including patients from intensive care units and specialty units, were retrieved. RIs were calculated using a previously validated indirect algorithm developed by the German Society of Clinical Chemistry and Laboratory Medicine\u27s Working Group on Guide Limits.Results: From a total of 108,845 results, after the exclusion of patients with multiple specimens, RIs were calculated for 24,628 males and 18,083 females with stratification into fine-grained age groups. These RIs demonstrate the complex age- and sex-related ALP dynamics occurring during physiological development.Conclusion: The population-specific RIs serve to allow an accurate understanding of the fluctuations in analyte activity with increasing age and to support clinical decision making

A pipeline for the fully automated estimation of continuous reference intervals using real-world data

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

Critical comments to a recent EFLM recommendation for the review of reference intervals

In a recent EFLM recommendation on reference intervals by Henny et al., the direct approach for determining reference intervals was proposed as the only presently accepted gold standard. Some essential drawbacks of the direct approach were not sufficiently emphasized, such as unacceptably wide confidence limits due to the limited number of observations claimed and the practical usability for only a limited age range. Indirect procedures avoid these disadvantages of the direct approach. Furthermore, indirect approaches are well suited for reference limits with large variations during lifetime and for common reference limits

Blood counts in adult and elderly individuals: defining the norms over eight decades of life

The blood count is one of the most common tests used for health assessment. In elderly individuals, selection of a ‘healthy’ reference population for laboratory assessment is difficult due to the high prevalence of chronic morbidities, leading to uncertainty regarding appropriate reference intervals. In particular, age‐specific lower haemoglobin reference limits to define anaemia are controversial. Here, we applied a data mining approach to a large dataset of 3 029 904 clinical routine samples to establish blood count reference intervals. We excluded samples from units/specialists with a high proportion of abnormal blood counts, samples from patients with an unknown or decreased estimated glomerular filtration rate, and samples with abnormal test results in selected other analytes. After sample exclusion, 566 775–572 060 samples from different individuals aged 20–100 years were available for analysis. We then used an established statistical algorithm to determine the distribution of physiological test results and calculated age‐ and sex‐specific reference intervals. Our results show substantial trends with age in haematology analytes' reference intervals. Most notably, haemoglobin and red cell counts decline in men with advanced age, accompanied by increases in red cell volume in both sexes. These findings were confirmed in an independent dataset, and suggest an at least partly physiologic cause

Correction: KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.

[This corrects the article DOI: 10.1371/journal.pone.0223010.]