Required steps for the validation of a Laboratory Information Management System

Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism

Objectives

Mass spectrometry-based steroidomics combined with machine learning (ML) provides a potentially powerful approach in endocrine diagnostics, but is hampered by limitations in the conveyance of results and interpretations to clinicians. We address this shortcoming by integration of the two technologies with a laboratory information management systems (LIMS) model.

Methods

The approach involves integration of ML algorithm-derived models with commercially available mathematical programming software and a web-based LIMS prototype. To illustrate clinical utility, the process was applied to plasma steroidomics data from 22 patients tested for primary aldosteronism (PA).

Results

Once mass spectrometry data are uploaded into the system, automated processes enable generation of interpretations of steroid profiles from ML models. Generated reports include plasma concentrations of steroids in relation to age- and sex-specific reference intervals along with results of ML models and narrative interpretations that cover probabilities of PA. If PA is predicted, reports include probabilities of unilateral disease and mutations of KCNJ5 known to be associated with successful outcomes of adrenalectomy. Preliminary results, with no overlap in probabilities of disease among four patients with and 18 without PA and correct classification of all four patients with unilateral PA including three of four with KCNJ5 mutations, illustrate potential utility of the approach to guide diagnosis and subtyping of patients with PA.

Conclusions

The outlined process for integrating plasma steroidomics data and ML with LIMS may facilitate improved diagnostic-decision-making when based on higher-dimensional data otherwise difficult to interpret. The approach is relevant to other diagnostic applications involving ML.

LIMS and Clinical Data Management

In order to achieve more accurate disease prevention, diagnosis, and treatment, clinical and genetic data need extensive and systematically associated study. As one way to achieve precision medicine, a laboratory information management system (LIMS) can effectively associate clinical data in a macrocosmic aspect and genomic data in a microcosmic aspect. This chapter summarizes the application of the LIMS in a clinical data management and implementation mode. It also discusses the principles of a LIMS in clinical data management, as well as the opportunities and challenges in the context of medical informatics.

Validation of a laboratory and hospital information system in a medical laboratory accredited according to ISO 15189

Introduction

The aim of the study was to present a protocol for laboratory information system (LIS) and hospital information system (HIS) validation at the Institute of Clinical Chemistry and Laboratory Medicine of the Merkur University Hospital, Zagreb, Croatia.

Materials and methods:

Validity of data traceability was checked by entering all test requests for virtual patient into HIS/LIS and printing corresponding barcoded labels that provided laboratory analyzers with the information on requested tests. The original printouts of the test results from laboratory analyzer(s) were compared with the data obtained from LIS and entered into the provided template. Transfer of data from LIS to HIS was examined by requesting all tests in HIS and creating real data in a finding generated in LIS. Data obtained from LIS and HIS were entered into a corresponding template. The main outcome measure was the accuracy of transfer obtained from laboratory analyzers and results transferred from LIS and HIS expressed as percentage (%).

Results:

The accuracy of data transfer from laboratory analyzers to LIS was 99.5% and of that from LIS to HIS 100%.

Conclusion:

We presented our established validation protocol for laboratory information system and demonstrated that a system meets its intended purpose.

Automation, parallelism, and robotics for proteomics

The speed of the human genome project (Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C. et al., Nature 2001, 409, 860-921) was made possible, in part, by developments in automation of sequencing technologies. Before these technologies, sequencing was a laborious, expensive, and personnel-intensive task. Similarly, automation and robotics are changing the field of proteomics today. Proteomics is defined as the effort to understand and characterize proteins in the categories of structure, function and interaction (Englbrecht, C. C., Facius, A., Comb. Chem. High Throughput Screen. 2005, 8, 705-715). As such, this field nicely lends itself to automation technologies since these methods often require large economies of scale in order to achieve cost and time-saving benefits. This article describes some of the technologies and methods being applied in proteomics in order to facilitate automation within the field as well as in linking proteomics-based information with other related research areas.

A Flexible Data Analysis Tool for Chemical Genetic Screens

High-throughput assays generate immense quantities of data that require sophisticated data analysis tools. We have created a freely available software tool, SLIMS (Small Laboratory Information Management System), for chemical genetics which facilitates the collection and analysis of large-scale chemical screening data. Compound structures, physical locations, and raw data can be loaded into SLIMS. Raw data from high-throughput assays are normalized using flexible analysis protocols, and systematic spatial errors are automatically identified and corrected. Various computational analyses are performed on tested compounds, and dilution-series data are processed using standard or user-defined algorithms. Finally, published literature associated with active compounds is automatically retrieved from Medline and processed to yield potential mechanisms of actions. SLIMS provides a framework for analyzing high-throughput assay data both as a laboratory information management system and as a platform for experimental analysis.