The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands

Analytical performance specifications for the measurement uncertainty of 24,25-dihydroxyvitamin D examinations

OBJECTIVES

The exploration of the metabolites in the degradation pathways of vitamin D (VTD) has gained importance in recent years and simultaneous quantitation of twenty-five-hydroxy vitamin D (25(OH)D) mass concentration together with 24,25-dihydroxyvitamin D (24,25(OH)2D) has been proposed as a newer approach to define VTD deficiency. Yet, no data are available on 24,25(OH)2D biological variation (BV). In this study, we evaluated 24,25(OH)2D's BV on the European Biological Variation Study (EuBIVAS) cohort samples to determine if analytical performance specifications (APS) for 24,25(OH)2D could be generated.

METHODS

Six European laboratories recruited 91 healthy participants. 25(OH)D and 24,25(OH)2D concentrations in K3-EDTA plasma were examined weekly for up to 10 weeks in duplicate with a validated LC-MS/MS method. The Vitamin D Metabolite Ratio (24,25(OH)2D divided by 25(OH)D × 100) was also calculated at each time point.

RESULTS

Linear regression of the mean 24,25(OH)2D concentrations at each blood collection showed participants were not in steady state. Variations of 24,25(OH)2D over time were significantly positively associated with the slopes of 25(OH)D concentrations over time and the concentration of 25(OH)D of the participant at inclusion, and negatively associated with body mass index (BMI), but not with age, gender, or location of the participant. The variation of the 24,25(OH)2D concentration in participants over a 10 weeks period was 34.6%. Methods that would detect a significant change linked to the natural production of 24,25(OH)2D over this period at p<0.05 would need a relative measurement uncertainty (u%)<14.9% while at p<0.01, relative measurement uncertainty should be <10.5%.

CONCLUSIONS

We have defined for the first time APS for 24,25(OH)2D examinations. According to the growing interest in this metabolite, several laboratories and manufacturers might aim to develop specific methods for its determination. The results presented in this paper are thus necessary prerequisites for the validation of such methods.

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

BACKGROUND

Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis.

METHODS

Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients' clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared.

RESULTS

In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively.

CONCLUSIONS

Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.

Biological variation: recent development and future challenges

Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.

A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories

OBJECTIVES

The Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills and technological predisposition in clinical laboratories.

METHODS

A questionnaire, focussing on digitization, information technology (IT) infrastructures, data accessibility, and BAI projects underway was sent to 1,351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets.

RESULTS

The 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%-including Laboratory Directors-cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient's health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the Laboratory Information System, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgements, indicating a growing interest.

CONCLUSIONS

The questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to all-around work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training.

How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.