What we work on.

Alex and Janne helped defining the Joint EFLM–COLABIOCLI Recommendation for venous blood collection — from patient identification over tube order, mixing, labelling to phlebotomist-patient communication.
Published open-access in Clinical Chemistry and Laboratory Medicine (CCLM) in 2018, it has since been adopted across Europe and Latin America and translated into multiple languages, making it one of the most widely used preanalytical guidelines worldwide.

Janne helped also dessigning a complete open-access teaching kit, hosted by the EFLM Committee on the Preanalytical Phase: https://www.eflm.eu/site/who-we-are/divisions/science-division/fu/c-preanalytical-phase

EFLM also runs a dedicated e-learning course for laboratory and clinical staff: https://www.eflm-elearning.eu/site/documents/Leaflet-Course_venuous-blood-sampling.pdf

Until 2020, stability claims for laboratory analytes were hard to compare across studies: protocols varied, statistics varied, and quality grading was largely subjective. The CRESS trilogy gives the field a single common framework.

Part I (CCLM 2020) — the CRESS Checklist for reporting stability studies.
Part II (CCLM 2023) — recommendations for the design of stability studies on clinical specimens.
Part III (CCLM 2024) — how to evaluate the quality of stability studies.

Together, these three EFLM C-PRE consensus papers define what a usable stability study should look like, from study design through reporting through quality grading. They are the methodological foundation for the DGKL/EFLM Sample Stability Database.

The EFLM Task Force on Preparation of Labs for Emergencies (TF-PLE) investigates how clinical laboratories can stay operational under disruption — pandemics, supply-chain failures, cyber-attacks and natural disasters. Each output is grounded in a survey of European labs followed by a consensus recommendation paper.

First wave (2023): a baseline survey of the Task Force's scope and an evaluation of how COVID-19 disrupted European laboratory operations.

Second wave (2024): a dedicated cybersecurity survey followed by the TF-PLE recommendations for reinforcing cyber-security and managing cyber-attacks in medical laboratories.

More publications are in the pipeline as the Task Force works through the remaining emergency scenarios.

The Stability Calculator turns pre/post-analytical stability studies from ad-hoc Excel sheets into a structured, multi-user web application. Researchers enter subjects, materials, storage conditions, time points and replicate measurements; the calculator runs the standard descriptive statistics (mean, SD, CV, % deviation), evaluates regression models, and derives the stability limit against CVmax = CVa·3 (or alternative criteria).

It is the natural companion to the DGKL/EFLM Sample Stability Database: the idea is that studies completed in the calculator can be exported directly into the DGKL_Stability_Data_Template.xlsx format, ready for ingestion into the central DGKL MedLab database. CRESS quality rating is captured at study setup, so each exported study is database-ready by design.

The CRESS trilogy (Cornes et al. 2020 / 2023 / 2024) gave the lab-medicine community a common framework for designing, reporting and grading preanalytical stability studies. Step 4 of the EFLM Committee on the Preanalytical Phase, of which Alex is the current and Janne the past chair, turns that framework into infrastructure: a central database where every published sample stability study is captured in a structured way with its CRESS quality rating, searchable by analyte, matrix and storage condition.

The LabMed Alliance contributes the database-structure and design as well as the data-extraction pipeline. The latter is done by a custom AI-agent/skill that reads stability publications and writes them into the DGKL/EFLM Excel template, ready for ingestion by the DGKL MedLab Database.

Goal: a single source of truth for "how long is this analyte stable, under which conditions, and how solid is the evidence?" — accessible to laboratorians and to AI-driven decision support alike.

Under the lead of Alex, as the current chair of the German DGKL section on the preanalytical phase, a guideline on venous blood collection was issued and still is the current guideline on this topic.
Peripheral venous blood collection is the single most common preanalytical procedure in laboratory medicine, and a leading source of erroneous results when done inconsistently. This DGKL Standard-Arbeitsanleitung (Standard Operating Procedure) sets out a step-by-step national consensus for German-speaking laboratories — covering responsibilities, test selection, patient preparation and identification, the draw itself, and sample transport.

It is the German-language (Germany, Austria and Switzerland) counterpart to the Joint EFLM–COLABIOCLI Recommendation: same evidence base, formatted as a directly usable SOP for clinical and laboratory staff in DACH institutions.

Vincent, as the newly appointed chair of the International Federation of Clinical Chemistry (IFCC) Working Group on Laboratory Errors and Patient Safety (WG-LEPS), following atfer Prof. Mario Plebani, issued a harmonised core panel of essential Quality indicators (QIs), designed for global adoption.
QIs are the only practical way to monitor — and compare — preanalytical, analytical and postanalytical performance across laboratories. The challenge has always been agreement: which indicators are essential, how should they be measured, and how can they be adopted in settings ranging from low-resource hospital labs to highly automated reference centres? The intent of this list of QIs is to make quality benchmarking comparable across countries and institutions, regardless of laboratory size or context.

Janne is an expert/consultant to the EFLM committee on Digitization and Artificial Intelligence. In this function, he helped defining reporting standards for AI and machine-learning studies in laboratory, which has different constraints than other medical data — analyte stability, preanalytical variation, reference intervals, traceability, IVDR — that the general frameworks do not handle well. This EFLM checklist closes the gap.

The checklist enhances existing frameworks (TRIPOD-AI, CONSORT-AI, STARD-AI, CLAIM, …) with laboratory-specific items, so studies become comparable across institutions, reproducible at the analytical level, and clinically usable downstream.

Aimed at researchers, journal editors, peer-reviewers and regulators evaluating AI/ML work in laboratory medicine.

Newborns and small children can lose a clinically significant share of their total blood volume to laboratory testing alone (iatrogenic anemia). The Sample Volume Calculator, designed by Janne, turns the minimum-volume calculation into a single, reproducible step: select the requested tests, enter the patient haematocrit, and the tool returns the minimum tube-by-tube volume needed — plasma, dead volume and total whole blood — across Heparin, Serum, EDTA, Urine and CSF tubes.

Test catalogues are provided as an Excel template, converted to JSON via a companion converter, and dropped into the web app. The calculator adds a 250 µl safety margin on every tube (except CSF) and warns when the computed volume exceeds the selected tube size. The methodology and validation are described in the accompanying CCLM Letter (Cadamuro et al., 2025).

To make it accessible to all who need it, the tool is Open-source under GPL-3. A live demo is hosted for testing at https://research.cadamu.ro/sample_volume_calculator and the full source code is on GitHub.