The severity of harm score database: an evidence-based foundation for laboratory test risk analysis

BACKGROUND: Patient risk management has become central to clinical laboratory quality standards, including the Clinical and Laboratory Standards Institute (CLSI) EP23 2nd edition (CLSI EP23-ED2), International Organization for Standardization (ISO) 15189:2022, ISO 22367:2026, and ISO 14971:2019. A key component of risk analysis is estimating the severity of harm (SoH) that may result from erroneous laboratory results. We present here the Severity of Harm Evidence Database, a structured, multi-study resource that integrates survey-based SoH assessments from the published independent studies. The database provides ordinal distributions and pooled SoH for 196 unique laboratory tests. METHODS: A canonical five-level scale (negligible, minor, serious, critical, catastrophic) was adopted, aligned with ISO 14971:2019. For the 19 tests common to both studies, direct count pooling was performed by summing category-level respondent counts. Wilson score confidence intervals were calculated for all proportions. The database is organized into nine relational sheets and is accompanied by an interactive web application. RESULTS: Out of the 196 tests covered, 19 have pooled estimates from both studies (total n=781 respondents). Pooled analysis confirms that cardiac biomarkers, blood gases, coagulation parameters, electrolytes (potassium, sodium), and therapeutic drug concentrations (digoxin, lithium) consistently receive the highest severity ratings, while certain endocrine markers receive lower scores. CONCLUSIONS: The SoH evidence database offers the first publicly available, structured repository of SoH assessments for clinical laboratory tests. It supports compliance with risk management requirements, informs risk-based strategies per CLSI EP23-ED2, and provides an evidence-based foundation for laboratory risk analysis.

Authors

Institutions

Publication Details

Journal
Clinical Chemistry and Laboratory Medicine (CCLM)
Published
2026-07-28
DOI
https://doi.org/10.1515/cclm-2026-0875
Primary Topic
Clinical Laboratory Practices and Quality Control
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The severity of harm score database: an evidence-based foundation for laboratory test risk analysis

Neda Milinković, Nathan Lorde, Oswald Sonntag, Mario Plebani et al.
Clinical Chemistry and Laboratory Medicine (CCLM)
Clinical Laboratory Practices and Quality Control
article

The severity of harm score database: an evidence-based foundation for laboratory test risk analysis

Neda Milinković, Nathan Lorde, Oswald Sonntag, Mario Plebani, Marc Thelen, Guilaine Boursier, Pika Meško Brguljan, Annelien Van Dalem, Amitava Dasgupta, Marith van Schrojenstein Lantman, Hikmet Can Çubukçu, Chiara Cosma, Charles R. Lefèvre, Giuseppe Lippi, Marija Kocijančić, Panagiotis Kanellopoulos
article en

Abstract

BACKGROUND: Patient risk management has become central to clinical laboratory quality standards, including the Clinical and Laboratory Standards Institute (CLSI) EP23 2nd edition (CLSI EP23-ED2), International Organization for Standardization (ISO) 15189:2022, ISO 22367:2026, and ISO 14971:2019. A key component of risk analysis is estimating the severity of harm (SoH) that may result from erroneous laboratory results. We present here the Severity of Harm Evidence Database, a structured, multi-study resource that integrates survey-based SoH assessments from the published independent studies. The database provides ordinal distributions and pooled SoH for 196 unique laboratory tests. METHODS: A canonical five-level scale (negligible, minor, serious, critical, catastrophic) was adopted, aligned with ISO 14971:2019. For the 19 tests common to both studies, direct count pooling was performed by summing category-level respondent counts. Wilson score confidence intervals were calculated for all proportions. The database is organized into nine relational sheets and is accompanied by an interactive web application. RESULTS: Out of the 196 tests covered, 19 have pooled estimates from both studies (total n=781 respondents). Pooled analysis confirms that cardiac biomarkers, blood gases, coagulation parameters, electrolytes (potassium, sodium), and therapeutic drug concentrations (digoxin, lithium) consistently receive the highest severity ratings, while certain endocrine markers receive lower scores. CONCLUSIONS: The SoH evidence database offers the first publicly available, structured repository of SoH assessments for clinical laboratory tests. It supports compliance with risk management requirements, informs risk-based strategies per CLSI EP23-ED2, and provides an evidence-based foundation for laboratory risk analysis.

Clinical Chemistry and Laboratory Medicine (CCLM)
University of Verona (IT), Ministry of Health (TR), University Hospitals Birmingham NHS Foundation Trust (GB), University of Padua (IT), Radboud University Nijmegen (NL), Inserm (FR), Université de Montpellier (FR), Radboud University Medical Center (NL), University of Belgrade (RS), Centre de Référence des Maladies Autoinflammatoires et des Amyloses (FR), University Clinic of Pulmonary and Allergic Diseases Golnik (SI), Laiko General Hospital of Athens (GR), Streekziekenhuis Koningin Beatrix (NL), University Children's Hospital Tübingen (DE), Universitair Ziekenhuis Brussel (BE), University of Kansas Medical Center (US), Centre Hospitalier Universitaire de Rennes (FR), Oldham Council (GB), Université de Rennes (FR), University of Tübingen (DE)
Partnerships for the goals
Openalex Percentile: Top 9%
Clinical Laboratory Practices and Quality Control
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.