Italian 
English CONTRIBUTI SCIENTIFICI – Scientific Papers
Volume:
Biochimica Clinica 2022; 46(1) 051-057
Pubblicato on-line:
Agosto 2, 2021
DOI:
10.19186/BC_2021.048
Errori di identificazione del paziente: un progetto SIBioC orientato alla gestione di un problema persistente
Wrong blood in tube: a SIBioC project for a persistent problem
AUTORI
1Dipartimento Didattico-Scientifico-Assistenziale (DIDAS)-Servizi di Diagnostica Integrata, Unità Operativa Complessa Medicina di Laboratorio, Ospedale-Università di Padova
2Unità Operativa Complessa Patologia Clinica, Azienda Ospedaliera Universitaria Senese, Siena
3Unità Operativa Complessa Laboratorio Analisi Chimico Cliniche VDE e AVC, Presidio Ospedaliero Felice Liotti, Pontedera, Pisa
4Laboratorio di Chimica Clinica, Ospedali Riuniti Padova Sud Schiavonia, AULSS6 Euganea Veneto
5Laboratorio di Analisi Chimico Cliniche, Ospedale di San Donato, Arezzo
6Servizio di Medicina di Laboratorio-ASST Bergamo Ovest, Treviglio, BG
7Laboratorio di Biochimica Clinica, Dipartimento di Scienze della Vita e della Riproduzione, Università di Verona
8Laboratorio di Chimica Clinica, Dipartimento della Salute, Ospedale Maggiore della Carità, Università del Piemonte Orientale, Novara
9Unità di Medicina di Laboratorio, Centro Cardiologico Monzino IRCCS, Milano
10Centro di Riferimento Regionale per la Qualità dei Servizi di Medicina di Laboratorio c/o ASST Grande Ospedale Metropolitano Niguarda, Milano
11Medicina di Laboratorio, Istituto Fiorentino di Cura e Assistenza (IFCA), Firenze
ABSTRACT
Wrong blood in tube: a SIBioC project for a persistent problem
Introduction: recently, multi-analytes delta-check (MDC) has been proposed as a more effective tool in identification errors (IE) prevention. In this context, “Haematology” and “Clinical Risk” SIBioC working groups launched a project aiming to develop a cell blood count (CBC) MDC. This work is aimed to describe the project and some preliminary results.
Methods: the project consists of four phases: collection of CBC results from 15 Italian laboratories to create an original dataset (OD); pilot study on a smaller dataset (SD) i.e., creation of an artificial mix-up dataset-MD containing IE by casual resampling of the SD and identification of the best statistical model to create a MDC; identification of the most accurate MDC on OD; testing the MDC in involved labs and verification of its effectiveness.
Results: the SD included 2,367 pair of consecutive results for the same patient (patients’ age: 0-100 years; the majority of repetitions were within days). The SD casual resampling generated a MD with 2,000 pair of patient-mixed consecutive results. When one of the most frequent used delta-check alert (ΔMCV=7fL) was applied to detect IE in MD, the method accuracy was low (AUC=0.542). On the contrary, testing of a multivariate model, obtained by a stepwise logistic analysis, allowed to obtain a more accurate MDC in IE detection (AUC=0.931, sensitivity=91.6%, specificity=94%).
Conclusions: MDC may offer a practical strategy to identify IE prior to test reporting, improving patient safety. However a good planning of project workflow, selection of methodology, tools and staff competence are key elements to reach the objectives