A Multicentre Pilot Study of a Two-Tier Newborn Sickle Cell Disease Screening Procedure with a First Tier Based on a Fully Automated MALDI-TOF MS Platform

Naubourg, Pierre; Osta, Marven El; Rageot, David; Grunewald, Olivier; Renom, Gilles; Ducoroy, Patrick; Périni, Jean-Marc

doi:10.3390/ijns5010010

Cited by 6 publications

(13 citation statements)

References 12 publications

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“…The data came from a previously described study of 6701 biological samples [11]. We analyzed residual blood spots from standard Guthrie cards (used in our laboratory's routine screening activity) for samples with full datasets (i.e., with clinical data, and avalidated Hb phenotypes using reference methods-the French neonatal screening program for SCDs was set up in 1995 under the aegis of the Association Française pour le Dépistage et la Prévention des Handicaps de l'Enfant [17]; the neonatal two-tier SCD screening strategy developed in France combines two of the following technics: Isoelectrofocusing (IEF), capillary electrophoresis (CE), or high performance liquid chromatography (HPLC) [18,19].…”

Section: Data Collectionmentioning

confidence: 99%

“…All samples were analyzed at two MALDI-TOF facilities (the University of Burgundy's CLIPP facility (Dijon, France) and Lille University Hospital's neonatal screening laboratory (Lille, France)), according to the previously described procedure [11]. At both facilities, samples were prepared for MS measurements using a research version of the NeoSickle ® kit (Biomaneo).…”

Section: Sample Processing and Analysismentioning

confidence: 99%

“…Parameter taken into account in the alert "a" Low Beta chain peak intensity "b" Ratio intensity 15,837/15,867 m/z peak "c" Intensity of Beta chain peak "d" Low HbS chain peak intensity "e" Low HbS chain peak resolution "f" resolving power of the HbS and HbA peaks "g" Ratio HbS/HbA peaks There were no alerts for newborns with S-thalassaemia (Sβ + or Sβ • ), since these samples were always classified as FS by the NeoSickle ® software [11].…”

Section: Alert Indexmentioning

confidence: 99%

“…Spectra were classified as "standard" and "non-standard", depending on their quality [11]. The MS profiles were considered to be "non-standard" for one of three reasons.…”

Section: Visual Assessment Of Ms Profilesmentioning

confidence: 99%

“…Hachani et al's pilot study was the first to demonstrate the MALDI-MS technique's potential in sickle cell disease (SCD) screening [10]. A more comprehensive study by Naubourg et al then demonstrated the relevance of the NeoSickle ® approach (i.e., automated, standardized sample preparation, followed by MALDI-MS analysis) and the NeoScreening ® sample processing and classification software [11]. Naubourg et al 's results showed that most of the samples had been correctly classified; however, there was still room for improvement, with a view to obtaining a completely automated, safe tool that enables technicians and biologists to validate results much more rapidly.…”

Section: Introductionmentioning

confidence: 99%

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The Reliable, Automatic Classification of Neonates in First-Tier MALDI-MS Screening for Sickle Cell Disease

Osta¹,

Naubourg²,

Grunewald

et al. 2019

IJNS

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Previous research has shown that a MALDI-MS technique can be used to screen for sickle cell disease (SCD), and that a system combining automated sample preparation, MALDI-MS analysis and classification software is a relevant approach for first-line, high-throughput SCD screening. In order to achieve a high-throughput "plug and play" approach while detecting "non-standard" profiles that might prompt the misclassification of a sample, we have incorporated various sets of alerts into the decision support software. These included "biological alert" indicators of a newborn's clinical status (e. g., detecting samples with no or low HbA), and "technical alerts" indicators for the most common non-standard profiles, i.e., those which might otherwise lead to sample misclassification. We evaluated these alerts by applying them to two datasets (produced by different laboratories). Despite the random generation of abnormal spectra by one-off technical faults or due to the nature and quality of the samples, the use of alerts fully secured the process of automatic sample classification. Firstly, cases of β-thalassemia were detected. Secondly, after a visual check on the tagged profiles and reanalysis of the corresponding biological samples, all the samples were correctly reclassified without prompting further alerts. All of the samples for which the results were not tagged were well classified (i.e., sensitivity and specificity = 1). The alerts were mainly designed for detecting false-negative classifications; all the FAS samples misclassified by the software as FA (a false negative) were marked with an alert. The implementation of alerts in the NeoScreening ® Laboratory Information Management System's decision support software opens up perspectives for the safe, reliable, automated classification of samples, with a visual check solely on abnormal results or samples. It should now be possible to evaluate the combination of the NeoSickle ® analytical solution and the NeoScreening ® Laboratory Information Management System in a real-life, prospective study of first-line SCD screening.

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Section: Data Collectionmentioning

confidence: 99%

Section: Sample Processing and Analysismentioning

confidence: 99%

Section: Alert Indexmentioning

confidence: 99%

“…Spectra were classified as "standard" and "non-standard", depending on their quality [11]. The MS profiles were considered to be "non-standard" for one of three reasons.…”

Section: Visual Assessment Of Ms Profilesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Reliable, Automatic Classification of Neonates in First-Tier MALDI-MS Screening for Sickle Cell Disease

Osta¹,

Naubourg²,

Grunewald

et al. 2019

IJNS

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Les principaux outils biologiques appliqués au dépistage néonatal

Cheillan

2021

Med Sci (Paris)

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Au cours des cinquante dernières années, le dépistage néonatal est devenu incontournable dans les programmes de santé publique de très nombreux pays. Durant toutes ces années, le nombre de maladies susceptibles d’être dépistées à la naissance n’a cessé de croître grâce aux possibilités offertes par les progrès techniques de la biologie clinique. Le test de Guthrie a permis la miniaturisation du prélèvement de sang, permettant ainsi le dépistage biologique dans la population des nouveau-nés. Par la suite, la fluorimétrie, l’immunoanalyse et, plus récemment, la spectrométrie de masse en tandem ont rendu possible le dépistage de nombreuses maladies qu’il est possible de traiter. Les nouvelles possibilités de séquençage du génome et d’intelligence artificielle vont probablement ouvrir une nouvelle ère, malgré les nombreuses questions éthiques qui se poseront. Cette revue propose de dresser le panorama des techniques biologiques utilisées actuellement pour le dépistage néonatal et de mettre en perspective la place de nouvelles évolutions techniques.

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A MALDI-TOF mass spectrometry-based haemoglobin chain quantification method for rapid screen of thalassaemia

et al. 2022

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Background Thalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden. Considering its high prevalence in low and middle-income countries, a cheap, accurate and high-throughput screening test of thalassaemia prior to a more expensive confirmatory diagnostic test is urgently needed. Methods In this study, we constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains in blood, and for the first time, evaluated its diagnostic efficacy in 674 thalassaemia (including both asymptomatic carriers and symptomatic patients) and control samples collected in three hospitals. Parameters related to haemoglobin imbalance (α-globin, β-globin, γ-globin, α/β and α-β) were used for feature selection before classification model construction with 8 machine learning methods in cohort 1 and further model efficiency validation in cohort 2. Results The logistic regression model with 5 haemoglobin peak features achieved good classification performance in validation cohort 2 (AUC 0.99, 95% CI 0.98–1, sensitivity 98.7%, specificity 95.5%). Furthermore, the logistic regression model with 6 haemoglobin peak features was also constructed to specifically identify β-thalassaemia (AUC 0.94, 95% CI 0.91–0.97, sensitivity 96.5%, specificity 87.8% in validation cohort 2). Conclusions For the first time, we constructed an inexpensive, accurate and high-throughput classification model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains and demonstrated its great potential in rapid screening of thalassaemia in large populations. Key messages Thalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden. We constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains to screen for thalassaemia.

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A Multicentre Pilot Study of a Two-Tier Newborn Sickle Cell Disease Screening Procedure with a First Tier Based on a Fully Automated MALDI-TOF MS Platform

Cited by 6 publications

References 12 publications

The Reliable, Automatic Classification of Neonates in First-Tier MALDI-MS Screening for Sickle Cell Disease

The Reliable, Automatic Classification of Neonates in First-Tier MALDI-MS Screening for Sickle Cell Disease

Les principaux outils biologiques appliqués au dépistage néonatal

A MALDI-TOF mass spectrometry-based haemoglobin chain quantification method for rapid screen of thalassaemia

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