International journal of laboratory hematology · 2025
Sysmex XN-Based Evaluation of the Diagnostic Performance of High-Fluorescent Cells From CSF as a Supportive Diagnostic Criterion in Neurological Diseases.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Schwarz Benedict, Hardt Christopher, Friedrich Katharina, Prpic Monika, Osterloh Anja, Heppner Frank L, Ruprecht Klemens, Kappert Kai, Jahic Amir
Аннотация
INTRODUCTION: Automatic cytological analysis of cerebrospinal fluid (CSF) by Sysmex XN-Series represents a convenient laboratory platform for quantitative examination of nucleated CSF cells (monomorphonuclear (MN), polymorphonuclear (PMN), high-fluorescent (HF)). HF cells (HFC), a research laboratory parameter so far, seem to be associated with certain clinical patterns. Hence, we aimed to determine the diagnostic HFC value for different clinical categories in neurological settings.
METHODS: Morphological classification of automatically detected HFC was carried out using manual light microscopy. Automatic method precision for cell differentiation was evaluated in comparison. In 284 cases, multiple correlation strategies and mathematical disease modellings enabled an explorative analysis of HFC suitability for case stratification into the categories Hemorrhage, Inflammation, Neoplasia, other, and unknown.
RESULTS: Manual microscopic reevaluation revealed plasma cells, macrophages, and malignant cells being HFC correlates in 80% of automatically detected HFC. Method correlation for automatic and manual CSF cell differentiation approaches was 95%, yielding a negative bias of 4.3% for MN and positive bias of 0.4% and 3.9% for PMN and HF, respectively. When HFC were used as a "stand-alone" predicting tool, diagnostic accuracy, specificity, and sensitivity depended on the clinical condition, ranging from > 0.5 up to > 0.7 for Hemorrhage, Inflammation, and Neoplasia. However, multiparametric correlation analyses combining laboratory CSF diagnostics and mathematical methods defined HFC as a relevant laboratory parameter for adequate clinical case stratification. With an expected random distribution of 25% for four clinical categories, almost 70% of cases were correctly classified when the HFC-based mathematical algorithm was applied.
CONCLUSION: HFC has significant diagnostic and/or predictive value for neurological diseases.
METHODS: Morphological classification of automatically detected HFC was carried out using manual light microscopy. Automatic method precision for cell differentiation was evaluated in comparison. In 284 cases, multiple correlation strategies and mathematical disease modellings enabled an explorative analysis of HFC suitability for case stratification into the categories Hemorrhage, Inflammation, Neoplasia, other, and unknown.
RESULTS: Manual microscopic reevaluation revealed plasma cells, macrophages, and malignant cells being HFC correlates in 80% of automatically detected HFC. Method correlation for automatic and manual CSF cell differentiation approaches was 95%, yielding a negative bias of 4.3% for MN and positive bias of 0.4% and 3.9% for PMN and HF, respectively. When HFC were used as a "stand-alone" predicting tool, diagnostic accuracy, specificity, and sensitivity depended on the clinical condition, ranging from > 0.5 up to > 0.7 for Hemorrhage, Inflammation, and Neoplasia. However, multiparametric correlation analyses combining laboratory CSF diagnostics and mathematical methods defined HFC as a relevant laboratory parameter for adequate clinical case stratification. With an expected random distribution of 25% for four clinical categories, almost 70% of cases were correctly classified when the HFC-based mathematical algorithm was applied.
CONCLUSION: HFC has significant diagnostic and/or predictive value for neurological diseases.