Purpose <p>Neonates admitted to intensive care units are at high risk of hospital-acquired infections. While blood cultures remain the gold standard for sepsis diagnosis, they do not detect infection in every case. Host immune response markers, such as C-reactive protein and procalcitonin, can assist in earlier detection but also have limitations and carry additional costs. We have developed a Neonatal Intensive Care Infection Score (NICIS), which leverages extended complete blood count (CBC+Diff + EIP) parameters, which may require specific analyser configuration or software licensing, to extract additional diagnostic value.</p> Methods <p>We conducted a retrospective cohort study of neonates admitted to the Neonatal Intensive Care Unit at the John Radcliffe Hospital (Oxford, UK) over one year. Eighteen CBC+Diff + EIP parameters were initially considered based on their ability to differentiate culture-positive patients from those without clinical suspicion of infection. NICIS was developed as a weighted score and validated internally using a temporally distinct dataset and externally using a four-year retrospective cohort from Erasmus University Medical Center (Rotterdam, The Netherlands).</p> Results <p>Incorporating eight CBC+Diff + EIP parameters, NICIS values ranged from 0 to 25, with higher scores indicating a greater likelihood of sepsis. NICIS achieved an area under the curve of 0.906 in training, 0.879 in internal validation, and 0.841 in external validation, outperforming C-reactive protein and total white blood cell count in all datasets.</p> Conclusions <p>NICIS provides an interpretable tool for sepsis detection using routinely collected CBC+Diff + EIP data without additional sampling. Its performance across internal and external cohorts suggests sufficient diagnostic accuracy, although prospective studies are warranted to further assess generalisability and clinical utility.</p>

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Development and validation of a diagnostic model for late-onset neonatal sepsis using the haematological profile: a retrospective cohort study

  • Emily Hyde,
  • Janno Schouten,
  • Jarob Saker,
  • H. R. Taal,
  • H. Russcher,
  • Eliane Wenstedt,
  • Stephen Kennedy,
  • Manu Vatish,
  • Mark Anthony

摘要

Purpose

Neonates admitted to intensive care units are at high risk of hospital-acquired infections. While blood cultures remain the gold standard for sepsis diagnosis, they do not detect infection in every case. Host immune response markers, such as C-reactive protein and procalcitonin, can assist in earlier detection but also have limitations and carry additional costs. We have developed a Neonatal Intensive Care Infection Score (NICIS), which leverages extended complete blood count (CBC+Diff + EIP) parameters, which may require specific analyser configuration or software licensing, to extract additional diagnostic value.

Methods

We conducted a retrospective cohort study of neonates admitted to the Neonatal Intensive Care Unit at the John Radcliffe Hospital (Oxford, UK) over one year. Eighteen CBC+Diff + EIP parameters were initially considered based on their ability to differentiate culture-positive patients from those without clinical suspicion of infection. NICIS was developed as a weighted score and validated internally using a temporally distinct dataset and externally using a four-year retrospective cohort from Erasmus University Medical Center (Rotterdam, The Netherlands).

Results

Incorporating eight CBC+Diff + EIP parameters, NICIS values ranged from 0 to 25, with higher scores indicating a greater likelihood of sepsis. NICIS achieved an area under the curve of 0.906 in training, 0.879 in internal validation, and 0.841 in external validation, outperforming C-reactive protein and total white blood cell count in all datasets.

Conclusions

NICIS provides an interpretable tool for sepsis detection using routinely collected CBC+Diff + EIP data without additional sampling. Its performance across internal and external cohorts suggests sufficient diagnostic accuracy, although prospective studies are warranted to further assess generalisability and clinical utility.