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Establishment of a predictive model for purulent meningitis in preterm infants
Xinru Cheng1,2,3, Qian Zhang1,2,3, Zhaoqin Fu1,2
1Department of Neonatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Background:
Purulent meningitis (PM) is an important cause of mortality and morbidity in the newborn population throughout the world. The subtle of specific clinical signs and low success rates of lumbar puncture make diagnosis of PM more difficult in preterm than in older children. The objective of this study was to establish a predict model for preterm PM in hopes of helping clinicians develop new diagnostic and treatment strategies.
Methods:
Premature infants who were admitted to The First Affiliated Hospital of Zhengzhou University from September 2017 to March 2020 were enrolled in this study. All the patients underwent lumbar puncture. We collected data encompassing maternal diseases and neonatal clinical features. Cerebrospinal fluid (CSF) culture is the gold standard for diagnosing meningitis. The PM was diagnosed according to the diagnostic criteria. All statistical analyses were performed using R 3.63 (https://www.r-project.org/). Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model of PM. The Brier score, calibration slope, and concordance (C)-index were used to verify the accuracy of prediction model.
Results:
A total of 168 preterm infants were enrolled in this study, 80 boys and 88 girls, the gestational age (GA) was 26.43-36.86 weeks (32.45±2.79 weeks), the birth weight (BW) was 700-3,400 g (1,814.05±568.84 g). There were 77 preterm infants with PM while 91 without. We identified seven variables as independent risk factors for PM in preterm infants by LASSO analysis [the optimal λ was 0.080960, and log(λ) = -2.5138], including procalcitonin (PCT) on the 1st day after birth, prenatal glucocorticoid use, albumin, the 1-minute Apgar score, the use of non-invasive biphasic positive airway pressure, hemoglobin, and sex. These were used to construct a risk prediction nomogram and verified its accuracy. The Brier score was 0.17, the calibration slope was 0.966, and the concordance index was 0.82018.
Conclusions:
Our prediction model could predict the risk of PM in preterm infants. Using this prediction model, it may be able to provide reference to determine whether lumbar puncture is performed and whether antibiotics are applied as soon as possible.
Insights
A new prediction model helps identify the risk of purulent meningitis (PM) in preterm infants. This tool can guide decisions on lumbar puncture and early antibiotic treatment for better outcomes.
Area of Science:
- Neonatal Medicine
- Infectious Diseases
- Pediatric Neurology
Background:
- Purulent meningitis (PM) poses significant mortality and morbidity risks in newborns globally.
- Diagnosing PM in preterm infants is challenging due to subtle clinical signs and difficulties with lumbar puncture.
- Developing effective diagnostic and treatment strategies for preterm infants with PM is crucial.
Purpose of the Study:
- To establish a predictive model for purulent meningitis (PM) in preterm infants.
- To aid clinicians in developing improved diagnostic and treatment strategies for preterm neonates.
- To enhance the early identification and management of PM in premature infants.
Main Methods:
- Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were employed.
- Data from 168 preterm infants (September 2017-March 2020) including maternal and neonatal features were collected.
- A risk prediction model was constructed and validated using Brier score, calibration slope, and concordance (C)-index.
Main Results:
- Seven independent risk factors for PM in preterm infants were identified: procalcitonin (PCT), prenatal glucocorticoid use, albumin, 1-minute Apgar score, non-invasive biphasic positive airway pressure, hemoglobin, and sex.
- A risk prediction nomogram was developed and its accuracy verified.
- The prediction model achieved a Brier score of 0.17, a calibration slope of 0.966, and a concordance index of 0.82018.
Conclusions:
- The developed prediction model accurately predicts the risk of purulent meningitis in preterm infants.
- This model can assist clinicians in determining the necessity of lumbar puncture.
- The model may facilitate timely decisions regarding the initiation of antibiotic therapy for preterm neonates.
