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Background <p>Neonatal seizures are sudden, abnormal brain activities that occur during the neonatal period, are the most common neurological emergencies in newborns, and are associated with high morbidity and mortality rates. Approximately 2–20% of neonates admitted to neonatal intensive care units (NICUs) worldwide experience neonatal seizures. Although established guidelines for managing neonatal seizures are available, no risk prediction models exist to help the clinical decision-making process; thus, the practices remain inconsistent across local facilities and resources. This study aims to develop and validate a neonatal risk prediction model for seizures among neonates admitted to NICUs at comprehensive specialized hospitals in Northwest Ethiopia.</p> Methods <p>A retrospective follow-up study was conducted among 907 neonates admitted to the NICU in comprehensive specialized hospitals in Northwest Ethiopia. A systematic random sampling technique was employed to select the neonates’ records. Predictors for multivariable binary logistic regression analysis were selected via penalized LASSO for the variable selection method. A risk prediction nomogram was constructed from the simplified model. The discriminatory, prediction and calibration power of the risk prediction model was evaluated. Using the Youden index-identified cutoff point criterion, the level of risk of neonates for seizures was determined. Finally, decision curve analysis was performed to assess its clinical utility. For analysis purposes, Stata 17 and R.4.4.0 were used.</p> Results <p>The incidence of neonatal seizures among neonates admitted to the NICU was 21.39% (95% CI: 18.83, 24.18%). The neonatal risk prediction nomogram was constructed using preterm birth, subgaleal hemorrhage, perinatal asphyxia, maternal history of seizure, hypoglycemia, and phototherapy as independent predictors. The internally validated discriminatory power of the model was 77.735% (95% CI: 73.912%, 81.634%), with a calibration plot test value of 0.056. The optimism coefficient of the simplified model was 0.0197. Decision curve analysis revealed a greater net benefit of the model in clinical practice than the “treat all” or “treat none” strategies did.</p> Conclusions <p>The incidence of neonatal seizures among neonates admitted to the NICU was relatively high. The predictive power of the constructed nomogram was good. Despite some limitations, the model offers superior net clinical benefit compared with the “treat all” or “treat none” strategies; however, further external validation is recommended for its wider application.</p>

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Development and validation of a clinical prediction model for neonatal seizures among neonates admitted to public health hospitals in Northwest Ethiopia: retrospective follow-up study

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  • 发表日期：

    2026年3月23日

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  • Tilahun Degu Tsega,
  • Almaw Genet Yeshiwas,
  • Wolde Melese Ayele,
  • Chalachew Abiyu Ayalew,
  • Assefa Andargie Kassa,
  • Abathun Temesgen,
  • Gashaw Melkie Bayeh,
  • Chalachew Yenew,
  • Getaneh Atikilt Yemata,
  • Tesfaneh Shimels,
  • Rahel Mulatie Anteneh,
  • Getasew Yirdaw,
  • Habitamu Mekonen,
  • Berhanu Abebaw Mekonnen,
  • Meron Asmamaw Alemayehu,
  • Sintayehu Simie Tsega,
  • Zeamanuel Anteneh Yigzaw,
  • Amare Genetu Ejigu,
  • Wondimnew Desalegn Addis,
  • Birhanemaskal Malkamu,
  • Abraham Teym,
  • Kalaab Esubalew Sharew,
  • Tamiru Alene,
  • Daniel Adane,
  • Ahmed Fentaw Ahmed

摘要

Background

Neonatal seizures are sudden, abnormal brain activities that occur during the neonatal period, are the most common neurological emergencies in newborns, and are associated with high morbidity and mortality rates. Approximately 2–20% of neonates admitted to neonatal intensive care units (NICUs) worldwide experience neonatal seizures. Although established guidelines for managing neonatal seizures are available, no risk prediction models exist to help the clinical decision-making process; thus, the practices remain inconsistent across local facilities and resources. This study aims to develop and validate a neonatal risk prediction model for seizures among neonates admitted to NICUs at comprehensive specialized hospitals in Northwest Ethiopia.

Methods

A retrospective follow-up study was conducted among 907 neonates admitted to the NICU in comprehensive specialized hospitals in Northwest Ethiopia. A systematic random sampling technique was employed to select the neonates’ records. Predictors for multivariable binary logistic regression analysis were selected via penalized LASSO for the variable selection method. A risk prediction nomogram was constructed from the simplified model. The discriminatory, prediction and calibration power of the risk prediction model was evaluated. Using the Youden index-identified cutoff point criterion, the level of risk of neonates for seizures was determined. Finally, decision curve analysis was performed to assess its clinical utility. For analysis purposes, Stata 17 and R.4.4.0 were used.

Results

The incidence of neonatal seizures among neonates admitted to the NICU was 21.39% (95% CI: 18.83, 24.18%). The neonatal risk prediction nomogram was constructed using preterm birth, subgaleal hemorrhage, perinatal asphyxia, maternal history of seizure, hypoglycemia, and phototherapy as independent predictors. The internally validated discriminatory power of the model was 77.735% (95% CI: 73.912%, 81.634%), with a calibration plot test value of 0.056. The optimism coefficient of the simplified model was 0.0197. Decision curve analysis revealed a greater net benefit of the model in clinical practice than the “treat all” or “treat none” strategies did.

Conclusions

The incidence of neonatal seizures among neonates admitted to the NICU was relatively high. The predictive power of the constructed nomogram was good. Despite some limitations, the model offers superior net clinical benefit compared with the “treat all” or “treat none” strategies; however, further external validation is recommended for its wider application.