Clinical Risk Index for Babies (CRIB II) Score in Predicting Neonatal Mortality in Preterm Babies Less Than or Equal To 32 Weeks of Gestation Admitted In NICU at Tertiary Care Hospital Tirupati

doi:10.61336/jccp/24-01

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Abstract
Introduction
Materials And Methods
Results
Discussion
Conclusion

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Research Article | Volume 10 Issue 1 (None, 2024) | Pages 317 - 320

Clinical Risk Index for Babies (CRIB II) Score in Predicting Neonatal Mortality in Preterm Babies Less Than or Equal To 32 Weeks of Gestation Admitted In NICU at Tertiary Care Hospital Tirupati

C. Mounika Lakshmi

Rajashaker Venkatappa

Naga Ramani DeviReddy

Manohar Badur

PG, Department of Paediatrics, S.V. Medical College, Tirupati, India

Assistant Professor, Department of Paediatrics, SV Medical College, Tirupati, India

Professor&HOD, Department of Paediatrics, S.V. Medical College Tirupati, India.

Under a Creative Commons license

Open Access

DOI : 10.61336/jccp/24-01

Received

May 11, 2024

Revised

May 26, 2024

Accepted

June 14, 2024

Published

June 26, 2024

Abstract

Background: Neonatal mortality contributes significantly to under-five deaths globally, with India accounting for nearly one-fourth of these losses. Prematurity, low birth weight, and birth asphyxia are leading causes. Early identification of high-risk neonates is critical for timely intervention and optimal use of intensive care resources. The Clinical Risk Index for Babies II (CRIB II) score offers a simplified, objective scoring system that can be applied within the first hour of NICU admission. Objective: To assess the predictive validity of CRIB II and a modified CRIB II (excluding base excess) in forecasting mortality and morbidity among preterm neonates (≤32 weeks) admitted to a tertiary NICU. Methods: A prospective observational study was conducted on 110 neonates ≤32 weeks. CRIB II scores were calculated upon admission. Mortality and major morbidities (IVH, BPD, ROP, sepsis) were recorded. ROC curve analysis and Youden’s index determined the optimal cut-offs. Statistical associations were analysed using Chi-square and Mann-Whitney U tests. Results: The CRIB II score demonstrated high predictive value for mortality with an AUC of 0.889 (with base excess) and 0.922 (without base excess). Mortality increased from 0% at score level I to 93.8% at level IV. Lower temperature, birth weight, and gestational age were significantly associated with non-survival (p < 0.0001). The CRIB II score also correlated with major morbidities, especially BPD and IVH. Conclusion: CRIB II is a valid and practical tool for early identification of at-risk neonates. Its simplified form remains effective even without base excess data, offering flexibility in low-resource NICUs.

INTRODUCTION

Globally, neonatal mortality remains a pressing public health concern, accounting for nearly 47% of all under-five deaths. In 2021, India contributed approximately 25% of global neonatal deaths, with most of these preventable through timely interventions. Prematurity, low birth weight, and complications arising from birth asphyxia constitute the leading causes. In this context, healthcare providers face a crucial challenge: how to identify the sickest newborns early and objectively, so as to allocate critical care resources efficiently. Over the years, various neonatal illness severity scores have been developed — including the Score for Neonatal Acute Physiology (SNAP), SNAP-II, SNAPPE-II, and the original Clinical Risk Index for Babies (CRIB). While accurate, many of these tools are cumbersome to apply and require laboratory data that may not be readily available, especially in low- and middle-income countries. CRIB II, developed in 2003, simplifies neonatal illness scoring by using only five readily available variables: gestational age, birth weight, sex, admission temperature, and base excess. It enables clinicians to stratify risk in the first few hours of NICU admission and make informed decisions about escalation of care. This study evaluates the performance of CRIB II in predicting mortality and major morbidities in neonates ≤32 weeks of gestation and also assesses a modified version of the score (without base excess), suitable for centres lacking routine ABG access.

MATERIALS AND METHODS

Study Design and Setting A prospective observational cohort study was conducted between April 2021 and March 2022 in the Level III NICU of SVRR Government General Hospital, Tirupati — a tertiary referral centre in South India. Inclusion Criteria • Neonates with gestational age ≤32 weeks • Admitted within the first 24 hours of life • Parental consent obtained Exclusion Criteria • Major congenital anomalies • Delayed admission (>24 hours) • Discharged against medical advice CRIB II Scoring CRIB II was calculated on NICU admission using: • Birth weight (g) • Gestational age (weeks) • Sex (male/female) • Body temperature (°C) • Base excess (from arterial blood gas) A modified CRIB II score excluding base excess was also evaluated in neonates where ABG was not feasible. Outcomes • Primary: Neonatal mortality (in-hospital death before discharge) • Secondary: Major neonatal morbidities (BPD, IVH, ROP, culture-positive sepsis) Statistical Analysis Data were analysed using SPSS v21. ROC curves were generated to evaluate predictive accuracy. Youden’s index determined optimal cut-off values. Comparisons between survivors and non-survivors were made using Mann-Whitney U tests and Chi-square tests (p < 0.05 considered significant).

RESULTS

Table 1: Neonatal Characteristics (n = 110) Parameter Value Male : Female 54 (49.1%) : 56 (50.9%) Median Gestational Age 30 weeks (IQR 28–32) Median Birth Weight 1135 g (IQR 978–1208) Median Admission Temperature 32.7°C (IQR 32–33.7) CRIB II score (with BE) 12.6 ± 3.5 CRIB II score (no BE) 7.1 ± 2.8 Mortality by CRIB II Score Levels Table 2: Mortality According to CRIB II Levels (With Base Excess) Score Level Alive Dead Mortality Rate Level I (0–5) 2 0 0% Level II (6–10) 16 2 11.1% Level III (11–15) 13 22 62.9% Level IV (>15) 1 15 93.8% Table 3: Mortality (Without Base Excess) Score Level Alive Dead Mortality Rate Level I 13 0 0% Level II 13 5 27.8% Level III 0 8 100% • With base excess: AUC = 0.889 • Without base excess: AUC = 0.922 The modified CRIB II showed slightly better discriminatory ability, which is highly valuable for low-resource settings. Diagnostic Accuracy Table 4: CRIB II Score Diagnostic Accuracy Metric Value Sensitivity 61.5% Specificity 60.3% PPV 58.2% NPV 63.6% Accuracy 60.9% Predictors of Mortality Table 5: Comparison between Survivors and Non-Survivors Parameter Survivors (Mean ± SD) Non-Survivors (Mean ± SD) p-value Birth Weight (g) 1221.4 ± 103.2 943.6 ± 162 <0.0001 Gestational Age (wks) 30.98 ± 1.1 28.3 ± 0.69 <0.0001 Admission Temp (°C) 33.1 ± 0.93 32.0 ± 1.03 <0.0001

DISCUSSION

This study demonstrated that CRIB II is a reliable predictor of neonatal mortality and morbidity in preterm neonates ≤32 weeks. Mortality rose steeply with increasing CRIB II levels. The findings correlate with previous research, including studies by Ezz-Eldin et al. and Gagliardi et al., which also validated CRIB II in NICU settings. Of particular interest was the performance of the modified CRIB II score. With an AUC of 0.922, the version without base excess outperformed the original in our cohort. This is highly relevant for many low- and middle-income countries where blood gas analysis is not routinely available. Admission hypothermia (temperature <33°C) was strongly associated with mortality. This supports the emphasis by WHO and UNICEF on improving thermal care, especially during transport and resuscitation. The CRIB II score also showed predictive ability for BPD and IVH. However, its performance for predicting ROP was limited, likely due to variability in oxygen exposure and inconsistent follow-up in some cases.

CONCLUSION

CRIB II is a simple, objective, and effective tool for predicting neonatal mortality and morbidity. It can be calculated within the first hour of admission using data that are routinely available in most NICUs. The modified version without base excess retains strong predictive ability, making it a practical solution in resource-limited settings. Its use can improve early risk stratification, guide decision-making, enhance parental counselling, and optimize use of neonatal intensive care resources.

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