The clinical risk index of babies (CRIB) score in India

Post-transport TOPS score as a predictive marker of mortality among transported neonates and its comparative analysis with SNAP-II PE

Aim

Multiple parameters are available to predict the outcome of critically sick neonates admitted in neonatal intensive care unit (NICU). Main aim of the study is to validate the role of TOPS, especially the post-transport TOPS score as a simplified assessment of neonatal acute physiology in predicting mortality among transported neonates admitted at level III NICU. Also, to compare the efficiency of post transport TOPS score with SNAP II PE in predicting mortality.

Methods

A prospective study carried out with 85 neonates transported from various primary health care centres to level III NICU. Physiological status of the neonates was assessed with the help of pre and post transport TOPS scores. Post-transport TOPS score was recorded immediately after the admission and SNAP II PE within 24 h of admission at level III NICU. Receiver operating characteristics analysis was performed to observe the mortality prediction efficiency of TOPS score and was compared with SNAP II PE.

Results

64 neonates were died due to asphyxia and preterm birth (32%) related complications. Strong significant association with the mortality rate was found between the total post transport TOPS score (0.001) and SNAP II PE (0.003). The AUC, sensitivity and specificity of post transport TOPS score for a cut-off value ≤7 were 0.900, 87.5% and 80% and significant (<0.001) and for SNAP II PE for a cut-off value >12 were 0.913, 75.5% and 100% and is significant (<0.001).

Conclusion

TOPS score, especially the post transport TOPS score has an equally good prediction capacity of mortality similar like SNAP II PE among mobilised critically ill neonates. Hence, the TOPS score can be used as a simple and effective method to predict mortality risk among transported neonates immediately after admission at level III NICU.

Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis

CONTEXT

Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants.

OBJECTIVE

Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality.

DATA SOURCES

Medline was searched for all articles (up to June 2020).

STUDY SELECTION

All developed or externally validated prognostic models for mortality prediction in liveborn infants born <32 weeks' gestation and/or <1500 g birth weight were included.

DATA EXTRACTION

Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool.

RESULTS

One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score.

LIMITATIONS

Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded.

CONCLUSIONS

Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.

Comparing mortality risk models in VLBW and preterm infants: systematic review and meta-analysis

OBJECTIVE

To compare the prognostic accuracy of six neonatal illness severity scores (CRIB, CRIB II, SNAP, SNAP II, SNAP-PE, and SNAP-PE II), birthweight (BW), and gestational age (GA) for predicting pre-discharge mortality among very low birth weight (VLBW) infants (<1500 g) and very preterm infants (<32 weeks' gestational age).

STUDY DESIGN

PubMed, EMBASE, and Scopus were the data sources searched for studies published before January 2019. Data were extracted, pooled, and analyzed using random-effects models and reported as AUC with 95% confidence intervals (CI).

RESULTS

Of 1659 screened studies, 24 met inclusion criteria. CRIB was the most discriminate for predicting pre-discharge mortality [AUC 0.88 (0.86-0.90)]. GA was the least discriminate [AUC 0.76 (0.72-0.80)].

CONCLUSIONS

Although the original CRIB score was the most accurate predictor of pre-discharge mortality, significant heterogeneity between studies lowers confidence in this pooled estimate. A more precise illness severity score to predict pre-discharge mortality is still needed.

Neonatal mortality risk assessment using SNAPPE- II score in a neonatal intensive care unit

Background

There are many scoring systems to predict neonatal mortality and morbidity in neonatal intensive care units (NICU). One of the scoring systems is SNAPPE-II (Score for Neonatal Acute Physiology with Perinatal extension-II). This study was carried out to assess the validity of SNAPPE-II score (Score for Neonatal Acute Physiology with Perinatal Extension-II) as a predictor of neonatal mortality and duration of stay in a neonatal intensive care unit (NICU).

Methods

This prospective, observational study was carried out over a period of 12 months from June 2015 to May 2016. Two hundred fifty five neonates, who met the inclusion criteria admitted to NICU in tertiary care hospital, BPKIHS Hospital, Nepal were enrolled in the study and SNAPPE-II score was calculated. Receiver Operating Characteristic (ROC) curve was constructed to derive the best SNAPPE-II cut-off score for mortality.

Results

A total of 305 neonates were admitted to NICU over a period of one year. Among them, 255 neonates fulfilled the inclusion criteria. Out of 255 neonates, 45 neonates (17.6%) died and 210 were discharged. SNAPPE-II score was significantly higher among neonates who died compared to those who survived [median (IQR) 57 (42–64) vs. 22 (14–32), P < 0.001]. SNAPPE II score had discrimination to predict mortality with area under ROC Curve (AUC): 0.917 (95% CI, 0.854–0.980). The best cut - off score for predicting mortality was 38 with sensitivity 84.4%, specificity 91%, positive predictive value 66.7% and negative predictive value 96.5%. SNAPPE II score could not predict the duration of NICU stay (P = 0.477).

Conclusion

SNAPPE- II is a useful tool to predict neonatal mortality in NICU. The score of 38 may be associated with higher mortality.

A Pilot Study on Neonatal Surgical Mortality: A Multivariable Analysis of Predictors of Mortality in a Resource-Limited Setting

Purpose:

The aim of this research is to study the predictors of neonatal surgical mortality (NSM)-defined as in-hospital death or death within 30 days of neonatal surgery.

Materials and Methods:

All neonates operated over the study period of 18 months were included to evaluate NSM. The evaluated preoperative and intraoperative variables were birth weight, gestation age, age at presentation, associated anomalies, site and duration of surgery, intraoperative blood loss, and temperature after surgery. Assessed postoperative variables included the need for vasopressors, postoperative ventilation, sepsis, reoperations, and time taken to achieve full enteral nutrition. Univariate and multivariate logistic regression was applied to find the predictors of mortality.

Results:

Based on patient's final outcome, patients were divided into two groups (Group 1-survival, n = 100 and Group 2-mortality, n = 50). Incidence of NSM in this series was 33.33%. Factors identified as predictors of NSM were duration of surgery >120 min (P = 0.007, odds ratio [OR]: 9.76), need for prolonged ventilation (P = 0.037, OR: 5.77), requirement of high dose of vasopressors (P = 0.003, OR: 25.65) and reoperations (P = 0.031, OR: 7.16 (1.20–42.81).

Conclusion:

NSM was largely dependent on intraoperative stress factors and postoperative care. Neonatal surgery has a negligible margin of error and warrants expertize to minimize the duration of surgery and complications requiring reoperations. Based on our observations, we suggest a risk stratification score for neonatal surgery.

Predicting deaths in a resource-limited neonatal intensive care unit in Nepal

Background

This study aimed to determine whether the Neonatal Acute Physiology (SNAP) scoring system (SNAP II) and with perinatal extension (SNAP II PE) can be used to predict neonatal deaths in a resource-limited neonatal intensive care unit in Nepal.

Methods

A prospective observational study was conducted in a neonatal intensive care unit (NICU) of Kanti Children's Hospital in Kathmandu, Nepal. Data required for the SNAP II and SNAP II PE scores were collected. The relationships between the SNAP II and SNAP II PE scores and neonatal mortality were analyzed.

Results

There were 135 neonates admitted during the 6 month study period, of whom 126 met the inclusion criteria. Of these 126 neonates, 29 (23.0%) died. Mortality was 83% (5/6) when SNAP II was >40, and 66.7% (6/9) when SNAP II PE was >50. A SNAP II score of ≥12 had a sensitivity of 75.9%, and specificity of 73.2% for predicting mortality, and a SNAP II PE score of ≥14 had a sensitivity of 82.8% and specificity of 67.0% for it.

Conclusions

SNAP II and SNAP II PE scoring of neonates can be used to predict prognosis of neonates in resource-limited NICUs in Nepal.

Assessment of sickness severity of illness in neonates: review of various neonatal illness scoring systems

Sickness severity scores are widely used for neonates admitted to neonatal intensive care units to predict severity of illness and risk of mortality and long-term outcome. These scores are also used frequently for quality assessment among various neonatal intensive care unit and hospital. Accurate and reliable measures of severity of illness are required for unbiased and reliable comparisons especially for benchmarking or comparative quality improvement care studies. These scores also serve to control for population differences when performing studies such as clinical trials, outcome evaluations, and evaluation of resource utilisation. Although presently there are multiple scores designed for neonates' sickness assessment but none of the score is ideal. Each score has its own advantages and disadvantages. We did literature search for identifying all neonatal sickness severity score and in this review article, we discuss these scores along with their merits and demerits.

Receiver operating characteristic (ROC) curve for medical researchers

Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.

Validation of CRIB II for prediction of mortality in premature babies

OBJECTIVE

Validation of Clinical Risk Index for Babies (CRIB II) score in predicting the neonatal mortality in preterm neonates < or = 32 weeks gestational age.

DESIGN

Prospective cohort study.

SETTING

Tertiary care neonatal unit.

SUBJECTS

86 consecutively born preterm neonates with gestational age < or = 32 weeks.

METHODS

The five variables related to CRIB II were recorded within the first hour of admission for data analysis. The receiver operating characteristics (ROC) curve was used to check the accuracy of the mortality prediction. HL Goodness of fit test was used to see the discrepancy between observed and expected outcomes.

RESULTS

A total of 86 neonates (males 59.6% mean birthweight: 1228 +/- 398 grams; mean gestational age: 28.3 +/- 2.4 weeks) were enrolled in the study, of which 17 (19.8%) left hospital against medical advice (LAMA) before reaching the study end point. Among 69 neonates completing the study, 24 (34.8%) had adverse outcome during hospital stay and 45 (65.2%) had favorable outcome. CRIB II correctly predicted adverse outcome in 90.3% (Hosmer Lemeshow goodness of fit test P=0.6). Area under curve (AUC) for CRIB II was 0.9032. In intention to treat analysis with LAMA cases included as survivors, the mortality prediction was 87%. If these were included as having died then mortality prediction was 83.1%.

CONCLUSION

The CRIB II score was found to be a good predictive instrument for mortality in preterm infants < or = 32 weeks gestation.

Refractive status at birth: its relation to newborn physical parameters at birth and gestational age

BACKGROUND

Refractive status at birth is related to gestational age. Preterm babies have myopia which decreases as gestational age increases and term babies are known to be hypermetropic. This study looked at the correlation of refractive status with birth weight in term and preterm babies, and with physical indicators of intra-uterine growth such as the head circumference and length of the baby at birth.

METHODS

All babies delivered at St. Stephens Hospital and admitted in the nursery were eligible for the study. Refraction was performed within the first week of life. 0.8% tropicamide with 0.5% phenylephrine was used to achieve cycloplegia and paralysis of accommodation. 599 newborn babies participated in the study. Data pertaining to the right eye is utilized for all the analyses except that for anisometropia where the two eyes were compared. Growth parameters were measured soon after birth. Simple linear regression analysis was performed to see the association of refractive status, (mean spherical equivalent (MSE), astigmatism and anisometropia) with each of the study variables, namely gestation, length, weight and head circumference. Subsequently, multiple linear regression was carried out to identify the independent predictors for each of the outcome parameters.

RESULTS

Simple linear regression showed a significant relation between all 4 study variables and refractive error but in multiple regression only gestational age and weight were related to refractive error. The partial correlation of weight with MSE adjusted for gestation was 0.28 and that of gestation with MSE adjusted for weight was 0.10. Birth weight had a higher correlation to MSE than gestational age.

CONCLUSION

This is the first study to look at refractive error against all these growth parameters, in preterm and term babies at birth. It would appear from this study that birth weight rather than gestation should be used as criteria for screening for refractive error, especially in developing countries where the incidence of intrauterine malnutrition is higher.

Simplified age-weight mortality risk classification for very low birth weight infants in low-resource settings

OBJECTIVE

To identify a valid neonatal mortality risk prediction score feasible for use in developing countries.

STUDY DESIGN

Retrospective study of 467 neonates, < or =1500 g, enrolled in trials during 1998 to 2005 at tertiary care children's hospitals in Dhaka, Bangladesh, and Cairo, Egypt, and a community field site in Sarlahi District, Nepal. We derived simplified mortality risk scores and compared their predictive accuracy with the modified Clinical Risk Index for Babies (CRIB) II. Outcome was death during hospital stay (Dhaka and Cairo) or end of the neonatal period (Nepal).

RESULTS

The area under the curve receiver operating characteristic was 0.62, 0.71, 0.68, and 0.69 on the basis of the (a) CRIB II applied to the Dhaka-Cairo dataset; (b) an 18-category, simplified age, weight, sex score; (c) a binary-risk simplified age-weight (SAW) classification derived from the Dhaka-Cairo dataset; and (d) external validation of the binary-risk SAW classification in the Nepal dataset, respectively. Mortality risk prediction with the SAW classification on the basis of gestational age (< or =29 weeks) or weight (<1000 g) was improved (P = .048) compared with CRIB II.

CONCLUSIONS

The SAW classification is a markedly simplified mortality risk prediction score for use in identifying high-risk, very low birth weight neonates in developing country settings for whom urgent referral is indicated.

A new score for predicting neonatal very low birth weight mortality risk in the NEOCOSUR South American Network

OBJECTIVE

To develop and validate a model for very low birth weight (VLBW) neonatal mortality prediction, based on commonly available data at birth, in 16 neonatal intensive care units (NICUs) from five South American countries.

STUDY DESIGN

Prospectively collected biodemographic data from the Neonatal del Cono Sur (NEOCOSUR) Network between October 2000 and May 2003 in infants with birth weight 500 to 1500 g were employed. A testing sample and crossvalidation techniques were used to validate a statistical model for risk of in-hospital mortality. The new risk score was compared with two existing scores by using area under the receiver operating characteristic curve (AUC).

RESULTS

The new NEOCOSUR score was highly predictive for in-hospital mortality (AUC=0.85) and performed better than the Clinical Risk Index for Babies (CRIB) and the NICHD risk models when used in the NEOCOSUR Network. The new score is also well calibrated - it had good predictive capability for in-hospital mortality at all levels of risk (HL test=11.9, p=0.85). The new score also performed well when used to predict in hospital neurological and respiratory complications.

CONCLUSIONS

A new and relatively simple VLBW mortality risk score had a good prediction performance in a South American network population. This is an important tool for comparison purposes among NICUs. This score may prove to be a better model for application in developing countries.