Modified Sick Neonatal Score (MSNS): A Novel Neonatal Disease Severity Scoring System for Resource-Limited Settings

Comparison of STOPS and SNAPPE-II in Predicting Neonatal Survival at Hospital Discharge: A Prospective, Multicentric, Observational Study

OBJECTIVE

To compare SNAPPE-II and STOPS admission severity scores in neonates admitted to neonatal intensive care unit (NICU) with a gestational age of ≥ 33 wk.

METHODS

In this multicenter, prospective, observational study, the sickness scoring was done on all the neonates at 12 h after admission to the NICUs. The scoring systems were compared by the area under the curve (AUC) on the receiver operating characteristics (ROC) curve.

RESULTS

A total of 669 neonates with gestational age ≥ 33 wk (mortality rate: 2.4%), who were admitted to five participating NICUs within 24 h of birth, were included. Both SNAPPE-II and STOPS had the good discriminatory and predictive ability for mortality with AUCs of 0.965 [95% confidence interval (CI): 0.94-0.98] and 0.92 (95% CI: 0.87-0.99), respectively. The STOPS scoring system with a cutoff score ≥ 4 on the ROC curve had 85% accuracy, whereas the SNAPPE-II cutoff score ≥ 33 on the ROC curve had 94% accuracy in predicting mortality.

CONCLUSION

In infants with the gestational age of ≥ 33 wk, SNAPPE-II and STOPS showed similar predictive ability, but the STOPS score, being a simpler clinical tool, might be more useful in resource-limited settings.

A Study of the Prediction of Mortality in a Tertiary Care Hospital Using the Modified Sick Neonatal Score (MSNS): An Observational Cross-Sectional Study

PURPOSE

India is a major contributor to neonatal deaths worldwide. There is a paucity of amenities for the management of neonatal health issues in rural areas of our country. Hence, there is a need to invent a reliable scoring system for the analysis of neonatal mortality.

AIM

The aim of the study is to evaluate the Modified Sick Neonatal Score (MSNS) as a predictor of mortality in neonatal care units in resource-limited settings.

MATERIALS AND METHODS

This cross-sectional observational study was performed in the intensive care unit of our hospital. All the data were collected and analyzed using IBM Corp.'s Statistical Package for Social Sciences (SPSS) software.

RESULTS

Overall, 71 participants were considered for the present study. The common clinical diagnoses noticed in our participants were meconium aspiration, malformation, and jaundice. The MSNS score compared between expired and discharged participants is found to be statistically significant with p<0.05.

CONCLUSION

The MSNS scoring system is considered an ideal scoring system for detecting early mortality in neonates.

Caregivers perception of common neonatal illnesses and their management among rural dwellers in Enugu state, Nigeria: a qualitative study

BACKGROUND

Neonatal mortality continues to be a challenge in Nigeria, where low-quality care, caregivers' ignorance of signs of neonatal illnesses, and prevalent use of unorthodox alternatives to health care predominate. Misconceptions originating and propagating as traditional practices and concepts can be linked to adverse neonatal outcomes and increased neonatal mortality. This study explores the perceptions of causes and management of neonatal illness among caregivers in rural communities in Enugu state, Nigeria.

METHODS

This was a cross-sectional qualitative study among female caregivers of children residing in rural communities in Enugu state. A total of six focus group discussions (FGDs) were conducted; three in each of the communities, using an FGD guide developed by the researchers. Using pre-determined themes, thematic content analysis was used to analyze the data.

RESULTS

The mean age of respondents was 37.2 ± 13.5 years. Neonatal illnesses were reportedly presented in two forms; mild and severe forms. The common causes of the mild illnesses reported were fever, jaundice, eye discharge, skin disorders, and depressed fontanelle. The severe ones were convulsion, breathlessness/difficulty or fast breathing, draining pus from the umbilicus, and failure-to-thrive. The caregivers' perceptions of causes and management of each illness varied. While some believed these illnesses could be managed with unorthodox treatments, others perceived the need to visit health centers for medical care.

CONCLUSIONS

Caregivers' perception on the causes and management of common neonatal illnesses in these communities is poor. Obvious gaps were identified in this study. There is a need to design appropriate interventions to dispel the myths and improve the knowledge of these caregivers on neonatal illnesses towards adopting good health-seeking behaviours.

Evaluation of Modified Extended Sick Neonate Score to Predict In-Hospital Mortality among Newborns Admitted to Resource-Poor Settings in Rural India

OBJECTIVES

To devise a score based on eight extended sick neonatal score (ESNS) variables by adding two more variables, birth weight, and gestational age to check its efficacy.

METHODS

A retrospective study was conducted on 521 neonates admitted from January 2018 to June 2020 in the neonatal intensive care unit (NICU) of a tertiary care center. The proposed score has the following components, respiratory effort, heart rate, axillary temperature, capillary refill time, random blood sugar, pulse oximeter saturation, Moro reflex, modified Downes score, gestation age, and birth weight. Each was scored as 0, 1, and 2. The total score for each neonate was calculated from the records available and outcome documented. Receiver operating characteristic (ROC) curve was generated; and a cutoff score was derived to predict mortality and compared with modified sick neonatal score (MSNS).

RESULTS

Modified ESNS has a strong correlation with the outcome. Area under the ROC curve was 0.995 (95% CI: 0.925-0.985) for modified ESNS and 0.933 (95% CI: 0.901-0.976) for MSNS. The optimum cutoff values for predicting mortality were 15.5 for modified ESNS and 12.5 for MSNS. For a cutoff score of ≤ 15, sensitivity and specificity were 86.27% and 86.60% for modified ESNS and 90.20% and 84.89%, respectively, for MSNS, in predicting mortality. Positive and negative predictive values were 41.12% and 98.31% for modified ESNS and 39.32% and 98.76%, respectively for MSNS.

CONCLUSION

The modified ESNS can predict in-hospital mortality among neonates, with good sensitivity and specificity.

Modified Sick Neonatal Score and Delta: Modified Sick Neonatal Scores As Prognostic Indicators in Neonatal Intensive Care Units

Background

Modified Sick Neonatal Score (MSNS) is a modification of the Sick Neonatal Score (SNS) by adding perinatal factors such as birth weight and gestational age to the scoring. A significantly higher sensitivity and specificity were obtained by adding the above parameters to SNS. The parameters in MSNS were simple and easy to score, but the scoring was done only once, and the utility of the score to assess the response to treatment was not analysed. In this study, we aimed to determine the role of MSNS as a prognostic indicator in the neonatal intensive care unit (NICU) and to study the correlation of the change in the MSNS (Delta-MSNS) 24 hours after admission with the outcomes and as a measure of response to treatment.

Methodology

A cross-sectional study was conducted for six months on all neonates admitted to the NICU during the study period. All babies were scored using MSNS containing eight basic clinical parameters, namely, respiratory rate, heart rate, axillary temperature, peripheral capillary refill time, random blood sugar, oxygen saturation, gestational age, and birth weight. Scoring by MSNS was done on the following two occasions: first score at admission, and second score 24 hours later. A change in the score during the first 24 hours of NICU stay (Delta-MSNS) was analysed for babies with an initial score of less than or equal to 12. All babies were followed up for analysis of outcomes, and outcomes were documented as discharge from the hospital or death. Length of hospital stay was documented for the babies that were discharged. Statistical analysis was done using the software RStudio v1.1.456. Kruskal-Wallis rank sum test was used to compare individual parameters of the score as well as the mean score between the expired and discharged groups. Spearman rank-order correlation coefficient was used to correlate the scores with length of hospital stay.

Results

A total of 248 neonates were considered for inclusion in the study. The mean score at admission was 7.94 (SD = 1.89) in the expired group and 14.46 (SD = 1.84) in the discharged group. The p-value for each of these was <0.001. Using MSNS as a test variable, a receiver operating characteristic (ROC) curve was generated, and as measured by the area under the curve was 0.98. A cut-off score of 10 was obtained that had a sensitivity of 88.24%, specificity of 95.2%, positive predictive value of 57.69%, and negative predictive value of 99%. A significant negative correlation was observed with a Spearman correlation coefficient of -0.67 when the initial MSNS was correlated with length of hospital stay in patients who were discharged. A significant negative correlation with a coefficient of -0.39 and p-value of 0.017 was determined when delta MSNS score was correlated with the discharged babies who had an initial score of less than 12.

Conclusions

MSNS is an easy-to-use bedside scoring system that requires minimal training and no invasive procedures. It has shown a high sensitivity in predicting mortality and length of hospital stay. Additionally, calculation of delta score was equally useful. It is a simplified score that enables providers to objectively assess the severity of illness with nominal training both in NICU as well as in low-resource settings.

Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit

Background: preterm and critically ill neonates often experience clinically suspected sepsis during their prolonged hospitalization in the neonatal intensive care unit (NICU), which can be the initial sign of final adverse outcomes. Therefore, we aimed to utilize machine learning approaches to predict neonatal in-hospital mortality through data-driven learning. Methods: a total of 1095 neonates who experienced clinically suspected sepsis in a tertiary-level NICU in Taiwan between August 2017 and July 2020 were enrolled. Clinically suspected sepsis was defined based on clinical features and laboratory criteria and the administration of empiric antibiotics by clinicians. The variables used for analysis included patient demographics, clinical features, laboratory data, and medications. The machine learning methods used included deep neural network (DNN), k-nearest neighbors, support vector machine, random forest, and extreme gradient boost. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: the final in-hospital mortality of this cohort was 8.2% (90 neonates died). A total of 765 (69.8%) and 330 (30.2%) patients were randomly assigned to the training and test sets, respectively. Regarding the efficacy of the single model that most accurately predicted the outcome, DNN exhibited the greatest AUC (0.923, 95% confidence interval [CI] 0.953–0.893) and the best accuracy (95.64%, 95% CI 96.76–94.52%), Cohen’s kappa coefficient value (0.74, 95% CI 0.79–0.69) and Matthews correlation coefficient value (0.75, 95% CI 0.80–0.70). The top three most influential variables in the DNN importance matrix plot were the requirement of ventilator support at the onset of suspected sepsis, the feeding conditions, and intravascular volume expansion. The model performance was indistinguishable between the training and test sets. Conclusions: the DNN model was successfully established to predict in-hospital mortality in neonates with clinically suspected sepsis, and the machine learning algorithm is applicable for clinicians to gain insights and have better communication with families in advance.

Rapid vs. Slow Rewarming for Management of Moderate to Severe Hypothermia in Low-Birth-Weight Pre-term Neonates-An Open Label Randomized Controlled Trial

BACKGROUND

Evidence is lacking regarding the optimal method of rewarming hypothermic low-birth-weight (LBW) pre-term neonates. We aim to evaluate the effect of rapid vs. slow rewarming in the management of moderate to severe hypothermia in LBW pre-term neonates.

METHODS

In this open label, randomized controlled trial, 100 LBW (<2.5 kg), pre-term (<37 weeks) neonates with moderate to severe hypothermia (<36°C) was randomized to two groups of 50 each and received either rapid (at >0.5°C/h) or slow (at ≤0.5°C/h) rewarming rate till normothermia. The primary outcome was stabilization score [TOPS (temperature, oxygenation, perfusion and saturation) and MSNS (modified sick neonatal score)] at baseline, 6 and 24 h and mortality until discharge. Other neonatal morbidities were assessed as secondary outcomes.

RESULTS

Mean TOPS score and MSNS score at baseline, 6 and 24 h of admission as well as change in score from baseline were similar between the two groups. The median rewarming rate [interquartile range (IQR)] was higher in rapid rewarming group than in the slow rewarming group [5.05°C/h (3.54-7.71) vs. 0.71°C/h (0.60-0.90); p < 0.001]. The median rewarming time taken in rapid rewarming group was lesser compared with that in the slow rewarming group [0.31 h (IQR 0.13-0.75) vs. 2.05 h (IQR 1.11-3.03); p < 0.001]. Mortality in rapid rewarming and slow rewarming group was similar [7/50 vs. 5/50; OR 1.46 (0.43-4.97), p = 0.538].

CONCLUSION

Rapid rewarming was as effective and safe as slow rewarming in the management of moderate to severe hypothermia in LBW pre-term neonates with similar short-term neonatal outcomes.

CTRI NUMBER

CTRI/2018/01/011187.

Intravenous vs intravenous plus aerosolized colistin for treatment of ventilator-associated pneumonia - a matched case-control study in neonates

BACKGROUND

Recently intravenous (IV) and aerosolized (ASZ) colistin have been used for treating ventilator-associated pneumonia (VAP) due to colistin susceptible multidrug-resistant Gram-negative bacteria (MDR-GNB). Colistin has limited lung penetration. We compared the efficacy and safety of IV-alone versus IV+ASZ-colistin for treating VAP in neonates.

METHODS

This retrospective matched case-control study was performed at NICU of the Aga Khan University Hospital, Pakistan between January 2015 and December 2018. Sixteen neonates with MDR-GNB associated VAP received IV-ASZ-colistin and were matched for date of birth, gestational age, birth weight, Apgar score, antenatal steroid history, disease severity, and duration of mechanical ventilation with 16 control neonates who received IV-colistin alone.

RESULTS

Both groups had similar MDR-GNB isolates and Acinetobacter baumannii (78%) was the most common pathogen. No colistin-resistant strain was isolated. Duration of IV-colistin and concomitant antibiotics use was significantly (p < 0.05) shorter in the IV-ASZ-colistin group. Significantly (p < 0.05) higher clinical cure and microbial eradication, along with lower ventilatory requirements, mortality rate, and colistin induced nephrotoxicity and electrolyte imbalance was observed in the IV-ASZ-colistin group.

CONCLUSIONS

With better lung penetration, ASZ-colistin offers effective and safe microbiological and clinical benefits as adjunctive or alternate treatment of VAP due to colistin susceptible MDR-GNB in neonates.