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Year : 2023  |  Volume : 12  |  Issue : 1  |  Page : 1-6

Role of modified sick neonatal score in predicting the neonatal mortality at limited-resource setting of central India

Department of Paediatrics, Government Medical College, Nagpur, Maharashtra, India

Date of Submission20-Aug-2022
Date of Decision12-Sep-2022
Date of Acceptance20-Sep-2022
Date of Web Publication03-Jan-2023

Correspondence Address:
Rajkumar Motiram Meshram
Department of Paediatrics, Government Medical College, Nagpur, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcn.jcn_83_22

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Background: The targets of Sustainable Development Goals cannot be achieved without reducing mortality of inborn as well as outborn neonates. Objective: The objective was to predict the mortality of neonates by applying Modified Sick Neonatal Score (MSNS). Material and Methods: Parameters of MSNS scoring system were applied to 450 neonates at the time of admission and followed up prospectively till discharge or death. The score and individual parameters were correlated with outcome. A receiver operating curve was plotted to determine the cutoff value for score to predict the mortality. Results: The common clinical diagnoses were respiratory distress (38%), sepsis (26%), and jaundice (14.44%). Sixty-two percent neonates were born at term and 38% were preterm while 55.56% neonates were low birth weight and 44.4% were weighted more than 2500 gms. Neonates who died were older at the time of admission, and duration of hospital stay was shorter compared to alive neonates (P < 0.001) with a mortality rate of 23.11%. The total MSNS for neonates who died was statistically significantly low, (mean ± standard deviation) 7.93 ± 2.70, compared to alive neonates, 12.02 ± 1.84 (P < 0.0001). With optimum cutoff score of ≤10, the sensitivity was 79.80% and the specificity was 82.37% while the positive predictive value and negative predictive value was 57.64% and 93.14% respectively with the area under curve was 0.89 (odds ratio-18.46, 95% confidence interval 10.3-33.64, P < 0.0001). Conclusion: The MSNS of ≤10 has a better sensitivity and specificity in predicting neonatal mortality and is easy to use with minimal resources to both preterm and term neonates.

Keywords: Modified Sick Neonatal Score, neonatal mortality, sick neonate score

How to cite this article:
Meshram RM, Nimsarkar RA, Nautiyal AP. Role of modified sick neonatal score in predicting the neonatal mortality at limited-resource setting of central India. J Clin Neonatol 2023;12:1-6

How to cite this URL:
Meshram RM, Nimsarkar RA, Nautiyal AP. Role of modified sick neonatal score in predicting the neonatal mortality at limited-resource setting of central India. J Clin Neonatol [serial online] 2023 [cited 2023 Mar 21];12:1-6. Available from: https://www.jcnonweb.com/text.asp?2023/12/1/1/366892

  Introduction Top

Despite remarkable improvement in obstetrics and neonatal health services, the neonatal phase of life carries the more risk of mortality. Globally, around two-thirds of neonatal deaths occur within the 1st week of life, and majority of them are on the 1st day of life.[1] Without focusing on the health and steps to control the mortality of extramural neonates, it is still difficult to attain the goal of reducing the neonatal mortality target at 12/1000 live births by 2030 given under the Sustainable Development Goals.[2] According to the recent National Family Health Survey (NFHS-5), the neonatal mortality rate in India is 24.9/1000 live births including interstate and rural–urban variations. Such high neonatal mortality can be attributed to the unavailability of effective antenatal, fetomaternal, and neonatal transport system as well as poor or no care during transport, self-transportation by parents, and without any pretreatment or stabilization of neonates.[3],[4],[5]

Such high risk of mortality in neonatal period demands a need of scoring system which would be simple, reliable, and reproducible with the possibility of making standardized comparison between performances of different units. This scoring system should be able to assess the well-being of neonate during transport as well as on arrival at the referral center.[6],[7]

A lot of scoring systems for the assessment of neonates were invented, such as clinical risk index for babies, Score for Neonatal Acute Physiology (SNAP), Score for Neonatal Acute Physiology-Perinatal Extension (SNAPPE), SNAP-2, SNAP-PE 2, and Neonatal Therapeutic Intervention Scoring System, but they had their own limitations.[8],[9],[10],[11],[12] Few improved systems such as Sick Neonatal Score (SNS)[13] and Extended SNS (ESNS)[14] are devised for predicting mortality of sick referral neonates; however, these scoring systems had limited reach because of the need of various instruments and devices which are not available in many resource-limited settings. Hence, Sick Neonate Score was modified by adding easily recordable parameters such as birth weight and gestational age, as Modified SNS (MSNS) which is now used to predict the neonatal mortality in limited-resource settings. This study was planned to use the MSNS to predict the neonatal mortality at limited-resources setting.

  Material and Methods Top

This cross-sectional analytical study was carried out at (Government Medical College and Hospital, Nagpur, Maharashtra, India) a tertiary care teaching government referral hospital of central India on inborn neonates for 1 year (June 2020 to May 2021). Our Neonatal Intensive Care Unit (NICU) is a 20-bedded level III NICU with facilities of mechanical ventilation, surfactant therapy, and renal replacement therapy (peritoneal dialysis). The sample size was calculated by assuming the sensitivity of MSNS of ≤10 is 80% with absolute precision of 1% with confidence interval (CI) of 95% using the following formula: N = Z21−α p (1-p)/d2, where N = Number of sample, α = Level of significance, Z1-α = Corresponding normal standard variant, P = Sensitivity (%), d = Absolute precision, and the sample size was 305. A total of 912 neonates were admitted during the study. Four hundred and sixty-two neonates were excluded by applying inclusion/exclusion criteria, and 450 neonates were included in the study by simple random method [Figure 1]. We recruited neonates of either sex of more than 28-week gestation after getting approval from the Institutional Ethical Committee of our institute (No 2041/EC/Pharmac/GMC/NGP date May 04, 2020) and informed valid consent from parents. Neonates with lethal congenital malformation, acute surgical emergencies, neonates whose parents were not willing to participate, and who left the hospital against medical advice were excluded from the study.
Figure 1: Flow chart of study participants

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A preparatory educational session was held for resident to standardize and improve the quality of observations and uniformity of screening the neonates and application of parameters of MSNS. Temperature assessment was done using the digital thermometer (EC-5004) by keeping it in the armpit of the neonate for 2 min. Two readings were recorded, and the lowest temperature reading was considered for the study. Oxygen saturation was measured at room air with a pulse oximeter (MD300C53). It was recorded twice, and the mean of two values was considered for the study. Capillary refill time was measured at the sternum by simultaneously activating stopwatch while applying gentle pressure until the skin is blanched. The stopwatch was inactivated when skin color returned to the baseline. A time period of 30 s was allowed before the next attempt if for any reason measurement had to be repeated. Blood sugar level was recorded by Glucostrips read by glucometer (AccuSure TD: 4183). Gestational age was assessed using the Modified New Ballard Score, and confirmation was done by either available maternal document mentioning the last menstrual period or relevant ultrasonography documents. Birth weight was recorded on electronic weighing scale with a minimal error of 10 gm. Heart rate was recorded on multipara monitor (Nihon Kohden Model BSM3763) for 1 min. Respiratory effort and rate were recorded clinically. The parameters of the MSNS were recorded on admission or as late as within half an hour. Scoring of each parameter was done as per the measurement and recorded by giving a score of 0, 1, and 2. Score "0" was denoting the worst, whereas score "2" is for the best. The maximum score was 16, and minimum was zero. Sociodemographic data and other clinical details were collected from either mother or caregiver in a specially designed structural data sheet for this study, and the whole process was supervised by senior residents. The details of parameters of MSNS are mentioned in [Table 1].[15] Relevant hematological, biochemical, and radiological investigations were done. The diagnosis was made on the basis of clinical findings and investigations. All the cases were managed according to the standard treatment protocol of our hospital and followed up to discharge or death.
Table 1: Parameters of the Modified Sick Neonatal Score

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Statistical analysis

Collected data were entered into Micro software spreadsheet, coded, and analyzed in a statistical software STATA version 14.0. Results were recorded as mean with standard deviation (SD), median with interquartile ranges (IQR), and percentage in appropriate tables. The Chi-square test was used to find significance of trend in mortality for ordinal categories, and Fisher's exact test was used for data with small frequency. The receiver operating characteristics (ROC) were generated with MSNS to predict mortality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the cutoff value. The Mann–Whitney U test was used to compare the scores between discharged and dead neonates in each of the individual parameters. P < 0.05 was considered statistically significant.

  Results Top

A total of 450 neonates were recruited in the study. The male-to-female ratio was 1.3:1. Sixty-two percentages of neonates were born at term, while 55.56% were of low birth weight. Other baseline characteristics are mentioned in [Table 2]. The predominant clinical diagnosis in study participants was respiratory distress in 171 (38%), sepsis in 117 (26%), followed by jaundice in 65 (14.44%) and birth asphyxia in 52 (11.56%). Malformation was noted in 22 (4.89%), while meconium aspiration syndrome in 12 (2.67%) and nonspecific causes were recorded in 11 (2.44%) neonates [Figure 2]. Neonates who died were older at the time of admission (mean ± SD 113.91 ± 176.26 h) compared to alive neonates (mean ± SD 70.32 ± 103.34 h) (P < 0.05), while the duration of hospital stay was significantly shorter for neonates who died (mean ± SD 4.35 ± 3.20 days) compared to alive neonates (mean ± SD 6.23 ± 3.78 days) (P < 0.001).
Figure 2: Clinical diagnosis of participants

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Table 2: Baseline characteristics of study participants (n=450)

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Neonates who were discharged had a higher frequency of better MSNS score across the parameters and the difference was statistically significant. The mean of the total MSNS score for discharged neonates was 12.02 ± 1.84 compared to 7.93 ± 2.70 for neonates who died (P < 0.0001). [Table 3] shows the frequencies of scores 0, 1, and 2 for each parameter of Modified Sick Neonate Score among discharged and died neonates.
Table 3: Distribution of parameters of the Modified Sick Neonatal Score among study participants

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[Table 4] depicts median (IQR) for each parameters of MSNS score among discharged and died neonates. The difference in scores among discharged and died neonates was statistically significant for all parameters except random blood sugar and birth weight as per the Mann–Whitney U test.
Table 4: Median (interquartile range) for parameters among survival and nonsurvival

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The ROC curve drawn with MSNS score as the test variable to predict mortality is shown in [Figure 3]. The area under curve was 0.89 (odds ratio [OR]-18.46, 95% CI 10.3–33.64, P < 0.0001). The maximum cutoff value for prediction of mortality was 10. The sensitivity and specificity were 79.80% and 82.37%, respectively, for the optimum cutoff score of ≤10. Positive predictive value and negative predictive value were 57.64% and 93.14%, respectively, while accuracy was 81.77%.
Figure 3: ROC curve with the total MSNS score <10 to predict mortality. ROC - Receiver operating characteristic; MSNS - Modified Sick Neonatal Score

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  Discussion Top

The neonatal disease severity scoring system is an important tool to assess the severity of neonatal illness for reducing the adverse neonatal outcome. Such scoring system should be easy to apply, less time-consuming, applicable in early course of hospitalization, ability to reproduce mortality and specific morbidity, and useful for all groups of neonates. Various scoring systems need invasive investigations and sophisticated instruments which are usually not available at all places, while few are specifically useful to preterm or term neonates and few are based on treatment received by neonates rather than pathophysiological factors. It is a well-known fact that hypothermia, hypoxemia, poor perfusion and low sugar, prematurity, and low birth weight are the major determinants of poor neonatal outcome.[16] The MSNS scoring system, which is a modification of SNS and ESNS, can be preferred in resource-limited settings due to nonrequirement of various sophisticated instruments and devices. It can be applied by both medical as well as paramedical personnel and applicable to both term and preterm neonates without decreasing the prognostic accuracy with ease of use as it is based on simple observable and measurable physiological with clinical variables such as temperature, oxygen saturation, blood sugar levels, skin perfusion, gestational age, and birth weight as well as clinical variables such as heart rate and respiratory efforts.

In the present study, 62% neonates were born at term and 38% were preterm while 55.56% neonates were low birth weight and 44.4% were weighted more than 2500 gms. Similar to the observations of other researchers, we encountered the common clinical problems such as sepsis, birth asphyxia, and respiratory distress.[5],[17],[18] We observed male dominance which might be attributed to biological vulnerability and social causes like preference of male children. Higher mortality rate (23.11%) in the current study might be due to late referral of complicated mother, as our institute is the largest government referral center serving to the socially and economically deprived population of central India. Our observations are concomitant with the findings of Chheda et al. (21.2%) and Verma et al. (20.76%); however, higher mortality rate is reported by Shah et al. (38%), Meshram et al. (31.98%), and Singh et al. (30.1%), while lower mortality is reported by Agrawal et al. (18%).[17],[18],[19],[20],[21],[22]

In the present study, MSNS (mean ± SD) was significantly low in neonates who died (7.93 ± 2.70) compared to discharged neonates (12.02 ± 1.84), and also noted lower score of each parameter in neonates who died compared to alive (P < 0.0001). We noted, MSNS had sensitivity of 79.80% and specificity of of 82.37% while positive predictive value and negative predictive value was 57.64% and 93.14% respectively with accuracy of 81.77%, when an optimum cutoff score of ≤10 to predict the mortality. The area under the ROC curve was 0.89 (OR-18.46, 95% CI 10.3–33.64, P < 0.0001), which is comparable to the findings of Mansoor et al. (sensitivity of 80%, specificity of 88.8% with an optimum cutoff score of ≤10, and the area under the ROC curve was 0.913). They also noted lower MSNS score and even lower score of individual parameters in expired neonates versus discharge neonates by applying a physiological and clinical variable (MSNS score) on 585 SCNU graduate, various authors reported variable predictability of SNS at variable cutoff score, while MSNS had a better sensitivity and specificity compared to SNS and ESNS.[13],[14],[22]

Although nowadays, neonatal illness severity scores are well-accepted tool, no single mathematical formula can completely capture the complex neonatal clinical process, and even the best scoring system is not completely accurate. The major limitation of this study was being a single-centered study and scoring was done at the time of hospitalization only. Maternal diseases, perinatal events, and hospital-acquired infection are not considered, which may influence the MSNS severity and mortality.

  Conclusion Top

MSNS had a better sensitivity and specificity with high negative predictive value and accuracy of 81.77% at cutoff score ≤10 and predict the neonatal mortality. It is easy to use by medical and paramedical personnel, with minimal resources to both preterm and term to assess neonatal disease severity and predict the prognosis of neonates in low- and middle-income countries. Further research is needed on larger participants and considerations of maternal variables, and serial scoring may have provided additional information.

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Conflicts of interest

There are no conflicts of interest.

  References Top

Neonatal Mortality-UNICEF Data. Available from: https://data.unicef.org/topic/child-survival/neonatal-mortality/. [Last accessed on 2022 Feb 10].  Back to cited text no. 1
National Family Health Survey-5 2019-2021 India Fact Sheet (pdf). Available from: http://rchiips.org/nfhs/factsheet_NFHS-5.shtml. [Last accessed on 2022 Feb 10].  Back to cited text no. 2
Mondal T, Khatun M, Md Habibulla SK, Ray S, Hazra A, Ivan MD, et al. Epidemiology of newborn transport in India - The reality check. Med J DY Patil Vidyapeeth 2021;14:308-13.  Back to cited text no. 3
  [Full text]  
Punitha P, Kumaravel KS, Pugalendhiraja KV, Santoshkumar. A study on the current status of neonatal transport to a special newborn care unit. Stanley Med J 2016;3:55-8.  Back to cited text no. 4
Meshram RM, Jain DL, Apte MU, Denge A. Morbidity and mortality pattern of intramural and extramural neonate: A prospective observational study. Int J Contemp Pediatr 2021;8:1006-113.  Back to cited text no. 5
Dorling JS, Field DJ, Manktelow B. Neonatal disease severity scoring systems. Arch Dis Child Fetal Neonatal Ed 2005;90:F11-6.  Back to cited text no. 6
Garg B, Sharma D, Farahbakhsh N. Assessment of sickness severity of illness in neonates: Review of various neonatal illness scoring systems. J Matern Fetal Neonatal Med 2018;31:1373-80.  Back to cited text no. 7
Cockburn F, Cooke RW, Gamsu HR, Greenough A, hopkins A, Mcintosh N, et al. The CRIB (clinical risk index for babies) score: A tool for assessing initial neonatal risk and comparing performance of neonatal Intensivr care units. The international Neonatal network. Lancet 1993;24:193-8.  Back to cited text no. 8
Parry G, Tucker J, Tarnow-Mordi W, UK Neonatal Staffing Study Collaborative Group. CRIB II: An update of the clinical risk index for babies score. Lancet 2003;361:1789-91.  Back to cited text no. 9
Richardson DK, Gray JE, McCormick MC, Workman K, Goldmann DA. Score for neonatal acute physiology: A physiologic severity index for neonatal intensive care. Pediatrics 1993;91:617-23.  Back to cited text no. 10
Richardson DK, Corcoran JD, Escobar GJ, Lee SK. SNAP-II and SNAPPE-II: Simplified newborn illness severity and mortality risk scores. J Pediatr 2001;138:92-100.  Back to cited text no. 11
Gray JE, Richardson DK, McCormick MC, Workman-Daniels K, Goldmann DA. Neonatal therapeutic intervention scoring system: A therapy-based severity-of-illness index. Pediatrics 1992;90:561-7.  Back to cited text no. 12
Rathod D, Adhisivam B, Bhat BV. Sick neonate score – A simple clinical score for predicting mortality of sick neonates in resource restricted settings. Indian J Pediatr 2016;83:103-6.  Back to cited text no. 13
Ray S, Mondal R, Chatterjee K, Samanta M, Hazra A, Sabui TK. Extended sick neonate score (ESNS) for clinical assessment and mortality prediction in sick newborns referred to tertiary care. Indian Pediatr 2019;56:130-3.  Back to cited text no. 14
Mansoor KP, Ravikiran SR, Kulkarni V, Baliga K, Rao S, Bhat KG, et al. Modified sick neonatal score (MSNS): A novel neonatal disease severity scoring system for Resource-limited settings. Crit Care res Pract 2019;2019:9059073.  Back to cited text no. 15
Mathur NB, Arora D. Role of TOPS (a simplified assessment of neonatal acute physiology) in predicting mortality in transported neonates. Acta Paediatr 2007;96:172-5.  Back to cited text no. 16
Singh J, Dalal P, Gathwala G, Rohilla R. Transport characteristics and predictors of mortality among neonates referred to a tertiary care Centre in North India: A prospective observational study. BMJ Open 2021;11:e044625.  Back to cited text no. 17
Verma SK, Nagaura CP, Goyal VK, Raheja KK, Singh A, Sharma P, et al. Status of transported neonate and evaluation of TOPS as a survival score. Indian J Neonatal Med Res 2017;5:PO01-05.  Back to cited text no. 18
Chheda A, Khadse S, Valvi C, Kulkarni R, Hiremath A. Importance of temperature, oxygen saturation, perfusion, sugar (TOPS) Parameters and the concept of TOPS score for neonatal transport in India – A pilot project. Pediatr Oncall J 2018;15:69-72.  Back to cited text no. 19
Shah BH, Gosai D, Pikle AS. Extended sick neonatal score in prediction of mortality of neonates transported to tertiary healthcare centre and its comparison with sick neonatal score and temperature, oxygenation, perfusion and blood sugar score. Int J Ceontemp Pediatr 2020;7:1996-9.  Back to cited text no. 20
Bokade CM, Meshram RM. Morbidity and mortality patterns among outborn referral neonates in Central India: Prospective observational study. J Clin Neonatol 2018;7:130-5.  Back to cited text no. 21
  [Full text]  
Agrawal J. Sick neonate score: Role in predicting mortality. Clin Mother Child Health 2020;17:355.  Back to cited text no. 22


  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3], [Table 4]


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