Ninety-seven sepsis patients were identified in this study from 3557 neonatal patients during the 3-year study period. Comparing the incidence of neonatal sepsis in countries was not easy since many reports used different criteria for early- and late-onset neonatal sepsis [42]. In Pakistan, Bosnia, and Malaysia, the incidences of LOS were 29, 71.3, and 90.2%, respectively [1, 23, 43]. Data from four other countries, including Thailand, found an incidence of 5 per 1000 live births [17]. The prevalence was 21.8 or more in Nigeria [12, 13]. A report from the largest hospital in Indonesia found an incidence of 35% [44].
Among all the cases of neonatal sepsis, the percentage of neonates weighing less than 2500 g was 64.1%. Based on the gestational age, the percentage of preterm neonates was 48.9, 69.2, and 59.8% for early-onset sepsis (EOS), LOS, and total sepsis, respectively. These results were similar to other body weight-based reports. Another study reported that the incidence of LOS among very low birth weight (VLBW) neonates was 25–30% and 6–10% in late preterm neonates, with the mortality rate of 36–51% [22]. Data from Kenya and Gambia showed a CFR of 26 and 31% [45, 46].
The percentage of gram-negative organisms in this study was 67.3% (35/52). Klebsiella pneumoniae and CONS were the most common microorganisms. These data were comparable with other developing countries [42, 47]. A 10-year prospective surveillance in Brazil revealed 51.6% episodes of neonatal infection caused by gram-negative rods (mainly Klebsiella spp. and E. coli) [48].
Antibiotics are one of the most important treatments for neonatal sepsis, although some people may not receive this treatment because of the facility limitation in some rural areas [8]. The first line of antibiotics for neonatal sepsis in many countries, like in the studied hospital, are a combination of penicillin group and gentamicin. At least 78% of the LOS patients in this study were administered ampicillin. However, broad-spectrum antibiotics can create problems of resistance. Multi-resistant organisms, such as A. baumanii and K. pneumoniae, are consistently increasing in many countries, especially in LMIC [8, 44]. Our study focused on bacterial sepsis. All neonatal sepsis patients used antibiotics. This was not used as a decisive variable in our study.
All possible proven neonatal sepsis patients during the 3-year period were included in this study. Nevertheless, this study had a larger sample size than previous studies. The NOSEP Score by Mahieu et al. (2000) used 43 proven episodes and 104 suspected sepsis episodes but did not use non-suspected sepsis patients [49]. Okascharoen et al. (2005) used 1870 neonates, with only 17 proven sepsis patients [16]; Singh et al. (2003) used 30 episodes of definite, 17 most probable, and 58 non-sepsis patients in their study [50]. Recently, the system by Singh was modified using 497 infants in Bangladesh [51]. In 1982, Tollner created the first neonatal sepsis score using basic clinical and laboratory data. He used 667 neonates in Ulm hospital [52].
The dependent variable for this study was proven neonatal sepsis. The proof was mostly based on the culture results, particularly hemoculture. All unproven sepsis patients were excluded. The clearly defined outcome variable is an essential requirement [53]. Confirmed sepsis guaranteed consistency and validity of the outcome [51]. The unproven neonatal sepsis patients were excluded from this study to avoid incorporation bias. This bias would appear if the possible predictive factors became part of the diagnostic criteria [3, 34].
Independent variables in the study originated from previous studies about the predictive model for neonatal sepsis and some scores for neonatal morbidity and mortality. In other clinical prediction rules, predictor variables were identified by the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relations [36]. In this study, the independent variables were added by some changes of continuous variables into qualitative forms. Others were made from the unification of some variables.
Initially, the original variables were classified as risk factors / history, clinical conditions, laboratory data, and treatment modalities, as suggested in some previous reports [54]. Some newer laboratory examinations such as procalcitonin [55], various interleukins [56, 57], and PCR methods [58] were not included in this study due to availability and financial reason.
The risk factors included demographic data and maternal history. In this study, the maternal history considered the mother’s habits (smoking, drug use), and the mother’s diseases (fever, amnionitis, history of antibiotics). Maternal diseases significantly contribute to neonatal sepsis—mostly for the early-onset sepsis. Puerperal infection was associated with 2:1 adjusted Risk Ratio for early neonatal mortality. Around 5% of all deaths in the first week of life were attributable to signs suggestive of puerperal infections [59].
To reduce the number of predictor variables and to make the statistical selection, some univariate tests were used as appropriate. In these tests p < 0.1 was used, although some other models used p < 0.2 [53]. Singh et al. did not use the univariate test for the study [50]. The selection of variables was based on the positive likelihood ratio. The results of the univariate tests were 68 (21 risk factors, 11 clinical condition, 34 laboratories, and 2 treatment modalities) variables.
Multivariate analysis used multiple logistic regression because the outcome variable was dichotomous, and this test was easy [53]. The reselection process was done based on clinical judgment, collinearities (more than 1 variables measured the same thing), similarities, and performances. If continuous and qualitative data were present, the qualitative would be chosen due to practicability. The use of dichotomized data was also accurate and more useful in clinical practice. The original continuous data in NOSEP score derivation did not improve the accuracy of the global scoring system [49].
All the variables were tried one by one several times if more than one choice were available. Gestational age did not pass the univariate test but this variable was tried to enter the multivariate analysis because of its clinical significance [16]. However, this variable still could not be included in the multiple logistic regression results. Some other significant risk factors could not enter the multivariate analysis probably because of the selection of the control group. The choice of non-sepsis neonates would influence the univariate and multivariate results. The final model was selected based on the variable composition, clinical judgment, and performance of the area under the ROC curve [16, 60].
The final equation used 6 variables (4 clinical conditions and 2 laboratory data). Abnormal heart rate had the second-highest adjusted OR after abnormal temperature. Abnormal heart rate characteristics (reduced variability and transient decelerations) occurred early in neonatal sepsis. These abnormalities were present 12–24 h before the clinical diagnosis of sepsis. This method was studied extensively by Griffin et al. in 2001 and 2003 (external validation) [61]. In this study, the normal value was simpler and not calculated using a sophisticated method. Reduced variability and transient decelerations in heart rate may be an early indicator of clinical instability [62, 63].
Abnormal temperature had the highest adjusted OR in the model. This was the most frequent clinical feature in some studies [16, 49]. For term infants, hyperthermia was a high predictive parameter. Some studies showed that more than 50% of the sepsis patients had a fever, while hypothermia was only found among 15% of the infants [64]. In this study, no infant with hypothermia developed late-onset sepsis. This is like the results by Okascharoen et al. (2005). The mortality rate was high among mild and moderate hypothermia in another study and the proportion of hyperthermia and hypothermia was 13 and 13.5%, respectively [65].
Abnormal leucocytes were determined according to Manroe’s criteria [66]. Leucocytes (total white blood cell (WBC) count) are one of the most common tests for evaluating bacterial infections. The criteria by Manroe were still used by some reference books despite its weaknesses, such as depending on the infant’s age, gestational age, and the blood vessels [66, 67]. Abnormal pH—mostly acidosis—would accompany hypoxemia. Metabolic acidosis is, most commonly, a consequence of lactic acid accumulation from anaerobic metabolism in hypoxic infants.
The NOSEP score had 5 final variables (1 risk factor, 1 clinical condition, and 3 laboratory data). The model from Okascharoen et al. had 6 variables (1 risk factor, 3 clinical conditions, and 2 laboratory data), and Singh et al. used 7 final variables (all clinical conditions) [16, 49, 50]. Later, the Hematology Scoring System was revalidated in India using 110 neonates with a good result [68]. Tollner in 1982 used seven clinical parameters, skin color, capillary refill, muscular hypotonia, apnea, respiratory distress, hepatomegaly, and gastrointestinal symptoms [52]. NEO-KISS was a score based on the German national surveillance scoring system. It includes clinical, biochemical, and hematological criteria [69].
Changing the equation into the scoring system will make the usage of the model easier. In comparison with the probability of the equation, the scoring system had a good result. The regression coefficients were used to determine the score [70]. At least 4 possibilities of rounding the coefficients were tried for each group. A different score would produce a different performance of the result. The best system was chosen based on the area under the curve (AUC) of the ROC curve and other performance indicators. The final scoring system for late-onset neonatal sepsis had AUC of 96.6%. The maximum score for this model was 23.
In this study, the AUC was 95.6% for the equation and 95.5% for the score. The sensitivity and specificity of the equation were above 80% for the probability cutoff of 20–40% (equation), or 2–3 (score) However, the choice of cutoff (including the PPV, NPV, LR+, and LR(−)) depends on the purpose of usage. For the balanced sensitivity and specificity, the choice would have to be above 70% of the value.
In the real clinical setting, the score proposes the use of antibiotics for “high” and “very high” groups. In contrast, no antibiotic is required for the “low” group of neonates. For the medium group, the antibiotic decision should be made individually by the attending physician. The clinical prediction rule is not a replacement for clinical judgment and should complement rather than supplant clinical opinion and intuition. Accurate clinical decision making is a central component of patient care [36, 37]. This clinical prediction rule can help the clinician diagnose late-onset neonatal sepsis.
Although some steps in the development were comparable, proper comparison with some other models could not be made easily since each model differs from each other in terms of age criteria, type of variables, validation process, and the purpose of the score. The NOSEP score and Okascharoen’s score use the age criteria of 3 days to determine early- or late-onset sepsis. Rodwell et al. only used the hematology parameter, while Singh et al. (2003) used just clinical conditions [16, 49, 50, 71].
The primary limitation of this study was its retrospective design. Information bias cannot be avoided using that design and data from medical records. The sample size of the study was limited since the total sample had to be divided into 2 groups. The missing data (as an unavoidable part of retrospective design study) was another limitation since any method, however perfect, can lead to biased estimates of the odds ratio and the model performance in predictive models [72]. Regarding the “worst” laboratory results, notably, several biochemistry results might be normal in a septic condition. The choice of patients in the control group (non-sepsis) may also affect the result of the study. For example, in this study, most of the non-sepsis cases had hyperbilirubinemia. The result for the icterus variable might be different if the predominant diagnoses were other diseases. This study also did not use a new data set. However, when our results were compared to the more recent literature, we considered our study to still be appropriate for some settings, especially underdeveloped and developing countries.
The chosen outcome was only proven sepsis. This could result in an underestimation of the true incidence. However, including unproven sepsis would cause incorporation bias. Lastly, validation of a new sample set was needed, either in the same setting or others. It is recommended to prospectively perform the validation process.
