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Majority of global neonatal deaths is due to sepsis. A vast portion of these deaths occurs in developing countries due to inaccessibility of hospitals or lack of resources. Blood culture is the test to confirm sepsis, but it requires the presence of laboratory and is time-consuming. Therefore, we require simple, easy to use methods to predict sepsis in homes. Majority of the available prediction models need invasive parameters and hence become useless in the rural areas of developing countries where laboratory facilities do not exist. Non-invasive prediction models overcome these challenges to predict neonatal sepsis in places where there is a scarcity of laboratories. The aim and objective of this study are as follows: (i) to develop a practical, non-invasive prediction-model for neonatal sepsis which can be used in the rural areas of developing countries and to validate its performance. (ii) To compare the prognostic performance of the non-invasive prediction model with invasive prediction model and (iii) to create a prototype of the hardware which calculates the probability of the sepsis in neonates and sends the real-time data to the cloud. For this retrospective analysis, we extracted the data of 1446 neonates from Medical Information Mart for Intensive care III (MIMIC) database. Using stepwise logistic regression analysis, we developed and validated two prediction models. These two models were named as model NI and model O. Model O contains invasive as well as non-invasive parameters whereas model NI contains only non-invasive parameters. Model NI performed equally well in comparison to Model O despite using different predictors. The area under ROC curves for model NI and model O were 0.879 (95% CI: 0.857 to 0.899) and 0.861 (95% CI: 0.838 to 0.881) respectively. Both models were statistically significant with [Formula: see text]-value[Formula: see text].

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Sepsis is one of the major causes of neonatal deaths worldwide. Majority of these neonatal deaths occurs in resource-poor countries due to inaccessibility of hospitals and absence of laboratories. Blood culture which is the gold standard to confirm sepsis is time-consuming and requires the presence of a laboratory. To start the antimicrobial therapy at the earliest, prediction models have been developed. A vast number of available prediction models require laboratory tests and cannot be used in the developing countries where such facilities do not exist. Therefore, there is a need for non-invasive prediction models. The objectives of this study are as follows: -(i) to train and test non-invasive prediction models for neonatal sepsis (ii) to compare the performance of the invasive with non-invasive prediction models. For this retrospective study, we extracted the data of 1446 neonates from the Medical Information Mart for Intensive Care (MIMIC) III data set. We trained and tested six prediction models using this data set. Three of these six models were trained using non-invasive parameters (model LR(NI), model ANN(NI) and model MDA (NI)) and three were trained using invasive and non-invasive parameters (model LR(O), model ANN(O) and model MDA(O)). The sensitivity of model LR(NI), model ANN(NI), model MDA(NI), model LR(O), model ANN(O) and model MDA(O ) at their optimum threshold values were 81.68%, 79.39%, 82.44%, 77.10%, 79.39% and 78.63% respectively. Whereas, specificity of the above mentioned models were 82.27%, 81.82%, 80.00%, 84.77%, 82.05% and 78.30% respectively. To decrease the neonatal mortality rate in resource-poor areas one may use non-invasive prediction models where invasive parameters are not available due to lack of resources, as shown by our study that non-invasive prediction models can achieve similar predictive capability as the invasive prediction models.

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