Detection Of Late-onset Sepsis Research Articles

Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System

A significant proportion of babies that are admitted to the neonatal intensive care unit (NICU) suffer from late onset sepsis (LOS). In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that the use of machine learning techniques might help detect LOS at an early stage. Some works have shown that linear methods (i.e., logistic regression) display a superior performance when predicting LOS. Nevertheless, as research on this topic is still in an early phase, it has not been ruled out that non-linear machine learning (ML) techniques can improve the predictive performance. Moreover, few studies have assessed the effect of parameters other than heart rate variability (HRV). Therefore, the current study investigates the effect of non-linear methods and assesses whether other vital parameters such as respiratory rate, perfusion index, and oxygen saturation could be of added value when predicting LOS. In contrast with the findings in the literature, it was found that non-linear methods showed a superior performance compared with linear models. In particular, it was found that random forest performed best (AUROC: 0.973), 24% better than logistic regression (AUROC: 0.782). Nevertheless, logistic regression was found to perform similarly to some non-linear models when trained with a short training window. Furthermore, when also taking training time into account, K-Nearest Neighbors was found to be the most beneficial (AUROC: 0.950). In line with the literature, we found that training the models on HRV features yielded the best results. Lastly, the results revealed that non-linear methods demonstrated a superior performance compared with linear methods when adding respiratory features to the HRV feature set, which ensured the greatest improvement in terms of AUROC score.

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability.

This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning. The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC). The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42h before start of antibiotics. These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns. The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.

Role of Serum Amyloid A Protein in the Early Detection of Late Onset Sepsis in Neonate

Background: Late onset sepsis (LOS) is an important cause of serious illness and deaths among neonates. Diagnosis of neonatal sepsis remains a challenge owing to nonspecific early clinical signs and the non-availability of a reliable biomarker. Serum amyloid A (SAA), the precursor protein in inflammation-associated reactive amyloidosis, whose level in the blood increases up to 1000 fold in response to inflammation and it is also an acute phase reactant like PCT and CRP. Objectives: assessing the accuracy and rapidity of SAA in detection of LOS in neonates. Method: This is a case-control study which was carried out on neonates admitted in (NICU) of Benha University Hospital and Benha Teaching Hospital during the period (from June 2018 to November 2019). Group 1 (patients group): 45 neonates with neonatal sepsis, group 2 (control group): 40 healthy neonates age and sex matched. SAA was measured. Results: mean value of SAA in septic group was 38.8 µg/ml compared to 1.26 µg/ml in control group , with statistically significant increase in patients than controls (p 2.8 µg/ml, SAA had a sensitivity 86.7%, specificity 85%, PPV 86.7%, and NPV 85% for early diagnosis of LOS. At a cut-off value of SAA>45.2 µg/ml, SAA had a sensitivity 83.3%, specificity 69.7%, PPV 50%, and NPV 92% for the prediction of mortality among LOS patients. Conclusion: The high sensitivity, specificity, positive predictive value and negative predictive value of SAA protein could help the clinicians for early diagnosis of LOS.

Presepsin for the Detection of Late-Onset Sepsis in Preterm Newborns

Presepsin for the Detection of Late-Onset Sepsis in Preterm Newborns