Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Methods of identifying surgical Necrotizing Enterocolitis-a systematic review and meta-analysis

BACKGROUND

Current data suggests potential benefit of earlier surgery for necrotizing enterocolitis (NEC) however this requires accurate prognostication early in the disease course. This study aims to identify and determine the effectiveness of previously reported methods or tests for the identification of surgical NEC.

METHODS

Systematic review and meta-analysis with registration on PROSPERO including articles describing a method of identifying surgical NEC. Outcomes of interest were effectiveness and repeatability of index test.

RESULTS

Of the 190 full-text articles screened, 90 studies were included which contained 114 methods of identifying surgical NEC in 9546 infants. Of these methods, 44 were a scoring system, 37 a single biomarker, 24 an imaging method, and 9 an invasive method. Sensitivity and specificity ranged from 12.8-100% to 13-100%, respectively. Some methods (9.6%) provided insufficient methods for repeatability within clinical practice or research. Meta-analyses were possible for only 2 methods, the metabolic derangement 7 score and abdominal ultrasound.

CONCLUSIONS

A range of methods for identifying surgical NEC have been identified with varying overall performance and uncertainties about reproducibility and superiority of any method. External validation in large multicentre datasets should allow direct comparison of accuracy and prospective study should evaluate impact on clinical outcomes.

IMPACT

Earlier identification of need for surgery in necrotizing enterocolitis (NEC) has the potential to improve the unfavourable outcomes in this condition. As such, many methods have been developed and reported to allow earlier identification of surgical NEC. This study is the first synthesis of the literature which identifies previously reported methods and the effectiveness of these. Many methods, including scoring systems and biomarkers, appear effective for prognostication in NEC and external validation is now required in multicentre datasets prior to clinical utility.

Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study

Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine learning (ML) algorithm to predict sNEC using perinatal factors obtained from the national cohort registry of very low birth weight (VLBW) infants. Data were collected from the medical records of 16,385 VLBW infants registered in the Korean Neonatal Network (KNN). Infants who underwent surgical intervention were identified with sNEC, and infants who received medical treatment, with medical NEC (mNEC). We used 38 variables, including maternal, prenatal, and postnatal factors that were obtained within 1 week of birth, for training. A total of 1085 patients had NEC (654 with sNEC and 431 with mNEC). VLBW infants showed a higher incidence of sNEC at a lower gestational age (GA) (p < 0.001). Our proposed ensemble model showed an area under the receiver operating characteristic curve of 0.721 for sNEC prediction.    Conclusion: Proposed ensemble model may help predict which infants with NEC are likely to develop sNEC. Through early prediction and prompt intervention, prognosis of sNEC may be improved. What is Known: • Machine learning (ML)-based techniques have been employed in NEC research for prediction, diagnosis, and prognosis, with promising outcomes. • While most studies have utilized abdominal radiographs and clinical manifestations of NEC as data sources, and have demonstrated their usefulness, they may prove weak in terms of early prediction. What is New: • We analyzed the perinatal factors of VLBW infants acquired within 7 days of birth and used ML-based analysis to identify which infants with NEC are vulnerable to clinical deterioration and at high risk for surgical intervention using nationwide cohort data.

[ROC curves: general characteristics and their usefulness in clinical practice]

The use of diagnostic tests to determine the presence or absence of a disease is essential in clinical practice. The results of a diagnostic test may correspond to numerical estimates that require quantitative reference parameters to be transferred to a dichotomous interpretation as normal or abnormal and thus implement actions for the care of a condition or disease. For example, in the diagnosis of anemia it is necessary to define a cut-off point for the hemoglobin variable and create two categories that distinguish the presence or absence of anemia. The method used for this process is the preparation of diagnostic performance curves, better known by their acronym in English as ROC (Receiver Operating Characteristic). The ROC curve is also useful as a prognostic marker, since it allows defining the cut-off point of a quantitative variable that is associated with greater mortality or risk of complications. They have been used in different prognostic markers in COVID-19, such as the neutrophil/lymphocyte ratio and D-dimer, in which cut-off points associated with mortality and/or risk of mechanical ventilation were identified. The ROC curve is used to evaluate the diagnostic performance of a test in isolation, but it can also be used to compare the performance of two or more diagnostic tests and define which one is more accurate. This article describes the basic concepts for the use and interpretation of the ROC curve, the interpretation of an area under the curve (AUC) and the comparison of two or more diagnostic tests.

State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis

Necrotizing Enterocolitis (NEC) is one of the leading causes of gastrointestinal emergency in preterm infants. Although NEC was formally described in the 1960's, there is still difficulty in diagnosis and ultimately treatment for NEC due in part to the multifactorial nature of the disease. Artificial intelligence (AI) and machine learning (ML) techniques have been applied by healthcare researchers over the past 30 years to better understand various diseases. Specifically, NEC researchers have used AI and ML to predict NEC diagnosis, NEC prognosis, discover biomarkers, and evaluate treatment strategies. In this review, we discuss AI and ML techniques, the current literature that has applied AI and ML to NEC, and some of the limitations in the field.

Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio

Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method (WSNR) are also compared with four well known feature selection methods. We found that the (WSNR) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that (WSNR) outperforms all the other methods included in the study.