Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants

Explainable artificial intelligence on safe balance and its major determinants in stroke patients

This study develops explainable artificial intelligence for predicting safe balance using hospital data, including clinical, neurophysiological, and diffusion tensor imaging properties. Retrospective data from 92 first-time stroke patients from January 2016 to June 2023 was analysed. The dependent variables were independent mobility scores, i.e., Berg Balance Scales with 0 (45 or below) vs. 1 (above 45) measured after three and six months, respectively. Twenty-nine predictors were included. Random forest variable importance was employed for identifying significant predictors of the Berg Balance Scale and testing its associations with the predictors, including Berg Balance Scale after one month and corticospinal tract diffusion tensor imaging properties. Shapley Additive Explanation values were calculated to analyse the directions of these associations. The random forest registered a higher or similar area under the curve compared to logistic regression, i.e., 91% vs. 87% (Berg Balance Scale after three months), 92% vs. 92% (Berg Balance Scale after six months). Based on random forest variable importance values and rankings: (1) Berg Balance Scale after three months has strong associations with Berg Balance Scale after one month, Fugl-Meyer assessment scale, ipsilesional corticospinal tract fractional anisotropy, fractional anisotropy laterality index and age; (2) Berg Balance Scale after six months has strong relationships with Fugl-Meyer assessment scale, Berg Balance Scale after one month, ankle plantar flexion muscle strength, knee extension muscle strength and hip flexion muscle strength. These associations were positive in the SHAP summary plots. Including Berg Balance Scale after one month, Fugl-Meyer assessment scale or ipsilesional corticospinal tract fractional anisotropy in the random forest will increase the probability of Berg Balance Scale after three months being above 45 by 0.11, 0.08, or 0.08. In conclusion, safe balance after stroke strongly correlates with its initial motor function, Fugl-Meyer assessment scale, and ipsilesional corticospinal tract fractional anisotropy. Diffusion tensor imaging information aids in developing explainable artificial intelligence for predicting safe balance after stroke.

Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning

BACKGROUND

Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories.

HYPOTHESIS

Integrating key maternal and postnatal infant variables in the first 2 weeks of age into machine learning (ML) algorithms will reliably predict survival and specific morbidities in VLBW preterm infants.

METHODS

ML algorithms were developed to integrate 47 features for predicting mortality, bronchopulmonary dysplasia (BPD), neonatal sepsis, necrotizing enterocolitis (NEC), intraventricular hemorrhage (IVH), cystic periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP). A retrospective cohort (n = 3341) was used to train and validate the models with a repeated 10-fold cross-validation strategy. These models were then tested on a separate cohort (n = 447) to evaluate the final model performance.

RESULTS

Among the seven ML algorithms employed, tree-based ensemble models, specifically Random Forest (RF) and XGBoost, had the best performance metrics. The area under the receiver operating characteristic curve (AUROC) of sepsis with or without meningitis (0.73), NEC (0.73), BPD (0.71), and mortality (0.74) exceeded 0.7, while the area under Precision-Recall curve (AUPRC) for all outcomes was greater than the prevalence, demonstrating effective risk stratification in VLBW preterm infants.

CONCLUSIONS

Our study demonstrates the potential of predictive analytics leveraging ML techniques in advancing precision medicine.

IMPACT

Reliable prediction of adverse outcomes before they occur has the potential to institute interventions and possibly improve health trajectories in VLBW preterm infants. We used machine learning to develop and test predictive models for mortality and five major morbidities in VLBW preterm infants. Individualized prediction of outcomes and individualized interventions will advance Precision Medicine in Neonatology.

Construction and evaluation of a risk model for adverse outcomes of necrotizing enterocolitis based on LASSO-Cox regression

Objective

This study aimed to develop a nomogram to predict adverse outcomes in neonates with necrotizing enterocolitis (NEC).

Methods

In this retrospective study on neonates with NEC, data on perinatal characteristics, clinical features, laboratory findings, and x-ray examinations were collected for the included patients. A risk model and its nomogram were developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses.

Results

A total of 182 cases of NEC were included and divided into a training set (148 cases) and a temporal validation set (34 cases). Eight features, including weight [p = 0.471, HR = 0.99 (95% CI: 0.98-1.00)], history of congenital heart disease [p < 0.001, HR = 3.13 (95% CI:1.75-5.61)], blood transfusion before onset [p = 0.757, HR = 0.85 (95%CI:0.29-2.45)], antibiotic exposure before onset [p = 0.003, HR = 5.52 (95% CI:1.81-16.83)], C-reactive protein (CRP) at onset [p = 0.757, HR = 1.01 (95%CI:1.00-1.02)], plasma sodium at onset [p < 0.001, HR = 4.73 (95%CI:2.61-8.59)], dynamic abdominal x-ray score change [p = 0.001, HR = 4.90 (95%CI:2.69-8.93)], and antibiotic treatment regimen [p = 0.250, HR = 1.83 (0.65-5.15)], were ultimately selected for model building. The C-index for the predictive model was 0.850 (95% CI: 0.804-0.897) for the training set and 0.7880.788 (95% CI: 0.656-0.921) for the validation set. The area under the ROC curve (AUC) at 8-, 10-, and 12-days were 0.889 (95% CI: 0.822-0.956), 0.891 (95% CI: 0.829-0.953), and 0.893 (95% CI:0.832-0.954) in the training group, and 0.812 (95% CI: 0.633-0.991), 0.846 (95% CI: 0.695-0.998), and 0.798 (95%CI: 0.623-0.973) in the validation group, respectively. Calibration curves showed good concordance between the predicted and observed outcomes, and DCA demonstrated adequate clinical benefit.

Conclusions

The LASSO-Cox model effectively identifies NEC neonates at high risk of adverse outcomes across all time points. Notably, at earlier time points (such as the 8-day mark), the model also demonstrates strong predictive performance, facilitating the early prediction of adverse outcomes in infants with NEC. This early prediction can contribute to timely clinical decision-making and ultimately improve patient prognosis.

Understanding necrotizing enterocolitis endotypes and acquired intestinal injury phenotypes from a historical and artificial intelligence perspective

Necrotizing enterocolitis (NEC) remains a devastating disease in preterm and term neonates. Despite significant progress made in understanding NEC pathogenesis over the last 50 years, the inability of current definitions to discriminate the various pathophysiological processes underlying NEC has led to an umbrella term that limits clinical and research progress. In this mini review, we provide a historical perspective on how NEC definitions and pathogenesis have evolved to our current understanding of NEC endotypes. We also discuss how artificial intelligence-based approaches are influencing our knowledge of risk-factors, classification and prognosis of NEC and other neonatal intestinal injury phenotypes.

Impact of Size for Gestational Age on Multivariate Analysis of Factors Associated with Necrotizing Enterocolitis in Preterm Infants: Retrospective Cohort Study

OBJECTIVE

Necrotizing enterocolitis (NEC) primarily affects preterm, especially small for gestational age (SGA), infants. This study was designed to (1) describe frequency and timing of NEC in SGA versus non-SGA infants and (2) assess whether NEC is independently associated with the severity of intrauterine growth failure.

STUDY DESIGN

Retrospective cohort study of infants without severe congenital malformations born <33 weeks' gestational age (GA) carried out from 2009 to 2021. The frequency and time of NEC were compared between SGA and non-SGA infants. Multivariate logistic regression was used to assess whether NEC was independently associated with intrauterine growth restriction. Severe growth restriction was defined as birth weight Z-score <  -2.

RESULTS

Among 2,940 infants, the frequency of NEC was higher in SGA than in non-SGA infants (25/268 [9.3%] vs. 110/2,672 [4.1%], respectively, p < 0.001). NEC developed 2 weeks later in SGA than non-SGA infants. In multivariate analysis, the adjusted odds of NEC increased with extreme prematurity (<28 weeks' GA) and with severe but not moderate growth restriction. The adjusted odds of NEC increased with urinary tract infection or sepsis within a week prior to NEC, were lower in infants fed their mother's own milk until discharge, and did not change over five epochs. NEC was independently associated with antenatal steroid (ANS) exposure in infants with birth weight (BW) Z-score < 0.

CONCLUSION

NEC was more frequent in SGA than in non-SGA infants and developed 2 weeks later in SGA infants. NEC was independently associated with severe intrauterine growth failure and with ANS exposure in infants with BW Z-score < 0.

KEY POINTS

· We studied 2,940 infants <33 weeks' GA.. · We assessed NEC.. · NEC was more frequent in SGA infants.. · NEC occurred 2 weeks later in SGA infants.. · NEC was associated with severe growth restriction..

Risk factors of necrotizing enterocolitis among neonates admitted to the neonatal intensive care unit at the selected public hospitals in southern Ethiopia, 2023

Introduction

Necrotizing enterocolitis (NEC) is a serious intestinal condition characterized by ischemic necrosis of the intestinal mucosa, inflammation, and invasion by gas-forming organisms, posing a significant threat to neonatal health. Necrotizing enterocolitis remains a significant cause of neonatal morbidity and mortality, particularly in developing countries. Due to limited research conducted in Ethiopia and the study area, there is a lack of information regarding the risk factors associated with necrotizing enterocolitis. Therefore, the goal of this study is to fill the aforementioned gap.

Objective

This study aims to identify the risk factors of necrotizing enterocolitis among neonates admitted to the neonatal intensive care unit (NICU) at selected general and referral hospitals in southern Ethiopia in the year 2023.

Methods and materials

A facility-based unmatched case-control study was conducted. All neonates admitted to the NICU and diagnosed with necrotizing enterocolitis by the attending physician during the data collection period were considered as cases, whereas neonates admitted to the NICU but not diagnosed with necrotizing enterocolitis during the data collection period were considered as controls. Data were collected through face-to-face interviews and record reviews using the Kobo toolbox platform. The binary logistic regression method was used to determine the relationship between a dependent variable and independent variables. Finally, a p-value of < 0.05 was considered statistically significant.

Results

This study included 111 cases and 332 controls. Normal BMI [AOR = 0.11, 95% CI: (0.02, 0.58)], history of khat chewing [AOR = 4.21, 95% CI: (1.96, 9.06)], term gestation [AOR = 0.06, 95% CI: (0.01, 0.18)], history of cigarette smoking [AOR = 2.86, 95% CI: (1.14, 7.14)], length of hospital stay [AOR = 3.3, 95% CI: (1.43, 7.67)], and premature rupture of membrane [AOR = 3.51, 95% CI: (1.77, 6.98)] were significantly associated with NEC.

Conclusion

The study identified several risk factors for necrotizing enterocolitis, including body mass index, history of khat chewing, gestational age, history of cigarette smoking, length of hospital stays, and premature rupture of membrane. Therefore, healthcare providers should be aware of these risk factors to identify newborns at high risk and implement preventive measures.

[Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates]

Necrotizing enterocolitis (NEC), with the main manifestations of bloody stool, abdominal distension, and vomiting, is one of the leading causes of death in neonates, and early identification and diagnosis are crucial for the prognosis of NEC. The emergence and development of machine learning has provided the potential for early, rapid, and accurate identification of this disease. This article summarizes the algorithms of machine learning recently used in NEC, analyzes the high-risk predictive factors revealed by these algorithms, evaluates the ability and characteristics of machine learning in the etiology, definition, and diagnosis of NEC, and discusses the challenges and prospects for the future application of machine learning in NEC.

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.