A data-driven algorithm integrating clinical and laboratory features for 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.

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning

BACKGROUND

Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.

METHODS

Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis.

RESULTS

Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster.

CONCLUSION

Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases.

IMPACT

Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

Development of a Prediction Model for Surgery or Early Mortality at the Time of Initial Assessment for Necrotizing Enterocolitis

OBJECTIVE

 No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week.

STUDY DESIGN

 This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birthweight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA SCORE (H: yperglycemia, H: yperkalemia, use of inotropes for H: ypotension during the prior week, A: cidemia, Neonatal S: equential O: rgan F: ailure A: ssessment [nSOFA: ] score) was compared with a similar model using the nSOFA score.

RESULTS

 Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively.

CONCLUSION

 Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC.

KEY POINTS

· No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week.. · In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score).. · The HASOFA-model had high discrimination (AUC 0.909) and excellent calibration and was internally validated..

Necrotizing enterocolitis: current understanding of the prevention and management

Necrotizing enterocolitis (NEC) is one of the diseases in neonates, with a high morbidity and mortality rate, especially in preterm infants. This review aimed to briefly introduce the latest epidemiology, susceptibility factors, and clinical diagnosis and presentation of NEC. We also organized new prevention strategies by risk factors according to different pathogeneses and then discussed new treatment methods based on Bell's staging and complications, and the classification of mild to high severity based on clinical and imaging manifestations. Such a generalization will help clinicians and researchers to gain a deeper understanding of the disease and to conduct more targeted classification, grading prevention, and exploration. We focused on prevention and treatment of the early and suspected stages of NEC, including the discovery of novel biomarkers and drugs to control disease progression. At the same time, we discussed its clinical application, future development, and shortcomings.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

The validity of hospital diagnostic and procedure codes reflecting morbidity in preterm neonates born <32 weeks gestation

OBJECTIVE

To determine the validity of diagnostic hospital billing codes for complications of prematurity in neonates <32 weeks gestation.

STUDY DESIGN

Retrospective cohort data from discharge summaries and clinical notes (n = 160) were reviewed by trained, blinded abstractors for the presence of intraventricular hemorrhage (IVH) grades 3 or 4, periventricular leukomalacia (PVL), necrotizing enterocolitis (NEC), stage 3 or higher, retinopathy of prematurity (ROP), and surgery for NEC or ROP. Data were compared to diagnostic billing codes from the neonatal electronic health record.

RESULTS

IVH, PVL, ROP and ROP surgery had strong positive predictive values (PPV > 75%) and excellent negative predictive values (NPV > 95%). The PPVs for NEC (66.7%) and NEC surgery (37.1%) were low.

CONCLUSION

Diagnostic hospital billing codes were observed to be a valid metric to evaluate preterm neonatal morbidities and surgeries except in the instance of more ambiguous diagnoses such as NEC and NEC surgery.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

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.

Biomarkers of necrotizing enterocolitis in the era of machine learning and omics

Necrotizing enterocolitis (NEC) continues to be a major cause of morbidity and mortality in preterm infants. Despite decades of research in NEC, no reliable biomarkers can accurately diagnose NEC or predict patient prognosis. The recent emergence of multi-omics could potentially shift NEC biomarker discovery, particularly when evaluated using systems biology techniques. Furthermore, the use of machine learning and artificial intelligence in analyzing this 'big data' could enable novel interpretations of NEC subtypes, disease progression, and potential therapeutic targets, allowing for integration with personalized medicine approaches. In this review, we evaluate studies using omics technologies and machine learning in the diagnosis of NEC. Future implications and challenges inherent to the field are also discussed.

Necrotizing Enterocolitis: The Role of Hypoxia, Gut Microbiome, and Microbial Metabolites

Necrotizing enterocolitis (NEC) is a life-threatening disease that predominantly affects very low birth weight preterm infants. Development of NEC in preterm infants is accompanied by high mortality. Surgical treatment of NEC can be complicated by short bowel syndrome, intestinal failure, parenteral nutrition-associated liver disease, and neurodevelopmental delay. Issues surrounding pathogenesis, prevention, and treatment of NEC remain unclear. This review summarizes data on prenatal risk factors for NEC, the role of pre-eclampsia, and intrauterine growth retardation in the pathogenesis of NEC. The role of hypoxia in NEC is discussed. Recent data on the role of the intestinal microbiome in the development of NEC, and features of the metabolome that can serve as potential biomarkers, are presented. The Pseudomonadota phylum is known to be associated with NEC in preterm neonates, and the role of other bacteria and their metabolites in NEC pathogenesis is also discussed. The most promising approaches for preventing and treating NEC are summarized.

Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Simple Scoring System that Predicts the Need for Surgical Intervention in Infants with Necrotizing Enterocolitis

BACKGROUND AND AIMS

It is difficult to identify those preterm infants who require surgical intervention. This study examined the predictive factors for bowel resection in infants with definitive and advanced necrotizing enterocolitis (NEC). We then developed a scoring system to better predict the need for surgical intervention.

METHODS

A total of 203 infants with modified Bell's stage 2 or higher NEC from January 2018-December 2020 were identified for this study. A retrospective study evaluated the association between several comprehensive variables and surgical intervention using a multiple logistic regression analysis, and then a scoring system was developed based on the sum of coefficients (β).

RESULTS

Of the 135 patients who met the inclusion criteria, 57 infants underwent a surgical intervention. The multivariable logistic regression analysis showed that birth weight (regression coefficient, β = 1.30), PCT (β = 2.33), not having received enteral nutrition before the diagnosis of NEC (β = 2.13), acidosis (β = 1.57), respiratory alkalosis (β = 2.42), hypokalemia (β = 2.14), peritonitis (β = 2.87) and coagulation disorders (β = 1.78) were associated with the occurrence of bowel resection. A scoring system ranging from 0-17 was developed based on the total coefficient obtained. It was found that a cut-off score of 5 may distinguish those infants needing surgical intervention from other infants with NEC.

CONCLUSION

We successfully developed a clinical decision-making tool associated with the need for surgical intervention among infants with advanced NEC. The risk scoring system could accurately identify infants who would benefit from surgical intervention.

Remote ischemic conditioning in necrotizing enterocolitis: study protocol of a multi-center phase II feasibility randomized controlled trial

PURPOSE

Remote ischemic conditioning (RIC) is a maneuver involving brief cycles of ischemia reperfusion in an individual's limb. In the early stage of experimental NEC, RIC decreased intestinal injury and prolonged survival by counteracting the derangements in intestinal microcirculation. A single-center phase I study demonstrated that the performance of RIC was safe in neonates with NEC. The aim of this phase II RCT was to evaluate the safety and feasibility of RIC, to identify challenges in recruitment, retainment, and to inform a phase III RCT to evaluate efficacy.

METHODS

RIC will be performed by trained research personnel and will consist of four cycles of limb ischemia (4-min via cuff inflation) followed by reperfusion (4-min via cuff deflation), repeated on two consecutive days post randomization. The primary endpoint of this RCT is feasibility and acceptability of recruiting and randomizing neonates within 24 h from NEC diagnosis as well as masking and completing the RIC intervention.

RESULTS

We created a novel international consortium for this trial and created a consensus on the diagnostic criteria for NEC and protocol for the trial. The phase II multicenter-masked feasibility RCT will be conducted at 12 centers in Canada, USA, Sweden, The Netherlands, UK, and Spain. The inclusion criteria are: gestational age < 33 weeks, weight ≥ 750 g, NEC receiving medical treatment, and diagnosis established within previous 24 h. Neonates will be randomized to RIC (intervention) or no-RIC (control) and will continue to receive standard management of NEC. We expect to recruit and randomize 40% of eligible patients in the collaborating centers (78 patients; 39/arm) in 30 months. Bayesian methods will be used to combine uninformative prior distributions with the corresponding observed proportions from this trial to determine posterior distributions for parameters of feasibility.

CONCLUSIONS

The newly established NEC consortium has generated novel data on NEC diagnosis and defined the feasibility parameters for the introduction of a novel treatment in NEC. This phase II RCT will inform a future phase III RCT to evaluate the efficacy and safety of RIC in early-stage NEC.

Spontaneous intestinal perforation (SIP) will soon become the most common form of surgical bowel disease in the extremely low birth weight (ELBW) infant

Recent data have revealed declines in the prevalence rates of NEC over the last decade in premature infants. In contrast, SIP has either remained steady or risen during the same epoch. These trends are consistent with our knowledge of the clinical arena. The ability to discern SIP contamination within NEC datasets has slowly improved. Additionally, quality improvement efforts are being utilized to reduce NEC through stewardship of antibiotics, acid inhibitors, central lines and blood products, as well as optimization of human milk diets. These forces are moving us to a new era, where NEC will no longer be the dominant surgical intestinal disease of the extremely preterm neonate. Indeed, in the extremely low birth weight (ELBW) population, SIP may already be the most prevalent reason for abdominal surgery. In this perspective, the reader will find supporting data and references for these assertions as well as predictions for the future.

Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota

Background

Necrotizing enterocolitis (NEC) is a common, potentially catastrophic intestinal disease among very low birthweight premature infants. Affecting up to 15% of neonates born weighing less than 1500 g, NEC causes sudden-onset, progressive intestinal inflammation and necrosis, which can lead to significant bowel loss, multi-organ injury, or death. No unifying cause of NEC has been identified, nor is there any reliable biomarker that indicates an individual patient’s risk of the disease. Without a way to predict NEC in advance, the current medical strategy involves close clinical monitoring in an effort to treat babies with NEC as quickly as possible before irrecoverable intestinal damage occurs. In this report, we describe a novel machine learning application for generating dynamic, individualized NEC risk scores based on intestinal microbiota data, which can be determined from sequencing bacterial DNA from otherwise discarded infant stool. A central insight that differentiates our work from past efforts was the recognition that disease prediction from stool microbiota represents a specific subtype of machine learning problem known as multiple instance learning (MIL).

Results

We used a neural network-based MIL architecture, which we tested on independent datasets from two cohorts encompassing 3595 stool samples from 261 at-risk infants. Our report also introduces a new concept called the “growing bag” analysis, which applies MIL over time, allowing incorporation of past data into each new risk calculation. This approach allowed early, accurate NEC prediction, with a mean sensitivity of 86% and specificity of 90%. True-positive NEC predictions occurred an average of 8 days before disease onset. We also demonstrate that an attention-gated mechanism incorporated into our MIL algorithm permits interpretation of NEC risk, identifying several bacterial taxa that past work has associated with NEC, and potentially pointing the way toward new hypotheses about NEC pathogenesis. Our system is flexible, accepting microbiota data generated from targeted 16S or “shotgun” whole-genome DNA sequencing. It performs well in the setting of common, potentially confounding preterm neonatal clinical events such as perinatal cardiopulmonary depression, antibiotic administration, feeding disruptions, or transitions between breast feeding and formula.

Conclusions

We have developed and validated a robust MIL-based system for NEC prediction from harmlessly collected premature infant stool. While this system was developed for NEC prediction, our MIL approach may also be applicable to other diseases characterized by changes in the human microbiota.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04618-w.

A critical evaluation of current definitions of necrotizing enterocolitis

BACKGROUND

Necrotizing enterocolitis (NEC) is a devastating intestinal disease of premature infants, with significant mortality and long-term morbidity among survivors. Multiple NEC definitions exist, but no formal head-to-head evaluation has been performed. We hypothesized that contemporary definitions would perform better in evaluation metrics than Bell's and range features would be more frequently identified as important than yes/no features.

METHODS

Two hundred and nineteen patients from the University of Iowa hospital with NEC, intestinal perforation, or NEC concern were identified from a 10-year retrospective cohort. NEC presence was confirmed by a blinded investigator. Evaluation metrics were calculated using statistics and six supervised machine learning classifiers for current NEC definitions. Feature importance evaluation was performed on each decision tree classifier.

RESULTS

Newer definitions outperformed Bell's staging using both standard statistics and most machine learning classifiers. The decision tree classifier had the highest overall machine learning scores, which resulted in Non-Bell definitions having high sensitivity (0.826, INC) and specificity (0.969, ST), while Modified Bell (IIA+) had reasonable sensitivity (0.783), but poor specificity (0.531). Feature importance evaluation identified nine criteria as important for diagnosis.

CONCLUSIONS

This preliminary study suggests that Non-Bell NEC definitions may be better at diagnosing NEC and calls for further examination of definitions and important criteria.

IMPACT

This article is the first formal head-to-head evaluation of current available definitions of NEC. Non-Bell NEC definitions may be more effective in identifying NEC based on findings from traditional measures of diagnostic performance and machine learning techniques. Nine features were identified as important for diagnosis from the definitions evaluated within the decision tree when performing supervised classification machine learning. This article serves as a preliminary study to formally evaluate the definitions of NEC utilized and should be expounded upon with a larger and more diverse patient cohort.

Biomarkers of gut injury in neonates - where are we in predicting necrotising enterocolitis?

Despite advances in neonatal care Necrotising Enterocolitis (NEC) continues to have a significant mortality and morbidity rate, and with increasing survival of those more immature infants the population at risk of NEC is increasing. Ischaemia, reperfusion, and inflammation underpin diseases affecting intestinal blood flow causing gut injury including Necrotising Enterocolitis. There is increasing interest in tissue biomarkers of gut injury in neonates, particularly those representing changes in intestinal wall barrier and permeability, to determine whether these could be useful biomarkers of gut injury. This article reviews current and newly proposed markers of gut injury, the available literature evidence, recent advances and considers how effective they are in clinical practice. We discuss each biomarker in terms of its effectiveness in predicting NEC onset and diagnosis or predicting NEC severity and then those that will aid in surveillance and identifying those infants are greatest risk of developing NEC.

Doppler ultrasound assessment of splanchnic perfusion and heart rate for the detection of necrotizing enterocolitis

PURPOSE

Monitoring disease progression is crucial to improve the outcome of necrotizing enterocolitis (NEC). A previous study indicates that intestinal wall flow velocity was reduced in NEC pups from the initial stages of the disease. This study aims to investigate whether splanchnic perfusion via the superior mesenteric artery (SMA) (i) is altered during NEC development and (ii) can be used as a monitoring tool to assess disease progression.

METHODS

NEC was induced in C57BL/6 mice via gavage feeding of formula, hypoxia, and oral lipopolysaccharide, from postnatal day 5 (P5) to P9 (AUP: 32,238). Breastfed littermates served as controls. Doppler ultrasound (U/S) of bowel loops was performed daily. Intestinal wall perfusion was calculated as average flow velocity (mm/s) of multiple abdominal regions. Groups were compared using one-way ANOVA.

RESULTS

The SMA flow velocity was not altered during the initial stage of NEC development, but become significantly reduced at P8 when the intestinal disease was more advanced. These changes occurred concomitantly with an increase in heart rate.

CONCLUSIONS

NEC is associated with intestinal hypo-perfusion at the periphery and flow in the SMA is reduced during the later stages of disease indicating the presence of intestinal epithelium damage. This study contributes to understanding NEC pathophysiology and illustrates the value of Doppler U/S in monitoring disease progression.

Remote ischemic conditioning counteracts the intestinal damage of necrotizing enterocolitis by improving intestinal microcirculation

Necrotizing enterocolitis (NEC) is a devastating disease of premature infants with high mortality rate, indicating the need for precision treatment. NEC is characterized by intestinal inflammation and ischemia, as well derangements in intestinal microcirculation. Remote ischemic conditioning (RIC) has emerged as a promising tool in protecting distant organs against ischemia-induced damage. However, the effectiveness of RIC against NEC is unknown. To address this gap, we aimed to determine the efficacy and mechanism of action of RIC in experimental NEC. NEC was induced in mouse pups between postnatal day (P) 5 and 9. RIC was applied through intermittent occlusion of hind limb blood flow. RIC, when administered in the early stages of disease progression, decreases intestinal injury and prolongs survival. The mechanism of action of RIC involves increasing intestinal perfusion through vasodilation mediated by nitric oxide and hydrogen sulfide. RIC is a viable and non-invasive treatment strategy for NEC.

Necrotizing enterocolitis (NEC) is one of the most lethal gastrointestinal emergencies in neonates needing precision treatment. Here the authors show that remote ischemic conditioning is a non-invasive therapeutic method that enhances blood flow in the intestine, reduces damage, and improves NEC outcome.

Defining necrotizing enterocolitis: current difficulties and future opportunities

Necrotizing enterocolitis (NEC) is a leading cause of morbidity and mortality in hospitalized infants. First classified through Bell staging in 1978, a number of additional definitions of NEC have been proposed in the subsequent decades. In this review, we summarize eight current definitions of NEC, and explore similarities and differences in clinical signs and radiographic features included within these definitions, as well as their limitations. We highlight the importance of a global consensus on defining NEC to improve NEC research and outcomes, incorporating input from participants at an international NEC conference. We also highlight the important role of patient-families in helping to redefine NEC.

Neonatal necrotizing enterocolitis: a case series examining clinical diagnosis with discrepant versus concordant autopsy results

OBJECTIVE

The objective of this study is to determine whether rapidity of death in necrotizing enterocolitis (NEC) increased odds of discordance between clinical and pathological diagnosis.

STUDY DESIGN

Retrospective case-series study including preterm infants admitted to the NICU.

RESULTS

Twenty-two infants met the selection criteria. Gross pathologic evidence of NEC was present in 1/6 cases (17%) where demise occurred <12 h after onset of symptoms, 3/5 cases (60%) within 12-24 h, and 8/11 cases (73%) in >24 h. Histological evidence of necrosis was present in 4/6 (67%) cases when death occurred <12 h, 4/5 (80%) in 12-24 h, and 9/11 (82%) in >24 h. The percentage with gross pathologic evidence showed a monotonic trend (P = 0.031), while the trend was less clear for histologic findings (P = 0.496).

CONCLUSION

Pathologic features of cell death may not have had sufficient time to develop. This study could reassure both healthcare providers and families when pathologic and clinical diagnoses are not consistent.

Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease that primarily affects preterm infants during their first weeks after birth. Mortality rates associated with NEC are 15-30%, and surviving infants are susceptible to multiple serious, long-term complications. The disease is sporadic and, with currently available tools, unpredictable. We are creating an early warning system that uses stool microbiome features, combined with clinical and demographic information, to identify infants at high risk of developing NEC. Our approach uses a multiple instance learning, neural network-based system that could be used to generate daily or weekly NEC predictions for premature infants. The approach was selected to effectively utilize sparse and weakly annotated datasets characteristic of stool microbiome analysis. Here we describe initial validation of our system, using clinical and microbiome data from a nested case-control study of 161 preterm infants. We show receiver-operator curve areas above 0.9, with 75% of dominant predictive samples for NEC-affected infants identified at least 24 hours prior to disease onset. Our results pave the way for development of a real-time early warning system for NEC using a limited set of basic clinical and demographic details combined with stool microbiome data.

Physical examination score predicts need for surgery in neonates with necrotizing enterocolitis

OBJECTIVE

To evaluate the utility of a standardized physical exam score (PE-NEC) in predicting need for surgery or death in neonates with necrotizing enterocolitis (NEC).

METHODS

This prospective, multicenter, observational study was conducted from 3/1/14 to 2/29/16 with three regional perinatal centers in upstate New York. Infants with NEC Bell's Stage ≥ 2 had physical exams and laboratory data recorded at 12-24 h intervals for 48 h following diagnosis. PE-NEC score was comprised of seven components: bowel sounds, capillary refill time, abdominal wall erythema, girth, discoloration, induration, and tenderness. Surgical timing was determined by surgeons blinded to the PE-NEC score. Optimal sensitivity and specificity of PE-NEC score for surgery/death (primary outcome) was determined by receiver operating characteristic curve analysis.

RESULTS

Of 100 infants with NEC, 5 had pneumoperitoneum at diagnosis and were excluded yielding 95 for analyses. Of those, 35 infants experienced the primary outcome: 3 died from NEC prior to surgery and 32 had surgery (30 laparotomies, 2 drains). The PE-NEC score was found to be sensitive and specific for need for surgery/death (AUC = 0.89, 95% CI 0.82-0.97); a score of ≥3 had a sensitivity of 0.88 (95% CI 0.72-0.97), specificity of 0.81 (95% CI 0.69-0.90). All components of the PE-NEC score were more likely to be present among infants with surgical NEC or who died.

CONCLUSION

PE-NEC score is sensitive and specific in predicting need for surgery in infants with NEC and should be validated as a clinical decision-making tool.

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R² > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO₂ and HCO₃); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.

Changing the paradigm of defining, detecting, and diagnosing NEC: Perspectives on Bell's stages and biomarkers for NEC

Better means to diagnose and define necrotizing enterocolitis are needed to guide clinical practice and research. Adequacy of Bell's staging system for clinical practice and clarity of cases used in NEC clinical datasets has been a topic of controversy for some time. This article provides reasons why a better global definition for NEC is needed and offers a simple alternative bedside definition for preterm NEC called the "Two out of Three" rule. Some argue that biomarkers may fill knowledge gaps and provide greater precision in defining relevant features of a clinical disease like NEC. NEC biomarkers include markers of inflammation, intestinal dysfunction, hematologic changes, and clinical features. Development and reporting of NEC biomarkers should be guided by the FDA's BEST Consensus resource, "Biomarkers, EndpointS, & other Tools" and consistently report metrics so that studies can be compared and results pooled. Current practice in the NICU would be enhanced by clinical tools that effectively inform the clinical team that a baby is at increasing risk of NEC. Ideally, these tools will incorporate both clinical information about the baby as well as molecular signals that are indicative of NEC. While meaningful biomarkers for NEC and clinical tools exist, translation into practice is mediocre.

Predicting Mortality or Intestinal Failure in Infants with Surgical Necrotizing Enterocolitis

OBJECTIVE

To compare existing outcome prediction models and create a novel model to predict death or intestinal failure (IF) in infants with surgical necrotizing enterocolitis (NEC).

STUDY DESIGN

A retrospective, observational cohort study conducted in a 2-campus health system in Atlanta, Georgia, from September 2009 to May 2015. Participants included all infants ≤37 weeks of gestation with surgical NEC. Logistic regression was used to model the probability of death or IF, as a composite outcome, using preoperative variables defined by specifications from 3 existing prediction models: American College of Surgeons National Surgical Quality Improvement Program Pediatric, Score for Neonatal Acute Physiology Perinatal Extension, and Vermont Oxford Risk Adjustment Tool. A novel preoperative hybrid prediction model was also derived and validated against a patient cohort from a separate campus.

RESULTS

Among 147 patients with surgical NEC, discrimination in predicting death or IF was greatest with American College of Surgeons National Surgical Quality Improvement Program Pediatric (area under the receiver operating characteristic curve [AUC], 0.84; 95% CI, 0.77-0.91) when compared with the Score for Neonatal Acute Physiology Perinatal Extension II (AUC, 0.60; 95% CI, 0.48-0.72) and Vermont Oxford Risk Adjustment Tool (AUC, 0.74; 95% CI, 0.65-0.83). A hybrid model was developed using 4 preoperative variables: the 1-minute Apgar score, inotrope use, mean blood pressure, and sepsis. The hybrid model AUC was 0.85 (95% CI, 0.78-0.92) in the derivation cohort and 0.77 (95% CI, 0.66-0.86) in the validation cohort.

CONCLUSIONS

Preoperative prediction of death or IF among infants with surgical NEC is possible using existing prediction tools and, to a greater extent, using a newly proposed 4-variable hybrid model.

Acylcarnitine Profiles Reflect Metabolic Vulnerability for Necrotizing Enterocolitis in Newborns Born Premature

OBJECTIVE

To evaluate the association between newborn acylcarnitine profiles and the subsequent development of necrotizing enterocolitis (NEC) with the use of routinely collected newborn screening data in infants born preterm.

STUDY DESIGN

A retrospective cohort study was conducted with the use of discharge records for infants born preterm admitted to neonatal intensive care units in California from 2005 to 2009 who had linked state newborn screening results. A model-development cohort of 94 110 preterm births from 2005 to 2008 was used to develop a risk-stratification model that was then applied to a validation cohort of 22 992 births from 2009.

RESULTS

Fourteen acylcarnitine levels and acylcarnitine ratios were associated with increased risk of developing NEC. Each log unit increase in C5 and free carnitine /(C16 + 18:1) was associated with a 78% and a 76% increased risk for developing NEC, respectively (OR 1.78, 95% CI 1.53-2.02, and OR 1.76, 95% CI 1.51-2.06). Six acylcarnitine levels, along with birth weight and total parenteral nutrition, identified 89.8% of newborns with NEC in the model-development cohort (area under the curve 0.898, 95% CI 0.889-0.907) and 90.8% of the newborns with NEC in the validation cohort (area under the curve 0.908, 95% CI 0.901-0.930).

CONCLUSIONS

Abnormal fatty acid metabolism was associated with prematurity and the development of NEC. Metabolic profiling through newborn screening may serve as an objective biologic surrogate of risk for the development of disease and thus facilitate disease-prevention strategies.

Current Research on the Epidemiology, Pathogenesis, and Management of Necrotizing Enterocolitis

Despite decades of research on necrotizing enterocolitis, we still do not fully understand the pathogenesis of the disease, or how to prevent or how to treat it. However, as a result of recent significant advances in the microbiology, molecular biology, and cell biology of the intestine of preterm infants and infants with necrotizing enterocolitis, there is some hope that research into this devastating disease will yield some important translation into effective prevention, more rapid diagnosis, and novel therapies.

Predictive scores for mortality in full-term infants with necrotizing enterocolitis: experience of a tertiary hospital in Southwest China

BACKGROUND

Although many risk factors for mortality of necrotizing enterocolitis (NEC) were investigated, most of them were obtained from preterm infants, and few works focused on the prognostic risk factors in fullterm infants. This study aimed to identify risk factors and develop a prediction score model for mortality in fullterm neonates with NEC.

METHODS

The risk factors were analyzed retrospectively by bivariate and multivariate logistic regression analysis in 153 full-term neonates with NEC, who were hospitalized in Children's Hospital of Chongqing Medical University from 2000 to 2013. A prediction score model was developed according to the regression coefficients of risk factors.

RESULTS

The mortality of the infants was 19.6% (30/153). The non-survivors had a younger age of diagnosis and advanced stage of NEC (P<0.05). They had a higher prevalence of respiratory failure, intestinal perforation, peritonitis and other complications, compared with the survivors (P<0.05). On the day of diagnosis, the nonsurvivors were more likely to have abnormal laboratory indicators than survivors (P<0.05). Age at diagnosis [odds ratio (OR)=0.91, 95% confidence interval (CI)=0.836-0.99], respiratory failure (OR=2.76, 95% CI=1.10-6.92), and peritonitis (OR=26.36, 95% CI=7.52-173.92) had significant independent contributions to death. A score model predicting death was developed, and the area under the receiver operating characteristic curve was 0.869 (95% CI=0.803-0.935). All infants with scores ≥8 died.

CONCLUSION

Younger age at diagnosis, peritonitis, and respiratory failure might be risk factors for the mortality of full-term infants with NEC. Infants with a predictive score of 8 were at high risk for death.

Necrotizing enterocolitis: controversies and challenges

Necrotizing enterocolitis is a devastating intestinal disease that affects ~5% of preterm neonates. Despite advancements in neonatal care, mortality remains high (30–50%) and controversy still persists with regards to the most appropriate management of neonates with necrotizing enterocolitis. Herein, we review some controversial aspects regarding the epidemiology, imaging, medical and surgical management of necrotizing enterocolitis and we describe new emerging strategies for prevention and treatment.

D, Y Yuan B.S. C, D Nicolas J, Griffiths M.D. A, Mestan M.D. K, J Hunter M.D. C. Urinary Claudin-2 Measurements as a Predictor of Necrotizing Enterocolitis: A Pilot Study

BACKGROUND

Necrotizing Enterocolitis (NEC) affects 5-10% of NICU patients where initially patients may have only nonspecific clinical findings. A noninvasive tool for detection would aid in diagnosis. Increased urinary claudins have been associated with active adult inflammatory bowel disease.

METHODS

Institutional Review Board approval was obtained. Neonatal intestinal tissue samples were obtained from patients with and without NEC. Immunofluorescence analysis of claudin-2 was performed on the intestinal tissue. Thirty two urine samples were collected from 6 NICU patients. Proteins were extracted and urinary claudin-2 expression was measured using Western Blot Analysis. All sample concentrations were normalized to urinary creatinine. Differences were analyzed with ANOVA or Student's T-test. Findings were correlated to the patient's clinical status.

RESULTS

Neonatal intestinal immunofluorescence analysis revealed that claudin-2 is present in healthy intestinal epithelium and is decreased in NEC intestinal tissue (p=0.0001). Of the six patients evaluated, three patients had NEC. All 3 patients with NEC had spikes in urinary claudin-2 levels (p<0.001, p<0.001, p 0.0598 respectively). Spikes did not appear to correlate with other etiologies of neonatal sepsis, medication use or need for mechanical ventilation. Levels during active NEC were almost twice that of NEC-free periods (p<0.0001).

CONCLUSION

A tool for early detection would facilitate early intervention and potential prevention of severe NEC. Preliminary findings indicate that urinary claudin-2 may represent a potential biomarker for NEC worth further investigation.

Impaired Activity of Blood Coagulant Factor XIII in Patients with Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is the most common gastrointestinal (GI) medical/surgical emergency of the newborn and a leading cause of preterm neonate morbidity and mortality. NEC is a challenge to diagnose since it often shares similar clinical features with neonatal sepsis. In the present study, plasma protein profiling was compared among NEC, sepsis and control cohorts using gel electrophoresis, immunoblot and mass spectrometry. We observed significant impairment in the formation of fibrinogen-γ dimers (FGG-dimer) in the plasma of newborns with NEC that could efficiently differentiate NEC and sepsis with a high level of sensitivity and specificity. Interestingly, the impaired FGG-dimer formation could be restored in NEC plasma by the addition of exogenous active factor XIII (FXIII). Enzymatic activity of FXIII was determined to be significantly lower in NEC subject plasma for crosslinking FGG when compared to sepsis. These findings demonstrate a potential novel biomarker and related biologic mechanism for diagnosing NEC, as well as suggest a possible therapeutic strategy.

Necrotizing enterocolitis: a clinical review on diagnostic biomarkers and the role of the intestinal microbiota

Necrotizing enterocolitis (NEC) remains one of the most frequent gastrointestinal diseases in the neonatal intensive care unit, with a continuing unacceptable high mortality and morbidity rates. Up to 20% to 40% of infants with NEC will need surgical intervention at some point. Although the exact pathophysiology is not yet elucidated, prematurity, use of formula feeding, and an altered intestinal microbiota are supposed to induce an inflammatory response of the immature intestine. The clinical picture of NEC has been well described. However, an early diagnosis and differentiation against sepsis is challenging. Besides, it is difficult to timely identify NEC cases that will deteriorate and need surgical intervention. This may interfere with the most optimal treatment of infants with NEC. In this review, we discuss the pathogenesis, diagnosis, and treatment of NEC with a focus on the role of microbiota in the development of NEC. An overview of different clinical prediction models and biomarkers is given. Some of these are promising tools for accurate diagnosis of NEC and selection of appropriate therapy.

Noninvasive biomarkers of necrotizing enterocolitis

Necrotizing enterocolitis is an acute inflammatory disease, which primarily affects preterm infants, and is a leading cause of morbidity and mortality in the neonatal intensive care unit. Unfortunately, necrotizing enterocolitis can be difficult to distinguish from other diseases and clinical conditions especially during the early course of the disease. This diagnostic uncertainty is particularly relevant to clinical evaluation and medical management and potentially leads to unnecessary and extended periods of cessation of enteral feedings and prolonged courses of parenteral nutrition and antibiotics. Biomarkers are molecular indicators of a disease process, diagnosis, prognosis and can be used to monitor the effects of disease management. Historically, there has been a paucity of reliable and robust biomarkers for necrotizing enterocolitis. However, several studies have recently identified promising biomarkers. Noninvasive samples for biomarker measurement are preferred and may have certain advantages in the preterm infant. In this review article, we focus on recent exciting and promising discoveries in noninvasive biomarkers for necrotizing enterocolitis.