A critical evaluation of current definitions of necrotizing enterocolitis

Clinical Characteristics of Necrotizing Enterocolitis Diagnosed by Independent Adjudication of Abdominal Radiographs, Laparotomy, or Autopsy in Preterm Infants in the "Connection Trial"

OBJECTIVE

 Necrotizing enterocolitis (NEC) classically is diagnosed by radiographic demonstration of pneumatosis intestinalis/portal venous gas (PI/PVG). This study examines clinical characteristics of NEC confirmed by independent evaluation of abdominal radiographs, taken for clinical signs of NEC, or by pathologic findings at laparotomy or autopsy (confirmed NEC [cNEC]).

STUDY DESIGN

 The investigated cohort included 1,382 extremely low birth weight (BW) infants (BW range: 500-1,000 g) with median 27 weeks (range: 23-32) gestational age (GA) at birth. They were randomized into the placebo-controlled "Connection Trial" of the new biological drug candidate IBP-9414 with cNEC as one primary endpoint.

RESULTS

 Total 119 infants (8.6%) had cNEC diagnosed at median 14 days of age by confirming PI/PVG at X-ray adjudication (n = 111) and/or by surgery/autopsy (n = 21). Sixteen percent of cNEC cases died. Adverse events of NEC were reported in 8.5% of infants and 4.1% had NEC diagnosed by radiology and surgery/autopsy at the participating centers. Regression analyses showed that the risk of cNEC decreased by 11 to 30% for every 100-g increment in BW and single-week increment in GA and associated cNEC with odds ratios (ORs) > 2.0 for gastrointestinal (GI) perforation and obstruction, hypotension, hypokalemia, hypophosphatemia, and death. Comparing risks of cNEC in infants below and above 750-g BW showed higher ORs (2.7-4.3) for GI perforation, hypotension, hypokalemia, and renal complications in the smaller infants, whereas the bigger infants had higher ORs (1.9-3.2) for serious non-GI events, late-onset sepsis (LOS), and death. Predictors of cNEC (hazard ratio, HR > 1.5) included serious non-GI events (mainly infections), hyponatremia, and hyperglycemia, whereas the HR was 0.52 for intravenous antibiotics. After cNEC diagnosis, there were higher rates of GI perforation and obstruction, hypotension, hypokalemia, and LOS.

CONCLUSION

 Independent adjudication of abdominal radiographs increased radiological recognition of NEC and proved to be feasible in a multicenter study setting as well as able to diagnose clinically relevant NEC.

KEY POINTS

· Independent adjudication of abdominal radiographs in ELBW infants increased NEC recognition.. · Risk of NEC decreased by 11 to 30% with every 100-g increment in BW and GA week.. · In infants with BW 750 to 1,000 g, the risk of death from NEC was almost twice that in infants with BW 500 to 749 g. · Infants with NEC received antibiotics during one-third and parenteral nutrition during half of the first 7 postnatal weeks..

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.

A Novel Integrated Clinical-Biochemical-Radiological and Sonographic Classification of Necrotizing Enterocolitis

OBJECTIVE

 To evaluate the sensitivity and specificity of clinical, laboratory, and radiological markers and the neonatologist-performed intestinal ultrasound (NP-IUS) for treatment interventions in preterm neonates who developed necrotizing enterocolitis (NEC).

STUDY DESIGN

 This was a case-control study of preterm neonates < 35 weeks with a diagnostic workup for NEC. The diagnostic workup included NP-IUS performed by trained neonatologists using a standard protocol, abdominal roentgenogram (AXR), and laboratory investigations. Intestinal ultrasound (IUS) performed by two neonatologists was standardized to detect 11 injury markers. AXRs were read independently by experienced pediatric radiologists. The investigators who retrospectively interpreted the IUS were blinded to the clinical and treatment outcomes.

RESULTS

 A total of 111 neonates were assessed. Fifty-four did not require intervention and formed the control group. Twenty cases were treated medically, 21 cases were treated with late surgery for stricture or adhesions, and 16 were treated with early surgery. The integrated model of cumulative severity of ultrasound markers, respiratory and hemodynamic instability, abdominal wall cellulitis, and C- reactive protein > 16 mg/L had an area under the curve (AUC) of 0.89 (95% confidence interval [CI]: 0.83-0.94%, p < 0.0001) for diagnosing NEC requiring surgical intervention. We also investigated the utility of Bell's classification to diagnose either the need for surgery or death, and it had an AUC of 0.74 (95% CI: 0.65-0.83%, p < 0.0001).

CONCLUSION

 In this cohort, a combination of specific IUS markers and clinical signs of instability, abdominal wall cellulitis, plus laboratory markers were diagnostic of NEC requiring interventions.

KEY POINTS

· The diagnosis of necrotizing enterocolitis requires a combination of markers.. · The combination of specific ultrasound markers, clinical signs, and laboratory markers were diagnostic of NEC requiring intervention.. · The intestinal ultrasound performed by a trained neonatologist was the most sensitive diagnostic marker of NEC requiring surgical intervention..

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..

Implications of maternal vitamin D administration for the neonatal respiratory distress syndrome: A randomized clinical trial

BACKGROUND

Vitamin D deficiency has been suggested to be a risk factor for neonatal respiratory distress syndrome (RDS). This study aimed to evaluate the effect of 25 (OH) D administrations in pregnant women with findings of preterm labor on the incidence of RDS in their preterm neonates.

MATERIALS AND METHODS

A randomized controlled clinical trial was conducted on pregnant mothers with gestational age (GA) of less than 34 weeks at risk of preterm delivery. 175 subjects were randomly assigned into two groups, including intervention (intramuscular injection of 50,000 units of 25(OH) D during 72 hours before delivery) and control (no injections). Serum concentrations of 25(OH) D were measured shortly after birth in both mothers and neonates. Then, clinical and laboratory results of mothers and their offspring were recorded (in a checklist). Short-term outcomes and the need for respiratory support were also assessed. Data were analyzed by independent t-test, Mann-Whitney U test, Fisher's exact test, and chi-square test.

RESULTS

Even though gestational age, birth weight, delivery method, and serum vitamin D levels are consistent among both groups, 45% of neonates in the control group and 20% in the intervention group developed respiratory distress syndrome (P = 0.05). The mean 25(OH) D level in neonates was 17.7±10.5 and 19.29±9.94 ng/mL in the intervention and control groups, respectively (P > 0.05).

CONCLUSION

A single dose of 50,000 units of intramuscular 25(OH)D in pregnant women at risk of preterm labor can lower the risk of RDS in the infant.

Novel scoring system for early diagnosis of necrotizing enterocolitis: integrating clinical and laboratory data with urinary caveolin-1 levels

Introduction

Necrotizing enterocolitis (NEC) poses a significant threat to preterm infants, with nonspecific early manifestations complicating timely diagnosis. Therefore, this study aimed to develop a novel scoring system for early diagnosis of NEC, incorporating clinical and laboratory data with urinary caveolin-1 levels.

Material and methods

A single-center prospective cohort study was conducted at a tertiary hospital in East Java, Indonesia. NEC diagnosis was established by Bell's criteria and proven gut dysbiosis. Urinary levels of claudin-2, caveolin-1, and epidermal growth factor (EGF) were assessed as potential indicators of tight junction disruption. The selected urine biomarker cutoff value was determined using symbolic classification analysis and combined with clinical and laboratory parameters from Bell's criteria to create an NEC scoring system, validated with the Aiken index. Sensitivity and specificity analyses were performed.

Results

Thirty-four neonates, comprising NEC, preterm non-NEC, and term infants, were included. qPCR analysis highlighted elevated Klebsiella, Lactobacillus, Clostridium, and Bacteroides levels in NEC patients, indicating a gut dysbiosis trend. Among 3 biomarkers, caveolin-1 ≥ 17.81 ng/dl on day 3 demonstrated 72.86% negative predictive value and 87.50% positive predictive value. The combined scoring system which comprised abdominal cellulitis, distension, radiology, advanced resuscitation at birth, prematurity or low birthweight, platelet count, sepsis, orogastric retention, metabolic acidosis and caveolin-1 findings exhibited an AUC of 0.922 (95% CI: 0.81-1.00, p < 0.001), with ≥ 1.81 as the cutoff, offering 93% sensitivity and 94% specificity.

Conclusions

Urine caveolin-1 on day 3 signifies enterocyte tight junction damage and the acute phase of NEC in premature infants. The proposed scoring system demonstrates good performance in predicting NEC incidence in preterm infants.

[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.

Development of an international core outcome set for treatment trials in necrotizing enterocolitis-a study protocol

AIM

Necrotizing enterocolitis (NEC) is the most lethal disease of the gastrointestinal tract of preterm infants. New and existing management strategies need clinical evaluation. Large heterogeneity exists in the selection, measurement, and reporting of outcome measures in NEC intervention studies. This hampers meta-analyses and the development of evidence-based management guidelines. We aim to develop a Core Outcome Set (COS) for NEC that includes the most relevant outcomes for patients and physicians, from moment of diagnosis into adulthood. This COS is designed for use in NEC treatment trials, in infants with confirmed NEC.

METHODS

This study is designed according to COS-STAD (Core Outcome Set-STAndards for Development) recommendations and the COMET (Core Outcome Measures in Effectiveness Trials) Initiative Handbook. We obtained a waiver from the Ethics Review Board and prospectively registered this study with COMET (Study 1920). We will approach 125 clinicians and/or researchers from low-middle and high-income countries based on their scientific output (using SCIVAL, a bibliometric tool). Patients and parents will be approached through local patient organisations. Participants will be separated into three panels, to assess differences in priorities between former patients and parents (1. lay panel), clinicians and researchers involved in the neonatal period (2. neonatal panel) and after the neonatal period (3. post-neonatal panel). They will be presented with outcomes currently used in NEC research, identified through a systematic review, in a Delphi process. Eligible outcome domains are also identified from the patients and parents' perspectives. Using a consensus process, including three online Delphi rounds and a final face-to-face consensus meeting, the COS will be finalised and include outcomes deemed essential to all stakeholders: health care professionals, parents and patients' representatives. The final COS will be reported in accordance with the COS-Standards for reporting (COS-STAR) statement.

CONCLUSIONS

Development of an international COS will help to improve homogeneity of outcome measure reporting in NEC, will enable adequate and efficient comparison of treatment strategies, and will help the interpretation and implementation of clinical trial results. This will contribute to high-quality evidence regarding the best treatment strategy for NEC in preterm infants.

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.

Predictive Factors for Mortality in Pre-Term Neonates with Necrotizing Enterocolitis: A Retrospective Cohort Study

Background: Necrotizing enterocolitis (NEC) is a serious neonatal condition. Over the last three decades, there has been progress in neonatal intensive care with an increase in the incidence of pre-term births. This has led to an increase in the incidence of NEC. However, research studies regarding NEC outcomes in low-income countries are scarce. Our study aimed to assess predictive factors for mortality in patients with NEC in a single center in Tunisia. Patients and Methods: We conducted a retrospective data collection through a review of the patients' medical records. All neonates with a medical or surgical management of NEC between January 1, 2010 and March 31, 2022 were included. Results: A total of 102 neonates were included with the overall survival of 47%. Outcomes of the univariable analysis showed that patients in the deceased group had lower gestational age, lower five-minute Apgar score, lower birth weight, and lower platelet count than those in the survivor group. Multivariable logistic analyses demonstrated that gestational age <32 weeks (p = 0.024; odds ratio [OR], 2.5), five-minute Apgar score <8 (p = 0.017; OR, 3.621), birth weight <1,500 g (p = 0.001; OR, 4.136), platelet count <50,000/mm³ (p = 0.029; OR, 2.5), Bell's stage 3 (p = 0.035; OR, 2.496), and sepsis during hospitalization (p < 0.001; OR, 5.971) were associated with mortality in neonates with NEC. Conclusions: Our study showed that gestational age <32 weeks, five-minute Apgar score <8, very low birth weight, severe thrombocytopenia, Bell's stage 3, and sepsis during hospitalization were predictive factors for mortality in neonates with NEC. These factors would be useful to refine treatment modalities for better disease outcomes.

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.

Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns

Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area. IMPACT: Only a few publications on the use of AI in NEC diagnosis are available although they offer some evidence that AI may be helpful in NEC diagnosis. AI requires large, multicentre, and multimodal datasets of high quality for model training and testing. Published results in the literature are based on data from single institutions and, as such, have limited generalisability. Large multicentre studies evaluating broad datasets are needed to evaluate the true potential of AI in diagnosing NEC in a clinical setting.

A new intestinal ultrasound integrated approach for the management of neonatal gut injury

Clinical management of gut injury has been a challenge to the clinician since the first description of necrotizing enterocolitis (NEC) about 50 years ago, and it is still poorly defined. Most of the diagnostic markers are non-specific, and there is no consensus yet on a reliable gold standard for diagnosis. This report describes our approach to integrating point of care intestinal ultrasound (IUS) as the primary radiological assessment modality with other clinical and biochemical markers.

CONCLUSIONS

This is the first clinical guideline integrating point of care IUS as a routine assessment of the gut injury. This integrated algorithm improves the quality of care of the gut injury, provides a more accurate diagnosis of NEC, and differentiates other categories of gut injury.

WHAT IS KNOWN

• Necrotizing enterocolitis is a poorly defined disease, and the routine assessment relying on AXR does not differentiate NEC from other categories of gut injury.

WHAT IS NEW

• Integrating point of care IUS with the routine clinical assessment of gut injury enables the gut injury to be classified according to the triggering factors and severity; this helps target the appropriate management.

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.