Critical Organ Dysfunction and Preoperative Mortality in Newborns with Hypoplastic Left Heart Syndrome.

Hypoplastic left heart syndrome (HLHS) is fatal without surgical intervention. An important subset of HLHS patients die prior to surgical intervention, but this population is underevaluated. The neonatal sequential organ failure assessment score (nSOFA) is an operational definition of organ dysfunction that can identify those with a high risk of mortality among neonatal intensive care unit (NICU) patients. The utility of the nSOFA to predict preoperative mortality in the unique HLHS population is unknown and could inform care, particularly care provided by neonatology staff. We performed a multicenter retrospective cohort study of HLHS cases across three level IV NICUs from January 1, 2009, to December 3, 2023. Patients were classified as either survived or died prior to surgical intervention. Demographic variables were curated from medical records including the maximum nSOFA (nSOFAmax) before surgical intervention or death. We identified 265 patients with HLHS over the study period. The nSOFAmax was greater in patients who died preoperatively (14/265; 5%) compared with survivors to surgical intervention (median 8 [interquartile range, 6, 12] vs. 2 [0, 4]; p < 0.001). The area under receiver operating characteristics curve for the nSOFAmax to discriminate for mortality was 0.93 (95% confidence interval, 0.88-0.98; p < 0.001). Compared with an nSOFAmax of 0, the likelihood ratio for preoperative death doubled at 2, tripled at 4, and was 10-fold at 9. This is the first demonstration of nSOFA utility in specific to congenital heart disease and HLHS. The nSOFAmax represents a novel, electronic health record-compatible, and generalizable method to identify patient-level organ dysfunction and risk for preoperative mortality in HLHS patients. KEY POINTS: · An important subset of HLHS patients die preoperatively.. · nSOFA can be used to measure preoperative HLHS severity.. · nSOFA predicts preoperative mortality risk in HLHS patients..

Hourly Kinetics of Critical Organ Dysfunction in Extremely Preterm Infants.

Rationale: Use of severity of illness scores to classify patients for clinical care and research is common outside of the neonatal ICU. Extremely premature (<29 weeks' gestation) infants with extremely low birth weight (<1,000 g) experience significant mortality and develop severe pathology during the protracted birth hospitalization. Objectives: To measure at high resolution the changes in organ dysfunction that occur from birth to death or discharge home by gestational age and time, and among extremely preterm infants with and without clinically meaningful outcomes using the neonatal sequential organ failure assessment score. Methods: A single-center, retrospective, observational cohort study of inborn, extremely preterm infants with extremely low birth weight admitted between January 2012 and January 2020. Neonatal sequential organ failure assessment scores were calculated every hour for every patient from admission until death or discharge. Measurements and Main Results: Longitudinal, granular scores from 436 infants demonstrated early and sustained discrimination of those who died versus those who survived to discharge. The discrimination for mortality by the maximum score was excellent (area under curve, 0.91; 95% confidence intervals, 0.88-0.94). Among survivors with and without adverse outcomes, most score variation occurred at the patient level. The weekly average score over the first 28 days was associated with the sum of adverse outcomes at discharge. Conclusions: The neonatal sequential organ failure assessment score discriminates between survival and nonsurvival on the first day of life. The major contributor to score variation occurred at the patient level. There was a direct association between scores and major adverse outcomes, including death.

Maximum vasoactive-inotropic score and mortality in extremely premature, extremely low birth weight infants.

OBJECTIVE: To determine the relationship between maximum vasoactive-inotropic (VISmax) and mortality in extremely premature (<29 weeks completed gestation), extremely low birth weight (ELBW, <1000 g) infants. STUDY DESIGN: Single center, retrospective, and observational cohort study. RESULTS: We identified 436 ELBW, <29 week, inborn infants cared for during the study period. Compared to infants with VISmax of 0, the frequency of mortality based on VISmax ranged from 3.3-fold to 46.1-fold. VISmax > 30 was associated with universal mortality. Multivariable modeling that included gestational age, birth weight, and VISmax revealed significant utility to predict mortality with negative predictive value of 87.0% and positive predictive value of 84.8% [adjusted AUROC: 0.90, (0.86-0.94)] among patients that received vasoactive-inotropic treatment. CONCLUSION: VISmax is an objective measure of hemodynamic/cardiovascular support that was directly associated with mortality in extremely premature ELBW infants. The VISmax represents an important step towards neonatal precision medicine and risk stratification of extremely premature ELBW infants.

Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool.

PURPOSE: Necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP) are devastating diseases in preterm neonates, often requiring surgical treatment. Previous studies evaluated outcomes in peritoneal drain placement versus laparotomy, but the accuracy of the presumptive diagnosis remains unknown without bowel visualization. Predictive analytics provide the opportunity to determine the etiology of perforation and guide surgical decision making. The purpose of this investigation was to build and evaluate machine learning models to differentiate NEC and SIP. METHODS: Neonates who underwent drain placement or laparotomy NEC or SIP were identified and grouped definitively via bowel visualization. Patient characteristics were analyzed using machine learning methodologies, which were optimized through areas under the receiver operating characteristic curve (AUROC). The model was further evaluated using a validation cohort. RESULTS: 40 patients were identified. A random forest model achieved 98% AUROC while a ridge logistic regression model reached 92% AUROC in differentiating diseases. When applying the trained random forest model to the validation cohort, outcomes were correctly predicted. CONCLUSIONS: This study supports the feasibility of using a novel machine learning model to differentiate between NEC and SIP prior to any intended surgical interventions. LEVEL OF EVIDENCE: level II TYPE OF STUDY: Clinical Research Paper.