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.

Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study.

Introduction: The aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort. Methods: We included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013-2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable. Results: We included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants. Conclusions: We developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field.

Regdanvimab for patients with mild-to-moderate COVID-19: a retrospective cohort study and subgroup analysis of patients with the Delta variant.

OBJECTIVES: To evaluate the efficacy and safety of regdanvimab, a neutralizing antibody, in patients with mild-to-moderate SARS-CoV-2 including against the Delta variant. METHODS: A single-center, retrospective, observational cohort study in adults with confirmed COVID-19. The primary end point was the proportion of patients deteriorating with peripheral oxygen saturation <90% in room air, requiring supplemental oxygen therapy above high flow, or experiencing mortality due to COVID-19 up to day 28. RESULTS: A total of 722 patients were eligible; 418 received regdanvimab and 304 received standard of care (SoC), of whom 71.1% (297/418, regdanvimab) and 37.8% (115/304, SoC) were infected with the Delta variant. The proportion of patients with a primary end point event was significantly lower with regdanvimab than SoC (3.1% vs 9.9%; difference: -6.8 [95% confidence interval: -10.9, -2.8]; P = 0.0002). A similar trend was observed in the Delta variant subgroup (regdanvimab, 2.7% vs SoC, 7.0%; difference -4.3 [95% confidence interval: -10.8, 0.2]; P = 0.0827). The secondary efficacy end points supported the primary analysis findings in the overall cohort and Delta variant subgroup. No new safety signals were identified. CONCLUSION: Regdanvimab demonstrated clinical efficacy in the overall cohort and may provide a clinical benefit for patients with mild-to-moderate COVID-19 infected with the Delta variant.

Clinical Effectiveness of Regdanvimab Treatment for Mild-to-Moderate COVID-19: A Retrospective Cohort Study.

Background: In a Phase III study, regdanvimab (CT-P59) reduced the risk of hospitalization or death versus placebo in patients with mild-to-moderate coronavirus disease 2019 (COVID-19). Purpose: We performed a retrospective cohort study of patients with COVID-19 to examine the effect of regdanvimab versus standard of care (SoC) on oxygen saturation. Methods: We reviewed patients with mild-to-moderate COVID-19 confirmed by reverse transcription-polymerase chain reaction at a single hospital in the Republic of Korea. The primary efficacy end point was the proportion of patients deteriorating with peripheral capillary oxygen saturation <94% on room air up to day 28. Results: A total of 127 patients were treated for COVID-19 with regdanvimab, 190 with SoC. The proportion of patients deteriorating with peripheral capillary oxygen saturation <94% on room air up to day 28 was 13.4% with regdanvimab and 39.5% with SoC (P < 0.0001); median time (range) until sustained recovery of fever was 2.0 (0.2-14.8) and 4.2 (0.1-17.1) days, respectively. Supplemental oxygen was required by 23.6% of patients with regdanvimab and 52.1% with SoC (P<0.0001) for a mean of 6.3 and 8.7 days, respectively (P = 0.0113); no patients needed mechanical ventilation. Compared with SoC, hospitalization was shorter with regdanvimab (mean = 11.1 vs 13.6 days; 63.8% vs 31.6% discharged within 11 days; both P values < 0.0001). Fewer regdanvimab-treated patients required remdesivir (14.2% vs 43.2%; P < 0.0001). There were no deaths. Two patients had adverse reactions with regdanvimab. Conclusions: This real-world study indicates that regdanvimab can prevent deterioration in patients with mild-to-moderate COVID-19. (Curr Ther Res Clin Exp. 2022; 83:XXX-XXX).

Modeling the early temporal dynamics of viral load in respiratory tract specimens of COVID-19 patients in Incheon, the Republic of Korea.

OBJECTIVE: To investigate the duration and peak of severe acute respiratory syndrome coronavirus 2 shedding as infectivity markers for determining the isolation period. METHODS: A total of 2,558 upper respiratory tract (URT) and lower respiratory tract (LRT) specimens from 138 patients with laboratory-confirmed coronavirus disease were analyzed. Measurements of sequential viral loads were aggregated using the cubic spline smoothing function of a generalized additive model. The time to negative conversion was compared between symptom groups using survival analysis. RESULTS: In URT samples, viral RNA levels peaked on day 4 after symptom onset and rapidly decreased until day 10 for both E and RdRp genes, whereas those in LRT samples immediately peaked from symptom onset and decreased until days 15.6 and 15.0 for E and RdRp genes, respectively. Median (interquartile range) time to negative conversion was significantly longer in symptomatic (18.0 [13.0-25.0] days) patients than in asymptomatic (13.0 [9.5-17.5] days) patients. The more types of symptoms a patient had, the longer the time to negative conversion. CONCLUSIONS: The viral load rapidly changes depending on the time after symptom onset; the viral shedding period may be longer with more clinical symptoms. Different isolation policies should be applied depending on disease severity.