Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study.

BACKGROUND: Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician's ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). METHODS: We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. RESULTS: A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P < 0.05). Our proposed model, tested on a dataset from March 4, 2019, to March 4, 2022. The mean AUROC of our proposed model for IMV support prediction performance demonstrated 0.861 (95%CI, 0.853-0.869). It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600-0.622), 0.837 (95%CI, 0.828-0.845), and 0.0.831 (95%CI, 0.821-0.845), respectively. The highest AUPRC value is shown in the proposed model at 0.327 (95%CI, 0.308-0.347). The proposed model performed more accurate predictions as gestational age decreased. Additionally, the model exhibited the lowest alarm rate while maintaining the same sensitivity level. CONCLUSION: Deep learning approaches can help accurately standardize the prediction of invasive mechanical ventilation for neonatal patients and facilitate advanced neonatal care. The results of predictive, recall, and alarm performances of the proposed model outperformed the other models.

Risk factors and clinical outcomes of extubation failure in very early preterm infants: a single-center cohort study.

BACKGROUND: Early extubation success (ES) in preterm infants may reduce various mechanical ventilation-associated complications; however, extubation failure (EF) can cause adverse short- and long-term outcomes. Therefore, the present study aimed to identify differences in risk factors and clinical outcomes between ES and EF in very early preterm infants. METHODS: This retrospective study was conducted between January 2017 and December 2021. Premature infants born at 32 weeks' gestational age in whom extubation had failed at least once were assigned to the EF group. Successfully extubated patients with a similar gestational age and birth weight as those in the EF group were assigned to the ES group. EF was defined as the need for re-intubation within 120 h of extubation. Various variables were compared between groups. RESULTS: The EF rate in this study was 18.6% (24/129), and approximately 80% of patients with EF required re-intubation within 90.17 h. In the ES group, there was less use of inotropes within 7 days of life (12 [63.2%] vs. 22 [91.7%], p = 0.022), a lower respiratory severity score (RSS) at 1 and 4 weeks (1.72 vs. 2.5, p = 0.026; 1.73 vs. 2.92, p = 0.010), and a faster time to reach full feeding (18.7 vs. 29.7, p = 0.020). There was a higher severity of bronchopulmonary dysplasia BPD (3 [15.8%] vs. 14 [58.3%], p = 0.018), longer duration of oxygen supply (66.5 vs. 92.9, p = 0.042), and higher corrected age at discharge (39.6 vs. 42.5, p = 0.043) in the EF group. The cutoff value, sensitivity, and specificity of the respiratory severity score (RSS) at 1 week were 1.98, 0.71, and 0.42, respectively, and the cutoff value, sensitivity, and specificity of RSS at 4 weeks were 2.22, 0.67, and 0.47, respectively. CONCLUSIONS: EF caused adverse short-term outcomes such as a higher BPD severity and longer hospital stay. Therefore, extubation in very early preterm infants should be carefully evaluated. Using inotropes, feeding, and RSS at 1 week of age can help predict extubation success.

Genetic Aspects of Small for Gestational Age Infants Using Targeted-Exome Sequencing and Whole-Exome Sequencing: A Single Center Study.

BACKGROUND: The etiology of small for gestational age (SGA) is multifactorial and includes maternal/uterine-placental factors, fetal epigenetics, and genetic abnormalities. We evaluated the genetic causes and diagnostic effectiveness of targeted-panel sequencing (TES) or whole-exome sequencing (WES) in SGA infants without a known cause. METHODS: A prospective study was conducted on newborn infants born with a birth weight of less than the 10th percentile for gestational age between January 2019 and December 2020 at the Pusan National University Hospital. We excluded infants with known causes of SGA, including maternal causes or major congenital anomalies or infections. SGA infants without a known etiology underwent genetic evaluation, including karyotyping, chromosomal microarray (CMA), and TES/WES. RESULTS: During the study period, 82 SGA infants were born at our hospital. Among them, 61 patients were excluded. A total of 21 patients underwent karyotyping and chromosomal CMA, and aberrations were detected in two patients, including one chromosomal anomaly and one copy number variation. Nineteen patients with normal karyotype and CMA findings underwent TES or WES, which identified three pathogenic or likely pathogenic single-gene mutations, namely LHX3, TLK2, and MED13L. CONCLUSIONS: In SGA infants without known risk factors, the prevalence of genetic causes was 22% (5/21). The diagnostic yield of TES or WES in SGA infants with normal karyotype and CMA was 15.7% (3/19). TES or WES was quite helpful in identifying the etiology in SGA infants without a known cause.

Clinical and Laboratory Findings of Nosocomial Sepsis in Extremely Low Birth Weight Infants According to Causative Organisms.

BACKGROUND: nosocomial sepsis remains a significant source of morbidity and mortality in extremely low birth weight (ELBW) infants. Early and accurate diagnosis is very important, but it is difficult due to the similarities in clinical manifestation between the causative microorganisms. We tried to identify the differences between causative microorganisms in clinical and laboratory findings and to help choose antibiotics, when sepsis was suspected in ELBW infants. METHODS: a retrospective study was conducted on preterm infants, born at less than 28 weeks of gestation, with a birth weight of less than 1000 g between January 2009 and December 2019. Clinical and laboratory findings of suspected sepsis, after the first 72 h of life, were assessed. We classified them into four groups according to blood culture results (gram positive, gram negative, fungal, and negative culture groups) and compared them. RESULTS: a total of 158 patients were included after using the exclusion criteria, with 45 (29%) in the gram positive group, 35 (22%) in the gram negative group, 27 (17%) in the fungal group, and 51 (32%) in the negative culture group. There were no significant differences in mean gestational age, birth weight, and neonatal morbidities, except for the age of onset, which was earlier in the fungal group than other groups. White blood cell (WBC) counts were the highest in the gram negative group and the lowest in the fungal group. The mean platelet counts were the lowest in the fungal group. C-reactive protein (CRP) levels were the highest in the gram negative group, while glucose was the highest in the fungal group. CONCLUSIONS: in conclusion, we showed that there are some differences in laboratory findings, according to causative microorganisms in the nosocomial sepsis of ELBW infants. Increased WBC and CRP were associated with gram negative infection, while decreased platelet and glucose level were associated with fungal infection. These data may be helpful for choosing empirical antibiotics when sepsis is suspected.