Adverse Perinatal and Early Life Outcomes following 15q11.2 CNV Diagnosis

The copy number variation (CNV) of 15q11.2, an emerging and common condition observed during prenatal counseling, is encompassed by four highly conserved and non-imprinted genes—TUBGCP5, CYFIP1, NIPA1, and NIPA2—which are reportedly related to developmental delays or general behavioral problems. We retrospectively analyzed 1337 samples from genetic amniocentesis for fetal CNV using microarray-based comparative genomic hybridization analysis between January 2014 and December 2019. 15q11.2 CNV showed a prevalence of 1.5% (21/1337). Separately, 0.7% was noted for 15q11.2 BP1–BP2 microdeletion and 0.8% for 15q11.2 microduplication. Compared to the normal array group, the 15q11.2 BP1–BP2 microdeletion group had more cases of neonatal intensive care unit transfer, an Apgar score of <7 at 1 min, and neonatal death. Additionally, the group was symptomatic with developmental delays and had more infantile deaths related to congenital heart disease (CHD). Our study makes a novel contribution to the literature by exploring the differences in the adverse perinatal outcomes and early life conditions between the 15q11.2 CNV and normal array groups. Parent-origin gender-based differences may help in the prognosis of the fetal phenotype; development levels should be followed up in the long term and echocardiography should be offered prenatally and postnatally for the prevention of a delayed diagnosis of CHD.

Impact of hospitalization duration before medical emergency team activation: A retrospective cohort study

Background The rapid response system has been implemented in many hospitals worldwide and, reportedly, the timing of medical emergency team (MET) attendance in relation to the duration of hospitalization is associated with the mortality of MET patients. We evaluated the relationship between duration of hospitalization before MET activation and patient mortality. We compared cases of MET activation for early, intermediate, and late deterioration to patient characteristics, activation characteristics, and patient outcomes. We also aimed to determine the relationship, after adjusting for confounders, between the duration of hospitalization before MET activation and patient mortality. Materials and methods We retrospectively evaluated patients who triggered MET activation in general wards from March 2009 to February 2015 at the Asan Medical Center in Seoul. Patients were categorized as those with early deterioration (less than 2 days after admission), intermediate deterioration (2–7 days after admission), and late deterioration (more than 7 days after admission) and compared them to patient characteristics, activation characteristics, and patient outcomes. Results Overall, 7114 patients were included. Of these, 1793 (25.2%) showed early deterioration, 2113 (29.7%) showed intermediate deterioration, and 3208 (45.1%) showed late deterioration. Etiologies of MET activation were similar among these groups. The clinical outcomes significantly differed among the groups (intensive care unit transfer: 34.1%, 35.6%, and 40.4%; p < 0.001 and mortality: 26.3%, 31.5%, and 41.2%; p < 0.001 for early, intermediate, and late deterioration, respectively). Compared with early deterioration and adjusted for confounders, the odds ratio of mortality for late deterioration was 1.68 (1.46–1.93). Conclusions Nearly 50% of the acute clinically-deteriorating patients who activated the MET had been hospitalized for more than 7 days. Furthermore, they presented with higher rates of mortality and ICU transfer than patients admitted for less than 7 days before MET activation and had mortality as an independent risk factor.

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Background In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days. Results We used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P < 0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P < 0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.