Trends in the characteristics and disease severity of patients using an after-hours house call (AHHC) medical service changed during the coronavirus disease (COVID-19) pandemic. However, there have been no reports on this issue since the start of the COVID-19 pandemic. This study aimed to investigate patients’ tendencies to utilize an AHHC medical service for fever or common cold symptoms during the COVID-19 pandemic. This retrospective cohort study compared the characteristics and disease severity of patients with fever or common cold symptoms utilizing an AHHC medical service offered by a single large company between the control period (December 1, 2018 to April 30, 2019) and the COVID-19 pandemic exposure period (December 1, 2019 to April 30, 2020). We also assessed the proportion of these patients in relation to all patients calling the service for any reason. During the control and COVID-19 pandemic exposure periods, a total of 6462 and 10,003 patients consulted the AHHC medical service, respectively. Of these, 5335 (82.6%) and 7423 (74.2%) patients had fever and common cold symptoms, respectively, during the control and COVID-19 pandemic exposure periods (P < 0.001). The corresponding median (interquartile range) ages were 8 (3–11) and 10 (4–33) years, respectively. The distribution of disease severity differed between the groups. The proportions of patients with mild, moderate, and severe illness were 71.1, 28.7, and 0.2% in the control period and 42.3, 56.7, and 0.9% in the COVID-19 pandemic exposure period, respectively (P < 0.001). During the COVID-19 pandemic, the proportion of patients with fever or common cold symptoms was lower than that in the control period, but disease severity was significantly higher.

BACKGROUND: Several studies have investigated postextubation complications of the positive-pressure and suctioning techniques; however, these studies yielded inconsistent results. Therefore, in this systematic review, we aimed to assess and compare the risk of complications between these techniques after extubation. METHODS: This study was registered with the International Prospective Register of Systematic Reviews (CRD42021272068). We searched for randomized controlled trials (RCT) or observational studies that compared positive-pressure and suctioning extubation techniques in medical literature databases. Our search was conducted from the databases’ inception to July 7, 2022. The included studies were assessed for quality by using a risk of bias tool. RESULTS: Six RCTs and 1 non-randomized controlled study were included in this systematic review ( N = 1,575 subjects), wherein the positive-pressure and suctioning techniques were applied to 762 and 813 subjects, respectively. Three studies were conducted in operating rooms, and 4 studies were conducted in ICUs. Five studies were conducted among adults, and 2 studies were conducted among children or neonates. All the studies except 1 RCT showed that the positive-pressure technique tended to have a lower but not statistically different risk of complications, including desaturation, airway obstruction, pneumonia, aspiration, atelectasis, and re-intubation, than the suctioning technique. Three of the 6 RCTs were determined to have a high risk of bias and the 1 non-randomized controlled study was determined to have a serious risk of bias. CONCLUSIONS: The positive-pressure technique tended to have a lower risk of complications than the suctioning technique. Further high-quality studies are warranted.

We compared the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and logistic regression (LR) with the least absolute shrinkage and selection operator (LASSO) for 30-day unplanned readmission, according to the number of predictor variables and presence/absence of blood-test results. We used electronic health records of patients discharged alive from 38 hospitals in 2015–2017 for derivation (n=339,513) and in 2018 for validation (n=118,074), including basic characteristics (age, sex, admission diagnosis category, number of hospitalizations in the past year, discharge location), diagnosis, surgery, procedure, and drug codes, and blood-test results. We created six patterns of datasets having different numbers of binary variables (that ≥5% or ≥1% of patients or ≥10 patients had) with and without blood-test results. For the dataset with the smallest number of variables (102), the c-statistic was highest for GBDT (0.740), followed by RF (0.734), LR-LASSO (0.720), and DNN (0.664). For the dataset with the largest number of variables (1543), the c-statistic was highest for GBDT (0.764), followed by LR-LASSO (0.755), RF (0.751), and DNN (0.720). We found that GBDT generally outperformed LR-LASSO, but the difference became smaller when the number of variables was increased and blood-test results were used.