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Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Methods: Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. Results: A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71–0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74–0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. Conclusions: We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus is a common cause of respiratory tract disease in children, predominantly presenting with mild symptoms. We present five cases of respiratory syncytial virus infection of the lower respiratory tract in immunocompromised adults suffering from severe respiratory insufficiency leading to bilateral pneumonia and fulfilling the criteria for acute respiratory distress syndrome. Respiratory syncytial virus was cultured as the only pathogen in the bronchoalveolar lavage fluid in four of these patients. Despite various therapeutic interventions, only one patient survived. Respiratory syncytial virus was implicated as a direct cause of respiratory failure. Respiratory syncytial virus may be an underestimated cause of severe respiratory failure and acute respiratory distress syndrome in the immunocompromised adult admitted to the intensive care unit. [ABSTRACT FROM AUTHOR]

Objective: To describe the 12-month mortality of Dutch COVID-19 intensive care unit patients, the total COVID-19 population and various subgroups on the basis of the number of comorbidities, age, sex, mechanical ventilation, and vasoactive medication use., Methods: We included all patients admitted with COVID-19 between March 1, 2020, and March 29, 2022, from the Dutch National Intensive Care (NICE) database. The crude 12-month mortality rate is presented via Kaplan-Meier survival curves for each patient subgroup. We used Cox regression models to analyze the effects of patient characteristics on 12-month mortality after hospital discharge., Results: We included 16,605 COVID-19 patients. The in-hospital mortality rate was 28.1%, and the 12-month mortality rate after intensive care unit admission was 29.8%. Among hospital survivors, 12-month mortality after hospital discharge was 2.5% (300/11,931). The hazard of death at 12 months after hospital discharge was greater in patients between 60 and 79 years (HR 4.74; 95%CI 2.23 - 10.06) and ≥ 80 years (HR 22.77; 95%CI 9.91 - 52.28) than in patients < 40 years of age; in male patients than in female patients (HR 1.38; 95%CI 1.07 - 1.78); and in patients with one (adjusted HR 1.95; 95%CI 1.5 - 2.53), two (adjusted HR 4.49; 95%CI 3.27 - 6.16) or more than two comorbidities (adjusted HR 4.99; 95%CI 2.62 - 9.5) than in patients with no comorbidities. Neither vasoactive medication use nor mechanical ventilation resulted in statistically significant results., Conclusion: For Dutch COVID-19 intensive care unit patients, most deaths occurred during their hospital stay. For hospital survivors, the crude 12-month mortality rate was low. Patient age (older than 60), sex and the number of comorbidities were associated with a greater hazard of death at 12 months after hospital discharge, whereas mechanical ventilation and vasoactive medication were not.

The IGF-binding protein (IGFBP) family consists of six proteins that are expressed and secreted in different tissues. The proteins are regulators of physiological processes throughout the body by modulating the activity of IGF-I and IGF-II. In this article, we describe the coordinated expression of IGFBP5 and MN1 in meningiomas. MN1 is a transcriptional co-activator and we show that MN1 stimulates the IGFBP5 promoter in Hep3B cells. A CACCC-containing sequence, located 140 bp upstream of the transcription start site of the promoter, is required for MN1 action. This sequence matches with the CACCCAC consensus sequence that was selected in an oligonucleotide selection assay performed for MN1. The CACCC element has also been shown to be important for induction of the IGFBP5 promoter by retinoic acid (RA) and progesterone (Pg). We were unable to confirm the effect of Pg on the promoter in Hep3B and U2-osteosarcoma cells regardless of the presence of MN1. On the other hand, we show that induction of the promoter by RA depends on co-expressed MN1 in Hep3B cells. MN1TEL, a leukemia-related fusion protein containing parts of the MN1 and TEL (ETV6) genes, is capable of stimulating the IGFBP5 promoter but is unable to cooperate with RA in Hep3B cells. This suggests that the effects of RA can be negatively affected in leukemias caused by MN1TEL.