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Abstract Background Less is known about the risk factors for acute respiratory distress syndrome (ARDS) in sepsis patients diagnosed according to sepsis 3.0 criteria. Moreover, the risk factors for ARDS severity remain unclear. Methods We retrospectively collected the characteristics of sepsis patients from the intensive care unit of the First Affiliated Hospital of China Medical University from January 2017 to September 2018. Logistic regression was used in determining the risk factors. Results 529 patients with sepsis were enrolled and 179 developed ARDS. The most common infection sites were acute abdominal infection (n = 304) and pneumonia (n = 117). Multivariate analysis showed that patients with pancreatitis with local infection (odds ratio [OR], 3.601; 95% confidence interval [CI], 1.429–9.073, P = 0.007), pneumonia (OR 3.486; 95% CI 1.890–6.430, P

Abstract Background This study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications. Patients and methods This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value ≥ 3) using the same training and test data sets. Results In total, 38,515 ventilation sessions occurred in 61,532 ICU admissions. VAP occurred in 212 of these sessions. We incorporated 42 VAP risk factors at admission and routinely measured the vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84% in the validation, 74% sensitivity and 71% specificity 24 h after intubation. The AUC of our VAP machine learning model is nearly 25% higher than the CPIS model, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively. Conclusions We developed and internally validated an automated model for VAP prediction using the MIMIC-III cohort. The VAP prediction model achieved high performance based on its AUC, sensitivity and specificity, and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

Abstract Background Prolonged and difficult weaning is associated with higher rates of complications and mortality. Therefore, it is important to identify the associated factors. Case presentation We describe our experience with a 37-year-old man diagnosed with severe viral pneumonia (influenza A). He presented with acute respiratory failure type I on admission. During intubation, his blood pressure and heart rate decreased, and epinephrine and norepinephrine were administered. Although his clinical condition improved 8 days after intensive care unit (ICU) admission, he experienced difficulty weaning. He remained conscious but had a poor spontaneous cough with sputum production and weak limb muscle strength. His cough reflex was absent during bronchoscopic sputum suction, and he used abdominal breathing during the T-tube test. Magnetic resonance imaging revealed an Arnold–Chiari malformation type I, posterior dislocation of the odontoid process, and syringomyelia, with compression and deformation of the medulla and high cervical cord. The patient was successfully weaned from the ventilator at 20 days after ICU admission. Conclusions Arnold–Chiari malformation type I and posterior dislocation of the odontoid process, which aggravate medullary compression and increase the risk of cervical nerve injury, might be a rare factor affecting prolonged weaning in critical illness.