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BackgroundAlthough multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable. ObjectiveWe aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization. MethodsPatients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort. ResultsOf 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively. ConclusionsFor patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

Author(s): Kensuke Nakamura [sup.1], Kentaro Ogura [sup.2] [sup.3], Hidehiko Nakano [sup.1], Hiromu Naraba [sup.1], Yuji Takahashi [sup.1], Tomohiro Sonoo [sup.1] [sup.3], Hideki Hashimoto [sup.1], Naoto Morimura [sup.4] Author Affiliations: (1) [...], Purpose Among patients surviving treatment in intensive care units (ICU), some cases exist for which inflammation persisted with prolonged hospital stays, referred as persistent inflammatory, immunosuppressed, catabolic syndrome (PIICS). C reactive protein (CRP) is regarded as the most important marker for PIICS. Nevertheless, the applicable cut-off of CRP for PIICS has never been described in the literature. Methods Data of patients admitted to the ICU/Emergency ward from May 2015 through June 2019 were analyzed retrospectively. Using K-means clustering, a 14-day CRP transition dataset was analyzed and categorized finally into 7 classes: 4 PIICS classes and 3 non-PIICS classes. Outcomes and the other PIICS characteristics were evaluated. Results From all 5513 admitted patients, this study examined data of 539 patients who had been admitted for more than 14 days, and for whom 14 day CRP transition analysis could be performed. By the CRP transitions of 7 categorized classes, the CRP cut-off for PIICS was regarded as 3.0 mg/dl on day 14. The Barthel Index at discharge, albumin, and total lymphocyte counts on day 14 were significantly lower in PIICS classes than those of non-PIICS classes. Creatinine kinase, antithrombin activity and thrombomodulin on admission were regarded as independent risk factors for PIICS. Conclusions Among patients with prolonged hospital stay, the PIICS population had elevated CRP, but lower Barthel Index, albumin, and total lymphocyte counts. The criterion of day 14 CRP for PIICS should be 3.0 mg/dl.

Background: Mechanical insufflation-exsufflation (MI-E) facilitates extubation. However, its potential to reduce the duration of ventilator use remains unclear. Therefore, the present study investigated whether the use of MI-E shortened the duration of mechanical ventilation in patients with high sputum retention. Methods: A randomized open-label trial was conducted at a single intensive care unit (ICU) in Japan between November 2017 and June 2019. Ventilated subjects requiring suctioning at least once every hour due to sputum retention were randomly assigned to the MI-E group or conventional care group. The primary endpoint was the number of ventilator-free days on day 28. Secondary endpoints were ventilator days in surviving subjects, the length of ICU stay, and mortality and tracheostomy rates among survivors. Results: Forty-eight subjects (81% males) with a median age of 72 years (interquartile range [IQR], 65–85 years) were enrolled. There were 27 subjects in the MI-E group and 21 in the control group. The median number of ventilator-free days was 21 (IQR, 13–24) and 18, respectively (IQR, 0–23) (P =.38). No significant differences were observed in the ICU length of stay (median, 10 days (IQR, 7–12) vs 12 days (IQR, 6–15); P =.31), mortality rate (19% vs 15%; odds ratio [OR], 1.36 [0.28–6.50]; P =.69), or tracheostomy rate among survivors (14% vs 28%; OR, 0.40 [0.08–1.91]; P =.25). Conclusion: In ventilated subjects in the ICU with high sputum retention, the use of MI-E did not significantly increase the number of ventilator-free days over that with conventional care. [ABSTRACT FROM AUTHOR]