ObjectiveTo investigate the association between time from hospital admission to
intensive care unit (ICU) admission (door to ICU time) and hospital
mortality in patients with sepsis.MethodsThis retrospective observational study included routinely collected
healthcare data from patients with sepsis. The primary endpoint was hospital
mortality, defined as the survival status at hospital discharge. Door to ICU
time was calculated and included in a multivariable model to investigate its
association with mortality.ResultsData from 13 115 patients were included for analyses, comprising 10 309
survivors and 2 806 non-survivors. Door to ICU time was significantly longer
for non-survivors than survivors (median, 43.0 h [interquartile range, 12.4,
91.3] versus 26.7 h [7.0, 74.2]). In the multivariable regression model,
door to ICU time remained significantly associated with mortality (odds
ratio [OR] 1.11, 95% confidence interval [CI] 1.006, 1.017) and there was a
significant interaction between age and door to ICU time (OR 0.99, 95% CI
0.99, 1.00).ConclusionA shorter time from hospital door to ICU admission was shown to be
independently associated with reduced hospital mortality in patients with
severe sepsis and/or septic shock.
Developmental dysplasia of the hip (DDH) is a common orthopedic disease. A simple and cost-effective scientific tool for assisting the early diagnosis of DDH is urgently needed. This study proposed a new artificial intelligence (AI) model for automated measure of the CE angle to aid the diagnosis of DDH by modifying the Mask R-CNN algorithm.13228 anteroposterior pelvic x-ray images were collected from the PACS system of the second Hospital of Jilin University, of which 104 images were randomly selected as test data. The rest of x-ray images were labelled and preprocessed for model development. The new AI model was the constructed based modified Mask R-CNN model to detect key points for CE angle measurement. The performance of AI model on measuring CE angle was verified by comparing with three attending orthopaedic doctors. The mean CE angles on left and right pelvis measured by the AI model was 29.46 ± 6.98°and 27.92 ± 6.56°, respectively, while the mean CE angle measured by the three doctors was 29.85 ± 6.92°and 27.75 ± 6.45°, respectively. AI model displayed a higly consistency with the doctors in measuring CE angles. Besides, AI model showed a much high efficiency in term of measuring time-consumption. In this study, we successfully constructed a new effective model for measuring CE angle by identifying key points, which provided a new intelligent measurement tool for orthopedic image measurement and evaluation.
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