Objective To determine the usefulness of serial measurements of the rapid shallow breathing index ( f/VT , RSBI) as a predictor for successfully weaning of patients undergoing prolonged mechanical ventilation ( gt; 72 hours) . Methods 76 mechanically ventilated patients were prospectively analyzed. 120-min spontaneous breathing trial was conducted after the patients having fullfiled the traditional weaning criteria, and RSBI were continuously monitored by the ventilator at five time points ( 5, 15, 30,60, and 120 min) . A repeated measure of general linear model in SPSS 15.0 was conducted to analyze the data. Results 62 patients completed 120-minute spontaneous breath trial and in which 20 patients failed weaning. There was no significant difference of RSBI at five time points during weaning ( P gt;0. 05) . But thevariation trends of RSBI during weaning time were significant different between the successful weaning patients and the failed weaning patients ( P lt; 0. 05) . Conclusions In patients undergoing prolonged mechanical ventilation, the variation trend of RSBI is more valuable than single RSBI in the prediction ofsuccessful weaning.
ObjectiveTo evaluate the value of imaging quantification parameters in artificial intelligence (AI) assisted diagnosis systems in clinical decision-making for lung nodules ≤2 cm and the diagnostic efficacy of AI. MethodsLung nodule patients admitted to Zhongshan Hospital affiliated with Dalian University from 2020 to 2023 were included. Imaging parameters of lung nodules were extracted using AI assisted diagnosis systems. Multifactor analysis was used to screen predictors for distinguishing benign and malignant nodules and high-risk predictors for recurrent invasive adenocarcinoma, and a diagnostic model was established and its performance evaluated. The diagnostic efficacy of the AI system was judged according to pathological results. ResultsA total of 594 patients with lung nodules were included, including 202 males and 392 females, with an average age of 24-82 (58.75±11.55) years. Volume, average CT value, and 3D maximum diameter of non-solid nodules were independent predictors of malignant nodules, with thresholds of 287.4 mm3, -491 HU, and 12.0 mm, respectively. The area under the curve (AUC) for diagnostic efficacy was ranked from high to low as combined model (0.802), volume (0.783), average CT value (0.749), and 3D maximum diameter (0.714); the average CT value and 3D long diameter of solid nodules were independent predictors of malignant nodules, with thresholds of -81 HU and 17.5 mm, respectively, and AUC values of 0.874 and 0.686, respectively, with the combined prediction AUC of 0.957; the mass of cystic nodules was an independent predictor of malignancy when the mass >180.7 mg. Independent predictors of high recurrence risk of invasive adenocarcinoma in non-solid nodules were consolidation-tumor ratio (CTR), average CT value, 3D long diameter, and volume, with thresholds of 0.14, -386 HU, 15.6 mm, and 1018.9 mm3, respectively, and diagnostic efficacy was ranked from high to low as combined model (0.788), 3D long diameter (0.735), volume (0.725), average CT value (0.720), and CTR (0.697). The accuracy of AI in predicting benign and malignant target nodules was 87.4%, with positive predictive value of 96.6% and negative predictive value of 58.9%. ConclusionIn clinical decision-making for lung nodules ≤2 cm, AI assisted diagnosis systems have high application value.