Background: Hydromorphone patient-controlled analgesia (PCA) provides satisfactory postoperative pain therapy, but its effect has not been assessed in acute pancreatitis (AP).Aim: To assess the safety and efficacy of intravenous hydromorphone PCA for pain relief in AP.Methods: This open-label trial included AP patients admitted within 72 h of symptom onset, aged 18–70 years old, and with Visual Analog Scale (VAS) for pain intensity ≥5. They were randomized to receive intravenous hydromorphone PCA (0.05 mg/h with 0.2 mg on-demand) or intramuscular pethidine (50 mg as required) for three consecutive days. Intramuscular dezocine (5 mg on demand) was the rescue analgesia. The primary outcome was the change of VAS score recorded every 4 h for 3 days. Interim analysis was conducted by an Independent Data and Safety Monitoring Committee (IDSMC).Results: From 26 July 2019 to 15 January 2020, 77 patients were eligible for the intention-to-treat analysis in the interim analysis (39 in the hydromorphone group and 38 in the pethidine group). Baseline parameters were comparable between groups. No difference in VAS between the two groups was found. Hydromorphone PCA was associated with higher moderately severe to severe cases (82.1% vs. 55.3%, p = 0.011), acute peripancreatic fluid collections (53.9% vs. 28.9%, p = 0.027), more cumulative opioid consumption (median 46.7 vs. 5 mg, p < 0.001), higher analgesia costs (median 85.5 vs. 0.5 $, p < 0.001) and hospitalization costs (median 3,778 vs. 2,273 $, p = 0.007), and more adverse events (20.5% vs. 2.6%, p = 0.087). The per-protocol analysis did not change the results. Although a sample size of 122 patients was planned, the IDSMC halted further recruitment as disease worsening or worse clinical outcomes between the groups in the interim analysis.Conclusion: Hydromorphone PCA was not superior to pethidine in relieving pain in AP patients and might have worse clinical outcomes. Therefore, its use is not recommended.Clinical Trial Registration: Chictr.org.cn. ChiCTR1900025971
ObjectivesTo develop and validate deep learning (DL) models for predicting the severity of acute pancreatitis (AP) by using abdominal nonenhanced computed tomography (CT) images.MethodsThe study included 978 AP patients admitted within 72 hours after onset and performed abdominal CT on admission. The image DL model was built by the convolutional neural networks. The combined model was developed by integrating CT images and clinical markers. The performance of the models was evaluated by using the area under the receiver operating characteristic curve.ResultsThe clinical, Image DL, and the combined DL models were developed in 783 AP patients and validated in 195 AP patients. The combined models possessed the predictive accuracy of 90.0%, 32.4%, and 74.2% for mild, moderately severe, and severe AP. The combined DL model outperformed clinical and image DL models with 0.820 (95% confidence interval, 0.759–0.871), the sensitivity of 84.76% and the specificity of 66.67% for predicting mild AP and the area under the receiver operating characteristic curve of 0.920 (95% confidence interval, 0.873–0.954), the sensitivity of 90.32%, and the specificity of 82.93% for predicting severe AP.ConclusionsThe DL technology allows nonenhanced CT images as a novel tool for predicting the severity of AP.
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