Acute pancreatitis as a rare complication of leptospirosis: A case report and literature review.

Leptospirosis is a zoonotic disease. We present a case of acute pancreatitis associated with leptospirosis. An 88-year-old woman was admitted to the hospital with high fever and severe myalgia of the lower extremities. Based on the clinical presentation, hepatic dysfunction with a mild increase in bilirubin, renal dysfunction, and life history, the possibility of leptospirosis was considered. Plain computed tomography of the trunk on admission revealed no special findings. Appropriate antimicrobial therapy was administered at an early stage. After treatment initiation, the clinical symptoms and blood test abnormalities began to improve, and the patient appeared to be doing well. Although no abdominal or back pain was consistently noted during hospitalization, the serum amylase level increased over time; therefore, the patient underwent another computed tomography scan on the ninth day. Acute pancreatitis, which was absent upon admission, was noted. Appropriate treatment for pancreatitis was administered, and the patient was discharged. A subsequent serum antibody test confirmed the diagnosis of leptospirosis. Herein, we also summarized previous cases of acute pancreatitis associated with leptospirosis. The time of onset for pancreatitis was inconsistent, and there were a few cases of pancreatitis without abdominal or back pain. In contrast, serum amylase or lipase levels were elevated in all patients, which could be an important trigger for suspected complications of pancreatitis. When leptospirosis is suspected, complications of pancreatitis should always be considered, even in the absence of apparent abdominal pain. Regular monitoring of pancreatic enzymes such as amylase and lipase is recommended.

Effectiveness and safety of lubiprostone after switching from stimulant laxatives in elderly patients with chronic constipation.

Background and Aim: Stimulant laxatives may cause electrolyte abnormalities, dehydration, and abdominal pain; their long-term use can lead to tolerance and subsequent refractory constipation. We investigated the effectiveness, safety, and quality of life after switching from stimulant laxatives to lubiprostone in elderly patients with chronic constipation (CC). Methods: This multicenter, interventional, open-label, single-arm, before-and-after comparison study enrolled 99 Japanese patients aged 65-90 years with CC who took stimulant laxatives for ≥2 weeks prior to switching to lubiprostone monotherapy. Results: The mean ± SD spontaneous defecations at Week 1 of 7.8 ± 6.2 times/week was not significantly different from that at baseline (8.3 ± 4.7). Spontaneous defecations were significantly reduced at Weeks 2 (-1.5 ± 4.0, P < 0.001) and 4 (-1.5 ± 3.7, P < 0.001). The Bristol Stool Form Scale score did not change from baseline (4.7 ± 0.9) at Weeks 1 (4.5 ± 1.3) or 4 (4.3 ± 1.3), but it did at Week 2 (4.3 ± 1.5, P < 0.05). The Patient Assessment of Constipation Quality of Life questionnaire score increased (0.36 ± 0.07, P < 0.001) after 28 days. Nausea was the only symptom that worsened from baseline and was the most frequently reported adverse drug reaction (15.2%). Conclusion: Switching to lubiprostone monotherapy for CC was not associated with significant concerns in short-term spontaneous defecation frequency and safety, but it might affect the efficacy and patient quality of life over 2 weeks. Careful treatment strategies facilitating gradual switching to lubiprostone monotherapy may be needed in patients using stimulant laxatives.

Machine learning prediction model for treatment responders in patients with primary biliary cholangitis.

Background and Aim: Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data. Methods: We conducted a single-center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out-of-sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver-related deaths were analyzed using Kaplan-Meier analysis. Results: Compared to logistic regression (AUC = 0.595, P = 0.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC = 0.84) and XGB (AUC = 0.83) models; however, the AUC was not significantly high for decision tree (AUC = 0.633) or naïve Bayes (AUC = 0.584) models. Kaplan-Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log-rank = 0.005 and 0.007). Conclusion: ML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation.