Natural history and growth prediction model of pancreatic serous cystic neoplasms.

OBJECTIVE: Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection. METHODS: Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package. RESULTS: Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5-160 mm), with a mean follow-up of 72 months (range 3-266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals. CONCLUSION: SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.

Comparative Effectiveness of Virtual Versus In-person Visits for Abdominal Pain During the COVID-19 Pandemic.

BACKGROUND AND AIMS: The COVID-19 pandemic has highlighted the importance of telemedicine in improving healthcare access and reducing costs. This study aimed to assess order compliance in the virtual versus in-person setting for the initial evaluation of abdominal pain (AP) prior to and during the pandemic. METHODS: A retrospective evaluation of virtual and in-person outpatient gastroenterology visits for AP were identified through natural language processing from January 2019 through September 2021 at the Cleveland Clinic main campus and regional hospitals in Ohio. We assessed the number and type of orders placed for patients and measured compliance through order completion. This study received Institutional Review Board approval (IRB 21-514). RESULTS: Among 20,356 patients at their initial visit, 79% had orders placed, of which 40% had pandemic in-person visits, 13% had pandemic virtual visits, and 47% had pre-pandemic in-person visits. Patients seen virtually were 65.1% less likely to complete orders compared to patients seen in-person (p < 0.001) during the pandemic. Patients seen in a pandemic virtual setting were 71.0% less likely to complete imaging orders (p < 0.001), 82.6% less likely to complete procedure orders (p < 0.001), and 60.5% less likely to complete lab orders (p < 0.001). CONCLUSION: Compared with in-person visits, patients seen virtually for their first presentation of AP were less likely to complete labs, imaging, and endoscopic evaluations. In-person visits were more successful with patient order completion during the pandemic. These findings highlight that virtual visits for AP, despite convenience, may compromise care delivery and warrant additional care coordination to achieve compliance with medical recommendations.