Background: The use of cost-effectiveness analysis for medical devices has proven to be challenging because of the existence of the learning effects in the device-operator interactions. The need for the relevant analytical framework for assessing the economic value of such technologies has been recognized.
Objectives: To present a modified difference-in-differences (DID) cost-effectiveness methodology that facilitates visualization of a new health technology's learning curve.
Methods: Using the Premier Perspective database (Premier Inc., Charlotte, NC), we examined the impact of physicians adopting a bipolar sealer (BPS) to control blood loss in primary unilateral total knee arthroplasties on hospital lengths of stay and total hospitalization costs when compared with two control groups. In our DID approach, we substituted month-from-adoption for the calendar-month-of-adoption in both graphical representations and ordinary least-squares regression results to estimate the effect of the BPS.
Results: The results clearly demonstrated a learning curve associated with the adoption of the BPS technology. Although the reductions in length of stay were immediate, the first postadoption year costs increased by $1335 (extrahospital controls) to $1565 (within-hospital controls). Importantly, and also consistent with a learning curve hypothesis, these initial higher costs were offset by subsequent cost savings in the second and third years postadoption.
Conclusions: The presented modified DID approach is a suitable and versatile analytical tool for economic evaluation of a slowly diffusing medical device or health technology. It provides a better understanding of the potential learning effects associated with relevant interventions.
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http://dx.doi.org/10.1016/j.jval.2017.03.002 | DOI Listing |
J Med Internet Res
January 2025
Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.
View Article and Find Full Text PDFAnn Surg Oncol
January 2025
Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of infection. This study aimed to develop a machine-learning model to predict postoperative infection after cytoreductive surgery (CRS) and HIPEC with splenectomy.
Methods: The study enrolled patients in the national TriNetX database and at the Johns Hopkins Hospital (JHH) who underwent splenectomy during CRS/HIPEC from 2010 to 2024.
Abdom Radiol (NY)
January 2025
The General Hospital of Western Theater Command, Chengdu, Sichuan 610083, China, Chengdu, China.
Background: Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of radiomics models in predicting PNI in CRC.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
Department of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.
Purpose: To evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF).
Methods: A total of 74 patients with severe endometrial fibrosis (SEF), 41 patients with mild to moderate fibrosis (MMEF) confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. The enrolled data were randomly stratified and divided into a train set (108 cases: 28 healthy women, 29 with MMEF, and 51 with SEF) and a test set (47 cases: 12 healthy women, 12 MMEF and 23 SEF) at a ratio of 7:3.
Radiol Artif Intell
January 2025
https://www.procancer-i.eu/.
Purpose To assess the impact of scanner manufacturer and scan protocol on the performance of deep learning models to classify prostate cancer (PCa) aggressiveness on biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5,478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC and the full dataset)-impacts model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC).
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