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Algorithmic Prediction of Delayed Radiology Turn-Around-Time during Non-Business Hours.
Radiology turnaround time is an important quality measure that can impact hospital workflow and patient outcomes. We aimed to develop a machine learning model to predict delayed turnaround time during non-business hours and identify factors that contribute to this delay.This retrospective study consisted of 15,117 CT cases from May 2018 to May 2019 during non-business hours at two hospital campuses after applying exclusion criteria. Of these 15,177 cases, 7,532 were inpatient cases and 7,585 were emergency cases. Order time, scan time, first communication by radiologist, free-text indications, and other clinical metadata were extracted. A combined XGBoost classifier and Random Forest natural language processing model was trained with 85% of the data and tested with 15% of the data. The model predicted two measures of delay: when the exam was ordered to first communication (total time) and when the scan was completed to first communication (interpretation time). The model was analyzed with the area under the curve (AUC) of receiver operating characteristic (ROC) and feature importance. Source code: https://bit.ly/2UrLiVJ RESULTS: The algorithm reached an AUC of 0.85, with a 95% confidence interval [0.83, 0.87], when predicting delays greater than 245 minutes for "total time" and 0.71, with a 95% confidence interval [0.68, 0.73], when predicting delays greater than 57 minutes for "interpretation time". At our institution, CT scan description (e.g. "CTA chest pulmonary embolism protocol"), time of day, and year in training were more predictive features compared to body part, inpatient status, and hospital campus for both interpretation and total time delay.This algorithm can be applied clinically when a physician is ordering the scan to reasonably predict delayed turnaround time. Such a model can be leveraged to identify factors associated with delays and emphasize areas for improvement to patient outcomes.
View details for DOI 10.1016/j.acra.2021.05.026
View details for PubMedID 34187741
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Intrathecal Access Through Suboccipital Port in Patients With Spinal Muscular Atrophy and Complex Spines: Case Series and Technical Note.
2020; 12 (8): e9525
Introduction Many patients with the spinal muscular atrophy (SMA) have complex spinal anatomy, secondary to thoraco-lumbar spinal fusions. Their fragile musculoskeletal anatomy potentiates limb and joint injury if conventional spinal fluid access modalities are utilized. This creates a challenge when attempting to deliver intrathecal medications such as nusinersen (Spinraza®). Catheter placement in the cervical subarachnoid space with a caudally directed tip is potentially beneficial. This article describes our experience with Spinraza injections into the thecal space through a suboccipital port. This allowed for simple, chronic, and reliable cerebrospinal fluid (CSF) aspiration and intrathecal injections. Methods A total of 15 patients with SMA and complex spinal anatomy were implanted with a cervical subarachnoid catheter, connected to a suboccipital access port. We retrospectively reviewed the charts of these patients for clinical outcomes and complications. All patients then underwent serial port cannulation, aspiration of CSF, and injection of Spinraza following standard manufacturer dosage guidelines. Results The age range was 3 to 49. Two had type-1 SMA, 10 had type-2 SMA, and three had type-3 SMA. We were able to successfully cannulate the port, aspirate CSF, and inject Spinraza during all access attempts. Two incidents of subcutaneous CSF leaks were resolved through reoperation and one incident of transient CSF leak was resolved without surgical repair. Conclusion Patients with SMA requiring intrathecal injections of Spinraza can be treated safely and efficiently with this novel implantation technique. The complication rates are low and the injection time is dramatically lower than with conventional injection techniques.
View details for DOI 10.7759/cureus.9525
View details for PubMedID 32905153
View details for PubMedCentralID PMC7465927