Evaluation of machine learning models as decision aids for anesthesiologists.

Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% (< 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p < 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.

The Challenges of Ultrasound-guided Thoracic Paravertebral Blocks in Rib Fracture Patients.

Thoracic paravertebral blocks (TPVBs) provide an effective pain relief modality in conditions where thoracic epidurals are contraindicated. Historically, TPVBs were placed relying solely on the landmark-based technique, but the availability of ultrasound imaging makes it a valuable and practical tool during the placement of these blocks. TPVBs also provide numerous advantages over thoracic epidurals, namely, minimal hypotension, absence of urinary retention, lack of motor weakness, and remote risk of an epidural hematoma. Utilization of both landmark-based and ultrasound-guided techniques may increase the successful placement of a TPVB. This article reviews relevant sonoanatomy as it pertains to TPVBs. However, certain patient-related issues, including pneumothoraces, surgical emphysema, body habitus, and transverse process fractures, all may make imaging with ultrasound challenging. The changes noted on ultrasound imaging as a result of these issues will be further described in this review.

Echocardiographic manifestation of esophagitis mimicking a posterior mediastinal mass.

Incidental extracardiac findings (ECFs) are commonly noted on cardiac imaging. The majority of the ECFs are noticed on computed tomography (CT), cardiac magnetic resonance scanning, and myocardial perfusion imaging. Although transthoracic echocardiography (TTE) is a widely used cardiac modality, there is scarcity of data describing ECF on TTE. ECFs have the potential to alter patient management. We present a rare case of a cystic mass seen in the posterior mediastinum on TTE, which led to further evaluation and diagnosis of esophagitis with ulceration.