Multimodal Pain Control Utilizing Buprenorphine for Robotic-assisted Laparoscopic Prostatectomy: A Quality Improvement Comparison to Conventional Opioid Management.

INTRODUCTION: This study investigated the effectiveness of buprenorphine as an alternative to the use of conventional opioids perioperatively in an effort to help mitigate the impact of the use of perioperative conventional opioids for patients undergoing robotic-assisted laparoscopic prostatectomy. METHODS: Outcomes of patients with localized prostate cancer undergoing robotic-assisted laparoscopic prostatectomy were examined before and after implementation of novel quality improvement study that included receiving buprenorphine compared to conventional opioids for pain control intraoperatively and postoperatively. The primary end point was adequate pain control with secondary end points being analgesic consumption at home, opioid-related side effects, and patient satisfaction. RESULTS: When analyzing the secondary end point of oral morphine milligram equivalents, the buprenorphine group received significantly less morphine milligram equivalent compared to the conventional opioid group (15.19 vs 47.91, P = .006). The buprenorphine group also had lower reported pain scores at discharge (4.3; scale 1-10) compared to the conventional opioid group (5.4), though this did not reach significance (P = .069). In the buprenorphine group, 76.9% strongly agreed that their pain was adequately controlled in the hospital compared to 57.5% of the conventional opioid group (P = .223). There was no difference in overall satisfaction at postoperative day 5 (P = .358). CONCLUSIONS: Our study demonstrates buprenorphine's analgesic capabilities to maintain adequate pain control and patient satisfaction compared to conventional opioids during robotic-assisted laparoscopic prostatectomy, while decreasing perioperative opioid use.

The Elusive Sarcoidosis, an Eight-Year Journey to the Diagnosis of Sarcoidosis: A Case Report.

We present a unique case of a patient coming to our internal medicine clinic with intermittent diffuse lymphadenopathy and non-specific symptoms for the past eight years. Initially, the patient was thought to have carcinoma of unknown primary origin, given the abnormalities seen in her imaging. The diagnosis of sarcoidosis was also dismissed, given that the patient had not responded to steroids with negative laboratory support. The patient was referred to several specialists, and only after a pulmonary biopsy was a non-caseating granuloma revealed after multiple prior failed biopsies. The patient was placed on infusion therapy and responded positively. This case demonstrates a challenging diagnosis and treatment which emphasizes the importance of considering alternative treatments if the initial therapy fails.

Automating Anesthesiology Resident Case Logs Reduces Reporting Variability.

Background: The Accreditation Council for Graduate Medical Education (ACGME) case log system for anesthesiology resident training relies on subjective categorization of surgical procedures and lacks clear guidelines for assigning credit roles. Therefore, resident reporting practices likely vary within and between institutions. Our primary aim was to develop a systematic process for generating automated case logs using data elements extracted from the electronic health care record. We hypothesized that automated case log reporting would improve accuracy and reduce reporting variability. Methods: We developed a systematic approach for automating anesthesiology resident case logs from the electronic health care record using a discrete classification system for assigning credit roles and Anesthesia Current Procedure Terminology codes to categorize cases. The median number of cases performed was compared between the automated case log and resident-reported ACGME case log. Results: Case log elements were identified in the electronic health care record and automatically extracted. A total of 42 individual case logs were generated from the extracted data and visualized in an external dashboard. Automated reporting captured a median of 1226.5 (interquartile range: 1097-1366) total anesthetic cases in contrast to 1134.5 (interquartile range: 899-1208) reported to ACGME by residents (P = .0014). Automation also decreased the case count interquartile range and the distribution approached normality, suggesting that automation reduces reporting variability. Conclusions: Automated case log reporting uniformly captures the resident training experience and reduces reporting variability. We hope this work provides a foundation for aggregating graduate medical education data from the electronic health care record and advances adoption of case log automation.

The Anesthetic Management of a Parturient With Osteogenesis Imperfecta Type I Undergoing Cesarean Delivery.

Osteogenesis imperfecta (OI) is a rare disorder of bone fragility caused by mutations in the COL1A1/2 genes, which encode type I procollagen. It commonly manifests with bone fractures, joint dislocations, and easy bruising. OI patients presenting for surgery may pose multiple challenges to the anesthesiologist such as management of a potentially difficult airway and heightened positional fracture risks. We present a case detailing the spinal anesthetic management of a 28-year-old woman with type I OI requiring cesarean delivery for a 32-week intrauterine pregnancy with fetal cardiac anomalies.