Background

Although clinical factors related to intraoperative opioid administration have been described, there is little research evaluating whether administration is influenced by drug formulation and, specifically, the unit dose of the drug. The authors hypothesized that the unit dose of hydromorphone is an independent determinant of the quantity of hydromorphone administered to patients intraoperatively.

Methods

This observational cohort study included 15,010 patients who received intraoperative hydromorphone as part of an anesthetic at the University of California, Los Angeles hospitals from February 2016 to March 2018. Before July 2017, hydromorphone was available as a 2-mg unit dose. From July 1, 2017 to November 20, 2017, hydromorphone was only available in a 1-mg unit dose. On November 21, 2017, hydromorphone was reintroduced in the 2-mg unit dose. An interrupted time series analysis was performed using segmented Poisson regression with two change-points, the first representing the switch from a 2-mg to 1-mg unit dose, and the second representing the reintroduction of the 2-mg dose.

Results

The 2-mg to 1-mg unit dose change was associated with a 49% relative decrease in the probability of receiving a hydromorphone dose greater than 1 mg (risk ratio, 0.51; 95% CI, 0.40-0.66; P < 0.0001). The reintroduction of a 2-mg unit dose was associated with a 48% relative increase in the probability of administering a dose greater than 1 mg (risk ratio, 1.48; 95% CI, 1.11-1.98; P = 0.008).

Conclusions

This observational study using an interrupted time series analysis demonstrates that unit dose of hydromorphone (2 mg vs. 1 mg) is an independent determinant of the quantity of hydromorphone administered to patients in the intraoperative period.

Background and aims

Determinants of pharmaceutical unit presentations are not well understood and often appear indiscriminate. However, the dose administered may play a key role in the patient's anesthetic course. A recent change in a pharmaceutical vendor at our institution resulted in a change in midazolam presentation. In this study, we sought to determine whether the dose in which midazolam was dispensed to anesthesiologists was associated with the quantity of midazolam administered perioperatively.

Material and methods

In this retrospective, observational study, we examined 310 adult patients who underwent general anesthesia at a single site, tertiary care, university hospital before and after a change in midazolam presentation from 2 mg to 3 mg. The primary outcome was the quantity of midazolam administered during the anesthetic. Additional clinical variables measured included patient age, weight, gender, and American Society of Anesthesiology (ASA) classification.

Results

The mean dose of midazolam administered to the 3 mg presentation cohort was 2.67 mg compared to 1.99 mg to the 2 mg presentation cohort (mean difference: 0.68 mg, 95% CI: 0.46-0.9 mg; P value <0.001). According to a logistic regression model, the odds of receiving a dose of 3 mg or greater in the 3 mg presentation cohort was 22 times greater than the odds of receiving such a dose in the 2 mg presentation cohort (OR: 22.3; 95% CI: 10.6-47.0; P < 0.001). This effect of presentation dose on administration dose was not observed in patients greater than or equal to 65 years of age.

Conclusions

Midazolam presentation dose influences the administration dose.

Objectives

To report our experience and review published reports on the laparoscopic management of peripelvic renal cysts. Peripelvic renal cysts represent a unique subset of renal cysts, as they are rare, commonly symptomatic, and more difficult to treat than simple peripheral renal cysts. Minimally invasive methods for the treatment of peripelvic renal cysts, including laparoscopic decortication, have recently become more common.

Methods

Four patients who presented with symptomatic peripelvic cysts underwent laparoscopic decortication at our institution. All four were men aged 47 to 65 years. One patient had undergone an unsuccessful prior cyst aspiration. All patients underwent preoperative computed tomography and retrograde pyelography. The mean number of peripelvic cysts per patient was 3.0, and the mean cyst size was 7.1 cm.

Results

The mean operative time was 259 minutes (range 240 to 293), and the mean estimated blood loss was 30 mL (range 10 to 50). No evidence of cystic renal cell carcinoma was found on aspiration cytology or cyst wall pathologic examination. The mean hospital stay was 1.3 days. No inadvertent collecting system injuries and no intraoperative or postoperative complications occurred. All 4 patients achieved symptomatic relief and were determined to have radiologic success as determined by the 6-month postoperative computed tomography findings.

Conclusions

Laparoscopic ablation of peripelvic renal cysts is more difficult than that of simple peripheral renal cysts and demands a heightened awareness of potential complications and, therefore, more advanced surgical skills. In addition to our experience, a thorough review of published reports found this procedure to be safe and effective with appropriate patient selection.

Prediction models of post-liver transplant mortality are crucial so that donor organs are not allocated to recipients with unreasonably high probabilities of mortality. Machine learning algorithms, particularly deep neural networks (DNNs), can often achieve higher predictive performance than conventional models. In this study, we trained a DNN to predict 90-day post-transplant mortality using preoperative variables and compared the performance to that of the Survival Outcomes Following Liver Transplantation (SOFT) and Balance of Risk (BAR) scores, using United Network of Organ Sharing data on adult patients who received a deceased donor liver transplant between 2005 and 2015 (n = 57,544). The DNN was trained using 202 features, and the best DNN's architecture consisted of 5 hidden layers with 110 neurons each. The area under the receiver operating characteristics curve (AUC) of the best DNN model was 0.703 (95% CI: 0.682-0.726) as compared to 0.655 (95% CI: 0.633-0.678) and 0.688 (95% CI: 0.667-0.711) for the BAR score and SOFT score, respectively. In conclusion, despite the complexity of DNN, it did not achieve a significantly higher discriminative performance than the SOFT score. Future risk models will likely benefit from the inclusion of other data sources, including high-resolution clinical features for which DNNs are particularly apt to outperform conventional statistical methods.