Oral morphine dosing predictions based on single dose in healthy children undergoing surgery.

BACKGROUND: Oral morphine has been proposed as an effective and safe alternative to codeine for after-discharge pain in children following surgery but there are few data guiding an optimum safe oral dose. AIMS: The aim of this study was to characterize the absorption pharmacokinetics of enteral morphine in order to simulate time-concentration profiles in children given common oral morphine dose regimens. METHODS: Children (2-6 years, n = 34) undergoing elective surgery and requiring opioid analgesia were randomized to receive preoperative oral morphine (100 mcg·kg-1 , 200 mcg·kg-1 , 300 mcg·kg-1 ). Blood sampling for morphine assay was performed at 30, 60, 90, 120, 180, and 240 min. Morphine serum concentrations were determined by liquid chromatography-mass spectroscopy and pharmacokinetic parameters were calculated using nonlinear mixed effects models. Current data were pooled with published time-concentration profiles from children (n = 1059, age 23 weeks postmenstrual age - 3 years) administered intravenous morphine, to determine oral bioavailability (F), absorption lag time (TLAG ), and absorption half-time (TABS ). These parameter estimates were used to predict concentrations in children given oral morphine (100, 200, 300, 400, 500 mcg·kg-1 ) at different dosing intervals (3, 4, 5, 6, 8, 12 h). RESULTS: The oral morphine formulation had F 0.298 (CV 36.5%), TLAG 0.45 (CV 63.6%) h and TABS 0.71 (CV 55%) h. A single-dose morphine 100 mcg·kg-1 achieved a mean CMAX 10 mcg·l-1 . Repeat 4-hourly dosing achieved mean steady-state concentration 13-18 mcg·l-1 ; concentrations associated with good analgesia after intravenous administration. Serum concentration variability was large ranging from 5 to 55 mcg·l-1 at steady state. CONCLUSIONS: Oral morphine 200 mcg·kg-1 then 100 mcg·kg-1 4 h or 150 mcg·kg-1 6 h achieves mean concentrations associated with analgesia. There was high serum concentration variability suggesting that respiration may be compromised in some children given these doses.

Dexmedetomidine and hydromorphone: a novel pain management strategy for the oncology ward setting during anti-GD2 immunotherapy for high-risk neuroblastoma in children.

BACKGROUND: Treatment of neuroblastoma with targeted immunotherapy using chimeric anti-GD2 monoclonal antibodies (ch14.18) is associated with significant pain requiring management with a high-dose opioid infusion. We present a case series of six children, for whom dexmedetomidine and hydromorphone infusions safely and effectively reduced the pain of ch14.18 therapy in the oncology ward setting. PROCEDURE: The ch14.18 infusion is administered for ≥ 10 hr over four consecutive days in each of 5 cycles. Hydromorphone (2-8 mcg.kg(-1) .hr(-1) ) and dexmedetomidine (0.1-0.6 mcg.kg(-1) .hr(-1) ) infusions were commenced 1 hr before starting ch14.18 immunotherapy and titrated to optimal clinical effect. One hour after stopping the ch14.18 infusion, dexmedetomidine was discontinued and hydromorphone weaned as tolerated. This strategy was supervised by the Acute Pain Service with strict monitoring and nursing policies. Patient charts were reviewed for evidence of side effects and quality of analgesia. RESULTS: Data from six patients, with median (range) age of 3.5 (2-12) years are reported. Median (range) utilization of hydromorphone was 2.9 (2.0-4.7) mcg.kg(-1) .hr(-1) , and of dexmedetomidine was 0.17 (0.10-0.20) mcg.kg(-1) .hr(-1) . A drop in mean arterial pressure ≥ 30% below baseline was identified during 30% of treatment days, but only prompted interrupting or reducing the ch14.18 infusion on 7% of treatment days; only one episode of grade 3 hypotension was recorded during this series. Hypoxemia occurred during 8% and bradycardia during 4% of treatment days. CONCLUSIONS: Dexmedetomidine infusion may be an effective and safe pain management adjunct to opioid therapy for the pain of ch14.18 infusion.

An evaluation of an expert system for detecting critical events during anesthesia in a human patient simulator: a prospective randomized controlled study.

BACKGROUND: Perioperative monitoring systems produce a large amount of uninterpreted data, use threshold alarms prone to artifacts, and rely on the clinician to continuously visually track changes in physiological data. To address these deficiencies, we developed an expert system that provides real-time clinical decisions for the identification of critical events. We evaluated the efficacy of the expert system for enhancing critical event detection in a simulated environment. We hypothesized that anesthesiologists would identify critical ventilatory events more rapidly and accurately with the expert system. METHODS: We used a high-fidelity human patient simulator to simulate an operating room environment. Participants managed 4 scenarios (anesthetic vapor overdose, tension pneumothorax, anaphylaxis, and endotracheal tube cuff leak) in random order. In 2 of their 4 scenarios, participants were randomly assigned to the expert system, which provided trend-based alerts and potential differential diagnoses. Time to detection and time to treatment were measured. Workload questionnaires and structured debriefings were completed after each scenario, and a usability questionnaire at the conclusion of the session. Data were analyzed using a mixed-effects linear regression model; Fisher exact test was used for workload scores. RESULTS: Twenty anesthesiology trainees and 15 staff anesthesiologists with a combined median (range) of 36 (29-66) years of age and 6 (1-38) years of anesthesia experience participated. For the endotracheal tube cuff leak, the expert system caused mean reductions of 128 (99% confidence interval [CI], 54-202) seconds in time to detection and 140 (99% CI, 79-200) seconds in time to treatment. In the other 3 scenarios, a best-case decrease of 97 seconds (lower 99% CI) in time to diagnosis for anaphylaxis and a worst-case increase of 63 seconds (upper 99% CI) in time to treatment for anesthetic vapor overdose were found. Participants were highly satisfied with the expert system (median score, 2 on a scale of 1-7). Based on participant debriefings, we identified avoidance of task fixation, reassurance to initiate invasive treatment, and confirmation of a suspected diagnosis as 3 safety-critical areas. CONCLUSION: When using the expert system, clinically important and statistically significant decreases in time to detection and time to treatment were observed for the endotracheal tube cuff Leak scenario. The observed differences in the other 3 scenarios were much smaller and not statistically significant. Further evaluation is required to confirm the clinical utility of real-time expert systems for anesthesia.