Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
BMJ Open. 2022 Sep 5;12(9):e064089. doi: 10.1136/bmjopen-2022-064089.
Accurately assessing the probability of significant respiratory depression following opioid administration can potentially enhance perioperative risk assessment and pain management. We developed and validated a risk prediction tool to estimate the probability of significant respiratory depression (indexed by naloxone administration) in patients undergoing noncardiac surgery.
Retrospective cohort study.
Single academic centre.
We studied n=63 084 patients (mean age 47.1±18.2 years; 50% men) who underwent emergency or elective non-cardiac surgery between 1 January 2007 and 30 October 2017.
A derivation subsample reflecting two-thirds of available patients (n=42 082) was randomly selected for model development, and associations were identified between predictor variables and naloxone administration occurring within 5 days following surgery. The resulting probability model for predicting naloxone administration was then cross-validated in a separate validation cohort reflecting the remaining one-third of patients (n=21 002).
The rate of naloxone administration was identical in the derivation (n=2720 (6.5%)) and validation (n=1360 (6.5%)) cohorts. The risk prediction model identified female sex (OR: 3.01; 95% CI: 2.73 to 3.32), high-risk surgical procedures (OR: 4.16; 95% CI: 3.78 to 4.58), history of drug abuse (OR: 1.81; 95% CI: 1.52 to 2.16) and any opioids being administered on a scheduled rather than as-needed basis (OR: 8.31; 95% CI: 7.26 to 9.51) as risk factors for naloxone administration. Advanced age (OR: 0.971; 95% CI: 0.968 to 0.973), opioids administered via patient-controlled analgesia pump (OR: 0.55; 95% CI: 0.49 to 0.62) and any scheduled non-opioids (OR: 0.63; 95% CI: 0.58 to 0.69) were associated with decreased risk of naloxone administration. An overall risk prediction model incorporating the common clinically available variables above displayed excellent discriminative ability in both the derivation and validation cohorts (c-index=0.820 and 0.814, respectively).
Our cross-validated clinical predictive model accurately estimates the risk of serious opioid-related respiratory depression requiring naloxone administration in postoperative patients.
准确评估阿片类药物给药后发生显著呼吸抑制的概率,可能有助于增强围手术期风险评估和疼痛管理。我们开发并验证了一种风险预测工具,以估计接受非心脏手术患者发生显著呼吸抑制(以纳洛酮给药为指标)的概率。
回顾性队列研究。
单家学术中心。
我们研究了 n=63084 名(平均年龄 47.1±18.2 岁;50%为男性)2007 年 1 月 1 日至 2017 年 10 月 30 日期间接受急症或择期非心脏手术的患者。
反映可用患者三分之二的推导子样本(n=42082)被随机选择用于模型开发,并确定预测变量与术后 5 天内发生纳洛酮给药之间的关联。然后,在反映剩余三分之一患者(n=21002)的单独验证队列中对预测纳洛酮给药的概率模型进行交叉验证。
在推导队列(n=2720(6.5%))和验证队列(n=1360(6.5%))中,纳洛酮给药率相同。风险预测模型确定了女性性别(OR:3.01;95%CI:2.73 至 3.32)、高风险手术程序(OR:4.16;95%CI:3.78 至 4.58)、药物滥用史(OR:1.81;95%CI:1.52 至 2.16)和任何阿片类药物的规定给药而不是按需给药(OR:8.31;95%CI:7.26 至 9.51)是纳洛酮给药的危险因素。年龄较大(OR:0.971;95%CI:0.968 至 0.973)、通过患者自控镇痛泵给予阿片类药物(OR:0.55;95%CI:0.49 至 0.62)和任何规定的非阿片类药物(OR:0.63;95%CI:0.58 至 0.69)与纳洛酮给药风险降低相关。纳入上述常见临床可用变量的综合风险预测模型在推导和验证队列中均显示出出色的区分能力(c 指数分别为 0.820 和 0.814)。
我们经过交叉验证的临床预测模型可准确估计术后需要纳洛酮治疗的严重阿片类药物相关呼吸抑制的风险。