Department of Emergency Medicine, Baylor University Medical Center, Dallas, Texas, USA
Baylor University Medical Center, Dallas, Texas, USA.
BMJ Open. 2021 Nov 24;11(11):e051663. doi: 10.1136/bmjopen-2021-051663.
Opioid-induced respiratory depression (OIRD) and oversedation are rare but potentially devastating adverse events in hospitalised patients. We investigated which features predict an individual patient's risk of OIRD or oversedation; and developed a risk stratification tool that can be used to aid point-of-care clinical decision-making.
Retrospective observational study.
Twelve acute care hospitals in a large not-for-profit integrated delivery system.
All inpatients ≥18 years admitted between 1 July 2016 and 30 June 2018 who received an opioid during their stay (163 190 unique hospitalisations).
The primary outcome was occurrence of sedation or respiratory depression severe enough that emergent reversal with naloxone was required, as determined from medical record review; if naloxone reversal was unsuccessful or if there was no evidence of hypoxic encephalopathy or death due to oversedation, it was not considered an oversedation event.
Age, sex, body mass index, chronic obstructive pulmonary disease, concurrent sedating medication, renal insufficiency, liver insufficiency, opioid naïvety, sleep apnoea and surgery were significantly associated with risk of oversedation. The strongest predictor was concurrent administration of another sedating medication (adjusted HR, 95% CI=3.88, 2.48 to 6.06); the most common such medications were benzodiazepines (29%), antidepressants (22%) and gamma-aminobutyric acid analogue (14.7%). The c-statistic for the final model was 0.755. The 24-point Oversedation Risk Criteria (ORC) score developed from the model stratifies patients as high (>20%, ≥21 points), moderate (11%-20%, 10-20 points) and low risk (≤10%, <10 points).
The ORC risk score identifies patients at high risk for OIRD or oversedation from routinely collected data, enabling targeted monitoring for early detection and intervention. It can also be applied to preventive strategies-for example, clinical decision support offered when concurrent prescriptions for opioids and other sedating medications are entered that shows how the chosen combination impacts the patient's risk.
阿片类药物引起的呼吸抑制（OIRD）和过度镇静是住院患者中罕见但潜在致命的不良事件。我们研究了哪些特征可以预测个体患者发生 OIRD 或过度镇静的风险；并开发了一种风险分层工具，可用于辅助床边临床决策。
回顾性观察性研究。
大型非营利性综合医疗系统中的 12 家急性护理医院。
2016 年 7 月 1 日至 2018 年 6 月 30 日期间入住的所有≥18 岁且在住院期间接受阿片类药物治疗的患者（163190 例独特的住院患者）。
主要结局是发生需要纳洛酮紧急逆转的镇静或呼吸抑制程度，通过病历回顾确定；如果纳洛酮逆转不成功或没有缺氧性脑病或因过度镇静而死亡的证据，则不认为是过度镇静事件。
年龄、性别、体重指数、慢性阻塞性肺疾病、同时使用镇静药物、肾功能不全、肝功能不全、阿片类药物初治、睡眠呼吸暂停和手术与过度镇静风险显著相关。最强的预测因素是同时使用另一种镇静药物（调整后的 HR，95%CI=3.88，2.48 至 6.06）；最常见的此类药物是苯二氮䓬类（29%）、抗抑郁药（22%）和γ-氨基丁酸类似物（14.7%）。最终模型的 c 统计量为 0.755。从模型中开发的 24 分过度镇静风险标准（ORC）评分将患者分为高危（>20%，≥21 分）、中危（11%-20%，10-20 分）和低危（≤10%，<10 分）。
ORC 风险评分从常规收集的数据中识别出发生 OIRD 或过度镇静风险高的患者，从而能够进行早期检测和干预的针对性监测。它还可以应用于预防策略-例如，当同时开出处方开阿片类药物和其他镇静药物时，提供临床决策支持，显示所选组合如何影响患者的风险。
