Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial

被引:143
作者
Khanna, Ashish K. [1 ,2 ]
Bergese, Sergio D. [3 ,4 ]
Jungquist, Carla R. [5 ]
Morimatsu, Hiroshi [6 ]
Uezono, Shoichi [7 ]
Lee, Simon [8 ]
Ti, Lian Kah [9 ]
Urman, Richard D. [10 ]
McIntyre, Robert, Jr. [11 ]
Tornero, Carlos [12 ]
Dahan, Albert [13 ]
Saager, Leif [14 ,15 ]
Weingarten, Toby N. [16 ]
Wittmann, Maria [17 ]
Auckley, Dennis [18 ]
Brazzi, Luca [19 ]
Le Guen, Morgan [20 ]
Soto, Roy [21 ]
Schramm, Frank [22 ]
Ayad, Sabry [23 ]
Kaw, Roop [23 ]
Di Stefano, Paola [24 ]
Sessler, Daniel I. [25 ]
Uribe, Alberto [3 ]
Moll, Vanessa [8 ]
Dempsey, Susan J. [11 ,26 ]
Buhre, Wolfgang [27 ]
Overdyk, Frank J. [28 ]
机构
[1] Wake Forest Sch Med, Dept Anesthesiol, Sect Crit Care Med, Winston Salem, NC USA
[2] Outcomes Res Consortium, Winston Salem, NC USA
[3] Ohio State Univ, Dept Anesthesiol, Med Ctr, Columbus, OH USA
[4] SUNY Stony Brook, Dept Anesthesiol, Sch Med, Stony Brook, NY USA
[5] Univ Buffalo, Sch Nursing, Buffalo, NY USA
[6] Okayama Univ Hosp, Dept Anesthesiol & Resuscitol, Okayama, Japan
[7] Jikei Univ, Sch Med, Tokyo, Japan
[8] Emory Univ, Dept Anesthesiol, Atlanta, GA 30322 USA
[9] Natl Univ Singapore, Dept Anaesthesia, Singapore, Singapore
[10] Brigham & Womens Hosp, Dept Anesthesiol Perioperat & Pain Med, Boston, MA USA
[11] Univ Colorado, Sch Med, Dept Surg, Aurora, CO USA
[12] Hosp Clin Univ Valencia, Dept Anesthesiol Resuscitat & Pain Therapeut, Valencia, Spain
[13] Leiden Univ, Dept Anesthesiol, Med Ctr, Leiden, Netherlands
[14] Univ Michigan, Dept Anesthesiol, Sch Med, Ann Arbor, MI 48109 USA
[15] Univ Med Gottingen, Klin Anasthesiol, Gottingen, Germany
[16] Mayo Clin, Dept Anesthesiol & Perioperat Med, Rochester, MN USA
[17] Univ Hosp Bonn, Dept Anaesthesiol, Bonn, Germany
[18] Case Western Reserve Univ, Dept Anaesthesiol, Cleveland, OH 44106 USA
[19] Univ Turin, Dept Anesthesia Intens Care & Emergency, Turin, Italy
[20] Hop Foch, Dept Anaesthesiol, Suresnes, France
[21] Beaumont Hosp, Dept Anesthesiol, Royal Oak, MI USA
[22] Providence Reg Med Ctr, Dept Anesthesiol, Everett, WA USA
[23] Cleveland Clin Fdn, Outcomes Res Consortium, Cleveland, OH USA
[24] Medtron Core Clin Solut, Study & Sci Solut, Rome, Italy
[25] Cleveland Clin, Dept Outcomes Res, Inst Anesthesiol, Cleveland, OH USA
[26] Univ Calif Los Angeles, Sch Nursing, Los Angeles, CA USA
[27] Univ Med Ctr, Dept Anesthesiol, Maastricht, Netherlands
[28] Trident Anesthesia Grp LLC, Charleston, SC USA
关键词
ADVERSE EVENTS; POSTOPERATIVE HYPOXEMIA; PAIN MANAGEMENT; VALIDATION; PATIENT; RISK; OUTCOMES; MODELS; SAFETY; INDEX;
D O I
10.1213/ANE.0000000000004788
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
BACKGROUND: Opioid-related adverse events are a serious problem in hospitalized patients. Little is known about patients who are likely to experience opioid-induced respiratory depression events on the general care floor and may benefit from improved monitoring and early intervention. The trial objective was to derive and validate a risk prediction tool for respiratory depression in patients receiving opioids, as detected by continuous pulse oximetry and capnography monitoring. METHODS: PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) was a prospective, observational trial of blinded continuous capnography and oximetry conducted at 16 sites in the United States, Europe, and Asia. Vital signs were intermittently monitored per standard of care. A total of 1335 patients receiving parenteral opioids and continuously monitored on the general care floor were included in the analysis. A respiratory depression episode was defined as respiratory rate <= 5 breaths/min (bpm), oxygen saturation <= 85%, or end-tidal carbon dioxide <= 15 or >= 60 mm Hg for >= 3 minutes; apnea episode lasting >30 seconds; or any respiratory opioid-related adverse event. A risk prediction tool was derived using a multivariable logistic regression model of 46 a priori defined risk factors with stepwise selection and was internally validated by bootstrapping. RESULTS: One or more respiratory depression episodes were detected in 614 (46%) of 1335 general care floor patients (43% male; mean age, 58 +/- 14 years) continuously monitored for a median of 24 hours (interquartile range [IQR], 17-26). A multivariable respiratory depression prediction model with area under the curve of 0.740 was developed using 5 independent variables: age >= 60 (in decades), sex, opioid naivety, sleep disorders, and chronic heart failure. The PRODIGY risk prediction tool showed significant separation between patients with and without respiratory depression (P< .001) and an odds ratio of 6.07 (95% confidence interval [CI], 4.44-8.30;P< .001) between the high- and low-risk groups. Compared to patients without respiratory depression episodes, mean hospital length of stay was 3 days longer in patients with >= 1 respiratory depression episode (10.5 +/- 10.8 vs 7.7 +/- 7.8 days;P< .0001) identified using continuous oximetry and capnography monitoring. CONCLUSIONS: A PRODIGY risk prediction model, derived from continuous oximetry and capnography, accurately predicts respiratory depression episodes in patients receiving opioids on the general care floor. Implementation of the PRODIGY score to determine the need for continuous monitoring may be a first step to reduce the incidence and consequences of respiratory compromise in patients receiving opioids on the general care floor.
引用
收藏
页码:1012 / 1024
页数:13
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