- Email: [email protected]
Preadmission chronic opioid usage and its association with 90-day mortality in critically ill patients: a retrospective cohort study
British Journal of Anaesthesia, xxx (xxx): xxx (xxxx) doi: 10.1016/j.bja.2019.03.032 Advance Access Publication Date: xxx Clinical Investigation
CLINICAL INVESTIGATION
Preadmission chronic opioid usage and its association with 90-day mortality in critically ill patients: a retrospective cohort study Tak Kyu Oh1,*, In-Ae Song1, Jae Ho Lee2, Cheong Lim3, Young-Tae Jeon1, Hee-Joon Bae4, You Hwan Jo5 and Hee-Jung Jee6 1
Department of Anesthesiology and Pain Medicine, South Korea, 2Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, South Korea, 3Department of Thoracic and Cardiovascular Surgery, South Korea, 4Department of Neurology, Stroke Center, South Korea, 5Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea and 6Department of Biostatistics, College of Medicine, Korea University, Seoul, South Korea
*Corresponding author. E-mail: [email protected]
Background: The aim of this study was to investigate the association of chronic opioid usage with 90-day mortality in critically ill patients after admission to the ICU. Methods: This retrospective cohort study analysed the medical records of adult patients admitted to ICUs in a tertiary academic hospital between January 2012 and December 2017. Patients taking opioids regularly for more than 4 weeks before ICU admission were defined as chronic opioid users, whereas the others were defined as opioid-naı¨ve patients. Results: We selected 18 409 patients for this study, including 757 (4.1%) chronic opioid users. After propensity matching, 2990 patients (chronic opioid users, 757; opioid-naı¨ve, patient: 2233) were included in the analysis. The odds of 90-day mortality were higher in chronic opioid users than in opioid-naı¨ve patients using both the generalised estimating equation model for the propensity-matched cohort (odds ratio¼1.90; 95% confidence interval, 1.57e2.31; P<0.001) and the multivariable logistic regression model for the entire cohort (odds ratio¼2.20; 95% confidence interval, 1.81e2.66; P<0.001). Additionally, this association was significant in cancer patients and non-chronic kidney disease (CKD) patients and was not significant in non-cancer and CKD patients. Conclusions: Our results suggest that in a cohort of critically ill adult patients, chronic opioid use is associated with an increase in 90-day mortality. This association was more evident in cancer patients and non-CKD patients. Keywords: analgesics; opioid; critical care; intensive care units; mortality; outcome; neoplasms; sepsis
Editorial decision : 21 March 2019; Accepted: 21 March 2019 © 2019 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. For Permissions, please email: [email protected]
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Editor’s key points Chronic opioid therapy has been associated with immune suppression and infection risk. Most previous studies could not disentangle confounding by indication, meaning the causes leading to opioid therapy are also contributing to poor outcomes. This study showed that premorbid chronic opioid use was independently associated with lower chances of survival after ICU admission.
As of 2015, more than a third (37.8%) of the noninstitutionalised adults in the USA (91.8 million) are reportedly using opioids.1 Furthermore, as of 2010, there are 15.5 million people worldwide who are opioid-dependent, thereby increasing the global burden.2 Increase in opioid-dependence is associated with an increase in mortality,3 making the opioid epidemic a public health crisis in several countries, including the USA.2,4e6 Increased opioid usage is typically related to chronic noncancer pain, cancer pain, and persistent postoperative pain.7e9 Chronic opioid usage adversely affects both the innate and adaptive immunity, thereby hindering immune functions.10 Use of prescription opioid has been associated with a higher risk of infections in patients with invasive pneumococcal disease11 and rheumatoid arthritis,12 increasing the risk of mortality among hospitalised patients, especially those admitted to ICUs.13,14 A recent study reported that the mortality rate in sepsis patients who used opioids before their admission was higher than that in opioid-naı¨ve patients who were not exposed to opioids.15 However, this study did not evaluate the effects of chronic opioid usage before ICU admission. Furthermore, no studies to date have analysed the association between preadmission chronic opioid usage and mortality in a general ICU population. This study aimed to compare the 90-day mortality after ICU admission between chronic opioid users and opioid-naı¨ve patients. We hypothesised that the former group would exhibit an increase in 90-day mortality after ICU admission.
Methods This retrospective observational study was approved by the Institutional Review Board (IRB) of the Seoul National University Bundang Hospital (SNUBH; Seoul, Korea; IRB approval number: B-1806/474-105). Considering the retrospective design of this study, the requirement for informed consent was waived by the IRB.
Data registry and patient selection This study utilised data stored and managed in the electronic medical record system of SNUBH on all adult patients (18 yr) who were admitted to ICUs between January 2012 and December 2017. All the ICU admission cases for the study period were screened by a group of medical record technicians in the medical informatics team who were not informed of the purpose of this study. If a patient was admitted to the ICU two times or more throughout the study period, only the latter admission, which was likely to be more severe, was included in the analysis. Patients with no clear information on the
duration or dosage of preadmission opioid use were excluded from the analysis. Additionally, patients who were admitted to the ICU 24 h after hospital admission were also excluded to avoid confounders related to opioid use during hospitalisation in the general ward, as we intended to focus on the effects of pre-hospital admission chronic opioid usage on the outcomes in critically ill patients.
Preadmission chronic opioid usage as an independent variable Preadmission medication history of all patients admitted to the ICU of SNUBH was assessed by a registered nurse, regardless of the prescribing clinician or the hospital. All medications with unclear ingredients were reported to the clinician in charge after identifying the main ingredient. Considering that 4 weeks of treatment with opioids is considered chronic opioid therapy,16 patients who had been regularly and persistently prescribed to take opioids for 4 weeks before ICU admission were defined as chronic opioid users, whereas the others were defined as opioid-naı¨ve patients. However, patients admitted to the ICU after surgery who were new users of opioids for pain control throughout the perioperative period were not considered for group assignment. The dose of opioid usage in chronic opioid users was calculated by converting the total amount of opioids used into morphine equivalent daily dose (MEDD, mg) using the standard conversion ratio17 (Supplementary Table S1). For patients who were prescribed a mixture of different opioids, the regular opioids (long acting or patch) were assumed to have been entirely consumed, whereas the short-acting opioids (pro re nata [PRN]) were assumed to have been taken twice a day.
Ninety-day mortality and overall survival after ICU admission as a dependent variable The primary end point of this study was 90-day mortality, which was defined as death within 90 days of ICU admission. Regardless of the follow-up status, the date of death for all patients who were discharged from SNUBH was confirmed via the Ministry of the Interior and Safety in South Korea, as of May 15, 2018. Based on this information, the overall survival, which was calculated as the period between the date of ICU admission and the date of death or May 15, 2018 (the last date of confirmed survival), was used as the secondary end point.
Potential covariates The medical records collected as covariates for this study included physical characteristics (sex, age, BMI), socioeconomic status, insurance type (national health insurance program/medical aid beneficiary program), marital status (never married/married or living together/divorced or separated/ widowed), occupation (office worker/licensed job/house work/ self-employed/student, military, labourer, or unemployed), highest educational attainment (lower than high school/more than or equal to high school, lower than college/more than or equal to college), comorbidities (hypertension, diabetes mellitus, coronary artery diseases from stable angina to myocardial infarction), liver disease (liver cirrhosis, hepatitis, fatty liver), cerebrovascular disease, chronic obstructive lung disease, chronic kidney disease (CKD), anaemia, cancer, cancer pain, and non-cancer pain. Although the government pays most of the hospital charges for patients in the medical aid
Opioid and mortality in critically ill patients
beneficiary program (low income earners), it pays approximately two-thirds of the charges for those in the national health insurance program. Comorbidity-related information was collected from the electronic medical record system based on the International Classification of Disease 10 codes.
Statistical analysis Whereas the continuous variables were represented using mean values with standard deviation (SD), the categorical variables were represented using numbers with percentages. First, propensity score matching, known to reduce confounders in an observational study effectively, was performed using the nearest neighbour method (1:3 ratio, without replacement, and calliper 0.3).18 The physical characteristics such as the socioeconomic status, comorbidities, cancer pain, non-cancer pain, and year of ICU admission were all included as covariates for propensity score matching, and logistic regression analysis was performed to calculate the propensity scores in a logistic model. The absolute value of the standardised mean difference (ASD) was used to evaluate the balance between chronic opioid users and opioid-naı¨ve patients before and after propensity score matching. This value was set below 0.1 to balance the two groups. After confirming that the two groups were well balanced, we applied a generalised estimating equation (GEE) for the clustering of observations within each propensity score matching stratum in the propensity score-matched cohort.19 In this model, binomial distribution and logistic function were assumed for the dependent variable (90-day mortality). Additionally, a robust estimator and an independent structure were used for the covariance matrix and working correlation matrix, respectively. The result of the GEE was presented as the odds ratio (OR) with a 95% confidence interval (CI). Next, we performed multivariable logistic regression analysis of 90-day mortality for the entire cohort to (a) determine if
Fig. 1. Flowchart summarising the steps used for patient selection.
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the results from the propensity score-matched cohort were generalisable for the entire cohort in our hospital, and (b) to determine the association between chronic opioid usage and 90-day mortality not in isolation, but along with other important covariates. All covariates (physical characteristics, socioeconomic status, comorbidities, cancer or non-cancer pain, and year of ICU admission) were included in this multivariable logistic regression model. In addition, MEDD (per 10 mg) was included as a new continuous variable to investigate the effect of opioid dosage in chronic opioid users in a sensitivity analysis. The results of the multivariable model have been presented as forest plots (OR with 95% CI). In the multivariable models, we tested the interaction of chronic opioid usage with all other covariates for 90-day mortality. In the presence of interactions, we performed a subgroup analysis using the same method as the multivariable logistic regression analysis used for the entire cohort. It was confirmed that there was no multicollinearity in all multivariable models with variance inflation factor < 2.0. Finally, the overall survival time before and after propensity score matching have been presented as KaplaneMeier curves, and log-rank tests were performed to test for statistical significance between the two groups. All statistical analyses were performed using the R software (version 3.5.2, R Development Core Team, Vienna, Austria), and P<0.05 was considered statistically significant.
Results Between January 2012 and December 2017, there were 40 533 cases of ICU admissions in total. Among these 10 135 duplicate admissions, 5440 paediatric patients (<18 yr), 30 cases with an unclear medical history of preadmission opioid usage, and 6519 patients who were admitted to the ICU 24 h after hospital admission were excluded. Overall, 18 409 patients were included in the analysis, of which 757 (4.1%) were chronic
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opioid users and 17 652 (95.9%) were opioid-naı¨ve patients (Fig. 1). The baseline characteristics of all patients are outlined in Supplementary Table S2. Of the analysed patients, 1914 (10.4%) died within 90 days of ICU admission. The mean dose of MEDD (SD) among the chronic opioid users was 12.5 (21.0) mg. After propensity score matching to balance the two groups, a total of 2990 patients (757 chronic opioid users and 2233 opioid-naı¨ve patients) were included in the final analysis. The balance between the two groups before and after propensity score matching is shown in Table 1 and Supplementary Fig. S1. The ASD between the two groups after propensity score matching was below 0.1, demonstrating a
good balance. The distribution of the propensity scores between the two groups became similar after propensity score matching (Supplementary Fig. S2).
Ninety-day mortality and preadmission chronic opioid usage in propensity score-matched cohort In the propensity matched cohort (Table 2), 90-day mortality in chronic opioid users was 28.8% (218/757), whereas that in opioid-naı¨ve patients was 17.0% (379/2233). In the GEE model, the chronic opioid users were at a 1.90-fold higher odds of 90
Table 1 Comparison of characteristics between preadmission chronic opioid user and opioid-naı¨ve patients before and after propensity score matching. Data are presented as n (%) or mean value (standard deviation). *The patients in the medical aid beneficiary program are those who are classified to have low income, and most of their hospital charges are paid by the government. Meanwhile, for the patients in the national health insurance program, approximately two-thirds of their hospital charges are covered by the government. ASD, absolute value of standardised mean difference; LC, liver cirrhosis. Variables
Sex, male Age, yr BMI, kg m2 Insurance type* National health insurance program Medical aid beneficiary program Marital status Never married Married or living together Divorced or separated Widowed Occupation Office worker Licensed job House work Self-employed Student, military, or labourer Unemployed Highest educational attainment Lower than high school More or equal to high school, lower than college More than or equal to college Comorbidities at hospital admission Hypertension Diabetes mellitus Coronary artery disease Cerebrovascular disease Chronic obstructive lung disease Liver disease (LC, hepatitis, fatty liver) Chronic kidney disease Anaemia (haemoglobin <10 g dl1) Cancer Complaints of cancer pain at hospital admission Complaints of non-cancer pain at hospital admission Year of ICU admission 2012 2013 2014 2015 2016 2017
Before propensity score matching (n¼18 409)
After propensity score matching (n¼2990)
Chronic opioid Opioid naı¨ve ASD Chronic opioid user n¼757 patients (n¼17 652) user (n¼757)
Opioid naı¨ve patients (n¼2233)
ASD
395 (52.2) 67.9 (12.8) 23.2 (3.2)
10 524 (59.6) 62.4 (15.7) 24.0 (3.4)
395 (52.2) 67.9 (12.8) 23.2 (3.2)
1169 (52.4) 67.7 (14.2) 23.3 (3.3)
0.02 <0.01 0.05 0.02
701 (92.6) 56 (7.4)
17 073 (96.7) 579 (3.3)
701 (92.6) 56 (7.4)
2073 (92.8) 160 (7.2)
29 (3.8) 589 (77.8) 30 (4.0) 109 (14.4)
1176 (6.7) 14 367 (81.4) 524 (3.0) 1585 (9.0)
29 (3.8) 589 (77.8) 30 (4.0) 109 (14.4)
85 (3.8) 1735 (77.7) 100 (4.5) 313 (14.0)
49 (6.5) 29 (3.8) 189 (25.0) 58 (7.7) 63 (8.3) 369 (48.7)
3608 (20.4) 932 (5.3) 3596 (20.4) 1987 (11.3) 1923 (10.9) 5606 (31.8)
49 (6.5) 29 (3.8) 189 (25.0) 58 (7.7) 63 (8.3) 369 (48.7)
152 (6.8) 105 (4.7) 559 (25.0) 186 (8.3) 187 (8.4) 1044 (46.8)
362 (47.8) 221 (29.2)
6679 (37.8) 4908 (27.8)
362 (47.8) 221 (29.2)
1049 (47.0) 647 (29.0)
174 (23.0)
6065 (34.4)
174 (23.0)
537 (24.0)
339 (44.8) 76 (10.0) 18 (2.4) 20 (2.6) 31 (4.1) 34 (4.5) 152 (20.1) 384 (50.7) 352 (46.5) 135 (17.8)
7578 (42.9) 1526 (8.6) 471 (2.7) 705 (4.0) 682 (3.9) 444 (2.5) 2849 (16.1) 3702 (21.0) 2134 (12.1) 523 (3.0)
0.04 0.05 0.02 0.08 0.01 0.04 0.20 0.51 0.69 0.39
339 (44.8) 76 (10.0) 18 (2.4) 20 (2.6) 31 (4.1) 34 (4.5) 152 (20.1) 384 (50.7) 352 (46.5) 135 (17.8)
996 (44.6) 213 (9.5) 50 (2.2) 63 (2.8) 106 (4.7) 96 (4.3) 448 (20.1) 1056 (47.3) 976 (43.7) 320 (14.3)
0.01 <0.01 <0.01 0.01 0.01 0.01 0.04 0.02 0.04 0.04
53 (7.0)
1893 (10.7)
0.15
53 (7.0)
164 (7.3)
0.02
51 (6.7) 81 (10.7) 127 (16.8) 129 (17.0) 171 (22.6) 198 (26.2)
1998 2358 2978 3072 3412 3834
51 (6.7) 81 (10.7) 127 (16.8) 129 (17.0) 171 (22.6) 198 (26.2)
180 245 377 388 509 534
0.15 0.43 0.24 0.16
0.15
0.03
0.34
0.04
0.20
0.03
0.10 (11.3) (13.4) (16.9) (17.4) (19.3) (21.7)
0.02 (8.1) (11.0) (16.9) (17.4) (22.8) (23.9)
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Table 2 Generalised estimating equation model for 90-day mortality before and after propensity score matching. *A binomial distribution and logistic function was assumed for the dependent variable (90-day mortality) in generalised estimating equation model. Additionally, robust estimator and independent structure were used for covariance matrix and working correlation matrix, respectively. CI, confidence interval. Variable Entire cohort (unadjusted) Preadmission opioid naı¨ve patients Preadmission chronic opioid user Propensity score-matched cohort (adjusted) Preadmission opioid naı¨ve patients Preadmission chronic opioid user
90-day mortality rate
Odds ratio* (95% CI)
P-value
9.6% (1696/17 652) 28.8% (218/757)
1 3.81 (3.23e4.49)
<0.001
17.0% (379/2233) 28.8% (218/757)
1 1.90 (1.57e2.31)
<0.001
day mortality than the opioid-naı¨ve patients in the propensitymatched cohort (OR¼1.90; 95% CI, 1.57e2.31; P<0.001; Table 2).
Ninety-day mortality and preadmission chronic opioid usage in the entire cohort A multivariable logistic regression model for the entire cohort showed that the chronic opioid users were at a 2.20-fold higher odds of 90-day mortality than the opioid-naı¨ve patients (OR¼2.20; 95% CI, 1.81e2.66; P<0.001; Fig. 2 and Table 3). In a sensitivity analysis, A 10 mg increase in the MEDD resulted in a 1.17-fold higher odds of 90-day mortality among the chronic opioid users, in the entire cohort (OR¼1.17; 95% CI, 1.04e1.33; P¼0.010; Table 3). Chronic opioid usage showed interactions with cancer, cancer pain, non-cancer pain, and CKD for 90-day mortality. The results of the subgroup analysis are presented in Supplementary Table S3. The chronic opioid users were at a 3.67-fold higher odds of 90-day mortality than the opioid-naı¨ve patients in cancer patients (OR¼3.67; 95% CI, 2.83e4.77; P<0.001), whereas no significant difference was seen in the non-cancer patients (P¼0.772). Additionally, the chronic opioid users were at a 2.77-fold higher odds of 90-day mortality than the opioid-naı¨ve patients in non-CKD patients (OR¼2.77; 95% CI, 2.23e3.44; P<0.001), whereas no significant difference was seen in the CKD patients (P¼0.787). The results of the univariable logistic regression model including all covariates for 90-day mortality are presented in Supplementary Table S4.
Overall survival time before and after propensity score matching The KaplaneMeier curve before and after propensity score matching showed that the overall survival was significantly shorter in the chronic opioid user group than in the opioidnaı¨ve group (both P<0.001 by log-rank test; Fig. 3).
Discussion This study showed that preadmission chronic opioid use was associated with greater 90-day mortality and shorter overall survival time after ICU admission. The strength of this association increased with increasing dose of MEDD. Additionally, this association was more evident in preadmission cancer and non-CKD patients. This study, which was conducted in a heterogeneous population of critically ill patients from mixed ICUs, not only attempted to minimise the confounders by propensity score matching but also confirmed that similar trends were observed in a multivariable logistic regression
model for the entire cohort. In addition to physical characteristics and comorbidities, we included socioeconomic status as an important covariate based on studies that demonstrated an association between socioeconomic factors and chronic opioid use.20 One of the possible reasons for the higher 90-day mortality in the chronic opioid users is the impact of opioids on the patient’s immunity which leads to an increased risk of infections. Immunity is the defensive host mechanism against pathogenesis. Immune responses in critically ill patients are suppressed21 owing to dysregulation of their innate and adaptive immune systems.22 Immunosuppression, which is known to be induced by long-term opioid therapy, can, therefore, be a key risk factor, resulting in increased rates of mortality owing to infections, in critically ill patients.23,24 Recent studies have reported that opioid users exhibit an increased risk of serious infections,11,12,25 which support the findings of this study. Patients admitted to ICUs are exposed to novel ICUacquired infections even if the initial admission was not for an infection. This is another key independent risk factor of hospital mortality.26,27 Considering that the propensity score matching was done to balance the characteristics of the two groups at the time of ICU admission, the findings of this study suggest that chronic opioid users may have been exposed to new nosocomial infections during their stay in the ICU. Overall, we demonstrate that although the chronic opioid users admitted to ICUs because of infections were likely to exhibit poorer prognosis than the opioid-naı¨ve patients; they were also more likely to contract new nosocomial infections. The endocrine effects of long-term opioid therapy should also be considered when interpreting the study outcomes. Long-term opioid use suppresses the hypothalamicepituitary functions and consequently induces hormonal abnormalities.28 A previous study has reported that chronic fentanyl application induces severe adrenal insufficiency,29 which is believed to be the most severe complication from suppression of hypothalamicepituitary functions. Critically ill patients often show adrenal insufficiency, which can lead to suppressed stress responses.30 Therefore, the findings of this study may have been influenced by the hormonal abnormalities that may or may not have been existent in chronic opioid users. Nonetheless, as there is inadequate information on how long-term opioid administration impacts hormonal activities and its effects on the prognosis of critically ill patients, additional studies should be performed in the future to evaluate these effects.
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Fig. 2. Odds ratios of all individual variables from multivariable logistic regression model in entire cohort. Occupation: (1) licensed job vs office worker; (2) house work vs office worker; (3) self-employed vs office worker; (4) student, military, or labourer vs office worker; and (5) unemployed vs office worker. Highest educational attainment: (1) more than or equal to high school, lower than college vs lower than high school; and (2) more than or equal to college vs lower than high school. Marital status: (1) married or living together vs never married; (2) divorced or separated vs never married; and (3) widowed vs never married. Adm year: 2012. Adm, admission; CAD, coronary artery disease; CI, confidence interval; CKD, chronic kidney disease; COPD, chronic obstructive lung disease; CVD, cerebrovascular disease; MEDD, morphine equivalent daily dose; OR, odds ratio.
Additionally, we should consider the cardiovascular effects of long-term opioid administration. Although most opioids exhibit little direct negative effects on cardiac contractility, it can induce cardiac dysfunction when used in parallel with certain medications, such as benzodiazepines.31 In this study, most of the chronic opioid users persistently used long-acting opioids, to avoid opioid withdrawal. Therefore, these patients may have had more cardiac dysfunctions than the opioid-naı¨ve patients, which may have affected the outcomes of this study. In fact, a previous retrospective study showed a greater association between increased cardiovascular outcomes (including myocardial infarction) and chronic opioid users than in the general population.32 However, there is little evidence supporting the association between the chronic opioid administration and increased risk of cardiacrelated adverse effects, and additional studies are required to evaluate it. The results of the subgroup analysis in patients with preadmission cancer or CKD were also notable in this study. In cancer patients, higher opioid usage or requirement has been shown to be an indicator of an advanced cancer stage or shortened survival.33,34 Therefore, there might have been more advanced cancer patients in the chronic opioid user group than in the opioid-naı¨ve group, which might have influenced the results in this study. Considering opioids are
known to cause infections in advanced cancer patients,35 chronic opioid use might be a significant risk factor for the development of infections in critically ill and cancer patients. Therefore, our results suggest that cancer patients who take opioids before admission are likely to have higher mortality because of various reasons including cancer progression and infections. In other words, critically ill and cancer patients who are chronic opioid users should be very carefully managed by the ICU physicians. Our findings in CKD patients indicate that clinicians usually try not to prescribe long-acting opioid analgesics for these patients because of the prolonged excretion of opioids,36 which in turn may influence the results of this study. However, further studies are required to clarify the complex relationship between opioids and renal function.37 Despite several intriguing findings, this study has several limitations. First, the retrospective nature of this study may have resulted in selection bias and thereby poorer data accuracy or quality than a prospective study. Second, this study was performed at a single centre, which limits the generalisability of the findings. Third, the standard conversion ratio has been utilised to assess different kinds of opioids used by the patients comprehensively. Lastly, we performed both propensity score matching and multivariable adjustment to reduce the effect of confounders in this study. However, the
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Table 3 Multivariable logistic regression analysis for 90-day mortality after ICU admission in entire cohort. Model fitting information in multivariable logistic regression model in entire cohort for 90-day mortality: e2log likelihood, 9783.7; c2, 2503.2; df, 31; P<0.001. Model fitting information in multivariable logistic regression model of sensitivity analysis for 90-day mortality in entire cohort: 2log likelihood, 9783.7; c2, 2503.2; df, 31; P<0.001. *MEDD of preadmission chronic opioid user was calculated using standard morphine conversion ratio in Table S1. CI, confidence interval; CKD, chronic kidney disease; MEDD, morphine equivalent daily dose; Preadm, preadmission. Variable
Multivariable model
Entire cohort (n¼18 409) Preadm opioid-naı¨ve patient Preadm chronic opioid user Sensitivity analysis in entire cohort (n¼18 409) Preadm opioid-naı¨ve patient Preadm chronic opioid user MEDD* (per 10 mg increase) Interaction in multivariable logistic regression model Comorbidity of preadmission cancer Preadm opioid-naı¨ve patient* non-cancer Preadm chronic opioid user* cancer Cancer pain before hospital admission Preadm opioid-naı¨ve patient* non-cancer pain Preadm chronic opioid user* cancer pain Comorbidity of preadmission CKD Preadm opioid-naı¨ve patient* non-CKD Preadm chronic opioid user* CKD Highest educational attainment Preadm opioid-naı¨ve patient* Lower than high school Preadm chronic opioid user* More than or equal to high school, lower than college Preadm chronic opioid user* More than or equal to college
Odds ratio (95% CI)
P-value
1 2.20 (1.81e2.66)
<0.001
1 1.79 (1.40e2.29) 1.17 (1.04e1.33)
<0.001 0.010
1 3.64 (2.33e5.70)
<0.001
1 0.48 (0.28e0.83)
0.009
1 0.36 (0.23e0.58)
<0.001
1 1.26 (0.82e1.94) 2.09 (1.34e3.27)
0.297 0.001
Fig. 3. KaplaneMeier curve of the overall survival time after ICU admission between the chronic opioid users and opioid naı¨ve patients, before (a) and after (b) propensity score matching.
ORs obtained from the propensity-matched cohort and those based on the entire cohort, adjusting for all covariates, are not directly comparable, because of the non-collapsibility of the OR. Therefore, the two results should be interpreted independently. Furthermore, propensity score matching and multivariable adjustment can only reduce the effect of known confounders. Considering that there might be unmeasured
confounders and selection bias, the observed results in this study may have been affected. In conclusion, this study showed that preadmission chronic opioid use is associated with increased 90-day mortality, as well as shorter overall survival time in critically ill adult patients. Additionally, this association was more evident in cancer patients and non-CKD patients.
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Authors’ contributions Study design: TKO. Interpretation of data: TKO. Drafting of the manuscript: TKO. Data collection: IAS, JHL, CL, YTJ, HJB, YHJ. Data analysis: HJJ. All authors gave approval for the final version of the manuscript.
Declaration of interest
14.
15.
16.
The authors declare that they have no conflicts of interest.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.bja.2019.03.032.
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mortality in the surgical intensive care unit. Clinics (Sao Paulo) 2013; 68: 1103e8 Ylipalosaari P, Ala-Kokko TI, Laurila J, Ohtonen P, Syrjala H. Intensive care acquired infection is an independent risk factor for hospital mortality: a prospective cohort study. Crit Care 2006; 10: R66 Zhang R, Meng J, Lian Q, et al. Prescription opioids are associated with higher mortality in patients diagnosed with sepsis: a retrospective cohort study using electronic health records. PLoS One 2018; 13, e0190362 Hauser W, Petzke F, Radbruch L, Tolle TR. The opioid epidemic and the long-term opioid therapy for chronic noncancer pain revisited: a transatlantic perspective. Pain Manag 2016; 6: 249e63 Mercadante S, Caraceni A. Conversion ratios for opioid switching in the treatment of cancer pain: a systematic review. Palliat Med 2011; 25: 504e15 Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984; 79: 516e24 Hanley JA, Negassa A, Edwardes MD, Forrester JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol 2003; 157: 364e75 Svendsen K, Fredheim OM, Romundstad P, Borchgrevink PC, Skurtveit S. Persistent opioid use and socio-economic factors: a population-based study in Norway. Acta Anaesthesiol Scand 2014; 58: 437e45 Surbatovic M, Vojvodic D, Khan W. Immune response in critically ill patients. Mediators Inflamm 2018; 2018: 9524315 Duggal NA, Snelson C, Shaheen U, Pearce V, Lord JM. Innate and adaptive immune dysregulation in critically ill ICU patients. Sci Rep 2018; 8: 10186 Roy S, Wang J, Kelschenbach J, Koodie L, Martin J. Modulation of immune function by morphine: implications for susceptibility to infection. J Neuroimmun Pharmacol 2006; 1: 77e89 Sacerdote P. Opioids and the immune system. Palliat Med 2006; 20: s9e15 Wiese AD, Griffin MR, Schaffner W, et al. Long-acting opioid use and the risk of serious infections: a retrospective cohort study. Clin Infect Dis Adv 2018; 15. https:// doi.org/10.1093/cid/ciy809. Access published on September Laupland KB, Zygun DA, Davies HD, Church DL, Louie TJ, Doig CJ. Population-based assessment of intensive care unit-acquired bloodstream infections in adults: incidence, risk factors, and associated mortality rate. Crit Care Med 2002; 30: 2462e7 Wang FD, Chen YY, Chen TL, Lin YT, Fung CP. Risk factors and mortality of nosocomial infections of methicillinresistant Staphylococcus aureus in an intensive care unit. J Crit Care 2011; 26: 82e8 Rhodin A, Stridsberg M, Gordh T. Opioid endocrinopathy: a clinical problem in patients with chronic pain and longterm oral opioid treatment. Clin J Pain 2010; 26: 374e80 Oltmanns KM, Fehm HL, Peters A. Chronic fentanyl application induces adrenocortical insufficiency. J Intern Med 2005; 257: 478e80 Marik PE, Zaloga GP. Adrenal insufficiency in the critically ill: a new look at an old problem. Chest 2002; 122: 1784e96 Chen A, Ashburn MA. Cardiac effects of opioid therapy. Pain Med 2015; 16: S27e31
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32. Carman WJ, Su S, Cook SF, Wurzelmann JI, McAfee A. Coronary heart disease outcomes among chronic opioid and cyclooxygenase-2 users compared with a general population cohort. Pharmacoepidemiol Drug Saf 2011; 20: 754e62 33. Oh TK, Do SH, Yoon YS, Song IA. Association between opioid use and survival time in patients with unresectable pancreatic cancer: 10 years of clinical experience. Pancreas 2018; 47: 837e42 34. Zylla D, Kuskowski MA, Gupta K, Gupta P. Association of opioid requirement and cancer pain with survival in
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advanced non-small cell lung cancer. Br J Anaesth 2014; 113: i109e16 35. Shao YJ, Liu WS, Guan BQ, et al. Contribution of opiate analgesics to the development of infections in advanced cancer patients. Clin J Pain 2017; 33: 295e9 36. Dean M. Opioids in renal failure and dialysis patients. J Pain Symptom Manage 2004; 28: 497e504 37. Mallappallil M, Sabu J, Friedman EA, Salifu M. What do we know about opioids and the kidney? Int J Mol Sci 2017; 18: 223
Handling editor: P.S. Myles
CLINICAL INVESTIGATION
Preadmission chronic opioid usage and its association with 90-day mortality in critically ill patients: a retrospective cohort study Tak Kyu Oh1,*, In-Ae Song1, Jae Ho Lee2, Cheong Lim3, Young-Tae Jeon1, Hee-Joon Bae4, You Hwan Jo5 and Hee-Jung Jee6 1
Department of Anesthesiology and Pain Medicine, South Korea, 2Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, South Korea, 3Department of Thoracic and Cardiovascular Surgery, South Korea, 4Department of Neurology, Stroke Center, South Korea, 5Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea and 6Department of Biostatistics, College of Medicine, Korea University, Seoul, South Korea
*Corresponding author. E-mail: [email protected]
Background: The aim of this study was to investigate the association of chronic opioid usage with 90-day mortality in critically ill patients after admission to the ICU. Methods: This retrospective cohort study analysed the medical records of adult patients admitted to ICUs in a tertiary academic hospital between January 2012 and December 2017. Patients taking opioids regularly for more than 4 weeks before ICU admission were defined as chronic opioid users, whereas the others were defined as opioid-naı¨ve patients. Results: We selected 18 409 patients for this study, including 757 (4.1%) chronic opioid users. After propensity matching, 2990 patients (chronic opioid users, 757; opioid-naı¨ve, patient: 2233) were included in the analysis. The odds of 90-day mortality were higher in chronic opioid users than in opioid-naı¨ve patients using both the generalised estimating equation model for the propensity-matched cohort (odds ratio¼1.90; 95% confidence interval, 1.57e2.31; P<0.001) and the multivariable logistic regression model for the entire cohort (odds ratio¼2.20; 95% confidence interval, 1.81e2.66; P<0.001). Additionally, this association was significant in cancer patients and non-chronic kidney disease (CKD) patients and was not significant in non-cancer and CKD patients. Conclusions: Our results suggest that in a cohort of critically ill adult patients, chronic opioid use is associated with an increase in 90-day mortality. This association was more evident in cancer patients and non-CKD patients. Keywords: analgesics; opioid; critical care; intensive care units; mortality; outcome; neoplasms; sepsis
Editorial decision : 21 March 2019; Accepted: 21 March 2019 © 2019 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. For Permissions, please email: [email protected]
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Editor’s key points Chronic opioid therapy has been associated with immune suppression and infection risk. Most previous studies could not disentangle confounding by indication, meaning the causes leading to opioid therapy are also contributing to poor outcomes. This study showed that premorbid chronic opioid use was independently associated with lower chances of survival after ICU admission.
As of 2015, more than a third (37.8%) of the noninstitutionalised adults in the USA (91.8 million) are reportedly using opioids.1 Furthermore, as of 2010, there are 15.5 million people worldwide who are opioid-dependent, thereby increasing the global burden.2 Increase in opioid-dependence is associated with an increase in mortality,3 making the opioid epidemic a public health crisis in several countries, including the USA.2,4e6 Increased opioid usage is typically related to chronic noncancer pain, cancer pain, and persistent postoperative pain.7e9 Chronic opioid usage adversely affects both the innate and adaptive immunity, thereby hindering immune functions.10 Use of prescription opioid has been associated with a higher risk of infections in patients with invasive pneumococcal disease11 and rheumatoid arthritis,12 increasing the risk of mortality among hospitalised patients, especially those admitted to ICUs.13,14 A recent study reported that the mortality rate in sepsis patients who used opioids before their admission was higher than that in opioid-naı¨ve patients who were not exposed to opioids.15 However, this study did not evaluate the effects of chronic opioid usage before ICU admission. Furthermore, no studies to date have analysed the association between preadmission chronic opioid usage and mortality in a general ICU population. This study aimed to compare the 90-day mortality after ICU admission between chronic opioid users and opioid-naı¨ve patients. We hypothesised that the former group would exhibit an increase in 90-day mortality after ICU admission.
Methods This retrospective observational study was approved by the Institutional Review Board (IRB) of the Seoul National University Bundang Hospital (SNUBH; Seoul, Korea; IRB approval number: B-1806/474-105). Considering the retrospective design of this study, the requirement for informed consent was waived by the IRB.
Data registry and patient selection This study utilised data stored and managed in the electronic medical record system of SNUBH on all adult patients (18 yr) who were admitted to ICUs between January 2012 and December 2017. All the ICU admission cases for the study period were screened by a group of medical record technicians in the medical informatics team who were not informed of the purpose of this study. If a patient was admitted to the ICU two times or more throughout the study period, only the latter admission, which was likely to be more severe, was included in the analysis. Patients with no clear information on the
duration or dosage of preadmission opioid use were excluded from the analysis. Additionally, patients who were admitted to the ICU 24 h after hospital admission were also excluded to avoid confounders related to opioid use during hospitalisation in the general ward, as we intended to focus on the effects of pre-hospital admission chronic opioid usage on the outcomes in critically ill patients.
Preadmission chronic opioid usage as an independent variable Preadmission medication history of all patients admitted to the ICU of SNUBH was assessed by a registered nurse, regardless of the prescribing clinician or the hospital. All medications with unclear ingredients were reported to the clinician in charge after identifying the main ingredient. Considering that 4 weeks of treatment with opioids is considered chronic opioid therapy,16 patients who had been regularly and persistently prescribed to take opioids for 4 weeks before ICU admission were defined as chronic opioid users, whereas the others were defined as opioid-naı¨ve patients. However, patients admitted to the ICU after surgery who were new users of opioids for pain control throughout the perioperative period were not considered for group assignment. The dose of opioid usage in chronic opioid users was calculated by converting the total amount of opioids used into morphine equivalent daily dose (MEDD, mg) using the standard conversion ratio17 (Supplementary Table S1). For patients who were prescribed a mixture of different opioids, the regular opioids (long acting or patch) were assumed to have been entirely consumed, whereas the short-acting opioids (pro re nata [PRN]) were assumed to have been taken twice a day.
Ninety-day mortality and overall survival after ICU admission as a dependent variable The primary end point of this study was 90-day mortality, which was defined as death within 90 days of ICU admission. Regardless of the follow-up status, the date of death for all patients who were discharged from SNUBH was confirmed via the Ministry of the Interior and Safety in South Korea, as of May 15, 2018. Based on this information, the overall survival, which was calculated as the period between the date of ICU admission and the date of death or May 15, 2018 (the last date of confirmed survival), was used as the secondary end point.
Potential covariates The medical records collected as covariates for this study included physical characteristics (sex, age, BMI), socioeconomic status, insurance type (national health insurance program/medical aid beneficiary program), marital status (never married/married or living together/divorced or separated/ widowed), occupation (office worker/licensed job/house work/ self-employed/student, military, labourer, or unemployed), highest educational attainment (lower than high school/more than or equal to high school, lower than college/more than or equal to college), comorbidities (hypertension, diabetes mellitus, coronary artery diseases from stable angina to myocardial infarction), liver disease (liver cirrhosis, hepatitis, fatty liver), cerebrovascular disease, chronic obstructive lung disease, chronic kidney disease (CKD), anaemia, cancer, cancer pain, and non-cancer pain. Although the government pays most of the hospital charges for patients in the medical aid
Opioid and mortality in critically ill patients
beneficiary program (low income earners), it pays approximately two-thirds of the charges for those in the national health insurance program. Comorbidity-related information was collected from the electronic medical record system based on the International Classification of Disease 10 codes.
Statistical analysis Whereas the continuous variables were represented using mean values with standard deviation (SD), the categorical variables were represented using numbers with percentages. First, propensity score matching, known to reduce confounders in an observational study effectively, was performed using the nearest neighbour method (1:3 ratio, without replacement, and calliper 0.3).18 The physical characteristics such as the socioeconomic status, comorbidities, cancer pain, non-cancer pain, and year of ICU admission were all included as covariates for propensity score matching, and logistic regression analysis was performed to calculate the propensity scores in a logistic model. The absolute value of the standardised mean difference (ASD) was used to evaluate the balance between chronic opioid users and opioid-naı¨ve patients before and after propensity score matching. This value was set below 0.1 to balance the two groups. After confirming that the two groups were well balanced, we applied a generalised estimating equation (GEE) for the clustering of observations within each propensity score matching stratum in the propensity score-matched cohort.19 In this model, binomial distribution and logistic function were assumed for the dependent variable (90-day mortality). Additionally, a robust estimator and an independent structure were used for the covariance matrix and working correlation matrix, respectively. The result of the GEE was presented as the odds ratio (OR) with a 95% confidence interval (CI). Next, we performed multivariable logistic regression analysis of 90-day mortality for the entire cohort to (a) determine if
Fig. 1. Flowchart summarising the steps used for patient selection.
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the results from the propensity score-matched cohort were generalisable for the entire cohort in our hospital, and (b) to determine the association between chronic opioid usage and 90-day mortality not in isolation, but along with other important covariates. All covariates (physical characteristics, socioeconomic status, comorbidities, cancer or non-cancer pain, and year of ICU admission) were included in this multivariable logistic regression model. In addition, MEDD (per 10 mg) was included as a new continuous variable to investigate the effect of opioid dosage in chronic opioid users in a sensitivity analysis. The results of the multivariable model have been presented as forest plots (OR with 95% CI). In the multivariable models, we tested the interaction of chronic opioid usage with all other covariates for 90-day mortality. In the presence of interactions, we performed a subgroup analysis using the same method as the multivariable logistic regression analysis used for the entire cohort. It was confirmed that there was no multicollinearity in all multivariable models with variance inflation factor < 2.0. Finally, the overall survival time before and after propensity score matching have been presented as KaplaneMeier curves, and log-rank tests were performed to test for statistical significance between the two groups. All statistical analyses were performed using the R software (version 3.5.2, R Development Core Team, Vienna, Austria), and P<0.05 was considered statistically significant.
Results Between January 2012 and December 2017, there were 40 533 cases of ICU admissions in total. Among these 10 135 duplicate admissions, 5440 paediatric patients (<18 yr), 30 cases with an unclear medical history of preadmission opioid usage, and 6519 patients who were admitted to the ICU 24 h after hospital admission were excluded. Overall, 18 409 patients were included in the analysis, of which 757 (4.1%) were chronic
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opioid users and 17 652 (95.9%) were opioid-naı¨ve patients (Fig. 1). The baseline characteristics of all patients are outlined in Supplementary Table S2. Of the analysed patients, 1914 (10.4%) died within 90 days of ICU admission. The mean dose of MEDD (SD) among the chronic opioid users was 12.5 (21.0) mg. After propensity score matching to balance the two groups, a total of 2990 patients (757 chronic opioid users and 2233 opioid-naı¨ve patients) were included in the final analysis. The balance between the two groups before and after propensity score matching is shown in Table 1 and Supplementary Fig. S1. The ASD between the two groups after propensity score matching was below 0.1, demonstrating a
good balance. The distribution of the propensity scores between the two groups became similar after propensity score matching (Supplementary Fig. S2).
Ninety-day mortality and preadmission chronic opioid usage in propensity score-matched cohort In the propensity matched cohort (Table 2), 90-day mortality in chronic opioid users was 28.8% (218/757), whereas that in opioid-naı¨ve patients was 17.0% (379/2233). In the GEE model, the chronic opioid users were at a 1.90-fold higher odds of 90
Table 1 Comparison of characteristics between preadmission chronic opioid user and opioid-naı¨ve patients before and after propensity score matching. Data are presented as n (%) or mean value (standard deviation). *The patients in the medical aid beneficiary program are those who are classified to have low income, and most of their hospital charges are paid by the government. Meanwhile, for the patients in the national health insurance program, approximately two-thirds of their hospital charges are covered by the government. ASD, absolute value of standardised mean difference; LC, liver cirrhosis. Variables
Sex, male Age, yr BMI, kg m2 Insurance type* National health insurance program Medical aid beneficiary program Marital status Never married Married or living together Divorced or separated Widowed Occupation Office worker Licensed job House work Self-employed Student, military, or labourer Unemployed Highest educational attainment Lower than high school More or equal to high school, lower than college More than or equal to college Comorbidities at hospital admission Hypertension Diabetes mellitus Coronary artery disease Cerebrovascular disease Chronic obstructive lung disease Liver disease (LC, hepatitis, fatty liver) Chronic kidney disease Anaemia (haemoglobin <10 g dl1) Cancer Complaints of cancer pain at hospital admission Complaints of non-cancer pain at hospital admission Year of ICU admission 2012 2013 2014 2015 2016 2017
Before propensity score matching (n¼18 409)
After propensity score matching (n¼2990)
Chronic opioid Opioid naı¨ve ASD Chronic opioid user n¼757 patients (n¼17 652) user (n¼757)
Opioid naı¨ve patients (n¼2233)
ASD
395 (52.2) 67.9 (12.8) 23.2 (3.2)
10 524 (59.6) 62.4 (15.7) 24.0 (3.4)
395 (52.2) 67.9 (12.8) 23.2 (3.2)
1169 (52.4) 67.7 (14.2) 23.3 (3.3)
0.02 <0.01 0.05 0.02
701 (92.6) 56 (7.4)
17 073 (96.7) 579 (3.3)
701 (92.6) 56 (7.4)
2073 (92.8) 160 (7.2)
29 (3.8) 589 (77.8) 30 (4.0) 109 (14.4)
1176 (6.7) 14 367 (81.4) 524 (3.0) 1585 (9.0)
29 (3.8) 589 (77.8) 30 (4.0) 109 (14.4)
85 (3.8) 1735 (77.7) 100 (4.5) 313 (14.0)
49 (6.5) 29 (3.8) 189 (25.0) 58 (7.7) 63 (8.3) 369 (48.7)
3608 (20.4) 932 (5.3) 3596 (20.4) 1987 (11.3) 1923 (10.9) 5606 (31.8)
49 (6.5) 29 (3.8) 189 (25.0) 58 (7.7) 63 (8.3) 369 (48.7)
152 (6.8) 105 (4.7) 559 (25.0) 186 (8.3) 187 (8.4) 1044 (46.8)
362 (47.8) 221 (29.2)
6679 (37.8) 4908 (27.8)
362 (47.8) 221 (29.2)
1049 (47.0) 647 (29.0)
174 (23.0)
6065 (34.4)
174 (23.0)
537 (24.0)
339 (44.8) 76 (10.0) 18 (2.4) 20 (2.6) 31 (4.1) 34 (4.5) 152 (20.1) 384 (50.7) 352 (46.5) 135 (17.8)
7578 (42.9) 1526 (8.6) 471 (2.7) 705 (4.0) 682 (3.9) 444 (2.5) 2849 (16.1) 3702 (21.0) 2134 (12.1) 523 (3.0)
0.04 0.05 0.02 0.08 0.01 0.04 0.20 0.51 0.69 0.39
339 (44.8) 76 (10.0) 18 (2.4) 20 (2.6) 31 (4.1) 34 (4.5) 152 (20.1) 384 (50.7) 352 (46.5) 135 (17.8)
996 (44.6) 213 (9.5) 50 (2.2) 63 (2.8) 106 (4.7) 96 (4.3) 448 (20.1) 1056 (47.3) 976 (43.7) 320 (14.3)
0.01 <0.01 <0.01 0.01 0.01 0.01 0.04 0.02 0.04 0.04
53 (7.0)
1893 (10.7)
0.15
53 (7.0)
164 (7.3)
0.02
51 (6.7) 81 (10.7) 127 (16.8) 129 (17.0) 171 (22.6) 198 (26.2)
1998 2358 2978 3072 3412 3834
51 (6.7) 81 (10.7) 127 (16.8) 129 (17.0) 171 (22.6) 198 (26.2)
180 245 377 388 509 534
0.15 0.43 0.24 0.16
0.15
0.03
0.34
0.04
0.20
0.03
0.10 (11.3) (13.4) (16.9) (17.4) (19.3) (21.7)
0.02 (8.1) (11.0) (16.9) (17.4) (22.8) (23.9)
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Table 2 Generalised estimating equation model for 90-day mortality before and after propensity score matching. *A binomial distribution and logistic function was assumed for the dependent variable (90-day mortality) in generalised estimating equation model. Additionally, robust estimator and independent structure were used for covariance matrix and working correlation matrix, respectively. CI, confidence interval. Variable Entire cohort (unadjusted) Preadmission opioid naı¨ve patients Preadmission chronic opioid user Propensity score-matched cohort (adjusted) Preadmission opioid naı¨ve patients Preadmission chronic opioid user
90-day mortality rate
Odds ratio* (95% CI)
P-value
9.6% (1696/17 652) 28.8% (218/757)
1 3.81 (3.23e4.49)
<0.001
17.0% (379/2233) 28.8% (218/757)
1 1.90 (1.57e2.31)
<0.001
day mortality than the opioid-naı¨ve patients in the propensitymatched cohort (OR¼1.90; 95% CI, 1.57e2.31; P<0.001; Table 2).
Ninety-day mortality and preadmission chronic opioid usage in the entire cohort A multivariable logistic regression model for the entire cohort showed that the chronic opioid users were at a 2.20-fold higher odds of 90-day mortality than the opioid-naı¨ve patients (OR¼2.20; 95% CI, 1.81e2.66; P<0.001; Fig. 2 and Table 3). In a sensitivity analysis, A 10 mg increase in the MEDD resulted in a 1.17-fold higher odds of 90-day mortality among the chronic opioid users, in the entire cohort (OR¼1.17; 95% CI, 1.04e1.33; P¼0.010; Table 3). Chronic opioid usage showed interactions with cancer, cancer pain, non-cancer pain, and CKD for 90-day mortality. The results of the subgroup analysis are presented in Supplementary Table S3. The chronic opioid users were at a 3.67-fold higher odds of 90-day mortality than the opioid-naı¨ve patients in cancer patients (OR¼3.67; 95% CI, 2.83e4.77; P<0.001), whereas no significant difference was seen in the non-cancer patients (P¼0.772). Additionally, the chronic opioid users were at a 2.77-fold higher odds of 90-day mortality than the opioid-naı¨ve patients in non-CKD patients (OR¼2.77; 95% CI, 2.23e3.44; P<0.001), whereas no significant difference was seen in the CKD patients (P¼0.787). The results of the univariable logistic regression model including all covariates for 90-day mortality are presented in Supplementary Table S4.
Overall survival time before and after propensity score matching The KaplaneMeier curve before and after propensity score matching showed that the overall survival was significantly shorter in the chronic opioid user group than in the opioidnaı¨ve group (both P<0.001 by log-rank test; Fig. 3).
Discussion This study showed that preadmission chronic opioid use was associated with greater 90-day mortality and shorter overall survival time after ICU admission. The strength of this association increased with increasing dose of MEDD. Additionally, this association was more evident in preadmission cancer and non-CKD patients. This study, which was conducted in a heterogeneous population of critically ill patients from mixed ICUs, not only attempted to minimise the confounders by propensity score matching but also confirmed that similar trends were observed in a multivariable logistic regression
model for the entire cohort. In addition to physical characteristics and comorbidities, we included socioeconomic status as an important covariate based on studies that demonstrated an association between socioeconomic factors and chronic opioid use.20 One of the possible reasons for the higher 90-day mortality in the chronic opioid users is the impact of opioids on the patient’s immunity which leads to an increased risk of infections. Immunity is the defensive host mechanism against pathogenesis. Immune responses in critically ill patients are suppressed21 owing to dysregulation of their innate and adaptive immune systems.22 Immunosuppression, which is known to be induced by long-term opioid therapy, can, therefore, be a key risk factor, resulting in increased rates of mortality owing to infections, in critically ill patients.23,24 Recent studies have reported that opioid users exhibit an increased risk of serious infections,11,12,25 which support the findings of this study. Patients admitted to ICUs are exposed to novel ICUacquired infections even if the initial admission was not for an infection. This is another key independent risk factor of hospital mortality.26,27 Considering that the propensity score matching was done to balance the characteristics of the two groups at the time of ICU admission, the findings of this study suggest that chronic opioid users may have been exposed to new nosocomial infections during their stay in the ICU. Overall, we demonstrate that although the chronic opioid users admitted to ICUs because of infections were likely to exhibit poorer prognosis than the opioid-naı¨ve patients; they were also more likely to contract new nosocomial infections. The endocrine effects of long-term opioid therapy should also be considered when interpreting the study outcomes. Long-term opioid use suppresses the hypothalamicepituitary functions and consequently induces hormonal abnormalities.28 A previous study has reported that chronic fentanyl application induces severe adrenal insufficiency,29 which is believed to be the most severe complication from suppression of hypothalamicepituitary functions. Critically ill patients often show adrenal insufficiency, which can lead to suppressed stress responses.30 Therefore, the findings of this study may have been influenced by the hormonal abnormalities that may or may not have been existent in chronic opioid users. Nonetheless, as there is inadequate information on how long-term opioid administration impacts hormonal activities and its effects on the prognosis of critically ill patients, additional studies should be performed in the future to evaluate these effects.
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Fig. 2. Odds ratios of all individual variables from multivariable logistic regression model in entire cohort. Occupation: (1) licensed job vs office worker; (2) house work vs office worker; (3) self-employed vs office worker; (4) student, military, or labourer vs office worker; and (5) unemployed vs office worker. Highest educational attainment: (1) more than or equal to high school, lower than college vs lower than high school; and (2) more than or equal to college vs lower than high school. Marital status: (1) married or living together vs never married; (2) divorced or separated vs never married; and (3) widowed vs never married. Adm year: 2012. Adm, admission; CAD, coronary artery disease; CI, confidence interval; CKD, chronic kidney disease; COPD, chronic obstructive lung disease; CVD, cerebrovascular disease; MEDD, morphine equivalent daily dose; OR, odds ratio.
Additionally, we should consider the cardiovascular effects of long-term opioid administration. Although most opioids exhibit little direct negative effects on cardiac contractility, it can induce cardiac dysfunction when used in parallel with certain medications, such as benzodiazepines.31 In this study, most of the chronic opioid users persistently used long-acting opioids, to avoid opioid withdrawal. Therefore, these patients may have had more cardiac dysfunctions than the opioid-naı¨ve patients, which may have affected the outcomes of this study. In fact, a previous retrospective study showed a greater association between increased cardiovascular outcomes (including myocardial infarction) and chronic opioid users than in the general population.32 However, there is little evidence supporting the association between the chronic opioid administration and increased risk of cardiacrelated adverse effects, and additional studies are required to evaluate it. The results of the subgroup analysis in patients with preadmission cancer or CKD were also notable in this study. In cancer patients, higher opioid usage or requirement has been shown to be an indicator of an advanced cancer stage or shortened survival.33,34 Therefore, there might have been more advanced cancer patients in the chronic opioid user group than in the opioid-naı¨ve group, which might have influenced the results in this study. Considering opioids are
known to cause infections in advanced cancer patients,35 chronic opioid use might be a significant risk factor for the development of infections in critically ill and cancer patients. Therefore, our results suggest that cancer patients who take opioids before admission are likely to have higher mortality because of various reasons including cancer progression and infections. In other words, critically ill and cancer patients who are chronic opioid users should be very carefully managed by the ICU physicians. Our findings in CKD patients indicate that clinicians usually try not to prescribe long-acting opioid analgesics for these patients because of the prolonged excretion of opioids,36 which in turn may influence the results of this study. However, further studies are required to clarify the complex relationship between opioids and renal function.37 Despite several intriguing findings, this study has several limitations. First, the retrospective nature of this study may have resulted in selection bias and thereby poorer data accuracy or quality than a prospective study. Second, this study was performed at a single centre, which limits the generalisability of the findings. Third, the standard conversion ratio has been utilised to assess different kinds of opioids used by the patients comprehensively. Lastly, we performed both propensity score matching and multivariable adjustment to reduce the effect of confounders in this study. However, the
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Table 3 Multivariable logistic regression analysis for 90-day mortality after ICU admission in entire cohort. Model fitting information in multivariable logistic regression model in entire cohort for 90-day mortality: e2log likelihood, 9783.7; c2, 2503.2; df, 31; P<0.001. Model fitting information in multivariable logistic regression model of sensitivity analysis for 90-day mortality in entire cohort: 2log likelihood, 9783.7; c2, 2503.2; df, 31; P<0.001. *MEDD of preadmission chronic opioid user was calculated using standard morphine conversion ratio in Table S1. CI, confidence interval; CKD, chronic kidney disease; MEDD, morphine equivalent daily dose; Preadm, preadmission. Variable
Multivariable model
Entire cohort (n¼18 409) Preadm opioid-naı¨ve patient Preadm chronic opioid user Sensitivity analysis in entire cohort (n¼18 409) Preadm opioid-naı¨ve patient Preadm chronic opioid user MEDD* (per 10 mg increase) Interaction in multivariable logistic regression model Comorbidity of preadmission cancer Preadm opioid-naı¨ve patient* non-cancer Preadm chronic opioid user* cancer Cancer pain before hospital admission Preadm opioid-naı¨ve patient* non-cancer pain Preadm chronic opioid user* cancer pain Comorbidity of preadmission CKD Preadm opioid-naı¨ve patient* non-CKD Preadm chronic opioid user* CKD Highest educational attainment Preadm opioid-naı¨ve patient* Lower than high school Preadm chronic opioid user* More than or equal to high school, lower than college Preadm chronic opioid user* More than or equal to college
Odds ratio (95% CI)
P-value
1 2.20 (1.81e2.66)
<0.001
1 1.79 (1.40e2.29) 1.17 (1.04e1.33)
<0.001 0.010
1 3.64 (2.33e5.70)
<0.001
1 0.48 (0.28e0.83)
0.009
1 0.36 (0.23e0.58)
<0.001
1 1.26 (0.82e1.94) 2.09 (1.34e3.27)
0.297 0.001
Fig. 3. KaplaneMeier curve of the overall survival time after ICU admission between the chronic opioid users and opioid naı¨ve patients, before (a) and after (b) propensity score matching.
ORs obtained from the propensity-matched cohort and those based on the entire cohort, adjusting for all covariates, are not directly comparable, because of the non-collapsibility of the OR. Therefore, the two results should be interpreted independently. Furthermore, propensity score matching and multivariable adjustment can only reduce the effect of known confounders. Considering that there might be unmeasured
confounders and selection bias, the observed results in this study may have been affected. In conclusion, this study showed that preadmission chronic opioid use is associated with increased 90-day mortality, as well as shorter overall survival time in critically ill adult patients. Additionally, this association was more evident in cancer patients and non-CKD patients.
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Authors’ contributions Study design: TKO. Interpretation of data: TKO. Drafting of the manuscript: TKO. Data collection: IAS, JHL, CL, YTJ, HJB, YHJ. Data analysis: HJJ. All authors gave approval for the final version of the manuscript.
Declaration of interest
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The authors declare that they have no conflicts of interest.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.bja.2019.03.032.
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