Abstract
This nested case-control study examines the association of risk of infection with opioid use in immunocompromised patients.
In a recent article, Edelman and colleagues1 identified an association between prescribed opioids and risk for community-acquired pneumonia among patients with and without HIV infection. This is of concern for immunocompromised patients receiving opioids for cancer pain. We used the prospective Cologne Cohort of Neutropenic Patients (NCT01821456) to confirm this observation in patients with chemotherapy-induced neutropenia.
Methods
The study was approved by the ethics committee of the University Hospital of Cologne, which waived written informed consent. We conducted a nested case-control study based on patients with cancer with an absolute neutrophil count of less than 500/μL treated between January 2008 and December 2018. A multivariable conditional logistic regression was performed resembling the analysis by Edelman et al1 (excluding vaccines; Veterans Aging Cohort Study score; benzodiazepines, including antibacterial/antifungal prophylaxis; and Charlson comorbidity index2). Opioids were categorized as previously described,1 adding buprenorphine to the nonimmunosuppressive groups3 as well as piritramide and tilidine to the unknown group. Case study patients were inpatients with hospital-acquired infection (fever ≥38 °C, blood stream infection, pneumonia, or intensive care unit admission) and no baseline infection. Controls were matched 1:1 by age (aged within 5 years), sex, underlying disease, and time of admission (within 1 year) using the Gower coefficient. Sensitivity analyses were performed including (1) the effect of the opioid treatment duration, (2) the effect of preexisting outpatient opioid treatment, and (3) the association of opioid treatment with pneumonia only instead of the composite end point.
Results
We identified 2135 matched pairs with complete data sets, based on 3942 cases and 2949 controls in the cohort database. Opioids were administered in 371 of 2135 patient cases (17.4%) and 354 of 2135 patient controls (16.6%) (Table). No association of opioid administration was observed with the risk for infection (adjusted odds ratio [aOR], 0.84; 95% CI, 0.64-1.11 with immunosuppressive properties and aOR, 1.11; 95% CI, 0.86-1.43 without immunosuppressive properties) (Table). Patients with opioids from the unknown category, mostly meperidine (33 of 50 patient cases; 14 of 22 patient controls), showed an increased infection risk.
Table. Patient Characteristics and Conditional Logistic Regression for Administered Opioids and Infection Riska.
| Characteristic | No. (%) | Univariate Analysis, OR (95% CI) | P Value | Multivariable Analysis, OR (95% CI) | P Value | |
|---|---|---|---|---|---|---|
| Incidence of Infectionb | No Incidence of Infectionb | |||||
| Total, No./total (%)c | 2135/3942 (54.2) | 2135/2949 (72.4) | ||||
| Observation, median (IQR), d | 9 (3-14) | 11 (7-19) | ||||
| Age, median (IQR), y | 54 (41-65) | 54 (40-65) | … | … | … | … |
| Sex | ||||||
| Male | 1322 (61.9) | 1322 (61.9) | … | … | … | … |
| Female | 813 (38.1) | 813 (38.1) | … | … | … | … |
| Underlying disease | ||||||
| Lymphoma | 918 (43.0) | 918 (43.0) | … | … | … | … |
| Acute myeloid leukemia | 318 (14.9) | 318 (14.9) | … | … | … | … |
| Acute lymphatic leukemia | 175 (8.2) | 175 (8.2) | … | … | … | … |
| Chronic lymphatic leukemia | 159 (7.4) | 159 (7.4) | … | … | … | … |
| Solid tumor | 156 (7.3) | 156 (7.3) | … | … | … | … |
| Otherd | 181 (8.4) | 181 (8.4) | … | … | … | … |
| Opioids | ||||||
| Any | 371 (17.4) | 354 (16.6) | … | … | … | … |
| None (reference) | 1764 (82.6) | 1781 (83.4) | … | … | … | … |
| Immunosuppressive propertiese | 128 (6.0) | 146 (6.8) | 0.88 (0.67-1.15) | .37 | 0.84 (0.64-1.11) | .22 |
| Nonimmunosuppressive propertiesf | 193 (9.0) | 186 (8.7) | 1.08 (0.84-1.38) | .55 | 1.11 (0.86-1.43) | .43 |
| Unknown immunosuppressive propertiesg | 50 (2.3) | 22 (1.0) | 2.49 (1.45-4.25) | <.001 | 2.59 (1.49-4.48) | <.001 |
| Antibiotic prophylaxis | 823 (38.5) | 1037 (48.6) | 0.63 (0.56-0.72 | <.001 | 0.61 (0.53-0.70) | <.001 |
| Antifungal prophylaxis | 1141 (53.4) | 1152 (54.0) | 0.98 (0.86-1.11) | .73 | 1.04 (0.92-1.19) | .52 |
| Glucocorticosteroids | 1124 (52.6) | 1089 (51.0) | 1.07 (0.95-1.21) | .27 | 1.13 (0.99-1.29) | .06 |
| Smoking (vs none and unknown) | 1959 (91.8) | 1944 (91.1) | ||||
| Current | 104 (4.9) | 107 (5.0) | 0.95 (0.69-1.30) | .73 | 0.90 (0.65-1.26) | .56 |
| Former | 72 (3.3) | 84 (3.9) | 0.82 (0.57-1.17) | .27 | 0.78 (0.54-1.13) | .19 |
| Diabetes mellitus | 233 (10.9) | 250 (11.7) | 0.91 (0.73-1.12 | .36 | 0.84 (0.67-1.04) | .11 |
| Hepatitis C | 43 (2.0) | 21 (1.0) | 2.22 (1.27-3.88) | .005 | 2.29 (1.30-4.07) | .004 |
| HIV | 92 (4.3) | 97 (4.5) | 0.92 (0.64-1.32) | .64 | 0.93 (0.62-1.39) | .72 |
| Chronic obstructive pulmonary disease | 82 (3.8) | 79 (3.7) | 1.06 (0.73-1.53) | .78 | 0.99 (0.67-1.46) | .95 |
| Congestive heart failure | 15 (0.7) | 20 (0.9) | 0.74 (0.37-1.47) | .39 | 0.72 (0.36-1.45) | .35 |
| Stroke | 12 (0.6) | 9 (0.4) | 1.33 (0.56-3.16) | .51 | 1.26 (0.52-3.06) | .62 |
| Pain-related diagnosish | 263 (12.3) | 222 (10.4) | 1.22 (1.00-1.48) | .046 | 1.26 (1.03-1.54) | .02 |
| Alcohol-related diagnosis | 15 (0.7) | 8 (0.4) | 2.00 (0.81-4.96) | .13 | 2.39 (0.93-6.18) | .07 |
| Charlson Comorbidity Index, median (IQR)i | 2 (2-6) | 2 (2-6) | 1.01 (0.99-1.03) | .34 | 1.01 (0.99-1.04) | .33 |
Abbreviations: HIV, human immunodeficiency virus; IQR, interquartile range; OR, odds ratio; and ellipses, variables used for matching, not included in regression analysis.
Clinical characteristics based on presence of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) Code and/or variable defined in the Cologne Cohort of Neutropenic Patients database (eg, medication, smoking, and alcohol). Hepatitis C defined by positive antibody or detectable RNA.
Infection defined as body temperature of 38°C or higher, bloodstream infection, pneumonia, or admission to intensive care unit.
1:1 matching by age (aged within 5 years), sex, underlying disease, and time of admission (within 1 year) using the Gower coefficient.
Myelodysplastic syndrome, chronic myeloid leukemia, other (eg, HIV).
Immunosuppressive properties: diamorphine, dihydrocodeine, fentanyl, morphinhydrochloride, morphine sulfate, sufentanil, sufentanil citrate.1
No immunosuppressive properties: buprenorphine,3 hydrocodone, hydromorphone, oxycodone, oxycodone/naloxone, tramadol.1
Unknown immunosuppressive properties: levomethadone, methadone, meperidine, tapentadol,1 piritramide, tilidine, tilidine/naloxone.
Defined according to Edelman et al.1
10-year survival probability using 0.983exp(CCI × 0.9), Charlson comorbidity index, determined using ICD-10 codes of diagnoses.2
Sensitivity analysis revealed an association of longer opioid treatment with increased infection risk (aOR, 0.95; 95% CI, 0.92-0.98 with immunosuppressive properties and aOR, 0.98; 95% CI, 0.96-0.99 without immunosuppressive properties). Overall results were stable after excluding patients with preexisting opioid treatment from multivariable analysis (censored patient cases n = 143, patient controls n = 141; aOR, 0.97; 95% CI, 0.67-1.42 and aOR, 1.26; 95% CI, 0.91-1.76, respectively). There was also no relevant change when using only pneumonia as a clinical end point (patient cases n = 1072; aOR, 1.00; 95% CI, 0.75-1.34 with immunosuppressive properties and aOR, 1.10; 95% CI, 0.79-1.53 without immunosuppressive properties).
Discussion
In our analysis, we were not able to identify an increased risk for pneumonia or any infection by any type of opioid in severely immunocompromised patients with an overall infection risk of more than 50% during the observational period. An association of infection with meperidine use is probably based on this drug being a standard treatment for severe mucositis, an acknowledged infection risk. In contrast to the study by Edelman et al,1 we observed patients during a rather short period of severe immunosuppression, and the previously observed effects of opioids may therefore not have had the same effect in our cohort. The severity of immunosuppression and the better supervision of inpatients in our cohort compared with outpatients in the study by Edelman et al1 may have masked a comparably smaller effect of opioids.
A strength of our study is the higher homogeneity of patients regarding the indication of opioid treatment (cancer pain). Despite the great efforts of Edelman et al1 to control confounders, opioid use in veterans with and without HIV infection is a powerful surrogate for a large range of detrimental health conditions (opioid use for musculoskeletal and bone diseases with decreased mobility4 or chronic cough [dihydrocodeine]).5
Further observation, ideally in a controlled prospective trial, is warranted before adapting current standards in cancer care.
References
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