Substance Use Disorder as Risk Factor for Intubation in Angioedema: A Nationwide Cohort Study
Case Western Reserve University School of Medicine, Cleveland, Ohio, U.S.A.
Department of Otolaryngology‐Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, Ohio, U.S.A.
Abstract
Objectives
Individuals with angioedema (AE) are at high risk for airway compromise and often require endotracheal intubation. Patient factors predisposing one to airway compromise are not well described. The objective of this study is to examine whether substance use disorder (SUD) in patients with AE is associated with need for airway intervention.
Methods
This population‐based retrospective cohort study compared AE patients with SUD versus propensity‐matched control groups. Outcomes were hospitalization, intubation, and tracheotomy. Using the TriNetX National Database, this study included 28,931 patients with SUD and 117,509 patients without SUD who presented with AE.
Results
Among patients with AE, those with each subtype of SUD (alcohol, cannabis, cocaine, tobacco, and opioids) were found to have higher risk of severe AE compared to propensity‐matched non‐SUD cohorts. Rate of hospitalization after cohort matching ranged from 20.4% for tobacco use disorder to 30.4% for cocaine use disorder, all significantly higher than the 8.0% in a population without SUD. Each SUD subtype was associated with a higher rate of intubation compared with matched non‐SUD groups, with cannabis use disorder having the highest relative risk (RR) of 3.67 (95% CI: 2.69–5.02). Tobacco (RR = 2.45, 95% CI: 1.79–3.34) and alcohol (RR = 2.82, 95% CI: 1.73–4.58) use disorders were both associated with significantly higher risk of tracheotomy.
Conclusion
These data suggest that patients with SUD, regardless of subtype, and after propensity matching for demographics and comorbidities are at higher risk for adverse outcomes when presenting with AE. This study highlights clinically relevant predictors of airway compromise.
Level of Evidence
3 Laryngoscope, 135:45–49, 2025
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Keywords: angioedema, intubation, national database, substance use disorder
Article notes
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Revised 2024 May 24; Received 2024 Jan 25; Accepted 2024 Jun 28; Issue date 2025 Jan.
INTRODUCTION
Angioedema (AE) is characterized by a rapid swelling of dermal, subcutaneous, or submucosal tissue, most commonly affecting the face and mucosa of the head and neck. 1 While symptoms of AE are generally self‐limited, involvement of the larynx can result in life‐threatening airway obstruction requiring emergency intervention. 2
Pathophysiologically, AE occurs due to increased vascular permeability and excessive fluid extravasation into surrounding tissue, and the process is classified by its primary inflammatory mediator: either histamine or bradykinin. 3 Histaminergic AE is the result of mast cell degranulation and is most often caused by an acute IgE‐mediated allergic reaction, which can be triggered by allergies to certain foods, insect‐stings, latex, drugs, or other stimuli. 4 Histaminergic AE can also be the result of non‐IgE‐mediated “pseudoallergic” responses, which are caused by direct mast cell stimulation at the MRGPRX2 receptor by certain drugs such as opioids and radiocontrast media. 5 Additional etiologies of mast cell‐related AE include aspirin or NSAID‐associated hypersensitivity reactions, as well as chronic urticaria. 6 , 7 , 8
Major etiologies of bradykinin‐mediated AE include drug action and C1‐esterase deficiency. Angiotensin‐converting enzyme (ACE) inhibitors, which inhibit the breakdown of bradykinin, are the most common cause of drug‐induced AE, with studies estimating that 0.1%–0.7% of patients taking ACE inhibitors develop AE. 2 , 6 , 9 Other medications including angiotensin receptor blockers (ARBs), neprilysin inhibitors, dipeptidyl peptidase 4 (DPP‐4) inhibitors, calcium channel blockers (CCBs), mTOR inhibitors, and fibrinolytic agents have also been associated with an increased risk of AE, especially when used in combination with ACE inhibitors. 10 , 11 , 12 , 13 , 14 , 15 C1‐inhibitor deficiency exists in the hereditary form, as in hereditary angioedema (HAE), or in the acquired form, most often seen in elderly patients with an associated lymphoproliferative or autoimmune disorder. 16 , 17
AE accounts for approximately 117,000 emergency department (ED) visits per year. 18 A 2019 study estimated the rate of AE‐related hospitalization in the US population at 4.4 per 100,000. 19 Tongue, glottis, or laryngeal swelling can put the patient with AE at risk for airway compromise and are frequent indications for hospitalization. 20 Therefore, hospitalization can be used as a proxy measure of the severity of AE. Typical initial medical management of undifferentiated AE includes antihistamines, corticosteroids, and epinephrine, although these are less likely to be effective in bradykinin‐mediated AE. 21 Evaluation by nasopharyngeal laryngoscopy is recommended for patients with suspected upper airway involvement. In severe cases of airway obstruction, intubation or cricothyrotomy/tracheotomy may be necessary. 22
Kostis et al. published results from a multicenter, randomized, double‐blind, active‐controlled trial studying 25,302 patients with hypertension that compared the safety and efficacy of omapatrilat, an inhibitor of both neprilysin and ACE, to the ACE inhibitor enalapril. Of the 12,557 patients in the enalapril group, 0.7% developed AE, compared with 2.2% in the omapatrilat group. 23 Analysis from the so‐named OCTAVE trial found populations with a higher risk for developing AE while on enalapril included those older than 65 years, African Americans, women, and patients with a history of drug rash or seasonal allergies. 10 , 24 , 25 Women, smokers, and patients with a prior history of AE have also been shown to have a higher risk of developing AE. 24 However, factors that contribute to the development of particularly severe presentations of AE are not yet well understood.
Substance use disorders (SUDs), which involve excessive use of alcohol, nicotine, cannabis or other illicit substances, have a lifetime prevalence of 2%–9% in the United States. 26 Recent studies have shown that SUD is associated with a systemic inflammatory state, which may result in increased baseline bradykinin levels in these patients. 27 Additionally, smokers were shown to have lower serum DPP‐4 activity leading to decreased bradykinin breakdown, which has been proposed as a mechanism for increased rates of AE in this population. 28 We hypothesized that SUD is a risk factor for severe presentations of AE and may lead to an increased rate of adverse outcomes. In this national database retrospective cohort study, we estimated the risk of hospitalization, intubation, and tracheotomy as a result of AE among patients with various SUDs compared with matched individuals without SUDs.
MATERIALS AND METHODS
Study Population
We used the TriNetX Analytics network platform, which allows access to de‐identified data of 84.5 million unique patients from 61 health care organizations in the United States, among whom 79.1 million (age ≥18 years) had medical encounter(s) with health care organizations since 2008. 29
The study population comprised 146,440 individuals who fulfilled the following inclusion criteria: (a) they had medical encounter(s) with health care organizations between August 1, 2008 and August 1, 2023; (b) they had presented with angioneurotic edema; and (c) they did not have a diagnosis of hereditary AE.
The AE study population included 28,931 patients with SUD and 117,509 patients without SUD. Among the population with SUD, 7772 patients had a diagnosis of alcohol use disorder, 3307 of cannabis use disorder, 2248 of cocaine use disorder, 2437 of opioid use disorder, and 23,150 of tobacco use disorder.
TriNetX Analytics provides web‐based real‐time secure access to patient electronic health records from hospital, primary care, and specialty treatment providers, covering diverse geographic locations, age groups, ethnic groups, and income levels. Though the data are de‐identified, end‐users can use the platform built‐in functions working on patient‐level data for cohort selection and matching, analyzing incidence and prevalence of events in a cohort and comparing characteristics and outcomes between matched cohorts.
The status of AE was based on the ICD‐10 diagnosis code of “Angioneurotic edema” (T78.3). The status of emergency room or hospital visit was based on the Current Procedural Terminology (CPT) code for Hospital Inpatient and Observation Care Services (1013659) or the TriNetX visit identifiers “Emergency,” “Inpatient Encounter,” or “Inpatient Non‐acute.”
The status of SUD was based on the ICD‐10 diagnosis code of “mental and behavioral disorders due to psychoactive substance use” (F10‐F19). The status of alcohol use disorder was based on the ICD‐10 diagnosis code of “alcohol related disorders” (F10); that of cannabis use disorder on the code of “cannabis related disorders” (F12); that of cocaine use disorder on the code of “cocaine related disorders” (F14); that of opioid use disorder on the code of “opioid related disorders” (F11); and that of tobacco use on the codes of “nicotine dependence” (F17) or “tobacco use” (Z72.0). Other subtypes of SUD, such as methamphetamine use disorder, were not examined due to their small sample sizes.
For outcome measures, the status of hospitalization was based on the CPT code “hospital inpatient services” (1013659), the status of endotracheal intubation was based on the CPT code “intubation, endotracheal, emergency procedure” (31500), and the status of tracheostomy was based on the CPT codes “tracheostomy, emergency procedure” (1005887) and “tracheostomy, planned” (1014613).
Procedures
We tested whether AE patients with SUD had higher risk for an adverse outcome, defined as hospitalization, intubation, or tracheostomy, compared with non‐SUD patients. Separate analyses were performed for alcohol use disorder, cannabis use disorder, cocaine use disorder, opioid use disorder, and tobacco use disorder.
The cohorts of SUD and non‐SUD patients, as well as the cohorts for subset comparisons, were created by propensity score matching for demographics (age, gender, ethnicity); adverse socioeconomic determinants of health (including “problems related to education and literacy”, “problems related to employment and unemployment”, “occupational exposure to risk factors”, and “problems related to housing and economic circumstances”, according to the ICD‐10); common lifetime comorbidities (hypertension, obesity, type 2 diabetes, and chronic respiratory diseases); and use of ACE inhibitor to match for potential cause of AE. All comorbidities and records of substance use were only tracked in the time window before the AE incident.
The outcomes were adverse events, defined as hospitalization, intubation, or tracheostomy within 14 days of presenting with AE. The TriNetX built‐in propensity score matching function was used (1:1 matching using a nearest neighbor greedy matching algorithm with a caliper of 0.25 times the standard deviation). The Risk Ratio (RR), a built‐in function in TriNetX, was used to describe the relative risk of adverse outcomes between propensity‐matched cohorts. Separate analyses were performed for SUD, SUD subtypes, and non‐SUD individuals.
RESULTS
The demographic characteristics of the patients with AE and the sample sizes as a function of SUD subtype are shown in Table I. Patients with SUD were older (mean age: 53.2 ± 14.3 years) than those without SUD (49.4 ± 20.3 years). There were more men in the SUD population (49% vs. 34%), and the percentage of African Americans was higher in the SUD (37%) than in the non‐SUD (24%) sample. The prevalence of adverse socioeconomic determinants of health was also higher in the SUD population than in patients without SUD (7% vs. 2%). AE patients with SUD had a higher lifetime prevalence of measured comorbidities and were more likely to be taking ACE inhibitors (all p < 0.001).
| AUD | CUD | Cocaine UD | OUD | TUD | SUD | Non‐SUD | |
|---|---|---|---|---|---|---|---|
| Total number of patients | 7808 | 3310 | 2252 | 2444 | 23,246 | 29,063 | 118,743 |
| Age (years, mean ± SD)* | 54.7 ± 13.1 | 46.3 ± 14.8 | 52.1 ± 11 | 51.3 ± 14.2 | 53.4 ± 14.3 | 53.2 ± 14.7 | 49.4 ± 20.3 |
| Gender (% male)* | 65 | 54 | 59 | 46 | 49 | 49 | 34 |
| Ethnicity (%) | |||||||
| White* | 47 | 35 | 19 | 48 | 46 | 47 | 55 |
| African American* | 38 | 50 | 62 | 38 | 38 | 37 | 24 |
| Hispanic/ Latino* | 4 | 4 | 3 | 5 | 4 | 4 | 7 |
| Asian* | 1 | 1 | 1 | 1 | 1 | 1 | 3 |
| Unknown | 11 | 11 | 15 | 11 | 12 | 24 | 27 |
| Adverse socioeconomic determinants of health (%)* | 11 | 17 | 18 | 15 | 7 | 7 | 2 |
| Lifetime medical conditions | |||||||
| Hypertension* | 72 | 64 | 77 | 70 | 64 | 63 | 35 |
| Obesity* | 25 | 30 | 28 | 36 | 27 | 27 | 16 |
| Type 2 Diabetes* | 24 | 25 | 32 | 32 | 25 | 24 | 14 |
| Chronic respiratory diseases* | 35 | 40 | 46 | 45 | 37 | 35 | 17 |
| On ACE inhibitors* | 50 | 43 | 54 | 47 | 42 | 41 | 19 |
Among the AE population, the risk of hospitalization ranged from 20.4% for tobacco use disorder to 30.4% for cocaine use disorder, all significantly higher than the 8.0% in the non‐SUD population (p < 0.001). The RRs for hospitalization between SUD and non‐SUD cohorts after propensity score matching for demographics (age, gender, and ethnicity), adverse socioeconomic determinants of health, and comorbid conditions remained significantly higher for all SUD subtypes, the highest being for alcohol and cannabis use disorders (RR = 1.85, 95% CI: 1.73–1.98 for alcohol; RR = 1.85, 95% CI: 1.68–2.04 for cannabis; RR = 1.79, 95% CI: 1.60–1.99 for cocaine; RR = 1.75, 95% CI: 1.67–2.04 for tobacco; and RR = 1.81, 95% CI: 1.62–2.03 for opioids) (see Table II).
| Cohort | Patients in Cohort | Risk in Cohort | Risk in Matched non‐SUD Cohort | Risk Ratio (95% CI) |
|---|---|---|---|---|
| AUD | 7797 | 25.18% | 13.62% | 1.85 (1.73, 1.98) |
| CUD | 3307 | 27.88% | 15.06% | 1.85 (1.68, 2.04) |
| CocaineUD | 2245 | 30.38% | 16.88% | 1.79 (1.60, 1.99) |
| OUD | 2444 | 27.37% | 15.39% | 1.81 (1.62, 2.03) |
| TUD | 22,860 | 19.71% | 11.51% | 1.75 (1.67, 2.04) |
| SUD (total) | 27,997 | 19.54% | 10.59% | 1.86 (1.78, 1.94) |
The Risk Ratios for intubation between SUD and non‐SUD cohorts after propensity score matching for also remained significantly higher for all SUD subtypes, the highest being for cannabis use disorder (RR = 2.75, 95% CI: 2.31–3.27 for alcohol; RR = 3.67, 95% CI: 2.69–5.02 for cannabis; RR = 2.96, 95% CI: 2.17–4.05 for cocaine; RR = 2.44, 95% CI: 2.44–2.78 for tobacco; and RR = 2.14, 95% CI: 1.89–3.51 for opioids) (see Table III).
| Cohort | Patients in Cohort | Risk in Cohort | Risk in Matched Non‐SUD Cohort | Risk Ratio (95% CI) |
|---|---|---|---|---|
| AUD | 7797 | 5.93% | 2.13% | 2.75 (2.31, 3.27) |
| CUD | 3307 | 5.44% | 1.51% | 3.67 (2.69, 5.02) |
| CocaineUD | 2245 | 6.73% | 2.27% | 2.96 (2.17, 4.05) |
| OUD | 2444 | 5.69% | 2.25% | 2.57 (1.89, 3.51) |
| TUD | 22,860 | 3.45% | 1.60% | 2.44 (2.14, 2.78) |
| SUD (total) | 27,997 | 3.50% | 1.30% | 2.78 (2.46, 3.15) |
The RRs for tracheotomy between SUD and non‐SUD cohorts after propensity score matching for were significantly higher for only alcohol and tobacco use disorders, the highest being for alcohol use disorder (RR = 2.82, 95% CI: 1.73–4.58 for alcohol; RR = 1.75, 95% CI: 0.82–3.55 for cannabis; RR = 1.6, 95% CI: 0.73–3.52 for cocaine; RR = 2.45, 95% CI: 1.79–3.34 for tobacco; and RR = 1, 95% CI: 0.42–2.40 for opioids) (see Table IV).
| Cohort | Patients in Cohort | Risk in Cohort | Risk in Matched Non‐SUD Cohort | Risk Ratio (95% CI) |
|---|---|---|---|---|
| AUD | 7797 | 0.80% | 0.30% | 2.82 (1.73, 4.58) |
| CUD | 3307 | 0.64% | 0.36% | 1.75 (0.82, 3.55) |
| CocaineUD | 2245 | 0.71% | 0.45% | 1.6 (0.73, 3.52) |
| OUD | 2444 | 0.41% | 0.41% | 1 (0.42, 2.40) |
| TUD | 22,860 | 0.59% | 0.32% | 2.45 (1.79, 3.34) |
| SUD (total) | 27,997 | 0.59% | 0.22% | 2.67 (1.99, 3.58) |
DISCUSSION
In this retrospective cohort study, the authors report that the overall risk for hospitalization among patients presenting with AE ranged from 20.4% for tobacco use disorder to 30.4% for cocaine use disorder, all significantly higher than the 8.0% in a population without SUD. The authors propose that hospitalization is an indicator of clinical severity in AE; therefore, all SUD subtypes in this study were associated with a higher risk for severe presentation of AE.
This study shows significant differences in adverse socioeconomic factors and comorbidities between the SUD group and non‐SUD group. After propensity matching for age, gender, ethnicity, adverse socioeconomic factors, and common comorbidities, the risk of hospitalization from AE remained significantly higher in the SUD subtype groups compared with the matched non‐SUD subtype groups. Although risk for hospitalization is likely dependent on a complex interaction of many factors, these results suggests that SUD independently increases risk of adverse outcomes in patients who develop nonhereditary AE.
The outcomes of intubation and tracheotomy were also studied using propensity‐matched SUD and non‐SUD groups. Each SUD subtype that was studied was associated with a higher risk of intubation following AE, ranging from RR of 2.44 in tobacco use disorder to RR of 3.96 in cannabis use disorder. A systemic inflammatory state caused by long‐term substance use may contribute to the increased risk of intubation. The outcome of tracheotomy, which may indicate that patients were difficult to intubate or were intubated for a prolonged period, was significantly higher only in the alcohol and tobacco use disorder groups compared with matched non‐SUD groups (RR = 2.82 and RR = 2.45, respectively).
To our knowledge, this is the first study looking specifically at individual SUDs as risk factors for severe AE. There are several studies in the literature exploring other risk factors or predictive clinical findings for adverse outcomes in AE. Loftus, et al. found in an 875 patient retrospective review that significant risk factors for severe cases of AE included older age, Hispanic race, ACEi‐induced AE type, American Society of Anesthesiologists class III or above, coexistent cardiopulmonary disease, and a positive smoking history. 30 They found that hospitalization or airway intervention was seen in 20.2% of smokers compared with 7.8% of nonsmokers. In comparison, our study saw 19.7% of smokers were hospitalized versus 11.5% of nonsmokers in propensity‐matched cohorts.
Kieu et al. found in a case series of 311 AE patients who were evaluated with flexible laryngoscopy that those with edema of the face, upper lip, or lower lip were at lower risk for airway intervention, while involvement of the tongue, soft palate, vallecula, aryepiglottic folds, and true vocal cords were at highest risk for airway intervention. 31 Additionally, they found that dysphagia, dysphonia, drooling, respiratory distress, and globus sensation were associated with higher risk of airway intervention. Clinical evaluation and direct visualization with laryngoscopy remain the mainstay of deciding whether to intubate.
Identifying high‐risk patients is important as the number of AE hospitalizations are increasing progressively, from 12,078 in 2001 to 18,050 in 2014 according to Shrestha, et al. 19 Further studies should examine if an increasing number of risk factors in an individual patient increases the risk of adverse events from AE.
Lochbaum et al. highlight the importance of concomitant medications that can increase risk of developing AE in patients also taking ACE inhibitors. Direct DPP‐4 inhibitors (i.e. gliptins) pose only a minor risk of AE development when taken alone, but may pose a higher risk when taken together with ACE inhibitors. 32 Similarly, mTOR inhibitors like sirolimus and everolimus, which are immunosuppressants typically used in transplant patients, have been implicated in increasing the risk of AE in patients taking ACE inhibitors. Byrd et al. compared 145 transplant patients who were taking ACE inhibitors versus non‐transplant, ACE inhibitor‐exposed controls and found that the transplant group was significantly more likely to develop AE. 28 Further studies should ask whether patients taking ACE inhibitors concomitantly with DPP‐4 inhibitors or immunosuppressants are at higher risk for severe cases of AE.
Strengths of this study include a large sample size which allows us to study a relatively infrequent outcome in specific patient populations (e.g., individual SUDs).
One limitation is that the ICD codes used by TriNetX do not categorize AE by etiology. For example, studying a cohort of ACE inhibitor AE patients may reveal high‐risk patients when prescribing ACE inhibitors. Furthermore, application of these data is difficult, as the laryngoscopy findings and clinical presentation are key for evaluating severity of AE in a time‐limited, practical setting.
CONCLUSION
This is the largest study to date studying SUD as a risk factor for severe forms of AE. Each SUD subtype studied was associated with a higher risk of hospitalization and intubation when presenting with AE. Physicians should be aware of certain risk factors that may require a higher acuity level of care, including history of tobacco and cannabis use.
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Footnote Group
BIBLIOGRAPHY
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