Risk of Hospital INpatient Opioid Overdose (RHINOO): A review of factors impacting naloxone administration in patients receiving opioids

doi:10.21203/rs.3.rs-4713521/v1

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Risk of Hospital INpatient Opioid Overdose (RHINOO): A review of factors impacting naloxone administration in patients receiving opioids

https://doi.org/10.21203/rs.3.rs-4713521/v1

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Purpose:
Opioid medications remain a common treatment for acute pain in hospitalized patients. This study aims to identify factors contributing to opioid overdose in the inpatient population, addressing the gap in data on which patients are at higher risk for opioid-related adverse events in the hospital setting.

Methods:
A retrospective chart review of inpatients receiving at least one opioid medication was performed at a large academic medical center from January 1, 2022, through December 31, 2022. Patients who received naloxone were designated as the study group, while those who received opioids without naloxone served as the control group. Suspected risk factors were included in a multivariable direct logistic regression model to identify patients at higher risk for opioid-related adverse events.

Results:
The review included 11,050 admitted patients who received an inpatient opioid, of whom 130 received naloxone. Analysis revealed that patients with creatinine clearance (CrCl) < 60 mL/min, co-administered benzodiazepine, body mass index (BMI) > 30 kg/m², underlying pulmonary disease, obstructive sleep apnea, chronic opioid use, and/or substance use disorder were at higher risk for requiring naloxone. These factors significantly influenced the likelihood and magnitude of in-hospital opioid overdose.

Conclusion:
These validated risk factors should be considered when administering opioid analgesics in the inpatient setting. Consideration should be given to reducing the dose and/or frequency of opioids in addition to the use of alternative analgesic modalities for patients with these risk factors to mitigate the risk of opioid-related adverse events. Incorporating these considerations into clinical practice can enhance patient safety and outcomes.

opioids

naloxone

adverse events

overdose

risk factors

risk management

In 1995, pain was deemed the fifth vital sign by Dr. James Campbell, former president of the American Pain Society, and has since been included in patient-reported outcomes tracked by hospitals (1). Not only does adequate pain control improve quality of life and patient satisfaction, but it has been shown to enhance clinical outcomes such as improved wound healing and immune function (2). While there are several modalities used to control pain, opioids continue to be the most utilized agent in the inpatient setting (3). However, due to the opioid epidemic, benefits of opioid-based pain control must be weighed against risks. Some of the risks include constipation, pruritis, rash, nausea, vomiting, withdrawal symptoms and overdosing, which in turn can lead to encephalopathy, respiratory depression and even death. Naloxone, an opioid antagonist, is an antidote for the rapid reversal of opioid overdose. While there is considerable literature on the utility of opioid risk tools in outpatient settings, limited information exists for the inpatient setting (2). To prevent inpatient hospital overdose, patients must be risk-stratified based on underlying conditions. By identifying these risk factors, advancements in patient safety in a hospital setting can be made.

The Joint Commission highlighted the risks of opioid therapy in 2012 through a Sentinel Event Alert entitled “Safe Use of Opioids in Hospitals,” which proposed strategies for identifying patients at considerable risk for opioid-induced respiratory depression (4). According to the literature, a range of factors can contribute to increased susceptibility to opioid overdose, including liver failure, renal failure, and increasing age (5-8). Hepatic impairment can greatly impact efficacy, metabolism, and elimination of several analgesic agents, including opioids, which can result in drug accumulation and increased adverse effects (5, 9). Similarly, patients with renal impairment face issues with metabolism and clearance of opioids, which can be problematic when trying to achieve adequate pain control and limit adverse effects (9, 10). Pain management in the geriatric population is additionally challenging. Geriatric patients are at higher risk for drug interactions given their increased rates of comorbid medical conditions, especially with medications such as central nervous symptom (CNS) depressants and inhibitors of the cytochrome P450 2D6 and 3A4 enzymes which can increase opioid serum levels (9, 10).

Many medications used concomitantly with opioids to achieve adequate pain control can contribute to adverse side effects, especially when it comes to respiratory depression. Membrane stabilizing agents, such as gabapentin, are often co-prescribed with opioids for the management of various neuropathic pain syndromes (11). While these drugs are typically seen as safe, they can be associated with drug-induced respiratory depression both on their own or when used concomitantly with opioids (11). This risk increases when other factors are considered, such as age, renal insufficiency, lung disease, and dosage (11). CNS depressants, such as muscle relaxants and benzodiazepines, are often used concomitantly with opioids for analgesia. Specifically, benzodiazepines carry the risk of misuse and abuse which can make them less safe when used in tandem with opioids. In addition, opioids and benzodiazepines are involved with respiratory control by reducing respiratory rate, tidal volume, and upper airway patency leading to obstructive apneas and hypopneas (12).

Pulmonary conditions, such as obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD), can contribute to opioid-induced respiratory compromise. At baseline, patients with OSA have difficulty protecting their airway due to relaxation of the throat muscles leading to blockage of airflow to the lungs. Opioid usage hinders a patient’s ability to arouse during times of hypoxemia and can lead to prolonged airway obstruction (13).

As noted above, there are many demographic factors and health conditions contributing to adverse events from opioid use, most of which are interrelated. However, few studies have looked at quantifying and comparing how these factors influence opioid overdose rates in the inpatient population. Therefore, the study's main objective is to identify clinically relevant risk factors associated with adverse events from opioid usage via the evaluation of naloxone administration on an inpatient basis, and to use these factors to create a risk assessment tool to stratify iatrogenic opioid overdose risk. Secondary endpoints included morphine milligram equivalents (MME) and length of stay between patients administered naloxone and control groups.

Study Design

This study was a retrospective chart review of electronic medical records (EMR) at a large academic medical center with locations across eastern Pennsylvania and northwestern New Jersey. It included admitted patients from 13 campuses who received opioid analgesics between January 1st, 2022, and December 31st, 2022. (IRB SLIR 2023-56)

Selection of Participants

Adult patients 18 years of age and older who were admitted for inpatient stays and received at least one dose of an opioid analgesic was our target demographic. Patients were excluded from chart review if they were pregnant, prisoners, intubated/mechanically ventilated, end of life/comfort/hospice care, or only received opioid analgesics in the operating room (OR), post-anesthesia care unit (PACU), and/or the emergency department (ED). Based off these criteria, there were 11,050 patients eligible for the study. This was further broken down into two groups: the study group of 130 patients who received naloxone and the control group of 10,920 who did not.

Selection of Risk Factors

A comprehensive literature review was performed by authors independently to identify factors that have been shown to influence opioid overdose rates irrespective of study population. Potential risk factors were then discussed as a group, and a consensus decision was made to focus on the proposed risk factors shown in Table 1. A consensus decision was also used to determine proxy measurements for certain risk factors, such as liver insufficiency—defined by elevated total bilirubin and INR—and renal insufficiency, defined by reduced creatinine clearance. These risk factors were compared across both groups to determine which of these were statistically significant.

Data Collection

A report was generated using the EMR identifying patients admitted to the hospital who received opioids between January 1st, 2022 and December 31st, 2022. Patients who overdosed within the hospital were identified if naloxone was administered during their stay. If naloxone was administered, progress notes were manually searched to see if there was a response after naloxone administration—indicating incidence of opioid overdose in the inpatient setting. In addition to the naloxone administration, data pulled from the EMR included patient demographic information, admission information, and proposed risk factors shown in Table 1, rapid response/code blue that occurred during hospital admission, naloxone dose and number of administrations, MME determined by type calculating the total amount of opioids received during the admission multiplied by a conversion factor , and length of stay. Collected data were de-identified and stored in a secure Microsoft Excel spreadsheet using the REDCap database.

Data Analysis:

To determine whether our proposed risk factors independently and significantly predicted likelihood of overdose, we constructed a multivariable direct logistic regression model.

To ascertain model fit, we reported the omnibus chi square statistic and the Hosmer-Lemeshow goodness-of-fit statistic. For each covariate, we present adjusted odds ratios (AOR), and 95% confidence intervals (CIs).

Next, we attempted to internally validate our regression model with sample replication using 500 bootstrapped models, given our large sample size. Bootstrapping is typically preferred over other internal validation methods, such as cross validation and split sampling, due to decreased bias and increased model stability (14). We reported the bias-corrected c-statistic for our validated model.

Finally, we created a scoring system based on our regression results using a method suggested by Ohno et al that obtains the square root of the adjusted odds ratio for the significant independent predictors, then rounds decimal points to the nearest whole number (15).

As a secondary outcome, we compared admission MME between patients receiving naloxone versus opioids using a Mann Whitney rank sums test due to the skewed distributions.

We used SAS version 9.4 (Cary, NC) to analyze our data, with p < .05 denoting statistical significance for all outcomes, and no adjustment for the multiple comparisons.

Participant Characteristics

There were 11,050 patients in our sample with complete data for analysis [mean age (standard deviation) = 64.49 years (15.95); 5,490 female (49.68%), 5,559 male (50.31%) 1 non-binary (.01%)]. Table 2 presents the frequency for overdose requiring naloxone for multivariable logistic regression modeling.

Most of the participants requiring naloxone were admitted to an Internal Medicine service 44.6% (n = 58), followed by Critical Care Medicine 13% (n = 17), Anesthesiology 9.2% (n = 12), Pulmonary Medicine 9.2% (n = 12), General Surgery 8.4% (n = 11), and Trauma 5.3% (n=7). Cardiothoracic Surgery, Colorectal Surgery, Family Medicine, Neurosurgery, Orthopedics, Otolaryngology, Surgical Oncology, and Vascular surgery comprising the remaining of the admitting services. The most common admitting diagnoses were respiratory conditions (shortness of breath, respiratory failure, cough) (14.6%) followed by cardiac (bradycardia, chest pain, congestive heart failure) (12.3%), pain (hip, neck, abdomen, back) (10%), trauma (fractures) (10%), gastrointestinal (nausea, bowel obstruction) (9.2%), cancer (6.1%), neurology (stroke, change in mental status) (5.3%), electrolyte imbalances (hyperkalemia, hyponatremia) (5.3%), and infections (cellulitis, urinary tract infection) (5.3%).

Outcome Data

Table 3 presents the multivariable logistic regression results. Before regression modeling, we assessed the presence of outliers and influential data points. There were only 130/11050 outliers (1.1%) based on examination of the normalized residuals, Cook’s D, and leverage statistics. Therefore, we retained these patients in our model to ensure the broadest possible external generalizability.

As indicated in the footnote, the model had adequate goodness-of-fit based on the omnibus chi square and Hosmer-Lemeshow p-values, with an overall classification of 79.3%er based on the included covariates. The following 7 covariates were independently and significantly associated with increased likelihood of overdose: 1) BMI > 30 (p < .0001); 2) creatinine clearance 30-59 (mL/min) (compared to > 60 (mL/min)) (p = .0110); 3) creatinine clearance < 29 (mL/min) (compared to > 60 (mL/min)) (p < .0001); 4) benzodiazepine and/or barbiturate usage (p = .0014); 5) pulmonary disease (composite of COPD and/or asthma) (p < .0001); 6) substance use (composite of chronic opioid dependence and/or substance abuse disorder) (p < .0001); and 7) obstructive sleep apnea (p = .0424).

Internal validation of our model revealed a bias-correct c-statistic of .774, which is remarkably close to the original value of .793, indicating consistently good model fit/performance when using 500 simulated random samples taken from the entire database.

Table 4 presents the score derived from our multivariable logistic regression model. The results illustrate that among the risk factors demonstrated to impact overdose, certain ones had a higher impact. Specifically, BMI > 30, creatinine clearance < 29 mL/min, pulmonary disease, and a history of substance abuse were found to have an impact twice as great as other factors.

For our secondary outcome, there was a statistically significant difference in the general distribution of admission MME (p < .001). Median (raw range) for the naloxone group (n = 130) was 114.0 (4.0 – 15,384) versus 57.0 (0.375 – 72,097) for the opioids group (n = 10,920).

Opioids prescribed for acute pain in a hospital setting cause unexpected in-hospital deaths, longer hospital stays, higher healthcare costs, and a greater likelihood of 30-day readmission (16). In our retrospective study, we used naloxone administration as a secondary measurement for opioid overdose in over 11,000 patients, enabling us to examine risk factors contributing to adverse events compared to the controlled population receiving opioids. While the patient group receiving naloxone had almost double the MME, we identified an additional seven statistically significant risk factors predisposing patients to adverse drug events, which included OSA, obstructive pulmonary disease, coadministration of benzodiazepines, BMI, CrCl, and substance use disorder history.

Per our study results, OSA was noted to be a statistically significant risk factor for adverse events with opioid administration. Of the patients that overdosed in the inpatient setting, 21.54% had a history of OSA compared to just 10.51% of patients who did not require naloxone use in the hospital (OR = 1.608, p = 0.0424). This is consistent with literature, which shows that patients with pre-existing OSA have a 1.4-fold risk of respiratory issues compared to control groups and is the cause of death in 50% of surgical patients within the first 24 hours of surgery (13). The u-opioid receptors responsible for opioid analgesia are also implicated in modulation of respiratory drive (17). Furthermore, obstructive apneas can also occur with opioids due to their direct inhibitory effect on genioglossus muscle activity and their impact on the central hypoglossal motor pool, along with the depression of the protective arousal response (17).

In our study, patients with iatrogenic opioid overdose were more than twice as likely to have concomitant obstructive pulmonary disease (47.69% vs. 22.75%). Our data suggests that obstructive pulmonary disease is more strongly associated with in-hospital opioid overdose compared to OSA. This may be due to the structural and functional changes associated with chronic obstructive pulmonary disease. Opioids administered to individuals with obstructive pulmonary disease may exacerbate respiratory depression, reduce mucous clearance due to cough suppression, and increase immunosuppressive events (18). A large cohort study identified an increase in all-cause mortality among recipients of 30 MME per day compared to those without advanced COPD (19). Another study found similar correlations with adverse effects, with a 2.27-fold risk of opioid-induced respiratory depression after surgery, in line with our findings (13).

The literature mentions a few types of medications which can negatively interact with opioids including benzodiazepines, muscle relaxers and gabapentinoids. Interestingly, in our study, only benzodiazepine/barbiturate use was associated with increased odds of iatrogenic opioid overdose (OR = 2.042, p = 0.0014), whereas gabapentinoid and muscle relaxant use was not found to be an independent risk factor for opioid overdose.

Benzodiazepines are the main culprit for adverse events when used alongside opioids for pain control. Both benzodiazepines and opioid analgesics are CNS depressants, with their coadministration demonstrating a potentiating effect on overdose (20). Two studies reported a 3.5 to 4-fold increased risk of overdose death with sedating nervous system medications; another study found increased cardiopulmonary and respiratory adverse events when opioids were combined with benzodiazepines (4, 11, 12). Muscle relaxers have been routinely co-prescribed with opioids, with one article highlighting that about 10% of those who use opioids for pain control are also prescribed muscle relaxers (21). This increases to 30% when the pain in question is specifically musculoskeletal (21). Regarding gabapentin, one study noted a 50% increase in opioid related death with concurrent use of gabapentin. This statistic nearly doubled when the gabapentin dose was high, supporting a drug-drug interaction between the two agents and thus its association with life-threatening consequences (11). Another study noted that continuation of home gabapentin or pregabalin was associated with a 6-fold increase in opioid-induced respiratory depression on surgical wards (22). Perhaps one contributing factor to the higher rates of opioid overdose in inpatients with recent benzodiazepine/barbiturate use is the longer half-life of these medications compared to gabapentinoids and muscle relaxants. For instance, the half-life of long-acting benzodiazepines ranges from 40-250 hours, whereas gabapentinoids and muscle relaxants generally have much shorter half-lives (23-25). In the inpatient population specifically, blood levels of gabapentinoids and muscle relaxants might not be high enough to potentiate effects of opioids after a few days, whereas benzodiazepines may be present in the bloodstream for longer.

BMI has also played a role in adverse events with opioid usage. Our research found that a BMI of greater than 30 was a statistically significant predictor of adverse events with opioid usage (OR = 3.656, p < 0.001). Patients with a higher BMI experience increased restrictive effects on lung function and a reduction in functional residual capacity. This is caused by reduced chest wall compliance due to added weight from adipose tissue and cephalad displacement of the diaphragm due to increased abdominal mass. This, in turn, can lead to atelectasis, hypoxemia, and diminished lung function (22, 26). One study found that not only was there a strong correlation of obesity and the use of opioids for pain control, but that correlation strengthened as BMI increased (27). Moreover, the duration of treatment with opioids was noted to be higher in those with obesity, leading to increased risk of opioid use disorder and mortality (27). Another study was able to discern that obesity, alongside other risk factors such as mild liver disease, Hispanic origin, and COPD, put patients at higher risk of cardiopulmonary and respiratory arrest in both medical and surgical settings (28).

Our research was able to identify that not only do patients with a CrCl < 29 (mL/min) benefit from renal dose adjustments with opioid usage, but that those with a CrCl of 30-59 (mL/min) were also at risk for adverse events, suggesting they might also benefit from dosage adjustments. In patients with kidney disease, the primary concern revolves around the accumulation of opioids and their active metabolites due to renal insufficiency. This accumulation is a consequence of decreased nephrons, glomerular filtration rate (GFR), tubular secretion, and renal blood flow required for the removal of opioids and their harmful metabolites. Furthermore, liver enzymes CYP2D6 and CYP3A4 undergo downregulation in advanced kidney disease, secondary to uremia (8). Agents such as oxycodone and hydromorphone should be used cautiously in patients with CrCl < 30 (mL/min) as there have been reports of drug accumulation leading to CNS toxicity and sedation if not properly dose adjusted (8, 9). However, opioids such as methadone and fentanyl seem safe to use, although dose adjustments are still highly recommended. Finally, opioids like codeine, morphine, and tramadol should be completely avoided due to accumulated active metabolites that can lead to adverse effects (8, 29).

In our study, the risk factor with the largest independent effect on risk of iatrogenic opioid overdose was history of substance use disorder (OR = 4.255, p < 0.0001). There may be a few reasons for this interaction. Firstly, substance use disorders are often comorbid with liver and renal disease, which may impair metabolism and excretion of opioids in these patients (30). Furthermore, substance use in the inpatient setting is often treated with drugs that may interact with opioids. For instance, alcohol withdrawal is typically treated with benzodiazepines, which we have shown increases the risk of inpatient opioid overdose.

Surprisingly, age and liver failure were not found to be risk factors. There is some precedent for these findings in the literature. Zedler et al, for example, also did not find hepatic dysfunction to be statistically significant to include as a risk factor for their own opioid risk tool (31). Regarding the geriatric population, adverse effects are thought to be due to changes in pharmacokinetics, pharmacodynamics, and drug-drug interactions (32). Our study, however, did not find age to be a risk factor associated with adverse events related to opioid use. While it is known that the elderly population are more likely to suffer from other comorbid conditions – which as noted above have been associated with adverse effects from opioids – all other factors constant, age itself is not the risk factor in question.

Limitations:

Our study was performed with data collected from a single health system in one geographical region; though diverse in terms of populations served, the health system only serves a small portion of the northeast United States. Although a thorough search of the EMR was performed, there is inherent potential for misclassification of diagnostic coding. For example, a diagnosis of heart failure may have been charted, even though the patient may have only had a prior history of reduced ejection fraction or simply a diastolic dysfunction. Thus, there may be variation among different physicians and even among the different admitting services which may lead to biases in how patient admissions are documented. Lastly, it is important to recognize that naloxone is often administered reflexively by rapid response teams as a reaction to alterations in consciousness even when opioids are not the most likely culprit. As such, there were certain cases in which it was unclear whether the naloxone administration was given for reversal of opioids or for ambiguous altered mental status of unknown etiology.

In this large, retrospective chart review, we found several patient characteristics that pose statistically significant risks of naloxone administration amongst those receiving opioids in the inpatient setting. Reduced CrCl, co-administration of benzodiazepines, BMI greater than 30 kg/m2, underlying pulmonary disease, obstructive sleep apnea, chronic opioid use, and substance use disorder were all found to be risk factors for adverse events and naloxone administration. Future directions can include using these risk factors to stratify iatrogenic opioid overdose risk and guide inpatient opioid administration. Providers prescribing opioids in the inpatient setting should strongly consider dose reduction and increasing frequency intervals based on the presence of these risk factors as a part of their clinical decision-making process.

Acknowledgments:

Joshua Melot, MD

Anna Ng Pellegrino, MD

Robert Langan, MD

Rob Menak, PharmD

Patrick Wende, MS

Arman Haveric, BS

Funding:

The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received.

Human Ethics and Consent to Participate Declarations:

Not applicable. Retrospective data analysis of chart review. RIB waiver obtained.

Ethical Approval:

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by St. Luke's University Health Network Institutional Review Board: registry of Office for Human Research Protections (OHRP) [IRB 00002757] IRB approval # SLIR 2023-56

Competing Interest Declaration:

The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication. The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Table 1. Summary of risk factors investigated

Proposed Risk Factors

Age

BMI

History of heart failure

Hepatic insufficiency

PT/INR
Total bilirubin

Renal insufficiency

Creatinine clearance (CrCl)

Obstructive pulmonary disease

Asthma
COPD

Concurrent sedative medication use

Benzodiazepine
Barbiturate
Gabapentinoid
Muscle relaxer (Robaxin; Flexeril)

History of substance use disorder

Morphine milligram equivalents

Table 2: Frequency of Risk Factors in Study and Control Patients

Risk Factor

Overdose

(n = 130)

No Overdose

(n = 10,920)

Age Categories

(n, %)

< 49 years: 20 (15.38%)

50-64 years: 52 (40.0%)

> 65 years: 58 (44.62%)

< 49 years: 1,871 (17.13%)

50-64 years: 3,255 (29.81%)

> 65 years: 5,794 (53.06%)

BMI > 30 kg/m2

(n, %)

38 (29.23%)

957 (8.76%)

Heart Failure

(n, %)

39 (30.0%)

2,765 (25.32%)

Liver Disease

(n, %)

50 (38.46%)

3,699 (33.87%)

Creatinine Clearance

(n, %) (mL/min)

> 60: 54 (41.54%)

30-59: 41 (31.54%)

< 29: 35 (26.92%)

> 60: 5,552 (50.84%)

30-59: 3,174 (29.07%)

2,194 (20.09%)

Sedating Medication

(n, %)

None: 32 (24.62%)

Gabapentin and/or Pregabalin: 18 (13.85%)

Muscle Relaxants: 16 (12.31%)

Benzodiazepines and/or Barbiturates: 64 (49.23%)

None: 4,588 (42.01%)

Gabapentin and/or Pregabalin: 1,482 (13.57%)

Muscle Relaxants: 1,285 (11.77%)

Benzodiazepines and/or Barbiturates: 3,565 (32.65%)

Pulmonary Disease

(Composite of COPD and/or Asthma)

(n, %)

62 (47.69%)

2,484 (22.75%)

Substance Use

(Composite of Chronic Opioid Dependence and/or Substance Abuse Disorder)

(n, %)

52 (40.0%)

1,198 (10.97%)

Obstructive Sleep Apnea

(n, %)

28 (21.54%)

1,148 (10.51%)

Table 3: Multivariable Direct Logistic Regression for Overdose

Risk Factor (Covariate)	Adjusted Odds Ratio (95% Confidence Interval)	p-value
Age (reference = < 49 years)
*50 – 64 years*	.980 (.569 – 1.688)	.9417
> *65 years*	.611 (.347 – 1.079)	.0893
BMI > 30 (kg/m2)	3.656 (2.436 – 5.487)	< .0001
Creatinine Clearance (reference = > 60)
*CrCl 30 – 59 (mL/min)*	1.791 (1.143 – 2.807)	.0110
*CrCl < 29 (mL/min)*	2.738 (1.694 – 4.427)	< .0001
Liver Disease	1.134 (.783 – 1.643)	.5053
Sedating Medication (reference = none)
*Gabapentin and/or Pregabalin*	1.209 (.666 – 2.194)	.5317
*Muscle Relaxants*	1.405 (.756 – 2.608)	.2820
*Benzodiazepines and/or Barbiturates*	2.042 (1.319 – 3.160)	.0014
Obstructive Pulmonary Disease (COPD and/or Asthma)	2.240 (1.547 – 3.242)	< .0001
Substance Abuse (Chronic Opioid Use and/or Substance Use Disorder)	4.255 (2.934 – 6.170)	< .0001
Heart Failure	.761 (.503 – 1.149)	.1939
Obstructive Sleep Apnea	1.608 (1.016 – 2.545)	.0424

Pearson chi square p < .0001; Hosmer-Lemeshow goodness-of-fit p = .653; c-statistic=.793

Table 4: Independent and Statistically Significant Predictors of Overdose

Risk Factor	Adjusted Odds Ratio (95% Confidence Interval)	SCORE*
BMI > 30 kg/m²	3.656 (2.436 – 5.487)	√3.656 = 1.9 = 2.0
Creatinine Clearance < 29 (ref: < 60)	2.738 (1.694 – 4.427)	√2.738 = 1.65 = 2.0
Creatinine Clearance 30-59 (ref: < 60)	1.791 (1.143 – 2.807)	√1.791 = 1.34 = 1.0
Benzodiazepines and/or Barbiturates (ref: none)	2.042 (1.319 – 3.160)	√2.042 = 1.43 = 1.0
Pulmonary Disease (composite of COPD and/or asthma)	2.240 (1.547 – 3.242)	√2.240 = 1.496 = 2.0
Substance Abuse (composite of chronic opioid dependence and/or substance use disorder)	4.255 (2.934 – 6.170)	√4.255 = 2.06 = 2.0
Obstructive Sleep Apnea	1.608 (1.016 – 2.545)	√1.608 = 1.27 = 1.0

* Based on taking the square root of the adjusted odds ratio and rounding decimal points

to the nearest whole number.

No competing interests reported.

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Reviewers agreed at journal
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Risk of Hospital INpatient Opioid Overdose (RHINOO): A review of factors impacting naloxone administration in patients receiving opioids

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