Touchpoints – Opportunities to predict and prevent opioid overdose: A cohort study

Larochelle, Marc R.; Bernstein, Ryan; Bernson, Dana; Land, Thomas; Stopka, Thomas J.; Rose, Adam J.; Bharel, Monica; Liebschutz, Jane M.; Walley, Alexander Y.

doi:10.1016/j.drugalcdep.2019.06.039

Cited by 144 publications

(100 citation statements)

References 47 publications

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“…The substance use disorder (SUD) epidemic has led to increased infectious complications of injection drug use (IDU) [6], including serious bacterial infections (SBIs), such as skin and soft tissue infections (SSTI), bacteremia or sepsis, osteomyelitis, infectious endocarditis, as well as hepatitis B virus (HBV), hepatitis C virus (HCV), and HIV infections [7][8][9][10]. IDUrelated SBIs are associated with high morbidity and mortality [11] and may be an important marker of SUD severity; those with an IDU-related SBI experienced a more than fifty-fold increase in overdose death compared to those without an IDU-related SBI [12]. Hospitalization rates and hospitalization costs associated with IDU-related SBIs are important measures of the social, economic, and public health burden of IDU.…”

Section: Introductionmentioning

confidence: 99%

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Capizzi

Leahy

Wheelock³

et al. 2020

PLoS ONE

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Background Injection drug use has far-reaching social, economic, and health consequences. Serious bacterial infections, including skin/soft tissue infections, osteomyelitis, bacteremia, and endocarditis, are particularly morbid and mortal consequences of injection drug use. Methods We conducted a population-based retrospective cohort analysis of hospitalizations among patients with a diagnosis code for substance use and a serious bacterial infection during the same hospital admission using Oregon Hospital Discharge Data. We examined trends in hospitalizations and costs of hospitalizations attributable to injection drug use-related serious bacterial infections from January 1, 2008 through December 31, 2018. Results From 2008 to 2018, Oregon hospital discharge data included 4,084,743 hospitalizations among 2,090,359 patients. During the study period, hospitalizations for injection drug use-related serious bacterial infection increased from 980 to 6,265 per year, or from 0.26% to 1.68% of all hospitalizations (P<0.001). The number of unique patients with an injection drug use-related serious bacterial infection increased from 839 to 5,055, or from 2.52% to 8.46% of all patients (P<0.001). While hospitalizations for all injection drug use-related serious bacterial infections increased over the study period, bacteremia/sepsis hospitalizations rose most rapidly with an 18-fold increase. Opioid use diagnoses accounted for the largest percentage of hospitalizations for injection drug use-related serious bacterial infections, but hospitalizations for amphetamine-type stimulant-related serious bacterial infections rose most rapidly with a 15-fold increase. People living with HIV and HCV experienced increases in hospitalizations for injection drug use-related serious bacterial infection during the study period. Overall, the total cost of hospitalizations for injection drug use-related serious bacterial infections increased from $16,305,129 in 2008 to $150,879,237 in 2018 (P<0.001). Conclusions In Oregon, hospitalizations for injection drug use-related serious bacterial infections increased dramatically and exacted a substantial cost on the health care system from 2008 to 2018. This increase in hospitalizations represents an opportunity to initiate substance use disorder treatment and harm reduction services to improve outcomes for people who inject drugs.

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Section: Introductionmentioning

confidence: 99%

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Capizzi

Leahy

Wheelock³

et al. 2020

PLoS ONE

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“…Further, hospitalization is a high-risk touchpoint after which people with opioid use disorder 83 are at increased risk for overdose and death. A recent study in Massachusetts found that hospitalization 84 for injection-related infection was associated with a 54-fold increase in mortality, and that MOUD can 85 mitigate this risk (Larochelle et al, 2019). Medication, combined with psychosocial interventions, 86 comprise first line treatment for moderate to severe alcohol use disorder.…”

Section: Introduction 68mentioning

confidence: 99%

Predictors of Opioid and Alcohol Pharmacotherapy Initiation at Hospital Discharge Among Patients Seen by an Inpatient Addiction Consult Service

Englander

King

Nicolaidis

et al. 2019

Journal of Addiction Medicine

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Background: Medications for opioid use disorder (MOUD) and alcohol use disorder (MAUD) are effective and under-prescribed. Hospital-based addiction consult services can engage out-of-treatment adults in addictions care. Understanding which patients are most likely to initiate MOUD and MAUD can inform interventions and deepen understanding of hospitals’ role in addressing substance use disorders (SUD). Objective: Determine patient- and consult-service level characteristics associated with MOUD/MAUD initiation during hospitalization. Methods: We analyzed data from a study of the Improving Addiction Care Team (IMPACT), an interprofessional hospital-based addiction consult service at an academic medical center. Researchers collected patient surveys and clinical data from September 2015 to May 2018. We used logistic regression to identify characteristics associated with medication initiation among participants with OUD, AUD, or both. Candidate variables included patient demographics, social determinants, and treatment-related factors. Results: Three hundred thirty-nine participants had moderate to severe OUD, AUD, or both and were not engaged in MOUD/MAUD care at admission. Past methadone maintenance treatment (aOR 2.07, 95%CI (1.17, 3.66)), homelessness (aOR 2.63, 95%CI (1.52, 4.53)), and partner substance use (aOR 2.05, 95%CI (1.12, 3.76) were associated with MOUD/MAUD initiation. Concurrent methamphetamine use disorder (aOR 0.32, 95%CI (0.18, 0.56)) was negatively associated with MOUD/MAUD initiation. Conclusions: The association of MOUD/MAUD initiation with homelessness and partner substance use suggests that hospitalization may be an opportunity to reach highly-vulnerable people, further underscoring the need to provide hospital-based addictions care as a health-system strategy. Methamphetamine's negative association with MOUD/MAUD warrants further study.

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“…Although we used an all-available lookback window to exclude patients with a prior overdose, do not know whether the patients in our study population had a diagnosed overdose prior to October 2011. Another limitation is that the list of candidate predictors does not encompass all of the important risk factors of overdose, such as behavioral health and criminal justice variables that are poorly captured in claims data [ 42 ]. Predictors were also measured during the previous 3–6 months, and we did not assess whether a longer covariate assessment period could have resulted in better prediction.…”

Section: Discussionmentioning

confidence: 99%

Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data

et al. 2020

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Introduction With increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention. Objective To develop an algorithm to predict overdose using routinely-collected healthcare databases. Methods Within a US commercial claims database (2011-2015), patients with �1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance. Results We identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In

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Touchpoints – Opportunities to predict and prevent opioid overdose: A cohort study

Cited by 144 publications

References 47 publications

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Predictors of Opioid and Alcohol Pharmacotherapy Initiation at Hospital Discharge Among Patients Seen by an Inpatient Addiction Consult Service

Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data

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