Inches, Centimeters, and Yards: Overlooked Definition Choices Inhibit Interpretation of Morphine Equivalence

Objective: Morphine-standardized doses are used in clinical practice and research to account for molecular potency. Ninety milligrams of morphine equivalents (MME) per day are considered a "high dose" risk threshold in guidelines, laws, and by payers. Although ubiquitously cited, the "CDC definition" of daily MME lacks a clearly defined denominator. Our objective was to assess denominator-dependency on "high dose" classification across competing definitions. Methods: To identify definitional variants, we reviewed literature and electronic prescribing tools, yielding 4 unique definitions. Using Prescription Drug Monitoring Programs data (July to September 2018), we conducted a population-based cohort study of 3,916,461 patients receiving outpatient opioid analgesics in California (CA) and Florida (FL). The binary outcome was whether patients were deemed "high dose" (>90 MME/d) compared across 4 definitions. We calculated I2 for heterogeneity attributable to the definition. Results: Among 9,436,640 prescriptions, 42% overlapped, which led denominator definitions to impact daily MME values. Across definitions, average daily MME varied 3-fold (range: 17 to 52 [CA] and 23 to 65 mg [FL]). Across definitions, prevalence of "high dose" individuals ranged 5.9% to 14.2% (FL) and 3.5% to 10.3% (CA). Definitional variation alone would impact a hypothetical surveillance study trying to establish how much more "high dose" prescribing was present in FL than CA: from 39% to 84% more. Meta-analyses revealed strong heterogeneity (I2 range: 86% to 99%). In sensitivity analysis, including unit interval 90.0 to 90.9 increased "high dose" population fraction by 15%. Discussion: While 90 MME may have cautionary mnemonic benefits, without harmonization of calculation, its utility is limited. Comparison between studies using daily MME requires explicit attention to definitional variation

Machine Learning Can Unlock Insights Into Mortality

There is no abstract associated with this publication

Distinguishing Death from Disenrollment in Claims Data Using a Readily Implemented Machine Learning Algorithm

Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims based research. If not an outcome, death is a competing risk and poses a threat to validity when treated as non-informative right censoring. Objectives: We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 2 years of continuous enrollment prior to disenrollment between 01/2007 and 01/2018. Leveraging unique linkages in addition to data that are typically unavailable in the publicly licensed data, we ascertained date of death from the Social Security Death Index, inpatient discharge status, and death indicators in the administrative data. Models including candidate predictors for age, sex, Census region, month of disenrollment, year of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity, specificity, and ROC associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: Overall, we identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the year of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97. At the same prediction threshold, the weighted algorithm classified death with a sensitivity of .947 and a specificity of 0.898 (ROC=.973). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment with high specificity and varying sensitivity depending on the chosen prediction threshold. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) to meet the needs of the specific study at hand

Four Competing Definitions of Morphine Equivalence Insidiously Inhibit Evidence Synthesis

Analysis of opioid milligrams of morphine equivalents (MME) per day definitions. Presented virtually at the 37th annual International Conference on Pharmacoepidemiology and Therapeutic Risk Management

External validation of a machine learning algorithm to distinguish death from disenrollment in claims data

Poster presentation from the 38th International Conference on Pharmacoepidemiology & Therapeutic Risk Managemen

Distinguishing Death from Disenrollment: Applying a Predictive Algorithm to Reduce Bias in Estimating the Risk of Rehospitalization

Background: The inability to identify dates of death in several insurance claims data sources can result in biased estimates when death is a competing event. To address this issue, an algorithm to predict when plan disenrollment is due to death was developed and validated using the MarketScan insurance claims data. Objectives: We illustrate the bias introduced when estimating the risk of rehospitalization within 90-days of acute myocardial infarction (AMI) if death is not accounted for as a competing event. We demonstrate how this validated algorithm can be used to reduce this bias. Methods: We use a 20% sample of Medicare claims (2007–2017) to identify patients with an incident admission for AMI. Patients were required to be 66+ years of age with employer-sponsored supplemental insurance. We compare 3 methods of estimating the risk of 90-day rehospitalization. The first method uses the true death data available in the Medicare enrollment data. We used cumulative incidence functions to estimate the risk of rehospitalization, accounting for death as a competing risk. The second method mimics scenarios where death data are unavailable, and patients are disenrolled from insurance coverage shortly after death. We used Kaplan Meier curves to estimate the risk of rehospitalization, treating death as non-informative censoring at the time of disenrollment. The third method applies the validated predictive algorithm to the Medicare claims where death date has been obscured. We used a predicted probability threshold of 0.99 to distinguish between plan disenrollment and death (sensitivity = 0.92, specificity = 0.90). We estimated the risk of rehospitalization accounting for predicted death as a competing risk. Results: We identified 12 753 patients with an index hospitalization for AMI (mean age = 77.8 years). When accounting for death as a competing risk using validated death dates, the estimated 90-day risk of rehospitalization was 21.6% (20.8%, 22.3%). When mimicking a scenario where death is treated as non-informative censoring at the time of disenrollment, the estimated 90-day risk was 24.8% (23.9%, 25.6%). When using the algorithm to distinguish between death and disenrollment and accounting for predicted death as a competing risk, the estimated 90-day risk was 21.7% (21.0%, 22.4%). Conclusions: When estimating the risk of rehospitalization following AMI in a cohort of Medicare patients, applying a claims-based algorithm to predict death resulted in estimates that closely mirrored the estimates using validated death data. Alternatively, failure to account for death as a competing risk resulted in an estimate that was biased upwards

Overlooked variations in calculating morphine equivalence raises questions of validity in epidemiology studies

Background Morphine-standardized analgesic doses account for molecular potency. 90 milligrams of morphine equivalents (MME) per day are considered a “high dose” risk threshold. Although ubiquitously cited, the “CDC definition” of daily MME lacks a clearly defined denominator. Objectives Assess the sensitivity of denominator-dependency on “high dose” classification across 4 competing definitions. Methods Review of 27 studies cited in the CDC Pain Management Guideline yielded 4 unique definitions. Using Prescription Drug Monitoring Programs data (Jul-Sep 2018), we conducted a population-based cohort study of 3,916,461 patients receiving opioid analgesics in California (CA) and Florida (FL). The outcome was patients deemed “high dose” (>90 MME/day) across 4 definitions. Results In a simple example, a 30-day supply of 2 partially overlapping oxycodone prescriptions have daily MME varying across 4 definitions: 76mg (total days supply); 93mg (on-therapy days); 31mg (fixed observation window); or 105mg (maximum daily dose). Among 9,436,640 prescriptions, 42% overlapped, impacting daily MME. Across definitions, average daily MME varied 3-fold [range: 17-52 (CA), 23-65 mg (FL)]. Across definitions, prevalence of “high dose” individuals ranged 5.9%-14.2% (FL), 3.5%-10.3% (CA). Definitional variation would impact a hypothetical surveillance study trying to establish how much more “high dose” prescribing was present in FL than CA: from 34% to 79% more. Conclusions Previously unrecognized definitional variation confounds surveillance metrics and constrains real-world applicability of observational studies. While 90 MME may have cautionary mnemonic benefits, without harmonization of calculation, its utility is limited. Comparison between studies using daily MME requires explicit attention to definitional variation for validity

Opioid MME Calculations

Objective: Morphine-standardized doses are used in clinical practice and research to account for molecular potency. Ninety milligram of morphine equivalents (MME) per day are considered a “high dose” risk threshold in guidelines, laws, and by payers. Although ubiquitously cited, the “CDC definition” of daily MME lacks a clearly defined denominator. Our objective was to assess denominator-dependency on “high dose” classification across competing definitions. Methods: To identify definitional variants, we reviewed literature and electronic prescribing tools, yielding 4 unique definitions. Using Prescription Drug Monitoring Programs data (July to September 2018), we conducted a population-based cohort study of 3,916,461 patients receiving outpatient opioid analgesics in California (CA) and Florida (FL). The binary outcome was whether patients were deemed “high dose” ( > 90 MME/d) compared across 4 definitions. We calculated I2 for heterogeneity attributable to the definition. Results: Among 9,436,640 prescriptions, 42% overlapped, which led denominator definitions to impact daily MME values. Across def- initions, average daily MME varied 3-fold (range: 17 to 52 [CA] and 23 to 65 mg [FL]). Across definitions, prevalence of “high dose” individuals ranged 5.9% to 14.2% (FL) and 3.5% to 10.3% (CA). A definitional variation would impact a hypothetical surveillance study trying to establish how much more “high dose” prescribing was present in FL than CA: from 34% to 79% more. Meta-analyses revealed strong heterogeneity (I2 range: 86% to 99%). In sensitivity analysis, including unit interval 90.0 to 90.9 increased “high dose” population fraction by 15%. Discussion: While 90 MME may have cautionary mnemonic bene- fits, without harmonization of calculation, its utility is limited. Comparison between studies using daily MME requires explicit attention to definitional variation. Key Words: opioids, milligrams of morphine equivalents (MME), definitions, epidemiology, Prescription Drug Monitoring Programs (PDMP)Additional information available at: https://www.opioiddata.org/studies/equations-for-calculating-mmes

An Algorithm to Predict Out-of-Hospital Death Using Insurance Claims Data

Slide presentation to accompany manuscript. Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims-based research. Deaths likely result in disenrollment; however, disenrollment may also reflect a change in insurance provider. We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 1 year of continuous enrollment prior to disenrollment in 2007-2018. Using Social Security Death Index, inpatient discharge status, and death indicators in the administrative data as the gold standard, we used claims in the prior year to predict death. Models including candidate predictors for age, sex, Census region, month of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity and specificity associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: We identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the 61 days of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) t