- Research
- Open Access
- Open Peer Review
Predicting opioid dependence from electronic health records with machine learning
- Randall J. Ellis1,
- Zichen Wang1,
- Nicholas Genes2 and
- Avi Ma’ayan1Email authorView ORCID ID profile
- Received: 27 October 2018
- Accepted: 22 January 2019
- Published: 29 January 2019
Abstract
Background
The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence.
Results
We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls.
Conclusions
The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room.
Keywords
- Opioid epidemic
- Opioid dependence
- Electronic health records
- Electronic medical records
- Machine learning
- Artificial intelligence
Introduction
In a highly visible report it was described how drug overdose deaths have substantially increased in the United States from 2010 to 2015 [1]. The estimated societal costs of prescription opioid overdoses, abuse, and dependence in the United States in 2013 totaled $78.5 billion [2]. The challenges for physicians combating the opioid epidemic include: 1) Determining which patients are at risk of developing opioid dependence when prescribed these medications for conventional pain treatment; 2) Determining which patients known to be addicted to opioids are most at risk of opioid overdose; and 3) Identifying drug-seeking patients who visit the Emergency Department (ED) for the secondary gain of obtaining an opioid prescription. Strategies to identify drug-seeking patients rely mostly on checking Prescription Drug Monitoring Programs (PDMPs) [3], examining past clinical perceptions (clinical gestalt), or exam findings such as withdrawal symptoms [4, 5]. Urine toxicology tests can detect opioid metabolites, but these tests are prone to false positives and negatives, and opioid metabolites only remain present in the urine for a short period [6].
Previous studies of biomedical variables predictive of opioid misuse and abuse have unraveled several salient factors, including chronic opioid prescriptions, history of psychiatric illness, non-opioid substance disorders, having a family member diagnosed with an opioid use disorder, the use of multiple pharmacies to fill prescriptions, having hepatitis C, and tobacco addiction [7–10]. These studies are based on various types of data, including pharmacy prescriptions, insurance claims, vital signs, and medical notes from electronic health records (EHR). For example, Ciesielski et al. [7] and Rice et al. [8], in two separate studies, used pharmacy and insurance claims information from over half a million patients to construct a multivariate logistic regression model to predict likelihood of opioid abuse. Similarly, Cochran et al. [9] and Dufour et al. [10] analyzed insurance claims databases to identify variables with predictive power to classify opioid use disorder patients. In a related study, Hylan et al. [11] tracked for four years 2752 patients that received chronic opioid treatment for their pain condition. To determine and predict opioid misuse, Hylan et al. also utilized natural language processing to analyze clinicians’ notes. All these past studies point to few common clinical factors that contribute to opioid pathology. Their observations support that the construction of predictive models of opioid misuse and abuse based on prior knowledge about the patient is feasible. So far, no prior work examined the predictive value of biological measures from standard lab tests for opioid misuse and abuse. In addition, all prior studies used either univariate statistics or multivariate linear models to discern associations between opioid misuse diagnosis and other clinical variables.
Finding a clinically objective signature of opioid abuse would assist physicians in offering the proper treatment to those patients who attempt to hide their addiction for other clinical conditions. Such a signature will be a composite biomarker that can be detected by machine learning methods. EHR systems have proliferated in the past decade, and are increasingly used to perform predictive diagnosis with non-linear machine learning methods [12–14]. EHR data include demographics, diagnoses, laboratory tests, vital signs, clinical notes, prescriptions, and procedures data. Examples of previous predictive studies that utilized EHR systems implementing machine learning methods include predicting the incidence of cardiovascular disease in patients with severe schizophrenia, bipolar disorder, or other non-organic psychosis [15]; length of hospital stay and time to readmission based on Research Domain Criteria in psychiatric patients [16]; unplanned readmission after discharge [17]; in-hospital mortality [18]; patient physiological age [19]; and many more. Here we describe the application of a machine learning classifier to predict substance dependence based on lab tests and vital signs using patient data derived from the Mount Sinai Medical Center (MSMC) EHR system. The lab tests and vital signs that are found to be the most useful in distinguishing substance dependent patients from controls were identified. Furthermore, the substance dependent population was clinically phenotyped by the over-representation of their diagnoses, prescriptions, and procedures during the five years prior to their first diagnosis of substance dependence.
Methods
Constructing the case and control populations
The MSMC EHR (Epic Systems, Verona, WI) data were organized into a de-identified collection. 42,825,357 diagnoses from the EHR were queried to find all patients with diagnoses belonging to the 304. * family of the International Classification of Diseases (ICD-9) codes (12,112 cases), referring to various forms of substance dependence [20]. Patients were excluded if their first 304.* diagnosis was made before they turned 20 years of age to avoid patients who were born with substance dependence or acquired substance dependence during childhood or adolescence. This filtering reduced the number of cases to 11,573. Lab tests and vital signs were obtained for all patients within a 20-day window around their 304 diagnosis. Initially, this analysis produced 873 types of lab tests and 51 types of vital signs. To construct a control population, the requirements were that all lab tests and vital signs are from patients older than 20 years, have no history of diagnoses in ICD-9 code families 291–293: alcohol- and drug-induced mental disorders and withdrawal; 303–305: alcohol/drug dependence, abuse; and 964.9–978.0: poisoning by psychoactive substances. This filtering step left 828,062 patients as controls.
Modified z-scores [21] were calculated for all lab tests and vital signs for the cases and controls. Some values in the EHR data are mistakenly entered, for example, a height of 2376 ft was observed. To remove outliers from the case and control populations, all lab tests and vital signs with modified z-scores below −2.5 or above 2.5 were removed. Additionally, percentages below 0 or above 100 were removed. After removing outliers, we retained 9518 cases and 707,015 controls.
a Distribution of tests (labs and vitals) per case before filtering. b Distribution of tests (labs and vitals) per case after removing patients with less than 17 tests and tests with 90% or greater missing values. c Distribution of tests (labs and vitals) per control after removing tests with 90% or greater missing values
Methods to compare cases and controls
Using the lab tests and vital signs from the cases and matched controls, median effect sizes were calculated for all 95 lab tests and vital signs. The value of the lab test or vital sign from each case is used to calculate an effect size, then for each lab test or vital sign, the median of these effect sizes is taken. Medians were calculated because they are more robust to outliers. Mean effect sizes were also calculated to check for consistency of their direction. Additionally, we examined the values of the lab tests and vital signs in both cases and controls during the 100 days prior to the diagnosis of substance dependence. For comparing these profiles to the controls, we examined the 100 days prior to the mean day of their lab tests and vital signs. Finally, diagnoses, prescriptions, and procedures in the five years preceding the first diagnosis of substance dependence were compared to those found in the age matched controls using odds ratios.
Opioid prescriptions analysis
To examine opioid prescriptions in the MSMC-EHR, the percentages of patients with at least one or more opioid prescriptions were calculated along with the percentage of total opioid prescriptions. Additionally, the distribution of opioid prescriptions by patient was examined, and a Wilcoxon rank-sum test was applied to quantify the difference between the number of opioid prescriptions given to patients with an opioid dependence prior to the substance abuse diagnosis, and the number of opioid prescriptions given to patients who have at least one opioid prescription, but no history of opioid dependence.
Classification of patients by substance dependence status
A Random Forest classifier was implemented with Scikit-learn [22] with 100 estimators, a Gini criterion, and a random state of 42. Cases and matched controls were iteratively classified using a bootstrapping procedure. 100 bootstraps of equal size to the case population were sampled from the matched controls, and 10-fold cross-validation was applied on each bootstrap. Area under the receiver operating characteristic curve (AUROC) was calculated as one way to assess classifier performance [23]. Gini importance was measured for each lab test and vital sign to assess the contribution of each feature. The 10 lab tests and vital signs (features) with the highest Gini importance were tested, and AUROCs were calculated. 10 random sets of 10 features were tested to determine baseline performance using random lab tests and vital signs. Finally, a dummy classifier making predictions by randomly picking from the population was employed to establish a performance baseline. F1 scores were calculated for all precision-recall combinations along the precision-recall curve, and confusion matrices were calculated using the threshold corresponding to the highest F1 score.
In the experiments using imputation by the mean or the median, classification performance was measured for including all patients, only those with no less than 17 lab tests and vital signs, and only those with less than 17 lab tests and vital signs. Additionally, we ran a test case with patients that had ICD-9 code families in the range of 291–293 but did not have ICD-9 codes in the 304.* family. These 291–293 ICD codes denote alcohol- and drug-induced mental disorders and withdrawal. Because the data in these analyses had higher dimensionality, i.e., more lab tests and vital signs due to the retention of all patients, only 10 bootstraps of equal size were sampled from the matched controls, and 10-fold cross-validation was conducted on each bootstrap.
The lab tests and vital signs during the 20 days prior to the first diagnosis of substance dependence, as well as 10 days before and 10 days after the first diagnosis, were used as the features to train the main set of classifiers. However, other classifiers were developed using only the diagnoses, prescriptions, and procedures during the 5 years prior to the first diagnosis of substance dependence. Furthermore, rather than predicting substance dependence status, we also constructed models to predict non-medical opioid poisoning events, i.e. overdose, denoted by ICD-9 codes 965.0, 965.00, 965.01, 965.02, 965.09, E850.0, E850.1, E850.2, using lab tests and vital signs during the 6 months prior to the event.
Results
Descriptive statistics of the case population
Distribution of ages for 11,573 cases given their first substance dependence diagnoses at 20 years of age or older
Opioid prescriptions in the Mount Sinai EHR
Histograms of (a) opioid prescriptions per patient, and (b) prescriptions per age in years
Quantifying differences between the case and control groups
Top 10 lab tests and vital signs by median effect size
Lab Test/Vital Sign | Median Effect Size | Mean Effect Size |
---|---|---|
Absolute lymphocyte count | 1.103 | 1.132 |
Oxygen saturation | 1.1 | 1.289 |
Lymphocytes percentage | 1.1 | 1.147 |
Partial pressure of oxygen | 1.092 | 1.208 |
Estimated glomerular filtration rate | 1.056 | 1.276 |
Total carbon dioxide level | 1.048 | 1.067 |
Platelet count | 1.045 | 1.081 |
Carbon dioxide pressure | 1.044 | 1.072 |
Alkaline phosphatase | 1.036 | 1.098 |
Aspartate aminotransferase | 1.035 | 1.111 |
Machine learning classifier to predict opioid dependence
Lab tests and vital signs from the cases and matched controls were used to train various Random Forest classifiers. A bootstrapping method was used to match different sets of controls to equal size of the case population. The initial set of n = 7797 case patients was achieved by the filtering steps described in the methods. Stratified 10-fold cross-validation was implemented to evaluate the performance of the classifiers.
Classifiers that use only labs and vitals dense data without imputation
Top 10 features by Gini importance
Lab Test/Vital Sign | Mean Gini | SD Gini | Case Mean | Control Mean | Case SD | Control SD |
---|---|---|---|---|---|---|
Red blood cell distribution width | 0.026 | 0.001 | 14.620 | 14.1 | 1.687 | 1.592 |
Albumin testing g/dL | 0.025 | 0.001 | 3.793 | 3.99 | 0.753 | 0.749 |
Total bilirubin mg/dL | 0.025 | 0.002 | 0.191 | 0.19 | 0.115 | 0.111 |
Lymphocytes percentage | 0.024 | 0.001 | 25.509 | 22.236 | 10.253 | 10.488 |
Total protein g/dL | 0.024 | 0.001 | 7.181 | 7.078 | 0.855 | 0.832 |
Neutrophils percentage | 0.022 | 0.001 | 63.032 | 67.512 | 12.542 | 12.707 |
Phosphorus mg/dL | 0.019 | 0.001 | 3.608 | 3.482 | 0.729 | 0.742 |
Absolute neutrophil count | 0.019 | 0.001 | 4.826 | 5.566 | 2.396 | 2.674 |
Hemoglobin g/dL | 0.018 | 0 | 12.545 | 12.886 | 2.021 | 2.031 |
Hematocrit test | 0.018 | 0 | 37.164 | 38.05 | 5.851 | 5.892 |
Gini importance values for the top 20 features by mean Gini importance
Classifiers that use imputed data with all labs and vitals
Because a significant number of lab tests, vital signs, and patients were discarded due to sparsity, an alternative approach is to impute the missing values with expected values. Using the imputation strategies of substituting missing values with the mean or the median yielded similar results. Without imputation, the AUROCs ranged from 0.832–0.87, with a mean AUROC of 0.856 (Additional file 6: Figure S6A). The confusion matrix showed that the classifier correctly identified 74.5% of controls and 81% of cases (Additional file 6: Figure S6B). Imputing by the mean, the AUROCs ranged from 0.822–0.87, with a mean AUROC of 0.847 (Additional file 6: Figure S6C). The confusion matrix showed that the classifier correctly identified 72.7% of controls and 80.9% of cases (Additional file 6: Figure S6D). Imputing by the median, the AUROCs ranged from 0.824–0.867, with a mean of 0.844 (Additional file 6: Figure S6E). The confusion matrix showed that the classifier correctly identified 72.1% of controls and 81% of cases (Additional file 6: Figure S6F).
Classifiers that use only dense labs and vitals data with imputation
Receiver operating characteristic curves, normalized, and non-normalized confusion matrices for classifiers using no imputation (a, b), imputation by the mean (c, d), and imputation by the median (e, f), retaining only patients with more than 17 labs and vitals
Patients with drug or alcohol induced mental disorders as a test case
In constructing our control sample, we excluded patients with a diagnosis of a drug- or alcohol-induced mental disorders, specifically, ICD-9 codes in the range of 291–293. Examining these patients as a potential test cases, there were 6573 patients who had these ICD-9 codes, but only 1466 of these patients had the 291–293 ICD-9 codes without additional diagnosis in the 304.* family. The classifier predicted that 57.6% of these patients belonged to the case group, compared to 21.3% of an equally-sized set of matched controls, suggesting that patients with drug- or alcohol-induced mental disorders are much more likely to also misuse opioids and develop dependence.
Classifiers that use lab test and vital signs from 20 day prior to initial diagnosis
So far, all classifiers described used vital signs and lab test from 10 day prior to initial diagnosis of substance dependence and 10 day post this diagnosis. Next, we modified the cases dataset to include only lab tests and vital signs during the 20 days prior to the initial diagnosis of substance dependence. We did this to assess whether the machine learning approach can operate in a practical clinical setting before diagnosis of substance dependence is detected and reported. Without imputation, AUROCs ranged from 0.791–0.857, with a mean of 0.833 (Additional file 7: Figure S7A). The confusion matrix showed that the classifier correctly identified 64.4% of controls and 84.5% of cases (Additional file 7: Figure S7B). Imputing by the mean, AUROCs ranged from 0.787–0.85, with a mean of 0.823 (Additional file 7: Figure S7C). The confusion matrix showed that the classifier correctly identified 63.8% of controls and 83.1% of cases (Additional file 7: Figure S7D). Imputing by the median, AUROCs ranged from 0.781–0.849, with a mean of 0.82 (Additional file 7: Figure S7E). The confusion matrix showed that the classifier correctly identified 66.4% of controls and 81.1% of cases (Additional file 7: Figure S7F). For these classifiers using lab tests and vital signs from 20 days prior to diagnosis of substance dependence, the AP scores were as follows: 0.829 (no imputation, Additional file 8: Figure S8A), 0.821 (mean imputation, Additional file 8: Figure S8B), and 0.818 (median imputation, Additional file 8: Figure S8C). Hence, we can retain comparable high quality predictions by shifting the window of 20 days to those days before initial diagnosis.
Classifiers that use diagnoses, prescriptions, and procedures
In addition to predicting substance dependence status from lab tests and vital signs, we also tested whether substance dependence status could be predicted only from 5-year clinical history of diagnoses, prescriptions, and procedures. Total number of diagnoses, prescriptions, and procedures from the 5 years before the first diagnosis of substance dependence were classified, with and without imputation. Without imputation, AUROCs ranged from 0.838–0.889, with a mean of 0.863 (Additional file 9: Figure S9A). The confusion matrix showed the classifier correctly identified 75.2% of controls and 81.8% of cases (Additional file 9: Figure S9B). Ranking all diagnoses, prescriptions, and procedures by Gini importance, the top 10 features were: methadone prescription, major depression diagnosis, trazodone prescription (used to treat major depression), interview/evaluation procedure, nicotine prescription, sodium chloride prescription, thiamine prescription, HIV diagnosis, lorazepam prescription, and personal history of allergy to penicillin diagnosis. Imputing by the mean, AUROCs ranged from 0.827–0.875, with a mean of 0.853 (Additional file 9: Figure S9C). The confusion matrix showed that the classifier correctly identified 72% of controls and 82.4% of cases (Additional file 9: Figure S9D). Imputing by the median, AUROCs ranged from 0.796–0.858, with a mean of 0.821 (Additional file 9: Figure S9E). The confusion matrix showed the classifier correctly identified 72.1% of controls and 75.4% of cases (Additional file 9: Figure S9F). For these classifiers, using the 5-year clinical history of diagnoses, prescriptions, and procedures prior to diagnosis of substance dependence, the AP scores were as follows: 0.865 (no imputation, Additional file 10: Figure S10A), 0.849 (mean imputation, Additional file 10: Figure S10B), and 0.829 (median imputation, Additional file 10: Figure S10C). Hence, we conclude that this strategy is also highly predictive. The most important features are consistent with the features described below when clinical phenotyping was applied to the original classifiers that utilized vital signs and lab tests.
Classifiers that predict overdose
Aside from predicting substance dependence status, we tested whether the diagnosis of a non-medical opioid poisoning, an overdose, could be predicted from lab tests and vital signs from data collected 6 months prior to the overdose event, with and without imputation. Lab tests and vital signs from the 6 months before the diagnosis of a non-medical opioid poisoning were classified, with and without imputation. Because these cases and control populations were small (477 cases, 4527 matched controls), the results showed more variability. Without imputation, AUROCs ranged from 0.694–0.922, with a mean of 0.822 (Additional file 11: Figure S11A). The confusion matrix showed the classifier correctly identified 67.2% of controls and 80.7% of cases (Additional file 11: Figure S11B). Imputing by the mean, AUROCs ranged from 0.69–0.951, with a mean of 0.815 (Additional file 11: Figure S11C). The confusion matrix showed that the classifier correctly identified 69.2% of controls and 79.3% of cases (Additional file 11: Figure S11D). Imputing by the median, AUROCs ranged from 0.665–0.933, with a mean of 0.811 (Additional file 11: Figure S11E). The confusion matrix showed that the classifier correctly identified 72.1% of controls and 77.5% of cases (Additional file 11: Figure S11F). Overall, these results suggest that non-medical opioid poisoning is somewhat predictive with prior knowledge about vital signs and lab tests. It is expected that with more cases, prediction quality will improve.
Clinical phenotyping of cases based on diagnoses, prescriptions, and procedures
Scatter plot of 483 diagnoses with statistically significant over- or underrepresentation in the cases compared to the controls measured using the Fisher Exact test. Each point is a diagnosis that was statistically significant. 31 diagnoses have odds ratios of less than 1
Top 10 differentially represented diagnoses during the 5 years prior to diagnosis of substance abuse (ranked by odds ratio)
Diagnosis | Odds ratio | p-value (Bonferroni-corrected) |
---|---|---|
Unspecified episodic mood disorder | 11.779 | 1.03E-138 |
Dysthymic disorder | 6.209 | 1.48E-114 |
Depressive disorder, not elsewhere classified | 6.081 | 0 |
Personal history of noncompliance with medical treatment, presenting hazards to health | 5.933 | 4.29E-107 |
Other unknown and unspecified cause of morbidity and mortality | 4.896 | 2.69E-112 |
Accidents occurring in unspecified place | 4.845 | 9.87E-110 |
Pain in limb | 4.565 | 1.66E-112 |
Cough | 4.54 | 9.00E-115 |
Lumbago | 4.301 | 1.54E-110 |
Human immunodeficiency virus [HIV] disease | 3.467 | 1.28E-136 |
Medical non-adherence, a condition where patients do not follow therapeutic recommendations, is overrepresented in the cases. This finding may support a socioeconomic difficulty in adhering to medical advice, or general apathy to medical treatment, or a refusal to take alternative medications that are not opioids, or a refusal for any psychiatric treatment. Lumbago, an older term for low back pain, is also overrepresented in the cases. Patients with lumbago are often treated with opioids, and may become addicted; or conversely, opioid users with correspondingly lower thresholds for pain may present to clinics or emergency departments with lumbago. Other pain-related diagnoses are also overrepresented in the cases. These include limb pain (OR = 4.56, p = 1.66E-112), backache (OR = 4.04, p = 5.82E-75), abdominal pain (OR = 2.68, p = 5.23E-66), chronic pain (OR = 6.47, p = 6.32E-63), chest pain (OR = 1.95, p = 3.49E-43), and others. Diagnoses underrepresented in the cases include those related with pregnancy, such as “supervision of other normal pregnancy,” “outcome of delivery, single liveborn,” and “post term pregnancy, delivered, with or without mention of antepartum condition.” This is related to the suggestion that pregnant patients are among the least likely to seek care from multiple institutions, while HIV and chronic pain patients are among the most likely to seek care from multiple institutions [30].
Top 10 differentially represented prescriptions during the 5 years prior to diagnosis of substance abuse (ranked by odds ratio)
Prescription | Odds ratio | p-value (Bonferroni-corrected) |
---|---|---|
Methadone | 45.956 | 0 |
Nicotine | 26.239 | 0 |
Thiamine | 12.861 | 1.58E-277 |
quetiapine | 11.553 | 3.27E-241 |
Trazodone | 9.863 | 0 |
Clonazepam | 8.38 | 7.21E-165 |
Haloperidol | 6.82 | 8.28E-178 |
Folic Acid | 5.28 | 5.03E-202 |
Lorazepam | 4.745 | 2.02E-231 |
Ibuprofen | 4.64 | 2.58E-202 |
Top 10 differentially represented procedures during the 5 years prior to diagnosis of substance abuse (ranked by odds ratio)
Procedure | Odds ratio | p-value (Bonferroni-corrected) |
---|---|---|
Other group therapy | 19.8 | 1.23E-69 |
Interview & Evaluation NEC | 11.578 | 4.06E-42 |
Psychiatric Mental Determination | 10.597 | 1.24E-29 |
Exploratory verbal psychotherapy | 10.371 | 2.41E-57 |
Brief interview & evaluation | 6.224 | 9.00E-201 |
Limited interview/evaluation | 5.804 | 2.71E-279 |
Interview & evaluation NOS | 5.745 | 6.16E-102 |
Comprehensive interview/evaluation | 5.065 | 1.71E-147 |
Other counselling | 4.3 | 3.93E-36 |
Other fetal monitoring | 0.218 | 1.14E-30 |
Discussion
Using lab tests and vital signs proximal to the diagnosis date of substance dependence as input, we tested the ability of a Random Forest machine learning classifier to predict whether a patient will be diagnosed with substance dependence. Using a baseline of 50/50 chance to diagnose a patient as substance dependent, the best classifier performed well above chance. The best classifier correctly predicted whether a patient is not substance-dependent ~ 76% of the times, and whether a patient is a substance-dependent ~ 92% of the times. While these results are promising, there is room for improvement before a clinical implementation. The measurements that distinguished substance-dependent patients from non-substance dependent patients, as determined by effect size, Gini importance, or by the Wilcoxon rank-sum test, were mostly related to white blood cells, protein, blood gases, blood volume and blood cell width. The relationships between these lab tests and vital signs in the context of substance dependence diagnosis can be explained. It is encouraging that the laboratory results and vital signs identified by the classifier have well-known relationships to pain syndromes and opioid use. Respiratory rate, for instance, has been shown to be elevated in many painful conditions, and decreased in opioid overdose. Respiratory rate will directly affect blood gases. White blood cell counts have also been shown to fluctuate in response to trauma and surgery, with a decline in lymphocytes and an increase in polymorphonuclear leukocytes (PMNs). Compared to the substance-dependent cases, our control cohort showed the same pattern as prior studies of trauma patients. In addition to classification using clinical measures, we attempted to classify patients with, as well as examined the prevalence of, diagnoses, prescriptions, and procedures in the case and control populations during the five years before diagnosis of substance dependence. The diagnoses most overrepresented were psychiatric, supporting the close association between substance abuse and psychiatric comorbidities as reported before [7–10]. Agreeing with this, the medical procedures most overrepresented in the cases were various types of psychiatric evaluations and interviews. The prescriptions most overrepresented in the cases were related to opioid treatment and malnourishment, as many drug abusers arrive at the hospital in a malnourished state. Examining all opioid prescriptions in the MSMC-EHR, opioids were prescribed to a large portion of patients, and patients diagnosed with an opioid use disorder had significantly more opioid prescriptions than patients who were given few opioid prescriptions. Future work may include other features for predicting substance dependence status. These can be combined with the clinical features we already used here. Additionally, other machine learning methods such as deep learning may perform better than the Random Forest classifier we employed. The case and control populations could also be made larger by integrating other EHR systems. It is possible that results will vary when examining distinct populations across hospitals in different cities and countries. The current study is focused on patients with diagnoses in the 304 family (drug dependence), but there are other ICD-9 families related to drug abuse. The 305 family, which denotes non-dependent substance abuse, was commonly used for patients with alcohol and tobacco use disorders. For this reason, we focused on the 304 family of ICD-9 diagnoses. Finally, future studies can examine gene variants that are enriched in the cases compared to the controls. Such analysis can identify genetics variants that may influence propensity for drug abuse and at the same time point further to mechanisms of action. The machine learning classifiers developed here can increase the size of the case population to improve the statistical power needed to identify true variants.
Conclusions
Through analyzing of the health records of hundreds of thousands individuals in the MSMC-EHR with a machine learning framework, we furthered characterized opioid dependent patients using physiological measurements. We found that opioid dependent patients have significantly higher WBC and respiratory disturbances. Opioid dependent patients are also commonly malnourished which is characterized by low RCDW and blood albumin compared to controls. Clinical phenotyping analysis discovered that opioid dependent patients are more likely to suffer from psychiatric disorders and manifest pain-related symptoms. The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room. It should be noted that marking a patient with an opioid dependency ICD code, which is commonly used for insurance purposes, is often inaccurate and inconclusive. Hence, we recommend that the results from our study should be considered preliminary, and the quality of the real cases disputable. The study should be validated by other independent EHR systems and different computational approaches. Regardless, the multi-variate non-linear characteristic of the classifiers developed here, combine unique mixture of the values of many measured variables together to produce predictions not possible by looking at a single biomarker. The complex relationships between measured variables would be difficult to detect via an in-person clinical assessment alone. Hence, the predictive machine learning classifiers we developed can alert physicians about the potential of patients to have opioid dependency from routine lab tests and vital signs. However, there are still technical, administrative, and bureaucratic barriers for real implementation.
Declarations
Acknowledgements
We thank the Dudley Lab at Mount Sinai for sharing their processed de-identified MSMC-EHR dataset with us for this project.
Funding
This work is supported by NIH grants U54-HL127624 (LINCS-DCIC), U24-CA224260 (IDG-KMC), and OT3-OD025467 (NIH Data Commons).
Availability of data and materials
None available.
Authors’ contributions
AM conceived and managed the research project. RJE and ZW performed all the analyses. NG provided clinical implications and interpretations. RJE, ZW, NG and AM wrote the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study has been granted exemption from human-subject research by the Program for the Protection of Human Subjects (PPHS) at the Institutional Review Boards (IRB), Mount Sinai Health System. The project number is HS#:18–00993.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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