Trends in Prescribing Opioids, Benzodiazepines, and Both Among Adults with Alcohol Use Disorder in New York State.

BACKGROUND: Alcohol use disorder (AUD) is a highly prevalent public health problem that contributes to opioid- and benzodiazepine-related morbidity and mortality. Even though co-utilization of these substances is particularly harmful, data are sparse on opioid or benzodiazepine prescribing patterns among individuals with AUD. OBJECTIVE: To estimate temporal trends and disparities in opioid, benzodiazepine, and opioid/benzodiazepine co-prescribing among individuals with AUD in New York State (NYS). DESIGN/PARTICIPANTS: Serial cross-sectional study analyzing merged data from the NYS Office of Addiction Services and Supports (OASAS) and the NYS Department of Health Medicaid Data Warehouse. Subjects with a first admission to an OASAS treatment program from 2005-2018 and a primary AUD were included. A total of 148,328 subjects were identified. MEASURES: Annual prescribing rates of opioids, benzodiazepines, or both between the pre- (2005-2012) and post- (2013-2018) Internet System for Tracking Over-Prescribing (I-STOP) periods. I-STOP is a prescription monitoring program implemented in NYS in August 2013. Analyses were stratified based on sociodemographic factors (age, sex, race/ethnicity, and location). RESULTS: Opioid prescribing rates decreased between the pre- and post-I-STOP periods from 25.1% (95% CI, 24.9-25.3%) to 21.3% (95% CI, 21.2-21.4; P <.001), while benzodiazepine (pre: 9.96% [95% CI, 9.83-10.1%], post: 9.92% [95% CI, 9.83-10.0%]; P =.631) and opioid/benzodiazepine prescribing rates remained unchanged (pre: 3.01% vs. post: 3.05%; P =.403). After I-STOP implementation, there was a significant decreasing trend in opioid (change, -1.85% per year, P <.0001), benzodiazepine (-0.208% per year, P =.0184), and opioid/benzodiazepine prescribing (-0.267% per year, P <.0001). Opioid, benzodiazepine, and co-prescription rates were higher in females, White non-Hispanics, and rural regions. CONCLUSIONS: Among those with AUD, opioid prescribing decreased following NYS I-STOP program implementation. While both benzodiazepine and opioid/benzodiazepine co-prescribing rates remained high, a decreasing trend was evident after program implementation. Continuing high rates of opioid and benzodiazepine prescribing necessitate the development of innovative approaches to improve the quality of care.

A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information - A merging of two administrative data sources.

OBJECTIVE: Despite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders. METHODS: Data from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims. RESULTS: A total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition. CONCLUSION: The integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study.

BACKGROUND: Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. OBJECTIVE: The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars. METHODS: We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA2DS2-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA2DS2­VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke. RESULTS: The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion. CONCLUSIONS: Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Improving patient safety reporting with the common formats: Common data representation for Patient Safety Organizations.

Medical errors and patient safety issues remain a significant problem for the healthcare industry in the United States. The Institute of Medicine report To Err is Human reported that there were as many as 98,000 deaths per year due to medical error as of 1999. Many authors and government officials believe that the first step on the path to improvement in patient safety is more comprehensive collection and analysis of patient safety events. The belief is that this will enable safety improvements based on data showing the nature and frequency of events that occur, and the effectiveness of interventions. This systematization of healthcare practice can be a step in the right direction toward a value based, safety conscious and effective healthcare system. To help standardize this reporting and analysis, AHRQ created Common Formats for Patient Safety data collection and reporting. This manuscript describes the development of patient safety reporting and learning through the Patient Safety Organizations (PSO)s and the Common Formats and gives readers an overview of how the system is expected to function and the breadth of development of the Common Formats to date.

Combating Ebola with Repurposed Therapeutics Using the CANDO Platform.

Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.

An observational study of the quality of care for chronic kidney disease: a Buffalo and Albany, New York metropolitan area study.

BACKGROUND: The database of a major regional health insurer was employed to identify the number and frequency of covered patients with chronic kidney disease (CKD). We then examined the characteristics of their care as defined, in part, by the frequency of physician visits and specialty referral, the characteristics of laboratory testing and total costs as indices of the quality of care of the subject population. METHODS: This retrospective, cross-sectional study analyzed insurance claims, laboratory results and medication prescription data. Patients with two estimated glomerular filtration rate readings below 60 ml/min/1.73 m(2) (n = 20,388) were identified and classified by CKD stage. RESULTS: The prevalence of CKD stages 3a and above was 12 %. Vascular comorbidities were common with prevalence increasing steadily from stage 3a through stage 5. Only 55.6 % of stage 4 CKD patients had claims for nephrology visits within one year of their index date. Fifty-nine percent of patients had claims for renin-angiotensin system (RAS) blockers. Twenty-five percent of patients in stage 3a CKD filled a prescription for non-steroidal anti-inflammatory drugs. Fifty-two percent of patients who developed end-stage renal disease received their first dialysis treatment as inpatients. CONCLUSIONS: The pattern of medical practice observed highlights apparent deficiencies in the care of CKD patients including inappropriate medication use, delayed nephrology referral, and a lack of preparation for dialysis. This study shows the potential value of using large patient databases available through insurers to assess and likely improve regional CKD care.

Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system. It was also assessed on the manually annotated, 2010 i2b2 challenge data. Performance was established with regard to precision, recall, and F-measure for each of the concepts within the VA documents using bootstrapping. Within that corpus, concepts identified by MCVS were broadly distributed throughout SNOMED CT, and the token-order-specific language model achieved better performance based on precision, recall, and F-measure (0.95±0.15, 0.96±0.16, and 0.95±0.16, respectively; mean±SD) than the bag-of-words based, naïve Bayes model (0.64±0.45, 0.61±0.46, and 0.60±0.45, respectively) that has previously been used for concept mapping. Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model. RapTAT required just 7.2ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models.

BACKGROUND: Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care. Improving predictive models can help providers and families navigate these unique challenges. OBJECTIVE: Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission. METHODS: Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model. RESULTS: Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension II (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications. CONCLUSIONS: Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information. Evaluation of larger, more diverse data sets may provide additional clarity on comparative performance.

Buprenorphine, Norbuprenorphine, and Naloxone Levels in Adulterated Urine Samples: Can They be Detected When Buprenorphine/Naloxone Film is Dipped into Urine or Water?

Reportedly, various urine manipulations can be performed by opioid use disorder (OUD) patients who are on buprenorphine/naloxone medications to disguise their non-compliance to the treatment. One type of manipulation is known as "spiking" adulteration, directly dipping a buprenorphine/naloxone film into urine. Identifying this type of urine manipulation has been the aim of many previous studies. These studies have revealed urine adulterations through inappropriately high levels of "buprenorphine" and "naloxone" and a very small amount of "norbuprenorphine." So, does the small amount of "norbuprenorphine" in the adulterated urine samples result from dipped buprenorphine/naloxone film, or is it a residual metabolite of buprenorphine in the patient's system? This pilot study utilized 12 urine samples from 12 participants, as well as water samples as a control. The samples were subdivided by the dipping area and time, as well as the temperature and concentration of urine samples, and each sublingual generic buprenorphine/naloxone film was dipped directly into the samples. Then, the levels of "buprenorphine," "norbuprenorphine," "naloxone," "buprenorphine-glucuronide" and "norbuprenorphine-glucuronide" were examined by Liquid Chromatography with tandem mass spectrometry (LC-MS/MS). The results of this study showed that high levels of "buprenorphine" and "naloxone" and a small amount of "norbuprenorphine" were detected in both urine and water samples when the buprenorphine/naloxone film was dipped directly into these samples. However, no "buprenorphine-glucuronide" or "norbuprenorphine-glucuronide" were detected in any of the samples. In addition, the area and timing of dipping altered "buprenorphine" and "naloxone" levels, but concentration and temperature did not. This study's findings could help providers interpret their patients' urine drug test results more accurately, which then allows them to monitor patient compliance and help them identify manipulation by examining patient urine test results.

The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective.

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.

Defining the Subtypes of Long COVID and Risk Factors for Prolonged Disease: Population-Based Case-Crossover Study.

BACKGROUND: There have been over 772 million confirmed cases of COVID-19 worldwide. A significant portion of these infections will lead to long COVID (post-COVID-19 condition) and its attendant morbidities and costs. Numerous life-altering complications have already been associated with the development of long COVID, including chronic fatigue, brain fog, and dangerous heart rhythms. OBJECTIVE: We aim to derive an actionable long COVID case definition consisting of significantly increased signs, symptoms, and diagnoses to support pandemic-related clinical, public health, research, and policy initiatives. METHODS: This research employs a case-crossover population-based study using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) data generated at Veterans Affairs medical centers nationwide between January 1, 2020, and August 18, 2022. In total, 367,148 individuals with ICD-10-CM data both before and after a positive COVID-19 test were selected for analysis. We compared ICD-10-CM codes assigned 1 to 7 months following each patient's positive test with those assigned up to 6 months prior. Further, 350,315 patients had novel codes assigned during this window of time. We defined signs, symptoms, and diagnoses as being associated with long COVID if they had a novel case frequency of ≥1:1000, and they significantly increased in our entire cohort after a positive test. We present odds ratios with CIs for long COVID signs, symptoms, and diagnoses, organized by ICD-10-CM functional groups and medical specialty. We used our definition to assess long COVID risk based on a patient's demographics, Elixhauser score, vaccination status, and COVID-19 disease severity. RESULTS: We developed a long COVID definition consisting of 323 ICD-10-CM diagnosis codes grouped into 143 ICD-10-CM functional groups that were significantly increased in our 367,148 patient post-COVID-19 population. We defined 17 medical-specialty long COVID subtypes such as cardiology long COVID. Patients who were COVID-19-positive developed signs, symptoms, or diagnoses included in our long COVID definition at a proportion of at least 59.7% (268,320/449,450, based on a denominator of all patients who were COVID-19-positive). The long COVID cohort was 8 years older with more comorbidities (2-year Elixhauser score 7.97 in the patients with long COVID vs 4.21 in the patients with non-long COVID). Patients who had a more severe bout of COVID-19, as judged by their minimum oxygen saturation level, were also more likely to develop long COVID. CONCLUSIONS: An actionable, data-driven definition of long COVID can help clinicians screen for and diagnose long COVID, allowing identified patients to be admitted into appropriate monitoring and treatment programs. This long COVID definition can also support public health, research, and policy initiatives. Patients with COVID-19 who are older or have low oxygen saturation levels during their bout of COVID-19, or those who have multiple comorbidities should be preferentially watched for the development of long COVID.

The implications of APOBEC3-mediated C-to-U RNA editing for human disease.

Intra-organism biodiversity is thought to arise from epigenetic modification of constituent genes and post-translational modifications of translated proteins. Here, we show that post-transcriptional modifications, like RNA editing, may also contribute. RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosine to uracil. RNAsee (RNA site editing evaluation) is a computational tool developed to predict the cytosines edited by these enzymes. We find that 4.5% of non-synonymous DNA single nucleotide polymorphisms that result in cytosine to uracil changes in RNA are probable sites for APOBEC3A/G RNA editing; the variant proteins created by such polymorphisms may also result from transient RNA editing. These polymorphisms are associated with over 20% of Medical Subject Headings across ten categories of disease, including nutritional and metabolic, neoplastic, cardiovascular, and nervous system diseases. Because RNA editing is transient and not organism-wide, future work is necessary to confirm the extent and effects of such editing in humans.

Exploring the frontier of electronic health record surveillance: the case of postoperative complications.

BACKGROUND: The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of 9 postoperative complications. METHODS: Postoperative complications from 6 medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complications. Algorithms were developed from VASQIP outcome definitions using NLP-coded concepts, regular expressions, and structured data. The VASQIP nurse reviewer served as the reference standard for evaluating sensitivity and specificity. The algorithms were designed in the development and evaluated in the test dataset. RESULTS: Sensitivity and specificity in the test set were 85% and 92% for acute renal failure, 80% and 93% for sepsis, 56% and 94% for deep vein thrombosis, 80% and 97% for pulmonary embolism, 88% and 89% for acute myocardial infarction, 88% and 92% for cardiac arrest, 80% and 90% for pneumonia, 95% and 80% for urinary tract infection, and 77% and 63% for wound infection, respectively. A third of the complications occurred outside of the hospital setting. CONCLUSIONS: Computer algorithms on data extracted from the electronic health record produced respectable sensitivity and specificity across a large sample of patients seen in 6 different medical centers. This study demonstrates the utility of combining NLP with structured data for mining the information contained within the electronic health record.

Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes.

BACKGROUND: An effective national biosurveillance system expedites outbreak recognition and facilitates response coordination at the federal, state, and local levels. The BioSense system, used at the Centers for Disease Control and Prevention, incorporates chief complaints but not data from the whole encounter note into its surveillance algorithms. OBJECTIVE: To evaluate whether biosurveillance by using data from the whole encounter note is superior to that using data from the chief complaint field alone. DESIGN: 6-year retrospective case-control cohort study. SETTING: Mayo Clinic, Rochester, Minnesota. PARTICIPANTS: 17,243 persons tested for influenza A or B virus between 1 January 2000 and 31 December 2006. MEASUREMENTS: The accuracy of a model based on signs and symptoms to predict influenza virus infection in patients with upper respiratory tract symptoms, and the ability of a natural language processing technique to identify definitional clinical features from free-text encounter notes. RESULTS: Surveillance based on the whole encounter note was superior to the chief complaint field alone. For the case definition used by surveillance of the whole encounter note, the normalized partial area under the receiver-operating characteristic curve (specificity, 0.1 to 0.4) for surveillance using the whole encounter note was 92.9% versus 70.3% for surveillance with the chief complaint field (difference, 22.6%; P < 0.001). Comparison of the 2 models at the fixed specificity of 0.4 resulted in sensitivities of 89.0% and 74.4%, respectively (P < 0.001). The relative risk for missing a true case of influenza was 2.3 by using the chief complaint field model. LIMITATIONS: Participants were seen at 1 tertiary referral center. The cost of comprehensive biosurveillance monitoring was not studied. CONCLUSION: A biosurveillance model for influenza using the whole encounter note is more accurate than a model that uses only the chief complaint field. Because case-defining signs and symptoms of influenza are commonly available in health records, the investigators believe that the national strategy for biosurveillance should be changed to incorporate data from the whole health record. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

Differential Perceptions of What Constitutes a Medical Error Associated with Electronic Medical Records.

Perceptions of errors associated with healthcare information technology (HIT) often depend on the context and position of the viewer. HIT vendors posit very different causes of errors than clinicians, implementation teams, or IT staff. Even within the same hospital, members of departments and services often implicate other departments. Organizations may attribute errors to external care partners that refer patients, such as nursing homes or outside clinics. Also, the various clinical roles within an organization (e.g., physicians, nurses, pharmacists) can conceptualize errors and their root causes differently. Overarching all these perceptual factors, the definitions, mechanisms, and incidence of HIT-related errors are remarkably conflictual. There is neither a universal standard for defining or counting these errors. This paper attempts to enumerate and clarify the issues related to differential perceptions of medical errors associated with HIT. It then suggests solutions.

Association Between Buprenorphine Dose and the Urine "Norbuprenorphine" to "Creatinine" Ratio: Revised.

Background: Utilizing a 1-year chart review as the data, Furo et al. conducted a research study on an association between buprenorphine dose and the urine "norbuprenorphine" to "creatinine" ratio and found significant differences in the ratio among 8-, 12-, and 16-mg/day groups with an analysis of variance (ANOVA) test. This study expands the data for a 2-year chart review and is intended to delineate an association between buprenorphine dose and the urine "norbuprenorphine" to "creatinine" ratio with a higher statistical power. Methods: This study performed a 2-year chart review of data for the patients living in a halfway house setting, where their drug administration was closely monitored. The patients were on buprenorphine prescribed at an outpatient clinic for opioid use disorder (OUD), and their buprenorphine prescription and dispensing information were confirmed by the New York Prescription Drug Monitoring Program (PDMP). Urine test results in the electronic health record (EHR) were reviewed, focusing on the "buprenorphine," "norbuprenorphine," and "creatinine" levels. The Kruskal-Wallis H and Mann-Whitney U tests were performed to examine an association between buprenorphine dose and the "norbuprenorphine" to "creatinine" ratio. Results: This study included 371 urine samples from 61 consecutive patients and analyzed the data in a manner similar to that described in the study by Furo et al. This study had similar findings with the following exceptions: (1) a mean buprenorphine dose of 11.0 ± 3.8 mg/day with a range of 2 to 20 mg/day; (2) exclusion of 6 urine samples with "creatinine" level <20 mg/dL; (3) minimum "norbuprenorphine" to "creatinine" ratios in the 8-, 12-, and 16-mg/day groups of 0.44 × 10-4 (n = 68), 0.1 × 10-4 (n = 133), and 1.37 × 10-4 (n = 82), respectively; however, after removing the 2 lowest outliers, the minimum "norbuprenorphine" to "creatinine" ratio in the 12-mg/day group was 1.6 × 10-4, similar to the findings in the previous study; and (4) a significant association between buprenorphine dose and the urine "norbuprenorphine" to "creatinine" ratios from the Kruskal-Wallis test (P < .01). In addition, the median "norbuprenorphine" to "creatinine" ratio had a strong association with buprenorphine dose, and this association could be formulated as: [y = 2.266 ln(x) + 0.8211]. In other words, the median ratios in 8-, 12-, and 16-mg/day groups were 5.53 × 10-4, 6.45 × 10-4, and 7.10 × 10-4, respectively. Therefore, any of the following features should alert providers to further investigate patient treatment compliance: (1) inappropriate substance(s) in urine sample; (2) "creatinine" level <20 mg/dL; (3) "buprenorphine" to "norbuprenorphine" ratio >50:1; (4) buprenorphine dose >24 mg/day; or (5) "norbuprenorphine" to "creatinine" ratios <0.5 × 10-4 in patients who are on 8 mg/day or <1.5 × 10-4 in patients who are on 12 mg/day or more. Conclusion: The results of the present study confirmed those of the previous study regarding an association between buprenorphine dose and the "norbuprenorphine" to "creatinine" ratio, using an expanded data set. Additionally, this study delineated a clearer relationship, focusing on the median "norbuprenorphine" to "creatinine" ratios in different buprenorphine dose groups. These results could help providers interpret urine test results more accurately and apply them to outpatient opioid treatment programs for optimal treatment outcomes.

The Role of C-to-U RNA Editing in Human Biodiversity.

Intra-organism biodiversity is thought to arise from epigenetic modification of our constituent genes and post-translational modifications after mRNA is translated into proteins. We have found that post-transcriptional modification, also known as RNA editing, is also responsible for a significant amount of our biodiversity, substantively expanding this story. The APOBEC (apolipoprotein B mRNA editing catalytic polypeptide-like) family RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosines to uracils (C>U) in specific stem-loop structures.1,2 We used RNAsee (RNA site editing evaluation), a tool developed to predict the locations of APOBEC3A/G RNA editing sites, to determine whether known single nucleotide polymorphisms (SNPs) in DNA could be replicated in RNA via RNA editing. About 4.5% of non-synonymous SNPs which result in C>U changes in RNA, and about 5.4% of such SNPs labelled as pathogenic, were identified as probable sites for APOBEC3A/G editing. This suggests that the variant proteins created by these DNA mutations may also be created by transient RNA editing, with the potential to affect human health. Those SNPs identified as potential APOBEC3A/G-mediated RNA editing sites were disproportionately associated with cardiovascular diseases, digestive system diseases, and musculoskeletal diseases. Future work should focus on common sites of RNA editing, any variant proteins created by these RNA editing sites, and the effects of these variants on protein diversity and human health. Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential.

COVID-19 vaccination and venous thromboembolism risk in older veterans.

Introduction: It is important for SARS-CoV-2 vaccine providers, vaccine recipients, and those not yet vaccinated to be well informed about vaccine side effects. We sought to estimate the risk of post-vaccination venous thromboembolism (VTE) to meet this need. Methods: We conducted a retrospective cohort study to quantify excess VTE risk associated with SARS-CoV-2 vaccination in US veterans age 45 and older using data from the Department of Veterans Affairs (VA) National Surveillance Tool. The vaccinated cohort received at least one dose of a SARS-CoV-2 vaccine at least 60 days prior to 3/06/22 (N = 855,686). The control group was those not vaccinated (N = 321,676). All patients were COVID-19 tested at least once before vaccination with a negative test. The main outcome was VTE documented by ICD10-CM codes. Results: Vaccinated persons had a VTE rate of 1.3755 (CI: 1.3752-1.3758) per thousand, which was 0.1 percent over the baseline rate of 1.3741 (CI: 1.3738-1.3744) per thousand in the unvaccinated patients, or 1.4 excess cases per 1,000,000. All vaccine types showed a minimal increased rate of VTE (rate of VTE per 1000 was 1.3761 (CI: 1.3754-1.3768) for Janssen; 1.3757 (CI: 1.3754-1.3761) for Pfizer, and for Moderna, the rate was 1.3757 (CI: 1.3748-1.3877)). The tiny differences in rates comparing either Janssen or Pfizer vaccine to Moderna were statistically significant (p < 0.001). Adjusting for age, sex, BMI, 2-year Elixhauser score, and race, the vaccinated group had a minimally higher relative risk of VTE as compared to controls (1.0009927 CI: 1.007673-1.0012181; p < 0.001). Conclusion: The results provide reassurance that there is only a trivial increased risk of VTE with the current US SARS-CoV-2 vaccines used in veterans older than age 45. This risk is significantly less than VTE risk among hospitalized COVID-19 patients. The risk-benefit ratio favors vaccination, given the VTE rate, mortality, and morbidity associated with COVID-19 infection.

Identification of patient characteristics associated with survival benefit from metformin treatment in patients with stage I non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) continues to be a major cause of cancer deaths. Previous investigation has suggested that metformin use can contribute to improved outcomes in NSCLC patients. However, this association is not uniform in all analyzed cohorts, implying that patient characteristics might lead to disparate results. Identification of patient characteristics that affect the association of metformin use with clinical benefit might clarify the drug's effect on lung cancer outcomes and lead to more rational design of clinical trials of metformin's utility as an intervention. In this study, we examined the association of metformin use with long-term mortality benefit in patients with NSCLC and the possible modulation of this benefit by body mass index (BMI) and smoking status, controlling for other clinical covariates. METHODS: This was a retrospective cohort study in which we analyzed data from the Veterans Affairs (VA) Tumor Registry in the United States. Data from all patients with stage I NSCLC from 2000 to 2016 were extracted from a national database, the Corporate Data Warehouse that captures data from all patients, primarily male, who underwent treatment through the VA health system in the United States. Metformin use was measured according to metformin prescriptions dispensed to patients in the VA health system. The association of metformin use with overall survival (OS) after diagnosis of stage I NSCLC was examined. Patients were further stratified according to BMI and smoking status (previous vs current) to examine the association of metformin use with OS across these strata. RESULTS: Metformin use was associated with improved survival in patients with stage I NSCLC (average hazard ratio, 0.82; P < .001). An interaction between the effect of metformin use and BMI on OS was observed (χ2 = 3268.42; P < .001) with a greater benefit of metformin use observed in patients as BMI increased. Similarly, an interaction between smoking status and metformin use on OS was also observed (χ2 = 2997.05; P < .001) with a greater benefit of metformin use observed in previous smokers compared with current smokers. CONCLUSIONS: In this large retrospective study, we showed that a survival benefit is enjoyed by users of metformin in a robust stage I NSCLC patient population treated in the VA health system. Metformin use was associated with an 18% improved OS. This association was stronger in patients with a higher BMI and in previous smokers. These observations deserve further mechanistic study and can help rational design of clinical trials with metformin in patients with lung cancer.