Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies

Informing evidence-based medicine for opioid use disorder using pharmacoeconomic studies

INTRODUCTION

The health and economic consequences of inadequately treated opioid use disorder (OUD) are substantial. Healthcare systems in the United States (US) and other countries are facing a growing healthcare crisis due to opioids. Although effective medications for OUD exist, relying solely on clinical information is insufficient for addressing the opioid crisis.

AREAS COVERED

In this review, the role of pharmacoeconomic studies in informing evidence-based medication treatment for OUD is discussed, with a particular emphasis on the US healthcare system, where the economic burden is significantly higher than the global average. The scope/objective of pharmacoeconomics as a distinct scientific research program is briefly defined, followed by a discussion of existing evidence informed by data from systematic reviews, in addition to a convenience sample of recently published pharmacoeconomic studies and protocols. The review also explores the need for methodological advancements in the field.

EXPERT OPINION

Despite the potential of pharmacoeconomic research in shaping evidence-based medicine for OUD, significant challenges limiting its real-world application remain. How to address these challenges are explored, including how to combine cost-effectiveness and budget impact analyses to address the needs of the healthcare system as a whole and specific stakeholders interested in adopting new OUD treatment strategies.

Risk of Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) Among Patients with Type 2 Diabetes Mellitus on Anti-Hyperglycemic Medications

Background

Observed activity of metformin in reducing the risk of severe COVID-19 suggests a potential use of the anti-hyperglycemic in the prevention of post-acute sequelae of SARS-CoV-2 infection (PASC). We assessed the 3-month and 6-month risk of PASC among patients with type 2 diabetes mellitus (T2DM) comparing metformin users to sulfonylureas (SU) or dipeptidyl peptidase-4 inhibitors (DPP4i) users.

Methods

We used de-identified patient level electronic health record data from the National Covid Cohort Collaborative (N3C) between October 2021 and April 2023. Participants were adults ≥ 18 years with T2DM who had at least one outpatient healthcare encounter in health institutions in the United States prior to COVID-19 diagnosis. The outcome of PASC was defined based on the presence of a diagnosis code for the illness or using a predicted probability based on a machine learning algorithm. We estimated the 3-month and 6-month risk of PASC and calculated crude and weighted risk ratios (RR), risk differences (RD), and differences in mean predicted probability.

Results

We identified 5596 (mean age: 61.1 years; SD: 12.6) and 1451 (mean age: 64.9 years; SD 12.5) eligible prevalent users of metformin and SU/DPP4i respectively. We did not find a significant difference in risk of PASC at 3 months (RR = 0.86 [0.56; 1.32], RD = -3.06 per 1000 [-12.14; 6.01]), or at 6 months (RR = 0.81 [0.55; 1.20], RD = -4.91 per 1000 [-14.75, 4.93]) comparing prevalent users of metformin to prevalent users of SU/ DPP4i. Similar observations were made for the outcome definition using the ML algorithm.

Conclusion

The observed estimates in our study are consistent with a reduced risk of PASC among prevalent users of metformin, however the uncertainty of our confidence intervals warrants cautious interpretations of the results. A standardized clinical definition of PASC is warranted for thorough evaluation of the effectiveness of therapies under assessment for the prevention of PASC.

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

The Impact of Longitudinal Data-Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores

The impact of electronic health record (EHR) discontinuity (i.e., receiving care outside of a given EHR system) on EHR-based risk prediction is unknown. We aimed to assess the impact of EHR-continuity on the performance of clinical risk scores. The study cohort consisted of patients aged ≥ 65 years with ≥ 1 EHR encounter in the 2 networks in Massachusetts (MA; 2007/1/1-2017/12/31, internal training and validation dataset), and one network in North Carolina (NC; 2007/1/1-2016/12/31, external validation dataset) that were linked with Medicare claims data. Risk scores were calculated using EHR data alone vs. linked EHR-claims data (not subject to misclassification due to EHR-discontinuity): (i) combined comorbidity score (CCS), (ii) claim-based frailty score (CFI), (iii) CHAD2 DS2 -VASc, and (iv) Hypertension, Abnormal renal/liver function, Stroke, Bleeding, Labile, Elderly, and Drugs (HAS-BLED). We assessed the performance of CCS and CFI predicting death, CHAD2 DS2 -VASc predicting ischemic stroke, and HAS-BLED predicting bleeding by area under receiver operating characteristic curve (AUROC), stratified by quartiles of predicted EHR-continuity (Q1-4). There were 319,740 patients in the MA systems and 125,380 in the NC system. In the external validation dataset, AUROC for EHR-based CCS predicting 1-year risk of death was 0.583 in Q1 (lowest) EHR-continuity group, which increased to 0.739 in Q4 (highest) EHR-continuity group. The corresponding improvement in AUROC was 0.539 to 0.647 for CFI, 0.556 to 0.637 for CHAD2 DS2 -VASc, and 0.517 to 0.556 for HAS-BLED. The AUROC in Q4 EHR-continuity group based on EHR alone approximates that based on EHR-claims data. The prediction performance of four clinical risk scores was substantially worse in patients with lower vs. high EHR-continuity.

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network

BACKGROUND

Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation.

RESULT

The experimental results on EMRs from medical and health institutions in Hainan Province, China show that the proposed MR-KPA model can effectively improve the accuracy of medication recommendation on small-scale longitudinal EMR data compared with existing representative methods.

CONCLUSION

The advantages of the proposed MR-KPA are mainly attributed to knowledge enhancement based on ontology embedding, the pre-training visit model and adversarial training. Each of these three optimizations is very effective for improving the capability of medication recommendation on small-scale longitudinal EMR data, and the pre-training visit model has the most significant improvement effect. These three optimizations are also complementary, and their integration makes the proposed MR-KPA model achieve the best recommendation effect.

An algorithm to predict data completeness in oncology electronic medical records for comparative effectiveness research

INTRODUCTION

Electronic health record (EHR) discontinuity (missing out-of-network encounters) can lead to information bias. We sought to construct an algorithm that identifies high EHR-continuity among oncology patients.

METHODS

Using a linked Medicare-EHR database and regression, we sought to 1) measure how often Medicare claims for outpatient encounters were substantiated by visits recorded in the EHR, and 2) predict continuity ratio, defined as the yearly proportion of outpatient encounters reported to Medicare that were captured by EHR data. The prediction model...s performance was evaluated with the coefficient of determination and Spearman...s correlation. We quantified variable misclassification by decile of continuity ratio using standardized difference and sensitivity.

RESULTS

A total of 79,678 subjects met all eligibility criteria. Predicted and observed continuity was highly correlated (σ_Spearman=0.86). On average across all variables measured, MSD was reduced by a factor of 1/7^th and sensitivity was improved 35-fold comparing subjects in the highest vs. lowest decile of CR.

CONCLUSION

In the oncology population, restricting EHR-based study cohorts to subjects with high continuity may reduce misclassification without greatly impacting representativeness. Further work is needed to elucidate the best manner of implementing continuity prediction rules in cohort studies.

Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study

BACKGROUND

While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size.

METHODS

Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome.

RESULTS

6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32-1.44), and mortality (RR 0.82, 0.41-1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28-0.98, p = 0.044) and mortality (RR 0.56, 95%CI 0.33-0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control.

CONCLUSIONS

There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide.

Effectiveness and Safety of Reduced and Standard Daily Doses of Direct Oral Anticoagulants in Patients with Nonvalvular Atrial Fibrillation: A Cohort Study Using National Database Representing the Japanese Population

Purpose

To compare the effectiveness and safety of reduced or standard daily doses of direct oral anticoagulants (DOACs) with warfarin in Japanese patients with nonvalvular atrial fibrillation (NVAF). We used post-hoc analyses to identify patient groups that could benefit from reduced-dose DOACs.

Patients and Methods

Using the National Database of Health Insurance Claims and Specific Health Checkups of Japan, we identified 944,776 patients with NVAF who had started an oral anticoagulant after at least one year of non-use between April 2011 and March 2016. We matched patients taking any, reduced, or standard doses of DOACs 1:1 with those taking warfarin. We measured treatment effectiveness based on admission due to stroke or systemic embolism (S/SE) and safety based on admission due to any bleeding (defined as major bleeding, MB). We compared both outcomes between DOACs and warfarin using the Cox proportional hazards model. We used post-hoc analysis to match patients receiving reduced-dose DOACs to those receiving standard-dose DOACs and compared treatment effectiveness and safety.

Results

More than half of patients receiving DOACs used a reduced dose. The occurrences of S/SE and MB in patients receiving any, reduced, or standard doses of DOACs were equal to or lower than those receiving warfarin. In the post-hoc analysis, the risk of S/SE and MB was similar between reduced and standard doses of DOACs except for those with a history of cerebral infarction and CHA2DS2-VASc score ≥3, where the risk of S/SE was lower for reduced doses of any and individual DOACs.

Conclusion

Findings from the current study are consistent with recent Asian and global studies but different from most studies conducted in North America and Europe, where patients receiving a reduced dose of DOACs had an increased risk of S/SE. Future studies should test the reproducibility of results from the current study.