Medication Changes and Diagnosis of New Chronic Illnesses in COVID-19 Intensive Care Unit Survivors

Background Currently, there is limited literature on the impact of the COVID-19 infection on medications and medical conditions in COVID-19 intensive care unit (ICU) survivors. Our study is, to our knowledge, the first multicenter study to describe the prevalence of new medical conditions and medication changes at hospital discharge in COVID-19 ICU survivors. Objective To determine the number of medical conditions and medications at hospital admission compared to at hospital discharge in COVID-19 ICU survivors. Methods Retrospective multicenter observational study (7 ICUs) evaluated new medical conditions and medication changes at hospital discharge in patients with COVID-19 infection admitted to an ICU between March 1, 2020, to March 1, 2021. Patient and hospital characteristics, baseline and hospital discharge medication and medical conditions, ICU and hospital length of stay, and Charlson comorbidity index were collected. Descriptive statistics were used to describe patient characteristics and number and type of medical conditions and medications. Paired t-test was used to compare number of medical conditions and medications from hospital discharge to admission. Results Of the 973 COVID-19 ICU survivors, 67.4% had at least one new medical condition and 88.2% had at least one medication change. Median number of medical conditions (increased from 3 to 4, P < .0001) and medications (increased from 5 to 8, P < .0001) increased from admission to discharge. Most common new medical conditions at discharge were pulmonary disorders, venous thromboembolism, psychiatric disorders, infection, and diabetes. Most common therapeutic categories associated with medication change were cardiology, gastroenterology, pain, hematology, and endocrinology. Conclusion and Relevance Our study found that the number of medical conditions and medications increased from hospital admission to discharge. Our results provide additional data to help guide providers on using targeted approaches to manage medications and diseases in COVID-19 ICU survivors after hospital discharge.

Change Matters: Medication Change Prediction with Recurrent Residual Networks

Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all medications for the current visit, which often overlaps with medications from previous visits. A more clinically relevant task is to identify medication changes. In this paper, we propose a new recurrent residual networks, named MICRON, for medication change prediction. MICRON takes the changes in patient health records as input and learns to update a hid- den medication vector and the medication set recurrently with a reconstruction design. The medication vector is like the memory cell that encodes longitudinal information of medications. Unlike traditional methods that require the entire patient history for prediction, MICRON has a residual-based inference that allows for sequential updating based only on new patient features (e.g., new diagnoses in the recent visit), which is efficient. We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. MICRON also requires fewer parameters, which significantly reduces the training time to 38.3s per epoch with 1.5× speed-up.

Medication Optimization Among People With Type 2 Diabetes Participating in a CGM-Driven Virtual Care Program: Prospective Trial (Preprint)

BACKGROUND The Onduo virtual care program for people with type 2 diabetes (T2D) includes a mobile app, remote lifestyle coaching, connected devices and telemedicine consultations with endocrinologists for medication management and prescription of real-time continuous glucose monitoring (RT-CGM) devices. In a previously described 4-month prospective study of this program, adults with T2D and baseline HbA1c ≥8.0% to ≤12.0% experienced a mean HbA1c decrease of 1.6% with no significant increase in hypoglycemia. OBJECTIVE The objective of this analysis was to evaluate medication optimization and management in the 4-month prospective T2D study. METHODS Study participants received at least 1 telemedicine consultation with an Onduo endocrinologist for diabetes medication management and used RT-CGM intermittently to guide therapy and dosing. Medication changes were analyzed. RESULTS A total of 48 (87%) out of 55 participants had a medication change consisting of a dose change, addition, or discontinuation. Of these, 15 (31%) of participants had a net increase in number of diabetes medications classes from baseline. Mean time to first medication change for these participants was 36 days. The percentage of participants taking a GLP-1 receptor agonist increased from 25% to 56%, while the percentages of participants taking a sulfonylurea or DPP-4 inhibitor decreased from 56% to 33% and 17% to 6%, respectively. Prescriptions of other anti-diabetic medication classes including insulin did not change significantly. CONCLUSIONS The Onduo virtual care program can play an important role in providing timely access to guideline-based diabetes management medications and technologies for people with T2D. CLINICALTRIAL ClinicalTrials.gov NCT0386538

Unprovoked Dystonic Reaction in a Child Taking Long-Term Methylphenidate

In this report, we discuss the case of a 9-year-old male with Attention Deficit Hyperactivity Disorder (ADHD) on long-term methylphenidate and guanfacine who experienced acute orofacial dystonia that resolved immediately with the administration of benztropine. Current literature describes various cases of methylphenidate-induced dystonia, but ours appears to be the first reported instance of spontaneous dystonia without a recent change in dose or medication change. This may suggest the possibility of methylphenidate-induced dystonia spontaneously occurring several years after initiation.

Safety of Using a Combinatorial Pharmacogenomic Test for Patients with Major Depressive Disorder in the GUIDED trial

BackgroundPharmacogenomic testing has emerged to aid medication selection for patients with major depressive disorder (MDD) by identifying potential gene-drug interactions (GDI). Many pharmacogenomic tests are available with varying levels of supporting evidence, including direct-to-consumer and physician-ordered tests. We retrospectively evaluated the safety of using a physician-ordered combinatorial pharmacogenomic test (GeneSight) to guide medication selection for patients with MDD in a large, randomized, controlled trial (GUIDED).Materials and MethodsPatients diagnosed with MDD who had an inadequate response to ≥1 psychotropic medication were randomized to treatment as usual (TAU) or combinatorial pharmacogenomic test-guided care (guided-care). All received combinatorial pharmacogenomic testing and medications were categorized by predicted GDI (no, moderate, or significant GDI). Patients and raters were blinded to study arm, and physicians were blinded to test results for patients in TAU, through week 8. Measures included adverse events (AEs, present/absent), worsening suicidal ideation (increase of ≥1 on the corresponding HAM-D17 question), or symptom worsening (HAM-D17 increase of ≥1). These measures were evaluated based on medication changes [add only, drop only, switch (add and drop), any, and none] and study arm, as well as baseline medication GDI.ResultsMost patients had a medication change between baseline and week 8 (938/1,166; 80.5%), including 269 (23.1%) who added only, 80 (6.9%) who dropped only, and 589 (50.5%) who switched medications. In the full cohort, changing medications resulted in an increased relative risk (RR) of experiencing AEs at both week 4 and 8 [RR 2.00 (95% CI 1.41–2.83) and RR 2.25 (95% CI 1.39–3.65), respectively]. This was true regardless of arm, with no significant difference observed between guided-care and TAU, though the RRs for guided-care were lower than for TAU. Medication change was not associated with increased suicidal ideation or symptom worsening, regardless of study arm or type of medication change. Special attention was focused on patients who entered the study taking medications identified by pharmacogenomic testing as likely having significant GDI; those who were only taking medications subject to no or moderate GDI at week 8 were significantly less likely to experience AEs than those who were still taking at least one medication subject to significant GDI (RR 0.39, 95% CI 0.15–0.99, p=0.048). No other significant differences in risk were observed at week 8.ConclusionThese data indicate that patient safety in the combinatorial pharmacogenomic test-guided care arm was no worse than TAU in the GUIDED trial. Moreover, combinatorial pharmacogenomic-guided medication selection may reduce some safety concerns. Collectively, these data demonstrate that combinatorial pharmacogenomic testing can be adopted safely into clinical practice without risking symptom degradation among patients.FundingMyriad Neuroscience/Assurex Health

Abstract P395: High Risk Diabetics Risk of Recurrent Stroke and Cardiovascular Disease Persists Even With Patient Personalized Subspecialty Care

Introduction: Diabetes management is an important aspect of stroke prevention. To our knowledge, studies that focus specifically on the role of multidisciplinary teams for adjusting diabetes medications and diabetes education for stroke and cardiovascular disease prevention to compliment standard stroke prevention and nursing education are lacking. Here we sought to evaluate whether high risk diabetics, hemoglobin A1c (HA1c) > 8%, admitted secondary to stroke would benefit from a multidisciplinary team model that also incorporates endocrinology consultation and diabetes education to personalized nursing education and education and management of the admitting service. Methods: Data was obtained from our Institutional Review Board approved stroke admission database from 2017 to November 2019. Regression analysis was used to identify significant associations between diabetes education (DE) and endocrine consultation (EC) with readmission rates with 30 days, re-admission within 30 days secondary to stroke, cardiovascular event or stroke within a year of the stroke admission, and medication change after controlling for age, sex, NIHSS, HbA1c, low density lipoprotein (LDL), reperfusion therapy for acute stroke. Follow-up HbA1c post hospitalization was available for only 17% of the population, and was not included in the regression models. Results: A total of 202 patients were included, median age 66 (interquartile range 56-75), 43% women, NIHSS median 5 (interquartile range (IQR, 2-9), LDL median 105 (IQR, 69-155), A1c median 9.5 (IQR, 8.5 -11.1), and 24% received reperfusion therapy. EC was associated with higher likelihood of a medication change (odds ratio (OR) 9.43, 95% confidence interval (CI) (3.22-30.69). DE was associated with younger age (OR 0.96, 95% CI 0.92-0.99); higher A1c value (OR 1.47, 95% CI 1.18 - 1.87) and higher likelihood of cardiovascular event within a year of the stroke (OR 3.38, 95% CI 1.23 - 9.70). Conclusion: While the endocrine consultation does lead to medications changes with the intent of improving post discharge glycemic control, cardiovascular events were still more likely, possibly from DM disease severity. Further continuation of follow up of these patients with EC and DE after hospital discharge may be needed.

Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control

Objective. Uncontrolled hypertension is driven by clinical uncertainty around blood pressure data. This research sought to determine whether decision support—in the form of enhanced data visualization—could improve judgments about hypertension control. Methods. Participants (Internet sample of patients with hypertension) in 3 studies ( N = 209) viewed graphs depicting blood pressure data for fictitious patients. For each graph, participants rated hypertension control, need for medication change, and perceived risk of heart attack and stroke. In study 3, participants also recalled the percentage of blood pressure measurements outside of the goal range. The graphs varied by systolic blood pressure mean and standard deviation, change in blood pressure values over time, and data visualization type. Results. In all 3 studies, data visualization type significantly affected judgments of hypertension control. In studies 1 and 2, perceived hypertension control was lower while perceived need for medication change and subjective perceptions of stroke and heart attack risk were higher for raw data displays compared with enhanced visualization that employed a smoothing function generated by the locally weighted smoothing algorithm. In general, perceptions of hypertension control were more closely aligned with clinical guidelines when data visualization included a smoothing function. However, conclusions were mixed when comparing tabular presentations of data to graphical presentations of data in study 3. Hypertension was perceived to be less well controlled when data were presented in a graph rather than a table, but recall was more accurate. Conclusion. Enhancing data visualization with the use of a smoothing function to minimize the variability present in raw blood pressure data significantly improved judgments about hypertension control. More research is needed to determine the contexts in which graphs are superior to data tables.