Elsberg Syndrome, Lumbosacral Radiculopathy, and Myelitis Due to Herpes Zoster in a Patient With Smoldering Myeloma.

Herpes zoster (HZ) is a common illness caused by the reactivation of latent varicella zoster virus (VZV) due to waning immunity, often secondary to old age or an underlying immunocompromised state. Its complications can manifest in variety of ways, including persistent neuralgias, vasculopathies, and stroke. Here, we describe a case of a 45-year-old man with a history of cryptogenic stroke and smoldering myeloma who was admitted with sacral HZ complicated by right lumbosacral radiculopathy and myelitis, otherwise known as Elsberg syndrome (ES). He was found to have an enhancing lesion in the peripheral conus medullaris on magnetic resonance imaging (MRI) with nonspecific inflammation and necrosis on biopsy pathology and cerebrospinal fluid (CSF) polymerase chain reaction (PCR) positive for VZV. The patient was initially treated with intravenous acyclovir and dexamethasone and discharged with a steroid taper and indefinite valacyclovir therapy. Twelve months postdischarge, the patient's right lumbosacral radiculopathy and myelitis had almost completely resolved; however, he continued to require bladder self-catheterization. We believe that the patient's underlying smoldering myeloma lead to an immunocompromised state, allowing for reactivation of latent VZV, resulting in both the patient's cryptogenic stroke years earlier and recent ES.

Patient Demographics and Clinic Type Are Associated With Patient Engagement Within a Remote Monitoring Program.

Background: Remote physiological monitoring (RPM) is accessible, convenient, relatively inexpensive, and can improve clinical outcomes. Yet, it is unclear in which clinical setting or target population RPM is maximally effective. Objective: To determine whether patients' demographic characteristics or clinical settings are associated with data transmission and engagement. Methods: This is a prospective cohort study of adults enrolled in a diabetes RPM program for a minimum of 12 months as of April 2020. We developed a multivariable logistic regression model for engagement with age, gender, race, income, and primary care clinic type as variables and a second model to include first-order interactions for all demographic variables by time. The participants included 549 adults (mean age 53 years, 63% female, 54% Black, and 75% very low income) with baseline hemoglobin A1c ≥8.0% and enrolled in a statewide diabetes RPM program. The main measure was the transmission engagement over time, where engagement is defined as a minimum of three distinct days per week in which remote data are transmitted. Results: Significant predictors of transmission engagement included increasing age, academic clinic type, higher annual household income, and shorter time-in-program (p < 0.001 for each). Self-identified race and gender were not significantly associated with transmission engagement (p = 0.729 and 0.237, respectively). Conclusions: RPM appears to be an accessible tool for minority racial groups and for the aging population, yet engagement is impacted by primary care location setting and socioeconomic status. These results should inform implementation of future RPM studies, guide advocacy efforts, and highlight the need to focus efforts on maintaining engagement over time.

Remote patient monitoring sustains reductions of hemoglobin A1c in underserved patients to 12 months.

AIMS: We sought to determine whether underserved patients enrolled in a statewide remote patient monitoring (RPM) program for diabetes achieve sustained improvements in hemoglobin A1c at 6 and 12 months and whether those improvements are affected by demographic and clinical variables. METHODS: Demographic and clinical variables were obtained at baseline, 6 months and 12 months. Baseline HbA1c values were compared with those obtained at 6 and 12 months via paired t-tests. A multivariable regression model was developed to identify patient-level variables associated with HbA1c change at 12 months. RESULTS: HbA1c values were obtained for 302 participants at 6 months and 125 participants at 12 months. Compared to baseline, HbA1c values were 1.8% (19 mmol/mol) lower at 6 months (p < 0.01) and 1.3% (14 mmol/mol) lower at 12 months (p < 0.01). Reductions at 12 months were consistent across clinical settings. A regression model for change in HbA1c showed no statistically significant difference for patient age, sex, race, household income, insurance, or clinic type. CONCLUSIONS: Patients enrolled in RPM had improved diabetes control at 6 and 12 months. Neither clinic type nor sociodemographic variables significantly altered the likelihood that patients would benefit from this type of technology. These results suggest the promise of RPM for delivering care to underserved populations.

Effect of Payor-Mandated Review of Prescription Drug Monitoring Program on Opioid Prescriber Rates.

OBJECTIVE: To evaluate the effect of a 2016 South Carolina payor mandate to query the state prescription drug monitoring program (PDMP) before prescribing controlled substances on the rate of opioid prescribers in South Carolina. METHODS: South Carolina PDMP datasets from 2010-2017 were evaluated using interrupted time series regression to compare changes in the rate of opioid prescribers before and after the 2016 mandate. The rate of opioid prescribers was defined as the number of prescribers who prescribed class II to IV opioids on any one prescription in each quarter divided by the total number of South Carolina prescribers who prescribed any one class II to IV medication. The rate of high-dose opioid prescribers was defined as the number of prescribers who prescribed ≥90-morphine milligram equivalent per day on any one prescription in each quarter divided by all of the prescribers who prescribed an opioid analgesic prescription. RESULTS: The rates of South Carolina opioid prescribers decreased from 75% in 2010 to 60% in 2017, with no significant change in slope (P = 0.24) after the 2016 payor mandates. The rates of South Carolina high-dose opioid prescribers decreased from 40% in 2010 to 32% in 2017, with a significant decrease in slope (P < 0.001) after the payor mandate. CONCLUSIONS: The slope of the South Carolina high-dose opioid prescriber rate significantly decreased after the 2016 South Carolina payor mandate, while the slope of the South Carolina opioid prescriber rate did not. The long-term outcomes related to the change in opioid prescriber rates are unknown and warrant further study.

Estimating the Economic Burden of Acute Myocardial Infarction in the US: 12 Year National Data.

BACKGROUND: Acute myocardial infarction (AMI) carries a substantial mortality and morbidity burden. The purpose of this study is to provide annual mean cost per patient and national level estimates of direct and indirect costs (lost productivity from morbidity and premature mortality) associated with AMI. METHODS: Nationally representative data spanning 12 years (2003-2014) with a sample of 324,869 patients with AMI from the Medical Expenditure Panel Survey (MEPS) were analyzed. A novel 2-part model was used to examine the excess direct cost associated with AMI, controlling for covariates. To estimate lost productivity from morbidity, an adjusted Generalized Linear Model was used for the differential in wage earnings between participants with and without AMI. Lost productivity from premature mortality was estimated based on published data. RESULTS: The total annual cost of AMI in 2016 dollars was estimated to be $84.9 billion, including $29.8 billion in excess direct medical expenditures, $14.6 billion in lost productivity from morbidity and $40.5 billion in lost productivity from premature mortality between 2003 and 2014. In the adjusted regression, the overall excess direct medical expenditure of AMI was $7,076 (95% confidence interval [CI] $6,028-$8,125) higher than those without AMI. After adjustment, annual wages for patients with AMI were $10,166 (95% CI -$12,985 to -$7,347) lower and annual missed work days were 5.9 days (95% CI 3.57-8.27) higher than those without AMI. CONCLUSIONS: The study finds that the economic burden of AMI is substantial, for which effective prevention could result in significant health and productivity cost savings.

Impact of Patient-Level Characteristics on In-hospital Mortality After Interhospital Transfer to Medicine Services: an Observational Study.

BACKGROUND: National administrative datasets have demonstrated increased risk-adjusted mortality among patients undergoing interhospital transfer (IHT) compared to patients admitted through the emergency department (ED). OBJECTIVE: To investigate the impact of patient-level data not available in larger administrative datasets on the association between IHT status and in-hospital mortality. DESIGN: Retrospective cohort study with logistic regression analyses to examine the association between IHT status and in-hospital mortality, controlling for covariates that were potential confounders. Model 1: IHT status, admit service. Model 2: model 1 and patient demographics. Model 3: model 2 and disease-specific conditions. Model 4: model 3 and vital signs and laboratory data. PARTICIPANTS: Nine thousand three hundred twenty-eight adults admitted to Medicine services. MAIN MEASURES: Interhospital transfer status, coded as an unordered categorical variable (IHT vs ED vs clinic), was the independent variable. The primary outcome was in-hospital mortality. Secondary outcomes included unadjusted length of stay and total cost. KEY RESULTS: IHT patients accounted for 180 out of 484 (37%) in-hospital deaths, despite accounting for only 17% of total admissions. Unadjusted mean length of stay was 8.4 days vs 5.6 days (p < 0.0001) and mean total cost was $22,647 vs $12,968 (p < 0.0001) for patients admitted via IHT vs ED respectively. The odds ratios (OR) for in-hospital mortality for patients admitted via IHT compared to the ED were as follows: model 1 OR, 2.06 (95% CI 1.66-2.56, p < 0.0001); model 2 OR, 2.07 (95% CI 1.66-2.58, p < 0.0001); model 3 OR, 2.07 (95% CI 1.63-2.61, p < 0.0001); model 4 OR, 1.70 (95% CI 1.31-2.19, p < 0.0001). The AUCs of the models were as follows: model 1, 0.74; model 2, 0.76; model 3, 0.83; model 4, 0.88, consistent with a good prediction model. CONCLUSIONS: Patient-level characteristics affect the association between IHT and in-hospital mortality. After adjusting for patient-level clinical characteristics, IHT status remains associated with in-hospital mortality.

When Do Clinicians Follow-up Abnormal Liver Tests in Primary Care?

BACKGROUND: Many guidelines addressing the approach to abnormal liver chemistries, including bilirubin, transaminases and alkaline phosphatase, recommend repeating the tests. However, when clinicians repeat testing is unknown. MATERIAL AND METHODS: This retrospective study followed adult patients with abnormal liver chemistries in a patient-centered medical home (PCMH) from 2007 to 2016. All PCMH patients possessing at least 1 abnormal liver test (total bilirubin, aminotransferases and alkaline phosphatase) were included. Patients were followed from the index abnormal liver chemistry until the next liver test result, or the end of the study period. The primary predictor variable of interest was the number of abnormal chemistries (out of 4) on index testing. Demographic and clinical variables served as other potential predictors of outcome. A Cox proportional hazards model was applied to investigate associations between the predictor variables and the time to repeat liver chemistry testing. RESULTS: Of 9,545 patients with at least 2 PCMH visits and 1 liver test abnormality, 6,489 (68%) obtained repeat testing within 1 year, and 80% of patients had follow-up tests within 2 years. Patients with multiple abnormal liver tests and those with higher degrees of abnormality were associated with shorter time to repeat testing. CONCLUSIONS: A large proportion of patients with abnormal liver tests still lack repeat testing at 1 year. The number of liver abnormal liver tests and degree of elevation were inversely associated with the time to repeat testing.

Patient perception of obesity versus physician documentation of obesity: A quality improvement study.

As the prevalence of obesity increases, the prevalence of associated comorbid diseases, obesity-related mortality rates and healthcare costs rise concordantly. Two main factors that hinder efforts to treat obesity include a lack of recognition by patients and documentation by physicians. This study evaluates the relationship between patient perception of obese weight and physician documentation of obesity. This quality improvement observational study surveyed patients of an academic internal medicine clinic on their perception of obesity. Responses were compared to longitudinal physician documentation of obesity and body mass index (BMI). A total of 59.9% of patients with obesity perceived their weight as obese. While 33.7% of patients with a BMI of 30 to 34.9 kg/m2 perceived themselves as having obesity, 71.4% of patients with a BMI of 45 to 49.9 kg/m2 perceived themselves as having obesity. A total of 42.4% of patients with obesity had physician documentation of obesity in the last year. While 25% of patients with a BMI of 30 to 34.9 kg/m2 had physician documentation of obesity, 85.7% of patients with a BMI of 45 to 49.9 kg/m2 had physician documentation of obesity. For patients with a BMI ≥50 kg/m2 , 52.9% perceived their weight to be obese and 76.5% had physician documentation of obesity in the last year. Both patient perception and physician documentation of obesity were significantly less than the prevalence of obesity. Patient perception of obesity and provider documentation of obesity increased as BMI increased until a BMI ≥50 kg/m2 . Both patients and providers must improve recognition of this disease.

Trends in Healthcare Expenditures Among US Adults With Hypertension: National Estimates, 2003-2014.

BACKGROUND: One in 3 US adults has high blood pressure, or hypertension. As prior projections suggest hypertension is the costliest of all cardiovascular diseases, it is important to define the current state of healthcare expenditures related to hypertension. METHODS AND RESULTS: We used a nationally representative database, the Medical Expenditure Panel Survey, to calculate the estimated annual healthcare expenditure for patients with hypertension and to measure trends in expenditure longitudinally over a 12-year period. A 2-part model was used to estimate adjusted incremental expenditures for individuals with hypertension versus those without hypertension. Sex, race/ethnicity, education, insurance status, census region, income, marital status, Charlson Comorbidity Index, and year category were included as covariates. The 2003-2014 pooled data include a total sample of 224 920 adults, of whom 36.9% had hypertension. Unadjusted mean annual medical expenditure attributable to patients with hypertension was $9089. Relative to individuals without hypertension, individuals with hypertension had $1920 higher annual adjusted incremental expenditure, 2.5 times the inpatient cost, almost double the outpatient cost, and nearly triple the prescription medication expenditure. Based on the prevalence of hypertension in the United States, the estimated adjusted annual incremental cost is $131 billion per year higher for the hypertensive adult population compared with the nonhypertensive population. CONCLUSIONS: Individuals with hypertension are estimated to face nearly $2000 higher annual healthcare expenditure compared with their nonhypertensive peers. This trend has been relatively stable over 12 years. Healthcare costs associated with hypertension account for about $131 billion. This warrants intense effort toward hypertension prevention and management.

Assessing the Burden of Abnormal LFTs and the Role of the Electronic Health Record: A Retrospective Study.

BACKGROUND: Primary care clinicians encounter abnormal liver function tests (LFTs) frequently. This study assesses the prevalence of abnormal LFTs and patient follow-up patterns in response. METHODS: This is a retrospective study from 2007-2016 of adult patients with abnormal LFTs seen in an internal medicine clinic. The proportion of patients with follow-up testing and the time (in days) to repeat LFTs were the primary outcomes measured. Results were evaluated before and after the implementation of the institution's electronic health record (EHR). RESULTS: This study identified a period prevalence for abnormal LFTs of 39%. Of these, 9,545 unique patients met inclusion criteria, with 8,415 patients (88.2%) possessing follow-up LFTs and no significant difference in the proportion of patients receiving follow-up by degree of initial abnormality. Median time to follow-up in mild abnormalities (1-2 times normal) was 138 days, compared to 21 days for severe abnormalities (>4 times normal, P < 0.0001). Reduced time to repeat testing across all spectrums of abnormality was observed following EHR implementation, but proportions of missing follow-up did not improve. A multivariable logistic regression model identified younger age, poverty, living over 50 miles from clinic, recent cohort entry and a lower magnitude of abnormality as predictors for missing repeat LFT testing (area under the curve = 0.838 [95% CI: 0.827-0.849]). CONCLUSIONS: Abnormal LFTs were detected in 39% of all patients seen. The degree of LFT abnormality did not influence rates of follow-up testing, but does appear to play a role in the timing of repeat testing, when obtained. Follow-up rates did not improve with EHR implementation.

Identification of High Utilization Inpatients on Internal Medicine Services.

BACKGROUND: As healthcare reform moves toward value based care, hospitals must reduce costs. As a first step, here we developed a predictive model to identify high-cost patients on admission. METHODS: We performed a retrospective observational study of 7,571 adults admitted to internal medicine services from July 1, 2013 to June 30, 2014. We compared the top 10% highest cost patients to other patients (controls) and identified clinical variables associated with high inpatient costs. Using logistic regression analyses, we developed a predictive model that could be used on admission to identify potential high utilization patients. RESULTS: In the 757 high utilizer patients, the median total hospital cost was $53,430 ± 60,679 compared to $8,431 ± 7,245 in the control group (P < 0.0001). The median length of stay for high utilization patients was 19.5 ± 32.5 days compared to 3.8 ± 3.9 days in the control group (P < 0.001). Variables associated with high utilization included transfer from an outside hospital (odds ratio [OR] = 1.6), admission to the pulmonary or medical intensive care unit (OR = 2.4), admission to cardiology (OR = 1.8), coagulopathy (OR = 2.6) and fluid and electrolyte disorders (OR = 2.1). A multivariate logistic regression model was used to fit a predictive model for high utilizers. The receiver operating characteristics curve of this prediction model yielded an area under the curve of 0.80. CONCLUSIONS: High resource utilization patients appear to have a specific phenotype that can be predicted with commonly available clinical variables. Our predictive formula holds promise as a tool that may help ultimately reduce hospital costs.