The clinical impact of estimating low-density lipoprotein cholesterol (LDL-C) using different equations in the general population.

BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is associated with atherosclerotic cardiovascular disease (ASCVD). Friedewald, Sampson, and Martin-Hopkins equations are used to calculate LDL-C. This study compares the impact of switching between these equations in a large geographically defined population. MATERIALS AND METHODS: Data for individuals who had a lipid panel ordered clinically between 2010 and 2019 were included. Comparisons were made across groups using the two-sample t-test or chi-square test as appropriate. Discordances between LDL measures based on clinically actionable thresholds were summarized using contingency tables. RESULTS: The cohort included 198,166 patients (mean age 54 years, 54% female). The equations perform similarly at the lower range of triglycerides but began to diverge at a triglyceride level of 125 mg/dL. However, at triglycerides of 175 mg/dL and higher, the Martin-Hopkins equation estimated higher LDL-C values than the Samson equation. This discordance was further exasperated at triglyceride values of 400 to 800 mg/dL. When comparing the Sampson and Friedewald equations, at triglycerides are below 175 mg/dL, 9% of patients were discordant at the 70 mg/dL cutpoint, whereas 42.4% were discordant when triglycerides are between 175 and 400 mg/dL. Discordance was observed at the clinically actionable LDL-C cutpoint of 190 mg/dL with the Friedewald equation estimating lower LDL-C than the other equations. In a high-risk subgroup (ASCVD risk score > 20%), 16.3% of patients were discordant at the clinical cutpoint of LDL-C < 70 mg/dL between the Sampson and Friedewald equations. CONCLUSIONS: Discordance at clinically significant LDL-C cutpoints in both the general population and high-risk subgroups were observed across the three equations. These results show that using different methods of LDL-C calculation or switching between different methods could have clinical implications for many patients.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

A model-based cost-effectiveness analysis of pharmacogenomic panel testing in cardiovascular disease management: preemptive, reactive, or none?

PURPOSE: Pharmacogenomics (PGx) studies how inherited genetic variations in individuals affect drug absorption, distribution, and metabolism. PGx panel testing can potentially help improve efficiency and accuracy in individualizing therapy. This study compared the cost-effectiveness between preemptive PGx panel testing, reactive PGx panel testing and usual care (no testing) in cardiovascular disease management. METHODS: We developed a decision analytic model from the US payer's perspective for a hypothetical cohort of 10,000 patients ≥45 years old, using a short-term decision tree and long-term Markov model. The testing panel included the following gene-drug pairs: CYP2C19-clopidogrel, CYP2C9/VKORC1-warfarin, and SLCO1B1-statins with 30 test-return days. Costs were reported in 2019 US dollars and effectiveness was measured in quality-adjusted life years (QALYs). The primary outcome was incremental cost-effectiveness ratio (ICER = ΔCost/ΔQALY), assuming 3% discount rate for costs and QALYs. Scenario and probabilistic sensitivity analyses were performed to assess the impact of demographics, risk level, and follow-up timeframe. RESULTS: Preemptive testing was found to be cost-effective compared with usual care (ICER $86,227/QALY) at the willingness-to-pay threshold of $100,000/QALY while reactive testing was not (ICER $148,726/QALY). Sensitivity analyses suggested that our cost-effectiveness results were sensitive to longer follow-up, and the age group 45-64 years. CONCLUSION: Compared with usual care, preemptive PGx panel testing was cost-effective in cardiovascular disease management.

Predictors of return visits to the emergency department among different age groups of older adults.

OBJECTIVE: To identify predictors of 30-day emergency department (ED) return visits in patients age 65-79 years and age ≥ 80 years. METHODS: This was a cohort study of older adults who presented to the ED over a 1-year period. A mixed-effects logistic regression model was used to identify predictors for returning to the ED within 30 days. We stratified the cohort into those aged 65-79 years and aged ≥80 years. Adjusted odds ratios (aORs) with 95% confidence intervals (CI) were reported. This study adhered to the STROBE reporting guidelines. RESULTS: A total of 21,460 ED visits representing 14,528 unique patients were included. The overall return rate was 15% (1998/13,300 visits) for age 65-79 years, and 16% (1306/8160 visits) for age ≥ 80 years. A history of congestive heart failure (CHF), dementia, or prior hospitalization within 2 years were associated with increased odds of returning in both age groups (for age 65-79: CHF aOR 1.36 [CI 1.16-1.59], dementia aOR 1.27 [CI 1.07-1.49], prior hospitalization aOR 1.36 [CI 1.19-1.56]; for age ≥ 80: CHF aOR 1.32 [CI 1.13-1.55], dementia aOR 1.22 [CI 1.04-1.42], and prior hospitalization aOR 1.27 [CI 1.09-1.47]). Being admitted from the ED was associated with decreased odds of returning to the ED within 30 days (aOR 0.72 [CI 0.64-0.80] for age 65-79 years and 0.72 [CI 0.63-0.82] for age ≥ 80). CONCLUSION: Age alone was not an independent predictor of return visits. Prior hospitalization, dementia and CHF were predictors of 30-day ED return. The identification of predictors of return visits may help to optimize care transition in the ED.

Vaccination Rates Among Patients Age 65 Years and Older Who Require Interpreter Services in the State of Minnesota.

In the United States, the growing population of older adults with limited English language proficiency (LEP) faces profound health care disparities. Previous research on vaccination of older adults has been based on self-reported data, without clinical verification. We compared pneumococcal vaccination rates between a patient group with LEP and a group of English speakers in an older community-dwelling population. A population-nested matched cohort of participants age 65 years and older was identified in Minnesota. Patients with LEP were identified through an electronic alert within the electronic health record, designed to determine the need for an interpreter. Patients were matched 1 to 1 for age, sex, and Charlson comorbidity index. We used conditional logistic regression for the final analysis. In total, 24,052 patients were identified as older patients (mean [SD] age, 74 [7] years). Of them, 617 patients (2.6%) had LEP. The most common primary languages were Somali (24%), Vietnamese (15%), and Spanish (13%). We found lower rates of vaccination with 13-valent pneumococcal conjugate vaccine (PCV13) in the LEP group compared with English speakers [62% vs 77%; odds ratio (OR) (95% CI) 2.07 (1.61-2.66); P < 0.001]. Results were similar for 23-valent pneumococcal polysaccharide vaccine (PPSV23) [60% vs 75%; OR (95% CI) 1.97 91.54-2.51); P < 0.001]. These data are suggestive that older adults who required a language interpreter during health care encounters were less likely to be vaccinated with PCV13 and PPSV23 than older adults who did not require an interpreter. Effectiveness studies are needed to determine which interventions can help improve vaccination rates in the LEP population of elderly patients.

Validation of the Elderly Risk Assessment Index in the Emergency Department.

OBJECTIVES: The Elderly Risk Assessment (ERA) score is a validated index for primary care patients that predict hospitalizations, mortality, and Emergency Department (ED) visits. The score incorporates age, prior hospital days, marital status, and comorbidities. Our aim was to validate the ERA score in ED patients. METHODS: Observational cohort study of patients age ≥ 60 presenting to an academic ED over a 1-year period. Regression analyses were performed for associations with outcomes (hospitalization, return visits and death). Medians, interquartile range (IQR), odds ratios (OR) and 95% confidence intervals (CI) were calculated. RESULTS: The cohort included 27,397 visits among 18,607 patients. Median age 74 years (66-82), 48% were female and 59% were married. Patients from 54% of visits were admitted to the hospital, 16% returned to the ED within 30 days, and 18% died within one year. Higher ERA scores were associated with: hospital admission (score 10 [4-16] vs 5 [1-11], p < 0.0001), return visits (11 [5-17] vs 7 [2-13], p < 0.0001); and death within one year (14 [7-20] vs 6 [2-13], p < 0.0001). Patients with ERA score ≥ 16 were more likely to be admitted to the hospital, OR 2.14 (2.02-2.28, p < 0.0001), return within 30 days OR 1.99 (1.85-2.14), and to die within a year, OR 2.69 (2.54-2.85). CONCLUSION: The ERA score can be automatically calculated within the electronic health record and helps identify patients at increased risk of death, hospitalization and return ED visits. The ERA score can be applied to ED patients, and may help prognosticate the need for advanced care planning.

Associations of Skilled Nursing Facility Quality Ratings With 30-Day Rehospitalizations and Emergency Department Visits.

Skilled nursing facilities (SNFs) increasingly provide care to patients after hospitalization. The Centers for Medicare & Medicaid Services reports ratings for SNFs for overall quality, staffing, health inspections, and clinical quality measures. However, the relationship between these ratings and patient outcomes remains unclear. In this retrospective cohort study, we reviewed the electronic health records of 3,923 adult patients discharged from the hospital and admitted to 9 SNFs served by a health care delivery system. We used Cox proportional hazards models to examine associations between the overall quality and individual ratings and our primary outcomes of 30-day rehospitalizations and 30-day emergency department visits. Patients in higher-rated facilities had a 13% lower risk of 30-day rehospitalization than patients in lower-rated facilities (hazard ratio, 0.87; 95% CI, 0.76-0.99). The risk of emergency department visits was also lower for patients in facilities with a higher overall quality rating and a higher quality measures rating. Staffing and health inspection ratings were not associated with our primary outcomes. These findings may help inform providers and nursing home policy makers.

Early temporal characteristics of elderly patient cognitive impairment in electronic health records.

BACKGROUND: The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly patients. Also, the use of a rich source of clinical free text in electronic health records (EHRs) to facilitate CI research has not been well explored. The aim of this study is to characterize and better understand early signals of elderly patient CI by examining temporal trends of patient ADL and analyzing topics of patient medical conditions in clinical free text using topic models. METHODS: The study cohort consists of physician-diagnosed CI patients (n = 1,435) and cognitively unimpaired (CU) patients (n = 1,435) matched by age and sex, selected from patients 65 years of age or older at the time of enrollment in the Mayo Clinic Biobank. A corpus analysis was performed to examine the basic statistics of event types and practice settings where the physician first diagnosed CI. We analyzed the distribution of ADL in three different age groups over time before the development of CI. Furthermore, we applied three different topic modeling approaches on clinical free text to examine how patients' medical conditions change over time when they were close to CI diagnosis. RESULTS: The trajectories of ADL deterioration became steeper in CI patients than CU patients approximately 1 to 1.5 year(s) before the actual physician diagnosis of CI. The topic modeling showed that the topic terms were mostly correlated and captured the underlying semantics relevant to CI when approaching to CI diagnosis. CONCLUSIONS: There exist notable differences in temporal trends of basic and instrumental ADL between CI and CU patients. The trajectories of certain individual ADL, such as bathing and responsibility of own medication, were closely associated with CI development. The topic terms obtained by topic modeling methods from clinical free text have a potential to show how CI patients' conditions evolve and reveal overlooked conditions when they close to CI diagnosis.

Which Readmissions May Be Preventable? Lessons Learned From a Posthospitalization Care Transitions Program for High-risk Elders.

BACKGROUND: Care transitions programs have been shown to reduce hospital readmissions. OBJECTIVES: The main objective of this study was to evaluate effects of the Mayo Clinic Care Transitions (MCCTs) Program on potentially preventable and nonpreventable 30-day unplanned readmissions among high-risk elders. RESEARCH DESIGN: This was a retrospective cohort study of patients enrolled in MCCT following hospitalization and propensity score-matched controls receiving usual primary care. SUBJECTS: The subjects were primary care patients, who were 60 years or older, at high-risk for readmission, and hospitalized for any cause between January 1, 2011 and June 30, 2013. MEASURES: Hospital readmission within 30 days. The 3M algorithm was used to identify potentially preventable readmissions. Readmissions for ambulatory care sensitive conditions, a subset of preventable readmissions identified by the 3M algorithm, were also assessed. RESULTS: The study cohort included 365 pairs of MCCT enrollees and propensity score-matched controls. Patients were similar in age (mean 83 y) and other baseline demographic and clinical characteristics, including reason for index hospitalization. MCCT enrollees had a significantly lower all-cause readmission rate [12.4% (95% confidence interval: CI, 8.9-15.7) vs. 20.1% (15.8-24.1); P=0.004] resulting from a decrease in potentially preventable readmissions [8.4% (95% CI, 5.5-11.3) vs. 14.3% (95% CI, 10.5-17.9); P=0.01]. Few potentially preventable readmissions were for ambulatory care sensitive conditions (6.7% vs. 12.0%). The rates of nonpotentially preventable readmissions were similar [4.3% (95% CI, 2.2-6.5) vs. 6.7% (95% CI, 4.0-9.4); P=0.16]. Potentially preventable readmissions were reduced by 44% (hazard ratio, 0.56; 95% CI, 0.36-0.88; P=0.01) with no change in other readmissions. CONCLUSIONS: The MCCT significantly reduces preventable readmissions, suggesting that access to multidisciplinary care can reduce readmissions and improve outcomes for high-risk elders.

Care of the dialysis patient: Primary provider involvement and resource utilization patterns - a cohort study.

BACKGROUND: Efficient and safe delivery of care to dialysis patients is essential. Concerns have been raised regarding the ability of accountable care organizations to adequately serve this high-risk population. Little is known about primary care involvement in the care of dialysis patients. This study sought to describe the extent of primary care provider (PCP) involvement in the care of hemodialysis patients and the outcomes associated with that involvement. METHODS: In a retrospective cohort study, patients accessing a Midwestern dialysis network from 2001 to 2010 linked to United States Renal Database System and with >90 days follow up were identified (n = 2985). Outpatient visits were identified using Current Procedural Terminology (CPT)-4 codes, provider specialty, and grouped into quartiles-based on proportion of PCP visits per person-year (ppy). Top and bottom quartiles represented patients with high primary care (HPC) or low primary care (LPC), respectively. Patient characteristics and health care utilization were measured and compared across patient groups. RESULTS: Dialysis patients had an overall average of 4.5 PCP visits ppy, ranging from 0.6 in the LPC group to 6.9 in the HPC group. HPC patients were more likely female (43.4% vs. 35.3%), older (64.0 yrs. vs. 60.0 yrs), and with more comorbidities (Charlson 7.0 vs 6.0). HPC patients had higher utilization (hospitalizations 2.2 vs. 1.8 ppy; emergency department visits 1.6 vs 1.2 ppy) and worse survival (3.9 vs 4.3 yrs) and transplant rates (16.3 vs. 31.5). CONCLUSIONS: PCPs are significantly involved in the care of hemodialysis patients. Patients with HPC are older, sicker, and utilize more resources than those managed primarily by nephrologists. After adjusting for confounders, there is no difference in outcomes between the groups. Further studies are needed to better understand whether there is causal impact of primary care involvement on patient survival.

Impact of Multiple Chronic Conditions in Patients Hospitalized with Stroke and Transient Ischemic Attack.

BACKGROUND: The prevalence and clinical impact of chronic conditions (CCs) have increasingly been recognized as an important public health concern. We evaluated the prevalence of coexisting CCs and their association with 30-day mortality and readmission in hospitalized patients with stroke and transient ischemic attack (TIA). METHODS: In a retrospective study of patients aged ≥18 years hospitalized for first-ever stroke and TIA, we assessed the prevalence of coexisting CCs and their predictive value for subsequent 30-day mortality and readmission. RESULTS: Study cohort comprised 6771 patients, hospitalized for stroke (n = 4068) and TIA (n = 2703), 51.4% men, with mean age of 68.2 years (standard deviation: ±15.6), mean number of CCs of 2.9 (±1.7), 30-day mortality rate of 8.6% (entire cohort), and 30-day readmission rate of 9.7% (in 2498 patients limited to Olmsted and surrounding counties). In multivariable models, significant predictors of (1) 30-day mortality were coexisting heart failure (HF) (odds ratio [OR]: 1.45, 95% confidence interval [CI]: 1.09-1.92), cardiac arrhythmia (OR: 1.74, 95% CI: 1.40-2.17), coronary artery disease (CAD) (OR: 1.64, 95% CI: 1.29-2.08), cancer (OR: 1.67, 95% CI: 1.31-2.14), and diabetes (HR: 1.28, 95% CI: 1.01-1.62); and (2) 30-day readmission (n = 2498) were CAD (OR: 1.50, 95% CI: 1.09-2.07), cancer (OR: 1.46, 95% CI: 1.01-2.10), and arthritis (OR: 1.62, 95% CI: 1.09-2.40). CONCLUSIONS: In patients hospitalized with stroke and TIA, CCs are highly prevalent and influence 30-day mortality and readmission. Optimal therapeutic and lifestyle interventions for CAD, HF, cardiac arrhythmia, cancer, diabetes, and arthritis may improve early clinical outcome.

Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank.

BACKGROUND: Deficits in health-related quality of life (HRQOL) may be associated with worse patient experiences, outcomes and even survival. While there exists evidence to identify risk factors associated with deficits in HRQOL among patients with individual medical conditions such as cancer, it is less well established in more general populations without attention to specific illnesses. This study used patients with a wide range of medical conditions to identify contributors with the greatest influence on HRQOL deficits. METHODS: Self-perceived general health and depressive symptoms were assessed using data from 21,736 Mayo Clinic Biobank (MCB) participants. Each domain was dichotomized into categories related to poor health: deficit (poor/fair for general health and ≥3 for PHQ-2 depressive symptoms) or non-deficit. Logistic regression models were used to test the association of commonly collected demographic characteristics and disease burden with each HRQOL domain, adjusting for age and gender. Gradient boosting machine (GBM) models were applied to quantify the relative influence of contributors on each HRQOL domain. RESULTS: The prevalence of participants with a deficit was 9.5 % for perception of general health and 4.6 % for depressive symptoms. For both groups, disease burden had the strongest influence for deficit in HRQOL (63 % for general health and 42 % for depressive symptoms). For depressive symptoms, age was equally influential. The prevalence of a deficit in general health increased slightly with age for males, but remained stable across age for females. Deficit in depressive symptoms was inversely associated with age. For both HRQOL domains, risk of a deficit was associated with higher disease burden, lower levels of education, no alcohol consumption, smoking, and obesity. Subjects with deficits were less likely to report that they were currently working for pay than those without a deficit; this association was stronger among males than females. CONCLUSIONS: Comorbid health burden has the strongest influence on deficits in self-perceived general health, while demographic factors show relatively minimal impact. For depressive symptoms, both age and comorbid health burden were equally important, with decreasing deficits in depressive symptoms with increasing age. For interpreting patient-reported metrics and comparison, one must account for comorbid health burden.

Emergency department and inpatient health care utilization among patients who require interpreter services.

BACKGROUND: Limited English proficiency is associated with health disparities and suboptimal health outcomes. Although Limited English proficiency is a barrier to effective health care, its association with inpatient health care utilization is unclear. The aim of this study was to examine the association between patients with limited English proficiency, and emergency department visits and hospital admissions. METHODS: We compared emergency department visits and hospitalizations in 2012 between patients requiring interpreter services and age-matched English-proficient patients (who did not require interpreters), in a retrospective cohort study of adult patients actively empanelled to a large primary health care network in a medium-sized United States city (n = 3,784). RESULTS: Patients who required interpreter services had significantly more Emergency Department visits (841 vs 620; P ≤ .001) and hospitalizations (408 vs 343; P ≤ .001) than patients who did not require interpreter services. On regression analysis the risk of a first Emergency Department visit was 60% higher for patients requiring interpreter services than those who did not (unadjusted hazard ratio [HR], 1.6; 95% confidence interval (CI), 1.4-1.9; P < .05), while that of a first hospitalization was 50% higher (unadjusted HR, 1.5; 95% CI, 1.2-1.8; P < .05). These findings remained significant after adjusting for age, sex, medical complexity, residency and outpatient health care utilization. CONCLUSIONS: Patients who required interpreter services had higher rates of inpatient health care utilization compared with patients who did not require an interpreter. Further research is required to understand factors associated with this utilization and to develop sociolinguistically tailored interventions to facilitate appropriate health care provision for this population.

Cost comparison between home telemonitoring and usual care of older adults: a randomized trial (Tele-ERA).

BACKGROUND: From 1992 to 2008, older adults in the United States incurred more healthcare expense per capita than any other age group. Home telemonitoring has emerged as a potential solution to reduce these costs, but evidence is mixed. The primary aim of the study was to evaluate whether the mean difference in total direct medical cost consequence between older adults receiving additional home telemonitoring care (TELE) (n=102) and those receiving usual medical care (UC) (n=103) were significant. Inpatient, outpatient, emergency department, decedents, survivors, and 30-day readmission costs were evaluated as secondary aim. MATERIALS AND METHODS: Multivariate generalized linear models (GLMs) and parametric bootstrapping method were used to model cost and to determine significance of the cost differences. We also compared the differences in arithmetic mean costs. RESULTS: From the conditional GLMs, the estimated mean cost differences (TELE versus UC) for total, inpatient, outpatient, and ED were -$9,537 (p=0.068), -$8,482 (p =0.098), -$1,160 (p=0.177), and $106 (p=0.619), respectively. Mean postenrollment cost was 11% lower than the prior year for TELE versus 22% higher for UC. The ratio of mean cost for decedents to survivors was 2.1:1 (TELE) versus 12.7:1 (UC). CONCLUSIONS: There were no significant differences in the mean total cost between the two treatment groups. The TELE group had less variability in cost of care, lower decedents to survivors cost ratio, and lower total 30-day readmission cost than the UC group.

Telemonitoring Blood Pressure by Secure Message on a Patient Portal: Use, Content, and Outcomes.

INTRODUCTION: Secure messages on a patient portal allow patients to asynchronously communicate with their healthcare teams. Patients can use this mode of communication to transmit data such as home blood pressure (BP) measurements. MATERIALS AND METHODS: In this retrospective study, we examined 52,373 secure messages for content related to home BP monitoring. Text searches of the messages were followed by manual message review to identify BP-related messages. Two physicians independently reviewed a sample of these messages and the provider responses. RESULTS: Of 19,545 total message users, there were 4,412 message users with a diagnosis of hypertension and 365 who sent BP-related messages. Of the 52,373 secure messages, 624 messages (1.2%) contained information about home BP. Providers responded to messages with a change in medication dose or a prescription in 17%. When new medications were recommended, providers needed more pharmacy information in 53%. Messages contained a concern about high BP in 27% and concern about low BP in 8.5%. BP data in patient messages only attained American Heart Association-endorsed measurement criteria in 7% of messages. CONCLUSIONS: Patient-generated secure messages with BP data often result in message responses from providers for a BP medication dose change or a new prescription. Despite its increasing use, BP management by secure message has significant limitations and might be better served by BP virtual visits (e-visits) containing specific data requirements such as an average BP value from at least 12 readings and a preferred pharmacy for a prescription.

Telemedicine barriers associated with regional quality measures.

Telemedicine practitioners are familiar with multiple barriers to delivering care at a distance. Licensing and reimbursement barriers are well known and are being addressed at national and state levels by the American Telemedicine Association. Another telemedicine barrier comes in the form of quality measures for diabetes. Minnesota medical practices are currently being compared on the proportion of their patients with diabetes who have attained goals for blood pressure, low-density lipoprotein cholesterol, and hemoglobin A1C. The quality measure for blood pressure specifically excludes measurements taken by the patient, thus precluding blood pressure telemonitoring as a way to meet the blood pressure goal. To counter this barrier, advocacy in telemedicine is needed so that telemonitoring as a data collection tool is included in quality measures.

Impact of Program Changes Including Telemedicine and Telephonic Care During the COVID-19 Pandemic in Preventing 30-Day Hospital Readmission for Patients in a Care Transitions Program.

INTRODUCTION/OBJECTIVES: To describe health outcomes of older adults enrolled in the Mayo Clinic Care Transitions (MCCT) program before and during the COVID-19 pandemic compared to unenrolled patients. METHODS: We conducted a retrospective cohort study of adults (age >60 years) in the MCCT program compared to a usual care control group from January 1, 2019, to September 20, 2022. The MCCT program involved a home, telephonic, or telemedicine visit by an advanced care provider. Outcomes were 30- and 180-day hospital readmissions, emergency department (ED) visit, and mortality. We performed a subgroup analysis after March 1, 2020 (during the pandemic). We analyzed data with Cox proportional hazards regression models and hazard ratios (HRs) with 95% CIs. RESULTS: Of the 1,012 patients total, 354 were in the MCCT program and 658 were in the usual care group with a mean (SD) age of 81.1 (9.1) years overall. Thirty-day readmission was 16.9% (60 of 354) for MCCT patients and 14.7% (97 of 658) for usual care patients (HR, 1.24; 95% CI, 0.88-1.75). During the pandemic, the 30-day readmission rate was 15.1% (28 of 186) for MCCT patients and 14.9% (68 of 455) for usual care patients (HR, 1.20; 95% CI, 0.75-1.91). There was no difference between groups for 180-day hospitalization, 30- or 180-day ED visit, and 30- or 180-day mortality. CONCLUSIONS: Numerous factors involving patients, providers, and health care delivery systems during the pandemic most likely contributed to these findings.

The Importance of Estimating Excess Deaths Regionally During the COVID-19 Pandemic.

National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner's offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, those aged 18 to 39 years (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 [95% CI, 1.05 to 1.56]) or 7 or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15]) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.

Discordance Between Very Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort.

BACKGROUND: Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low-density lipoprotein cholesterol (VLDL-C) and lipid discordance could be contributors to the residual risk of ASCVD. METHODS AND RESULTS: Cardiovascular disease-free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low-density lipoprotein cholesterol (LDL-C) and VLDL-C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow-up. The association of LDL-C and VLDL-C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL-C and VLDL-C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL-C (per 10-mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05-1.09]; P<0.001]) after adjustment for traditional risk factors. The interaction between LDL-C and VLDL-C was not statistically significant (P=0.11). Discordant individuals with high VLDL-C and low LDL-C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person-years, during follow-up. CONCLUSIONS: VLDL-C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.

Aims: We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results: In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion: The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new data sets.