P6351A point-of-care risk score predicts 30-day readmission in patients hospitalized with heart failure (HF): derivation and validation of the LENT index

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
H G C Van Spall ◽  
S F Lee ◽  
T Averbuch ◽  
U Erbas Oz ◽  
R Perez ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Length Of Stay ◽  
Risk Score ◽  
Prediction Models ◽  
Point Of Care ◽  
Risk Index ◽  
Emergency Department Visits ◽  
Internal Validation ◽  
Risk Prediction Models

Abstract Background Risk prediction models in heart failure (HF) are typically complex, derived retrospectively from administrative databases, and modest in their ability to discriminate between high, medium, and low risk categories. The complexity of these models makes them difficult to use at the point of care. Purpose To determine if a simple risk index using Length of hospital stay (L), number of Emergency department visits in the preceding 6 months (E), and either admission or discharge N-Terminal (NT) prohormone of Brain Natriuretic Peptide (pro-BNP) at the point of care can predict 30-day readmissions in patients hospitalized for HF. Methods This is a sub-study of the Patient-Centered Care Transitions in HF (PACT-HF) stepped-wedge cluster randomized trial. We included 772 patients hospitalized for HF at 10 Canadian hospitals. We used log-binomial regression models with Length of stay, Emergency department visits in the preceding 6 months, and either admission or discharge N-Terminal prohormone of Brain Natriuretic Peptide (NT-pro-BNP) as the predictor variables and 30-day all-cause readmission as the outcome. We derived the LENT risk score from the β-coefficients of the regression model (Fig. 1). All the models were adjusted for post-discharge services. We assessed model discrimination with C-statistics and model calibration with the net reclassification index (NRI). We used the bootstrapping approach with 100 runs for internal validation. Results The LENT index had a possible score ranging from 1 to 13 (Fig 1). Increments in the LENT risk score were associated with an increased risk of 30-day readmission; a 1-point increase in the LENT index using the admission and discharge NT-pro-BNP predicted a 23% and 19% increase in 30-day readmission risk, respectively. The internal validation produced similar results. Compared to a null model, the LE index had an NRI of 0.35 [95% CI 0.18, 0.53], and admission and discharge NT-pro-BNP further improved calibration of the LE index (NRI 0.15 [95% CI 0, 0.32] and 0.20 [95% CI 0.03, 0.37], respectively). The LENT index offered modest discrimination for 30-day readmission (C-statistic 0.64 [95% CI 0.59, 0.69]), similar to more complex risk models. Figure 1. The LENT index scoring system Conclusion A simple risk index based on Length of stay, Emergent visits, and NT-pro-BNP at the point of care can reliably predict 30-day readmissions. The LENT index offers ease of use over traditional risk prediction models. Acknowledgement/Funding Canadian Institutes of Health Research, Ontario MOHLTC, Roche Diagnostics

Impact of a nurse-managed heart failure clinic: a pilot study

2000 ◽  
Vol 9 (2) ◽  
pp. 140-146 ◽  
Author(s):  
S Paul
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Congestive Heart Failure ◽  
Length Of Stay ◽  
Hospital Readmissions ◽  
Emergency Department Visits ◽  
Economic Management ◽  
Collection Rate ◽  
Heart Failure Clinic ◽  
Hospital Days

BACKGROUND: One approach to optimize clinical and economic management of congestive heart failure is the use of multidisciplinary outpatient clinics in which advanced practice nurses coordinate care. One such clinic was developed in 1995 at a southeastern university hospital to enhance management of patients with chronic congestive heart failure. OBJECTIVES: To evaluate the effects of a multidisciplinary outpatient heart failure clinic on the clinical and economic management of patients with congestive heart failure. METHODS: Data on hospital readmissions, emergency department visits, length of stay, charges, and reimbursement from the 6 months before 15 patients joined a heart failure clinic were compared with data from the 6 months after the patients joined the clinic. RESULTS: The patients had a total of 38 hospital admissions (151 hospital days) in the 6 months before joining the clinic and 19 admissions (72 hospital days) in the 6 months afterward. The mean length of stay decreased from 4.3 days in the 6 months before joining to 3.8 days in the 6 months afterward, and the number of emergency department visits also decreased, although neither decrease was statistically significant. Mean inpatient hospital charges decreased from $10,624 per patient admission to $5893. Reimbursements were $7751 (73% collection rate) and $5138 (87% collection rate), respectively. CONCLUSIONS: Patients seemed to benefit from participation in the heart failure clinic. If a healthcare provider is available to manage early signs and symptoms of worsening heart failure, hospital readmissions may be decreased and patients' outcomes may be improved.

P4152Implications of the ACC/AHA risk score for heart failure risk prediction and its comparison with existing heart failure risk prediction models: A prospective population-based cohort study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
B Arshi ◽  
J C Van Den Berge ◽  
B Van Dijk ◽  
J W Deckers ◽  
M A Ikram ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Model Performance ◽  
Population Based ◽  
Abc Model ◽  
Risk Prediction Models ◽  
Failure Risk ◽  
C Statistic

Abstract Background In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) developed a score for assessment of cardiovascular risk. Due to between study variability in ascertainment and adjudication of heart failure (HF), incident HF was not included as an endpoint in the ACC/AHA risk score. Purpose To assess the performance of the ACC/AHA risk score for HF risk prediction in a large population-based cohort and to compare its performance with the existing HF risk prediction models including the Atherosclerosis Risk in Communities (ARIC) model and the Health Aging and Body Composition (Health ABC) model. Methods The study included 2743 men and 3646 women from a prospective population-based cohort study. Cox proportional hazards models were fitted using risk factors applied by the ACC/AHA model for cardiovascular risk, the ARIC model and the Health ABC model. Independent relationship of each predictor with 10-year HF incidence was estimated in men and women. Next, N-terminal pro-b-type natriuretic peptide (NT-pro-BNP) was added to the ACC/AHA model. The performance of all fitted models was evaluated and compared in terms of discrimination, calibration and the Akaike Information Criterion (AIC). In addition, area under the receiver operator characteristic curve (AUC), sensitivity and specificity of each model in predicting 10-year incident of HF was assessed. The incremental value of NT-pro-BNP to the ACC/AHA model, was assessed using the continuous net reclassification improvement index (NRI). Results During a median follow-up of 13 years (63127 person-years), 387 HF events in women and 259 in men were recorded. The Optimism-corrected c-statistic for ACC/AHA model was 0.76 (95% confidence interval (CI): 0.73–0.79) for men and 0.76 (95% CI: 0.74–0.79) for women. The ARIC model provided the largest c-statistic for both men [0.82 (95% CI: 0.80–0.84)] and women [95% CI: 0.81 (0.79–0.83)] among the three models. Calibration of the models was reasonable. Addition of NT-pro-BNP to the ACC/AHA model considerably improved model fitness for men and for women. The AIC improved from 3104.62 to 2976.28 among men and from 5161.63 to 4921.51 among women. The c-statistic also improved to 0.81 (0.78–0.84) in men and 0.79 (0.77–0.81) in women. The continuous NRI for the addition of NT-pro-BNP to the base model was 5.3% (95% CI: −12.3–28.6%) for men and 15.9% (95% CI: 2.7–24.7%) for women. Conclusions Compared to HF-specific models, the ACC/AHA model, containing routine clinically available risk factors, had a reasonable performance in prediction of HF risk. Inclusion of NT-pro-BNP in the ACC/AHA model strongly increased the model performance. To achieve a better model performance for 10-year prediction of incident HF, updating the simple ACC/AHA risk score with the addition of NT-pro-BNP is recommended.

scholarly journals Evaluating risk prediction models for adults with heart failure: A systematic literature review

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0224135 ◽  
Author(s):  
Gian Luca Di Tanna ◽  
Heidi Wirtz ◽  
Karen L. Burrows ◽  
Gary Globe
Keyword(s):  
Heart Failure ◽  
Literature Review ◽  
Risk Prediction ◽  
Systematic Literature Review ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Internet search and medicaid prescription drug data as predictors of opioid emergency department visits

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sean D. Young ◽  
Qingpeng Zhang ◽  
Jiandong Zhou ◽  
Rosalie Liccardo Pacula
Keyword(s):  
Emergency Department ◽  
Prescription Drug ◽  
Drug Utilization ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Treatment Preferences ◽  
Internet Search ◽  
Utilization Data ◽  
Ed Visits ◽  
Medicaid Prescription

AbstractThe primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.

scholarly journals Death, hospitalization, emergency department visits and out-patient visits in patients with heart failure in contemporary practice: results from the prospective Europeam 9069-patient ARIADNE registry

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L.H Lund ◽  
U Zeymer ◽  
A.L Clark ◽  
V Barrios ◽  
T Damy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Primary Care Physicians ◽  
Nyha Class ◽  
Class Ii ◽  
Emergency Department Visits ◽  
Funding Source ◽  
Office Visits ◽  
Private Company ◽  
Contemporary Practice

Abstract Background In Europe, heart failure (HF) is managed in variable settings and frequently in office-based practice. In HF with reduced ejection fraction (HFrEF) there is now extensive evidence based therapy, but implementation is inconsistent, variable and overall inadequate. The Assessment of Real lIfe cAre –Describing EuropeaN hEart failure management (ARIADNE) registry aimed to assess in detail how outpatients with HFrEF are managed in Europe in contemporary practice. Methods ARIADNE was a prospective non-interventional registry of patients with HFrEF (NYHA class II-IV) treated by office-based cardiologists or selected primary care physicians (recognized as HF specialists) in a real world setting. Patients were enrolled in 687 centres in 17 European countries, and studied at baseline and after 6 and 12 months. Key pre-specified outcomes were deaths, hospitalizations, emergency department visits, and office visits, and their primary reasons. Results Over 20 months, we enrolled 9069 patients; median age 69 (19–96) years, 24% women, with 30% older than 75 years, 61% NYHA class II, with a median EF 35% (30–40%). Over a median follow-up of 353 (1–631) days, 382 patients (4.3%) died, with 171 cardiovascular deaths (1.9%). The rates of total hospitalizations overall, for HF, and for non-HF cardiovascular reasons were 19.3, 8.1, and 4.8 per 100 patient years, respectively; and rates of emergency department visits overall, for HF reasons, and for non-HF CV reason were 7.7, 1.6, and 1.8, respectively. The number of HF office visits were on average 1.0 per patient. Conclusions In this large multinational HFrEF registry with detailed data on cause-specific outcomes and health care utilization, incidence of death was low and outpatient HF visits were few, but incidence of HF and CV hospitalization and emergency department visits was high. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Novartis AG, Switzerland

scholarly journals Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

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