HOSPITAL Score, LACE Index and LACE+ Index as predictors of 30-day readmission in patients with heart failure

Early repeat hospitalization for fluid overload in individuals with cardiovascular disease and risks: a retrospective cohort study

AIMS

Fluid overload is a common manifestation of cardiovascular and kidney disease and a leading cause of hospitalizations. To identify patients at risk of recurrent severe fluid overload, we evaluated the incidence and risk factors associated with early repeat hospitalization for fluid overload among individuals with cardiovascular disease and risks.

METHODS

Single-center retrospective cohort study of 3423 consecutive adults with an index hospitalization for fluid overload between January 2015 and December 2017 and had cardiovascular risks (older age, diabetes mellitus, hypertension, dyslipidemia, kidney disease, known cardiovascular disease), but excluded if lost to follow-up or eGFR < 15 ml/min/1.73 m2. The outcome was early repeat hospitalization for fluid overload within 30 days of discharge.

RESULTS

The mean age was 73.9 ± 11.6 years and eGFR was 54.1 ± 24.6 ml/min/1.73 m2 at index hospitalization. Early repeat hospitalization for fluid overload occurred in 291 patients (8.5%). After adjusting for demographics, comorbidities, clinical parameters during index hospitalization and medications at discharge, cardiovascular disease (adjusted odds ratio, OR 1.66, 95% CI 1.27-2.17), prior hospitalization for fluid overload within 3 months (OR 2.52, 95% CI 1.17-5.44), prior hospitalization for any cause in within 6 months (OR 1.33, 95% CI 1.02-1.73) and intravenous furosemide use (OR 1.58, 95% CI 1.10-2.28) were associated with early repeat hospitalization for fluid overload. Higher systolic BP on admission (OR 0.992, 95% 0.986-0.998) and diuretic at discharge (OR 0.50, 95% CI 0.26-0.98) reduced early hospitalization for fluid overload.

CONCLUSION

Patients at-risk of early repeat hospitalization for fluid overload may be identified using these risk factors for targeted interventions.

Developing 60-Day Readmission Risk Score among Home Healthcare Patients with Heart Failure

Heart failure (HF) readmissions are common, costly, and often preventable. Despite the implementation of HF programs across clinical settings, rehospitalization is still common. Efforts to identify risk factors for 60-day rehospitalization among HF patients exist, but risk scoring has not been utilized in home healthcare. The purpose of this study was to develop a 60-day rehospitalization risk score for home care patients with HF. This study is a secondary data analysis of a retrospective cross-sectional dataset that was composed of data using the Outcome Assessment Information Set (OASIS)-C version for patients with HF. We computed the Charlson Comorbidity Index (CCI) to use as a confounder. The risk score was computed from the final logistic regression model regression coefficients. The median age was 78 years old, 45.4% were male, and 81.0% were White. We identified 10 significant risk factors including CCI score. The risk score achieved a c-statistic of 0.70 in this patient sample. This risk score could prove useful in clinical practice for guiding attention and decision-making for personalized care of patients with unrecognized or under-treated health needs.

Cohort design to assess the association between post-hospital primary care physician follow-up visits and hospital readmissions

While multifaceted post-hospitalization interventions can succeed in preventing hospital readmissions, many of these interventions are labor-intensive and costly. We hypothesized that a timely post-discharge primary care physician (PCP) visit alone might prevent hospital readmission. We conducted a retrospective cohort study to assess whether post-hospitalization PCP visits within 14 days of discharge were associated with lower rates of 30-day hospital readmission. In a secondary analysis we also assessed: whether visits with a PCP at 7-days post-discharge changed rates of hospital readmissions and whether post-hospitalization PCP visits were associated with decreased 90-day hospital readmissions. We included all adults with a PCP who were discharged from an inpatient medical service in a large, urban integrated academic health system from January 1, 2019 to September 9, 2019 in our analysis. We performed unadjusted bivariate analyses to measure the associations between having a PCP visit within 14 and 7 days of discharge and hospital readmission within 30 and 90 days. Then we constructed multivariate logistic regression models including patient medical and utilization characteristics to estimate the adjusted odds of a patient with a post-hospitalization PCP visit experiencing a 30-day hospital readmission (primary outcome) and 90-day readmission (secondary outcome). A total of 9236 patients were discharged; mean age was 57.9 years and 59.7% were female. Of the study population, 35.6% (n = 3284) and 24.1% (n = 2224) of patients had a post-hospitalization PCP visit within 14 days and or 7 days, respectively. Overall, 1259 (13.6%) and 2153 (23.3%) of discharged patients were readmitted at 30 and 90 days, respectively. In unadjusted analyses, having a post discharge PCP visit was not associated with decreased hospital readmission rates, but after adjusting for sociodemographic, medical and utilization characteristics, having a post-hospitalization PCP visit at 14 and 7 days was associated with lower hospital readmission rates at 30 days: 0.68 (95% CI 0.59-0.79) and 0.76 (95% CI 0.66-0.89), respectively; and 90 days: 0.76 (95% CI 0.68-0.85) and 0.80 (95% CI 0.70-0.91), respectively. In this large integrated urban academic health system, having a post-hospitalization PCP visit within 14- and 7-days of hospital discharge was associated with lower rates of readmission at 30 and 90 days. Further studies should examine whether improving access to PCP visits post hospitalization reduces readmissions rates.

The association between urinary sodium concentration (UNa) and outcomes of acute heart failure: a systematic review and meta-analysis

AIMS

Urinary sodium concentration (UNa) is a simple test advocated to assess diuretics efficacy and predict outcomes in acute heart failure (AHF). We performed a systematic review and meta-analysis to examine the association of UNa with outcomes of AHF.

METHODS AND RESULTS

We searched Embase and Medline for eligible studies that reported the association between UNa and outcomes of urinary output, weight loss, worsening renal function, length of hospital stay, re-hospitalization, worsening heart failure, and all-cause mortality in AHF. Nineteen observational studies out of 1592 screened records were included. For meta-analyses of outcomes, we grouped patients into high vs. low UNa, with most studies defining high UNa as &gt;48-65 mmol/L. In the high UNa group, pooled data showed a higher urinary output (mean difference 502 mL, 95% CI 323-681, P &lt; 0.01), greater weight loss (mean difference 1.6 kg, 95% CI 0.3-2.9, P = 0.01), and a shorter length of stay (mean difference -1.4 days, 95% CI -2.8 to -0.1, P = 0.03). There was no significant difference in worsening kidney function (OR 0.54, 95% CI 0.25-1.16, P = 0.1). Due to the small number of studies, we did not report pooled estimates for re-hospitalization and worsening heart failure. High UNa was associated with lower odds of 30-day (OR 0.27; 95% CI 0.14-0.49, P &lt; 0.01), 90-day (OR 0.39,95% CI 0.25-0.59, P &lt; 0.01) and 12-month (OR 0.35; 95% CI 0.20-0.61, P &lt; 0.01) mortality.

CONCLUSION

High UNa after diuretic administration is associated with higher urinary output, greater weight loss, shorter length of stay, and lower odds of death. UNa is a promising marker of diuretic efficacy in AHF which should be confirmed in randomized trials.

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

Background

The LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).

Methods

This was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.

Results

Of the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.

Conclusion

The LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.

Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction

Aims: This study aimed to utilize the existing LACE index (length of stay, acuity of admission, comorbidity index and emergency room visit in the past six months) to predict the risk of 30-day readmission and to find the associated factors in patients with AMI. Methods: This was a retrospective study and LACE index scores were calculated for patients admitted with AMI between 2015 and 2019. Data were utilized from the hospital’s electronic medical record. Multivariate logistic regression was performed to find the association between covariates and 30-day readmission. The risk prediction ability of the LACE index for 30-day readmission was analyzed by receiver operating characteristic curves with the C statistic. Results: A total of 205 (5.7%) patients were readmitted within 30 days. The odds ratio of older age group (OR = 1.78, 95% CI: 1.54–2.05), admission via emergency ward (OR = 1.45; 95% CI: 1.42–1.54) and LACE score ≥10 (OR = 2.71; 95% CI: 1.03–4.37) were highly associated with 30-day readmissions and statistically significant. The receiver operating characteristic curve C statistic of the LACE index for AMI patients was 0.78 (95% CI: 0.75–0.80) and showed favorable discrimination in the prediction of 30-day readmission. Conclusion: The LACE index showed a good discrimination to predict the risk of 30-day readmission for hospitalized patients with AMI. Further study would be recommended to focus on additional factors that can be used to predict the risk of 30-day readmission; this should be considered to improve the model performance of the LACE index for other acute conditions by using the national-based administrative data.

Prognostic risk scores for patients with heart failure

Heart failure has many causes. Although new drugs, devices and technologies are available, the survival rate and prognosis of patients with heart failure remain poor, placing a significant burden on individuals and society. Attempts to improve outcomes for patients with heart failure include developing prognostic risk scores. With medical advances, however, previous heart failure risk scores are not fully applicable to current practice, particularly because of the classification as heart failure with reduced ejection fraction, heart failure with mildly reduced ejection fraction, and heart failure with preserved ejection fraction. This article describes the use of risk prediction scores for heart failure patients with different clinical status and discusses their clinical applicability.

LACE Index to Predict the High Risk of 30-Day Readmission: A Systematic Review and Meta-Analysis

The LACE index accounts for: Length of stay (L), Acuity of admission (A), Comorbidities (C), and recent Emergency department use (E). This study aimed to explore the LACE index to predict the high risk of 30-day readmission in patients with diverse disease conditions by an updated systematic review. A systematic review carried out by electronic databases from 2011−2021. The studies included a LACE index score for 30-day of readmission and patients with all types of diseases and were published in the English language. The meta-analysis was performed by using a random-effects model with a 95% confidence interval. Of 3300 records, a total of 16 studies met the inclusion criteria. The country of publication was primarily the USA (n = 7) and study designs were retrospective and perspective cohorts. The average mean age was 64 years. The C-statistics was 0.55 to 0.81. The pooled random effects of relative risk readmission were overall (RR, 0.20; 95% CI, 0.12−0.34) and it was favorable. The subgroup analysis of the opted disease-based relative risk of readmissions of all causes, cardiovascular and pulmonary diseases, and neurological diseases were consistent and statistically significant at p < 0.001 level. Current evidence of this review suggested that incorporating a high-risk LACE index showed favorable to risk prediction and could be applied to predict 30-day readmission with chronic conditions. Future study would be planned to predict the high risk of 30-day readmission in acute clinical care for utility, and applicability of promising LACE index in South Korean hospitals.

HOSPITAL Score and LACE Index to Predict Mortality in Multimorbid Older Patients

Background

Estimating life expectancy of older adults informs whether to pursue future investigation and therapy. Several models to predict mortality have been developed but often require data not immediately available during routine clinical care. The HOSPITAL score and the LACE index were previously validated to predict 30-day readmissions but may also help to assess mortality risk. We assessed their performance to predict 1-year and 30-day mortality in hospitalized older multimorbid patients with polypharmacy.

Methods

We calculated the HOSPITAL score and LACE index in patients from the OPERAM (OPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly) trial (patients aged ≥ 70 years with multimorbidity and polypharmacy, admitted to hospital across four European countries in 2016–2018). Our primary and secondary outcomes were 1-year and 30-day mortality. We assessed the overall accuracy (scaled Brier score, the lower the better), calibration (predicted/observed proportions), and discrimination (C-statistic) of the models.

Results

Within 1 year, 375/1879 (20.0%) patients had died, including 94 deaths within 30 days. The overall accuracy was good and similar for both models (scaled Brier score 0.01–0.08). The C-statistics were identical for both models (0.69 for 1-year mortality, p = 0.81; 0.66 for 30-day mortality, p = 0.94). Calibration showed well-matching predicted/observed proportions.

Conclusion

The HOSPITAL score and LACE index showed similar performance to predict 1-year and 30-day mortality in older multimorbid patients with polypharmacy. Their overall accuracy was good, their discrimination low to moderate, and the calibration good. These simple tools may help predict older multimorbid patients’ mortality after hospitalization, which may inform post-hospitalization intensity of care.

Prediction value of the LACE index to identify older adults at high risk for all-cause mortality in South Korea: a nationwide population-based study

Background

As a tool to predict early hospital readmission, little is known about the association between LACE index and all-cause mortality in older adults. We aimed to validate the LACE index to predict all-cause mortality in older adults and also analyzed the LACE index outcome of all-cause mortality depending on the disease and age of the participants.

Methods

We used the National Health Insurance Service (NHIS) cohort, a nationwide claims database of Koreans. We enrolled 7491 patients who were hospitalized at least once between 2003 and 2004, aged ≥65 years as of the year of discharge, and subsequently followed-up until 2015. We estimated the LACE index using the NHI database. The Cox proportional hazards model was used to estimate the hazard ratio (HR) for all-cause mortality. Furthermore, we investigated all-cause mortality according to age and underlying disease when the LACE index was ≥10 and < 10, respectively.

Results

In populations over 65 years of age, patients with LACE index ≥10 had significantly higher risks of all-cause mortality than in those with LACE index < 10. (HR, 1.44; 95% confidence interval, 1.35–1.54). For those patients aged 65–74 years, the HR of all-cause mortality was found to be higher in patients with LACE index≥10 than in those with LACE index < 10 in almost all the diseases except CRF and mental illnesses. And those patients aged ≥75 years, the HR of all- cause mortality was found to be higher in patients with LACE index ≥10 than in those with LACE index < 10 in the diseases of pneumonia and MACE.

Conclusion

This is the first study to validate the predictive power of the LACE index to identify older adults at high risk for all-cause mortality using nationwide cohort data. Our findings have policy implications for selecting or managing patients who need post-discharge management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02848-4.

A Prospective Observational Study Assessing the Impacts of Health Literacy and Psychosocial Determinants of Health on 30-day Readmission Risk

Our objective was to assess the utility of an assessment battery capturing health literacy (HL) and biopsychosocial determinants of health in predicting 30-day readmission in comparison to a currently well-adopted readmission risk calculator. We also sought to capture the distribution of inpatient HL, with emphasis on inadequate and marginal HL (an intermediate HL level). A prospective observational study was conducted to obtain HL and biopsychosocial data on general medicine inpatients admitted to the UCLA health system. Five hundred thirty-seven subjects were tracked prospectively for 30-day readmission after index hospitalization. HL was significantly better at predicting readmission compared to LACE + (Length, admission acuity, comorbidities, emergency room visits) alone (P = .013). A multivariate model including education, insurance, and language comfort was a strong predictor of adequate HL (P < .001). In conclusion, HL offered significant improvement in risk stratification in comparison to LACE + alone. Patients with marginal HL were high-risk, albeit difficult to characterize. Incorporating robust HL and biopsychosocial determinant assessments may allow hospital systems to allocate educational resources towards at-risk patients, thereby mitigating readmission risk.

Tailored risk assessment of 90-day acute heart failure readmission or all-cause death to heart failure with preserved versus reduced ejection fraction

BACKGROUND

After incident heart failure (HF) admission, patients are vulnerable to readmission or death in the 90-day post-discharge. Although risk models for readmission or death incorporate ejection fraction (EF), patients with HF with preserved EF (HFpEF) and those with HF with reduced EF (HFrEF) represent distinct cohorts. To better assess risk, this study developed machine learning models and identified risk factors for the 90-day acute HF readmission or death by HF subtype.

METHODS AND RESULTS

Approximately 1965 patients with HFpEF and 1124 with HFrEF underwent an index admission. Acute HF rehospitalization or death occurred in 23% of HFpEF and 28% of HFrEF groups. Of the 101 variables considered, multistep variable selection identified 24 and 25 significant factors associated with 90-day events in HFpEF and HFrEF, respectively. In addition to risk factors common to both groups, factors unique to HFpEF patients included cognitive dysfunction, low-pulse pressure, β-blocker, and diuretic use, and right ventricular dysfunction. In contrast, factors unique to HFrEF patients included a history of arrhythmia, acute HF on presentation, and echocardiographic characteristics like left atrial dilatation or elevated mitral E/A ratio. Furthermore, the model tailored to HFpEF (area under the curve [AUC] = 0.770; 95% confidence interval [CI] 0.767-0.774) outperformed a model for the combined groups (AUC = 0.759; 95% CI 0.756-0.763).

CONCLUSION

The UF 90-day post-discharge acute HF Re admission or Death Risk Assessment (UF90-RADRA) models help identify HFpEF and HFrEF patients at higher risk who may require proactive outpatient management.

External validation of the modified LACE+, LACE+, and LACE scores to predict readmission or death after hospital discharge

BACKGROUND

Unplanned hospital readmissions are common adverse events. The LACE+ score has been used to identify patients at the highest risk of unplanned readmission or death, yet the external validity of this score remains uncertain.

METHODS

We constructed a cohort of patients admitted to hospital between 1 October 2014 and 31 January 2017 using population-based data from British Columbia (Canada). The primary outcome was a composite of urgent hospital readmission or death within 30 days of index discharge. The primary analysis sought to optimize clinical utility and international generalizability by focusing on the modified LACE+ (mLACE+) score, a variation of the LACE+ score which excludes the Case Mix Group score. Predictive performance was assessed using model calibration and discrimination.

RESULTS

Among 368,154 hospitalized individuals, 31,961 (8.7%) were urgently readmitted and 5428 (1.5%) died within 30 days of index discharge (crude composite risk of readmission or death, 9.95%). The mLACE+ score exhibited excellent calibration (calibration-in-the-large and calibration slope no different than ideal) and adequate discrimination (c-statistic, 0.681; 95%CI, 0.678 to 0.684). Higher risk dichotomized mLACE+ scores were only modestly associated with the primary outcome (positive likelihood ratio 1.95, 95%CI 1.93 to 1.97). Predictive performance of the mLACE+ score was similar to that of the LACE+ and LACE scores.

CONCLUSION

The mLACE+, LACE+ and LACE scores predict hospital readmission with excellent calibration and adequate discrimination. These scores can be used to target interventions designed to prevent unplanned hospital readmission.

External validation of EPIC's Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

Introduction

Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC’s Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC’s Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.

Methods

A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1^st of January 2018 and the 31^st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X² goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.

Results

At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676–0.708), 0.703 (95% CI 0.687–0.719) and 0.705 (95% CI 0.690–0.720). The Hosmer-Lemeshow X² tests had values of p<0.001.

Conclusion

In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability—model updating is warranted.

The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission

Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

OBJECTIVE

To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.

DESIGN

Systematic review and meta-analysis.

DATA SOURCE

Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.

PRIMARY AND SECONDARY OUTCOME MEASURES

Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.

RESULTS

Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.

CONCLUSION

Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.

PROSPERO REGISTRATION NUMBER

CRD42020159839.

Implementing standards of care for heart failure patients in general practice - the IMPACT-B study protocol

BACKGROUND

Heart failure (HF) is an important health problem. Most chronic HF management occurs in primary care. Although guidelines exist, there is an important implementation gap in current HF care in Belgium.

METHODS

We will conduct a non-randomised, non-controlled prospective observational trial to implement guideline-recommended disease management interventions in primary care in Leuven, a region of ±100.000 inhabitants. These interventions include education of general practitioners, reimbursement of the analysis of circulating natriuretic peptides and audits in the electronic health record (EHR), training and implementation of HF educators in primary care, and a protocol to structure transition to primary care after discharge. The main objective is to study and implement interventions in an iterative implementation process.

CONCLUSIONS

We will evaluate the implementation of several guideline-recommended disease management interventions to optimise the diagnosis and treatment of heart failure in a real-world primary care setting.

TRIAL REGISTRATION

NCT04334447 (clinicaltrials.gov).

Heart Failure with Preserved Ejection Fraction and 30-Day Readmission

OBJECTIVE

Several studies identify heart failure (HF) as a potential risk for hospital readmission; however, studies on predictability of heart failure readmission is limited. The objective of this work was to investigate whether a specific type of heart failure (HFpEF or HFrEF) has a higher association to the rate of 30-day hospital readmission and compare their predictability with the two risk scores: HOSPITAL score and LACE index.

DESIGN

Retrospective study from single academic center.

METHODS

Sample size included adult patients from an academic hospital in a two-year period (2015 - 2017). Exclusion criteria included death, transfer to another hospital, and unadvised leave from hospital. Baseline characteristics, diagnosis-related group, and ICD diagnosis codes were obtained. Variables affecting HOSPITAL score and LACE index and types of heart failure present were also extracted. Qualitative variables were compared using Pearson chi² or Fisher's exact test (reported as frequency) and quantitative variables using non-parametric Mann-Whitney U test (reported as mean ± standard deviation). Variables from univariate analysis with P values of 0.05 or less were further analyzed using multivariate logistic regression. Odds ratio was used to measure potential risk.

RESULTS

The sample size of adult patients in the study period was 1,916. All eligible cohort of patients who were readmitted were analyzed. Cumulative score indicators of HOSPITAL Score, LACE index (including the Charlson Comorbidity Index) predicted 30-day readmissions with P values of <0.001. The P value of HFpEF was found to be significant in the readmitted group (P < 0.001) compared to HFrEF (P = 0.141). Multivariate logistic regression further demonstrated the association of HFpEF with higher risk of readmission with odds ratio of 1.77 (95% CI: 1.25 - 2.50) and P value of 0.001.

CONCLUSIONS

Our data from an academic tertiary care center supports HFpEF as an independent risk factor for readmission. Multidisciplinary management of HFpEF may be an important target for interventions to reduce hospital readmissions.

Frailty and Sarcopenia Assessment upon HospitalAdmission to Internal Medicine Predicts Length ofHospital Stay and Re-Admission: A ProspectiveStudy of 980 Patients

Background: Frailty and sarcopenia are associated with frequent hospitalizations and poor clinical outcomes in geriatric patients. Ascertaining this association for younger patients hospitalized in internal medicine departments could help better prognosticate patients in the realm of internal medicine. Methods: During a 1-year prospective study in an internal medicine department, we evaluated patients upon admission for sarcopenia and frailty. We used the FRAIL questionnaire, blood alanine-amino transferase (ALT) activity, and mid-arm muscle circumference (MAMC) measurements. Results: We recruited 980 consecutive patients upon hospital admission (median age 72 years (IQR 65–79); 56.8% males). According to the FRAIL questionnaire, 106 (10.8%) patients were robust, 368 (37.5%) pre-frail, and 506 (51.7%) were frail. The median ALT value was 19IU/L (IQR 14–28). The median MAMC value was 27.8 (IQR 25.7–30.2). Patients with low ALT activity level (<17IU/L) were frailer according to their FRAIL score (3 (IQR 2–4) vs. 2 (IQR 1–3); p < 0.001). Higher MAMC values were associated with higher ALT activity, both representing robustness. The rate of 30 days readmission in the whole cohort was 17.4%. Frail patients, according to the FRAIL score (FS), had a higher risk for 30 days readmission (for FS > 2, HR = 1.99; 95CI = 1.29–3.08; p = 0.002). Frail patients, according to low ALT activity, also had a significantly higher risk for 30 days readmission (HR = 2.22; 95CI = 1.26–3.91; p = 0.006). After excluding patients whose length of stay (LOS) was ≥10 days, 252 (27.5%) stayed in-hospital for 4 days or longer. Frail patients according to FS had a higher risk for LOS ≥4 days (for FS > 2, HR = 1.87; 95CI = 1.39–2.52; p < 0.001). Frail patients, according to low ALT activity, were also at higher risk for LOS ≥4 days (HR = 1.87; 95CI = 1.39–2.52; p < 0.001). MAMC values were not correlated with patients’ LOS or risk for re-admission. Conclusion: Frailty and sarcopenia upon admission to internal medicine departments are associated with longer hospitalization and increased risk for re-admission.

Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility

Background

The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) methods, which have been applied as measures of spatial accessibility, focusing on their ability to predict the need for health care in the inpatient sector in Germany.

Methods

We tested three FCA methods (enhanced (E2SFCA), modified (M2SFCA) and integrated (iFCA)) for their accuracy in predicting hospital visits regarding six medical diagnoses (atrial flutter/fibrillation, heart failure, femoral fracture, gonarthrosis, stroke, and epilepsy) on national level in Germany. We further used the closest provider approach for benchmark purposes. The predicted visits were compared with the actual visits for all six diagnoses using a correlation analysis and a maximum error from the actual visits of ± 5%, ± 10% and ± 15%.

Results

The analysis of 229 million distances between hospitals and population locations revealed a high and significant correlation of predicted with actual visits for all three FCA methods across all six diagnoses up to ρ = 0.79 (p < 0.001). Overall, all FCA methods showed a substantially higher correlation with actual hospital visits compared to the closest provider approach (up to ρ = 0.51; p < 0.001). Allowing a 5% error of the absolute values, the analysis revealed up to 13.4% correctly predicted hospital visits using the FCA methods (15% error: up to 32.5% correctly predicted hospital). Finally, the potential of the FCA methods could be revealed by using the actual hospital visits as the measure of hospital attractiveness, which returned very strong correlations with the actual hospital visits up to ρ = 0.99 (p < 0.001).

Conclusion

We were able to demonstrate the impact of FCA measures regarding the prediction of hospital visits in non-emergency settings, and their superiority over commonly used methods (i.e. closest provider). However, hospital beds were inadequate as the measure of hospital attractiveness resulting in low accuracy of predicted hospital visits. More reliable measures must be integrated within the proposed methods. Still, this study strengthens the possibilities of FCA methods in health care planning beyond their original application in measuring spatial accessibility.

Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions

BACKGROUND

Electronic health record (EHR) vendors now offer "off-the-shelf" artificial intelligence (AI) models to client organizations. Our health system faced difficulties in promoting end-user utilization of a new AI model for predicting readmissions embedded in the EHR.

OBJECTIVES

The aim is to conduct a case study centered on identifying barriers to uptake/utilization.

METHODS

A qualitative study was conducted using interviews with stakeholders. The interviews were used to identify relevant stakeholders, understand current workflows, identify implementation barriers, and formulate future strategies.

RESULTS

We discovered substantial variation in existing workflows around readmissions. Some stakeholders did not perform any formal readmissions risk assessment. Others accustomed to using existing risk scores such as LACE+ had concerns about transitioning to a new model. Some stakeholders had existing workflows in place that could accommodate the new model, but they were not previously aware that the new model was in production. Concerns expressed by end-users included: whether the model's predictors were relevant to their work, need for adoption of additional workflow processes, need for training and change management, and potential for unintended consequences (e.g., increased health care resource utilization due to potentially over-referring discharged patients to home health services).

CONCLUSION

AI models for risk stratification, even if "off-the-shelf" by design, are unlikely to be "plug-and-play" in health care settings. Seeking out key stakeholders and defining clear use cases early in the implementation process can better facilitate utilization of these models.

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488–0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582–0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068–0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician’s model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.