Applicability of the heart failure Readmission Risk score: A first European study

Hospital Readmissions for Fluid Overload among Individuals with Diabetes and Diabetic Kidney Disease: Risk Factors and Multivariable Prediction Models

AIMS

Hospital readmissions due to recurrent fluid overload in diabetes and diabetic kidney disease can be avoided with evidence-based interventions. We aimed to identify at-risk patients who can benefit from these interventions by developing risk prediction models for readmissions for fluid overload in people living with diabetes and diabetic kidney disease.

METHODS

This was a single-center retrospective cohort study of 1,531 adults with diabetes and diabetic kidney disease hospitalized for fluid overload, congestive heart failure, pulmonary edema, and generalized edema between 2015 and 2017. The multivariable regression models for 30-day and 90-day readmission for fluid overload were compared with the LACE score for discrimination, calibration, sensitivity, specificity, and net reclassification index (NRI).

RESULTS

Readmissions for fluid overload within 30 days and 90 days occurred in 8.6% and 17.2% of patients with diabetes, and 8.2% and 18.3% of patients with diabetic kidney disease, respectively. After adjusting for demographics, comorbidities, clinical parameters, and medications, a history of alcoholism (HR 3.85, 95% CI: 1.41-10.55) and prior hospitalization for fluid overload (HR 2.50, 95% CI: 1.26-4.96) were independently associated with 30-day readmission in patients with diabetic kidney disease, as well as in individuals with diabetes. Additionally, current smoking, absence of hypertension, and high-dose intravenous furosemide were also associated with 30-day readmission in individuals with diabetes. Prior hospitalization for fluid overload (HR 2.43, 95% CI: 1.50-3.94), cardiovascular disease (HR 1.44, 95% CI: 1.03-2.02), eGFR ≤45 mL/min/1.73 m2 (HR 1.39, 95% CI: 1.003-1.93) was independently associated with 90-day readmissions in individuals with diabetic kidney disease. Additionally, thiazide prescription at discharge reduced 90-day readmission in diabetic kidney disease, while the need for high-dose intravenous furosemide predicted 90-day readmission in diabetes. The clinical and clinico-psychological models for 90-day readmission in individuals with diabetes and diabetic kidney disease had better discrimination and calibration than the LACE score. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetes was 22.4% and 28.9%, respectively. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetic kidney disease was 5.6% and 38.9%, respectively.

CONCLUSION

The risk models can potentially be used to identify patients at risk of readmission for fluid overload for evidence-based interventions, such as patient education or transitional care programs to reduce preventable hospitalizations.

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.

Comparison of linear and non-linear machine learning models for time-dependent readmission or mortality prediction among hospitalized heart failure patients

Although many models are available to predict prognosis of heart failure patients, most tools combining survival analysis are based on proportional hazard model. Non-linear machine learning algorithms would overcome the limitation of the time-independent hazard ratio assumption and provide more information in readmission or mortality prediction among heart failure patients. The present study collected the clinical information of 1796 hospitalized heart failure patients surviving during hospitalization in a Chinese clinical center from December 2016 to June 2019. A traditional multivariate Cox regression model and three machine learning survival models were developed in derivation cohort. Uno's concordance index and integrated Brier score in validation cohort were calculated to evaluate the discrimination and calibration of different models. Time-dependent AUC and Brier score curves were plotted to assess the performance of models at different time phases.

Role of the right ventricular contractile reserve during low-load exercise in predicting heart failure readmission

BACKGROUND

Exercise intolerance in patients with heart failure (HF) increases HF-associated readmission, and right ventricular (RV) contractile reserve assessed by low-load exercise stress echocardiography (ESE) is associated with exercise intolerance. This study investigated the impact of RV contractile reserve evaluated by low-load ESE on HF readmission.

METHODS

We prospectively examined 81 consecutive patients hospitalized for HF who underwent low-load ESE under a stabilized HF condition between May 2018 and September 2020. We performed a 25-W low-load ESE and defined RV contractile reserve as the increment in RV systolic velocity (RV s'). The primary outcome was hospital readmission. Incremental values of the change in RV s' over a readmission risk (RR) score were analyzed using the receiver operating characteristic (ROC) area under the curve; internal validation using bootstrapping was performed. The association between RV contractile reserve and HF readmission was illustrated with the Kaplan-Meier curve.

RESULTS

Eighteen (22 %) patients were readmitted due to worsening HF during the observation period (median 15.6 months). The cut-off value of 0.68 cm/s for the change in RV s' to predict HF readmission with the ROC curve analysis indicated good sensitivity (100 %) and specificity (76.2 %). The discriminatory ability for HF readmission was significantly improved by adding the change in RV s' to the RR score (p = 0.006), and the c-statistic using the bootstrap method was 0.92. The cumulative survival rate free of HF readmission was significantly lower in patients with reduced-RV contractile reserve (log-rank test, p < 0.001).

CONCLUSIONS

The change in RV s' during low-load exercise had an incremental prognostic value for predicting HF readmission. The results demonstrated the loss of RV contractile reserve assessed by low-load ESE was associated with HF readmission.

Post discharge management of heart failure patients: clinical findings at the first medical visit in a single-center study

BACKGROUND

The Post Discharge Management of patients with heart failure impact significantly their incomes. This study aims to analyze the clinical findings and management at the first medical visit of these patients in our context.

MATERIAL AND METHODS

This is a retrospective cross-sectional descriptive study on consecutive files of patients hospitalized for heart failure from January to December 2018 in our Department. We analyse data from the first post discharge medical visit including medical visit time, clinical conditions and management.

RESULTS

Three hundred and eight patients (mean age: 53.4 ± 17.0 years, 60% males) were hospitalized on median duration of 4 days [1-22 days]. One hundred and fifty-three patients (49,67%) were presented at the first medical visit after 66.53 days[0.06-369] on average, 10 (3.24%) patients died before this first medical visit and 145 (47.07%) had been lost to follow-up. The re-hospitalization and treatment non-compliance rates were 9.4% and 3.6%, respectively. Male gender (p = 0.048), renal failure (p = 0.010), and Vitamin K antagonist (VKA) /direct oral anticoagulant (DOAC) (p = 0.049) were the main lost to follow-up factors in univariate analysis without statistic signification in multivariate analysis. Hyponatremia (OR = 2.339; CI 95% = 0.908-6.027; p = 0.020) and atrial fibrillation (OR = 2.673; CI 95% = 1.321-5.408; p = 0.012) were the major mortality factors.

CONCLUSION

The management of patients with heart failure after discharge from hospital seems to be insufficient and inadequate. A specialized unit is required to optimize this management.

The Charlson comorbidity index and short-term readmission in patients with heart failure: A retrospective cohort study

The relationship between the Charlson comorbidity index (CCI) and short-term readmission is as yet unknown. Therefore, we aimed to investigate whether the CCI was independently related to short-term readmission in patients with heart failure (HF) after adjusting for other covariates. From December 2016 to June 2019, 2008 patients who underwent HF were enrolled in the study to determine the relationship between CCI and short-term readmission. Patients with HF were divided into 2 categories based on the predefined CCI (low < 3 and high > =3). The relationships between CCI and short-term readmission were analyzed in multivariable logistic regression models and a 2-piece linear regression model. In the high CCI group, the risk of short-term readmission was higher than that in the low CCI group. A curvilinear association was found between CCI and short-term readmission, with a saturation effect predicted at 2.97. In patients with HF who had CCI scores above 2.97, the risk of short-term readmission increased significantly (OR, 2.66; 95% confidence interval, 1.566-4.537). A high CCI was associated with increased short-term readmission in patients with HF, indicating that the CCI could be useful in estimating the readmission rate and has significant predictive value for clinical outcomes in patients with HF.

Machine Learning Patient-Specific Prediction of Heart Failure Hospitalization Using Cardiac MRI-Based Phenotype and Electronic Health Information

Background

Heart failure (HF) hospitalization is a dominant contributor of morbidity and healthcare expenditures in patients with systolic HF. Cardiovascular magnetic resonance (CMR) imaging is increasingly employed for the evaluation of HF given capacity to provide highly reproducible phenotypic markers of disease. The combined value of CMR phenotypic markers and patient health information to deliver predictions of future HF events has not been explored. We sought to develop and validate a novel risk model for the patient-specific prediction of time to HF hospitalization using routinely reported CMR variables, patient-reported health status, and electronic health information.

Methods

Standardized data capture was performed for 1,775 consecutive patients with chronic systolic HF referred for CMR imaging. Patient demographics, symptoms, Health-related Quality of Life, pharmacy, and routinely reported CMR features were provided to both machine learning (ML) and competing risk Fine-Gray-based models (FGM) for the prediction of time to HF hospitalization.

Results

The mean age was 59 years with a mean LVEF of 36 ± 11%. The population was evenly distributed between ischemic (52%) and idiopathic non-ischemic cardiomyopathy (48%). Over a median follow-up of 2.79 years (IQR: 1.59–4.04) 333 patients (19%) experienced HF related hospitalization. Both ML and competing risk FGM based models achieved robust performance for the prediction of time to HF hospitalization. Respective 90-day, 1 and 2-year AUC values were 0.87, 0.83, and 0.80 for the ML model, and 0.89, 0.84, and 0.80 for the competing risk FGM-based model in a holdout validation cohort. Patients classified as high-risk by the ML model experienced a 34-fold higher occurrence of HF hospitalization at 90 days vs. the low-risk group.

Conclusion

In this study we demonstrated capacity for routinely reported CMR phenotypic markers and patient health information to be combined for the delivery of patient-specific predictions of time to HF hospitalization. This work supports an evolving migration toward multi-domain data collection for the delivery of personalized risk prediction at time of diagnostic imaging.

Short-Term Mortality in Patients with Heart Failure at the End-of-Life Stages: Hades Study

Background: Information regarding short-term vital prognosis in patients with heart failure at advanced stages of the disease is scarce. Objective: To develop a three-month mortality predictive model for patients with advanced heart failure. Methods: Prospective observational study carried out in primary care and a convalescence community facility. Heart failure patients either New York Heart Association (NYHA) III with at least two HF hospitalizations during the previous six months or NYHA IV with/without previous recent hospitalization were included in the study. Multivariable predictive models using Cox regression were performed. Results: Of 271 patients included, 55 (20.3%) died during the first three months of follow-up. Mean age was 84.2 years (SD 8.3) and 59.8% were women. Predictive model including NT-proBNP had a C-index of 0.78 (95% CI 0.71; 0.85) and identified male gender, low body mass index, high potassium and NT-proBNP levels, and moderate-to-severe dependence for daily living activities (Barthel index < 40) as risk factors of mortality. In the model without NT-proBNP, C index was 0.72 (95% CI 0.64; 0.79) and, in addition to gender, body mass index, low Barthel index, and severe reductions in glomerular filtration rate showed the highest predictive hazard ratios for short-term mortality. Conclusions: In addition to age, male gender, potassium levels, low body mass index, and low glomerular filtration, dependence for activities of daily living add strong power to predict mortality at three months in patients with advanced heart failure.

Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction

Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

BACKGROUND

 Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.

OBJECTIVES

 To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.

METHODS

 In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.

RESULTS

 While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64].

CONCLUSION

 PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

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.

Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis

Simple Summary

Structured survey on the predictive analysis of postoperative complications in oncology, bridging classic risk scores with machine learning advances, and further establishing principles to guide the design of cohort studies and the predictive modeling of postsurgical risks.

Abstract

Postoperative complications can impose a significant burden, increasing morbidity, mortality, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.

Identifying Targets to Improve Heart Failure Outcomes for Patients Receiving Home Healthcare Services: The Relationship of Functional Status and Pain

Heart failure (HF) is one of the leading causes of rehospitalization in the United States. Due to the complex nature of HF, the provision of Medicare-certified home healthcare services has increased. Medicare-certified home healthcare agencies measure and report patients' outcomes such as functional status, activities of daily living (ADL), and instrumental activities of daily living to the Centers for Medicare and Medicaid Services. These metrics are assessed using the Outcome and Assessment Information Set (OASIS). As a large data set, OASIS has been used to advance care quality in multiple ways including identifying risk factors for negative patient outcomes. However, there is a lack of OASIS analyses to assess the relationship between functional status and the role of other factors, such as pain, in impeding recovery after hospitalization among HF patients. Therefore, the purpose of this study is to identify the relationship between functional status and pain using the OASIS database. Among 489 HF patients admitted to home healthcare, 83% were White, 57% were female, and the median age was 80. Patients who reported daily but not constant activity-interfering pain at discharge demonstrated the least improvement in functional performance as measured by ADLs, whereas patients without activity-interfering pain demonstrated the greatest improvement in ADL performance (p value = 0.0284). Tracking individual patient ADL scores, particularly the frequency of activity-interfering pain, could be a key indicator for clinical focus for patients with HF in the home healthcare setting.

Impact of Prior Ischemic Stroke on Outcomes in Patients With Heart Failure - A Propensity-Matched Study

BACKGROUND

Whether ischemic stroke per se, rather than older age or additional comorbidities, accounts for the adverse prognosis of heart failure (HF) is uncertain. The present study examineed the intrinsic association of ischemic stroke with outcomes in a propensity-matched cohort.Methods and Results:Of 1,351 patients hospitalized with HF, 388 (28.7%) had prior ischemic stroke. Using propensity score for prior ischemic stroke, estimated for each patient, a matched cohort of 379 pairs of HF patients with and without prior ischemic stroke, balanced on 32 baseline characteristics was assembled. At 30 days, prior ischemic stroke was associated with significantly higher risks of the combined endpoint of all-cause death or readmission (hazard ratio [HR]: 1.91; 95% confidence interval [CI]: 1.38 to 2.65; P<0.001), all-cause death (HR: 2.08; 95% CI: 1.28 to 3.38; P=0.003), all-cause readmission (HR: 2.67; 95% CI: 1.78 to 4.01; P<0.001), and HF readmission (HR: 2.11; 95% CI: 1.19 to 3.72; P=0.010). Prior ischemic stroke was associated with a significantly higher risk of all 4 outcomes at both 6 months and 1 year.

CONCLUSIONS

Prior ischemic stroke was a potent and persistent risk predictor of death and readmission among patients with HF after accounting for clinical characteristics.

A novel nomogram to predict all-cause readmission or death risk in Chinese elderly patients with heart failure

Aims

Elderly patients with heart failure (HF) are associated with frequent all‐cause readmission or death. The present study sought to develop an accurate and easy‐to‐use model to predict all‐cause readmission or death risk in Chinese elderly patients with HF.

Methods and results

This was a prospective cohort study in patients with HF aged 65 or older. Demographic, co‐morbidity, laboratory, and medication data were collected. A Cox regression model was used to identify factors for the prediction of readmission or death at 30 days and 1 year. A nomogram was developed with bootstrap validation. Of the included 854 patients, the cumulative all‐cause readmission and mortality rates were 10.5% and 11.6% at 30 days and 34.9% and 19.7% at 1 year, respectively. The independent risk factors associated with both 30 day and 1 year readmission or death were older age, stroke, diastolic blood pressure < 60 mmHg, body mass index ≤ 18.5 kg/m², lower estimated glomerular filtration rate, and BNP > 400 pg/mL (all P < 0.05). Anaemia, abnormal neutrophils, and admission without angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers were the specific independent risk factors of 30 day all‐cause readmission or death (all P < 0.05), whereas serum sodium ≤ 140 mmol/L and admission without beta‐blockers were the specific independent risk factors of 1 year all‐cause readmission or death (all P < 0.05). The C‐index of the 30 day and 1 year diagnosis prediction model was 0.778 [95% confidence interval (CI) 0.693–0.862] and 0.738 (95% CI 0.640–0.836), respectively.

Conclusions

We developed accurate and easy‐to‐use nomograms to predict all‐cause readmission or death in Chinese elderly patients with HF. The nomograms will assist in reducing the all‐cause readmission and mortality rates.

Short term prognosis of heart failure after first hospital admission

BACKGROUND

Death and unexpected readmission are frequent among heart failure patients. We aimed to assess 30-day readmission and mortality rate as well as to identify predictive factors for patients discharged from a first HF related hospital admission.

METHODS AND RESULTS

Retrospective, single-center, cohort study, using administrative data from a tertiary care hospital in Barcelona, Spain. Patients discharged alive from a first HF related admission from 2010 to 2014 were assessed for 30-day death, readmission and adverse outcome rate. A Linear Logistic Regression Model was fitted for each outcome. The set accounted for 3642 patients; 50.1% female and 49.9% male. Mean age was 76 years (SD=12). 30-Days rates were 9.2% for readmission, 5.6% for death and 13.8% for adverse outcome. Admission to an ED within 30 days was strongly linked to readmission (OR=6.97), death (OR=2.31) and adverse outcome (OR=8.55), as well as chronic kidney disease (OR=1.44/1.61/2.86 respectively). Discharge to a Long Stay Care (LSC) facility was linked to lower readmission and adverse event rates (OR=.57 and OR=.15).

CONCLUSION

Pre and post-index discharge use of health care resources is related to adverse outcome rates. Our findings point out the potential benefit for a more tailored approach in the management of HF patients.

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

Background

The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers’ demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB).

Methods

Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including all-cause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation.

Conclusions

The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined.

Registration number

The SLR was registered in Prospero (ID: CRD42018100709).

Usefulness of systolic blood pressure combined with heart rate measured on admission to identify 1-year all-cause mortality risk in elderly patients firstly hospitalized due to acute heart failure

BACKGROUND

Systolic blood pressure (SBP) and heart rate (HR) are well-known prognostic factors in heart failure (HF).

AIMS

Our objective was to assess the value of the combination of admission SBP and HR to estimate 1-year mortality risks in elderly patients admitted due to a first episode of acute HF (AHF).

METHODS

During a 36-month period, we retrospectively reviewed 901 consecutive patients aged ≥ 75 admitted because of a first episode of AHF. According to admission SBP-HR combinations, three groups were defined: "low-risk" (HR < 70 bpm and SBP ≥ 140 mmHg), "moderate-risk" (HR < 70 bpm and SBP < 140 mmHg or HR ≥ 70 bmp and SBP ≥ 120 mmHg), and "high-risk" (HR ≥ 70 bpm and SBP < 120 mmHg). We analyzed all-cause mortality using Cox mortality analysis.

RESULTS

One-year mortality ranged from 16.5% for patients in the low-risk group to 50% for those in the high-risk group (p < 0.0001). Multivariate Cox regression for 1-year mortality showed hazard risk (HzR) ratios, compared to that (HzR 1) of the low-risk reference group, of 1.759 (95% CI 1.035-2.988, p = 0.037) for moderate-risk, and 3.171 (95% CI 1.799-5.589, p = 0.0001) for high-risk group. Prior use of a high number of chronic therapies (HzR 1.045), lower admission diastolic BP (HzR 0.986) and higher admission serum potassium values (HzR 1.534) were also significantly associated with mortality.

CONCLUSION

In elderly population firstly hospitalized due to AHF, the simple combined admission measurement of SBP and HR predicts higher risk for 1-year all-cause mortality.

Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability Study

Background

Heart failure (HF) is a condition that affects approximately 6.2 million people in the United States and has a 5-year mortality rate of approximately 42%. With the prevalence expected to exceed 8 million cases by 2030, projections estimate that total annual HF costs will increase to nearly US $70 billion. Recently, the advent of remote monitoring technology has significantly broadened the scope of the physician’s reach in chronic disease management.

Objective

The goal of our program, named the Heart Health Program, was to examine the feasibility of using digital health monitoring in real-world home settings, ascertain patient adoption, and evaluate impact on 30-day readmission rate.

Methods

A digital medicine software platform developed at Mount Sinai Health System, called RxUniverse, was used to prescribe a digital care pathway including the HealthPROMISE digital therapeutic and iHealth mobile apps to patients’ personal smartphones. Vital sign data, including blood pressure (BP) and weight, were collected through an ambulatory remote monitoring system that comprised a mobile app and complementary consumer-grade Bluetooth-connected smart devices (BP cuff and digital scale) that send data to the provider care teams. Care teams were alerted via a Web-based dashboard of abnormal patient BP and weight change readings, and further action was taken at the clinicians’ discretion. We used statistical analyses to determine risk factors associated with 30-day all-cause readmission.

Results

Overall, the Heart Health Program included 58 patients admitted to the Mount Sinai Hospital for HF. The 30-day hospital readmission rate was 10% (6/58), compared with the national readmission rates of approximately 25% and the Mount Sinai Hospital’s average of approximately 23%. Single marital status (P=.06) and history of percutaneous coronary intervention (P=.08) were associated with readmission. Readmitted patients were also less likely to have been previously prescribed angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (P=.02). Notably, readmitted patients utilized the BP and weight monitors less than nonreadmitted patients, and patients aged younger than 70 years used the monitors more frequently on average than those aged over 70 years, though these trends did not reach statistical significance. The percentage of the 58 patients using the monitors at least once dropped from 83% (42/58) in the first week after discharge to 46% (23/58) in the fourth week.

Conclusions

Given the increasing burden of HF, there is a need for an effective and sustainable remote monitoring system for HF patients following hospital discharge. We identified clinical and social factors as well as remote monitoring usage trends that identify targetable patient populations that could benefit most from integration of daily remote monitoring. In addition, we demonstrated that interventions driven by real-time vital sign data may greatly aid in reducing hospital readmissions and costs while improving patient outcomes.

Sex differences in 1-year mortality risks in older patients experiencing a first acute heart failure hospitalization

AIM

To assess whether 1-year mortality in older patients experiencing a first admission for acute heart failure was related to sex, and to explore differential characteristics according to sex.

METHODS

We reviewed the medical records of 1132 patients aged >70 years of age admitted within a 3-year period because of a first episode of acute heart failure. We analyzed sex differences. Mortality was assessed using multivariate Cox analysis.

RESULTS

There were 648 (57.2%) women (mean age 82.1 years) and 484 men (mean age 80.1 years). There were some differences in risk factors: women more often had hypertension, and less frequently had coronary heart disease and comorbidities (women more often had dementia, and men more often had chronic obstructive pulmonary disease, chronic kidney disease and stroke). Women were treated more frequently with spironolactone. The 1-year all-cause mortality rate was 30.2% (30.7% women and 29.5% men). Multivariate Cox analysis identified an association between reduced heart failure (hazard ratio [HR] 0.35, 95% confidence interval [95% CI] 0.21-0.59), hemoglobin <10 g/dL (HR 1.99, 95% CI 1.16-3.40), systolic blood pressure (HR 0.98, 95% CI 0.97-0.99), previous diagnosis of dementia (HR 2.07, 95% CI 1.12-3.85), number of chronic therapies (HR 1.12, 95% CI 1.05-1.19) and 1-year mortality in women. In men, an association with mortality was found for low systolic blood pressure (HR 0.97, 95% CI 0.97-0.98) and higher potassium values (HR 1.42, 95% CI 1.01-2.00).

CONCLUSIONS

Among older patients hospitalized for the first acute heart failure episode, there is a slightly higher predominance of women. There are sex differences in risk factors and comorbidities. Although the mortality rate is similar, the factors associated with it according to sex are different. Geriatr Gerontol Int 2019; 19: 184-188.

Risk prediction for 30-day heart failure-specific readmission or death after discharge: Data from the Korean Acute Heart Failure (KorAHF) registry

BACKGROUND

Identifying patients with acute heart failure (HF) at high risk for readmission or death after hospital discharge will enable the optimization of treatment and management. The objective of this study was to develop a risk score for 30-day HF-specific readmission or death in Korea.

METHODS

We analyzed the data from the Korean Acute Heart Failure (KorAHF) registry to develop a risk score. The model was derived from a multiple logistic regression analysis using a stepwise variable selection method. We also proposed a point-based risk score to predict the risk of 30-day HF-specific readmission or death by simply summing the scores assigned to each risk variable. Model performance was assessed using an area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) index to evaluate discrimination, calibration, and reclassification, respectively.

RESULTS

Data from 4566 patients aged ≥40 years were included in the analysis. Among them, 446 (9.8%) had 30-day HF-specific readmission or death. The final model included 12 independent variables (age, New York Heart Association functional class, clinical history of hypertension, HF admission, chronic obstructive pulmonary disease, etiology of cardiomyopathy, systolic blood pressure, left ventricular ejection fraction, serum sodium, brain natriuretic peptide, N-terminal prohormone of brain natriuretic peptide at discharge, and prescription of β-blockers and angiotensin-converting enzyme inhibitors or angiotensin II receptor antagonists at discharge). The point risk score showed moderate discrimination (AUC of 0.710; 95% confidence interval, 0.685-0.735) and good calibration (χ²=8.540, p=0.3826).

CONCLUSIONS

The risk score for the prediction of the risk of 30-day HF-specific readmission or death after hospital discharge was developed using 12 predictors. It can be utilized to guide appropriate interventions or care strategies for patients with HF.

Lower admission blood pressure as an independent predictor of 1-year mortality in elderly patients experiencing a first hospitalization for acute heart failure

BACKGROUND

Systolic blood pressure (SBP) is an acknowledged prognostic factor in patients with heart failure (HF). Admission SBP should be a risk factor for 1-year mortality even in elderly patients experiencing a first admission for HF, and this risk may persist in the oldest subset of patients.

DESIGN

Methods: We reviewed the medical records of 1031 patients aged 70 years or older admitted within a 3-year period for a first episode of acute heart failure (AHF). The cohort was divided according to admission SBP values in quartiles. We analyzed all-cause mortality as a function of these admission SBP quartiles.

RESULTS

Mean age was 82.2 ± 6 years; their mean admission SBP was 138.6 ± 25 mmHg. A statistically significant association was present between mortality at 30 (p < 0.0001), 90 (p < 0.0001), and 365 days (p < 0.0001) after hospital discharge and lower admission SBP quartiles. One-year mortality ranged from 14.7% for patients within the upper SBP quartile to 41.4% for those in the lowest quartile. The multivariate analysis confirmed this association (HR: 0.884; 95% CI: 0.615-0.76; p = 0.0001), which remained significant when admission SBP was evaluated as a continuous variable (HR: 0.980; 95% CI: 0.975-0.985; p = 0.0001). The association between SBP and 1-year mortality remained when the sample was divided into old (70-82 years) and "oldest-old" (>82 years) patients.

CONCLUSIONS

Lower SBP at admission is an independent predictor of midterm postdischarge mortality for elderly patients experiencing a first admission for AHF.

Lymphocyte-to-white blood cells ratio in older patients experiencing a first acute heart failure hospitalization

PURPOSE

Low lymphocyte counts are related to poor health results in heart failure (HF) patients. We assess whether a low lymphocyte-to-white blood cells ratio (LWR) is related to 1-year mortality in older patients experiencing a first hospitalization for acute HF.

METHODS

We evaluated 859 patients > 75 years of age admitted within a 33-month period because of a first episode of acute HF. Patients were divided into four groups according to LWR quartiles.

RESULTS

Patients' mean age was 83.5 ± 5.5 years and their median LWR was 16.7%. After 1 year of follow-up 270 patients (31.43%) died. Mean LWR values were significatively lower in the group of patients who died (15.1 vs. 17.4%; p = 0.001). Mortality rates were significantly higher in the lower LWR quartile either at 1 month, 3 months, and 1 year after the index acute HF episode. The univariate logistic regression analysis identified the LWR (either as quartiles or continuous variable) to be independently associated with higher risk of 1-year post-discharge mortality. Multivariate analysis confirmed this association (HR for LWR as a quartiles variable 1.525; 95% CI 1.161-2.003 and for LWR as a continuous variable 1.145; 95% CI 1.069-1854) besides older age, a higher comorbidity and higher admission potassium.

CONCLUSIONS

As is the case in other HF scenarios, a simple routine admission laboratory test such as lymphocyte count can independently predict 1-year mortality for older patients hospitalized for first time due to acute HF.

High comorbidity, measured by the Charlson Comorbidity Index, associates with higher 1-year mortality risks in elderly patients experiencing a first acute heart failure hospitalization

BACKGROUND

Comorbidity is related to poor health results in chronic heart failure (HF).

AIMS

The purpose of the study was to assess whether a high Charlson Comorbidity Index score (CCI) relates to 1 year mortality after a first hospitalization for acute HF (AHF).

METHODS

We reviewed the medical records of 897 patients > 65 years of age admitted within a two-year period because of a first episode of AHF. We analyzed two groups: low (CCI ≤ 2) and high (CCI > 2) comorbidity.

RESULTS

Patients' mean CCI was 2.2 ± 1.7; 344 patients (38.35%) had a CCI > 2. 1-year all-cause mortality rate in the high comorbidity group was 32.6%, worse than that among low comorbidity group patients (23.7%, p = 0.002). Cox multivariate analysis identified a CCI > 2 as an independent risk factor for 1-year mortality (p = 0.002; HR: 1.525; CI 95% 1.161-2.003), along with older age, history of arterial hypertension, and higher admission heart rate and serum potassium values. Analyzing CCI as a continuous variable, the association remained is also significant (p = 0.0001; HR 1.145; CI 95% 1.069-1.854).

CONCLUSIONS

Higher global comorbidity (CCI > 2) at the time of a first hospitalization because of AHF is an independent predictor of mid-term post-discharge mortality among elderly HF patients.