Development and Validation of a Nomogram to Predict the 180-Day Readmission Risk for Chronic Heart Failure: A Multicenter Prospective Study

Construction and evaluation of a 180-day readmission prediction model for chronic heart failure patients based on sCD40L

The high readmission rate of patients with chronic heart failure (HF) can cause waste of medical resources and economic losses. Establishing an effective HF readmission model can effectively alleviate medical pressure and improve the quality of treatment. In this study, we conducted a comprehensive analysis of clinical and laboratory data from 248 patients with chronic HF who received treatment at our medical center between January 2021 to January 2022. We also measured soluble CD40 ligand (sCD40L) levels to determine their association with readmission due to HF during follow-up. To analyze the data, we employed various statistical methods including one-way ANOVA, correlation analysis, univariate COX regression, and Least Absolute Shrinkage and Selection Operator COX regression. Using these techniques, we organized the data and constructed a predictive model that was both trained and validated. We developed a nomogram to assess the likelihood of readmission within 180 days for patients with chronic HF. Our findings revealed that monocytes, creatinine, sCD40L, and hypertension history were all independent risk factors for 180-day HF readmissions. Additionally, our model's AUC was 0.731 in the training dataset and 0.704 in the validation dataset. This study provides new insights for predicting readmission within 180 days for patients with chronic HF. And sCD40L is an important predictive indicator for readmission of HF patients within 180 days, and clinical doctors can develop appropriate treatment plans based on sCD40L.

Nomogram Predicting 90-Day Readmission in Patients with Diabetes: A Prospective Study

Purpose

Readmission within a period time of discharge is common and costly. Diabetic patients are at risk of readmission because of comorbidities and complications. It is crucial to monitor patients with diabetes with risk factors for readmission and provide them with target suggestions. We aim to develop a nomogram to predict the risk of readmission within 90 days of discharge in diabetic patients.

Patients and Methods

This is a prospective observational survey. A total of 784 adult patients with diabetes recruited in two tertiary hospitals in central China were randomly assigned to a training set or a validation set at a ratio of 7:3. Depression, anxiety, self-care, physical activity, and sedentary behavior were assessed during hospitalization. A 90-day follow-up was conducted after discharge. Multivariate logistic regression was employed to develop a nomogram, which was validated with the use of a validation set. The AUC, calibration plot, and clinical decision curve were used to assess the discrimination, calibration, and clinical usefulness of the nomogram, respectively.

Results

In this study, the 90-day readmission rate in our study population was 18.6%. Predictors in the final nomogram were previous admissions within 1 year of the index admission, self-care scores, anxiety scores, physical activity, and complicating with lower extremity vasculopathy. The AUC values of the predictive model and the validation set were 0.905 (95% CI=0.874-0.936) and 0.882 (95% CI=0.816-0.947). Hosmer-Lemeshow test values were p = 0.604 and p = 0.308 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. Decision curve analysis indicated that the nomogram improved the clinical net benefit within a probability threshold of 0.02-0.96.

Conclusion

The nomogram constructed in this study was a convenient tool to evaluate the risk of 90-day readmission in patients with diabetes and contributed to clinicians screening the high-risk populations.

Development and validation of a nomogram for major adverse cardiovascular events after chronic total occlusion percutaneous coronary intervention for ischemic heart failure

BACKGROUND

Chronic total occlusion percutaneous coronary intervention (CTO-PCI) is an available means of revascularization in patients with ischemic heart failure (IHF). However, the prognosis of IHF patients undergoing CTO-PCI remains unclear due to the lack of reliable clinical predictive tools.

AIM

This study aimed to establish a nomogram for major adverse cardiovascular events (MACE) after CTO-PCI in IHF patients.

METHODS

Sixty-seven potential predictive variables for MACE in 560 IHF patients undergoing CTO-PCI were screened using least absolute shrinkage and selection operator regression. A nomogram was constructed based on multivariable Cox regression to visualize the risk of MACE, and then evaluation was carried out using the concordance index (C-index), time-independent receiver operating characteristic (timeROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

During a median follow-up of 32.0 months, there were 208 MACE occurrences. Seven variables were selected for nomogram construction: age, left ventricular ejection fraction, left ventricular end-diastolic diameter, N-terminal precursor B-type diuretic peptide, bending, and use of intravascular ultrasound and beta-blockers. The C-index was 0.715 (0.680-0.750) and the internal validation result was 0.715 (0.676-0.748). The timeROC area under the curve at 6 months, 1 year, and 2 years was 0.750 (0.653-0.846), 0.747 (0.690-0.804), and 0.753 (0.708-0.798), respectively. The calibration curves and DCA showed the nomogram had acceptable calibration and clinical applicability.

CONCLUSIONS

We developed a simple and efficient nomogram for MACE after CTO-PCI in IHF patients, which helps in early risk stratification and postoperative management optimization.

A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States

Background and aims

Heart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death.

Methods and results

Data (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO2, systolic blood pressure (SBP), and temperature (P < 0.01). The AUC and Brier score of the nomogram were 0.71 (0.67, 0.75) and 0.12 (0.11, 0.15) in the testing set and 0.73 (0.70, 0.75), 0.13 (0.12, 0.15), 0.65 (0.62, 0.68), and 0.13 (0.12, 0.13) in the external validation set, respectively. The RCS plot showed that the cutoff values of variables were HR of 96 bmp, 24h UOP of 1.2 L, serum calcium of 8.7 mg/dL, BUN of 30 mg/dL, NT-pro-BNP of 5121 pg/mL, SpO2 of 93%, SBP of 137 mmHg, and a temperature of 36.4°C.

Conclusion

Decreased temperature, decreased SpO2, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

Construction of a web-based dynamic nomogram for predicting the prognosis in acute heart failure

AIMS

The early identification and appropriate management may provide clinically meaningful and substained benefits in patients with acute heart failure (AHF). This study aimed to develop an integrative nomogram with myocardial perfusion imaging (MPI) for predicting the risk of all-cause mortality in AHF patients.

METHODS AND RESULTS

Prospective study of 147 patients with AHF who received gated MPI (59.0 [47.5, 68.0] years; 78.2% males) were enrolled and followed for the primary endpoint of all-cause mortality. We analysed the demographic information, laboratory tests, electrocardiogram, and transthoracic echocardiogram by the least absolute shrinkage and selection operator (LASSO) regression for selection of key features. A multivariate stepwise Cox analysis was performed to identify independent risk factors and construct a nomogram. The predictive values of the constructed model were compared by Kaplan-Meier curve, area under the curves (AUCs), calibration plots, continuous net reclassification improvement, integrated discrimination improvement, and decision curve analysis. The 1, 3, and 5 year cumulative rates of death were 10%, 22%, and 29%, respectively. Diastolic blood pressure [hazard ratio (HR) 0.96, 95% confidence interval (CI) 0.93-0.99; P = 0.017], valvular heart disease (HR 3.05, 95% CI 1.36-6.83; P = 0.007), cardiac resynchronization therapy (HR 0.37, 95% CI 0.17-0.82; P = 0.014), N-terminal pro-B-type natriuretic peptide (per 100 pg/mL; HR 1.02, 95% CI 1.01-1.03; P < 0.001), and rest scar burden (HR 1.03, 95% CI 1.01-1.06; P = 0.008) were independent risk factors for patients with AHF. The cross-validated AUCs (95% CI) of nomogram constructed by diastolic blood pressure, valvular heart disease, cardiac resynchronization therapy, N-terminal pro-B-type natriuretic peptide, and rest scar burden were 0.88 (0.73-1.00), 0.83 (0.70-0.97), and 0.79 (0.62-0.95) at 1, 3, and 5 years, respectively. Continuous net reclassification improvement and integrated discrimination improvement were also observed, and the decision curve analysis identified the greater net benefit of the nomogram across a wide range of threshold probabilities (0-100% at 1 and 3 years; 0-61% and 62-100% at 5 years) compared with dismissing the included factors or using either factor alone.

CONCLUSIONS

A predictive nomogram for the risk of all-cause mortality in patients with AHF was developed and validated in this study. The nomogram incorporated the rest scar burden by MPI is highly predictive, and may help to better stratify clinical risk and guide treatment decisions in patients with AHF.

A Novel Nomogram Integrated with Systemic Inflammation Markers and Traditional Prognostic Factors for Adverse Events' Prediction in Patients with Chronic Heart Failure in the Southwest of China

Objective

Inflammation contributes to the pathogenesis and progression of heart failure (HF). This study aimed to construct a nomogram based on systemic inflammatory markers and traditional prognostic factors to assess the risk of adverse outcomes (cardiovascular readmission and all-cause death) in patients with chronic heart failure (CHF).

Methods

Data were retrospectively collected from patients with HF admitted to the Department of Cardiovascular Medicine at the First Affiliated Hospital of Chongqing Medical University from January 2018 to April 2020, and each patient had complete follow-up information. The follow-up duration was from June 2018 to May 31, 2022. 550 patients were included and randomly assigned to the derivation and validation cohorts with a ratio of 7:3, and prognostic risk factors of CHF were identified by Cox regression analysis. The nomogram chart scoring model was constructed.

Results

The Cox multivariate regression analysis showed that traditional prognostic factors such as age (P=0.011), BMI (P=0.048), NYHA classification (P<0.001), creatinine (P<0.001), and systemic inflammatory markers including LMR (P=0.001), and PLR (P=0.015) were independent prognostic factors for CHF patients. Integrated with traditional and inflammatory prognostic factors, a nomogram was established, which yielded a C-index value of 0.739 (95% CI: 0.714-0.764) in the derivation cohort and 0.713 (95% CI: 0.668-0.758) in the validation cohort, respectively. The calibration curves exhibited good performance of the nomogram in predicting the adverse outcomes for patients with CHF. In subgroups (HFrEF, HFmrEF, and HFpEF groups), the systematic inflammatory markers-based nomograms proved to be effective prediction tools for patients' adverse overcomes, as well.

Conclusion

The nomogram combining systemic inflammatory markers and traditional risk factors has satisfactory predictive performance for adverse outcomes (mortality and readmission) in patients with CHF.

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

Background

In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.

Materials and methods

A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.

Results

A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.

Conclusion

The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.

Prevalence and impact of malnutrition on readmission among hospitalized patients with heart failure in China

AIMS

Malnutrition is common in patients with heart failure (HF) and is associated with poorer quality of life and increased mortality; however, an effective screening tool for malnutrition and its impact on the readmission of patients with HF is uncertain. Our objectives were to study (i) the nutritional status of Chinese hospitalized patients with HF and its impact on readmission and (ii) the validity of seven malnutrition screening tools.

METHODS AND RESULTS

In this study, univariate and multivariate analyses of Cox proportional hazards regression were used to determine important predictors of readmission. The endpoint was readmission due to HF or non-HF. A total of 402 patients were included (66.4% male, median age 62 years [range: 20-92 years], median NT-proBNP 5,229 ng/L). During a median follow-up of 159 days, 150 patients (37%) were readmitted to the hospital. After adjusting for confounders, only malnutrition assessed using the Controlling Nutritional Status (CONUT) nutrition score was independently associated with readmission (P = 0.0293). A base model for predicting readmission with a C-statistic of 0.680 and subsequent addition of various nutritional screening tools improved its performance over the base model. Patients with malnutrition had a twofold increased risk of readmission.

CONCLUSIONS

We found that the prevalence of malnutrition among hospitalized patients with HF in China is very high and that malnutrition significantly increases the risk of readmission in these patients. CONUT is a validated screening tool for malnutrition and may provide valuable prognostic information.

Associations between the gut microbiome, gut microbiology and heart failure: Current understanding and future directions

The role of the gut microbiome in pathophysiology, prognostication and clinical management of heart failure (HF) patients is of great clinical and research interest. Both preclinical and clinical studies have shown promising results, and the gut microbiome has been implicated in other cardiovascular conditions that are risk factors for HF. There is an increasing interest in the use of biological compounds produced as biomarkers for prognostication as well as exploration of therapeutic options targeting the various markers and pathways from the gut microbiome that are implicated in HF. However, study variations exist, and targeted research for individual putative biomarkers is necessary. There is also limited evidence pertaining to decompensated HF in particular. In this review, we synthesize current understandings around pathophysiology, prognostication and clinical management of heart failure (HF) patients, and also provide an outline of potential areas of future research and scientific advances.

A Survival Prediction for Acute Heart Failure Patients via Web-Based Dynamic Nomogram with Internal Validation: A Prospective Cohort Study

Purpose

The current study aimed to develop a convenient and accurate prognostic dynamic nomogram model for the risk of all-cause death in acute heart failure (AHF) patients that incorporates clinical characteristics including N-terminal pro-brain natriuretic peptide (NT-pro BNP) and growth stimulation expresses gene 2 protein (ST2).

Patients and Methods

We prospectively studied 537 consecutive AHF patients and derived a clinical prediction model. The least absolute shrinkage and selection operator regression model combined with clinical characteristics were used for dimensional reduction and feature selection. Multivariate Cox proportional hazard analysis and “Dynnom” package were used to build the dynamic nomogram for prediction of 1-,2-,and 5-year overall survival for AHF. With bootstrap validation, the time-dependent concordance index (C-index) and calibration curves were used to assess predictive discrimination and accuracy. The contributions of NT-pro BNP and ST2 to the nomogram were evaluated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI), while decision curve analysis (DCA) was used to assess clinical value.

Results

Patients were randomly divided into derivation (74.9%, n=402) and validation (25.1%, n=135) cohorts. Optimal independent prognostic factors for 1-,2-, and 5-year all-cause mortality were BS-ACMR (B: NT-pro BNP; S: ST2; A: age; C: complete right bundle branch block; M: mean arterial pressure; and R: red cell distribution width >14.5%); these were incorporated into the dynamic nomogram (https://bs-acmr-nom.shinyapps.io/dynnomapp/) with bootstrap validation. The C-indexes in the derivation (0.793) and validation (0.782) cohorts were consistent with comparable performance parameters. The calibration curve showed good agreement between the nomogram-predicted and actual survival. Adding NT-pro BNP and ST2 provided a significant net benefit and improved performance over other less adequate schemes in terms of DCA of survival probability compared to those neglecting either of these two factors.

Conclusion

The study constructed a dynamic BS-ACMR nomogram, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis in AHF patients.