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

Abstract

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.

The relationship between a series of inflammatory markers on the risk of heart failure in different gender groups: Analysis from NHANES 2015–2018

“…A study found that NLR can be used to predict the development of atrial fibrillation in diabetes patients [ 19 ]. A high NLR in conjunction with neutrophil transcriptional activation is associated with systemic inflammation and functional impairment in HF with preserved ejection fraction (HFpEF) [ 4 ].—Liu Z established a novel nomogram integrated with LMR (HR: 0.85, 95% CI: 0.78–0.93) and PLR (HR: 1.002, 95% CI: 1.000–1.004) to evaluate the risk of cardiovascular readmission or all-cause mortality in patients with CHF [ 20 ]. The above are consistent with our research results and verified synchronously in the same large sample population based on NHANES data.…”

A Predictive Model of Early Readmission for Patients with Heart Failure

“…Anemia, particularly persistent and new-onset anemia and milder temporary anemia, are suggested to be harmful to HF patients [23]. Inflammatory markers have been confirmed to be associated with adverse outcomes in HF patients [24]. NT-ProBNP secreted by the ventricle with increased wall stress is a prognostic HF biomarker [25].…”

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters

“…It is often used in the medical field for the prediction of adverse events of diseases, such as the prediction of adverse events of chronic heart failure. 14 At present, the nomogram prediction model has also been studied for the prediction of clinical metastasis of NSCLC, 15 and the novel International Association for the Study of Lung Cancer (IASLC) grade of invasive lung adenocarcinoma, 16 all of which reflect a good adjuvant effect in the treatment of patients.…”