With the development of the advanced and accurate new medical technologies and the improved health care system, the average human age has improved compared with previous decades. On the other hand, due to the new sedentary lifestyle, the incident of chronic cardiovascular diseases such as coronary heart disease, hypertension, diabetes, and obesity also increased. Heart failure is a serious late-stage event of cardiovascular diseases with high readmission and mortality rate (1–4). Meanwhile, the development of heart failure will eventually lead to adverse outcomes such as stroke and death (5–7). Therefore, the early identification of individuals with high risk factors in the community will give chance to the early intervention and prevention of early adverse effects for those individuals. Therefore, it will reduce their late-stage health care costs and mortality rate (8, 9).

The Framingham Heart Study and other studies have shown that risk factors such as age, gender, BMI, EF, diabetes, hypertension, obesity, and cardiovascular disease consistently predispose individuals to heart failure (10–13). Proper early intervention for patients with acute heart failure could play a massive role in the prevention of severe adverse effects and delay bad prognosis thus reducing costs and MACE.

N-terminal pro-B type natriuretic peptide (NT-proBNP) is used extensively to evaluate the severity of cardiac dysfunction in patients with heart failure, which is considered as a momentous assessment tool for physicians (14–18). However, due to the high costs of laboratory tests, it is not readily available in basic care settings such as community health care units, which are the first approach for the patients as a primary health care center. As a result, many patients are misdiagnosed or receive delayed assessments and treatment. Therefore, it is very necessary to establish an easy scoring model to have quick hint and assessment of severity and long-term prognosis of patients with heart failure. We aimed to formulate a risk algorithm composed of accessible clinical factors that can be assessed in primary care settings, by using lasso penalized regression for individual patients. It would allow early prevention and appropriate targeted intervention, and could decrease health-care costs and length of hospital stay (19).

The main goal of this study is to establish a scoring system for primary health care institutions to predict the risk and severity of cardiovascular events for patients with chronic heart failure, the risk of MACE of chronic heart failure patients, the length of hospital stay, and treatment cost via readily available clinical factors and methods in primary care settings, especially when NT-proBNP and other specific and time-consuming medical investigations are unavailable. The study highlighted different prognostic influencing factors of heart failure correlated with admission of NT-proBNP in a retrospective registry study. The results confirmed that the risk factors such as (1) body-mass index, (2) ejection fraction, (3) serum creatinine, (4) hemoglobin, (5) C-reactive protein, and (6) neutrophil lymphocyte ratio were associated with NT-proBNP. Age was not included into the risk score model as it was already taken into account when calculating the multiples of NT-proBNP elevation. The variables included in the model were consistent with the risk factors mentioned in the current studies (22–29).

This study focused on the early identification of acute exacerbations in patients with heart failure. It was conducted through identifying the severity of heart failure as the primary endpoint, and by creating an easy-to-use scoring system for evaluation. On the other hand, Long-term indicators such as MACE, length of hospitalization, and cost were used as secondary endpoints to confirm the feasibility of this prediction model. Although, diagnosis and assessment of heart failure in initial stages has been always challenging and could be misdiagnosed until occurrence of adverse events or sequelae, however, the early identification of severity of heart failure in the patients allow us to monitor, give proper timely treatment and get better prognosis (30).

In fact, large cohort studies and meta-analyses have shown the association between the risk factors in heart failure patients, such as NT pro-BNP, cardiac EF level, plasma level of calcium, hemoglobin, creatinine, uric acid, high-sensitivity C-reactive protein (CRP), overweight, chronic heart disease, malignancy and pulmonary infection with the prognosis of heart failure (31, 32). However, only few studies focused on the relationship between these risk factors and severity of heart failure, reflected by NT pro-BNP, and cardiac EF level, in-hospital MACE events, length, and the cost of in-hospital stay.

Furthermore, a number of risk prediction models have been published to statistically predict the risk of future outcomes associated with heart failure (33–36). Yet, only few models fit the Chinese population. For example, Framingham Heart Study Risk has set up an assessment for incident of heart failure but only in patients with atrial fibrillation (10) and not to assist patients with acute exacerbation of chronic heart failure, while “Bo Zhuang” in November has developed a multiple Cox regression model to predict long-term mortality and readmission risk of Chinese patients with chronic heart disease (37), with C index values of 0.69 (95%CI:0.65–0.72) and 0.62 (95%CI:0.57–0.67) in the derivation cohort and validation cohorts. However, the sample size of that study is insufficient, and it needs to conduct cardiopulmonary exercise tests (CPETs) to monitor the respiratory and circulatory parameters of patients under exercise and to obtain comprehensive indicators of cardiopulmonary functions, which is inappropriate for patients presenting with acute exacerbations of heart failure. The ACUTE HF score reporte by M. Cameli et al. was used to predict long-term prognosis (30-day, 6-month, and 5-year mortality) in patients with AHF (38). However, it had limited reference value for the short-term decision-making of doctors in treating patients with chronic heart failure. This study focused on the early identification of the severity of heart failure in patients, and also had a certain predictive value for long-term prognosis. There are also many studies using existing patient-centered holistic assessment methods to predict outcomes in patients with heart failure. For example, ALBI score, MELD XI score (39), Norton score (40), and SOFA score (41). Matsue et al. explored the relationship between ALBI score and MELD XI score using total bilirubin, serum creatinine, serum albumin, and prognosis in patients with AHF, and proved that ALBI had better predictive ability (39). However, since albumin synthesis and liver function are affected by many factors, the prognostic predictive ability of ALBI score in patients with AHF remains to be discussed. Natanzon et al. demonstrated that the admission Norton score was a strong predictor of prognosis in different heart failure populations and a predictor of short-term (30 and 90 days) and long-term (1-year) mortality after hospitalization in patients with AHF (40). But the Norton score is a tool used to evaluate the frailty of patients, including evaluation of mental state, which will be affected by the subjectivity of the evaluator. In addition, the Norton score is not simple enough and may be more suitable for inpatients, while it takes a certain amount of time for outpatient doctors to evaluate. Similarly, the SOFA score evaluated by Elias et al. is more complex, more time-consuming, and even requires dynamic assessment, and its application scenario is usually in the intensive care unit (41). The scoring model of this study was constructed with objective indicators, which was more reliable and could help outpatient doctors to obtain evaluation results quickly, which was one of the purposes and advantages of this study. Moreover, the AUC of the training and validation dataset in this study were 0.770 (95% CI:0.746–0.794) and 0.756 (95% CI:0.717–0.795), respectively, which indicate moderate levels of predictive ability and were high among those from previously published heart failure risk prediction models (35).

The current study has several strengths. This includes having a considerable sample size. Also, it provides a scoring system for primary medical workers with an easily applicable method to quickly assess the patient’s condition in routine clinical practice, and conduct clinical decision-making, which may contribute to improving patients’ quality of life and subsequently decrease mortality, morbidity, treatment costs, and length of hospital stay. Furthermore, the indicators included in the model are simple and easy to obtain, which help its vigorous promotion in primary health care settings and contributes to the improvement of China’s graded diagnosis and treatment system.

On the other hand, each study has some limitations, as well. First, the risk score was developed and validated with a Chinese middle-aged to elderly population with an average to good renal function. Therefore, the scoring system might be inapplicable to other ethnic groups or young individuals, since we had too few young adults with chronic heart failure. It was acknowledged that the risk score needed to be validated, and potentially recalibrated for other ethnic groups and age-groups. Similarly, MACE incidences tend to increase over time, therefore, the risk score might need to be recalibrated and the time factor might need to be considered to account for these changes. Secondly, the study was a retrospective analysis while the external validity of the risk function needs to be proven prospectively in independent samples. Thirdly, the epidemiological nature of the study also contributes to some limitations. We purposely incorporated clinical risk factors that are readily and routinely accessible in primary care settings such as blood routine and biochemistry, nevertheless, other factors could also be incorporated into risk models. For example, symptoms and signs can contribute in the assessment of risk factors. Despite its limitation, this risk score model conveniently provides a useful tool for prediction and patient triage.

This risk score model incorporating BMI, EF, serum creatinine, hemoglobin, CRP, and NLR, effectively evaluates patients’ severity classification of heart failure, closely related to MACE. It would help physicians to identify the severity of heart failure patients’ condition and target high-risk individuals for preventive measures, which contributes to lowering the incidence of MACE and minimizing hospital stay and treatment cost.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Ethics Committee of Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

MW, WZ, and GF designed the study. ML and JZ performed the statistical analysis. ML drafted the manuscript. All authors gave comments and suggestions, and approved publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.