A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure

Re-admission is the main adverse outcome for patients with heart failure (HF). Because it could be associated with a high mortality, lead to a decreased quality of life, and cause a significant financial burden [1‐3]. Thus, it is important to assess the prognosis of HF, as patients at higher risk of poor outcomes could receive more intensive therapy and close monitoring [4]. There are already efforts to develop novel prognostic models for HF [5]. A number of clinical studies predicting hospitalization for the deterioration of HF have been summarized in detail by Rahimi et al. [6, 7] The majority of effective models were built based on clinical data collected from discharge records of patients [8]; however, research indicates that high health-related quality of life (HRQOL) predicts a more favorable prognosis in patients with cardiovascular disease [9]. Thus, HRQOL should be used as a new prognostic indicator for HF patients [10, 11], and as an independent predictive factor of readmission with HF [12, 13].

The patient-reported outcomes measure (PROM) evaluates a patient’s quality of life by the way of scales to promote communication, inspect and identify HRQOL issues, enhance patient-centered treatment, and increase patient satisfaction [14]. PROM is widely used in HRQOL and combines all aspects of emotional, psychological, physical, and social functions, and even includes an individual’s subjective perception of health [15, 16]. Over the years, there has been considerable progress in the measurement, analysis, and interpretation of PROM, which has been validated by multifarious studies [17]. Thus, PROM is helpful in diagnosis and therapy, and is of significant importance in clinical practice [18, 19] and widely recognized by medical professionals.

Nomograms have been regarded as dependable instruments by creating simple intuitive diagrams of the predictive models that quantify the risk of clinical adverse events [20, 21]. In recent years, nomograms have also been used to predict the prognosis of HF [22‐24]. This study, as far as we know, is the first attempt to construct a prognostic nomogram of chronic heart failure (CHF) based on the Chinese PROM data of 454 patients with CHF, to predict the possibility of readmission.

Many studies show that HRQOL is independent predictive factors of prognosis for inpatients with HF and that they have important predictive value [12, 13] and nomogram based on Cox regression has been widely used to predict the survival time of chronic diseases, especially cancer [30‐32]. However, as far as we know, there have been no studies based on it to construct the quantitative model to predict the probability of readmission for patients with CHF. This study constructed a simple intuitive graph of the prediction model based on PROM to quantify the risk of readmission for CHF. This can be an important aid when doctors make treatment recommendations for patients with CHF.

There are some things that may be highlighted in this study. First, PROM used in our study is a questionnaire in Chinese based on the different cultural and societal value systems of mainland China as well as the medical and economic environments of the country. The reliability and validity of the scale have been verified by Tian et al. [26], and they were further verified and screened in this study. Second, only patients with CHF were selected in the study, regardless of etiology, LVEF, complications, etc. Thus, the database had covered, and was representative of, a wider population, further promoting the clinical application of the model. Third, internal validation through a bootstrap resampling method demonstrated moderate discrimination and excellent calibration, illustrating that the nomogram based on PROM may be valuable for patients with CHF.

This study, using data from strict screening and regular follow-up of CHF patients, confirms the significance of some demographic characteristics for prognosis; the results are consistent with those from other studies [33‐36]. In our prediction model, anxiety showed the greatest effect on the risk of readmission, followed by paranoia, health care, independence, income, support and appetite-sleep, while the smallest contributors were gender and depression.

Recently, a prospective observational study provided evidence of physical weakness, independence, support from society and family, anxiety, and depression being likely predictors of 30-day prognosis after hospitalization for HF [37]. Staniute et al. further also demonstrated that anxiety, depression and social support can indirectly affect the quality of life of patients with HF [38]. Moreover, anxiety, appetite and sleep were confirmed to be predictors of readmission for CHF in retrospective studies, which may have been impacted by the fact that the patient’s status influences the risk of readmission [39‐42]. Kitamura M not only confirmed that daily activities were independent predictors of readmission in heart failure patients within 90 days, but also calculated the cut-off value by ROC curve [43] and the study have also proved that self-care and daily activities are the mediating factors of readmission of heart failure [44]. While Hochang Benjamin Lee et al. emphasized that personality disorders as predictors of incident cardiovascular disease increased risk disease [45], our results are quite the opposite, which may be because the subjects studied were different; Apart from the results on patient paranoia, these findings were similar to the results of our reports on the readmission risk factors for CHF. We found that gender, income, health care, appetite-sleep, anxiety, depression, paranoia, support, and independence were predictors of readmission for CHF.

Many clinicians are usually able to make a preliminary assessment of a patient’s prognosis through clinical data; however, combining it with PRO can provide a more comprehensive and accurate understanding of the real health status of the patient. In practice, the process has been streamlined as the simple nomogram can be incorporated in mobile applications.

It would also be important to note some limitations of the study. First, according to the analysis, patients who refuse to follow up may be worse off than those who cooperate with follow-up, so that excluding these patients will underestimate the rehospitalization rate of patients with chronic heart failure. In addition, because the scale was subjectively filled by the patient, part of the content in the scale may be missing. Although we have imputed the missing data, there may still be bias. Second, most critical patients were not included in the study due to their inability to complete the scale. Due to this selection bias, our model may underestimate patient readmission rates. If all patients who meet the inclusion criteria could be selected, the actual C-index might be higher. Third, though the internally validated model demonstrated moderate discrimination and splendid calibration, considering the epidemiological and clinical behavioral differences between regions, the universality of this nomogram still requires additional databases to be used for external validation, especially from other provinces.

In conclusion, by using PROM as a measurement tool, this study has constructed and internally validated a neoteric nomogram to predict the probability of readmission for CHF patients. The nomogram is moderate accurate, easy to use, and displays splendid calibration; so, it can assist clinicians in assessing the risk of readmission for CHF patients, and further, make personalized treatment recommendations for them.