At present, Nomogram is widely used in the medical field, which is more accurate and easier to understand in estimating the survival rate of patients with different diseases, and can better guide clinical decision-making. This study established and validated a new and well-performing predictive model that can be used to evaluate the risk of hospital death in patients with ADCHF and diabetes, especially in those patients after 48 hours of Intensive Care Unit. A nomogram was constructed based on nine key variables (age, HR, SBP, RDW, Shock, ACEI/ARBs, β-blockers, assisted ventilation, and BUN) screened by LASSO regression in this study. Compared with SAPSII, SOFA score, and GWTG-HF score, the nomogram model showed better calibration ability and clinical application value, especially the high C-index indicated that the nomogram model had good predictive ability.

Due to the large number of variables involved in this study, in order to avoid too many variables into the final model which might lead to overfitting and reduced clinical applicability, LASSO regression was adopted to select the key variables in this study, which was a linear regression that avoided overfitting by imposing penalties on the size of model coefficients, and selectively puts key variables into the model to obtain better performance parameters [12]. Therefore, we constructed the first visual nomogram that can calculate the hazard of hospital death in ADCHF and diabetes, and the C-index was 0.857, indicating that this model has good distinguishing ability. Importantly, the nine variables in this nomogram were available after admission and easy to calculate. It was convenient for clinicians to quickly assess the risk of hospital mortality after admission, identify high-risk patients, and provide early interventions to reduce hospital mortality, which had good clinical application value.

Of important, among the nine key variables screened by LASSO regression to plot the nomogram, Shock and age were more heavily weighted in the scores. Shock is the most serious manifestation of ADHF, accounting for approximately 5% of ADHF, and is an independent hazard factor for hospital mortality in patients with ADHF, with a mortality of 30–50% [13]. The occurrence of Shock in patients with ADCHF and diabetes was 7.7% (67/867), and hospital mortality reached 38.8% (26/67), with a strong association with high mortality, which was also reported by other studies [14]. Furthermore, since the patients of this study was ADCHF with diabetes, abnormal vascular endothelial function, abnormal myocardial electrophysiology and high thrombotic load caused by long-term blood glucose fluctuations make ADCHF more likely to progress to Shock, significantly increasing the risk of death [15]. Therefore, early diagnosis, early identification and early evaluation and treatment are very important to improve the clinical prognosis of patients as much as possible.

Advanced age is one important risk factor for poor prognosis in various cardiovascular diseases and is significantly correlated with sarcopenia [16], frailty [17] and multimorbidity, in addition to being a chronic inflammatory condition in itself. In our study, it was found that patients in the Death group were relatively older, which also confirmed that advanced age was related to a high risk of death. Appropriate nutritional intervention, physical activity and control of underlying diseases were particularly important. ACEI/ARBs and β-blockers have been repeatedly shown to reduce HF hospitalization rates and improve survival, and are the cornerstone of treatment for HF [18]. The imbalance of renin-angiotensin-aldosterone system (RAAS) is a characteristic of ADCHF combined with diabetes. Studies have shown that ACEI / ARBs can not only reduce the all-cause mortality and readmission rates of HF with diabetes, but also improve the renal function of patients by reducing proteinuria [19]. In recent years, several studies have reported that RDW is an inflammatory marker, favored by many researchers. It is associated with the severity and prognosis of various cardiovascular diseases including HF, and its potential mechanisms may be related to inflammation, oxidative stress, and ineffective erythropoiesis [20]. Xanthopoulos A et al. found that RDW was a marker of poor prognosis in AHF and DM patients among 218 AHF patients [21], consistent with the results of this study.

Moreover, assisted ventilation and BUN ≥ 20 mg/dL were included in the final model. Assisted ventilation, including non-invasive and mechanically assisted ventilation, is widely used in patients with ADHF, especially in those with acute pulmonary edema symptoms. to improve oxidation by reducing pulmonary edema, and is recommended as an effective treatment strategy [22], but its impact on mortality is unclear. Sharon, A et al. found that intermittent biphasic positive airway pressure was associated with increased acute myocardial infarction and in-hospital mortality in the treatment of ADHF [23]. Other researchers believe that assisted ventilation is an effective treatment strategy to improve the prognosis of ADHF [24]. In another real-world study, Yukino M, et al. found that the in-hospital mortality rate of 3927 patients with noninvasive ventilator was 5.9% higher than that of those without noninvasive ventilator (3.5%). Although there was no statistical difference, the trend was basically consistent with this study [25]. BUN is a sensitive indicator of hemodynamics and renal perfusion, and is a hazard for cardiovascular diseases such as ACS and ADHF [26, 27]. In a large registry of acute decompensated heart failure, the best single predictor of mortality among 39 potential clinical and laboratory variables was high BUN levels at admission [28]. Angraal S et al. developed a model using machine learning to predict readmission and all-cause mortality HF with preserved ejection fraction, in which BUN is one of the important predictors [29]. In a study on the epidemiology of hospitalized HF patients in China, zhang et al. found that high BUN levels were significantly associated with higher mortality, in addition to common variables such as infection, acute myocardial infarction, and low SBP as predictors of death [30]. In our study, similar result was found that BUN ≥ 20 mg/dL was an independent predictor of hospital death in patients with ADCHF and diabetes. Resting heart rate has previously been recognized a potential predictor of mortality in patients with chronic heart failure, but little is known about its role in patients with ADHF. One study found that HR at discharge was independently associated with 1-year mortality in patients with AHF [31]. In this study, in patients with ADCHF combined with diabetes, HR on admission was found to be independently associated with in-hospital mortality, which is consistent with the findings of Lancellotti, Patrizio et al [32].

A few limitations of this study were as following: Firstly, the study population was derived from a single-center ICU, which could not exclude selection bias and might limit the application of nomogram in a larger population and also required external validation of data from different health care institutions; Secondly, due to missing data values > 20%, N-terminal probrain natriuretic peptide (NT-proBNP) and left ventricular ejection fraction(LVEF), which were previously considered as independent risk factors, were not included in the model, and the model should be used with caution before evaluating these two conditions. Thirdly, the data of the study were extracted from the MIMIC-III database, which contains multiple years of data (2001–2012), during which the treatment of cardiovascular diseases, especially heart failure, has been continuously updated and may affect the application of the nomogram. Therefore, it is not yet fully confirmed whether it can be applied to the present population, and further confirmation of the nomogram is needed in the future for larger populations and depending on the year. Finally, because of the nature of retrospective studies, we might not be able to fully adjust for potential confounders, which would partially affect our results, but should not affect its validity.