Factors influencing unplanned readmission within 30 days in patients with heart failure and their predictive value: a prospective study

doi:10.21203/rs.3.rs-4839258/v1

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Factors influencing unplanned readmission within 30 days in patients with heart failure and their predictive value: a prospective study

https://doi.org/10.21203/rs.3.rs-4839258/v1

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Background Heart failure imposes a significant healthcare burden, with early unplanned readmissions post-discharge linked to poor outcomes. Identifying risk factors and their predictive value is crucial for targeted interventions.

Objective To explore factors influencing 30-day unplanned readmission in heart failure and their predictive value.

Methods A prospective study of heart failure patients hospitalized in Beijing Hospital from October 2023 to March 2024. Patients were assessed for nutritional status using Mini-Nutritional Assessment Scale Short Version (MNA-SF), frailty using Groningen Frailty Index (GFI), and Appendicular Skeletal Muscle Mass Index (ASMI) calculation. Multifactorial COX regression analyse was conducted, and ROC curves plotted for predictive modeling.

Results A total of 121 heart failure patients (60.3% males), aged (69.87±11.9) years were included. Within 30 days-median follow-up duration, 25 (20.7%) patients with readmission. COX regression analysis stratified by gender showed that age, regular smoking, nutritional status, left ventricular ejection fraction(LVEF), brain natriuretic peptide(BNP), GFI, and ASMI were independent influences on readmission within 30 days in patients with heart failure (P<0.050). ROC curve analysis showed that age, BNP, ASMI, smoking status, LVEF, nutritional status, and GFI individually as well as in combination predicted readmission within 30 days in patients with heart failure; the joint model performed optimally, with an AUC value reaching 0.877 (95%CI 0.801~0.952, P<0.001), corresponded to a sensitivity of 0.920 and a specificity of 0.729.

Conclusion A multifactorial approach including age, BNP, ASMI, smoking status, LVEF, nutritional status, and GFI predicts 30-day readmission risk, offering a basis for clinical intervention strategies to improve patient outcomes.

Heart Failure

Readmission

Nutrition

Frailty

ASMI

Heart failure (hereinafter referred to as heart failure) is a clinical syndrome characterized by high morbidity, mortality, and rehospitalization rates, and has become a major global public health problem in the context of accelerated aging due to the increasing socioeconomic burden it causes[1] The latest large-scale epidemiologic data show that According to the latest large-scale epidemiological data, the prevalence of stage A heart failure and stage B heart failure in the general population aged ≥ 35 years is 35.8% and 42.8%, respectively, and is the main cause of cardiovascular mortality in China.[2, 3] which are the leading causes of cardiovascular death in China. Readmission is a recognized indicator used to measure the quality of care and outcomes of heart failure patients. Previous foreign systematic evaluations have shown that the 30-day readmission rate of heart failure patients is around 20%, and has been on the rise in recent years; whereas data on 30-day readmission for heart failure in China are scarce, results of a retrospective study have shown that readmission occurs within 30 days for about 22.8% of heart failure patients[4, 5] These patients have a higher risk of all-cause mortality, which seriously affects their prognosis and quality of life.[6] and need to be controlled or avoided by early risk identification and intervention[7–9] The need for early risk identification and intervention to control or avoid. With the increasing research on risk factors for cardiovascular events, the results of previous studies on factors influencing short-term readmission in hospitalized heart failure patients have lagged significantly. Recent studies have shown that decreased skeletal muscle mass, an important manifestation of sarcopenia, is a key factor in the progression of heart failure, and that heart failure patients with comorbid muscle atrophy have a worse prognosis[10, 11] The prevalence of weakness in heart failure patients averages approximately 50%, and the presence of weakness significantly increases the incidence of poor prognosis in heart failure patients, but previous assessments of weakness have mostly been limited to physical aspects, ignoring psychological and cognitive dimensions of the decline[12–14]. Therefore, the purpose of this study is to analyze the influencing factors of unplanned readmission in heart failure patients within 30 days after discharge and its predictive value based on the perspective of clinical practice, combined with the latest research advances, from which we can explore the factors that can be intervened by caregivers in a leading way, in order to provide reference for the improvement of the quality of care of hospitalized patients with heart failure, and to improve the prognosis of the patients.

Study design and population

This study is a prospective longitudinal study. Convenience sampling method was used to consecutively select heart failure patients who were hospitalized in the Department of Cardiovascular Medicine of Beijing Hospital from September 2023 to March 2024 for the study. According to the estimation of rate and hypothesis testing formula while considering the 15% loss of visit rate, the final calculation was made to get the required sample size of at least 104 cases. Inclusion criteria: (1) meeting the diagnostic criteria for heart failure in the National Heart Failure Guidelines 2018[15] ; (2) age ≥ 18 years; (3) willingness to participate and cooperate, and sign the informed consent. Exclusion criteria: (1) those with combined psychiatric diseases or significant cognitive and language dysfunction; (2) those with combined tumors and other serious diseases such as heart, lung and liver; (3) pregnant patients. This study was approved by the Ethics Committee of Beijing Hospital (approval number: 2023BJYYEC-356-01).

Methodology

Investigation content: (1) General information. It mainly includes age, gender, smoking status, drinking history, cardiac function grading and so on. (2) Nutritional status. The short version of Mini Nutrition Assessment-SF (MNA-SF) developed by Laurence et al. in 2001 was used, which is suitable for the Chinese hospitalized population.[16] which is suitable for the Chinese hospitalized population. The scale consists of 6 entries, including whether the patient has lost appetite in the past three months, whether he/she has lost weight, mobility, whether he/she has been traumatized or acutely ill in the past three months, psychopathology, and body mass index (BMI) or calf circumference. A score of 12–14 is considered normal nutrition, 8–11 is considered at risk of malnutrition, and 0–7 is considered malnourished[17]. (3) Frailty status. The Groningen Frailty Index (GFI) was used. The scale consists of 15 entries and includes 3 dimensions: 9 somatic (mobility, multiple health problems, somatic fatigue, vision, hearing), 1 cognitive (cognitive functioning), and 5 psychosocial (emotional isolation, depressed mood, and feelings of anxiety).GFI scores ranging from 0–15 reflect a range from healthy to severely debilitated, with a score of greater than 4 being in a state of debilitation[18]. (4) Extremity Skeletal Muscle Mass Index (ASMI). An anthropometric formula validated for large-scale populations was used to calculate the patient's ASM as follows:

ASM = 0.193 × weight + 0.107 × height − 4.157 × sex − 0.037 × age − 2.631

Weight, height and age are expressed in kilograms, centimeters and years, respectively; for sex, it is expressed as 1 for males and 2 for females.[19, 20] ASMI is expressed as ASM/height in meters2, with ASMI < 7.05 kg/m for men2 or < 5.63 kg/m for women2 as reduced skeletal muscle mass.[21] (5) Grip strength. Grip strength was measured with an electronic grip strength meter on the dominant hand and non-dominant hand, and a grip strength of < 28 kg for men or < 18 kg for women was considered to be reduced grip strength.

Observation indicators: (1) Baseline information. Including age, gender, smoking history, drinking history, left ventricular ejection fraction(LVEF), ASMI, grip strength, blood sodium, blood potassium, brain natriuretic peptide(BNP), nutritional status, GFI. (2) Readmission within 30 days. For patients who were readmitted to the department within 30 days, the duration and reason for the patient's hospitalization were recorded; for patients who were not admitted to the department within 30 days, the patients were asked by telephone around the 35th day after their discharge whether there had been any readmission during that period and the reason for the readmission.

Quality control methods: the study subjects were selected from the patients admitted to the hospital in strict accordance with the inclusion and exclusion criteria. Within 2 days after the patients were admitted to the hospital, general status and disease-related information was collected through the inpatient electronic medical record system; nutritional assessment and grip strength measurement were performed by one trained investigator, and the completed case report forms were checked by two investigators. The study was fully communicated to the study participants and their families before the start of the investigation, and the informed consent form was signed by the study participants.

Statistical methods: SPSS25.0 software was used for data analysis. Measurement data conforming to normal distribution were expressed as mean ± standard deviation (‾χ ± s), and t-test was used for comparison between groups; count data not conforming to normal distribution were expressed as median and quartiles [M(P25, P75)], and rank sum test was used for comparison between groups. Count data were expressed as frequencies or percentages (%), and comparisons between groups were performed using theχ² test. Because the results of 30-day readmission rates in previous studies differed between genders, this study was stratified by gender in the multifactorial analysis[22]. Differential variables were incorporated into the final predictive model by univariate analysis and multifactorial COX proportional risk modeling, and subject work characteristic curves (ROCs) were plotted and areas under the curve (AUCs) were calculated. p < 0.05 was considered a statistically significant difference.

Comparison of patients' baseline data

A total of 121 patients with heart failure were finally enrolled in the study. During the (30 ± 8.17) days-median follow-up duration, unplanned readmissions occurred in 25 (20.7%) patients within 30 days, and the rest of the patients (96, 79.3%) did not have readmission events.There was no statistically significant difference in the comparison of gender, history of alcohol consumption, grip strength, blood sodium and potassium between the two groups of the readmission group and the non-readmission group (P > 0.05), and there was no statistically significant difference in the comparison of age, history of smoking, EF, BNP, ASMI, nutritional status and GFI were compared with statistically significant differences (P < 0.05)(Table 1).

Table 1

Comparison of baseline data in patients with heart failure
sports event	Readmission group (25)	Non-readmission group (96)	t/χ² /Z value	P-value
Age (years,‾ χ±s)	75.36 ± 10.21	68.44 ± 12.00	−2.65	< 0.010
Gender
daughter	13	35	2.00	0.165
male	12	61	2.00	0.165
Regular smoking
No	14	82	10.47	< 0.010
Yes	11	14	10.47	< 0.010
Regular alcohol consumption
No	33	63	0.36	0.553
Yes	7	18	0.36	0.553
LVEF (%,‾ χ ± s )	40.88 ± 11.48	53.27 ± 10.78	5.88	< 0.010
ASMI, (kg/m ,²‾ χ ± s)	6.41 ± 1.09	7.74 ± 2.11	3.04	< 0.010
Grip strength, kg	22.07 ± 8.84	26.489 ± 10.76	1.89	0.061
Blood sodium, (mmol/L,‾ χ ± s).	140.83 ± 4.017	142.36 ± 2.78	1.80	0.079
Blood potassium (mmol/L,‾ χ±s).	4.10 (3.70, 4.40)	4.00 (3.70,4.40)	−0.46	0.640
BNP (pg/mL,‾ χ±s).	648.20 ± 68.51	296.85 ± 66.26	2.38	0.019
Nutritional status
Normal	11	70	11.81	< 0.010
At risk of malnutrition	10	24
Malnutrition	4	2
GFI
Normal	6	36	15.74	< 0.010
Problematic or dependent	11	54
Frail	8	6

BNP = brain natriuretic peptide; LVEF = left ventricular ejection fraction; GFI = Groningen Frailty Index.

Multifactorial COX proportional model analysis of unplanned readmissions within 30 days in patients with heart failure

Influential factors with statistically significant differences in univariate analysis of heart failure patients, including age, BNP, ASMI, smoking status, LVEF, nutritional status, and GFI were jointly included in the COX proportional risk model and stratified by gender, and the results showed that all of them were independent influences on unplanned readmission within 30 days in patients with heart failure (P < 0.05), as shown in Table 2.The analysis of the stratification by gender showed that men had a higher risk of 30-day readmission compared to women(Fig. 1).

Table 2

Multifactorial Cox regression analysis of factors influencing unplanned readmission within 30 days after discharge of heart failure patients
Factor	SE	Waldχ² values	P-value	HR (95% CI)
Age	0.03	7.88	< 0.01	1.07 (1.02 ~ 1.13)
BNP	0.68	4.90	0.03	4.54 (1.19–17.33)
ASMI	0.48	14.74	< 0.01	0.16 (0.06 ~ 0.41)
Regular smoking	0.68	4.11	0.04	4.00 (1.05–15.27)
LVEF	0.02	4.33	0.04	0.96 (0.92 ~ 0.97)
Nutritional assessment		6.97	0.03
At risk of malnutrition	0.56	3.85	0.05	3.02 (1.00-9.13)
Malnutrition	0.78	5.75	0.02	6.48 (1.41–29.90)
GFI		7.09	0.03
Problematic or dependent	0.81	5.07	0.02	6.19 (1.27–30.24)
Frail	0.89	6.66	0.01	10.03 (1.74–57.84)

BNP = brain natriuretic peptide; LVEF = left ventricular ejection fraction; GFI = Groningen Frailty Index.

ROC curves for unplanned readmissions in heart failure patients within 30 days of hospital discharge

Based on the results of multifactorial COX regression analysis and previous studies, ROC curves for unplanned readmission within 30 days after discharge of heart failure patients were constructed, and the results showed that the AUCs of age, BNP, ASMI, regular smoking or not, LVEF, nutritional status, and GFI individually and in combination predicted unplanned readmission within 30 days of patients with heart failure, respectively, were 0.677 (95% CI 0.556 ~ 0.797, P = 0.007), 0.364 (95% CI 0.238 ~ 0.490, P = 0.036), 0.698 (95% CI 0.568 ~ 0.829, P = 0.002), 0.647 (95% CI 0.516 ~ 0.778, P = 0.024), 0.190 (95% CI 0.097 ~ 0.283, P < 0.001), 0.660 (95%CI 0.532 ~ 0.789, P = 0.014), 0.747 (95%CI 0.636 ~ 0.857, P < 0.001), and 0.877 (95%CI 0.801 ~ 0.952, P < 0.001), and the Yoden exponent of the joint prediction model was 0.649, corresponding to a sensitivity of 0.920 and a specificity of 0.729(Fig. 2).

With the aging of the global population, the incidence of various cardiovascular-related chronic diseases is on the rise, and heart failure is at the end of the cardiovascular chain of events. Frequent readmission of patients with heart failure is significantly associated with higher mortality rates, and fewer studies have been conducted on readmission of patients with heart failure in China, and past measures relying on medication have not significantly improved the risk of readmission and the poor prognosis of patients after discharge from the hospital.[23] The risk of readmission and poor prognosis after hospital discharge have not been significantly improved by past measures relying on drug therapy. Some multidisciplinary disease management programs may reduce the number of readmissions and improve the quality of life of patients, but they may lead to an increase in the cost of treatment.[24, 25]. Therefore, nonpharmacologic disease management interventions are considered to be more cost-effective and can significantly improve patient self-care and quality of prognosis, including reducing the risk of readmission.[26]. The results of this study showed that the incidence of unplanned readmission within 30 days was high in patients with heart failure, and patient age, BNP, ASMI, regular smoking or not, EF, nutritional status, and GFI were the independent influencing factors and had a good predictive value of the risk of readmission; among them, targeted non-pharmacological interventions could be proposed on the basis of scientific assessment of the nutritional, debilitating, and skeletal muscle mass decline. To guide patients' home self-care in order to improve the prognostic quality of heart failure patients.

Previous epidemiologic surveys have shown that the readmission rate of heart failure patients within 30 days of discharge is 25%[27, 28], and the 30-day unplanned readmission rate in this study was 20.66%, which was slightly lower than that reported in the literature, probably due to the exclusion of a portion of planned admissions as well as differences in sample size and source. Frequent readmission not only affects the quality of life of heart failure patients, but also aggravates the economic burden on families and society, so an in-depth discussion of the influencing factors of readmission in heart failure patients from multiple perspectives is conducive to accurately identifying high-risk patients and formulating personalized intervention strategies to improve the quality of life of the patients and to reduce the cost of health care[26, 27, 29] The study is a good example of how to improve the quality of life of patients and reduce healthcare costs.

Age is an important demographic factor influencing the quality of prognosis for a number of chronic diseases, including heart failure, and more than 50% of hospitalized heart failure patients are 75 years of age or older.[30]. Studies have shown that the incidence of all-cause readmission in heart failure patients increases with age, with the risk of all-cause readmission in heart failure patients ≥ 65 and ≥ 80 years of age being 3.3 and 4.1 times higher than that in patients under 65 years of age, respectively[31]. Smoking is also a traditional cardiovascular risk factor, and the China Chronic Disease Prospective Study showed that among people aged 30–79 years without cardiovascular metabolic disease at baseline, smokers had an increased risk of first-ever ischemic heart disease and ischemic stroke, respectively 23% and 14%, with a 40% increased risk of death[32]. In this study, age and regular smoking had a significant effect on unplanned readmission within 30 days in patients with heart failure and had an important predictive value, suggesting the need to pay close attention to the progression of the disease in elderly patients in the clinical work, to educate patients with heart failure who are currently smoking, to emphasize the impact of tobacco use on cardiac function and overall health status, so as to make it clear to them that smoking cessation is important for the quality of the prognosis; in the period of preparation for hospital discharge Enhance health education and continuity of care for the patients themselves and their families.

In this study, the cumulative risk of unplanned readmission within 30 days was higher in men compared to women. Conclusions on the impact of gender on readmission in heart failure patients have been inconsistent in previous studies, and Hoang-Kim et al.[22] used a scoping review of the literature on heart failure readmission based on gender analysis, and 12 of the 34 studies ultimately included reported higher rates of heart failure readmission in men and 6 reported higher rates of heart failure readmission in women. Differences between the results of the studies may be due to the influence of social factors such as comorbidities, medication adherence, or type of occupation. Therefore, the effect of gender on the occurrence of short-term readmissions in heart failure patients needs to be further analyzed after more careful differentiation based on the characteristics of the study population.

LVEF and BNP levels are traditionally important indicators of heart failure severity, and both were independent risk factors for 30-day unplanned readmission in patients with heart failure in the present study, consistent with previous findings.[33–35]. However, BNP was a poor predictor of readmission in the present study, in contrast to Valle et al.[36] and Logeart et al.[37] The results of the study were different, probably due to the fact that there were more missing BNP data in the present study, which may still lead to the deviation between the analyzed results and the real situation, although reasonable interpolation was performed during data processing. LVEF performed the worst in this study in terms of readmission risk prediction, which is similar to Yu et al.[38] 's findings that EF shows a U-shaped relationship with prognosis in heart failure patients, that high EF does not represent a low risk of adverse events such as readmission in heart failure patients, and that 60–65% may be the range with the lowest prognostic risk in heart failure patients, and that the demographic factor of gender may have an influence in this. This complex relationship between LVEF and prognosis in heart failure patients may therefore be an important factor influencing the predictive value of LVEF in this study. A similar relationship is seen in the New York Heart Function Classification (NYHA), a more subjective classification system that suffers from poor consistency, which has been found in recent studies to discriminate little between patients with mildly impaired cardiac function, and previous studies have found that improvement in NYHA classification is not associated with better clinical outcomes when survival time is considered[39, 40]. This feature suggests that the prognostic significance of LVEF should be interpreted cautiously in clinical practice and that it needs to be applied in combination with other indicators, rather than simply judging according to ultrasound results, to avoid misjudging the prognostic risk of certain patients.

A recent cohort study investigating the use of 7 different nutritional assessment tools found that the prevalence of malnutrition among hospitalized heart failure patients in China ranged from 5.7–78.1%, depending on the screening tool used. In the present study, the prevalence of malnutrition in hospitalized heart failure patients was approximately 5.0%, which was slightly lower than the results previously reported in the literature, possibly due to the non-objective indicators of the nutritional assessment tool chosen for this study and the demographic characteristics of the included population. In this study, nutritional status was an independent predictive risk factor affecting unplanned readmission within 30 days in heart failure patients, and the risk of readmission in malnourished patients was more than 6 times higher than that in patients with normal nutritional status, which is in general agreement with the results of the Lim et al.[41] 's findings are in general agreement. The mechanism of combined malnutrition in patients with heart failure is not yet fully understood, and a generally recognized reason is that due to decreased cardiac function in heart failure patients, peripheral circulatory resistance and pulmonary circulatory pressures are elevated, leading to stagnation of the digestive tract, which in turn affects the digestion and absorption of nutrients, further exacerbating fluid retention and impaired anabolism in patients with heart failure, which may ultimately lead to a malignant state[42–44]. In contrast, studies have shown that nutritional status can be improved by nurses through health education and dietary guidance during hospitalization and during the transition period to discharge, and that all-cause readmission status and prognostic quality of patients with heart failure can be effectively improved by increasing the patients' attention to scientific diets and their adherence to nutritional supplementation interventions[23, 45] .

Nutritional status is closely related to muscle content and quality. Heart failure patients with malnutrition of nutrients, inadequate protein intake, insufficient exercise, and comorbid metabolic disorders such as diabetes can lead to abnormal loss of skeletal muscle and eventually progress to sarcopenia[46] The term "sarcopenia" is used to describe the loss of skeletal muscle mass. Sarcopenia, a loss of skeletal muscle mass and strength, has a prevalence of approximately 20% in patients with chronic heart failure and is an important predictor of the long-term prognosis of heart failure patients.[47–49] ASMI is an important indicator of skeletal muscle content and diagnosis of sarcopenia. The results of this study showed that ASMI was a protective factor for unplanned readmission within 30 days in patients with heart failure and had the high individual predictive value with an AUC value of 0.698; in another retrospective cohort study with a median follow-up of 1.75 years[50], ASMI was obtained as an independent predictor of all-cause mortality in patients with heart failure by anthropometric formulae, suggesting that ASMI is an important indicator for screening patients with a higher risk of poor prognosis.The intrinsic mechanisms by which ASMI affects the prognosis of patients with heart failure are not yet fully understood, and a variety of factors have been suggested to be associated with both co-morbidities such as malabsorption, advanced age, comorbid diabetes mellitus, a low BMI, an elevated BNP level, an elevated renal function, and an elevated renal function. The specific pathophysiological mechanisms include disorders of protein anabolism and hormonal balance, apoptosis, oxidative stress, and inflammation.[48, 51, 52] The gold standard for ASMI measurement is magnetic resonance imaging (MRI) and computed tomography (CT), and dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are also widely used in clinical research, but the above methods have problems such as high cost, radiation exposure, and poor reliability, which make it difficult to promote the determination of ASMI in the clinic and thus affect the assessment of the patient's prognostic risk, so we used the anthropometric ASM equation model to calculate the ASMI in the present study. Measurement ASM equation model was used in this study to calculate ASMI in patients with heart failure, which has been validated and applied on a large scale in the Chinese population, and has a strong agreement with DXA[19–21] In this study, the ASMI was calculated using the anthropometric ASM equation model in heart failure patients. This study shows that the ASMI calculated by anthropometric equation has a high predictive value for the occurrence of unplanned readmission within 30 days in heart failure patients, which is worth popularizing and applying in clinical work; doctors or nurses should pay attention to the calculation and evaluation of ASMI when they conduct physical examination of patients, and for patients with low ASMI, they need to focus on the guidance of nutrition and exercise, and pay attention to the adverse effects of medication on patients' The patients with low ASMI need to focus on the guidance of nutrition and exercise, and pay attention to the adverse effects of drugs on the patient's muscle mass and function and the need to give special nutrition and exercise prescriptions.

Frailty is a syndrome characterized by an excessive decline in multisystem function and reserve, in which the individual has increased sensitivity and decreased tolerance to various adverse events, and is a common co-morbidity in elderly patients with heart failure[53, 54] Meta-analysis showed that the overall prevalence of comorbid frailty in heart failure patients was about 50%, and it could reach about 76% in hospitalized heart failure patients, and the prevalence of suspected frailty and definite frailty in the present study was about 61.2%, and the reason for the differences between studies was mainly due to the differences in the tools used to assess frailty.[13, 55, 56] The Fried Frailty Inventory or FRAIL scale was mainly used in previous studies, which can only screen for frailty in physical functioning, whereas the GFI used in this study is more complex, covering multiple dimensions such as physical, psychological, and social functioning, and provides a more objective and comprehensive assessment of frailty; as frailty is closely related to aging, and is mainly concentrated in patients over 75–80 years of age, the average age of patients enrolled in this study was younger (1.5 years) than in the previous study. The average age of the patients was younger (69.87 ± 11.9), which may also affect the prevalence of debility; in addition, the occurrence of debility in heart failure patients is also related to the type of heart failure and the stage of heart failure progression[14]. Comorbid debility increases the symptom burden in heart failure patients, who usually face a greater risk of adverse prognosis such as readmission and death and have a poorer quality of life than the average patient, and this effect is independent of factors such as gender, age, and co-morbidities[57–59]. The results of the present study indicate that patients assessed as "problematic or dependent" and "debilitated" by the GFI had a 30-day risk of unplanned readmission that was more than 6 and 10 times higher than that of other patients with heart failure, respectively, and had the highest predictive value of the variables individually, which strongly suggests the need for clinicians or nurses to utilize a comprehensive approach. This suggests that clinicians or nurses need to use a comprehensive tool to assess the frailty of patients with heart failure. In addition to physical frailty, they should also pay attention to the frailty of the patient's mental, psychological, and social functions, etc. It is recommended that the assessment of frailty be incorporated into the routine care of patients with heart failure, and that patients with suspected frailty or definite frailty should be subjected to strengthened observation of the condition and daily nursing care, and be alerted to the occurrence of serious changes in their condition or falls, which may lead to an increased burden of disease for the patient. Weakness is usually accompanied by malnutrition or decreased skeletal muscle mass, so it is necessary to pay attention to the patient's diet and exercise, participate in the development and implementation of the patient's diet and exercise program, and do a good job of related health education, especially in the preparatory period for discharge, and if necessary, provide continuity of care and guidance, in order to minimize the patient's readmission in the short term; Currently, there are a variety of scales for the assessment of weakness, but the most suitable scale for clinical patients with heart failure is the one that can be applied to patients with heart failure. The most appropriate scale for clinical heart failure patients is not yet clear, and future in-depth exploration is needed to study the best specialized tool to improve the quality of debilitation assessment in heart failure patients.

There are several limitations to our study. Firstly, the study's generalizability is constrained by its single-center design and relatively small sample size, which may not fully capture the heterogeneity of heart failure patients across different healthcare settings. Secondly, the study did not differentiate between the subtypes of heart failure, which could influence the impact of certain risk factors. Additionally, the reliance on some missing data, which was addressed through imputation, may introduce some degree of bias or uncertainty into the findings. Lastly, while the study employed a comprehensive approach to risk factor assessment, the cross-sectional nature of the data collection limits the ability to establish causality or temporality in the observed associations. Future research with larger, multicenter cohorts and longitudinal data is needed to validate and expand upon these findings.

In summary, this study found that age, BNP, ASMI, regular smoking, LVEF, nutritional status, and GFI are the influencing factors of unplanned readmission within 30 days after discharge of heart failure patients, and the prediction model jointly constructed based on the above five indexes has good differentiation and clinical practicability; among them, the smoking habit, nutrition, ASMI, and GFI status can be controlled or even reversed by non-pharmacological methods such as health education, psychology, and lifestyle change. Smoking habit, nutrition, ASMI, and GFI status can be controlled or even reversed by non-pharmacological methods such as health education, psychological care, and lifestyle change. These findings will contribute to enhancing the patient care and procedural skills of clinical physicians and nurses. The translation of this study findings is expected to foster the evolution of preventive strategies and treatment of disease in the community. Healthcare professionals can use this as a starting point to think about and explore assessment and intervention programs, promote multidisciplinary cooperation, enhance patients' out-of-hospital self-management ability, and ultimately improve the prognosis of patients with heart failure, as well as improve the quality of healthcare and nursing care.

Acknowledgements

Not applicable.

Author contributions

LC contributed to conception and design of the study. XW, RY, MD,YH, AT organized the database. XW, TL performed the statistical analysis. LC, XW wrote the first draft of the manuscript. TL, RY, MD wrote sections of the manuscript. HW contribute to leadership responsibility for the research activity planning and execution. All authors contributed to manuscript revision and agreed to publish.

Funding

2023 Special Funds for Clinical Research Business of Central High-Level Hospitals-Talent Cultivation Special Fund(BJ-2023-136). The funders had no role in the study design, analysis, decision to publish, nor preparation of the manuscript.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Ethics Approval and consent to participate

This study was approved by the Ethics Committee of Beijing Hospital (approval number: 2023BJYYEC-356-01).

Consent for publication

Not applicable.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist.

Competing interests

The authors declare no competing interests.

Declaration of interests：

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

All authors have access to the data and a role in writing the manuscript. There are no relationships with any industry.

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Factors influencing unplanned readmission within 30 days in patients with heart failure and their predictive value: a prospective study

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ROC curves for unplanned readmissions in heart failure patients within 30 days of hospital discharge

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