
==== Front
BMC Musculoskelet Disord
BMC Musculoskelet Disord
BMC Musculoskeletal Disorders
1471-2474
BioMed Central London

7843
10.1186/s12891-024-07843-x
Research
Developing a prediction model for preoperative acute heart failure in elderly hip fracture patients: a retrospective analysis
Yu Qili 1
Fu Mingming 3
Hou Zhiyong drzyhou@hebmu.edu.cn

2
Wang Zhiqian 37800709@hebmu.edu.cn

1
1 https://ror.org/004eknx63 grid.452209.8 0000 0004 1799 0194 Department of Geriatric Orthopedics, Third Hospital of Hebei Medical University, Shijiazhuang, 050051 Hebei China
2 https://ror.org/004eknx63 grid.452209.8 0000 0004 1799 0194 Department of Orthopaedic Surgery, Third Hospital of Hebei Medical University, Shijiazhuang, 050051 Hebei China
3 https://ror.org/004eknx63 grid.452209.8 0000 0004 1799 0194 Third Hospital of Hebei Medical University, Shijiazhuang, 050051 Hebei China
14 9 2024
14 9 2024
2024
25 73612 4 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis.

Objective

This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model.

Methods

This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model’s calibration.

Results

Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381–0.734, P < 0.001), Age (OR = 1.760, 95% CI: 1.251–2.479, P = 0.001), Coronary Heart Disease (OR = 1.977, 95% CI: 1.454–2.687, P < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95% CI: 1.154–5.346, P = 0.020), Complications (OR = 1.516, 95% CI: 1.033–2.226, P = 0.033), Anemia (OR = 2.668, 95% CI: 1.850–3.847, P < 0.001), and Hypoalbuminemia (OR 2.442, 95% CI: 1.682–3.544, P < 0.001). The linear prediction model of acute heart failure was Logit(P) = -2.167–0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia, and the nomogram prediction model was established. The AUC of the predictive model was 0.763, indicating good predictive performance. Decision curve analysis revealed that the prediction model offers the greatest net benefit when the threshold probability ranges from 4 to 62%.

Conclusion

The prediction model we developed exhibits excellent accuracy in predicting the onset of acute heart failure preoperatively in elderly patients with hip fractures. It could potentially serve as an effective and useful clinical tool for physicians in conducting clinical assessments and individualized treatments.

Keywords

Acute heart failure
Hip fracture
Preoperative
Nomogram
Prediction model
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

With the aging of the global population, health issues faced by the elderly are garnering increased attention. The demographic shift towards an older population has made geriatric hip fractures a pressing public health concern, especially in individuals aged 65 and above. Among these health issues, hip fractures are undeniably one of the most common clinical problems in this demographic [1, 2]. Hip fractures usually result from external forces such as falls, vehicular accidents, or sports injuries. In the elderly, due to decreased bone density and increased bone fragility, the incidence of such injuries has significantly risen [3].

Heart failure, a common but often overlooked complication in elderly patients with hip fractures, arises from various causes that impair cardiac contraction or relaxation, impeding effective tissue perfusion [4]. Research by Vedat and colleagues has found that trauma, pain, reduced blood volume, surgical stress, the effects of anesthetics, and the trauma from the surgery itself may trigger acute heart failure in elderly hip fracture patients, or exacerbate existing heart failure conditions [5]. The presence of heart failure in hip fracture patients significantly increases surgical risks, slows post-operative recovery, and heightens the likelihood of complications such as arrhythmias, worsening heart failure, and acute myocardial infarctions [6]. These complexities not only extend hospital stays but might also impact the patient’s long-term quality of life and prognosis [7].

Despite the critical impact of acute heart failure on surgical outcomes in elderly hip fracture patients, there is a notable gap in research exploring the interrelationship between these conditions. In emergency situations, when patients are rapidly transferred to surgery due to hip fractures, the urgency of time often leads to the omission of some crucial steps during the preoperative assessment. We have observed that during the swift preoperative evaluation, most surgeons may overlook the testing of biomarkers for heart failure, such as brain natriuretic peptide (BNP) or N-terminal pro-brain natriuretic peptide (NT-proBNP), since these tests are not included in the standard preoperative laboratory examinations. Such oversights can lead to the failure to timely identify patients who have progressed to the stage of acute heart failure, thereby missing the opportunity for effective intervention in this high-risk state.

We hypothesize that a robust clinical predictive model, developed through detailed analysis of preoperative data and specific biomarkers, can significantly improve the identification and management of acute heart failure in elderly patients with hip fractures. This model aims to enhance surgical outcomes by enabling better preoperative assessment and tailored interventions, ultimately improving survival rates and quality of life for this high-risk patient group.

Materials and methods

Study population

In this retrospective study, we meticulously selected elderly patients using the electronic medical record system at our institution. Eligibility was confined to those aged 65 years or older who were admitted for hip fracture treatment and subsequently underwent orthopedic surgery between January 2018 and December 2019. Inclusion Criteria: (1) Age Requirement: Patients must be at least 65 years old at the time of hospital admission due to hip fracture. (2) Diagnosis of Hip Fracture: Diagnosis confirmed by radiological evidence. (3) Surgical Treatment: Hospitalized patients who underwent surgical treatment for hip fracture. (4) Complete Medical Records: Only patients with complete medical records, including detailed preoperative data, were included to ensure the availability of necessary clinical information for analysis. Exclusion Criteria: (1) Systemic Steroid Use: Patients regularly using systemic steroids were excluded due to the potential risks associated with long-term high-dose corticosteroid use, such as increased blood pressure, elevated blood sugar, and abnormal lipid levels, which are risk factors for heart disease and heart failure. (2) No Multi-Site Fractures: Patients with fractures at multiple sites were excluded to focus on isolated hip fractures. (3) Immune System Disorders: Those with immune system disorders were also excluded to avoid the potential confounding effects of chronic systemic inflammation or altered immune response on the outcomes.

Ethical approval

This study was a retrospective analysis of existing case data, ensuring that all patient data collection and analysis were conducted anonymously to protect patient privacy. Furthermore, the study complied with the Declaration of Helsinki and was approved by the Institutional Review Board of Hebei Medical University Third Hospital (approval number: 2021-087-1). It is important to note that patients were waived from informed consent due to the retrospective nature of the study, as approved by the named committee.

Definition of heart failure

According to the Chinese Guidelines for the Diagnosis and Treatment of Heart Failure 2018, heart failure is defined as a clinical syndrome characterized by the presence of suspicious symptoms and/or signs such as dyspnea, ankle swelling, and fatigue. This definition is supported by diagnostic evidence, which includes abnormal findings on an electrocardiogram, chest Xray indications of pulmonary congestion, pulmonary edema, or cardiac enlargement. Furthermore, echocardiographic evidence of cardiac structural and/or functional abnormalities supports the diagnosis. In our study, the diagnosis of heart failure was not only based on the elevation of serum natriuretic peptides but also aligned with specific age-adjusted thresholds, enhancing the precision of our diagnostic process: BNP: For all age groups, a diagnostic threshold of ≥ 300 pg/ml was used to guide the diagnosis of acute heart failure. NT-proBNP: Differentiated by age, the thresholds were set as follows: For individuals under 55 years: NT-proBNP > 450 pg/ml. For those aged 55 to 75 years: NT-proBNP > 900 pg/ml. For those older than 75 years: NT-proBNP > 1800 pg/ml. These biomarker thresholds significantly assist in the confirmation of acute heart failure. The focus on acute changes in these biochemical markers (BNP or NT-proBNP) alongside clinical presentations helps in distinguishing acute heart failure episodes from chronic conditions. This rigorous approach was used to identify cases of heart failure in our retrospective study conducted from 2018 to 2019 [8].

Data collection and study methodology

The primary outcome was the occurrence of heart failure preoperatively. We compared the characteristics of patients with and without heart failure and investigated predictors of preoperative heart failure. Baseline and preoperative variables related to heart failure were collected and analyzed through the electronic medical record system.

To ensure consistency in the research sample, strict inclusion and exclusion criteria were adhered to. One researcher was responsible for data input, which was subsequently verified by a consulting researcher who also participated in this study. Any discrepancies or suspicious data were rectified by re-examining the medical records.

In our study, the data collection process was meticulously designed to assess the clinical indicators and complications relevant to the preoperative phase for elderly patients with hip fractures. During the initial hospital admission, complications including pulmonary infections, pulmonary embolism, acute cerebral infarction, intracerebral hemorrhage, acute myocardial infarction, and acute renal failure were documented. This approach allowed us to evaluate their potential impact on the risk of developing acute heart failure before surgery and to tailor preemptive interventions accordingly.

For a clear understanding and consistent analysis, the clinical indicators were defined as follows: Anemia was primarily considered as iron-deficiency anemia, diagnosed with hemoglobin concentrations below 110 g/L in women and 120 g/L in men. Hyponatremia was identified with serum sodium concentrations falling below 135mmol/L. Hypokalemia was defined by serum potassium levels below 3.5mmol/L, and hypoalbuminemia was considered when serum albumin was less than 35 g/L. Lower Extremity Deep Vein Thrombosis (DVT) was diagnosed based on clinical symptoms and confirmed through duplex ultrasonography, particularly in high-risk preoperative screenings.

For the statistical analyses, SPSS 24.0 and R software were utilized. In statistical analytics, continuous variables were described by mean ± standard deviation (X ± SD), while categorical variables were represented by ratios or percentages. The independent sample t-test or Mann-Whitney U test was used for inter-group comparisons of continuous variables. Additionally, through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, we selected variables significantly associated with the risk of preoperative acute heart failure in elderly patients with hip fractures from multiple potential predictive factors. Subsequently, using multivariable logistic regression analysis, we evaluated whether these selected variables independently predict the risk of acute heart failure and calculated the coefficients for each variable, considering the relevant features with Odds Ratios (OR) and P-values within a 95% Confidence Interval (CI), with a significance level set at P < 0.05. These coefficients reflect the relative contribution of each predictive factor towards the prediction target (i.e., preoperative acute heart failure). Utilizing this information, we designed a nomogram. In the nomogram, each predictive factor is assigned a score based on its regression coefficient. By aggregating the scores of relevant predictive factors for an individual patient, a total score is obtained, corresponding to the probability of the prediction target occurring. To assess collinearity among variables, the Variance Inflation Factor (VIF) and tolerance were calculated. Typically, a VIF value below 5 and a tolerance above 0.1 are considered standards indicating no significant collinearity between variables. The accuracy of the predictive model in terms of risk was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC). Calibration curves were plotted to assess the calibration of the model, and the C-index was calculated to quantify its discriminatory performance. Bootstrap validation (with 1,000 bootstrap resamples) was conducted to calculate the corrected C-index. The clinical utility of the model was assessed by using decision curve analysis to quantify the net benefits at different threshold probabilities within the cohort, and the clinical impact curve was also employed to further evaluate the effects in the cohort.

Results

General patient characteristics

Between January 2018 and December 2019, a total of 1962 elderly fracture patients were included in our study. After filtering, 1273 patients were incorporated into the analysis. Exclusions were multi-site fractures or pathological fractures (324 patients); non-surgical patients (227); autoimmune diseases (28); and those with incomplete data (110) (Fig. 1). Of the analyzed patients, 386 were male (30.2%) and 888 were female (69.8%). The average age was 79.2 ± 7.9 years. Common comorbidities included hypertension (50.4%), cerebral infarction (39.3%), coronary heart disease (28.1%), diabetes (21.7%), COPD (2.8%), cancer (4.7%), and arrhythmias (6.4%). Complications arose during the hospital stay for 13.4% of patients, with primary involvements in the respiratory, digestive, urinary, hematologic, endocrine, and nervous systems (Table 1). Significant statistical differences were observed between the heart failure and non-heart failure groups in terms of gender was male, age, coronary heart disease, COPD, arrhythmias, and complications (p < 0.05).

Fig. 1 The patient flow chart in our study

Table 1 Baseline clinical characteristics of hip fracture patients classified by heart failure

Variables	Total (N = 1273)	Non-heart failure (N = 946)	Heart failure (N = 327)	P-value	
Gender, N (%)					
 Male	385(30.2%)	312(33.0%)	73(22.3%)	<0.001	
 Female	888(69.8%)	634(67.0%)	254(77.7%)		
Age, mean ± SD (years)	79.2 ± 7.9	78.3 ± 8.0	81.8 ± 7.1	0.011	
Age group, N (%)					
 <75 years	357(28.0%)	299(31.6%)	58(17.7%)	<0.001	
 ≥ 75 years	916(72.0%)	647(68.4%)	269(82.3%)		
Comorbidity N (%)					
 Hypertension					
  Yes	642(50.4%)	477(50.4%)	165(50.5%)	0.991	
  No	631(49.6%)	469(49.6%)	162(49.5%)		
 Stroke					
  Yes	500(39.3%)	359(37.9%)	141(43.1%)	0.099	
  No	773(60.7%)	587(62.1%)	186(56.9%)		
 Coronary_heart_disease					
  Yes	358(28.1%)	241(25.5%)	117(35.8%)	<0.001	
  No	915(71.9%)	705(74.5%)	210(64.2%)		
 Diabetes					
  Yes	276(21.7%)	207(21.9%)	69(21.1%)	0.786	
  No	997(78.3%)	739(78.1%)	258(78.9%)		
 COPD					
  Yes	36(2.8%)	21(2.2%)	15(4.6%)	0.026	
  No	1237(97.2%)	925(97.8%)	312(95.4%)		
 Cancer					
  Yes	60(4.7%)	43(4.5%)	17(5.2%)	0.631	
  No	1230(95.3%)	903(95.5%)	310(94.8%)		
 Arrhythmia					
  Yes	82(6.4%)	53(5.6%)	29(8.9%)	0.038	
  No	1191(93.6%)	893(94.4%)	298(91.1%)		
Complications					
 Yes	171(13.4%)	107(11.3%)	64(19.6%)	<0.001	
 No	1102(86.6%)	839(88.7%)	263(80.4%)		
Values are presented as mean±standard deviation, median (interquartile range), or number (percentage) as appropriate, SD Standard deviation, COPD Chronic Obstructive Pulmonary Disease

Univariate analysis of laboratory data and ultrasonographic results

The heart failure group had significantly higher rates of anemia, hypokalemia, hyponatremia, and hypoalbuminemia compared to the non-heart failure group (p < 0.05, Table 2). There was no significant difference between the two groups concerning lower limb venous thrombosis.

Table 2 The results of univariate analysis of laboratory data and ultrasound examination

Variables	Total (N = 1273)	Non-heart failure (N = 946)	Heart failure (N = 327)	P-value	
Anemia					
 Yes	475(37.3%)	264(27.9%)	211(64.5%)	<0.001	
 No	798 (62.7%)	682(72.1%)	116(35.5%)		
Hypokalemia					
 Yes	197(15.5%)	111(11.7%)	86(26.3%)	<0.001	
 No	1076(84.5%)	835(88.3%)	241(73.7%)		
Hyponatremia					
 Yes	295(23.2%)	176(18.6%)	119(36.4%)	<0.001	
 No	978(76.8%)	770(81.4%)	208(63.6%)		
Hypoalbuminemia					
 Yes	513(40.3%)	294(31.1%)	219(67.0%)	<0.001	
 No	760(59.7%)	625(68.9%)	108(33.0%)		
Lower extremity venous thrombosis					
 Yes	509(40.0%)	365(38.6%)	144(44.0%)	0.083	
 No	764(60.0%)	581(61.4%)	183(56.0%)		
Values are presented as median (interquartile range), or number (percentage) as appropriate

Selection of predictive variables and derivation of predictive model

Based on LASSO regression analysis results, 12 initial variables with higher relevance were selected. Using a logistic regression model, these 12 initial variables were further analyzed to understand their relationship with the target variable (occurrence of heart failure) and determine influential variables (Fig. 2). Among them, Gender was male (OR = 0.529, 95%CI: 0.381–0.734, P < 0.001), Age (OR = 1.760, 95%CI: 1.251–2.479, P = 0.001), Coronary Heart Disease (OR = 1.977, 95%CI: 1.454–2.687, P < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95%CI: 1.154–5.346, P = 0.020), Complications (OR = 1.516, 95%CI: 1.033–2.226, P = 0.033), Anemia (OR = 2.668, 95%CI: 1.850–3.847, P < 0.001), and Hypoalbuminemia (OR 2.442, 95%CI: 1.682–3.544, P < 0.001) stood out (Table 3). Multivariate logistic regression analysis then shortlisted these 7 variables from the initial 12. These variables were independent predictive factors, and to visually demonstrate each variable’s influence on the target variable, a nomogram was presented (Fig. 3). The prediction model of acute heart failure was Logit(P) = -2.167–0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia. We have calculated the Variance Inflation Factor (VIF) within our model, and the results show that all predictor variables have VIF values well below the commonly used threshold of 5, specifically: Male 1.03, Age 1.00, Coronary heart disease 1.04, COPD 1.01, Complications 1.03, Anemia 1.72 and Hypoalbuminemia 1.73.

Fig. 2 Data statistics and clinical feature selection using the LASSO binary logistic regression model. (A) Employing ten-fold cross-validation to assess the accuracy of the model. In the binary bias displayed as log(lambda), the vertical dashed lines represent the optimal lambda value position, with the minimum standard error (1-SE standard) plotted on the graph. The vertical dashed line on the left indicates the lambda value corresponding to the minimum standard, while the one on the right indicates the lambda value corresponding to the minimum standard plus 1 SE. (B) The LASSO regression analysis was performed to select variables highly correlated with the incidence of heart failure from a pool of 15 variables. A coefficient profile plot was generated by plotting the log (lambda) sequence against the coefficients. At the optimal lambda value (λ = 0.005569645), 12 variables exhibited non-zero coefficients, indicating their inclusion in the final predictive model

Table 3 Prediction factors of preoperative heart failure in geriatric patients with hip fracture

	B	SE	Wald	P value	Odds ratio	95%CI	
Gender was male	-0.637	0.167	14.561	<0.001	0.529	0.381–0.734	
Age	0.566	0.174	10.529	0.001	1.760	1.251–2.479	
Coronary heart disease	0.682	0.157	18.935	<0.001	1.977	1.454–2.687	
COPD	0.910	0.391	5.412	0.020	2.484	1.154–5.346	
Complications	0.416	0.196	4.519	0.033	1.516	1.033–2.226	
Anemia	0.981	0.187	27.617	<0.001	2.668	1.850–3.847	
Hypoalbuminemia	0.893	0.190	22.053	<0.001	2.442	1.682–3.544	
Constant	-2.167	0.220	141.707	<0.001	0.073		
COPD Chronic Obstructive Pulmonary Disease

Fig. 3 A nomogram model for predicting acute heart failure occurring in elderly patients with hip fractures before surgery

Manifestation of the heart failure risk nomogram in the cohort

The calibration curve, built using Bootstrap (1,000 Bootstrap resampling), apparent line, and bias-corrected line, showed slight deviation from the ideal line, indicating good consistency between predictive and observed outcomes (Fig. 4). The nomogram prediction model had an AUC of 0.763 with a 95% CI of 0.732–0.793 (Fig. 5). The C-index for the model, determined through internal validation using bootstrap (resampling = 1,000), was 0.756, indicating high predictive accuracy.

Fig. 4 Calibration curves of the acute heart failure nomogram prediction in the cohort. The x-axis represents the predicted acute heart failure risk. The y-axis represents the actual diagnosed acute heart failure. The ideal line represents a perfect prediction by an ideal model. The apparent line represents the prediction results obtained by the model using the original sample data, while the bias-corrected line indicates the forecast results after bias correction through resampling

Fig. 5 Analysis of ROC curve for the predictive values of preoperative acute heart failure

Clinical application

The decision curve analysis for the heart failure nomogram in predicting heart failure risk in elderly hip fracture patients is presented in Fig. 6. The decision curve indicates that the nomogram is beneficial when the threshold probability ranges from 4 to 62%. The Clinical Impact Curve (CIC) illustrates how the number of patients predicted by the model changes with different threshold settings.

Fig. 6 Decision curve analysis and clinical impact curve. A shows a Decision Curve Analysis (DCA) comparing the net benefit of the acute heart failure risk nomogram across training and test datasets. The blue lines denote the nomogram’s performance, which outweighs the grey lines representing the extremes of assuming all or no patients have acute heart failure. The blue lines’ position indicates the nomogram’s positive net benefit within certain probability thresholds, suggesting its value in clinical decision-making. B’s Clinical Impact Curve (CIC) contrasts the number of high-risk patients identified by the nomogram (solid blue line) against the actual heart failure cases (dashed red line) for both datasets. This curve illustrates the real-world impact of applying the nomogram, indicating its effectiveness and efficiency at various thresholds by showing how many patients would actually require further clinical attention

Discussion

Many elderly hip fracture patients present with acute heart failure preoperatively, a condition that significantly heightens surgical risks and worsens post-operative outcomes. Our findings indicate that 25.7% of geriatric hip fracture patients exhibited signs of acute heart failure prior to surgery—a notably high percentage. Therefore, timely and precise assessment and prevention of acute heart failure risk in this patient group are imperative. This study primarily focuses on potential risk factors for acute heart failure in geriatric hip fracture patients preoperatively, equipping clinicians to early identify critical cases and offer timely, individualized treatment plans.

In this retrospective cohort study, we delved into the characteristics of acute heart failure in geriatric hip fracture patients preoperatively. We identified Gender was male, Age, Coronary heart disease, COPD, Complications, Anemia, and Hypoalbuminemia as independent risk factors for acute heart failure onset before surgery. By leveraging these seven risk factors, we developed a predictive model, the practicability of which is assured by the easy accessibility of these variables, providing a robust clinical basis for risk prediction of acute heart failure in geriatric hip fracture patients.

Anemia emerged as a significant risk factor for the onset of acute heart failure in our cohort (OR = 2.668, 95% CI = 1.850–3.847, P < 0.001). This finding is consistent with those of other studies, which have highlighted anemia as a critical factor exacerbating heart failure, particularly in populations with comorbidities [9]. In our study, 37.3% of the patients were anemic, similar to the prevalence reported in earlier research, underscoring the commonality of this condition in geriatric patients with hip fractures [10]. Although this compensatory response can temporarily enhance oxygen supply, it has its limits. With persistent and worsening anemia, the heart faces undue stress. Over time, the perpetual strain can lead to myocardial fatigue and exhaustion, causing a drastic decline in cardiac function [11, 12]. Concurrently, anemia can induce endothelial dysfunction, attenuating the response of endothelium-derived relaxation factors, leading to aberrant vascular tone regulation. This dysfunction can accentuate both preload and afterload on the heart, thus amplifying the risk of acute heart failure [13]. Anemia can also trigger the overactivation of neurohormonal systems, including the sympathetic nervous system and the renin-angiotensin-aldosterone system, the excessive stimulation of which can expedite pathological cardiac alterations, culminating in acute heart failure [14].

In our research, we also discerned that Chronic Obstructive Pulmonary Disease (COPD) is a significant risk factor for acute heart failure among elderly hip fracture patients. Our findings are consistent with current research that emphasizes the impact of COPD on cardiovascular health. Elderly patients with COPD inherently have compromised pulmonary function. The long-standing nature of COPD results in airway inflammation and alveolar damage, further reducing pulmonary compliance and oxygen exchange efficiency. This creates a chronic hypoxic environment in the body, which can adversely affect cardiomyocyte function and structure, leading to cardiac remodeling and increased vulnerability to stress and injury [15–17]. When these patients suffer from a hip fracture, the severe trauma can ignite a cascade of inflammatory and stress responses, releasing inflammatory cytokines and hormones. These agents can cause vasodilation and increased vascular permeability, changing intravascular fluid distribution and increasing cardiac workload [18]. Additionally, the inflammatory response can further impair pulmonary function, leading to elevated pulmonary arterial pressures and exacerbated respiratory distress. In this scenario, the combined effects of increased cardiac workload, heightened pulmonary arterial pressure, and respiratory distress might precipitate acute heart failure [19].

Post-hip fracture, due to the generally compromised baseline health status of elderly patients, they are potentially susceptible to a slew of complications before surgery. This includes pulmonary infections, pulmonary embolism, acute cerebral infarction, intracerebral hemorrhage, acute myocardial infarction, and acute renal failure. Prior research indicates that these complications can either directly or indirectly induce acute heart failure [5]. Our findings echo these reports. For instance, pulmonary infections and embolisms can lead to blood oxygen and hemodynamic anomalies, thereby exacerbating cardiac workload and elevating acute heart failure risks [20, 21]. Acute cerebral infarctions and hemorrhages can impact cardiac function via neurogenic reflex pathways, increasing cardiac burden and the likelihood of heart failure [22]. Additionally, acute myocardial infarctions directly impair a portion of the myocardium, affecting the heart’s pumping efficacy and are prime triggers for acute heart failure [23]. Acute renal failure, by inducing fluid retention and electrolyte imbalances, can impede cardiac function, increasing its burden and establishing itself as a pivotal factor for acute heart failure [24]. Hence, when managing geriatric hip fracture patients, clinicians should prioritize early identification and management of these complications to prevent acute heart failure, ensuring the overall well-being and safety of the patient.

It is noteworthy that our study has shown that gender was male (OR = 0.529, 95% CI = 0.381–0.734, P < 0.001) is an independent predictor for the onset of acute heart failure in elderly patients prior to hip fracture surgery. Compared to elderly men, elderly women are more susceptible to acute heart failure following a hip fracture. Several reasons can explain this phenomenon. Firstly, physiological differences likely play a crucial role in this outcome. Elderly women generally have more severe osteoporosis than men, which not only increases the risk of hip fractures but may also trigger a more intense physiological stress response from the fracture [25]. Studies indicate that osteoporosis is correlated with the occurrence of heart disease, particularly more pronounced in elderly women [26]. Secondly, during the recovery process from fractures, elderly women, who may have underlying poorer cardiac conditions, are more prone to heart failure [27]. For instance, the asymptomatic nature of cardiac diseases in elderly women can mean that some degree of cardiac dysfunction may already be present before any heart problems are perceptibly felt. Furthermore, the risk of heart failure is also associated with the patients’ overall health status and quality of life. Elderly women may lack adequate social and psychological support, which impacts their overall rehabilitation and response to acute incidents [28, 29]. A deficiency in sufficient support and resources might make this group more likely to suffer complications, including acute heart failure, after a hip fracture. In summary, gender significantly influences the prediction of acute heart failure in elderly patients with hip fractures. This phenomenon is likely the result of a combination of physiological, psychological, and social factors. Further research in this area could enhance our understanding of gender differences in the prevention and treatment of cardiovascular diseases, especially tailored strategies for the elderly population. This refined focus may ultimately improve outcomes in this vulnerable group.

Limitations

This study has several limitations. Firstly, being a single-center retrospective cohort study, there’s an inherent selection bias that may have influenced the accuracy of our findings. Secondly, we couldn’t account for other key variables that might influence heart failure risk, such as detailed medication histories and lifestyle factors of the patients. Furthermore, due to data sourcing constraints, we were unable to include certain essential variables like biomarkers, which might affect the predictive accuracy and scope of our model. Lastly, we didn’t fully assess the patients’ cardiovascular functional status and other potential comorbidities, factors that might be significantly linked with geriatric hip fractures and heart failure.

Conclusion

This study successfully established an evaluation model for predicting acute heart failure preoperatively in elderly hip fracture patients. It aims to offer clinicians a valuable tool for estimating acute heart failure risk before surgery. The application of this model can enable medical teams to conduct a more precise cardiovascular health assessment and intervention preoperatively, thus enhancing surgical safety and outcomes. Additionally, this model can serve as a foundation for communicating with patients and devising personalized treatment strategies. While our model has shown efficacy in internal validation, it still requires external validation across a broader population. Subsequent studies should also explore whether targeted interventions based on this model can genuinely reduce the incidence of preoperative acute heart failure, thereby further ensuring the surgical safety and long-term prognosis of elderly hip fracture patients.

Acknowledgements

We are grateful to all those who took part in or assisted with this study project.

Author contributions

QLY conceived of the study and drafted the manuscript. MMF gathered and processed the data. ZQW and ZYH supervision, and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

None.

Data availability

The datasets utilized in the present study are contained within the internal network of the Third Hospital of Hebei Medical University. Due to existing data privacy policies, these datasets are not publicly accessible. However, they can be made available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The ethical review board of the Third Hospital of Hebei Medical University evaluated and sanctioned this research protocol, ensuring adherence to the Helsinki Declaration. The approval was granted under the reference number 2021–087 − 1. Due to the retrospective nature of data gathering in this study, patients were waived from informed consent by the Institutional Review Board of Hebei Medical University Third Hospital. Prior to analysis, all patient data were anonymized to protect privacy.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Cooper C Cole ZA Holroyd CR Earl SC Harvey NC Dennison EM Melton LJ Cummings SR Kanis JA Secular trends in the incidence of hip and other osteoporotic fractures Osteoporos Int 2011 22 5 1277 88 10.1007/s00198-011-1601-6 21461721
Cooper C, Cole ZA, Holroyd CR, Earl SC, Harvey NC, Dennison EM, Melton LJ, Cummings SR, Kanis JA. Secular trends in the incidence of hip and other osteoporotic fractures. Osteoporos Int. 2011;22(5):1277–88.21461721 10.1007/s00198-011-1601-6
2. Zhang C Feng J Wang S Gao P Xu L Zhu J Jial J Liu L Liu G Wang J Incidence of and trends in hip fracture among adults in urban China: a nationwide retrospective cohort study Plos Med 2020 17 8 e1003180 10.1371/journal.pmed.1003180 32760065
Zhang C, Feng J, Wang S, Gao P, Xu L, Zhu J, Jial J, Liu L, Liu G, Wang J, et al. Incidence of and trends in hip fracture among adults in urban China: a nationwide retrospective cohort study. Plos Med. 2020;17(8):e1003180.32760065 10.1371/journal.pmed.1003180
3. Christiansen BA Harrison SL Fink HA Lane NE Study OOFR For TSOO Incident fracture is associated with a period of accelerated loss of hip BMD: the study of osteoporotic fractures Osteoporos Int 2018 29 10 2201 9 10.1007/s00198-018-4606-6 29992510
Christiansen BA, Harrison SL, Fink HA, Lane NE, Study OOFR, For TSOO. Incident fracture is associated with a period of accelerated loss of hip BMD: the study of osteoporotic fractures. Osteoporos Int. 2018;29(10):2201–9.29992510 10.1007/s00198-018-4606-6
4. Mcdonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, Butler J. elutkienė J, Chioncel O, et al. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42(36):3599–726.
5. Cicek V Cinar T Hayiroglu MI Kilic S Keser N Uzun M Orhan AL Preoperative cardiac risk factors associated with in-hospital mortality in elderly patients without heart failure undergoing hip fracture surgery: a single-centre study Postgrad Med J 2021 97 1153 701 5 10.1136/postgradmedj-2020-138679 32913033
Cicek V, Cinar T, Hayiroglu MI, Kilic S, Keser N, Uzun M, Orhan AL. Preoperative cardiac risk factors associated with in-hospital mortality in elderly patients without heart failure undergoing hip fracture surgery: a single-centre study. Postgrad Med J. 2021;97(1153):701–5.32913033 10.1136/postgradmedj-2020-138679
6. Rostagno C Buzzi R Campanacci D Boccacini A Cartei A Virgili G Belardinelli A Matarrese D Ungar A Rafanelli M In Hospital and 3-Month Mortality and functional recovery rate in patients treated for hip fracture by a Multidisciplinary Team PLoS ONE 2016 11 7 e0158607 10.1371/journal.pone.0158607 27389193
Rostagno C, Buzzi R, Campanacci D, Boccacini A, Cartei A, Virgili G, Belardinelli A, Matarrese D, Ungar A, Rafanelli M, et al. In Hospital and 3-Month Mortality and functional recovery rate in patients treated for hip fracture by a Multidisciplinary Team. PLoS ONE. 2016;11(7):e0158607.27389193 10.1371/journal.pone.0158607
7. Mitchell R Draper B Harvey L Wadolowski M Brodaty H Close J Comparison of hospitalised trends, treatment cost and health outcomes of fall-related hip fracture for people aged ≥ 65 years living in residential aged care and the community Osteoporos Int 2019 30 2 311 21 10.1007/s00198-018-4800-6 30569228
Mitchell R, Draper B, Harvey L, Wadolowski M, Brodaty H, Close J. Comparison of hospitalised trends, treatment cost and health outcomes of fall-related hip fracture for people aged ≥ 65 years living in residential aged care and the community. Osteoporos Int. 2019;30(2):311–21.30569228 10.1007/s00198-018-4800-6
8. [Chinese guidelines for the Diagnosis and treatment of heart failure 2018] Zhonghua Xin xue guan bing za zhi 2018 46 10 760 89 30369168
[Chinese guidelines for the. Diagnosis and treatment of heart failure 2018]. Zhonghua Xin xue guan bing za zhi. 2018;46(10):760–89.30369168
9. Arshi A Lai WC Iglesias BC Mcpherson EJ Zeegen EN Stavrakis AI Sassoon AA Blood transfusion rates and predictors following geriatric hip fracture surgery Hip Int 2021 31 2 272 9 10.1177/1120700019897878 31912747
Arshi A, Lai WC, Iglesias BC, Mcpherson EJ, Zeegen EN, Stavrakis AI, Sassoon AA. Blood transfusion rates and predictors following geriatric hip fracture surgery. Hip Int. 2021;31(2):272–9.31912747 10.1177/1120700019897878
10. Beladan CC Botezatu SB Anemia and management of heart failure patients Heart Fail Clin 2021 17 2 195 206 10.1016/j.hfc.2020.12.002 33673945
Beladan CC, Botezatu SB. Anemia and management of heart failure patients. Heart Fail Clin. 2021;17(2):195–206.33673945 10.1016/j.hfc.2020.12.002
11. Anand IS Gupta P Anemia and Iron Deficiency in Heart failure Circulation 2018 138 1 80 98 10.1161/CIRCULATIONAHA.118.030099 29967232
Anand IS, Gupta P. Anemia and Iron Deficiency in Heart failure. Circulation. 2018;138(1):80–98.29967232 10.1161/CIRCULATIONAHA.118.030099
12. Okada S Iwahana T Kobayashi Y Is High Heart Rate always harmful to heart failure patients? Circ J 2020 84 9 1673 4 10.1253/circj.CJ-20-0198 32741844
Okada S, Iwahana T, Kobayashi Y. Is High Heart Rate always harmful to heart failure patients? Circ J. 2020;84(9):1673–4.32741844 10.1253/circj.CJ-20-0198
13. Verbrugge FH Guazzi M Testani JM Borlaug BA Altered hemodynamics and End-Organ damage in Heart failure Circulation 2020 142 10 998 1012 10.1161/CIRCULATIONAHA.119.045409 32897746
Verbrugge FH, Guazzi M, Testani JM, Borlaug BA. Altered hemodynamics and End-Organ damage in Heart failure. Circulation. 2020;142(10):998–1012.32897746 10.1161/CIRCULATIONAHA.119.045409
14. Borges JI Ferraino KE Cora N Nagliya D Suster MS Carbone AM Lymperopoulos A Adrenal G protein-coupled receptors and the failing heart: a Long-distance, yet intimate Affair J Cardiovasc Pharm 2022 80 3 386 10.1097/FJC.0000000000001213
Borges JI, Ferraino KE, Cora N, Nagliya D, Suster MS, Carbone AM, Lymperopoulos A. Adrenal G protein-coupled receptors and the failing heart: a Long-distance, yet intimate Affair. J Cardiovasc Pharm. 2022;80(3):386.10.1097/FJC.0000000000001213
15. Adeloye D Song P Zhu Y Campbell H Sheikh A Rudan I Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis Lancet Respiratory Med 2022 10 5 447 58 10.1016/S2213-2600(21)00511-7
Adeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respiratory Med. 2022;10(5):447–58.10.1016/S2213-2600(21)00511-7
16. Du Y Ge Y Xu Z Aa N Gu X Meng H Lin Z Zhu D Shi J Zhuang R Hypoxia-inducible factor 1 alpha (HIF-1α)/Vascular endothelial growth factor (VEGF) pathway participates in angiogenesis of myocardial infarction in muscone-treated mice: preliminary study Med Sci Monit 2018 24 8870 7 10.12659/MSM.912051 30531686
Du Y, Ge Y, Xu Z, Aa N, Gu X, Meng H, Lin Z, Zhu D, Shi J, Zhuang R, et al. Hypoxia-inducible factor 1 alpha (HIF-1α)/Vascular endothelial growth factor (VEGF) pathway participates in angiogenesis of myocardial infarction in muscone-treated mice: preliminary study. Med Sci Monit. 2018;24:8870–7.30531686 10.12659/MSM.912051
17. Beà A Valero JG Irazoki A Lana C López Lluch G Portero Otín M Pérez Galán P Inserte J Ruiz Meana M Zorzano A Cardiac fibroblasts display endurance to ischemia, high ROS control and elevated respiration regulated by the JAK2/STAT pathway FEBS J 2022 289 9 2540 61 10.1111/febs.16283 34796659
Beà A, Valero JG, Irazoki A, Lana C, López Lluch G, Portero Otín M, Pérez Galán P, Inserte J, Ruiz Meana M, Zorzano A, et al. Cardiac fibroblasts display endurance to ischemia, high ROS control and elevated respiration regulated by the JAK2/STAT pathway. FEBS J. 2022;289(9):2540–61.34796659 10.1111/febs.16283
18. Hasselbach L Weidner J Elsässer A Theilmeier G Heart failure relapses in response to Acute stresses - role of immunological and inflammatory pathways Front Cardiovasc Med 2022 9 809935 10.3389/fcvm.2022.809935 35548445
Hasselbach L, Weidner J, Elsässer A, Theilmeier G. Heart failure relapses in response to Acute stresses - role of immunological and inflammatory pathways. Front Cardiovasc Med. 2022;9:809935.35548445 10.3389/fcvm.2022.809935
19. Mcglothlin DP Granton J Klepetko W Beghetti M Rosenzweig EB Corris PA Horn E Kanwar MK Mcrae K Roman A ISHLT consensus statement: Perioperative management of patients with pulmonary hypertension and right heart failure undergoing surgery J Heart Lung Transpl 2022 41 9 1135 94 10.1016/j.healun.2022.06.013
Mcglothlin DP, Granton J, Klepetko W, Beghetti M, Rosenzweig EB, Corris PA, Horn E, Kanwar MK, Mcrae K, Roman A, et al. ISHLT consensus statement: Perioperative management of patients with pulmonary hypertension and right heart failure undergoing surgery. J Heart Lung Transpl. 2022;41(9):1135–94.10.1016/j.healun.2022.06.013
20. Peng Q Yang Q Risk factors and management of pulmonary infection in elderly patients with heart failure: a retrospective analysis Medicine 2021 100 38 e27238 10.1097/MD.0000000000027238 34559121
Peng Q, Yang Q. Risk factors and management of pulmonary infection in elderly patients with heart failure: a retrospective analysis. Medicine. 2021;100(38):e27238.34559121 10.1097/MD.0000000000027238
21. Goldberg JB Spevack DM Ahsan S Rochlani Y Dutta T Ohira S Kai M Spielvogel D Lansman S Malekan R Survival and right ventricular function after Surgical Management of Acute Pulmonary Embolism J Am Coll Cardiol 2020 76 8 903 11 10.1016/j.jacc.2020.06.065 32819463
Goldberg JB, Spevack DM, Ahsan S, Rochlani Y, Dutta T, Ohira S, Kai M, Spielvogel D, Lansman S, Malekan R. Survival and right ventricular function after Surgical Management of Acute Pulmonary Embolism. J Am Coll Cardiol. 2020;76(8):903–11.32819463 10.1016/j.jacc.2020.06.065
22. Chen Z Venkat P Seyfried D Chopp M Yan T Chen J Brain–Heart Interaction Circ Res 2017 121 4 451 68 10.1161/CIRCRESAHA.117.311170 28775014
Chen Z, Venkat P, Seyfried D, Chopp M, Yan T, Chen J. Brain–Heart Interaction. Circ Res. 2017;121(4):451–68.28775014 10.1161/CIRCRESAHA.117.311170
23. Frantz S Hundertmark MJ Schulz-Menger J Bengel FM Bauersachs J Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies Eur Heart J 2022 43 27 2549 61 10.1093/eurheartj/ehac223 35511857
Frantz S, Hundertmark MJ, Schulz-Menger J, Bengel FM, Bauersachs J. Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies. Eur Heart J. 2022;43(27):2549–61.35511857 10.1093/eurheartj/ehac223
24. Holgado JL Lopez C Fernandez A Sauri I Uso R Trillo JL Vela S Nuñez J Redon J Ruiz A Acute kidney injury in heart failure: a population study Esc Heart Fail 2020 7 2 415 22 10.1002/ehf2.12595 32059081
Holgado JL, Lopez C, Fernandez A, Sauri I, Uso R, Trillo JL, Vela S, Nuñez J, Redon J, Ruiz A. Acute kidney injury in heart failure: a population study. Esc Heart Fail. 2020;7(2):415–22.32059081 10.1002/ehf2.12595
25. Brozek W Reichardt B Zwerina J Dimai HP Klaushofer K Zwettler E Antiresorptive therapy and risk of mortality and refracture in osteoporosis-related hip fracture: a nationwide study Osteoporos Int 2016 27 1 387 96 10.1007/s00198-015-3415-4 26576544
Brozek W, Reichardt B, Zwerina J, Dimai HP, Klaushofer K, Zwettler E. Antiresorptive therapy and risk of mortality and refracture in osteoporosis-related hip fracture: a nationwide study. Osteoporos Int. 2016;27(1):387–96.26576544 10.1007/s00198-015-3415-4
26. Pirrotta F Cavati G Mingiano C Merlotti D Nuti R Gennari L Palazzuoli A Vitamin D Deficiency and Cardiovascular Mortality: Retrospective Analysis Siena osteoporosis cohort Nutrients 2023 15 15 3303 10.3390/nu15153303 37571241
Pirrotta F, Cavati G, Mingiano C, Merlotti D, Nuti R, Gennari L, Palazzuoli A. Vitamin D Deficiency and Cardiovascular Mortality: Retrospective Analysis Siena osteoporosis cohort. Nutrients. 2023;15(15):3303.37571241 10.3390/nu15153303
27. You F Ma C Sun F Liu L Zhong X The risk factors of heart failure in elderly patients with hip fracture: what should we care Bmc Musculoskel Dis 2021 22 1 832 10.1186/s12891-021-04686-8
You F, Ma C, Sun F, Liu L, Zhong X. The risk factors of heart failure in elderly patients with hip fracture: what should we care. Bmc Musculoskel Dis. 2021;22(1):832.10.1186/s12891-021-04686-8
28. Dyer SM Crotty M Fairhall N Magaziner J Beaupre LA Cameron ID Sherrington C A critical review of the long-term disability outcomes following hip fracture Bmc Geriatr 2016 16 1 158 10.1186/s12877-016-0332-0 27590604
Dyer SM, Crotty M, Fairhall N, Magaziner J, Beaupre LA, Cameron ID, Sherrington C. A critical review of the long-term disability outcomes following hip fracture. Bmc Geriatr. 2016;16(1):158.27590604 10.1186/s12877-016-0332-0
29. Kuo LY Hsu PT Wu WT Lee RP Wang JH Chen HW Chen IH Yu TC Peng CH Liu KL The incidence of mental disorder increases after hip fracture in older people: a nationwide cohort study Bmc Geriatr 2021 21 1 249 10.1186/s12877-021-02195-w 33858356
Kuo LY, Hsu PT, Wu WT, Lee RP, Wang JH, Chen HW, Chen IH, Yu TC, Peng CH, Liu KL, et al. The incidence of mental disorder increases after hip fracture in older people: a nationwide cohort study. Bmc Geriatr. 2021;21(1):249.33858356 10.1186/s12877-021-02195-w
