Characteristics of preoperative atrial fibrillation in geriatric patients with hip fracture and construction of a clinical prediction model: a retrospective cohort study
BMC Geriatrics volume 23, Article number: 310 (2023)
Atrial fibrillation is the most common atrial arrhythmia in the perioperative period and is associated with prolonged hospital stay, increased costs, and increased mortality. However, there are few data on the predictors and incidence of preoperative atrial fibrillation in hip fracture patients. Our aim was to identify predictors of preoperative atrial fibrillation and to propose a valid clinical prediction model.
Predictor variables included demographic and clinical variables. LASSO regression analyzes were performed to identify predictors of preoperative atrial fibrillation, and models were constructed and presented as nomograms. Area under the curve, calibration curve, and decision curve analysis (DCA) were used to examine the discriminative power, calibration, and clinical efficacy of the predictive models. Bootstrapping was used for validation.
A total of 1415 elderly patients with hip fractures were analyzed. Overall, 7.1% of patients had preoperative atrial fibrillation, and they were at significant risk for thromboembolic events. Patients with preoperative AF had a significantly longer delay in surgery than those without preoperative atrial fibrillation (p < 0.05). Predictors for preoperative atrial fibrillation were hypertension (OR 1.784, 95% CI 1.136–2.802, p < 0.05), C-reactive protein at admission (OR 1.329, 95% CI 1.048–1.662, p < 0.05), systemic inflammatory response index at admission (OR 2.137, 95% CI, 1.678–2.721 p < 0.05), Age-Adjusted Charlson Comorbidity Index (OR 1.542, 95% CI 1.326–1.794, p < 0.05), low potassium(OR 2.538, 95% CI 1.623–3.968, p < 0.05), anemia(OR 1.542, 95% CI 1.326–1.794, p < 0.05). Good discrimination and calibration effect of the model was showed. Interval validation could still achieve the C-index value of 0.799. DCA demonstrated this nomogram has good clinical utility.
This model has a good predictive effect on preoperative atrial fibrillation in elderly patients with hip fractures, which can help to better plan clinical evaluation.
The number of hip fractures worldwide is increasing due to an aging population. Surgery remains the treatment of choice for hip fractures to date [1, 2]. Atrial fibrillation is a common arrhythmia during perioperative period, with incidence rates ranged between 2%—60%, and it is related to poor outcomes [3,4,5,6].
There are many studies about atrial fibrillation associated with cardiothoracic surgery . Underlying cardiac disease and direct surgical manipulation of the heart are thought to be associated with the development of perioperative atrial fibrillation. Hypertension and chronic obstructive pulmonary disease also identified as risk factors for perioperative atrial fibrillation associated with cardiothoracic surgery . Orthopedic surgery is considered to have a relatively low perioperative incidence of atrial fibrillation compared with cardiothoracic surgery. Elderly and frail, functional limitations and comorbidities were patient characteristics in most hip fracture patients. So perioperative atrial fibrillation was common and considerable burdens still remained for hip fracture surgery .
In previous studies reported by Leibowitz D et.al , there was a highly correlation between perioperative atrial fibrillation and one-year mortality in elderly hip fracture patients, which was not attenuated by drug treatment of atrial fibrillation. Given the above, greater emphasis on prevention of perioperative atrial fibrillation might be warranted. Seong Jun Bae et.al  reported age and chronic obstructive pulmonary disease were predictors of postoperative atrial fibrillation in patients with hip fracture, which is one of the few studies. Whether these results apply to preoperative atrial fibrillation in hip fracture patients remains to be assessed. In addition, there are limited data on the characteristics of atrial fibrillation before hip fracture surgery.
At present, mechanisms and triggers of preoperative atrial fibrillation are still unclear in hip surgery. There is no gold standard risk score to predict individual risks of preoperative atrial fibrillation. We aimed to identify predictors of preoperative atrial fibrillation and propose a effective clinical prediction model.
Materials and methods
Electronic medical records were reviewed for all patients with hip fracture aged 65 years and over who underwent orthopedic repair surgeries from April 2017 to November 2020 in our department. Patients with multiple fractures, pathological fractures, malignant tumors, autoimmune disease, the history of chronic atrial fibrillation, time from injury to admission more than 1 week, and incomplete clinical data were not studied. This study obtained ethics approval from ethical review board of our hospital, and the informed consent was waived due to the retrospective design.
Data collection and variables definition
The primary outcome was the new-onset atrial fibrillation before surgery. We compared characteristics of two groups and studied the predictors of preoperative atrial fibrillation. Baseline and perioperative variables related to preoperative atrial fibrillation were collected and analyzed. The following information was extracted from electronic medical records: gender, age, comorbidities, fracture type, perioperative complications, surgery delay time, hematological and biochemical indices at admission, left atrial size, and the information about preoperative atrial fibrillation.
Preoperative atrial fibrillation was defined as new-onset irregular cardiac rhythm without identifiable p waves on the ECG that was persistent and required medication. Cardiac arrhythmias can be detected by an electrocardiogram or ECG monitoring system.
Differences between groups were compared by the Student's t-test or the Mann–Whitney U test for continuous variables. For categorical variables, the chi-square test or Fisher's exact test was used. The prediction model was developed by least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression, and draw a nomogram. Area under the curve (AUC) of receiver operating characteristic curve (ROC) analysis was performed in order to assess how accurate the nomogram was at predicting risk. The calibration curve and decision curve analysis were used to assess calibration and clinical value of the nomogram separately. We used SPSS statistical software and R statistical software for our statistical analyses and graphics. The level of significance was set at p < 0.05.
Characteristics of patients
From April 2017 to Sept 2019, a total of 2308 patients were screened to participate in this study. Among them, 893 patients were excluded and 1415 cases remained in the final analysis. Specifically, 138 patients had multiple fractures or pathological fractures; 281 patients received non-surgical treatment; 108 patients had chronic atrial fibrillation; 217 patients admitted with a delay of more than 1 week; 54 patients had malignant tumors and autoimmune disease, and 95 patients had incomplete data. Among analyzed patients, 101 (7.1%) occurred preoperative atrial fibrillation.
Table 1 summarizes characteristics of hip fracture patients. Most of the patients were women (71.4%) and mean age of patients was 79.6 years, with hypertension (46.6%) and cerebrovascular disease (41.0%) as the top two comorbidities. No differences in sex was found between groups, but a difference present in age, with preoperative atrial fibrillation patients being older than no-preoperative atrial fibrillation subjects (preoperative atrial fibrillation 82.3 ± 6.6 years, no-preoperative atrial fibrillation 79.4 ± 7.5 years, p < 0.001). The burden of chronic diseases, as assessed by Age-Adjusted Charlson Comorbidity Index (ACCI) was higher in patients with preoperative atrial fibrillation than without preoperative atrial fibrillation (5.3(1.3) versus 4.3(1.3), p < 0.001). The difference was statistically significant in the history of hypertension and coronary heart disease (p < 0.05). Although patients with preoperative atrial fibrillation seemed to have more diabetes and cerebrovascular disease, there was no significant difference. In addition to the above, C-reactive protein (CRP) and SIRI was found to be significantly higher in patients with preoperative atrial fibrillation.
Among the 101 patients with preoperative atrial arrhythmia, the mean CHA2DS2‐VASc score was 4.5 ± 1.6 and 99 (98.1%) had CHA2DS2‐VASc score ≥ 2.
Comparison of other preoperative complications that might induce preoperative atrial fibrillation and surgery delay time
The comparison of other preoperative complications that might induce preoperative atrial fibrillation and surgery delay time were shown in Table 2. In terms of type of complications, Patients with preoperative atrial fibrillation had a elevation in incidence of low potassium and anemia (p < 0.001). No differences were found in the incidence of heart failure between two groups. Moreover, surgery delay time of patients with no-preoperative atrial fibrillation was shorter compared to patients with preoperative atrial fibrillation (p < 0.001).
Selection of variables as predictors and derivation of the prediction model
With LASSO regression, 8 variables were selected. All 8 variables were then analyzed with the logistic regression model (shown in Fig. 1). There were 6 variables that remained in the model:hypertension (OR 1.784, 95% CI 1.136–2.802, p < 0.05), CRP at admission (OR 1.329, 95% CI 1.048–1.662, p < 0.05), SIRI at admission (OR 2.137, 95% CI,1.678–2.721 p < 0.05), ACCI (OR 1.542, 95% CI 1.326–1.794, p < 0.05), low potassium(OR 2.538, 95% CI 1.623–3.968, p < 0.05), anemia(OR 1.542, 95% CI 1.326–1.794, p < 0.05). Table 3 summarizes the findings of the analysis among hypertension,CRP at admission, SIRI at admission, ACCI, low potassium and anemia. By combining these independent predictors, a model was built and presented as a nomogram (shown in Fig. 2).
Evaluation of the prediction model
Calibration curves used for estimating preoperative atrial fibrillation in hip fracture patients showed good agreement across the cohort.The AUC for the predictive nomogram model was 0.799(95% CI 0.754–0.844) (shown in Fig. 3). With a p value of 0.948, The calibration curve indicated a good calibration effect of the nomogram. The internal validation C-index using bootstrapping (resampling = 1000) is 0.799. DCA demonstrated this nomogram has good clinical utility (shown in Fig. 4).
Characteristics of preoperative atrial fibrillation in elderly patients with hip fracture was analyzed and a nomogram prediction was constructed in this study. In our results, the incidence of preoperative atrial fibrillation is 7.1%, and they had a considerable risk of thromboembolic events. Moreover, we found surgery delay time of patients with no-preoperative atrial fibrillation was shorter compared to patients with preoperative atrial fibrillation. Six prediction factors constitute the prediction model, including hypertension, CRP, SIRI, ACCI, low potassium and anemia, which are easily available in clinical practice. The nomogram could predict the preoperative atrial fibrillation risk in hip fracture patients accurately.
Nearly all elderly hip fracture patients with preoperative atrial fibrillation are at significant risk for thromboembolic events based on the CHA2DS2-VASc score. Previous research has shown that many episodes of atrial fibrillation are asymptomatic and may go undetected until a thromboembolic event or other serious complication occurs. Furthermore, hip fracture patients of our study are the elderly with a variety of comorbidities, and the presentation of illnesses in this population is often atypical, making clinical diagnosis difficult . Therefore, the true incidence of atrial fibrillation in our study population may have been underestimated. Given the detrimental impact of AF on the prognosis of patients with hip fractures, prevention, timely diagnosis, and aggressive treatment are crucial. As with any preventable disease, predictors can be divided into modifiable and non-modifiable predictors. For modifiable predictors, identifying and avoiding its exposure is essential. However, high-quality research remains scarce, which is about the prediction model for preoperative atrial fibrillation on elderly hip fracture patients. Compared with no-preoperative atrial fibrillation, surgery delay time of patients with preoperative atrial fibrillation was significant longer. In addition to atrial fibrillation, the prolonged surgery delay time is likely related to overall condition of the patient. Left atrial dilatation is an important part of atrial adverse remodelling and is a strong predictor of atrial fibrillation. large left atrial volumes were associated with a high prevalence of persistent/permanent atrial fibrillation. In our study, there was no difference in left atrial size between the two groups.This is because most episodes of atrial fibrillation in our study were associated with perioperative factors, and most patients had only one episode without progression to persistent atrial fibrillation. Therefore, there was no significant change in left atrial volume. Of course, further studies on the mechanism of perioperative atrial fibrillation are needed to better explain this phenomenon.
Hypertension, CRP, SIRI, ACCI, low potassium and anemia were significant predictors of preoperative atrial fibrillation. The pathophysiology of the development of preoperative atrial fibrillation is not fully understood. Some studies reported that the pathogenesis of postoperative atrial fibrillation might be related to intraoperative sympathetic nerve activation, oxidative stress, systemic inflammatory response and complement activation . We believed that this pathogenesis also applied to preoperative atrial fibrillation. It is common for elderly patients to suffer a severe traumatic stress response after hip fractures, including neuroendocrine and immunoinflammatory responses and changes in metabolic function [14,15,16]. As a result of stress, the sympathetic nervous system releases catecholamines and glucocorticoids, which cause sodium retention, vasoconstriction, and heart rate to increase and ultimately leading to perioperative myocardial ischemia and injury. On the other hand, immuno-inflammatory promoted production of inflammatory cytokines and superoxide, leading to perioperative myocardial injury. Changes in the metabolic function may lead to anemia, low albumin, low potassium and others.
Gregory M Marcus et al. discovered that atrial fibrillation was independently associated with inflammation, but the role of inflammation in the genesis, maintenance, or propagation of atrial fibrillation is poorly understood . An indicator of the inflammatory response, CRP is used to assess the severity of traumatic stress [18,19,20]. Surgical trauma, fracture, infection and other factors affect the prognosis. The systemic inflammatory response index (SIRI) is based on the number of monocytes, lymphocytes and neutrophils in the peripheral blood and is used to describe the balance between the inflammatory response and the patient's immune status . It is an affordable and accessible inflammatory marker. Previous research reported that SIRI might be related to the acute coronary syndrome . In present study, we found CRP and SIRI were significant predictors of preoperative atrial fibrillation, that was to say inflammation was likely related to the genesis of preoperative atrial fibrillation.
Previous studies reported hypertension was related to atrial fibrillation [23,24,25]. In our study, hypertension was also one of significant predictors of preoperative atrial fibrillation in geriatric hip fracture patients. This was consistent with findings reported in previous studies. The mechanism might be associated with both electrical and structural atrial remodeling due to long-term elevated blood pressure. Activation of the renin–angiotensin–aldosterone system is the common pathophysiological basis of hypertension and atrial fibrillation. RAAS activity is increased in elderly patients with hip fracture and hypertension. As the main drug of the renin-angiotensin system, angiotensin II can increase intracellular free calcium ions, change potassium conductance, and induce the occurrence of transient inward current, thereby promoting the development of angiotensin II, which might contribute to the development of atrial fibrillation [26, 27].
Developed by Charlson et al., the Charlson Comorbidity Index is a measure of mortality risk resulting from comorbidities . When age is taken into account as a correction variable, ACCI is used to evaluate prognosis, which is based on a calculation that incorporates both age and important underlying diseases [29,30,31]. In addition, ACCI demonstrated remarkable accuracy in predicting complications after surgery . Studies showed that comorbidities were independently associated with atrial fibrillation, such as heart disease and chronic kidney disease etc. However, there was no report on the relationship between ACCI and preoperative atrial fibrillation in hip fracture patients. In our study, ACCI was one of significant predictors of preoperative atrial fibrillation in geriatric hip fracture patients. Age and coronary heart disease were risk factors of atrial fibrillation in previous studies. In contrast, we did not find a relationship between age and coronary heart disease and the risk of preoperative atrial fibrillation. Although there was a significant difference between the two groups in terms of age and coronary heart disease in univariate analysis, no significant difference was found in multivariate regression analysis.It was likely related to that ACCI had combined the effect of both age and coronary heart disease.
Elderly patients with hip fractures may also have other complications before or during the preoperative onset of atrial fibrillation, such as pulmonary infection, low albumin, hypokalemia, anemia, etc. Previous studies have reported that these complications may be predisposing factors for postoperative atrial fibrillation. Our results are similar to previous reports. Hypokalemia and anemia were significant predictors of preoperative atrial fibrillation. Anemia excited sympathetic nerves to induce atrial fibrillation. Low potassium causes cellular hyperpolarization, elevated resting potential, and increased cellular excitability, increasing the risk of arrhythmia.
This study has several limitations. First, it is a single-center, retrospective cohort study, which has an inherent limitation. Second, whether patients accept hospitalization is affected by many factors, including the economic level of the patient, and the number of beds of department, which may cause selection bias. Third, affected by incidence, the population is relatively small. Although we used clinical diagnoses as labels, the diagnoses of atrial fibrillation themselves may be a bit biased due to suspicious medical histories. Although nomogram had been extensively tested in self-initiated in-house validation testing, but further studies on multiple patients and external data from multiple locations are required to further confirm the results.
Availability of data and materials
Data are available from the authors upon reasonable request and with permission of corresponding author.
Prince MJ, Wu F, Guo Y, Gutierrez Robledo LM, O’Donnell M, Sullivan R, et al. The burden of disease in older people and implications for health policy and practice. Lancet. 2015;385(9967):549–62.
Saul D, Riekenberg J, Ammon JC, Hoffmann DB, Sehmisch S. Hip fractures: therapy, timing, and complication spectrum. Orthop Surg. 2019;11(6):994–1002.
Bhave PD, Goldman LE, Vittinghoff E, Maselli J, Auerbach A. Incidence, predictors, and outcomes associated with postoperative atrial fibrillation after major noncardiac surgery. Am Heart J. 2012;164(6):918–24.
Alonso-Coello P, Cook D, Xu SC, Sigamani A, Berwanger O, Sivakumaran S, et al. Predictors, prognosis, and management of new clinically important atrial fibrillation after noncardiac surgery: a prospective cohort study. Anesth Analg. 2017;125(1):162–9.
Oesterle A, Weber B, Tung R, Choudhry NK, Singh JP, Upadhyay GA. Preventing postoperative atrial fibrillation after Noncardiac surgery: a meta-analysis. Am J Med. 2018;131(7):795-804.e5.
Vidotti E, Vidotti LFK, Arruda Tavares CAG, Ferraz ÉDZ, Oliveira V, de Andrade AG, et al. Predicting postoperative atrial fibrillation after myocardial revascularization without cardiopulmonary bypass: a retrospective cohort study. J Card Surg. 2019;34(7):577–82.
Bessissow A, Khan J, Devereaux PJ, Alvarez-Garcia J, Alonso-Coello P. Postoperative atrial fibrillation in non-cardiac and cardiac surgery: an overview. J Thromb Haemost. 2015;13(Suppl 1):S304–12.
Philip I, Berroeta C, Leblanc I. Perioperative challenges of atrial fibrillation. Curr Opin Anaesthesiol. 2014;27(3):344–52.
Conen D, Alonso-Coello P, Douketis J, Chan MTV, Kurz A, Sigamani A, et al. Risk of stroke and other adverse outcomes in patients with perioperative atrial fibrillation 1 year after non-cardiac surgery. Eur Heart J. 2020;41(5):645–51.
Leibowitz D, Abitbol C, Alcalai R, Rivkin G, Kandel L. Perioperative atrial fibrillation is associated with increased one-year mortality in elderly patients after repair of hip fracture. Int J Cardiol. 2017;15(227):58–60.
Bae SJ, Kwon CH, Kim TY, Chang H, Kim BS, Kim SH, et al. Predictors and prognostic impact of post-operative atrial fibrillation in patients with hip fracture surgery. World J Clin Cases. 2022;10(11):3379–88.
Cauley JA, Cawthon PM, Peters KE, Cummings SR, Ensrud KE, Bauer DC, et al. risk factors for hip fracture in older men: the osteoporotic fractures in men study (MrOS). J Bone Miner Res. 2016;31(10):1810–9.
Zakkar M, Ascione R, James AF, Angelini GD, Suleiman MS. Inflammation, oxidative stress and postoperative atrial fibrillation in cardiac surgery. Pharmacol Ther. 2015;154:13–20.
Desborough JP. The stress response to trauma and surgery. Br J Anaesth. 2000;85(1):109–17.
Herman JP, McKlveen JM, Ghosal S, Kopp B, Wulsin A, Makinson R, et al. Regulation of the hypothalamic-pituitary-adrenocortical stress response. Compr Physiol. 2016;6(2):603–21.
Deussing JM, Chen A. The corticotropin-releasing factor family: physiology of the stress response. Physiol Rev. 2018;98(4):2225–86.
Marcus GM, Smith LM, Ordovas K, Scheinman MM, Kim AM, Badhwar N, et al. Intracardiac and extracardiac markers of inflammation during atrial fibrillation. Heart Rhythm. 2010;7(2):149–54.
Fakler JK, Grafe A, Dinger J, Josten C, Aust G. Perioperative risk factors in patients with a femoral neck fracture - influence of 25-hydroxyvitamin D and C-reactive protein on postoperative medical complications and 1-year mortality. BMC Musculoskelet Disord. 2016;1(17):51.
Markozannes G, Koutsioumpa C, Cividini S, Monori G, Tsilidis KK, Kretsavos N, et al. Global assessment of C-reactive protein and health-related outcomes: an umbrella review of evidence from observational studies and Mendelian randomization studies. Eur J Epidemiol. 2021;36(1):11–36.
Fu M, Zhang Y, Guo J, Zhao Y, Hou Z, Wang Z, et al. Application of integrated management bundle incorporating with multidisciplinary measures improved in-hospital outcomes and early survival in geriatric hip fracture patients with perioperative heart failure: a retrospective cohort study. Aging Clin Exp Res. 2022;34(5):1149–58.
Wang J, Zhang X, Tian J, Li H, Tang H, Yang C. Predictive values of systemic inflammatory responses index in early neurological deterioration in patients with acute ischemic stroke. J Integr Neurosci. 2022;21(3):94.
Dziedzic EA, Gąsior JS, Tuzimek A, Paleczny J, Junka A, Dąbrowski M, et al. Investigation of the Associations of Novel Inflammatory Biomarkers-Systemic Inflammatory Index (SII) and Systemic Inflammatory Response Index (SIRI)-With the Severity of Coronary Artery Disease and Acute Coronary Syndrome Occurrence. Int J Mol Sci. 2022;23(17):9553.
Huxley RR, Lopez FL, Folsom AR, Agarwal SK, Loehr LR, Soliman EZ, et al. Absolute and attributable risks of atrial fibrillation in relation to optimal and borderline risk factors: the Atherosclerosis Risk in Communities (ARIC) study. Circulation. 2011;123(14):1501–8.
Goette A, Kalman JM, Aguinaga L, Akar J, Cabrera JA, Chen SA, et al. EHRA/HRS/APHRS/SOLAECE expert consensus on atrial cardiomyopathies: Definition, characterization, and clinical implication. Heart Rhythm. 2017;14(1):e3–40.
Kivimaki M, Nyberg ST, Batty GD, Kawachi I, Jokela M, Alfredsson L, et al. Long working hours as a risk factor for atrial fibrillation: a multi-cohort study. Eur Heart J. 2017;38(34):2621–8.
Chen YJ, Chen YC, Tai CT, Yeh HI, Lin CI, Chen SA. Angiotensin II and angiotensin II receptor blocker modulate the arrhythmogenic activity of pulmonary veins. Br J Pharmacol. 2006;147(1):12–22.
Zhao J, Li J, Li W, Li Y, Shan H, Gong Y, et al. Effects of spironolactone on atrial structural remodelling in a canine model of atrial fibrillation produced by prolonged atrial pacing. Br J Pharmacol. 2010;159(8):1584–94.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.
Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom. 2022;91(1):8–35.
Liu J, Li J, He J, Zhang H, Liu M, Rong J. The Age-adjusted Charlson Comorbidity Index predicts post-operative delirium in the elderly following thoracic and abdominal surgery: a prospective observational cohort study. Front Aging Neurosci. 2022;14:979119.
Zhang XM, Wu XJ, Cao J, Guo N, Bo HX, Ma YF, et al. Effect of the age-adjusted Charlson comorbidity index on all-cause mortality and readmission in older surgical patients: a national multicenter, prospective cohort study. Front Med (Lausanne). 2022;9:896451.
Ethics approval and consent to participate
This study protocol was reviewed and approved by ethics committee of third Hospital of Hebei Medical University in compliance with the Helsinki, approval number 2021–087-1. An exemption from the informed consent was obtained from ethics committee of third Hospital of Hebei Medical University due to the retrospective data collection. All data were anonymized before the analysis to safeguard patient privacy.
Consent for publication
Mingming Fu,Yaqian Zhang, Yuqi Zhao, Junfei Guo, Yingze Zhang, Zhiyong Hou and Zhiqian Wang declare that they have no conflict of interest.
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Fu, M., Zhang, Y., Zhao, Y. et al. Characteristics of preoperative atrial fibrillation in geriatric patients with hip fracture and construction of a clinical prediction model: a retrospective cohort study. BMC Geriatr 23, 310 (2023). https://doi.org/10.1186/s12877-023-03936-9