https://orcid.org/0000-0003-3088-3337
Figures
Abstract
Objective
This study aimed to investigate risk factors associated with severe postoperative complications following hip fracture surgery in elderly patients and to develop a nomogram-based risk prediction model for these complications.
Methods
A total of 627 elderly patients with hip fractures treated at Yongchuan Hospital of Chongqing Medical University from January 2015 to April 2024 were collected. 439 patients were assigned to the training cohort for model development, and 188 to the validation cohort for model assessment. The training cohort was stratified based on the presence or absence of severe complications. We employed LASSO regression, as well as univariate and multivariate logistic regression analyses, to identify significant factors. A nomogram was constructed based on the outcomes of the multivariate regression. The model’s discriminative ability was assessed using the area under the receiver operating characteristic curve (AUC), while calibration plots and decision curve analysis (DCA) evaluated its calibration and stability. Internal validation was performed using the validation cohort.
Results
Out of the 627 patients, 118 (18.82%) experienced severe postoperative complications. Both LASSO regression and multivariate logistic analysis identified the modified 5-item frailty index (mFI-5) and the preoperative C-reactive protein to albumin ratio (CAR) as significant predictors of severe complications. The nomogram model, derived from the multivariate analysis, exhibited strong discriminative ability, with an AUC of 0.963 (95% CI: 0.946–0.980) for the training cohort and 0.963 (95% CI: 0.938–0.988) for the validation cohort. Calibration plots demonstrated excellent agreement between the nomogram’s predictions and actual outcomes. Decision curve analysis (DCA) indicated that the model provided clinical utility across all patient scenarios. These findings were consistent in the validation cohort.
Citation: Wei Z, Jiang L, Zhang M, Chen X (2024) Development and validation of a risk prediction model for severe postoperative complications in elderly patients with hip fracture. PLoS ONE 19(11): e0310416. https://doi.org/10.1371/journal.pone.0310416
Editor: Barry Kweh, National Trauma Research Institute, AUSTRALIA
Received: July 17, 2024; Accepted: August 31, 2024; Published: November 13, 2024
Copyright: © 2024 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Aging in China has led to a significant annual increase in the incidence of hip fractures among the elderly, impacting their health [1]. The elderly population experiencing hip fractures is growing by 30% per decade [2], characterized by severe osteoporosis, reduced physical capabilities, and slower reactions, making them vulnerable to falls. Common types include femoral neck, intertrochanteric, and subtrochanteric fractures, with the former two comprising 40%-50% of cases. The one-year mortality rate for elderly hip fractures ranges from 20% to 30%, and more than half of the patients suffer some form of disability [3,4]. Treatment options include conservative and surgical approaches, with surgery increasingly favored due to lower mortality risk compared to conservative methods [5], which are limited to patients unable or unwilling to undergo surgery. Advances in surgical techniques and materials, such as hip replacement and proximal femoral nail anti-rotation (PFNA), have become prevalent [6–9]. However, perioperative complications stemming from patient frailty, surgical trauma, bleeding, anesthesia, and pain significantly impact recovery. Thus, effective prevention and management of these complications are crucial in treating elderly hip fracture patients.
The majority of elderly hip fracture patients present with more than five chronic internal diseases prior to injury [10], complicating clinical management. Current clinical strategies emphasize personalized care and enhanced recovery after surgery (ERAS) principles, crucial for minimizing complications and promoting early recovery. Given the diversity in underlying conditions and physiological states among patients, individualized assessment to prevent complications is essential. Nomograms serve as valuable predictive tools by integrating various patient risk factors to anticipate complication risks. While previous studies have explored individual risk factors for postoperative complications in elderly hip fracture patients, a comprehensive risk prediction model has not yet been established. This study aims to develop and validate a predictive model and nomogram, offering a framework for assessing complication risks clinically and guiding treatment strategies.
Materials and methods
Patient data
The study included elderly patients with hip fractures admitted to the Joint Surgery Department of our institution from January 2015 to April 2024. We accessed the data for research purposes on June 03, 2024. The study was approved by the Ethics Committee of Yongchuan Hospital of Chongqing Medical University (approval number: 2024-kelunshen-61), with informed consent waived due to its retrospective nature.
Eligibility criteria
Inclusion criteria were as follows: (1) Age 65 years or older; (2) Diagnosis of a single acute hip fracture (including femoral neck and intertrochanteric fractures) due to low-energy trauma; (3) Underwent surgical intervention: Total hip arthroplasty or proximal femoral nail antirotation (PFNA) internal fixation.
Exclusion criteria consisted of: (1) Pathologic fractures; (2) Concurrent fractures at multiple sites; (3) Open fractures; (4) History of old fractures or prior hip surgery; (5) Incomplete clinical records.
Postoperative severe complications included: deep surgical site infections, prolonged mechanical ventilation (>48 hours), unplanned reintubation, acute renal failure, sepsis or septic shock, venous thromboembolism (deep vein thrombosis and pulmonary embolism), cerebrovascular accident, stress ulcer bleeding, myocardial infarction, heart failure, respiratory failure, cardiac arrest, and mortality.
Clinical data collection
Data were extracted from patient hospital records, encompassing gender, age, body mass index (BMI), fracture location, modified Five-Item Frailty Index (mFI-5) score, intraoperative transfusion status, preoperative levels of C-reactive protein (CRP), C-reactive protein to albumin ratio (CAR), serum albumin, hemoglobin, fibrinogen, uric acid, creatinine, triglycerides, preoperative ultrasound findings, operation duration, American Society of Anesthesiologists (ASA) physical status classification, preoperative fasting duration, interval from injury to surgery, surgical approach, and anesthesia type. The primary outcome was the incidence of severe postoperative complications, with data collection concluding at the date of hospital discharge.
Statistical analysis
Data from the Affiliated Yongchuan Hospital of Chongqing Medical University were randomly divided into training and validation cohorts at a 7:3 ratio, with comparisons made across various variables. Non-normally distributed data were presented as medians with interquartile ranges. Univariate analysis employed chi-square or Fisher’s exact tests for categorical variables and Student’s t-test or rank-sum tests for continuous variables. LASSO logistic regression was performed on the training set to identify independent predictors and construct a predictive model for severe complications. Nomogram performance was evaluated using ROC curves and calibration plots, with AUC values ranging from 0.5 to 1 indicating discriminative power. Decision curve analysis (DCA) determined the threshold for predictive net benefit, with statistical significance set at p < 0.05. Statistical analyses were conducted using SPSS (version 22.0) and R software (version 4.2.2).
Results
Patient demographics and baseline characteristics
This study included 627 elderly patients with hip fractures, among whom 118 (18.82%) experienced severe postoperative complications. Using random number table, 70% of patients were allocated to the training set (439 cases), and 30% to the validation set (188 cases). Statistically significant differences (P < 0.05) were observed in surgery duration and the American Society of Anesthesiologists (ASA) physical status classification. However, parameters such as gender, age, Body Mass Index (BMI), fracture location, Modified Frailty Index-5 (mFI-5), intraoperative blood transfusion, preoperative C-reactive protein to albumin ratio (CAR), preoperative hemoglobin (HGB), preoperative fibrinogen, preoperative uric acid, preoperative creatinine, preoperative triglycerides, preoperative ultrasound findings, preoperative fasting duration, time from injury to surgery, surgical approach, and anesthesia type showed no significant differences (P > 0.05), as detailed in Table 1.
Construction of the nomogram model
In the training cohort, four indicators showed significant differences: mFI-5 (P < 0.001), HGB (P < 0.05), CAR (P < 0.05), and type of surgery (P < 0.05), as shown in Table 2. Through LASSO regression analysis, these were narrowed down to two potential predictive factors. The coefficients are presented in Table 3, and the regression curve is depicted in Fig 1A. The cross-validation error plot for the LASSO model is shown in Fig 1B, indicating optimal regularization for the most parsimonious model. Fig 2 displays the AUCs for these variables, with mFI-5 and CAR achieving AUCs of 0.594 and 0.944, respectively. Multivariate logistic regression analysis of the training cohort is detailed in Table 4. The final model, integrating two independent predictors (mFI-5 and CAR), is illustrated as a nomogram in Fig 3. Model performance is further illustrated in Fig 4, demonstrating AUCs of 0.963 (95% CI: 0.946–0.980) for the training set and 0.963 (95% CI: 0.938–0.988) for the internal validation set, both surpassing individual predictor AUCs and indicating robust predictive accuracy. Calibration plots in Fig 5A and 5B reveal strong alignment between observed and predicted severe postoperative complications, affirming the nomogram’s validity. The study confirms the nomogram’s reliability in the validation set, with calibration curves closely approximating the ideal, indicating consistency between predictions and actual outcomes. DCA curves related to the nomogram, as depicted in Fig 6A and 6B, highlight the clinical net benefit across various risk threshold probabilities. The findings underscore the substantial clinical utility of the nomogram, as evidenced by its DCA curves.
Discussion
To mitigate the incidence of complications following hip fracture surgery in the elderly, surgical intervention has become the predominant treatment approach. THA and PFNA are widely adopted due to their minimally invasive nature and quick recovery times. However, challenges such as declining physical function, poor compliance, prolonged immobilization, and slow recovery among elderly patients have contributed to an increased risk of postoperative complications. These complications primarily encompass infections, lower limb deep vein thrombosis, heart and renal failure, gastrointestinal dysfunction, delirium, and mortality, significantly impacting surgical outcomes [11,12]. Previous studies have indicated that up to 20%-40% of elderly hip fracture patients experience postoperative complications, consistent with the findings of this study [13]. There exist diverse risk factors contributing to postoperative complications. This study specifically identified the mFI-5 and CAR as independent predictors of complications following hip fracture surgery. A predictive model was developed and a nomogram constructed, both of which demonstrated strong discriminatory ability, consistency, and effectiveness upon internal validation. These findings offer valuable insights for clinical practice, facilitating timely interventions to mitigate complication risks and promote expedited postoperative recovery.
There are numerous methods and tools for assessing patient frailty, included Fried Frailty Phenotype (FFP)[14], Clinical Frailty Scale (CFS) [15], FRAIL Index [16], Short Physical Performance Battery(SPPB) [17], Edmonton Frailty Scale(EFS) [18], Modified Frailty Index(mFI), each method has its own emphasis, such as difficulties in implementation, incomplete evaluation, too many evaluation indicators, need long time. mFI was often used to assessment surgical preoperative frailty, it include mFI-11 and mFI-5, mFI-5 is improved from mFI-11, retained relevant evaluation variables, has highest accuracy for preoperative frailty screening [19], and easy to collect data in clinical. Therefore, we choose mFI-5 as the variable to research.
The mFI-5 is a prognostic tool that incorporates five factors: hypertension, congestive heart failure, chronic obstructive pulmonary disease (COPD), diabetes, and functional dependency prevalence. An mFI-5 score ≥3 indicates frailty [20], providing a holistic assessment of patient physiological status by integrating data from the respiratory, circulatory, endocrine, nervous, and musculoskeletal systems [21]. This non-specific indicator is valuable for early identification of frailty risk in elderly surgical patients and can forecast adverse perioperative outcomes, including postoperative falls, infections, and mortality [22]. The mFI-5 has found broad application across various surgical settings. Lim’s research highlighted frailty’s impact on prognosis post-coronary artery bypass grafting, influencing recovery of cardiac function and extending hospital stays [23]. Huang’s study underscored mFI-5’s high sensitivity in predicting severe complications, prolonged hospitalization, and mortality following curative resection of colorectal cancer [24]. Ma’s study involving 294 elderly gynecological patients affirmed that the mFI-5 serves as an independent predictor of postoperative complications [25]. Olson’s research in orthopedics demonstrated that spinal surgery patients with higher frailty indexes face significantly increased risks of postoperative complications, whereas controlled frailty indicators do not elevate perioperative risks [26]. Dave’s findings underscored that frail elderly patients have a notably heightened risk of postoperative deep vein thrombosis and reoperation within 30 days [27]. Kim’s extensive study of 7,540 patients following THA validated mFI-5’s accuracy in predicting complications such as pulmonary infections, readmission rates, and other post-surgical complications, establishing it as a robust independent predictive tool [28]. Similarly, Shen [29] investigation of 965 elderly hip fracture patients aged 60 to 100, identified a preoperative frailty prevalence of 13.06%, with frailty strongly correlating with the severity of complications. These research findings consistently support the inclusion of mFI-5 in preoperative planning across various surgical specialties.
Some studies suggest that the mFI-5 may have minimal impact on the hospital stay of elderly patients undergoing surgical treatment for hip fractures, possibly due to effective preoperative management [30]. Notably, delirium, another area extensively studied with mFI-5, was not assessed in this study. At our institution, comprehensive preoperative education for elderly patients includes cognitive training, family support, and enhanced sleep management with dexmedetomidine when necessary during surgery. Additionally, psychological counseling, nutritional support, and postoperative functional recovery programs are provided. Consequently, the incidence of postoperative delirium is minimized, and hospital stays are not prolonged as a result. In this study, specific complications such as lower limb deep vein thrombosis were prevalent. Several factors contribute to this high incidence: 1. Patients with comorbidities such as cardiovascular and pulmonary diseases, as well as diabetes, often exhibit increased blood viscosity [31]. 2. The combination of hip fracture and surgical trauma triggers platelet activation, vascular endothelial damage, fibrinolysis activation, and other coagulation-related changes, alongside activation of the complement cascade and innate immune response due to tissue injury. 3. Postoperative fluid deficit. 4. Lower limb edema exacerbates these risks.
CRP is synthesized by the liver in response to infection, inflammation, and tissue damage, acting as a specific inflammatory indicator [32]. It reflects the patient’s post-injury stress state and the effect of postoperative recovery. High CRP levels have been linked to various postoperative complications. Early studies indicated that elevated preoperative CRP levels significantly increased the risk of infection, cardiovascular, and cerebrovascular complications [33]. Research by Song demonstrated a positive correlation between CRP levels and postoperative delirium. Specifically, when postoperative CRP exceeded 100 mg/L, the incidence of complications and mortality rose [34]. A systematic review suggested that while CRP cannot independently diagnose infection, it serves as an indicator necessitating follow-up and analysis of pre- and post-surgery CRP levels to understand underlying causes [35]. Research by Kim concluded that elevated preoperative CRP levels, particularly >1000 mg/L, are associated with increased one-year mortality in elderly patients following hip fracture surgery [36].
Serum albumin levels serve as a critical clinical marker for assessing nutritional status in elderly patients undergoing hip fracture surgery. Variations in albumin levels, influenced by surgical stress, blood loss, immobilization, and poor nutritional intake, significantly impact patient outcomes [37]. In a retrospective analysis involving 278 elderly hip fracture patients, Wang observed that decreased perioperative serum albumin levels and increased C-reactive protein (CRP) levels were associated with adverse postoperative events [38]. Residori’s prospective study demonstrated that preoperative serum albumin levels below 30g/L posed a significantly higher risk of postoperative complications, irrespective of the surgical approach—internal fixation or arthroplasty [39]. Subsequently, Wang’s risk prediction model, based on 720 elderly hip fracture patients, identified preoperative hypoalbuminemia as an independent risk factor for postoperative pulmonary infections [40]. Kieffer’s research linked preoperative hypoalbuminemia with prolonged hospital stays and increased one-year mortality rates in this patient population [41].
While previous research has often concentrated on single factors, this study integrates the CRP-to-albumin ratio (CAR) to offer a more comprehensive preoperative assessment. CAR is increasingly recognized as a prognostic indicator useful for assessing the prognosis of malignant tumors and predicting perioperative complications [42]. In orthopedics, CAR has been identified as an independent risk factor for postoperative complications and mortality in elderly hip fracture patients, demonstrating high sensitivity and specificity [43], which aligns with the findings presented here.
Limitations of this study include its retrospective nature and relatively small sample size, which may introduce bias and instability in the findings. Future research should prioritize larger-sample, multicenter, randomized controlled trials to validate these results. Additionally, while our predictive model and nomogram incorporate established risk factors, there may be unidentified variables influencing postoperative complications following hip fracture, necessitating further investigation and validation. The applicability of the model developed here is limited to surgical patients, and its generalizability to non-surgical patients requires additional exploration. Furthermore, although various postoperative complications were segmented and analyzed statistically, such as pulmonary infections, venous thromboses, heart failure, renal failure, gastrointestinal symptoms, and urinary tract infections, the model’s integration of multiple indicators without detailed analysis of each specific complication may impact prediction accuracy.
Conclusion
This study confirms that mFI-5 and CAR are predictive of severe postoperative complications in elderly patients undergoing hip fracture surgery. The nomogram model, which integrates these indicators, demonstrates robust predictive capability for severe postoperative complications in this elderly cohort following hip fracture surgery.
Acknowledgments
Authors would like to thanks the PLOS ONE’s editor and reviewers for their valuable comments and suggestions that helped to enhance the quality of the manuscript.
References
- 1. Chen M, Zhang Y, Du Y, Hong W, Tang W, Li H, et al. Epidemiological and clinical study of hip fracture in hospitalized elderly patients in Shanghai, China. Arch Osteoporos. 2019 Mar 12;14(1):37. pmid:30868420.
- 2. Veronese N, Maggi S. Epidemiology and social costs of hip fracture. Injury. 2018 Aug;49(8):1458–1460. Epub 2018 Apr 20. pmid:29699731.
- 3. Loggers SAI, Van Lieshout EMM, Joosse P, Verhofstad MHJ, Willems HC. Prognosis of nonoperative treatment in elderly patients with a hip fracture: A systematic review and meta-analysis. Injury. 2020 Nov;51(11):2407–2413. Epub 2020 Aug 23. pmid:32907702.
- 4. de Joode SGCJ, Kalmet PHS, Fiddelers AAA, Poeze M, Blokhuis TJ. Long-term functional outcome after a low-energy hip fracture in elderly patients. J Orthop Traumatol. 2019 Apr 11;20(1):20. pmid:30976999.
- 5. Tay E. Hip fractures in the elderly: operative versus nonoperative management. Singapore Med J. 2016 Apr;57(4):178–81. pmid:27075376.
- 6. Wang Y, Jiang Q, Long H, Chen H, Wei J, Li X, et al. Trends and benefits of early hip arthroplasty for femoral neck fracture in China: a national cohort study. Int J Surg. 2024 Mar 1;110(3):1347–1355. pmid:38320106.
- 7. Schneider F, Oettle C, Runer A, Wagner M, Arora R, Lindtner RA. Retrospective evaluation of radiological and clinical outcomes after surgical treatment of proximal femoral fractures utilizing PFNA and PFNA augmented. Arch Orthop Trauma Surg. 2024 Jun;144(6):2603–2608. Epub 2024 May 3. pmid:38700673.
- 8. R Ridha M, Al-Jabri T, Stelzhammer T, Shah Z, Oragui E, Giannoudis PV. Osteosynthesis, hemiarthroplasty, total hip arthroplasty in hip fractures: All I need to know. Injury. 2024 Mar;55(3):111377. Epub 2024 Jan 24. pmid:38324951
- 9. Li L, Fang X, Gao J, Han W, Kong X. Comparison of InterTAN and PFNA internal fixation for elderly patients with intertrochanteric fracture: A retrospective cohort study. Pak J Med Sci. 2024 Mar-Apr;40(4):589–594. pmid:38544993.
- 10. Napoli N, Schwartz AV, Palermo L, Jin JJ, Wustrack R, Cauley JA, et al. Risk factors for subtrochanteric and diaphyseal fractures: the study of osteoporotic fractures. J Clin Endocrinol Metab. 2013 Feb;98(2):659–67. Epub 2013 Jan 23. pmid:23345099.
- 11. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020 Aug 8;396(10248):413–446. Epub 2020 Jul 30. Erratum in: Lancet. 2023 Sep 30;402(10408):1132. pmid:32738937.
- 12. Kassahun WT. The effects of pre-existing dementia on surgical outcomes in emergent and nonemergent general surgical procedures: assessing differences in surgical risk with dementia. BMC Geriatr. 2018 Jul 3;18(1):153. pmid:29970028.
- 13. Seitz DP, Adunuri N, Gill SS, Rochon PA. Prevalence of dementia and cognitive impairment among older adults with hip fractures. J Am Med Dir Assoc. 2011 Oct;12(8):556–564. Epub 2011 Mar 8. pmid:21450227.
- 14. Konishi M. Scoring the physical frailty phenotype of patients with heart failure. J Cachexia Sarcopenia Muscle. 2022 Feb;13(1):5–7. Epub 2021 Nov 21. pmid:34806334.
- 15. Fornaess KM, Nome PL, Aakre EK, Hegvik TA, Jammer I. Clinical frailty scale: Inter-rater reliability of retrospective scoring in emergency abdominal surgery. Acta Anaesthesiol Scand. 2022 Jan;66(1):25–29. Epub 2021 Sep 2. pmid:34425015.
- 16. Lee H, Lee E, Jang IY. Frailty and Comprehensive Geriatric Assessment. J Korean Med Sci. 2020 Jan 20;35(3):e16. pmid:31950775; PMCID: PMC6970074.
- 17. Welch SA, Ward RE, Beauchamp MK, Leveille SG, Travison T, Bean JF. The Short Physical Performance Battery (SPPB): A Quick and Useful Tool for Fall Risk Stratification Among Older Primary Care Patients. J Am Med Dir Assoc. 2021 Aug;22(8):1646–1651. Epub 2020 Nov 13. pmid:33191134.
- 18. Sieber F, Gearhart S, Bettick D, Wang NY. Edmonton frailty scale score predicts postoperative delirium: a retrospective cohort analysis. BMC Geriatr. 2022 Jul 15;22(1):585. pmid:35840875.
- 19. Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. The 5-Factor Modified Frailty Index in the Geriatric Surgical Population. Am Surg. 2021 Sep;87(9):1420–1425. Epub 2020 Dec 30. pmid:33377791.
- 20. Weaver DJ, Malik AT, Jain N, Yu E, Kim J, Khan SN. The Modified 5-Item Frailty Index: A Concise and Useful Tool for Assessing the Impact of Frailty on Postoperative Morbidity Following Elective Posterior Lumbar Fusions. World Neurosurg. 2019 Apr;124:e626–e632. Epub 2019 Jan 11. pmid:30639495.
- 21. Morisaki K, Furuyama T, Matsubara Y, Inoue K, Kurose S, Yoshino S, et al. External validation of CLI Frailty Index and assessment of predictive value of modified CLI Frailty Index for patients with critical limb ischemia undergoing infrainguinal revascularization. Vascular. 2019 Aug;27(4):405–410. Epub 2019 Mar 19. pmid:30890090.
- 22. Ondeck NT, Bohl DD, Bovonratwet P, Anandasivam NS, Cui JJ, McLynn RP, et al. Predicting Adverse Outcomes After Total Hip Arthroplasty: A Comparison of Demographics, the American Society of Anesthesiologists class, the Modified Charlson Comorbidity Index, and the Modified Frailty Index. J Am Acad Orthop Surg. 2018 Oct 15;26(20):735–743. pmid:30130353.
- 23. Lim A, Choi M, Jang Y, Lee H. Preoperative frailty based on laboratory data and postoperative health outcomes in patients undergoing coronary artery bypass graft surgery. Heart Lung. 2022 Nov-Dec;56:1–7. Epub 2022 May 19. pmid:35598421.
- 24. Huang L, Li Z, Jian M, Wu X, Chen H, Qin H, et al. Application of MFI-5 in severe complications and unfavorable outcomes after radical resection of colorectal cancer. World J Surg Oncol. 2023 Sep 26;21(1):307. pmid:37752577.
- 25. Ma HR, Liu J, Li SX, Guo X, Zhang YF, Lin JY. A retrospective study on the relationship between 5 modified frailty index (5-mFI) and postoperative complications of gynecological elderly patients undergoing abdominal surgery. BMC Anesthesiol. 2023 Apr 18;23(1):127. pmid:37072699.
- 26. Olson J, Mo KC, Schmerler J, Durand WM, Kebaish KM, Skolasky RL, et al. Impact of Controlled Versus Uncontrolled mFI-5 Frailty on Perioperative Complications After Adult Spinal Deformity Surgery. Clin Spine Surg. 2024 Mar 27. Epub ahead of print. pmid:38531820.
- 27. Dave DR, Zeiderman M, Li AI, Pereira C. Modified Frailty Index Identifies Increased Risk of Postoperative Complications in Geriatric Patients After Open Reduction Internal Fixation for Distal Radius and Ulna Fractures: Analysis of 5654 Geriatric Patients, From the 2005 to 2017 the National Surgical Quality Improvement Project Database. Ann Plast Surg. 2023 May 1;90(5S Suppl 3):S295–S304. Epub 2023 Mar 4. pmid:36880758.
- 28. Kim AG, Grits D, Zhong J, Chiu AM,Zuke W, Kamath AF. 5-Factor Modified Frailty Index as a Predictor of Outcomes After Hemiarthroplasty or Total Hip Arthroplasty for Femoral Neck Fracture. J Am Acad Orthop Surg. 2024 Jul 1;32(13):e634–e641. Epub 2024 Apr 2. pmid:38569220.
- 29. Shen Y, Hao Q, Wang Y, Chen X, Jiang J, Dong B, Guyatt G. The association between preoperative modified frailty index and postoperative complications in Chinese elderly patients with hip fractures. BMC Geriatr. 2021 Jun 16;21(1):370. pmid:34134662.
- 30. Li CJ, Wang BJ, Mu DL, Hu J, Guo C, Li XY, et al. Randomized clinical trial of intraoperative dexmedetomidine to prevent delirium in the elderly undergoing major non-cardiac surgery. Br J Surg. 2020 Jan;107(2):e123–e132. pmid:31903588.
- 31. Kakkos SK, Gohel M, Baekgaard N, Bauersachs R, Bellmunt-Montoya S, Black SA, et al. Editor’s Choice—European Society for Vascular Surgery (ESVS) 2021 Clinical Practice Guidelines on the Management of Venous Thrombosis. European journal of vascular and endovascular surgery: the official journal of the European Society for Vascular Surgery, 2021;61(1), 9–82. pmid:33334670
- 32. Liu Z, Shi H, Chen L. Prognostic role of pre-treatment C-reactive protein/albumin ratio in esophageal cancer: a meta-analysis. BMC Cancer. 2019 Nov 29;19(1):1161. pmid:31783812.
- 33. Beloosesky Y, Grinblat J, Pirotsky A, Weiss A, Hendel D. Different C-reactive protein kinetics in post-operative hip-fractured geriatric patients with and without complications. Gerontology. 2004 Jul-Aug;50(4):216–22. pmid:15258426.
- 34. Song Y, Zhang D, Wang Q, Liu Y, Chen K, Sun J, et al. Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations. Transl Psychiatry. 2024 Jan 25;14(1):57. pmid:38267405.
- 35. van den Kieboom J, Bosch P, Plate JDJ, IJpma FFA, Kuehl R, McNally MA, et al. Diagnostic accuracy of serum inflammatory markers in late fracture-related infection: a systematic review and meta-analysis. Bone Joint J. 2018 Dec;100-B(12):1542–1550. pmid:30499325.
- 36. Kim BG, Lee YK, Park HP, Sohn HM, Oh AY, Jeon YT, et al. C-reactive protein is an independent predictor for 1-year mortality in elderly patients undergoing hip fracture surgery: A retrospective analysis. Medicine (Baltimore). 2016 Oct;95(43):e5152. pmid:27787371.
- 37. Zhang Y, Lin S, Yang X, Wang R, Luo L. Prognostic value of pretreatment systemic immune-inflammation index in patients with gastrointestinal cancers. J Cell Physiol. 2019 May;234(5):5555–5563. Epub 2018 Oct 23. pmid:30353545.
- 38. Wang Y, Liu W, Li B. Perioperative albumin drop is a marker for surgical stress and correlate with clinical outcomes in the elderly patients undergoing hip arthroplasty for femoral neck fractures. Journal of Orthopaedic Surgery and Research 2024 March;15.
- 39. Residori L, Bortolami O, Di Francesco V. Hypoalbuminemia increases complications in elderly patients operated for hip fracture. Aging Clin Exp Res. 2023 May;35(5):1081–1085. Epub 2023 Mar 31. pmid:37000402.
- 40. Wang Y, Li X, Ji Y, Tian H, Liang X, Li N, Wang J. Preoperative Serum Albumin Level As A Predictor Of Postoperative Pneumonia After Femoral Neck Fracture Surgery In A Geriatric Population. Clin Interv Aging. 2019 Nov 13;14:2007–2016. pmid:32009780.
- 41. Kieffer WK, Rennie CS, Gandhe AJ. Preoperative albumin as a predictor of one-year mortality in patients with fractured neck of femur. Ann R Coll Surg Engl. 2013 Jan;95(1):26–8. pmid:23317722.
- 42. Socha MW, Malinowski B, Puk O, Wartęga M, Bernard P, Nowaczyk M, et al. C-reactive protein as a diagnostic and prognostic factor of endometrial cancer. Crit Rev Oncol Hematol. 2021 Aug;164:103419. Epub 2021 Jul 8. pmid:34245857.
- 43. Balta O, Altınayak H, Gürler Balta M, Astan S, Uçar C, Kurnaz R, et al. Can C-reactive protein-based biomarkers be used as predictive of 30-day mortality in elderly hip fractures?A retrospective study. Ulus Travma Acil Cerrahi Derg. 2022 Jun;28(6):849–856. pmid:35652864.