Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Relationship between preoperative red blood cell distribution width and postoperative pneumonia in elderly patients with hip fracture: a retrospective cohort study

Journal of Orthopaedic Surgery and Research volume 18, Article number: 253 (2023) Cite this article

Abstract

Objective

The relationship between the preoperative red blood cell distribution width and postoperative pneumonia in elderly patients with hip fractures remains unclear. This study investigated whether the preoperative red blood cell distribution width was associated with postoperative pneumonia in elderly patients with hip fractures.

Methods

Clinical data of patients with hip fractures in the Department of orthopedics of a hospital from January 2012 to December 2021 were retrospectively analyzed. A generalized additive model was used to identify both linear and nonlinear relationships between red blood cell distribution width and postoperative pneumonia. A two-piecewise linear regression model was used to calculate the saturation effect. Subgroup analyses were performed using stratified logistic regression.

Results

This study included a total of 1444 patients. The incidence of postoperative pneumonia was 6.30% (91/1444), the mean age of the patients was 77.55 ± 8.75 years, and 73.06% of them (1055/1444) were female. After full adjustment for covariates, the preoperative red blood cell distribution width showed a nonlinear relationship with postoperative pneumonia. The two-piecewise regression model showed an inflection point at 14.3%. On the left side of the inflection point, the incidence of postoperative pneumonia increased by 61% for every 1% increase in red blood cell distribution width (OR: 1.61, 95% CI 1.13–2.31, P = 0.0089). The effect size was not statistically significant on the right side of the inflection point (OR: 0.83, 95% CI 0.61–1.12, P = 0.2171).

Conclusion

The relationship between preoperative red blood cell distribution width and incidence or postoperative pneumonia was nonlinear in elderly patients with hip fractures. The incidence of postoperative pneumonia was positively correlated with red blood cell distribution width when it was < 14.3%. A saturation effect was observed when the red blood cell distribution width reached 14.3%.

Introduction

Hip fractures are becoming more frequent due to the increasing population of the elderly, and as a prevalent condition, its mortality and disability rates remain high [1]. According to a retrospective study [2] that examined hip fractures in urban Chinese adults, the incidence of hip fractures among those aged 55 years and older was 148.75 per 100,000, with the incidence in women being higher (180.72 per 100,000) than that in men (121.86 per 100,000). The incidence of hip fractures also increases with age. For elderly patients with hip fractures, surgery is the preferred treatment option, and the choice of an intramedullary nail or hip replacement depends on the type of fracture [3,4,5]. The mortality rate for patients with hip fractures remains as high as 17–27% within 1 year after surgery and more than 40% at 5 years postoperatively, despite ongoing advancements in medical care [6]. Pneumonia has been identified as the primary cause of postoperative death in elderly individuals with hip fractures [7, 8]. Postoperative pneumonia (POP) is a common complication of hip fracture in the elderly, with an incidence of 5.1–14.9% [9,10,11]. There are many risk factors for POP in patients with hip fractures; however, there is no shortage of contentious risk factors.

The red blood cell distribution width (RDW) is a measurement of variations in the size of red blood cells [12]. RDW is a reliable indicator of a disease under several conditions. RDW has been found to be associated with cardiovascular disease [13, 14] (e.g., stroke, atrial fibrillation, and heart failure), malignancy [15], and death [16, 17]. In recent years, RDW has been found to be an independent predictor of inflammatory and infectious diseases [18,19,20]. Elevated RDW has been reported to be associated with pulmonary infections after hip fracture surgery in the elderly; however, there are variations in available reports [21, 22].

To determine whether preoperative RDW and POP were related, we conducted a retrospective cohort study with Chinese patients with hip fracture as the study population.

Methods

Participants and methods

A total of 1444 patients were included in this retrospective cohort study. We collected clinical data on elderly patients with hip fracture who were `admitted to the orthopedic department of Shenzhen Second People's Hospital between January 2012 and December 2021. The collected clinical data included general patient demographic information, preoperative laboratory test results, intraoperative data, and short-term prognostic information.

Hip fractures with a confirmed radiographic diagnosis (including femoral neck or intertrochanteric fractures), age ≥ 60 years, and surgical intervention were included. Old fractures (time from injury to admission > 3 weeks), pathological fractures, periprosthetic fractures, multiple fractures, or open fractures, preoperatively diagnosed pulmonary infections, combined hematological and immune system diseases (like leukemia, rheumatoid arthritis, and systemic lupus erythematosus), and incomplete data were among the exclusion criteria. The Clinical Research Ethics Committee of Shenzhen Second People's Hospital approved this study, which was conducted in compliance with the Declaration of Helsinki and medical ethics guidelines (20,210,620,213,357,012-FS01). The data were retrospective and anonymous; therefore, the requirement for informed consent was waived. See the flowchart for details (Fig. 1).

Fig. 1
figure 1

Flow chart of patient selection

Full size image

Clinical variables and outcomes

The RDW at admission served as the independent variable and the incidence of POP served as the dependent variable. Covariates included demographic data [e.g., age, sex, body mass index (BMI), classification of fracture, etc.], comorbidities [e.g., hypertension, diabetes mellitus, coronary artery disease, stroke, chronic obstructive pulmonary disease (COPD), etc.], preoperative laboratory indices (e.g., hemoglobin, leukocyte count, neutrophil count, monocyte count, albumin, globulin, blood urea nitrogen, and serum creatinine), surgery-related variables [operation duration, anesthesia methods, operation methods, American Society of Anesthesiologists (ASA) classification, intraoperative blood loss, etc.], and short-term prognostic information [e.g., length of hospital stay, intensive care unit (ICU) admission, and in-hospital death].

Definition of pneumonia

The diagnosis of pneumonia was based on the American Thoracic Society guidelines for healthcare-associated pneumonia [23] and Guidelines for the Diagnosis and Treatment of Adult hospital-acquired pneumonia and ventilator-associated pneumonia in China (2018 edition) [24]: (1) newly developed cough, sputum production, or aggravation of symptoms of original respiratory disease and purulent sputum with or without chest pain after admission; (2) fever; (3) physical examination showing signs of lung consolidation and/or wet rales; (4) white blood cell count > 10 × 109/L or < 4 × 109/L; and (5) chest X-ray film showing patchy infiltrating shadow or interstitial changes. If a patient had any of the above items 1–4 plus item 5 and did not have other pulmonary diseases, such as tuberculosis, lung cancer, or pulmonary embolism, pneumonia was diagnosed. POP was used as the study outcome indicator from the first 24 h after surgery until discharge.

Statistical analysis

Continuous variables are expressed as mean ± standard deviation (normal distribution) or median (interquartile range) (skewed distribution), and categorical variables are expressed as frequencies or percentages.

One-way analysis of variance (ANOVA) (normal distribution), Kruskal–Wallis H (skewed distribution) test, and chi-square test (categorical variables) were used to determine any significant differences among the groups according to the RDW quartiles. To group the RDW in quartiles, the RDW data were ordered from smallest to largest and the ordered distribution was divided into four parts of equal size. Patients were classified into four groups according to quartiles of RDW, and baseline characteristics were compared among the groups. Second, univariate analysis with logistic regression was performed to evaluate the association between variables and POP. The results are expressed as the effect size with odds ratios (OR) and 95% confidence intervals (CI). Statistical analysis consisted of three main steps.

Step 1: Univariate and multivariate binary logistic regression models were used. We constructed three distinct models: a non-adjusted model (with unadjusted covariates), a minimally adjusted model (with adjusted sociodemographic variables only), and a fully adjusted model (with adjusted covariates, as shown in Table 1). Variance Infusion Factors > 10 were removed from the fully adjusted model. Step 2: Considering that logistic regression cannot handle the nonlinear relationship and the possibility of a nonlinear relationship between RDW and the incidence of POP cannot be ruled out, smooth curve fitting (penalized spline method) was used to address nonlinearity. We used a two-piecewise linear regression model to examine the saturation effect of RDW on the incidence of POP. The inflection point for the RDW was determined using “exploratory” analyses, which involved moving the trial inflection point along the predefined interval and picking up the one that gave the maximum model likelihood. We also performed a log-likelihood ratio test and compared the one-line linear regression model with a two-piecewise linear model. Step 3: To ensure the robustness of the data analysis, we converted RDW into a categorical variable based on quartiles for sensitivity analysis to verify the results of RDW as a continuous variable.

Table 1 Baseline characteristics of the 1444 participants
Full size table

Analyses were performed using the statistical software packages R (http://www.R-project.org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc, Boston, MA, USA). P values < 0.05 (two-sided) were considered statistically significant.

Results

Baseline characteristics of participants

The baseline characteristics of the participants is shown in Table 1. We divided RDW into four groups (Q1–Q4) based on the quartile principle. The mean age of the 1444 patients was 77.55 ± 8.75 years; among them, 73.06% (1055/1444) were female and the incidence of POP was 6.30% (91/1444). Compared with the Q1–Q3 group, patients in the Q4 group had a higher mean age, longer time from fracture to surgery, and length of hospital stay; higher levels of platelet count, serum creatinine, and blood urea nitrogen; and a higher percentage of POP, femoral neck fracture, coronary artery disease, hypertension, and ASA classification ≥ 3 (P < 0.05). However, patients had a lower monocyte count, hemoglobin, albumin, and albumin–globulin ratio (AGR) (P < 0.05).

Univariate analysis

Univariate analysis showed that age, sex, comorbidity, coronary heart disease, stroke, Parkinson’s disease, white blood cell (WBC) count, neutrophil count, monocyte lymphocyte ratio (MLR), neutrophil lymphocyte ratio (NLR), hemoglobin, albumin, AGR, serum creatinine, blood urea nitrogen, ASA classification, intraoperative blood loss, ICU admission, and length of hospital stay were positively correlated with the incidence of POP (Table 2).

Table 2 Univariate analysis of postoperative pneumonia
Full size table

Results of the relationship between RDW and POP

Three models were used to evaluate the relationship between RDW and POP (Table 3). In the unadjusted model, each 1% increase in RDW was associated with a 32% increase in the incidence of POP (OR, 1.32; 95% CI 1.15–1.51, P < 0.0001). In the minimally adjusted model for age and sex, an association was also identified (OR, 1.21; 95% CI 1.05–1.41; P = 0.0076). However, in adjusted model II, we did not find a linear relationship between RDW and POP (P = 0.2354).

Table 3 Relationship between RDW and POP in different models
Full size table

For sensitivity analysis, we divided RDW into four groups, and the results showed the same increasing trend in the incidence of POP in the unadjusted and minimally adjusted models (All P < 0.05), but the same trend was not found in adjusted model II (P = 0.0670).

Results of nonlinear association between RDW and POP

As shown in the smoothing spline, the RDW was found to have a nonlinear relationship with POP (Fig. 2). The two-piecewise regression model showed that the inflection point was 14.3% after adjusting for covariates (age, sex, comorbidity, coronary heart disease, stroke, Parkinson’s disease, WBC count, MLR, NLR, hemoglobin, albumin, AGR, serum creatinine, blood urea nitrogen, ASA classification, intraoperative blood loss, and ICU admission). On the left side of the inflection point, the incidence increased by 61% per 1% increase in RDW (95% CI 1.13–2.31, P = 0.0089 (Table 4). The effect size was not statistically significant on the right side of the inflection point (OR,0.83; 95% CI 0.61–1.12, P = 0.2171).

Fig. 2
figure 2

The correlation between RDW and POP in patients with hip fracture. Adjust: age, sex, comorbidity, coronary heart disease, stroke, Parkinson’s disease, WBC count, MLR, NLR, hemoglobin, albumin, AGR, serum creatinine, blood urea nitrogen, ASA classification, intraoperative blood loss, ICU admission

Full size image
Table 4 Nonlinearity explanation of RDW and POP using the two-phase linear model
Full size table

Results of subgroup analyses

We did not find any statistically significant interactions between age, sex, comorbidity, coronary heart disease, stroke, Parkinson’s disease, WBC count, NLR, MLR, hemoglobin, albumin, AGR, serum creatinine, blood urea nitrogen, ASA classification, intraoperative blood loss, and ICU admission (Table 5).

Table 5 Effect size of preoperative RDW on POP in prespecified and exploratory subgroups
Full size table

Discussion

In this study, we used a generalized linear model to investigate the association between preoperative RDW and POP in elderly patients with hip fractures who were treated at the Department of Orthopedics of the Second People's Hospital of Shenzhen over a 10-year period. A nonlinear association between preoperative RDW and the incidence of postoperative pneumonia was discovered after 1444 patients' data were analyzed. To our knowledge, no previous research has mentioned this link. We found different correlations on both sides of the inflection point. On the left side of the inflection point, RDW was positively correlated with the incidence of POP (OR: 1.61, 95% CI 1.13–2.31, P = 0.0089), while on the right side of the inflection point, the effect seemed to be saturated with no statistical significance (OR, 0.83; 95% CI 0.61–1.12; P = 0.2171). Although medical technology has made great progress, there are still many problems associated with hip fractures, such as the decline in body function [25] and the occurrence of pneumonia.

Pneumonia, a common postoperative complication in elderly patients with hip fractures [9, 10, 26], causes prolonged hospitalization, increases cost and mortality, and increases the risk of readmission [27, 28], which greatly drains social resources and is a burden to patients' lives. Therefore, clinical care providers are required to take measures to reduce the incidence of POP. Numerous studies have identified risk factors for POP. Although some of these studies have identified RDW as a risk factor for POP [21], the fundamental relationship is still difficult to establish. Through this study, we have not only shown that preoperative RDW is a risk factor for POP but also pinpointed the inflection point, and this would give clinical healthcare practitioners a reference point on which to base future actions.

RDW was initially used to differentiate between anemia and other conditions [29], and a recent study demonstrated that increased RDW is a sign of an inflammatory response [30]. Preoperative RDW was demonstrated to be a significant predictor of POP in a study on the risk factors for pneumonia following meningioma excision [31]. Similarly, in a study of POP in patients with hip fracture [22], preoperative RDW was found to have a significant impact in predicting the development of pneumonia following hip fracture surgery. Additionally, another study [32] found that in patients undergoing coronary artery bypass grafting, the incidence of postoperative pneumonia increased from 1.5 to 4.1% in patients with RDW ≥ 12.36% compared to that of those with RDW < 11.72%, with a relative risk (RR) of 2.72 when unadjusted for variables (95% CI 1.37–5.44, P = 0.004), which remained statistically significant after multifactorial correction (RR:1.95, 95% CI 1.02–3.75, P = 0.044). Consistent with these results, our study also showed a similarly increased risk of POP in elderly patients with hip fractures with increasing RDW in the unadjusted model (OR: 1.32, 95% CI 1.15–1.51, P < 0.0001). Therefore, our study confirms that preoperative RDW is associated with POP. Clinical staffs can increase their attention to RDW for early assessment of patients at high risk of developing pneumonia.

The findings of this study demonstrated a correlation between preoperative RDW and POP incidence. Increased production of pro-inflammatory cytokines may be the cause of the inflammatory response that leads to poor maturation of peripheral blood erythrocytes and an increase in naïve erythrocytes, which in turn contributes to the increase in RDW [33]. Previous studies [32] have shown that RDW is associated with the risk of postoperative inflammatory complications in patients. RDW is a valuable and sensitive marker of high levels of inflammatory activity in patients with community-acquired pneumonia [34]. RDW is a straightforward and low-cost metric that reflects the degree of red blood cell volume heterogeneity. Because less deformable red blood cells worsen tissue oxygenation, elevated RDW may be associated with worse prognosis [29]. However, the relationship between RDW and pneumonia remains unclear. RDW is connected to interleukin-6 (IL-6), C-reactive protein, and erythrocyte sedimentation rate, indicating that RDW levels may be a measure of inflammation, which is one of the mechanisms that has been considered [35,36,37]. Elevated RDW levels suggest that patients may develop organ dysfunction, which could have a negative impact on their outcomes [38, 39]. Chen et al. [21] retrospectively analyzed clinical data from elderly patients with hip fractures and found that the risk of developing POP was 5.097 times higher for patients with RDW > 14.8% than for patients with RDW < 14.8% (95% CI 2.036–12.759, P = 0.001). Another study found the same association between RDW in patients with coronavirus disease 2019 (COVID-19) and their mortality and disease severity [33, 40], with higher levels of RDW being associated with poorer outcomes in patients with COVID-19 and higher RDW in non-survivors and patients with more severe symptoms than in survivors and patients with less severe symptoms. However, Zhao et al. [41] found that preoperative RDW was not a risk factor for POP in a study of 1495 elderly patients (> 65 years of age) who underwent surgery for femoral intertrochanteric fractures. Considering that such a problem exists because of the inconsistent population studied, we conducted a retrospective cohort study adjusted for relevant variables and used a generalized additive model (GAM) to explore the relationship between preoperative RDW and POP, which was determined to be associated with POP by smoothed curve fitting.

However, our study did not find any correlation between BMI, smoking status, and POP. In contrast, Xiang [11] and Gao [42] demonstrated that BMI and smoking were independent risk factors for POP. There was no correlation between smoking and pneumonia due to the limitations of the retrospective study, in which the prevalence of smoking in patients was much lower than in the actual situation; moreover, our study population was predominantly female. Therefore, additional prospective studies should be conducted to determine whether smoking is an independent risk factor of POP.

This study has several advantages. It utilizes the GAM to elucidate the nonlinear relationship between RDW and POP. The use of GAM fits a smoothing spline and helps in understanding the real relationship between variables and outcomes. Rigorous statistical adjustment was performed for confounding factors owing to the retrospective nature of the study. Interaction analysis also improves the reliability of the data. Notably, the study found that when the preoperative RDW was < 14.3%, the incidence of POP increased by 61% for every 1% increase in RDW. In clinical practice, this association can be used as a predictor of POP risk. Therefore, the size of the preoperative RDW can be used to determine whether appropriate preventive measures, such as the employment of a dedicated ortho-geriatrician in an orthopedic department, allow for the optimization of patients’ clinical conditions [43].

Limitations

This study has some limitations. The evidence for an association between exposure and outcome was weaker in this study than in previous prospective studies. In addition, this study was conducted in a population aged > 60 years; therefore, future studies should be conducted in younger and middle-aged populations to determine the correlation between preoperative RDW and POP. This was an observational study; therefore, it was inevitably subject to confounding factors. Hence, we could only adjust for measurable confounders but not for unmeasurable ones. For example, our study did not collect information on whether the patients had consumed alcohol [44] or previously had pneumonia; furthermore, preoperative laboratory markers (e.g., C-reactive protein and erythrocyte sedimentation rate were not collected [45, 46]). These variables are risk factors for POP, and could have affected the accuracy of our results. Therefore, prospective studies with larger sample sizes and more variables are required to validate the relationship between preoperative RDW and POP. Finally, patients with preoperative pneumonia were excluded from this study. Preoperative pneumonia has been found to be associated with the risk of postoperative adverse events in patients with hip fractures [47, 48]. Therefore, future studies on preoperative pneumonia in elderly patients with hip fracture should be conducted to determine whether RDW and preoperative pneumonia have the same trend as POP.

Conclusion

There was a nonlinear relationship between preoperative RDW and the incidence of POP in elderly patients with hip fractures. The incidence of POP was positively correlated with the RDW when the RDW was below 14.3%; however, no correlation was found when the RDW reached 14.3%.

Availability of data and materials

The datasets used or analyzed during the current study are available from the Corresponding author upon reasonable request.

Abbreviations

OR:

Odds ratio

CI:

Confidence interval

POP:

Postoperative pneumonia

RDW:

Red blood cell distribution width

BMI:

Body mass index

ICU:

Intensive care unit

COPD:

Chronic obstructive pulmonary disease

WBC:

White blood cell

MLR:

Monocytes lymphocyte ratio

NLR:

Neutrophil lymphocyte ratio

AGR:

Albumin–globulin ratio

ASA:

American Society of Anesthesiologists

RR:

Relative risk

COVID-19:

Coronavirus disease 2019

GAM:

Generalized additive model

IL:

Interleukin

References

  1. Veronese N, Maggi S. Epidemiology and social costs of hip fracture. Injury. 2018;49(8):1458–60. https://doi.org/10.1016/j.injury.2018.04.015.

    Article  PubMed  Google Scholar 

  2. Zhang C, Feng J, Wang S, 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. https://doi.org/10.1371/journal.pmed.1003180.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Maffulli N, Aicale R. Proximal femoral fractures in the elderly: a few things to know, and some to forget. Medicina (Kaunas). 2022;58(10):1314. https://doi.org/10.3390/medicina58101314.

    Article  PubMed  Google Scholar 

  4. Marsillo E, Pintore A, Asparago G, et al. Cephalomedullary nailing for reverse oblique intertrochanteric fractures 31A3 (AO/OTA). Orthop Rev (Pavia). 2022;14(6):38560. https://doi.org/10.52965/001c.38560.

    Article  PubMed  Google Scholar 

  5. Gargano G, Poeta N, Oliva F, et al. Zimmer natural nail and ELOS nails in pertrochanteric fractures. J Orthop Surg Res. 2021;16(1):509. https://doi.org/10.1186/s13018-021-02634-9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang XW, He HY, Zhang JZ, et al. Long-term mortality in the elderly with hip fracture and its related risk factors. Chinese J Orthop Trauma. 2018;20(9):763–7. https://doi.org/10.3760/cma.j.issn.1671-7600.2018.09.005. (Chinese).

    Article  Google Scholar 

  7. Barceló M, Torres OH, Mascaró J, et al. Correction to: hip fracture and mortality: study of specific causes of death and risk factors. Arch Osteoporos. 2021;16(1):53. https://doi.org/10.1007/s11657-021-00912-x.

    Article  PubMed  Google Scholar 

  8. Wang X, Dai L, Zhang Y, Lv Y. Gender and low albumin and oxygen levels are risk factors for perioperative pneumonia in geriatric hip fracture patients. Clin Interv Aging. 2020;15:419–24. https://doi.org/10.2147/CIA.S241592.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhang X, Shen ZL, Duan XZ, et al. Postoperative pneumonia in geriatric patients with a hip fracture: incidence, risk factors and a predictive nomogram. Geriatr Orthop Surg Rehabil. 2022;13:21514593221083824. https://doi.org/10.1177/21514593221083824.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Shin KH, Kim JJ, Son SW, et al. Early postoperative hypoalbuminaemia as a risk factor for postoperative pneumonia following hip fracture surgery. Clin Interv Aging. 2020;15:1907–15. https://doi.org/10.2147/CIA.S272610.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Xiang G, Dong X, Xu T, et al. A nomogram for prediction of postoperative pneumonia risk in elderly hip fracture patients. Risk Manag Healthc Policy. 2020;13:1603–11. https://doi.org/10.2147/RMHP.S270326.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lippi G, Plebani M. Red blood cell distribution width (RDW) and human pathology. One size fits all. Clin Chem Lab Med. 2014;52(9):1247–9. https://doi.org/10.1515/cclm-2014-0585.

    Article  CAS  PubMed  Google Scholar 

  13. Li N, Zhou H, Tang Q. Red blood cell distribution width: a novel predictive indicator for cardiovascular and cerebrovascular diseases. Dis Markers. 2017;2017:7089493. https://doi.org/10.1155/2017/7089493.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Feng GH, Li HP, Li QL, et al. Red blood cell distribution width and ischaemic stroke. Stroke Vasc Neurol. 2017;2(3):172–5. https://doi.org/10.1136/svn-2017-000071.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Montagnana M, Danese E. Red cell distribution width and cancer. Ann Transl Med. 2016;4(20):399. https://doi.org/10.21037/atm.2016.10.50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tonelli M, Wiebe N, James MT, et al. Red cell distribution width associations with clinical outcomes: a population-based cohort study. PLoS ONE. 2019;14(3):e0212374. https://doi.org/10.1371/journal.pone.0212374.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Li J, Yang X, Ma J, et al. Relationship of red blood cell distribution width with cancer mortality in hospital. Biomed Res Int. 2018;2018:8914617. https://doi.org/10.1155/2018/8914617.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Camon S, Quiros C, Saubi N, et al. Full blood count values as a predictor of poor outcome of pneumonia among HIV-infected patients. BMC Infect Dis. 2018;18(1):189. https://doi.org/10.1186/s12879-018-3090-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ku NS, Kim HW, Oh HJ, et al. Red blood cell distribution width is an independent predictor of mortality in patients with gram-negative bacteremia. Shock. 2012;38(2):123–7. https://doi.org/10.1097/SHK.0b013e31825e2a85.

    Article  PubMed  Google Scholar 

  20. Wang B, Gong Y, Ying B, et al. Relation between red cell distribution width and mortality in critically ill patients with acute respiratory distress syndrome. Biomed Res Int. 2019;2019:1942078. https://doi.org/10.1155/2019/1942078.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chen XJ, Zhu XP, Tian MM, et al. Construction and validation of a prediction model for pulmonary infection in elderly patients with hip fracture after. Chin J Nurs. 2021;56(5):659–66. https://doi.org/10.3761/j.issn.0254-1769.2021.05.003.(Chinese).

    Article  Google Scholar 

  22. Lv H, Yin P, Long A, et al. Clinical characteristics and risk factors of postoperative pneumonia after hip fracture surgery: a prospective cohort study. Osteoporos Int. 2016;27(10):3001–9. https://doi.org/10.1007/s00198-016-3624-5.

    Article  CAS  PubMed  Google Scholar 

  23. American Thoracic Society; Infectious Diseases Society of America. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med. 2005;171(4):388–416. https://doi.org/10.1164/rccm.200405-644ST.

    Article  Google Scholar 

  24. Respiratory Branch Chinese Medical Association. Guidelines for diagnosis and treatment of acquired pneumonia and respiratory associated pneumonia in Chinese adult hospitals. Chinese J Tuberc Respir. 2018;41(4):255–80. https://doi.org/10.3760/cma.j.issn.1001-0939.2018.04.006. (Chinese).

    Article  Google Scholar 

  25. Chen YP, Kuo YJ, Hung SW, et al. Loss of skeletal muscle mass can be predicted by sarcopenia and reflects poor functional recovery at one year after surgery for geriatric hip fractures. Injury. 2021;52(11):3446–52. https://doi.org/10.1016/j.injury.2021.08.007.

    Article  PubMed  Google Scholar 

  26. Byun SE, Shon HC, Kim JW, et al. Risk factors and prognostic implications of aspiration pneumonia in older hip fracture patients: a multicenter retrospective analysis. Geriatr Gerontol Int. 2019;19(2):119–23. https://doi.org/10.1111/ggi.13559.

    Article  PubMed  Google Scholar 

  27. Bohl DD, Sershon RA, Saltzman BM, et al. Incidence, risk factors, and clinical implications of pneumonia after surgery for geriatric hip fracture. J Arthroplast. 2018;33(5):1552–1556.e1. https://doi.org/10.1016/j.arth.2017.11.068.

    Article  Google Scholar 

  28. Salarbaks AM, Lindeboom R, Nijmeijer W. Pneumonia in hospitalized elderly hip fracture patients: the effects on length of hospital-stay, in-hospital and thirty-day mortality and a search for potential predictors. Injury. 2020;51(8):1846–50. https://doi.org/10.1016/j.injury.2020.05.017.

    Article  CAS  PubMed  Google Scholar 

  29. Salvagno GL, Sanchis-Gomar F, Picanza A, et al. Red blood cell distribution width: a simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52(2):86–105. https://doi.org/10.3109/10408363.2014.992064.

    Article  PubMed  Google Scholar 

  30. Olafsson HB, Sigurdarson GA, Christopher KB, et al. A retrospective cohort study on the association between elevated preoperative red cell distribution width and all-cause mortality after noncardiac surgery. Br J Anaesth. 2020;124(6):718–25. https://doi.org/10.1016/j.bja.2020.02.009.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zuo MR, Liang RF, Li M, et al. A comprehensive study of risk factors for post-operative pneumonia following resection of meningioma. BMC Cancer. 2019;19(1):100. https://doi.org/10.1186/s12885-019-5271-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. van Koeverden ID, den Ruijter HM, Scholtes VPW, et al. A single preoperative blood test predicts postoperative sepsis and pneumonia after coronary bypass or open aneurysm surgery. Eur J Clin Invest. 2019;49(3):e13055. https://doi.org/10.1111/eci.13055.

    Article  CAS  PubMed  Google Scholar 

  33. Wang C, Zhang H, Cao X, et al. Red cell distribution width (RDW): a prognostic indicator of severe COVID-19. Ann Transl Med. 2020;8(19):1230. https://doi.org/10.21037/atm-20-6090.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Braun E, Domany E, Kenig Y, et al. Elevated red cell distribution width predicts poor outcome in young patients with community acquired pneumonia. Crit Care. 2011;15(4):R194. https://doi.org/10.1186/cc10355.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hu L, Li M, Ding Y, et al. Prognostic value of RDW in cancers: a systematic review and meta-analysis. Oncotarget. 2017;8(9):16027–35. https://doi.org/10.18632/oncotarget.13784.

    Article  PubMed  Google Scholar 

  36. Lippi G, Targher G, Montagnana M, et al. Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients [published correction appears in Arch Pathol Lab Med. 2009 Aug;133(8):1186]. Arch Pathol Lab Med. 2009;133(4):628–632. https://doi.org/10.5858/133.4.628

  37. Vayá A, Sarnago A, Fuster O, et al. Influence of inflammatory and lipidic parameters on red blood cell distribution width in a healthy population. Clin Hemorheol Microcirc. 2015;59(4):379–85. https://doi.org/10.3233/CH-141862.

    Article  CAS  PubMed  Google Scholar 

  38. Hu Z, Sun Y, Wang Q, et al. Red blood cell distribution width is a potential prognostic index for liver disease. Clin Chem Lab Med. 2013;51(7):1403–8. https://doi.org/10.1515/cclm-2012-0704.

    Article  CAS  PubMed  Google Scholar 

  39. Wang PF, Song SY, Guo H, et al. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: a meta-analysis of 49 studies. J Cancer. 2019;10(18):4305–17. https://doi.org/10.7150/jca.31598.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lee JJ, Montazerin SM, Jamil A, et al. Association between red blood cell distribution width and mortality and severity among patients with COVID-19: a systematic review and meta-analysis. J Med Virol. 2021;93(4):2513–22. https://doi.org/10.1002/jmv.26797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhao K, Zhang J, Li J, et al. In-hospital postoperative pneumonia following geriatric intertrochanteric fracture surgery: incidence and risk factors. Clin Interv Aging. 2020;15:1599–609. https://doi.org/10.2147/CIA.S268118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gao YC, Zhang YW, Shi L, et al. What are risk factors of postoperative pneumonia in geriatric individuals after hip fracture surgery: a systematic review and meta-analysis. Orthop Surg. 2023;15(1):38–52. https://doi.org/10.1111/os.1363.

    Article  PubMed  Google Scholar 

  43. Quaranta M, Miranda L, Oliva F, et al. Haemoglobin and transfusions in elderly patients with hip fractures: the effect of a dedicated orthogeriatrician. J Orthop Surg Res. 2021;16(1):387. https://doi.org/10.1186/s13018-021-02524-0.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Simou E, Britton J, Leonardi-Bee J. Alcohol and the risk of pneumonia: a systematic review and meta-analysis. BMJ Open. 2018;8(8):e022344. https://doi.org/10.1136/bmjopen-2018-022344.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Cheng X, Liu Y, Wang W, et al. Preoperative risk factor analysis and dynamic online nomogram development for early infections following primary hip arthroplasty in geriatric patients with hip fracture. Clin Interv Aging. 2022;17:1873–83. https://doi.org/10.2147/CIA.S392393.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ali A, Abbasi AS, Amjad T, et al. Erythrocyte sedimentation rate and C-reactive protein as marker of acute versus chronic medical conditions. J Ayub Med Coll Abbottabad. 2019;31(1):39–45.

    PubMed  Google Scholar 

  47. Patterson JT, Bohl DD, Basques BA, et al. Does preoperative pneumonia affect complications of geriatric hip fracture surgery? Am J Orthop (Belle Mead NJ). 2017;46(3):E177–85.

    PubMed  Google Scholar 

  48. Shen JW, Zhang PX, An YZ, et al. Prognostic implications of preoperative pneumonia for geriatric patients undergoing hip fracture surgery or arthroplasty. Orthop Surg. 2020;12(6):1890–9. https://doi.org/10.1111/os.12830.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Dr. Xinglin Chen for providing guidance and for modifying the study design and content. We also thank Shenzhen Second People's Hospital for supporting this study financially.

Funding

This study was supported by the hospital-level clinical research project of Shenzhen Second People's Hospital, Project No. (20213357012).

Author information

Author notes

  1. Daxue Zhang and Yu Zhang contributed to the work equally and should be regarded as co-first authors.

Authors and Affiliations

  1. School of Nursing, Anhui Medical University, Hefei, China

    Daxue Zhang, Shiwei Yang, Lixin Sun & Ning Zhang

  2. Department of Orthopedics, Zhejiang Hospital, Hangzhou, China

    Yu Zhang

  3. Teaching Office, Shenzhen Second People’s Hospital, 3002 Sungang Road West, Futian District, Shenzhen City, 518000, China

    Shiwei Yang

  4. Department of Bone and Joint Bone Oncology, Shenzhen Second People’s Hospital, Shenzhen, China

    Shaocai Huang

  5. First Affiliated Hospital of Shenzhen University, Shenzhen, China

    Shiwei Yang

Authors
  1. Daxue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiwei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lixin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shaocai Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DZ analyzed the data and wrote the first draft of the manuscript. YZ contributed to statistical analysis and improved the manuscript. SY provided guidance, contributed to the data, and improved the manuscript. NZ, LS, and SH offered suggestions for content modification. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Shiwei Yang.

Ethics declarations

Ethics approval and consent to participate

This was a retrospective study of patients admitted to Shenzhen Second People's Hospital. The data are retrospective and anonymous, and the requirement for informed consent was therefore waived. This study was approved by the Clinical Research Ethics Committee of Shenzhen Second People's Hospital (20210620213357012-FS01) and the protocol was registered with ClinicalTrials.gov (registration number: ChiCTR2100047560). This study was conducted in accordance with the principles of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Zhang, Y., Yang, S. et al. Relationship between preoperative red blood cell distribution width and postoperative pneumonia in elderly patients with hip fracture: a retrospective cohort study. J Orthop Surg Res 18, 253 (2023). https://doi.org/10.1186/s13018-023-03732-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13018-023-03732-6

Keywords

Journal of Orthopaedic Surgery and Research

ISSN: 1749-799X