Abstract
Background
Hip fractures are a common and serious health concern in older adults. The ability to perform basic activities of daily living (BADL) is a key indicator of long-term functional status. This study aimed to evaluate the prognostic value of the Prognostic Nutritional Index (PNI) in identifying older patients at risk of moderate-to-severe BADL dependence two years after hip fracture surgery.
Methods
A total of 526 older patients with hip fractures were included in the analysis. Baseline characteristics and preoperative indicators were systematically examined. Univariate and multivariate logistic regression models were applied to identify independent factors associated with moderate-to-severe BADL dependence. Restricted cubic spline (RCS) analysis was used to explore potential nonlinear relationships, while receiver operating characteristic (ROC) curves evaluated the discriminatory performance of PNI. Subgroup analysis was performed to explore population differences. Propensity score matching (PSM) was performed to enhance robustness and control for confounding factors.
Results
The median age of the cohort was 73.0 years (interquartile range: 67.0–79.0), and 69.96% of participants were female. A total of 43 patients (8.17%) exhibited moderate-to-severe BADL dependence at two years postoperatively. Multivariate logistic regression revealed that higher preoperative PNI was independently associated with a lower risk of long-term dependence (OR: 0.90; 95% CI:0.82–0.99; P = 0.026). ROC analysis demonstrated a moderate discriminatory ability (AUC = 0.659), and RCS analysis indicated a linear relationship between PNI and BADL dependence (P-nonlinearity = 0.674). The association remained robust after PSM.
Conclusion
Preoperative PNI is an independent predictor of long-term functional dependence in older patients undergoing hip fracture surgery. These findings underscore the importance of nutritional assessment and optimization in preoperative care to improve postoperative functional outcomes in this vulnerable population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06278-w.
Keywords: Hip fracture, Prognostic nutritional index, Basic activities of daily living, Older adults
Background
Hip fractures in older adults represent a major health issue, primarily resulting from falls in individuals with osteoporosis-induced fragile bones [1]. The American Academy of Orthopaedic Surgeons reports over 300,000 hip fractures annually in the United States alone, with this number rising globally. Hip fractures rank among the top ten causes of disability worldwide, affecting 4.5 million people each year-a figure projected to increase to 21 million over the next four decades [2]. This escalating burden exerts significant pressure on healthcare systems, with annual treatment costs in the U.S. expected to reach $9.8 billion by 2040 [3].
The impact of hip fractures on individual patients is severe, often leading to complications such as pressure ulcers, deep vein thrombosis, pneumonia, and cognitive decline due to prolonged immobility [4, 5]. Timely surgical intervention is essential to restore mobility and mitigate these risks [6]. Despite advances in surgical techniques and postoperative care, many patients do not regain functional independence [6].
Restoration of basic activities of daily living (BADL) is a critical measure of quality of life and long-term outcomes following hip fracture surgery. Identifying modifiable preoperative factors that can enhance postoperative functional status is critical for improving outcomes in this vulnerable population [7, 8]. Nutritional status is a well-documented determinant of surgical recovery, especially in frail, older patients [9]. The Prognostic Nutritional Index (PNI), initially developed for cancer prognosis, has demonstrated predictive value in various malignancies, including prostate, liver, gastric, and colorectal cancers [10–15]. Its applicability has extended to non-cancer conditions such as Kawasaki disease, heart failure, and post-stroke cognitive impairment, underscoring its broader clinical relevance [16–18].
However, the potential role of PNI in predicting long-term functional dependence after hip fracture surgery remains inadequately studied. This study aims to investigate the association between preoperative PNI and postoperative BADL status, as assessed by the Barthel Index (BI). We hypothesize that higher preoperative PNI scores are associated with a lower risk of moderate-to-severe functional dependence two years after surgery. By elucidating this relationship, our findings may support the integration of nutritional assessment and optimization into preoperative care protocols for older adults with hip fractures.
Methods
Patient selection
This retrospective study included older patients (≥ 60 years) who underwent surgical treatment for hip fractures, encompassing femoral neck and intertrochanteric fractures, at The First Affiliated Hospital of Soochow University between January 2018 and December 2020. Exclusion criteria included: (1) non-recent fractures; (2) fractures resulting from high-energy trauma, such as falls from heights or motor vehicle accidents; (3) pre-fracture moderate to severe functional dependence resulting from major comorbidities, including cerebrovascular disease, Alzheimer’s disease, Parkinson’s disease, epilepsy, mental disorders, or malignancies; and (4) loss to follow-up or refusal to participate. The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of The First Affiliated Hospital of Soochow University (Approval No. 2025060). Informed consent was obtained from all participants at the time of follow-up.
This research was funded by the National Natural Science Foundation of China (No. 82102619), the Jiangsu Provincial Traditional Chinese Medicine Science and Technology Development Project (No. ZD202232), and the Extracurricular Academic Research Fund of Soochow University (No. KY20201019B).
Baseline characteristics were extracted from medical records, including age, gender, body mass index (BMI), fracture type (femoral neck vs. intertrochanteric fracture), and surgical procedure (internal fixation vs. arthroplasty). Preoperative comorbidities, including hypertension, diabetes, cardiovascular disease (CVD), and chronic respiratory diseases (CRD), were documented. Data on postoperative Denosumab treatment was also collected. Preoperative hematological parameters, measured one day before surgery, included serum albumin, lymphocyte count, hemoglobin (Hb), red blood cell count (RBC), white blood cell count (WBC), neutrophil count, platelet count (PLT), alanine transaminase (ALT), aspartate transaminase (AST), serum calcium, serum phosphorus, triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and glucose. Other perioperative clinical parameters were collected, including length of hospital stay, duration of anesthesia, American Society of Anesthesiologists (ASA) grade, and perioperative complications including postoperative thrombosis and pneumonia. In addition, information on postoperative denosumab treatment and the occurrence of refracture within two years after surgery were collected.
Evaluation of PNI and BADL
Preoperative nutritional status was evaluated using the PNI, calculated as: serum albumin (g/L) + 5 × total lymphocyte count (/L) [19]. To minimize the influence of extreme values and maintain consistency with previous methodologies [20–23], PNI was divided into tertiles based on the population distribution. The PNI range for the T1 group was 32.8 to 42.2, for the T2 group was 42.2 to 45.8, and for the T3 group was 45.8 to 66.1.
Functional status was assessed using the BI, a widely used instrument to evaluate BADL in older adults [24, 25]. We used a BI score of 60 points as the threshold to define moderate-to-severe dependence in BADL, based on regionally validated classification criteria applied in Chinese older populations [26]. Accordingly, participants were divided into two groups: those with a BI score > 60 were considered to have attained BADL independence, and those with a score ≤ 60 were classified as having persistent moderate-to-severe dependence at 2-year follow-up.
Statistical analysis
Statistical analyses were performed using R software (version 4.2.2) and MSTATA software (www.mstata.com). Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR), while categorical variables were presented as frequencies and percentages. Comparisons of continuous variables between groups were conducted using the Student’s t-test or the Mann-Whitney U test, depending on data distribution. The Kruskal-Wallis test was applied for comparisons across PNI tertiles. Categorical variables were analyzed using Chi-square or Fisher’s exact test, as appropriate. Univariate logistic regression was conducted to screen for potential influencing variables, with a significance threshold set at P < 0.05. To detect and eliminate variables with severe multicollinearity, variance inflation factor (VIF) analysis was performed prior to multivariate modeling. Subsequently, multivariate logistic regression was performed to identify independent predictors of moderate-to-severe dependence in BADL [27]. Subgroup analyses were performed according to gender, hypertension, diabetes, Denosumab treatment, and the occurrence of refracture within two years after surgery. Restricted cubic spline (RCS) analysis was used to assess linear or nonlinear relationships between PNI and the risk of moderate-to-severe ADL dependence, while receiver operating characteristic (ROC) curves were used to evaluate the predictive value of PNI [28, 29]. To minimize confounding, propensity score matching (PSM) was conducted using the nearest neighbor algorithm with a caliper of 0.20 and a 1:4 matching ratio. Matching variables included age, gender, BMI, type of fracture, operation type, diabetes, Hb, RBC and refracture, ensuring covariate balance (|SMD| < 0.1) [30, 31]. Reanalysis of the matched data confirmed the robustness of the results. Statistical significance was defined as a two-sided P < 0.05.
Results
As illustrated in Fig. 1 and 526 subjects were included in this study after applying the inclusion and exclusion criteria. The age of the patients ranged from 60 to 85 years, with 69.96% being female. Among the included patients, 59.89% had femoral neck fractures, and 51.71% underwent internal fixation surgery. According to the level of functional dependence in BADL assessed two years after surgery, patients were categorized into two groups: those with functional independence (BI > 60) and those with moderate to severe postoperative dependence (BI ≤ 60). Forty-three patients (8.2% of the study population) were classified as having moderate to severe postoperative dependence.
Fig. 1.
Flowchart of participant recruitment and exclusion
Baseline characteristics
As shown in Supplementary Table 1, no significant differences were found between the BI > 60 group and the BI ≤ 60 group regarding gender, fracture type, surgical type, preoperative comorbidities (including hypertension, CVD, and CRD), Denosumab treatment, length of hospital stay, anesthesia time, perioperative thrombosis, pneumonia, or various hematological parameters such as WBC, neutrophil count, PLT, ALT, AST, serum phosphorus, lipid profiles, and blood glucose (all P > 0.05). However, patients with BI ≤ 60 were significantly older (76.00 [71.00, 81.00] vs. 73.00 [67.00, 79.00] years, P = 0.005), and had a higher proportion of individuals with low BMI (18.60% vs. 6.63%, P = 0.005) and diabetes (34.88% vs. 18.63%, P = 0.011). They also had a higher prevalence of ASA class III-IV (32.56% vs. 16.15%, P = 0.014) and increased incidence of refracture within two years postoperatively (27.91% vs. 15.94%, P = 0.045). Additionally, hemoglobin (111.44 ± 16.98 vs. 119.23 ± 16.85 g/L, P = 0.006), RBC (3.68 vs. 3.96 × 10¹²/L, P = 0.010), and serum calcium (2.13 vs. 2.18 mmol/L, P = 0.046) were significantly lower in the BI ≤ 60 group. Notably, preoperative PNI was significantly higher in patients with BI > 60 (44.10 vs. 41.25, P < 0.001).
As presented in Table 1, higher PNI levels were significantly associated with younger age, higher BMI, and a greater proportion of females (P < 0.01 for all). Patients in the highest PNI tertile also exhibited a higher prevalence of femoral neck fractures and lower rates of CRD (P < 0.001). Hematological parameters, including Hb, RBC, and serum calcium, increased significantly across PNI tertiles (P < 0.001). Additionally, lipid profiles such as TC, LDL-C, and HDL-C were elevated in patients with higher PNI levels (P < 0.001). Furthermore, patients in the lowest PNI tertile had a higher incidence of perioperative pneumonia compared to the middle and highest tertiles (7.47% vs. 2.29% and 3.39%, P = 0.044). Notably, the proportion of patients with moderate-to-severe dependence in BADL at two years postoperatively decreased from 12.64% in the lowest PNI tertile to 3.95% in the highest tertile (P = 0.012), suggesting a strong association between higher preoperative PNI and better postoperative functional status.
Table 1.
Baseline characteristics of the participants according to the PNI
| Characteristic | PNI index levels | P-value | |||
|---|---|---|---|---|---|
| Overall, N = 526 | (32.8,42.2), N = 174 | (42.2,45.8), N = 175 | (45.8,66.1), N = 177 | ||
| Age (years) | 73.00 (67.00, 79.00) | 77.00 (71.00, 82.00) | 73.00 (67.00, 78.00) | 69.00 (64.00, 75.00) | < 0.001 |
| Gender | 0.007 | ||||
| Female | 368 (69.96%) | 111 (63.79%) | 118 (67.43%) | 139 (78.53%) | |
| Male | 158 (30.04%) | 63 (36.21%) | 57 (32.57%) | 38 (21.47%) | |
| BMI (kg/m2) | 0.009 | ||||
| < 18.5 | 40 (7.60%) | 22 (12.64%) | 10 (5.71%) | 8 (4.52%) | |
| ≥ 25 | 81 (15.40%) | 28 (16.09%) | 20 (11.43%) | 33 (18.64%) | |
| 18.5–25 | 405 (77.00%) | 124 (71.26%) | 145 (82.86%) | 136 (76.84%) | |
| Type of fracture | < 0.001 | ||||
| Femoral neck fracture | 315 (59.89%) | 86 (49.43%) | 106 (60.57%) | 123 (69.49%) | |
| Intertrochanteric fracture | 211 (40.11%) | 88 (50.57%) | 69 (39.43%) | 54 (30.51%) | |
| Operation type | 0.247 | ||||
| Arthroplasty | 254 (48.29%) | 75 (43.10%) | 89 (50.86%) | 90 (50.85%) | |
| Internal fixation | 272 (51.71%) | 99 (56.90%) | 86 (49.14%) | 87 (49.15%) | |
| Hypertension | 0.258 | ||||
| No | 243 (46.20%) | 86 (49.43%) | 84 (48.00%) | 73 (41.24%) | |
| Yes | 283 (53.80%) | 88 (50.57%) | 91 (52.00%) | 104 (58.76%) | |
| Diabetes | 0.516 | ||||
| No | 421 (80.04%) | 144 (82.76%) | 139 (79.43%) | 138 (77.97%) | |
| Yes | 105 (19.96%) | 30 (17.24%) | 36 (20.57%) | 39 (22.03%) | |
| CVD | 0.730 | ||||
| No | 480 (91.25%) | 157 (90.23%) | 162 (92.57%) | 161 (90.96%) | |
| Yes | 46 (8.75%) | 17 (9.77%) | 13 (7.43%) | 16 (9.04%) | |
| CRD | < 0.001 | ||||
| No | 501 (95.25%) | 157 (90.23%) | 170 (97.14%) | 174 (98.31%) | |
| Yes | 25 (4.75%) | 17 (9.77%) | 5 (2.86%) | 3 (1.69%) | |
| Denosumab treatment | 0.802 | ||||
| No | 221 (42.02%) | 72 (41.38%) | 77 (44.00%) | 72 (40.68%) | |
| Yes | 305 (57.98%) | 102 (58.62%) | 98 (56.00%) | 105 (59.32%) | |
| Hb (g/L) | 118.59 ± 16.98 | 111.08 ± 18.31 | 119.39 ± 15.48 | 125.19 ± 13.91 | < 0.001 |
RBC (
/L)
|
3.94 (3.53, 4.27) | 3.68 (3.19, 4.11) | 3.94 (3.55, 4.26) | 4.11 (3.77, 4.38) | < 0.001 |
WBC (
/L)
|
7.98 (6.67, 9.60) | 7.58 (6.20, 8.80) | 7.84 (6.65, 9.42) | 8.52 (7.27, 10.09) | < 0.001 |
Neutrophil (
/L)
|
6.01 (4.82, 7.42) | 5.90 (4.64, 7.03) | 5.90 (4.82, 7.50) | 6.27 (5.22, 7.87) | 0.076 |
PLT (
/L)
|
162.00 (131.00, 202.00) | 147.50 (116.00, 199.00) | 164.00 (131.00, 201.00) | 170.00 (140.00, 210.00) | < 0.001 |
| ALT (U/L) | 14.00 (10.90, 19.10) | 13.60 (10.30, 19.00) | 13.50 (10.70, 18.40) | 15.30 (11.50, 19.60) | 0.053 |
| AST (U/L) | 19.40 (15.70, 23.70) | 19.35 (15.20, 24.50) | 19.70 (16.70, 23.50) | 19.30 (15.30, 23.00) | 0.770 |
| Serum calcium (mmol/L) | 2.18 (2.10, 2.24) | 2.11 (2.06, 2.17) | 2.18 (2.12, 2.23) | 2.23 (2.17, 2.30) | < 0.001 |
| Serum phosphorus (mmol/L) | 1.04 (0.90, 1.15) | 1.01 (0.90, 1.17) | 1.04 (0.89, 1.16) | 1.06 (0.92, 1.14) | 0.357 |
| TG (mmol/L) | 0.97 (0.77, 1.28) | 0.95 (0.73, 1.18) | 0.97 (0.75, 1.25) | 1.02 (0.84, 1.43) | 0.003 |
| TC (mmol/L) | 4.27 (3.78, 4.88) | 3.86 (3.42, 4.39) | 4.37 (3.94, 4.87) | 4.57 (4.05, 5.18) | < 0.001 |
| LDL-C (mmol/L) | 2.38 (1.96, 2.93) | 2.17 (1.75, 2.55) | 2.44 (1.99, 2.97) | 2.57 (2.13, 3.12) | < 0.001 |
| HDL-C (mmol/L) | 1.19 (1.01, 1.43) | 1.12 (0.98, 1.28) | 1.21 (1.07, 1.44) | 1.25 (1.04, 1.48) | < 0.001 |
| Glucose (mmol/L) | 5.97 (5.38, 6.84) | 5.96 (5.32, 6.79) | 5.94 (5.37, 6.70) | 5.99 (5.46, 7.12) | 0.466 |
| Length of hospital stay (days) | 10.90 (7.98, 14.70) | 10.86 (7.88, 14.85) | 11.01 (8.01, 14.72) | 10.76 (8.60, 14.01) | 0.949 |
| Anesthesia time (minutes) | 117.50 (95.00, 150.00) | 120.00 (95.00, 150.00) | 120.00 (95.00, 150.00) | 115.00 (94.00, 148.00) | 0.481 |
| ASA | 0.054 | ||||
| I | 28 (5.32%) | 8 (4.60%) | 8 (4.57%) | 12 (6.78%) | |
| II | 406 (77.19%) | 123 (70.69%) | 141 (80.57%) | 142 (80.23%) | |
| III | 88 (16.73%) | 41 (23.56%) | 24 (13.71%) | 23 (12.99%) | |
| IV | 4 (0.76%) | 2 (1.15%) | 2 (1.14%) | 0 (0.00%) | |
| Perioperative thrombosis | 0.076 | ||||
| No | 470 (89.35%) | 148 (85.06%) | 161 (92.00%) | 161 (90.96%) | |
| Yes | 56 (10.65%) | 26 (14.94%) | 14 (8.00%) | 16 (9.04%) | |
| Pneumonia | 0.044 | ||||
| No | 503 (95.63%) | 161 (92.53%) | 171 (97.71%) | 171 (96.61%) | |
| Yes | 23 (4.37%) | 13 (7.47%) | 4 (2.29%) | 6 (3.39%) | |
| Refracture | 0.660 | ||||
| No | 437 (83.08%) | 141 (81.03%) | 148 (84.57%) | 148 (83.62%) | |
| Yes | 89 (16.92%) | 33 (18.97%) | 27 (15.43%) | 29 (16.38%) | |
| BADL dependence | 0.012 | ||||
| Yes | 483 (91.83%) | 152 (87.36%) | 161 (92.00%) | 170 (96.05%) | |
| No | 43 (8.17%) | 22 (12.64%) | 14 (8.00%) | 7 (3.95%) | |
Logistic regression analysis
As depicted in Supplementary Fig. 1, VIF analysis was performed to exclude covariates exhibiting variables with multicollinearity, including LDL-C, neutrophils, WBC, and TC. The univariate analysis (Table 2) identified several factors significantly associated with moderate-to-severe BADL dependence: advanced age (OR: 1.07; 95% CI: 1.02–1.12; P = 0.005), low BMI (OR: 2.82; 95% CI: 1.20–6.61; P = 0.017), diabetes (OR: 2.34; 95% CI: 1.20–4.56; P = 0.013), reduced Hb levels (OR: 0.97; 95% CI: 0.96–0.99; P = 0.004), reduced RBC levels (OR: 0.51; 95% CI: 0.30–0.85; P = 0.010), refracture (OR: 2.04; 95% CI: 1.00-4.15; P = 0.049) and lower PNI (OR: 0.86; 95% CI: 0.80–0.94; P < 0.001). In the multivariate analysis, diabetes (OR: 2.83; 95% CI: 1.38–5.81; P = 0.005), refracture (OR: 2.35; 95% CI: 1.10–5.03; P = 0.027) and lower PNI (OR: 0.90; 95% CI: 0.82–0.99; P = 0.026) remained independent predictors of moderate-to-severe BADL dependence.
Table 2.
Univariate and multivariate logistic regression analysis
| Characteristic | Univariable | Multivariable | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| N | Event N | OR | 95% CI | P-value | N | Event N | OR | 95% CI | P-value | |
| Age (years) | 526 | 43 | 1.07 | 1.02, 1.12 | 0.005 | 526 | 43 | 1.03 | 0.98, 1.09 | 0.220 |
| Gender | ||||||||||
| Female | 368 | 29 | — | — | 368 | 29 | — | — | ||
| Male | 158 | 14 | 1.14 | 0.58, 2.21 | 0.707 | 158 | 14 | 1.12 | 0.51, 2.46 | 0.787 |
| BMI (kg/m2) | ||||||||||
| < 18.5 | 405 | 33 | — | — | 405 | 33 | — | — | ||
| ≥ 25 | 40 | 8 | 2.82 | 1.20, 6.61 | 0.017 | 40 | 8 | 1.98 | 0.78, 5.04 | 0.153 |
| 18.5–25 | 81 | 2 | 0.29 | 0.07, 1.21 | 0.090 | 81 | 2 | 0.26 | 0.06, 1.15 | 0.076 |
| Type of fracture | ||||||||||
| Femoral neck fracture | 315 | 23 | — | — | 315 | 23 | — | — | ||
| Intertrochanteric fracture | 211 | 20 | 1.33 | 0.71, 2.49 | 0.373 | 211 | 20 | 0.90 | 0.22, 3.68 | 0.887 |
| Operation type | ||||||||||
| Arthroplasty | 254 | 20 | — | — | 254 | 20 | — | — | ||
| Internal fixation | 272 | 23 | 1.08 | 0.58, 2.02 | 0.808 | 272 | 23 | 0.81 | 0.22, 3.02 | 0.754 |
| Hypertension | ||||||||||
| No | 243 | 20 | — | — | ||||||
| Yes | 283 | 23 | 0.99 | 0.53, 1.84 | 0.966 | |||||
| Diabetes | ||||||||||
| No | 421 | 28 | — | — | 421 | 28 | — | — | ||
| Yes | 105 | 15 | 2.34 | 1.20, 4.56 | 0.013 | 105 | 15 | 2.83 | 1.38, 5.81 | 0.005 |
| CVD | ||||||||||
| No | 480 | 38 | — | — | ||||||
| Yes | 46 | 5 | 1.42 | 0.53, 3.80 | 0.487 | |||||
| CRD | ||||||||||
| No | 501 | 40 | — | — | ||||||
| Yes | 25 | 3 | 1.57 | 0.45, 5.48 | 0.478 | |||||
| Denosumab treatment | ||||||||||
| No | 221 | 20 | — | — | ||||||
| Yes | 305 | 23 | 0.82 | 0.44, 1.53 | 0.534 | |||||
| Hb (g/L) | 526 | 43 | 0.97 | 0.96, 0.99 | 0.004 | 526 | 43 | 0.99 | 0.93, 1.04 | 0.602 |
RBC (
/L)
|
526 | 43 | 0.51 | 0.30, 0.85 | 0.010 | 526 | 43 | 1.00 | 0.22, 4.60 | 0.995 |
PLT (
/L)
|
526 | 43 | 1.00 | 1.00, 1.01 | 0.166 | |||||
| ALT (U/L) | 526 | 43 | 1.00 | 0.98, 1.02 | 0.850 | |||||
| AST (U/L) | 526 | 43 | 1.02 | 0.99, 1.04 | 0.143 | |||||
| Serum calcium (mmol/L) | 526 | 43 | 0.05 | 0.00, 1.02 | 0.052 | |||||
| Serum phosphorus (mmol/L) | 526 | 43 | 1.89 | 0.44, 8.10 | 0.391 | |||||
| TG (mmol/L) | 526 | 43 | 0.80 | 0.42, 1.54 | 0.507 | |||||
| HDL-C (mmol/L) | 526 | 43 | 0.64 | 0.23, 1.77 | 0.391 | |||||
| Glucose (mmol/L) | 526 | 43 | 0.99 | 0.85, 1.16 | 0.901 | |||||
| Length of hospital stay (days) | 526 | 43 | 1.04 | 0.98, 1.10 | 0.166 | |||||
| Anesthesia time (minutes) | 526 | 43 | 1.00 | 0.99, 1.01 | 0.829 | |||||
| ASA | ||||||||||
| I | 28 | 4 | — | — | ||||||
| II | 406 | 25 | 0.39 | 0.13, 1.22 | 0.107 | |||||
| III | 88 | 14 | 1.14 | 0.34, 3.78 | 0.836 | |||||
| IV | 4 | 0 | 0.00 | 0.00, Inf | 0.985 | |||||
| Perioperative thrombosis | ||||||||||
| No | 470 | 40 | — | — | ||||||
| Yes | 56 | 3 | 0.61 | 0.18, 2.04 | 0.420 | |||||
| Pneumonia | ||||||||||
| No | 503 | 40 | — | — | ||||||
| Yes | 23 | 3 | 1.74 | 0.49, 6.10 | 0.389 | |||||
| Refracture | ||||||||||
| No | 437 | 31 | — | — | 437 | 31 | — | — | ||
| Yes | 89 | 12 | 2.04 | 1.00, 4.15 | 0.049 | 89 | 12 | 2.35 | 1.10, 5.03 | 0.027 |
| PNI | 526 | 43 | 0.86 | 0.80, 0.94 | < 0.001 | 526 | 43 | 0.90 | 0.82, 0.99 | 0.026 |
RCS curve analysis
As illustrated in Fig. 2, the RCS curve reveals a negative linear relationship between PNI and the odds of moderate-to-severe BADL dependence. The nonlinearity test yielded a P-value of 0.674, providing no evidence of a nonlinear association. Higher PNI values correlate with reduced odds of moderate-to-severe BADL dependence, suggesting that better preoperative nutritional status may reduce the risk of functional dependence after surgery.
Fig. 2.
RCS fitting for the association between PNI and BADL dependence
ROC curve analysis
Figure 3 demonstrated that the ROC curve indicates the PNI has moderate predictive ability for postoperative moderate-to-severe BADL dependence in patients with hip fractures, with an AUC of 0.659 (95% CI: 0.573–0.744), specificity of 76.4%, and sensitivity of 51.2%.
Fig. 3.
ROC curve for the association between PNI and BADL dependence
Subgroup analysis
As shown in Fig. 4, subgroup analyses revealed a significant association between higher PNI values and reduced risk of moderate-to-severe BADL dependence in specific populations. This association was remained significant among females (OR: 0.86, 95% CI: 0.76–0.96, P = 0.008), non-hypertensive individuals (OR: 0.85, 95% CI: 0.72-1.00, P = 0.045), non-diabetic patients (OR: 0.87, 95% CI: 0.77–0.98, P = 0.021) and those not receiving Denosumab treatment (OR: 0.84, 95% CI: 0.71–0.98, P = 0.029). No significant associations were observed in males, patients with hypertension or diabetes, those treated with Denosumab, or in subgroups stratified by refracture within two years postoperatively. Interaction tests indicated no significant differences across subgroups, suggesting that PNI is a consistent predictor of BADL dependence across diverse clinical contexts.
Fig. 4.
Subgroup analysis for the association between PNI and BADL dependence
PSM results
PSM, conducted using a 1:4 nearest neighbor algorithm (caliper = 0.20), was employed to reduce bias and control for confounding factors, with all standardized mean differences (SMDs) below 0.1, as depicted in Supplementary Fig. 2. After matching, 188 participants were included in the analysis (41 patients with moderate-to-severe dependence and 147 patients without). Baseline characteristics were generally well balanced between the groups after matching, with no statistically significant differences observed in most covariates (all P > 0.05), except for PNI, which remained significantly different (P < 0.05), as shown in Supplementary Table 2. Following matching, the association between PNI and functional dependence remained significant. Multivariate logistic regression showed an adjusted OR of 0.87 for PNI, affirming its role as an independent predictor (P = 0.018, Table 3). RCS analysis confirmed a linear relationship between PNI and functional dependence, with no evidence of nonlinearity (P-nonlinearity = 0.704, Supplementary Fig. 3). ROC analysis demonstrated modest discriminatory power for predicting functional dependence, with an AUC of 0.604 (Supplementary Fig. 4).
Table 3.
Univariate and multivariate logistic regression analysis after matching
| Characteristic | Univariable | Multivariable | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| N | Event N | OR | 95% CI | P-value | N | Event N | OR | 95% CI | P-value | |
| Age (years) | 188 | 41 | 1.01 | 0.96, 1.06 | 0.714 | 188 | 41 | 0.99 | 0.94, 1.05 | 0.859 |
| Gender | ||||||||||
| Female | 136 | 28 | — | — | 136 | 28 | — | — | ||
| Male | 52 | 13 | 1.29 | 0.61, 2.73 | 0.513 | 52 | 13 | 0.91 | 0.38, 2.15 | 0.826 |
| BMI (kg/m2) | ||||||||||
| < 18.5 | 155 | 33 | — | — | 155 | 33 | — | — | ||
| ≥ 25 | 23 | 6 | 1.30 | 0.48, 3.57 | 0.605 | 23 | 6 | 1.23 | 0.41, 3.68 | 0.714 |
| 18.5–25 | 10 | 2 | 0.92 | 0.19, 4.56 | 0.923 | 10 | 2 | 1.46 | 0.28, 7.72 | 0.656 |
| Type of fracture | ||||||||||
| Femoral neck fracture | 105 | 22 | — | — | 105 | 22 | — | — | ||
| Intertrochanteric fracture | 83 | 19 | 1.12 | 0.56, 2.24 | 0.749 | 83 | 19 | 1.09 | 0.23, 5.08 | 0.917 |
| Operation type | ||||||||||
| Arthroplasty | 89 | 19 | — | — | 89 | 19 | — | — | ||
| Internal fixation | 99 | 22 | 1.05 | 0.53, 2.11 | 0.885 | 99 | 22 | 0.96 | 0.23, 4.02 | 0.951 |
| Hypertension | ||||||||||
| No | 83 | 20 | — | — | ||||||
| Yes | 105 | 21 | 0.79 | 0.39, 1.58 | 0.500 | |||||
| Diabetes | ||||||||||
| No | 134 | 28 | — | — | 134 | 28 | — | — | ||
| Yes | 54 | 13 | 1.20 | 0.57, 2.54 | 0.633 | 54 | 13 | 1.56 | 0.68, 3.57 | 0.294 |
| CVD | ||||||||||
| No | 174 | 37 | — | — | ||||||
| Yes | 14 | 4 | 1.48 | 0.44, 4.99 | 0.526 | |||||
| CRD | ||||||||||
| No | 179 | 38 | — | — | ||||||
| Yes | 9 | 3 | 1.86 | 0.44, 7.76 | 0.397 | |||||
| Denosumab treatment | ||||||||||
| No | 79 | 20 | — | — | ||||||
| Yes | 109 | 21 | 0.70 | 0.35, 1.41 | 0.323 | |||||
| Hb (g/L) | 188 | 41 | 1.00 | 0.98, 1.02 | 0.991 | 188 | 41 | 1.02 | 0.96, 1.09 | 0.494 |
RBC (
/L)
|
188 | 41 | 0.92 | 0.50, 1.70 | 0.796 | 188 | 41 | 0.71 | 0.11, 4.46 | 0.715 |
PLT (
/L)
|
188 | 41 | 1.00 | 1.00, 1.01 | 0.179 | |||||
| ALT (U/L) | 188 | 41 | 1.00 | 0.98, 1.02 | 0.860 | |||||
| AST (U/L) | 188 | 41 | 1.01 | 0.99, 1.04 | 0.261 | |||||
| Serum calcium (mmol/L) | 188 | 41 | 0.10 | 0.00, 2.33 | 0.150 | |||||
| Serum phosphorus (mmol/L) | 188 | 41 | 1.09 | 0.23, 5.17 | 0.918 | |||||
| TG (mmol/L) | 188 | 41 | 0.78 | 0.40, 1.50 | 0.452 | |||||
| HDL-C (mmol/L) | 188 | 41 | 0.69 | 0.24, 2.01 | 0.502 | |||||
| Glucose (mmol/L) | 188 | 41 | 0.92 | 0.76, 1.11 | 0.361 | |||||
| Length of hospital stay (days) | 188 | 41 | 1.00 | 0.94, 1.06 | 0.925 | |||||
| Anesthesia time (minutes) | 188 | 41 | 1.00 | 0.99, 1.01 | > 0.999 | |||||
| ASA | ||||||||||
| I | 8 | 4 | — | — | ||||||
| II | 136 | 25 | 0.23 | 0.05, 0.96 | 0.044 | |||||
| III | 42 | 12 | 0.40 | 0.09, 1.86 | 0.243 | |||||
| IV | 2 | 0 | 0.00 | 0.00, Inf | 0.988 | |||||
| Perioperative thrombosis | ||||||||||
| No | 169 | 38 | — | — | ||||||
| Yes | 19 | 3 | 0.65 | 0.18, 2.34 | 0.506 | |||||
| Pneumonia | ||||||||||
| No | 180 | 38 | — | — | ||||||
| Yes | 8 | 3 | 2.24 | 0.51, 9.80 | 0.283 | |||||
| Refracture | ||||||||||
| No | 141 | 29 | — | — | 141 | 29 | — | — | ||
| Yes | 47 | 12 | 1.32 | 0.61, 2.87 | 0.476 | 47 | 12 | 1.47 | 0.64, 3.36 | 0.362 |
| PNI | 188 | 41 | 0.90 | 0.83, 0.99 | 0.023 | 188 | 41 | 0.87 | 0.78, 0.98 | 0.018 |
Discussion
This study highlights preoperative PNI as an independent predictor of postoperative functional status, as measured by the BI, in older patients undergoing hip fracture surgery. Higher PNI scores were consistently associated with better functional independence in BADL, even after adjusting for confounding factors and validating results through propensity score matching. These findings underscore the critical role of nutritional and immune status in optimizing postoperative outcomes in this vulnerable population.
The BI has become a gold standard for assessing BADL, particularly in older and rehabilitation populations. Its robust psychometric properties, including an internal consistency reliability coefficient of 0.90 [32], establish it as a reliable and valid measure of functional independence. Moreover, its validity and reliability in telephone-based assessments have been confirmed, enhancing its applicability in remote and telehealth settings [24]. Recently, the BI has gained traction as a prognostic tool across various clinical contexts. Wang et al. [33] demonstrated that lower BI scores significantly predicted increased mortality risk in 398 Chinese patients with COVID-19, highlighting the BI’s potential in risk stratification and personalized treatment strategies. In a prospective study of 250 older Japanese patients undergoing gastrointestinal surgery, Shibahashi et al. [34] found that preoperative BI scores were predictive of postoperative complications, underscoring the BI’s utility in preoperative risk assessment for aging populations. The BI’s application in patients with hip fractures has also gained importance. Pan et al. [35] studied 444 Chinese patients and found a strong association between discharge BI scores and 1-year postoperative survival rates following hip fracture surgery. Specifically, patients with discharge BI scores ≥ 50 exhibited significantly lower long-term mortality, as demonstrated by Kaplan-Meier survival analyses. This highlights the role of the BI in postoperative risk stratification and care planning. Furthermore, Mayoral et al. [36] evaluated the evolution of BI scores over a 1-year follow-up in 208 Spanish patients with osteoporotic hip fractures. They observed a significant decline in BI scores from admission to follow-up, emphasizing the BI’s sensitivity to changes in functional recovery and its utility as a monitoring tool. Collectively, these findings reinforce the growing clinical significance of BI as a comprehensive tool for assessing functional outcomes and predicting recovery trajectories.
Previous studies have underscored the significance of the PNI in predicting postoperative complications in patients with hip fractures. Wang et al. conducted a large-scale retrospective study involving 3,351 patients with hip fractures aged ≥ 45 years from 2000 to 2022. Their findings revealed that patients in the middle and high PNI tertiles exhibited significantly lower risks of postoperative complications (OR: 0.69, 95% CI: 0.48–0.98; OR: 0.61, 95% CI: 0.40–0.93). Additionally, Xing et al. [37] identified that low preoperative PNI (< 47.45) and advanced age (> 71.5 years) were significant risk factors for postoperative delirium (POD) in older patients with hip fractures. They suggested that preoperative interventions aimed at improving PNI, such as albumin supplementation or liver function enhancement, could reduce POD incidence. Mi et al. [38], through a secondary analysis of 369 patients with hip fractures, further demonstrated the predictive value of PNI when combined with other factors, such as anesthesia choice and preoperative MMSE (Mini-Mental State Examination) scores. They proposed a predictive model incorporating PNI and these parameters to reliably identify patients at higher risk of POD. These studies highlight the multifaceted role of PNI in perioperative risk assessment and management of hip fractures.
While the predictive value of PNI for perioperative complications is well-documented, its role in assessing postoperative functional status, particularly basic activities of daily living (BADL), remains underexplored in hip fracture populations. This study addresses this gap by demonstrating a robust association between higher preoperative PNI and better postoperative BADL outcomes. To our knowledge, it is the first to investigate this relationship, revealing that higher preoperative PNI values are significantly linked to a lower likelihood of moderate-to-severe functional dependence. Multivariate logistic regression analysis confirmed PNI as an independent protective factor against functional decline. Moreover, restricted cubic spline analysis showed a linear relationship between PNI and functional status, suggesting that even modest improvements in preoperative nutritional condition may confer meaningful benefits for postoperative functional independence.
The association between higher PNI and better postoperative functional status in patients with hip fractures can be explained by several interconnected physiological mechanisms. PNI, derived from serum albumin levels and total lymphocyte count, serves as a comprehensive indicator of both nutritional and immune status. Serum albumin, a key component of PNI, is essential for maintaining colloid osmotic pressure, protein synthesis, and tissue repair [39, 40]. Low albumin levels are associated with muscle atrophy and weakness, which can compromise physical function and impede postoperative rehabilitation [41, 42]. Higher PNI values likely reflect better protein reserves, aiding muscle repair and mitigating sarcopenia-a prevalent concern in older patients with hip fractures. Moreover, serum albumin contributes to collagen synthesis and mineral metabolism, both of which are essential for maintaining the structural integrity of bone tissue and promoting healing, thereby facilitating functional recovery [43, 44]. The lymphocyte count, another component of PNI, reflects immune competence. A robust immune system is vital for preventing infections, a common complication in patients with hip fractures that can delay mobilization and recovery [45]. Enhanced immune responses help reduce the risk of wound infections and systemic complications such as sepsis, thus supporting more efficient rehabilitation [46, 47].
Clinical implications
The results of this study highlight the critical importance of routine nutritional screening in older patients undergoing hip fracture surgery. As a simple and accessible marker, the PNI could be incorporated into preoperative assessments to identify patients at higher risk of poor postoperative functional status. Targeted nutritional interventions, including protein supplementation, vitamin D optimization, and personalized dietary plans, may contribute to better functional outcomes. Moreover, a multidisciplinary approach involving dietitians, physiotherapists, and geriatric specialists could further enhance patient care. Considering the significant burden of hip fractures and the growing prevalence of malnutrition among older adults, future research should prioritize evaluating the effectiveness of perioperative nutritional optimization strategies. Randomized controlled trials are essential to establish causal links between improved nutritional status and better postoperative functional performance.
Study limitations
This study has several limitations. First, its retrospective design inherently limits the ability to establish causal relationships between the PNI and postoperative functional dependence. Second, the single-center nature of the cohort may limit the generalizability of the findings to other clinical settings or broader populations. Third, although extensive adjustments were made for known confounding variables, the possibility of residual confounding due to unmeasured factors, such as detailed nutritional intake, cognitive function, or physical activity levels, cannot be fully excluded. In addition, the lack of BI scores before surgery or at discharge in this study limits the ability to assess baseline functional status and interpret longitudinal changes in postoperative function. This limitation is since the elderly patients with hip fracture in our study were bedridden at admission, and we considered them to be in a state of moderate to severe BADL dependence (although no specific BI score was given). Finally, the modest discriminative performance of PNI, as reflected by AUC, suggests that it may be more effective when used in combination with other clinical indicators to enhance risk stratification.
Conclusion
In conclusion, preoperative PNI is an independent predictor of postoperative functional status in older patients undergoing hip fracture surgery. These findings highlight the crucial role of nutritional and immune status in influencing surgical outcomes and support the incorporation of nutritional assessment and optimization into preoperative care protocols.
Supplementary Information
Acknowledgements
We thank all the staff for their valuable assistance during the data collection stage. In addition, we would like to thank the reviewers for their input on this study. We are also grateful to Bullet Edits Limited for language editing and proofreading of this manuscript.
Abbreviations
- ALT
Alanine Transaminase
- AST
Aspartate Transaminase
- BADL
Basic Activities of Daily Living
- BI
Barthel Index
- BMI
Body Mass Index
- CRD
Chronic Respiratory Diseases
- CVD
Cardiovascular Disease
- Hb
Hemoglobin
- HDL-C
High-Density Lipoprotein Cholesterol
- IQR
Interquartile Range
- LDL-C
Low-Density Lipoprotein Cholesterol
- PLT
Platelet Count
- PNI
Prognostic Nutritional Index
- POD
Postoperative Delirium
- PSM
Propensity Score Matching
- RBC
Red Blood Cell Count
- RCS
Restricted Cubic Spline
- ROC
Receiver Operating Characteristic
- SD
Standard Deviation
- SMD
Standardized Mean Difference
- TC
Total Cholesterol
- TG
Triglycerides
- WBC
White Blood Cell Count
Authors' contributions
QW: Conceptualization, Methodology, investigation and writing the original draft. WQD: Data curation, software and writing the original draft. ZTZ: Conceptualization, methodology, data curation and validation. RL: Conceptualization, methodology and data curation. DQY: Conceptualization, methodology and data curation. QFD: Conceptualization, methodology and data curation. ZGZ: Conceptualization, methodology and data curation. YHC: Conceptualization, methodology and data curation. LXH: Conceptualization, methodology, data curation and validation. LSL: Conceptualization, methodology, data curation, validation, review & editing and funding acquisition. DHJ: Conceptualization, Methodology, Data curation, validation, review & editing and funding acquisition. SHW: Conceptualization, methodology, data curation, investigation, software, review & editing and funding acquisition. All authors approved the final manuscript and consented to its publication.
Funding
This research was funded by the National Natural Science Foundation of China (No. 82102619), the Jiangsu Provincial Traditional Chinese Medicine Science and Technology Development Project (No. ZD202232), and the Extracurricular Academic Research Fund of Soochow University (No. KY20201019B).
Data availability
The data supporting this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study protocol was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Soochow University (No.2025060), which complies with the principles of the 1964 Declaration of Helsinki and its subsequent amendments. Informed consent was obtained from all participants at the time of follow-up. All data were anonymized before analysis to protect patient privacy.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qian Wu, Wenquan Ding and Zhongtai Zhang contributed equally to this work.
Contributor Information
Lisong Li, Email: lilisong1989@suda.edu.cn.
Dinghua Jiang, Email: jdhyzjyh@126.com.
Shenghao Wang, Email: wsh_suzhou@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data supporting this study are available from the corresponding author upon reasonable request.