Abstract
Introduction
This study aimed to evaluate the prognostic value of a composite score integrating the neutrophil-to-lymphocyte ratio (NLR), serum albumin, and D-dimer levels for predicting 1-year mortality in geriatric patients with proximal femur fractures.
Materials and methods
This retrospective cohort study included 300 patients aged ≥ 65 years who were admitted with proximal femur fractures and underwent surgical treatment between November 2023 and March 2026. Demographic, clinical, and laboratory data were collected from hospital records. A composite score based on NLR, albumin, and D-dimer was calculated. An exploratory unweighted 0–3 composite score was constructed from cohort-derived ROC thresholds for NLR, albumin, and D-dimer. Logistic regression, receiver operating characteristic (ROC) curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), Kaplan–Meier survival analysis, Cox regression, calibration analysis, and decision curve analysis (DCA) were performed to evaluate predictive performance.
Results
A total of 300 patients were analyzed, of whom 75 (25.0%) died within 1 year. Non-survivors were older and had significantly higher NLR and D-dimer levels and lower albumin levels (all p < 0.001). In mutually exclusive adjusted regression models, higher NLR and D-dimer levels, lower albumin levels, and a higher composite score were associated with 1-year mortality. In the composite-score model, each 1-point increase in score was associated with mortality after adjustment for clinical covariates (adjusted OR: 2.82, 95% CI: 1.95–4.08; p < 0.001). The composite score and its constituent biomarkers were not entered simultaneously into the same multivariable model. The composite score showed an apparent AUC of 0.86 (95% CI: 0.81–0.90), compared with AUCs of 0.74, 0.76, and 0.78 for NLR, albumin, and D-dimer, respectively. Addition of the score to the basic clinical model yielded a category-free NRI of 0.27 (95% CI: 0.14–0.40; p < 0.001) and an IDI of 0.068 (95% CI: 0.035–0.101; p < 0.001), indicating improved apparent risk separation within the derivation cohort. Kaplan–Meier and Cox regression analyses showed a stepwise increase in mortality risk across score groups (HR: 3.12 for high-risk group, p < 0.001). Calibration and decision-curve analyses suggested favorable apparent model performance and potential net benefit within the present study cohort.
Conclusions
In this retrospective single-center cohort, the composite score integrating NLR, albumin, and D-dimer was associated with 1-year mortality and showed promising apparent discriminatory performance. The score should be considered an exploratory prognostic tool that may complement comprehensive clinical assessment and support future risk-stratification research. Prospective multicenter studies with external validation and evaluation of clinical impact are required before its use in routine practice can be considered.
Keywords: Proximal femur fracture, Mortality, Neutrophil-to-lymphocyte ratio, Albumin, D-dimer, Composite score, Elderly, Risk stratification
Introduction
Proximal femur fractures are among the leading causes of morbidity, mortality, and loss of independence in the geriatric population. With increasing life expectancy worldwide, the incidence of osteoporotic fractures has risen substantially, and hip fractures in particular have become a major public health concern due to their considerable clinical, social, and economic burden [1, 2]. Beyond the immediate orthopedic consequences, these fractures often initiate a cascade of complications including prolonged immobilization, functional decline, infection, thromboembolic events, and cardiovascular deterioration [3, 4]. As a result, the prognosis of elderly patients with proximal femur fractures remains poor despite advances in perioperative care and surgical techniques.
The mortality associated with hip fractures is especially striking. Previous studies have reported that the 1-year mortality rate after hip fracture ranges from approximately 20% to 30%, which is comparable to the mortality observed in several malignant diseases [5–8]. This high mortality rate reflects not only the severity of the fracture itself, but also the frailty of the affected population. Advanced age, multiple comorbidities, sarcopenia, impaired nutritional reserve, systemic inflammatory activation, and reduced physiological resilience all contribute to adverse outcomes after fracture [5, 7]. Therefore, early identification of patients at increased risk of mortality is important for supporting perioperative planning, postoperative monitoring, and multidisciplinary management.
In geriatric patients, mortality after proximal femur fracture is not determined solely by fracture pattern or surgical intervention. Rather, it is shaped by a complex interaction between systemic inflammation, nutritional status, coagulation abnormalities, and the burden of accompanying chronic diseases [9, 10]. For this reason, there has been increasing interest in simple, inexpensive, and reproducible laboratory-based biomarkers that can be obtained at hospital admission and used for risk stratification in routine clinical practice. Such biomarkers may help clinicians identify vulnerable patients at an early stage, guide the intensity of perioperative surveillance, and improve individualized treatment planning.
Among these markers, the neutrophil-to-lymphocyte ratio (NLR) has emerged as a practical indicator of systemic inflammatory response. NLR reflects the balance between innate immune activation and adaptive immune regulation, and elevated levels have been associated with poor outcomes in a wide range of clinical settings, including cardiovascular disease, malignancy, trauma, and major surgery [11, 12]. In elderly patients with hip fracture, higher NLR values have been linked to increased postoperative mortality, suggesting that exaggerated inflammatory activation may be an important determinant of both short- and long-term prognosis [10, 13]. Because complete blood count testing is universally available and inexpensive, NLR is particularly attractive as a risk marker in emergency orthopedic settings.
Serum albumin is another widely recognized biomarker with important prognostic implications in elderly fracture patients. Albumin reflects not only nutritional status, but also systemic inflammation, hepatic synthetic function, and overall physiological reserve. Hypoalbuminemia is common in frail older adults and has consistently been associated with impaired wound healing, prolonged hospitalization, postoperative complications, and increased mortality after hip fracture [14]. In this respect, albumin serves as a surrogate marker of both chronic vulnerability and acute stress response, making it highly relevant for outcome prediction in geriatric orthopedic trauma.
D-dimer, a degradation product of cross-linked fibrin, represents another important component of the pathophysiological response to trauma and surgery. Elevated D-dimer levels indicate activation of coagulation and fibrinolysis and are frequently observed after fractures, especially in older adults [15–17]. In addition to its established role in the assessment of venous thromboembolism, D-dimer has been increasingly recognized as a marker of systemic stress and adverse prognosis. In geriatric hip fracture patients, higher preoperative D-dimer levels may reflect a more pronounced thrombo-inflammatory state and have been associated with unfavorable outcomes [16, 18–20]. Thus, D-dimer may provide prognostic information beyond thrombosis risk alone.
Although NLR, albumin, and D-dimer each reflect distinct but interrelated biological domains—namely inflammation, nutritional/physiological reserve, and coagulation activation—most previous studies have evaluated these parameters individually [17, 21]. This single-marker approach may not adequately capture the multidimensional pathophysiology underlying mortality after proximal femur fracture in elderly patients. In real-world clinical settings, prognosis is rarely determined by one isolated mechanism; instead, it is influenced by the cumulative impact of inflammatory burden, frailty, malnutrition, and hemostatic imbalance. Therefore, a composite score integrating these routinely available biomarkers may offer a more comprehensive and clinically useful means of risk assessment.
The development of a practical composite score based on NLR, albumin, and D-dimer may provide several advantages. First, it may improve the early identification of high-risk patients using laboratory parameters that are already measured during initial admission. Second, it may support more rational allocation of perioperative resources, including closer monitoring, geriatric consultation, nutritional intervention, and intensive care planning when needed. Third, by combining biomarkers from complementary biological pathways, such a score may have stronger predictive performance than each component alone. Despite this potential, evidence regarding integrated inflammation–nutrition–coagulation scoring systems in geriatric proximal femur fracture patients remains limited.
Accordingly, the present study aimed to evaluate the role of a composite score based on NLR, serum albumin, and D-dimer in predicting 1-year mortality among geriatric patients with proximal femur fracture. We hypothesized that a score integrating these three routinely available laboratory parameters would provide a simple and objective exploratory approach to mortality risk stratification in this vulnerable patient population.
Materials and methods
Study design and population
This study was designed as a retrospective cohort study. Ethical approval was obtained from the Non-Interventional Clinical Research Ethics Committee of Gaziantep City Hospital (Approval No: 408/2026; Date: January 21, 2026). All procedures were conducted in accordance with the principles of the Declaration of Helsinki. The study was conducted at the Department of Orthopedics and Traumatology, Gaziantep City Hospital, a tertiary referral center. Patients who were followed and treated in both outpatient and inpatient settings were included. The study period covered November 2023 to March 2026.
The study population consisted of geriatric patients aged ≥ 65 years who were admitted with a diagnosis of proximal femur fracture, including femoral neck, intertrochanteric, and subtrochanteric fractures, and who underwent surgical treatment. Only patients with complete clinical records and available preoperative laboratory data at the time of hospital admission were included in the final analysis. The primary analysis therefore used a complete-case approach; no imputation was performed for missing biomarker, clinical, or follow-up data. Radiological confirmation of fracture was required for all cases. Patients were excluded if they had pathological fractures (e.g., malignancy-related), polytrauma, active infection or inflammatory disease at admission, chronic hematological disorders, or severe hepatic dysfunction that could affect albumin levels. Additionally, patients with missing or incomplete medical records or those lost to follow-up were excluded from the study.
Data collection and definition of biomarkers and composite score
All data were retrospectively obtained from the hospital’s electronic medical record system. Demographic and clinical variables, including age, sex, comorbidities, fracture type, and perioperative characteristics, were recorded. All laboratory values used in the analysis were obtained from the first venous blood sample collected at the initial hospital assessment on admission, before surgical intervention. When repeated laboratory tests were available, only the admission values were used for calculation of the NLR and composite score. Neutrophil count, lymphocyte count, serum albumin concentration, and D-dimer level were recorded from this initial assessment. NLR was calculated as the absolute neutrophil count divided by the absolute lymphocyte count. Time to surgery was defined as the interval, in hours, between the documented time of hospital admission and the time of surgical skin incision recorded in the electronic operating-room record. In regression analyses, time to surgery was modeled per 12-hour increase. NLR was used as an indicator of systemic inflammatory response. Serum albumin levels were considered a marker of nutritional and physiological status, while D-dimer levels reflected activation of coagulation and fibrinolysis. To evaluate the combined contribution of inflammation, nutritional/physiological reserve, and coagulation activity, an exploratory composite score was developed using NLR, serum albumin, and D-dimer. Thresholds were derived from receiver operating characteristic (ROC) analyses conducted in the present study cohort, using the cut-off that maximized the Youden index for 1-year mortality. The resulting thresholds were NLR > 7.1, albumin ≤ 3.2 g/dL, and D-dimer > 1800 ng/mL. These thresholds were data-driven and were not prospectively prespecified or independently validated. Each biomarker meeting its adverse threshold was assigned 1 point, yielding an unweighted total score ranging from 0 to 3. Equal weighting was selected pragmatically to preserve transparency and ease of calculation, rather than because the three biomarkers were assumed to have equal biological or statistical effects. For descriptive survival analyses, patients with 0–1, 2, and 3 abnormal biomarker domains were categorized as low-, intermediate-, and high-score groups, respectively. These categories were exploratory and should not be interpreted as established clinical risk thresholds.
Outcome measures
The primary outcome of this study was all-cause 1-year mortality following proximal femur fracture. Secondary outcomes included in-hospital mortality, length of hospital stay, and the occurrence of postoperative complications, such as infection, thromboembolic events, and cardiovascular complications. Mortality data were obtained from hospital records and, when available, verified through national health registry systems to ensure accuracy. The time to event was defined as the duration from hospital admission to death or last follow-up. All outcomes were evaluated in relation to the composite biomarker score to assess its prognostic value in predicting short- and long-term clinical outcomes.
Statistical analysis
All statistical analyses were performed using IBM SPSS Statistics version 27.0 (IBM Corp., Armonk, NY, USA) and R software (version 4.3.0; R Foundation for Statistical Computing, Vienna, Austria). A two-tailed p value < 0.05 was considered statistically significant. Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR) depending on data distribution, while categorical variables were presented as frequency (percentage). The normality of distribution was assessed using the Kolmogorov–Smirnov test. Comparisons between groups were performed using Student’s t-test or one-way ANOVA for normally distributed variables and Mann–Whitney U or Kruskal–Wallis tests for non-normally distributed variables, as appropriate, while categorical variables were compared using the chi-square test or Fisher’s exact test.
Univariable logistic regression analyses were initially performed to describe associations with 1-year mortality. For adjusted analyses, two mutually exclusive multivariable logistic regression models were specified using the same prespecified clinical covariates: age, sex, hypertension, diabetes mellitus, coronary artery disease, fracture type, and time to surgery. The individual-biomarker model included continuous NLR, albumin, and D-dimer values in addition to these clinical covariates. The composite-score model included the composite score and the same clinical covariates, while excluding NLR, albumin, and D-dimer. Thus, the composite score and its constituent biomarkers were not entered simultaneously into any adjusted model. Variance inflation factors were examined in the individual-biomarker model to assess collinearity among covariates. Results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs).
Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of NLR, albumin, D-dimer, and the composite score, and for the exploratory composite score, ROC-derived thresholds were selected in the same study cohort by maximizing the Youden index. Accordingly, all discrimination, reclassification, calibration, and decision-curve estimates represent apparent performance in the derivation cohort and require validation in independent data. The area under the curve (AUC) was calculated, and comparisons between ROC curves were performed using the DeLong test.
To assess the incremental apparent prognostic information provided by the composite score beyond the reference models, category-free (continuous) net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated with 95% confidence intervals. A positive NRI indicates that, after addition of the composite score, predicted risks increased more often among patients who died and decreased more often among survivors. IDI represents the absolute change in the discrimination slope, defined as the difference between the mean predicted risks among non-survivors and survivors. These measures were evaluated in the derivation cohort and should therefore be interpreted as apparent incremental-performance estimates.
Model performance was additionally evaluated in terms of discrimination and calibration. Calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, and internal validation was performed using a bootstrapping method with 1000 resamples to reduce potential overfitting. Survival analysis was conducted using the Kaplan–Meier method, and differences between groups were assessed using the log-rank test. When appropriate, Cox proportional hazards regression analysis was used to estimate hazard ratios (HRs) with 95% confidence intervals.
To avoid structural collinearity, the composite score and its individual components were not entered simultaneously into the same multivariable model. Two mutually exclusive adjusted logistic regression models were specified. The individual-biomarker model included the prespecified clinical covariates together with continuous NLR, albumin, and D-dimer values. The composite-score model included the same clinical covariates together with the composite score, while excluding NLR, albumin, and D-dimer. Variance inflation factors were assessed in the individual-biomarker model to evaluate collinearity among covariates. For survival analyses based on composite-score categories, the score category was similarly modeled without simultaneous inclusion of the individual score components.
Results
A total of 330 patients were initially assessed for eligibility. After exclusion of 30 patients due to missing laboratory data, incomplete medical records, pathological fractures, polytrauma, or loss to follow-up, 300 patients were included in the final analysis (Fig. 1).
Fig. 1.
Flowchart of the study
Baseline demographic and clinical characteristics are presented in Table 1. A total of 300 patients were included in the final analysis, of whom 225 (75.0%) survived and 75 (25.0%) died within 1 year. Non-survivors were significantly older than survivors (82.9 ± 7.1 vs. 75.8 ± 6.7 years, p < 0.001), and coronary artery disease was more frequent among non-survivors (41.3% vs. 26.7%, p = 0.016). Time to surgery was also longer in non-survivors than in survivors (46 [30–62] vs. 32 [22–46] hours, p < 0.001). Sex, hypertension, diabetes mellitus, and fracture-type distributions did not differ significantly between groups (Table 1).
Table 1.
Baseline demographic and clinical characteristics of the study population according to 1-year survival status
| Variable | Total (n = 300) |
Survivors (n = 225) |
Non-survivors (n = 75) |
p-value |
|---|---|---|---|---|
| Age (years) | 77.6 ± 7.5 | 75.8 ± 6.7 | 82.9 ± 7.1 | < 0.001 |
| Female, n (%) | 181 (60.3) | 140 (62.2) | 41 (54.7) | 0.240 |
| Hypertension, n (%) | 193 (64.3) | 139 (61.8) | 54 (72.0) | 0.101 |
| Diabetes mellitus, n (%) | 106 (35.3) | 74 (32.9) | 32 (42.7) | 0.122 |
| Coronary artery disease, n (%) | 91 (30.3) | 60 (26.7) | 31 (41.3) | 0.016 |
| Fracture type, n (%) | 0.280 | |||
| Femoral neck | 108 (36.0) | 84 (37.3) | 24 (32.0) | |
| Intertrochanteric | 153 (51.0) | 111 (49.3) | 42 (56.0) | |
| Subtrochanteric | 39 (13.0) | 30 (13.3) | 9 (12.0) | |
| Time to surgery (hours) | 35 (24–50) | 32 (22–46) | 46 (30–62) | < 0.001 |
Admission biomarker values and composite scores according to 1-year survival status are presented in Table 2. Non-survivors had higher NLR values (9.5 [6.9–13.1] vs. 5.1 [3.7–7.2]), lower albumin levels (3.03 ± 0.45 vs. 3.57 ± 0.41 g/dL), higher D-dimer levels (2380 [1580–3480] vs. 1180 [840–1760] ng/mL), and higher composite scores (2.5 ± 0.6 vs. 1.2 ± 0.7) than survivors (all p < 0.001) (Table 2).
Table 2.
Admission biomarker values and composite score according to 1-year survival status
| Variable | Survivors (n = 225) | Non-survivors (n = 75) | p-value |
|---|---|---|---|
| NLR (median, IQR) | 5.1 (3.7–7.2) | 9.5 (6.9–13.1) | < 0.001 |
| Albumin (g/dL, mean ± SD) | 3.57 ± 0.41 | 3.03 ± 0.45 | < 0.001 |
| D-dimer (ng/mL, median, IQR) | 1180 (840–1760) | 2380 (1580–3480) | < 0.001 |
| Composite score (mean ± SD) | 1.2 ± 0.7 | 2.5 ± 0.6 | < 0.001 |
In mutually exclusive multivariable logistic regression analyses, older age and longer time to surgery were associated with increased 1-year mortality in both models (Table 3). In the individual-biomarker model, higher NLR (adjusted OR [aOR]: 1.10 per 1-unit increase, 95% CI: 1.04–1.16; p = 0.001) and D-dimer levels (aOR: 1.09 per 500-ng/mL increase, 95% CI: 1.03–1.15; p = 0.003) were associated with higher mortality, whereas higher albumin levels were associated with lower mortality (aOR: 0.52 per 1-g/dL increase, 95% CI: 0.33–0.81; p = 0.004). In the separate composite-score model, each 1-point increase in the composite score was associated with increased 1-year mortality (aOR: 2.82, 95% CI: 1.95–4.08; p < 0.001). The composite score and its individual components were not entered simultaneously into the same multivariable model (Table 3).
Table 3.
Mutually exclusive multivariable logistic regression models for 1-year mortality in geriatric proximal femur fractures
| Variable | Individual-biomarker model, adjusted OR (95% CI) | p-value | Composite-score model, adjusted OR (95% CI) | p-value |
|---|---|---|---|---|
| Age, per 1-year increase | 1.05 (1.01–1.10) | 0.013 | 1.06 (1.02–1.11) | 0.003 |
| Female sex | 0.78 (0.43–1.41) | 0.410 | 0.75 (0.41–1.36) | 0.342 |
| Hypertension | 1.19 (0.65–2.17) | 0.574 | 1.22 (0.67–2.22) | 0.519 |
| Diabetes mellitus | 1.25 (0.68–2.32) | 0.468 | 1.28 (0.70–2.34) | 0.423 |
| Coronary artery disease | 1.52 (0.83–2.80) | 0.176 | 1.58 (0.87–2.88) | 0.131 |
| Intertrochanteric fracture | 1.10 (0.53–2.28) | 0.796 | 1.08 (0.52–2.24) | 0.839 |
| Subtrochanteric fracture | 0.91 (0.33–2.52) | 0.854 | 0.94 (0.34–2.61) | 0.904 |
| Time to surgery, per 12-h delay | 1.13 (1.00–1.28) | 0.048 | 1.15 (1.01–1.30) | 0.032 |
| NLR, per 1-unit increase | 1.10 (1.04–1.16) | 0.001 | – | – |
| Albumin, per 1-g/dL increase | 0.52 (0.33–0.81) | 0.004 | – | – |
| D-dimer, per 500-ng/mL increase | 1.09 (1.03–1.15) | 0.003 | – | – |
| Composite score, per 1-point increase | – | – | 2.82 (1.95–4.08) | < 0.001 |
Reference category for fracture type: femoral neck fracture. The individual-biomarker model included NLR, albumin, and D-dimer together with clinical covariates. The composite-score model included the same clinical covariates and the composite score, but did not include NLR, albumin, or D-dimer. Thus, the composite score and its constituent biomarkers were never entered simultaneously into the same multivariable model.
ROC analysis showed that the composite score had an apparent AUC of 0.86 (95% CI: 0.81–0.90) for 1-year mortality, compared with AUCs of 0.74 (95% CI: 0.68–0.80) for NLR, 0.76 (95% CI: 0.70–0.82) for albumin, and 0.78 (95% CI: 0.72–0.84) for D-dimer. The ROC-derived cut-off for the composite score was ≥ 2, with a sensitivity of 80.0% and specificity of 78.7%. Comparisons by the DeLong test indicated statistically significant differences between the composite score and the individual biomarkers (Table 4; Fig. 2).
Table 4.
ROC curve analysis of NLR, albumin, D-dimer, and composite score for predicting 1-year mortality
| Variable | AUC (95% CI) | Cut-off value | Sensitivity (%) | Specificity (%) | p-value |
|---|---|---|---|---|---|
| NLR | 0.74 (0.68–0.80) | > 7.1 | 72.0 | 68.4 | < 0.001 |
| Albumin (g/dL) | 0.76 (0.70–0.82) | ≤ 3.2 | 70.7 | 72.9 | < 0.001 |
| D-dimer (ng/mL) | 0.78 (0.72–0.84) | > 1800 | 74.7 | 71.6 | < 0.001 |
| Composite score | 0.86 (0.81–0.90) | ≥ 2 | 80.0 | 78.7 | < 0.001 |
Fig. 2.
ROC curves comparing the predictive performance of NLR, albumin, D-dimer, and the composite score
Cut-off values were derived by ROC analysis using the Youden index in the same derivation cohort. They were not externally validated.
The addition of the composite score to the baseline model yielded a positive category-free NRI of 0.29 (95% CI: 0.15–0.43; p < 0.001), indicating favorable directional changes in predicted risk among non-survivors and survivors within the study cohort. The IDI was 0.072 (95% CI: 0.038–0.106; p < 0.001), corresponding to a 7.2-percentage-point increase in the discrimination slope. Similar positive NRI and IDI estimates were observed when the composite score was added to models based on the individual biomarkers (Table 5).
Table 5.
Category-free net reclassification improvement and integrated discrimination improvement after addition of the composite score to biomarker-based models
| Model Comparison | NRI (95% CI) | p-value | IDI (95% CI) | p-value |
|---|---|---|---|---|
| Baseline model vs. + composite score | 0.29 (0.15–0.43) | < 0.001 | 0.072 (0.038–0.106) | < 0.001 |
| NLR model vs. + composite score | 0.24 (0.11–0.38) | 0.001 | 0.061 (0.029–0.093) | < 0.001 |
| Albumin model vs. +lposite score | 0.21 (0.08–0.34) | 0.002 | 0.055 (0.022–0.088) | 0.001 |
| D-dimer model vs. + composite score | 0.19 (0.06–0.32) | 0.004 | 0.049 (0.018–0.080) | 0.002 |
Positive NRI indicates favorable directional changes in predicted risk among events and non-events. IDI represents the absolute increase in the discrimination slope. Both measures were calculated in the derivation cohort and do not establish clinical impact
Kaplan–Meier survival analysis demonstrated significant differences in 1-year survival rates across composite score groups. Cox proportional hazards regression analysis showed that patients in the high-risk group (composite score = 3) had a significantly increased risk of 1-year mortality compared to the low-risk group (score 0–1) (HR: 3.12, 95% CI: 2.01–4.85, p < 0.001). Similarly, patients in the intermediate-risk group (score = 2) also had an increased risk of mortality compared to the low-risk group (HR: 1.98, 95% CI: 1.25–3.14, p = 0.004) (Fig. 3).
Fig. 3.
Kaplan–Meier survival curves according to composite score groups
Calibration analysis of the multivariate model demonstrated good agreement between predicted and observed 1-year mortality probabilities. The calibration curve showed close alignment with the reference line across the range of predicted risks. The Hosmer–Lemeshow goodness-of-fit test indicated no significant deviation (p > 0.05). The calibration slope was estimated as 0.97, and the calibration intercept was −0.02, indicating adequate model calibration (Fig. 4).
Fig. 4.
Calibration plot of the multivariate model for predicting 1-year mortality
Decision-curve analysis suggested a higher modeled net benefit for the composite-score model than for models based on individual biomarkers across the examined threshold probabilities, particularly between 10% and 60%. The “treat-all” and “treat-none” strategies were displayed for comparison. These findings reflect model-based estimates within the derivation cohort and do not demonstrate improved clinical decisions or patient outcomes.
The apparent discrimination of the age-only, basic clinical, composite-score, and combined models is presented in Table 6. The age-only model had an AUC of 0.74 (95% CI: 0.68–0.80), whereas the basic clinical model had an AUC of 0.79 (95% CI: 0.73–0.84). The composite-score model had an AUC of 0.86 (95% CI: 0.81–0.90), corresponding to an absolute AUC difference of 0.07 compared with the basic clinical model (DeLong p < 0.001). The combined clinical-plus-composite-score model had the highest apparent AUC, at 0.89 (95% CI: 0.85–0.93) (Table 6; Fig. 5).
Table 6.
Comparison of predictive performance of non-laboratory clinical models and biomarker-based models for 1-year mortality
| Model | Variables Included | AUC (95% CI) | DeLong p-value |
|---|---|---|---|
| Model 1 | Age | 0.74 (0.68–0.80) | Reference |
| Model 2 | Age, sex, hypertension, diabetes mellitus, coronary artery disease, fracture type, time to surgery | 0.79 (0.73–0.84) | 0.041 vs. Model 1 |
| Model 3 | Composite score | 0.86 (0.81–0.90) | < 0.001 vs. Model 2 |
| Model 4 | Age, sex, hypertension, diabetes mellitus, coronary artery disease, fracture type, time to surgery, composite score | 0.89 (0.85–0.93) | 0.018 vs. Model 3 |
Fig. 5.
ROC curves comparing age-only, clinical, composite-score, and combined models
Addition of the composite score to the basic clinical model produced a positive category-free NRI of 0.27 (95% CI: 0.14–0.40; p < 0.001) and an IDI of 0.068 (95% CI: 0.035–0.101; p < 0.001). These estimates indicate improved apparent risk separation within the derivation cohort; they do not demonstrate that use of the score improves treatment decisions or patient outcomes (Table 7).
Table 7.
Category-free net reclassification improvement and integrated discrimination improvement after addition of the composite score to the basic clinical model
| Model Comparison | NRI (95% CI) | p-value | IDI (95% CI) | p-value |
|---|---|---|---|---|
| Basic clinical model vs. Clinical + composite score | 0.27 (0.14–0.40) | < 0.001 | 0.068 (0.035–0.101) | < 0.001 |
Positive NRI indicates favorable directional changes in predicted risk among events and non-events. IDI represents the absolute increase in the discrimination slope. Both measures were calculated in the derivation cohort and do not establish clinical impact
Decision curve analysis demonstrated the net benefit of the age-only, clinical, composite score, and combined models across a range of threshold probabilities (Fig. 6). The combined model showed higher net benefit values across most threshold probabilities, particularly between approximately 10% and 60%. The composite score model also demonstrated favorable net benefit compared to the age-only and clinical models within this range. The “treat-all” and “treat-none” strategies were also displayed for comparison (Fig. 6).
Fig. 6.
Decision curve analysis comparing age-only, clinical, composite-score, and combined models
Discussion
In this study, we evaluated the prognostic value of a composite score integrating inflammatory (NLR), nutritional (albumin), and coagulation (D-dimer) parameters for predicting 1-year mortality in geriatric patients with proximal femur fractures. The main findings demonstrated that the composite score was an independent predictor of mortality and showed higher apparent discrimination than the individual biomarkers in this cohort. In addition, ROC, NRI, IDI, calibration, and decision-curve analyses suggested improved apparent risk classification and potential modeled net benefit. These findings should be interpreted cautiously because all analyses were performed in the same retrospective single-center cohort.
The intended clinical role of the composite score warrants emphasis. In contemporary hip-fracture care, surgery remains the usual management for medically optimizable patients and is generally undertaken to enable early mobilization and recovery of basic function [22]. Accordingly, this score is not designed to determine whether surgery should be performed, withheld, or delayed. Rather, it provides an early estimate of 1-year prognosis that may support communication with patients and families, alignment of treatment with the patient’s values and goals, and planning of the appropriate intensity of perioperative and postoperative care. In patients with profound frailty or limited life expectancy, a high predicted risk may appropriately prompt early orthogeriatric, anesthesiology, and palliative-care involvement, together with discussion of the likely benefits and burdens of treatment. In this setting, surgery may still be goal-concordant and palliative—for example, when comfort, pain relief, or facilitation of sitting and transfers is the principal objective—and subsequent mortality should not automatically be interpreted as a failure of care [23].
Frailty, cognitive impairment, and pre-fracture functional status are also central determinants of prognosis after hip fracture. The present composite score does not capture these multidimensional clinical domains and should therefore be interpreted as a complement to, rather than a substitute for, comprehensive clinical assessment. In routine practice, laboratory-based risk estimation should be considered alongside a structured evaluation of frailty, cognitive status, pre-fracture mobility and independence in activities of daily living, comorbidity burden, and anesthetic risk. This combined approach is likely to provide a more clinically meaningful interpretation of prognosis and support individualized care planning. Importantly, because these variables were not systematically recorded in the present retrospective cohort, we could not assess the incremental predictive value of the composite score beyond frailty, cognition, and functional status. Future prospective studies should evaluate the performance, calibration, and clinical usefulness of an integrated model that combines the present score with standardized frailty and functional assessments [24, 25].
Consistent with previous literature, advanced age was identified as a strong predictor of mortality. Haentjens et al. reported that mortality after hip fracture increases significantly with age due to frailty and comorbidity burden [5]. Guzon-Illescas et al. demonstrated that age is one of the most important determinants of mortality following osteoporotic hip fractures [6]. In our study, non-survivors were significantly older, supporting the established relationship between aging and poor outcomes. Additionally, delayed surgery was associated with increased mortality risk, which is consistent with findings from Barbosa et al., who reported that perioperative delays contribute to increased complications and mortality in elderly patients [9].
Inflammation has been widely recognized as a key factor influencing prognosis after hip fracture. Aydin et al. demonstrated that systemic inflammatory markers are strongly associated with mortality in orthopedic trauma patients [12]. Yao et al. reported that NLR is a useful predictor of postoperative complications and adverse outcomes in elderly hip fracture patients [13]. More recently, Long et al. showed that elevated NLR is significantly associated with increased mortality risk in hip fracture patients, while Chen et al. demonstrated that inflammatory indices are closely related to postoperative mortality [26, 27]. In line with these findings, our study showed that NLR was significantly higher in non-survivors and remained an independent predictor of mortality. These results support the role of systemic inflammatory response as a major contributor to adverse outcomes.
Nutritional status, reflected by serum albumin levels, is another critical determinant of prognosis. Wang et al. demonstrated that albumin-based indices are strongly associated with mortality in elderly hip fracture patients [14]. Hypoalbuminemia reflects not only malnutrition but also systemic inflammation and reduced physiological reserve. Choi et al. also reported that albumin-related indices are significant predictors of mortality in hip fracture patients [28]. In our study, lower albumin levels were significantly associated with mortality and remained an independent predictor in multivariate analysis, confirming its prognostic importance.
Coagulation abnormalities also play a significant role in the pathophysiology of hip fracture outcomes. Lin et al. reported that D-dimer levels are associated with thrombotic complications in fracture patients [15]. Yao et al. demonstrated that D-dimer-based indices are useful for predicting adverse outcomes in geriatric hip fracture patients [16]. Atlas et al. further emphasized the importance of coagulation and inflammatory markers in mortality prediction [29]. In addition, Eygi et al. reported that albumin-to-D-dimer ratios are associated with mortality in critically ill patients [18]. In our study, D-dimer levels were significantly higher in non-survivors and remained independently associated with mortality, highlighting the importance of coagulation activation in this patient population.
Importantly, the most notable finding of our study is that the composite score integrating NLR, albumin, and D-dimer outperformed individual biomarkers in predicting mortality. Previous studies have generally focused on single biomarkers or dual indices. Lu et al. demonstrated that combined indices incorporating NLR and D-dimer improve prognostic performance in hip fracture patients [30]. Kilinc et al. highlighted that integrating inflammatory and metabolic markers enhances mortality prediction in femur fracture patients [31]. Wang et al. demonstrated that composite hematological indices improve prognostic accuracy compared to individual parameters [14], while Yao et al. emphasized the value of combining inflammatory and coagulation markers [16]. However, these approaches do not fully capture the multidimensional nature of mortality risk. Our study extends the existing literature by integrating three key biological domains— inflammation, nutrition, and coagulation—into a single, practical scoring system. In the present derivation cohort, this multidimensional approach showed an apparent AUC of 0.86 and positive NRI and IDI estimates relative to the examined reference models. These findings suggest potential incremental prognostic information but do not establish superiority to existing hip-fracture prognostic tools or readiness for clinical implementation.
The methodology used to construct the composite score also warrants cautious interpretation. The biomarker thresholds were selected from ROC analyses performed in the same cohort in which the score was subsequently evaluated. Consequently, the observed discrimination and risk-group separation may be optimistic because threshold selection and performance assessment were not independent. In addition, the equal-weight 0–3 scoring approach was chosen to provide a transparent and readily calculable exploratory measure; it does not establish that NLR, albumin, and D-dimer contribute equally to mortality risk. Although dichotomization facilitates bedside interpretation, it may also result in loss of prognostic information compared with modeling the biomarkers continuously. The score categories were similarly based on the number of abnormal biomarker domains and should be considered descriptive rather than clinically validated risk strata. Future prospective studies should compare this simple unweighted score with regression-based or penalized models and evaluate its calibration and transportability in independent cohorts.
When compared with the non-laboratory clinical models evaluated in this study, the composite-score model had an apparent AUC of 0.86, compared with 0.79 for the basic clinical model. This absolute AUC difference of 0.07 was statistically significant but may be clinically modest. The combined model had the highest apparent discrimination; however, these estimates were obtained in the same derivation cohort and may be optimistic. The NRI, IDI, and decision-curve analyses provide complementary estimates of apparent incremental performance, but they do not show that applying the score improves treatment decisions or patient outcomes. In addition, because the present study did not directly compare the composite score with established hip-fracture prognostic tools, no claim of superiority to existing models can be made. The score should therefore be regarded as a potentially informative adjunct to comprehensive clinical assessment, pending external validation and formal evaluation of clinical impact.
The absolute increase in apparent AUC from 0.79 for the basic clinical model to 0.86 for the composite-score model was 0.07. Although this difference was statistically significant, its clinical importance may be modest and cannot be established from discrimination metrics alone. In particular, an improvement in AUC does not demonstrate that applying the score changes clinical decisions, improves perioperative care, or improves patient outcomes. The accompanying NRI, IDI, calibration, and decision-curve findings provide complementary estimates of apparent model performance within this derivation cohort, but they likewise require confirmation in independent populations and formal clinical-impact evaluation.
Another important finding is the clear separation of survival curves according to composite score groups. Kaplan–Meier analysis showed progressively lower survival probabilities with increasing exploratory score categories, and Cox regression showed a stepwise association between score category and mortality risk. These findings indicate separation of survival within the present cohort but should not be interpreted as validation of clinically established risk thresholds.
Compared with previously reported models, our composite score demonstrates both promising apparent discriminative performance and feasibility based on routinely available laboratory tests. While previous studies have reported moderate predictive performance for individual biomarkers or limited combinations [12–14, 30], our model achieved a higher level of discrimination and provided additional apparent prognostic information in the present cohort. This suggests that combining multiple biological pathways may provide a more comprehensive assessment of patient risk.
The strengths of this study include the integration of multiple pathophysiological pathways into a single scoring system, the use of routinely available laboratory parameters, and the application of advanced statistical methods, including ROC analysis, NRI, IDI, calibration, and decision curve analysis. These features support the exploratory prognostic evaluation; however, they do not establish clinical effectiveness or implementation readiness.
However, some limitations should be acknowledged. First, the retrospective design may have introduced selection bias and limited the availability of potentially relevant clinical variables. Second, the single-center design may limit generalizability because patient characteristics, referral patterns, perioperative pathways, laboratory assays, and thresholds for postoperative monitoring may differ across institutions and healthcare systems. Third, the analysis was restricted to patients with complete clinical records, available preoperative biomarker measurements, and 1-year follow-up. This complete-case approach may have introduced selection bias if excluded patients differed systematically from the analyzed cohort with respect to frailty, acute illness severity, access to care, or mortality risk. Because detailed outcome data were not available for all excluded patients, the magnitude and direction of this potential bias could not be determined. Accordingly, the findings may not be directly generalizable to patients with incomplete records, missing laboratory data, unavailable follow-up, or different clinical-care settings. Third, frailty, cognitive status, and baseline functional status—including pre-fracture mobility and dependence in activities of daily living—were not systematically recorded and could not be incorporated into the analyses. These factors are important determinants of mortality and recovery after hip fracture; accordingly, the composite score should be considered an adjunct to comprehensive geriatric and perioperative assessment, rather than a standalone clinical decision tool. Fourth, only baseline laboratory parameters were evaluated, and dynamic changes over time were not assessed. In addition, the biomarker thresholds were derived from the same dataset used for model evaluation, and the score used equal, pragmatic weighting of dichotomized variables. These choices may have introduced optimism, information loss, and overfitting; therefore, the reported performance metrics should be regarded as apparent derivation estimates rather than validated measures of predictive performance. Moreover, because the cohort was restricted to surgically treated patients, the score cannot be used to compare operative with nonoperative management or to determine surgical eligibility. Furthermore, the composite score was not directly compared with established hip-fracture prognostic tools; therefore, its relative performance and potential added value beyond such tools remain unknown. Finally, external validation in independent cohorts is required to confirm the generalizability and clinical applicability of the composite score. Prospective studies should assess whether the score provides incremental prognostic value when combined with standardized frailty, cognitive, and functional assessments.
Conclusion
In this retrospective single-center complete-case cohort, a composite score based on NLR, albumin, and D-dimer was associated with 1-year mortality and showed apparent discrimination within the derivation data. These findings do not establish a validated clinical prediction tool or demonstrate improved patient outcomes. The score should not replace assessment of frailty, cognition, baseline function, comorbidity burden, or patient goals, and it is not intended to determine operative eligibility or treatment selection. External validation, recalibration, direct comparison with established hip-fracture prognostic tools, and prospective clinical-impact studies are required before routine use can be considered.
Author contributions
BB contributed to conceptualization, methodology, formal analysis, writing—original draft preparation, supervision, and project administration. OK contributed to data curation and investigation. CÖ contributed to methodology, validation, and writing—review and editing. EA contributed to investigation and visualization. AÇ contributed to data curation. All authors have read and agreed to the published version of the manuscript.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
The authors declare no competing interests.
Ethical approval
This retrospective study was approved by the Institutional Review Board for non-interventional clinical research at Gaziantep City Hospital, Gaziantep, Turkey (Approval No: 408/2026; Date: January 21, 2026). All procedures were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all individual participants included in the study.
Informed Consent
Due to the retrospective design of the study and the use of anonymized data obtained from the hospital electronic medical record system, the requirement for written informed consent was waived by the Ethics Committee. All patient data were anonymized and de-identified prior to analysis to ensure patient confidentiality and data protection.
Footnotes
Publisher’s Note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.