Fracture Type Modifies the Prognostic Impact of Acute Kidney Injury on In-Hospital Mortality after Hip Fracture Surgery in Elderly Patients: A Retrospective Cohort Study with Risk Stratification

Abstract

Introduction

In-hospital mortality following hip fracture surgery remains a significant outcome in elderly patients, yet perioperative determinants — particularly those modifiable during hospitalization — are incompletely characterized. Whether the prognostic impact of acute kidney injury (AKI) on in-hospital mortality differs according to fracture type has not been previously examined.

Materials and Methods

This retrospective cohort study included 342 patients aged ≥65 years who underwent surgical treatment for hip fracture at a tertiary referral center between January 2018 and January 2022. The primary outcome was in-hospital mortality. Independent predictors were identified using multivariable logistic regression. Model performance was assessed by ROC analysis, Hosmer–Lemeshow calibration testing, and bootstrap internal validation with 1000 iterations. A simple additive risk score was derived from the final model. Interaction analysis evaluated whether fracture type modified the association between AKI and mortality.

Results

In-hospital mortality occurred in 29 of 342 patients (8.5%). Independent predictors were postoperative AKI (OR 5.46, 95% CI 2.20–13.51), lower preoperative serum albumin (OR per 1 g/dL increase 0.38, 95% CI 0.18–0.78), and lower admission oxygen saturation (OR per 1% increase 0.86, 95% CI 0.77–0.95). The model demonstrated acceptable discrimination (AUC = 0.766, 95% CI 0.655–0.877). A three-variable additive risk score stratified mortality from 3.4% (score 0) to 39.1% (score 2–3). Interaction analysis revealed that fracture type significantly modified the prognostic impact of AKI (interaction p = 0.016), with a substantially stronger effect in extracapsular fractures (OR 14.3, 95% CI 4.0–50.0) than intracapsular fractures (OR 1.32, 95% CI 0.30–5.88).

Conclusions

Postoperative AKI, hypoalbuminemia, and reduced admission oxygen saturation independently predict in-hospital mortality after hip fracture surgery. The mortality risk conferred by AKI is substantially greater in extracapsular fractures, highlighting the need for fracture-type–specific perioperative vigilance and early renal protection strategies.

Keywords

hip fracture in-hospital mortality acute kidney injury albumin risk stratification level of evidence: level III retrospective comparative study

Introduction

Hip fractures are among the most serious fragility injuries in older adults and represent a growing public health burden, with annual incidence projected to exceed six million cases by 2050.^1,2 Despite advances in perioperative care, in-hospital mortality following hip fracture surgery remains considerable, with reported rates ranging from 1.2% to 11.4%.^3-5 Existing studies have predominantly focused on longer-term outcomes and have largely emphasized static baseline characteristics — such as advanced age, elevated ASA score, and comorbidity burden — as predictors of mortality.^3,5-7 These variables, however, reflect chronic frailty rather than the dynamic perioperative physiological disturbances that arise during the index hospitalization and may be more directly amenable to intervention.

The immediate postoperative period is characterized by acute physiological stress, hemodynamic instability, and early organ dysfunction, during which potentially modifiable complications such as acute kidney injury (AKI), hypoalbuminemia, and hypoxemia may decisively influence survival.^4,7,8 Despite the clinical relevance of these perioperative factors, their independent contribution to in-hospital mortality in elderly hip fracture patients remains incompletely defined. Critically, whether the prognostic impact of AKI differs according to hip fracture subtype — intracapsular versus extracapsular, two entities that differ substantially in biomechanics, surgical management, and physiological burden — has not been previously investigated.

The aim of the present study was to identify independent perioperative predictors of in-hospital mortality in elderly hip fracture patients and to examine whether fracture type modifies the association between postoperative AKI and early death. We hypothesized that dynamic perioperative indicators of systemic compromise would independently predict in-hospital mortality, and that the prognostic impact of AKI would differ significantly by fracture subtype.

Materials and Methods

Study Design and Ethical Approval

This retrospective cohort study evaluated patients aged ≥65 years who underwent surgical treatment for hip fracture at a tertiary referral center between January 2018 and January 2022. Ethical approval was obtained from the institutional Non-Interventional Clinical Research Ethics Committee (Approval No: 18/11/2021/732), and the study was conducted in accordance with the Declaration of Helsinki. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, with careful attention to the recommended reporting items. Informed consent was waived given the retrospective nature of the study. Clinical, laboratory, and perioperative data were retrieved from electronic medical records and archived patient files.

Patient Selection

All consecutive patients aged ≥65 years undergoing surgical treatment for hip fracture during the study period were screened for eligibility. Exclusion criteria were: death before surgical treatment (n=3), history of previous ipsilateral hip surgery (n=5), polytrauma (n=3), bilateral hip fractures (n=0), age <65 years (n=4), and subtrochanteric femur fractures (n=11). Subtrochanteric fractures were defined as fractures occurring within 5 cm distal to the lesser trochanter and were excluded because they represent a biomechanically and surgically distinct entity from intracapsular and intertrochanteric fractures. Reverse obliquity fracture patterns were classified as extracapsular fractures. After exclusions, 342 patients were included in the final analysis (Figure 1).

Outcome Definition and Fracture Classification

The primary outcome was in-hospital mortality, defined as death occurring during the index hospitalization. Hip fractures were classified radiographically as intracapsular or extracapsular (intertrochanteric). In our institutional practice, femoral neck fractures in patients aged ≥65 years are routinely treated with arthroplasty (hemiarthroplasty or total hip arthroplasty), and internal fixation with cannulated screws is generally reserved for younger patients and was not performed in the present study cohort. Extracapsular fractures are routinely treated with intramedullary nailing, and plate fixation methods are not commonly used.

Clinical and Perioperative Variables

Recorded variables included demographic characteristics, comorbidities, fracture type, ASA score, Charlson Comorbidity Index (CCI), anesthesia type, time to surgery, surgical procedure (osteosynthesis or arthroplasty), operation duration, day and timing of surgery, prior hospitalization within one year, preoperative anticoagulant use, and perioperative blood transfusion. Perioperative blood transfusion was performed according to institutional protocols, generally based on hemoglobin levels below 8 g/dL or the presence of symptomatic anemia. Postoperative complications including acute kidney injury (AKI), infection, prolonged drainage, and intraoperative cardiac arrest were recorded. Postoperative AKI was defined according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria⁹ as an increase in serum creatinine of ≥0.3 mg/dL within 48 hours or ≥1.5 times the baseline value during hospitalization.

The type of anesthesia (spinal or general) was selected based on patient comorbidities, overall clinical condition, and anesthesiologist preference, rather than a standardized protocol. Surgical procedures were performed by multiple experienced orthopedic surgeons, with generally comparable case distribution and operative volume among surgeons.

Laboratory Parameters

Preoperative and postoperative laboratory values were obtained from hospital records and included hemoglobin, white blood cell count (WBC), platelet count, INR, creatinine, serum albumin, sodium, potassium, and admission oxygen saturation. Standard clinical thresholds were applied for the definition of electrolyte disturbances.

Statistical Analysis

Continuous variables were expressed as mean ± standard deviation and categorical variables as frequency and percentage. Normality was assessed using the Shapiro–Wilk test. Between-group comparisons were performed using Student's t-test or Mann–Whitney U test for continuous variables and chi-square or Fisher’s exact test for categorical variables. Variables with p < 0.10 on univariate analysis and those with established clinical relevance were entered into a multivariable logistic regression model to identify independent predictors of in-hospital mortality. Model discrimination was assessed by receiver operating characteristic (ROC) curve analysis, and calibration was evaluated using the Hosmer–Lemeshow goodness-of-fit test. Internal validity was assessed by bootstrap resampling with 1000 iterations, with bias-corrected and accelerated (BCa) 95% confidence intervals reported for regression coefficients. A simple additive clinical risk score was derived from the independent predictors identified in the multivariable model, and mortality stratification across score categories was assessed using the chi-square test. Interaction analysis was performed to evaluate whether fracture type modified the association between postoperative AKI and in-hospital mortality, followed by stratified multivariable regression analyses. Kaplan–Meier survival analysis with the log-rank test was used to compare survival across risk score groups. All analyses were performed using IBM SPSS Statistics version 23.0 (IBM Corp., Armonk, NY, USA). Statistical significance was set at p < 0.05.

Results

Patient Characteristics and In-Hospital Mortality

A total of 368 patients were initially screened, and 342 met the inclusion criteria after exclusions (Figure 1). The cohort comprised 217 women (63.5%) and 125 men (36.5%), with a mean age of 79.61 ± 8.31 years. Extracapsular fractures accounted for 58.5% (n=200) and intracapsular fractures for 41.5% (n=142) of cases. ASA score distribution was as follows: ASA I–II in 8.5%, ASA III in 60.8%, and ASA IV in 30.7%. The Charlson Comorbidity Index was higher in non-survivors than survivors (6.31 ± 2.38 vs 5.47 ± 2.33, p=0.039). Among comorbidities, atrial fibrillation (p<0.001), prior myocardial infarction (p=0.006), congestive heart failure (p=0.013), and chronic kidney disease (p=0.016) were significantly associated with in-hospital mortality, whereas hypertension, diabetes mellitus, COPD, dementia, cerebrovascular disease, and malignancy were not (Table 1).

Table 1.

Baseline Demographic, Clinical Characteristics, and Comorbidities

Variable	Total (n=342)	Survivors (n=313)	In-hospital Mortality (n=29)	p value
Demographics
Age, years	79.61 ± 8.31	79.52 ± 8.20	80.59 ± 9.53	0.508
Sex				0.333
Men	125 (36.5%)	112 (35.8%)	13 (44.8%)
Women	217 (63.5%)	201 (64.2%)	16 (55.2%)
Fracture side				0.770
Left	174 (50.9%)	160 (51.1%)	14 (48.3%)
Right	168 (49.1%)	153 (48.9%)	15 (51.7%)
Fracture type				0.706
Intracapsular	142 (41.5%)	129 (41.2%)	13 (44.8%)
Extracapsular	200 (58.5%)	184 (58.8%)	16 (55.2%)
ASA score				0.005
ASA I	1 (0.3%)	1 (100%)	0 (0%)
ASA II	28 (8.2%)	28 (100%)	0 (0%)
ASA III	208 (60.8%)	194 (93.3%)	14 (6.7%)
ASA IV	105 (30.7%)	90 (85.7%)	15 (14.3%)
Charlson Comorbidity Index	5.54 ± 2.35	5.47 ± 2.33	6.31 ± 2.38	0.039
Anesthesia type				0.016
Spinal	243 (71.1%)	228 (93.8%)	15 (6.2%)
General	99 (28.9%)	85 (85.9%)	14 (14.1%)
Surgical procedure				0.220
Osteosynthesis	178 (52.0%)	166 (93.3%)	12 (6.7%)
Arthroplasty	164 (48.0%)	147 (89.6%)	17 (10.4%)
Comorbidities
Hypertension	271 (79.2%)	246 (90.8%)	25 (9.2%)	0.334
Diabetes mellitus	139 (40.6%)	127 (91.4%)	12 (8.6%)	0.933
History of myocardial infarction	111 (32.5%)	95 (85.6%)	16 (14.4%)	0.006
COPD	90 (26.3%)	81 (90.0%)	9 (10.0%)	0.546
Dementia	74 (21.6%)	70 (94.6%)	4 (5.4%)	0.284
Atrial fibrillation	78 (22.8%)	63 (80.8%)	15 (19.2%)	<0.001
Congestive heart failure	44 (12.9%)	36 (81.8%)	8 (18.2%)	0.013
Chronic kidney disease	37 (10.8%)	30 (81.1%)	7 (18.9%)	0.016
Cerebrovascular disease	57 (16.7%)	50 (87.7%)	7 (12.3%)	0.259
Malignancy	24 (7.0%)	23 (95.8%)	1 (4.2%)	0.432

ASA, American Society of Anesthesiologists; COPD, chronic obstructive pulmonary disease. Values are mean ± SD or n (%). P values from Student's t-test, Mann–Whitney U, chi-square, or Fisher’s exact test as appropriate.

In-hospital mortality occurred in 29 of 342 patients (8.5%). Mean age did not differ significantly between survivors and non-survivors (79.52 ± 8.20 vs 80.59 ± 9.53 years, p=0.508), nor did sex (p=0.333) or fracture side (p=0.770). ASA score was significantly associated with mortality, with rates of 0%, 6.7%, and 14.3% in ASA I–II, III, and IV patients, respectively (p=0.005).

Perioperative Variables and Complications

General anesthesia was associated with higher mortality compared with spinal anesthesia (14.1% vs 6.2%, p=0.016). Patients operated beyond 48 hours had significantly higher mortality than those operated within 48 hours (16.9% vs 6.5%, p=0.007). Admission oxygen saturation was lower in non-survivors (92.24 ± 5.24% vs 94.89 ± 3.10%, p<0.001). Preoperative hyponatremia was present in 24 patients (7.0%) and was associated with significantly higher mortality compared with normonatremia (20.8% vs 7.5%, p=0.041). Postoperative AKI was strongly associated with in-hospital mortality (28.2% vs 5.9%, p<0.001), as were intraoperative cardiac arrest (p<0.001), intraoperative blood transfusion (p=0.011), infection (p=0.009), and preoperative anticoagulant use (p=0.001) (Table 2).

Table 2.

Perioperative Variables, Anticoagulation, and Complications

Variable	Total (n=342)	Survivors (n=313)	In-hospital Mortality (n=29)	p value
Surgical Timing and Perioperative Variables
Length of hospital stay, days	7.47 ± 9.38	6.46 ± 6.13	18.45 ± 22.71	0.180
Prior hospitalization within 1 year	84 (24.6%)	74 (88.1%)	10 (11.9%)	0.194
Time to surgery				0.007
≤48 hours	277 (81.0%)	259 (93.5%)	18 (6.5%)
>48 hours	65 (19.0%)	54 (83.1%)	11 (16.9%)
Operation time, min	118.44 ± 44.26	117.89 ± 37.82	124.31 ± 88.80	0.456
Day of surgery				0.021
Weekend	84 (24.6%)	82 (97.6%)	2 (2.4%)
Weekday	258 (75.4%)	231 (89.5%)	27 (10.5%)
Surgery timing				0.256
Out-of-hours	176 (51.5%)	164 (93.2%)	12 (6.8%)
In-hours	166 (48.5%)	149 (89.8%)	17 (10.2%)
Admission oxygen saturation, %	94.66 ± 3.41	94.89 ± 3.10	92.24 ± 5.24	<0.001
Anticoagulation
Preoperative anticoagulant use	151 (44.2%)	130 (86.1%)	21 (13.9%)	0.001
Anticoagulant type				0.003
None	191 (55.9%)	183 (58.4%)	8 (27.6%)
Aspirin	50 (14.6%)	46 (14.7%)	4 (13.8%)
LMWH	7 (2.0%)	6 (1.9%)	1 (3.4%)
P2Y12 antagonist/clopidogrel	40 (11.7%)	36 (11.5%)	4 (13.8%)
Warfarin	10 (2.9%)	8 (2.6%)	2 (6.9%)
NOAC	44 (12.9%)	34 (10.9%)	10 (34.5%)
Perioperative Complications
Intraoperative cardiac arrest	4 (1.2%)	1 (25.0%)	3 (75.0%)	<0.001
Intraoperative blood transfusion	43 (12.6%)	35 (81.4%)	8 (18.6%)	0.011
Postoperative blood transfusion	176 (51.5%)	158 (89.8%)	18 (10.2%)	0.232
Acute kidney injury (AKI)	39 (11.4%)	28 (71.8%)	11 (28.2%)	<0.001
Infection	21 (6.1%)	16 (76.2%)	5 (23.8%)	0.009
Prolonged drainage	91 (26.6%)	79 (86.8%)	12 (13.2%)	0.060

LMWH, low molecular weight heparin; NOAC, non-vitamin K oral anticoagulant. Values are mean ± SD or n (%).

Laboratory Findings

Preoperative albumin was lower in non-survivors (3.64 ± 0.62 vs 4.02 ± 0.46 g/dL, p<0.001), as were postoperative albumin levels (2.77 ± 0.61 vs 3.23 ± 0.41 g/dL, p<0.001). Preoperative WBC count was higher in non-survivors (14.39 ± 8.16 vs 10.00 ± 3.74 ×10³/µL, p<0.001), and preoperative creatinine was also elevated (1.40 ± 0.87 vs 1.11 ± 0.91 mg/dL, p=0.003) (Table 3).

Table 3.

Laboratory Parameters in Survivors and Non-survivors

Variable	Survivors (n=313)	Non-survivors (n=29)	p value
Preoperative hemoglobin, g/dL	11.72 ± 1.94	11.43 ± 2.23	0.461
Postoperative day-1 hemoglobin, g/dL	10.24 ± 1.65	9.58 ± 1.97	0.042
Hemoglobin decrease, g/dL	−1.48 ± 1.50	−1.86 ± 1.86	0.085
Preoperative WBC count, ×10³/µL	10.00 ± 3.74	14.39 ± 8.16	<0.001
Preoperative platelet count, ×10³/µL	223.48 ± 80.25	255.07 ± 141.64	0.635
Preoperative INR	1.12 ± 0.34	1.19 ± 0.25	0.031
Preoperative creatinine, mg/dL	1.11 ± 0.91	1.40 ± 0.87	0.003
Postoperative day-1 creatinine, mg/dL	1.06 ± 0.88	1.78 ± 1.01	<0.001
Creatinine change, mg/dL	−0.05 ± 0.44	0.39 ± 0.65	<0.001
Preoperative albumin, g/dL	4.02 ± 0.46	3.64 ± 0.62	<0.001
Postoperative day-1 albumin, g/dL	3.23 ± 0.41	2.77 ± 0.61	<0.001
Albumin decrease, g/dL	−0.79 ± 0.41	−0.88 ± 0.59	0.438
Preoperative serum sodium, mmol/L	140.26 ± 3.73	138.69 ± 4.94	0.036
Preoperative serum potassium, mmol/L	4.49 ± 0.62	4.61 ± 0.65	0.316

WBC, white blood cell; INR, international normalized ratio. Values are mean ± SD.

Multivariable Analysis and Risk Score

Multivariable logistic regression identified three independent predictors of in-hospital mortality: postoperative AKI (presence vs absence OR 5.46, 95% CI 2.20–13.51, p<0.001), lower preoperative serum albumin (OR per 1 g/dL increase 0.38, 95% CI 0.18–0.78, p=0.008), and lower admission oxygen saturation (OR per 1% increase 0.86, 95% CI 0.77–0.95, p=0.003). The model demonstrated good overall fit (Omnibus χ²=34.65, p<0.001; Nagelkerke R²=0.219) and acceptable discrimination (AUC=0.766, 95% CI 0.655–0.877) (Figure 2). Bootstrap resampling with 1000 iterations confirmed the stability of all three predictors. Calibration analysis showed some deviation between predicted and observed mortality across risk deciles (Hosmer–Lemeshow p=0.018), discussed further in the Limitations section (Figure 3) (Table 4).

Table 4.

Multivariable Logistic Regression Analysis — Independent Predictors of In-Hospital Mortality

Variable	OR	95% CI	p value
Postoperative acute kidney injury	5.46	2.20–13.51	<0.001
Preoperative serum albumin (per 1 g/dL increase)	0.38	0.18–0.78	0.008
Admission oxygen saturation (per 1% increase)	0.86	0.77–0.95	0.003

Omnibus χ² = 34.65, p <0.001 | Nagelkerke R² = 0.219 | AUC = 0.766 (95% CI 0.655–0.877) | Hosmer–Lemeshow p = 0.018.

Bootstrap internal validation: 1000 iterations; BCa 95% CIs did not cross unity for any predictor.

OR, odds ratio for in-hospital mortality; CI, confidence interval. For AKI (binary variable), OR >1 indicates increased mortality risk. For serum albumin and admission oxygen saturation (continuous variables), OR <1 indicates that higher values are associated with reduced mortality risk.

A simple additive risk score was derived by assigning one point for each independent predictor present. Mortality rates increased significantly across score categories: 3.4% (score 0, n=238), 14.8% (score 1, n=81), and 39.1% (score 2–3, n=23) (χ²=40.2, p<0.001) (Table 5). Kaplan–Meier analysis confirmed significantly poorer survival in the high-risk group (log-rank χ²=6.91, p=0.009) (Figure 4).

Table 5.

Clinical Risk Score Derived From Multivariable Model. One Point Assigned per Predictor Present (AKI, Hypoalbuminemia, Hypoxia). Overall χ² = 40.2, p <0.001

Risk Score	Patients, n	In-hospital Mortality, n (%)
0	238	8 (3.4%)
1	81	12 (14.8%)
2–3	23	9 (39.1%)

Interaction Analysis: Fracture Type as a Modifier of AKI-Associated Mortality

Interaction analysis revealed that fracture type significantly modified the association between postoperative AKI and in-hospital mortality (interaction p=0.016). In stratified multivariable models, AKI was a strong and significant predictor of mortality in patients with extracapsular fractures (OR 14.3, 95% CI 4.0–50.0, p<0.001), whereas no significant association was observed in intracapsular fractures (OR 1.32, 95% CI 0.30–5.88, p=0.718). Preoperative albumin remained an independent predictor of mortality in both fracture subtypes (Table 6). In patients without postoperative AKI, in-hospital mortality rates did not significantly differ between intracapsular and extracapsular fractures (7.4% vs 5.0%, p = 0.385).

Table 6.

Stratified Multivariable Analysis According to Fracture Type

Predictor	Intracapsular OR (95% CI)	p	Extracapsular OR (95% CI)	p
Postoperative AKI	1.32 (0.30–5.88)	0.718	14.3 (4.0–50.0)	<0.001
Preoperative serum albumin	0.24 (0.08–0.75)	0.015	0.27 (0.10–0.73)	0.009
Admission oxygen saturation	0.42 (0.10–1.75)	0.237	0.23 (0.07–0.75)	0.015

Interaction term for AKI × fracture type: p = 0.016.

OR, odds ratio for in-hospital mortality; CI, confidence interval; AKI, acute kidney injury.

ORs are expressed per unit increase for continuous variables and as presence vs absence for binary variables.

Discussion

The present study identified postoperative acute kidney injury, lower preoperative serum albumin, and reduced admission oxygen saturation as independent predictors of in-hospital mortality following hip fracture surgery in elderly patients. Derived from three routinely available perioperative variables, the proposed additive risk score stratified mortality risk from 3.4% in the lowest-risk group to 39.1% in the highest-risk group, demonstrating that clinically meaningful prognostication is achievable without complex scoring systems. Crucially, this study provides the first evidence that fracture type significantly modifies the prognostic impact of postoperative AKI on in-hospital mortality, with renal dysfunction conferring substantially greater mortality risk in extracapsular than intracapsular fractures.

Postoperative AKI emerged as the strongest independent predictor of in-hospital mortality in the present cohort, with an odds ratio of 5.46 — a magnitude consistent with, and extending, prior evidence in elderly hip fracture populations. AKI following hip fracture surgery is not an isolated renal event but rather a systemic marker of perioperative physiological deterioration, reflecting the convergence of hemodynamic instability, surgical stress, volume shifts, and impaired renal reserve that characterize the early postoperative course in older adults.^10-12 Reported AKI incidence rates in this population range from approximately 11% to 24%, and the associated increase in mortality risk is well established across multiple retrospective and prospective cohorts.^10-12 The present study’s AKI incidence of 11.4% falls within the lower end of this range, likely reflecting the relatively selective surgical eligibility criteria applied and the predominantly spinal anesthesia approach in our cohort. More importantly, the interaction analysis revealed that the association between AKI and in-hospital mortality differed substantially according to fracture subtype — with AKI conferring a markedly elevated mortality risk in extracapsular fractures (OR 14.3, 95% CI 4.0–50.0) but no statistically significant association in intracapsular fractures (OR 1.32, p=0.718; interaction p=0.016). This divergence may be explained by the distinct pathophysiological profile of extracapsular fractures, which are typically associated with greater intraoperative blood loss, more extensive soft tissue disruption, and a higher systemic inflammatory burden compared with their intracapsular counterparts.^4,13-15 These factors may collectively amplify perioperative hemodynamic stress, compromise renal perfusion, and lower the threshold at which AKI translates into irreversible systemic deterioration.^7,12 Although the interaction between fracture subtype and renal outcomes has not been directly examined in prior hip fracture mortality studies, our findings align with the broader surgical literature demonstrating that the prognostic weight of organ dysfunction is modulated by the magnitude of the underlying physiological insult.¹⁶ Taken together, these findings suggest that AKI surveillance and early renal protective strategies may warrant particular prioritization in patients undergoing surgery for extracapsular hip fractures. Preoperative hypoalbuminemia independently predicted in-hospital mortality in our cohort, a finding that is consistent with an extensive body of evidence linking low serum albumin to adverse perioperative outcomes in elderly surgical patients.^7,8,14,17 Albumin occupies a unique position among preoperative biomarkers in that it simultaneously reflects nutritional status, hepatic synthetic capacity, systemic inflammatory burden, and overall physiological reserve — dimensions of frailty that are each independently relevant to postoperative prognosis.¹⁷ In elderly hip fracture patients specifically, preoperative hypoalbuminemia has been associated with higher rates of postoperative complications, prolonged hospitalization, and increased short-term mortality.^7,17,18 Beyond its role as a nutritional marker, albumin also exerts direct physiological functions including oncotic pressure maintenance, drug binding, and antioxidant activity, deficits in which may further compromise recovery from surgical stress.^17,19 The present findings reinforce the value of preoperative albumin assessment as a routine component of perioperative risk evaluation in this population, and raise the question of whether targeted perioperative nutritional optimization may represent a modifiable pathway to improved outcomes — a hypothesis that warrants prospective investigation.

Reduced admission oxygen saturation was the third independent predictor identified in the present study. Although respiratory parameters are less frequently incorporated into hip fracture prognostic models compared with comorbidity indices or laboratory markers, impaired oxygenation at presentation likely reflects underlying cardiopulmonary disease, blunted physiological reserve, or acute systemic compromise secondary to the fracture itself.^3,5 In elderly patients, the cardiopulmonary consequences of hip fracture — including pain-mediated splinting, immobility-related atelectasis, and perioperative fluid shifts — may be superimposed on pre-existing pulmonary or cardiac dysfunction, creating a state of compounded physiological vulnerability.⁵ Low admission oxygen saturation may therefore serve as a simple and readily available marker of this systemic compromise, providing prognostic information that complements and extends traditional clinical risk scores at the point of initial assessment.

Unlike conventional hip fracture mortality models that rely exclusively on preoperative variables, the present model deliberately incorporates postoperative AKI as a dynamic perioperative indicator of evolving systemic deterioration.^3,5,20 This distinction is methodologically important: rather than a preoperative screening tool, the proposed risk score is intended as a perioperative stratification instrument that can be applied during the index hospitalization to identify patients at elevated risk of early death and prompt intensified monitoring, early nephrology or critical care involvement, and targeted multidisciplinary intervention. Bootstrap internal validation confirmed the stability of all three predictors across 1000 resampling iterations, and bias-corrected confidence intervals did not cross unity for any variable, supporting the robustness of the identified associations and a low risk of substantial overfitting. Nevertheless, external validation in geographically and institutionally diverse cohorts remains an essential prerequisite before the score can be recommended for routine clinical application. Beyond the three predictors incorporated into the risk score, several other perioperative variables were associated with in-hospital mortality on univariate analysis, including surgical timing, anesthesia type, and preoperative anticoagulant use. The observed association between surgery beyond 48 hours and higher in-hospital mortality is consistent with prior registry-based evidence linking delayed surgery to increased 30-day and 90-day mortality in proximal femoral fractures,²¹ and supports ongoing efforts to minimize time to surgical intervention in this population.

An unexpected finding in the present cohort was the lower in-hospital mortality rate observed among patients operated on weekends (2.4%) compared with weekdays (10.5%; p=0.021). This result appears counterintuitive in light of the well-described “weekend effect” — the phenomenon whereby patients admitted on weekends experience higher complication rates and mortality owing to reduced staffing levels and diminished access to specialist services — which has been documented in several hip fracture registry studies.²² However, the direction of the association we observed may reflect selection bias inherent to retrospective data: patients undergoing surgery on weekends in our institution may represent a more selected, hemodynamically stable subgroup with fractures deemed amenable to weekend scheduling, whereas higher-risk or more complex cases may be deferred to early weekdays. The small number of weekend surgeries (n=84) and the retrospective design preclude definitive conclusions, and this finding should be interpreted with caution.

Several limitations of this study merit consideration. The retrospective, single-center design introduces the potential for selection bias, limits causal inference, and constrains generalizability to other healthcare settings and patient populations. Important clinical variables — including validated frailty indices, pre-fracture functional status, intraoperative hemodynamic parameters, and detailed nutritional assessments — were not available in the dataset and could not be incorporated into the analyses. A formal a priori sample size calculation was not performed due to the retrospective design of the study. The Hosmer–Lemeshow test indicated borderline-acceptable calibration (p=0.018), reflecting some deviation between predicted and observed mortality across risk deciles; this finding should temper the interpretation of the model’s absolute predictive accuracy and underscores the need for recalibration prior to external application. The stratified multivariable analyses performed within each fracture subtype were limited by a relatively low number of events per variable (EPV approximately 4–5), which constrains the precision of the stratum-specific estimates and necessitates cautious interpretation; these findings should be regarded as hypothesis-generating pending replication in larger cohorts. Additionally, Kaplan–Meier survival analysis was performed using length of hospital stay as a surrogate for in-hospital survival duration; as patients who survived to discharge were treated as censored observations, this approach approximates rather than directly measures in-hospital survival time and should be interpreted accordingly. Intraoperative hemodynamic parameters, including vasopressor use, were not consistently available in the dataset and could not be incorporated into the analysis. Further subgroup analysis comparing hemiarthroplasty and total hip arthroplasty was not performed due to the limited number of events. Intraoperative blood loss was not routinely and consistently recorded and therefore could not be included in the statistical analysis. Additionally, although all extracapsular fractures were treated with intramedullary nailing, variations in surgical technique (e.g., open versus closed reduction) may have influenced intraoperative blood loss. The study was limited to in-hospital mortality and did not capture 30-day or one-year outcomes, which are established endpoints in hip fracture research and may provide additional prognostic context. Finally, as the risk score includes postoperative variables, it cannot function as a purely preoperative prediction tool, and residual confounding inherent to observational study design cannot be entirely excluded.

Conclusions

Postoperative acute kidney injury, preoperative hypoalbuminemia, and reduced admission oxygen saturation independently predict in-hospital mortality following hip fracture surgery in elderly patients. A simple additive risk score derived from these three routinely available perioperative variables demonstrated meaningful mortality stratification across risk categories. Notably, fracture type significantly modified the prognostic impact of AKI, with renal dysfunction conferring substantially greater mortality risk in extracapsular than intracapsular fractures — a finding that may inform fracture-type–specific perioperative monitoring strategies. External validation in larger, multicenter cohorts is warranted to confirm these findings and facilitate broader clinical implementation.

Footnotes

ORCID iD

Ozan Altun

Ethical Considerations

This study was approved by the Karabük University Non-Interventional Clinical Research Ethics Committee (Approval No: 18/11/2021/732). The study was conducted in accordance with the Declaration of Helsinki.

Consent to Participate

Due to the retrospective design of the study, the requirement for informed consent was waived by the ethics committee (Karabük University Non-Interventional Clinical Research Ethics Committee, Approval No: 18/11/2021/732).

Author Contributions

OA and UD conceived the study. OA, YE, and EO designed the methodology. OA and OÇ collected the data. OA performed the formal analysis and prepared the visualizations. OA, OÇ, and MNY conducted the investigation. UD and MNY provided resources for the study. UD and YE supervised the project. EO and YE validated the analyses. OA drafted the manuscript. EO, YE, UD, and MNY critically reviewed and edited the manuscript. All authors read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.*

Use of Artificial Intelligence

Artificial intelligence (AI)-assisted tools were used during the preparation of this manuscript for the purposes of language editing and manuscript drafting. All AI-generated content was critically reviewed, revised, and approved by the authors. The authors take full responsibility for the accuracy, integrity, and originality of the submitted work.

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