Comparative Predictive Performance of HALP Score and Prognostic Nutritional Index for Mortality in ICU Patients After Hip Fracture Surgery

Comparative Predictive Performance of HALP Score and Prognostic Nutritional Index for Mortality in ICU Patients After Hip Fracture Surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Predictive Performance of HALP Score and Prognostic Nutritional Index for Mortality in ICU Patients After Hip Fracture Surgery Hülya Tosun Söner, Fatma Acil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9271468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hip fractures in elderly patients are associated with high morbidity and mortality. Simple and reliable prognostic biomarkers are needed to guide perioperative management. Hemoglobin–Albumin–Lymphocyte–Platelet (HALP) score and Prognostic Nutritional Index (PNI) reflect nutritional and immunological status and may predict postoperative outcomes. We aimed to investigate the association of preoperative HALP score and PNI with 1-year all-cause mortality in elderly patients admitted to the intensive care unit (ICU) after surgical treatment for hip fractures. Methods A retrospective study was performed on 257 patients aged 65 years and older who were admitted to the ICU following surgical intervention for hip fractures. Demographic information, comorbidities, and laboratory results were all gathered. The patients were categorized into two groups: non-survivors (n = 40) and survivors (n = 217). Univariable and multivariable Cox regression analyses were performed to identify independent predictors of 1-year all-cause mortality. Results Non-survivors had significantly lower PNI (34.1 ± 7.7 vs. 37.9 ± 6.6; p = 0.004) and HALP scores (median 11.3 vs. 17.3; p = 0.003). Multivariable analysis identified low PNI (HR 0.910; 95% CI 0.841–0.984; p = 0.018), male gender (HR 3.054; p = 0.007), malignancy (HR 7.303; p = 0.014), and high Charlson Comorbidity Index (HR 2.404; p < 0.001) as independent predictors of mortality. ROC analysis demonstrated moderate discriminative power for PNI (AUC 0.642) and HALP (AUC 0.648). Conclusions Preoperative PNI is an independent predictor of mortality in elderly hip fracture patients, whereas HALP shows moderate prognostic value. These simple nutritional indices may aid early risk stratification and guide perioperative care. Hip fracture surgery HALP score prognostic nutritional index intensive care unit all-cause mortality Figures Figure 1 Figure 2 Figure 3 1. Introduction The number of hip fractures in older people has gone up a lot because the world's population is getting older. This is becoming a bigger public health issue. Hip fractures are a major public health issue for both patients and healthcare systems because they lead to longer hospital stays, more complications after surgery, and higher death rates. Epidemiological projections predict that the annual number of hip fractures worldwide will increase from approximately 1.6 million in 2000 to 6.3 million by 2050 [ 1 ]. Current data show that 30-day mortality rates after hip fracture range from 1–7%, while one-year mortality rates are approximately 23–30% [ 2 , 3 ]. Although it has been shown that performing surgery within the first 24–48 hours after admission can reduce postoperative complications [ 4 ], many patients with hip fractures continue to experience significant reductions in functional capacity and adverse clinical outcomes [ 5 ]. Consequently, the identification of reliable and easily applicable biomarkers that can predict mortality and adverse clinical outcomes in patients with hip fractures is becoming increasingly crucial. One indicator that has attracted attention in recent years is the Hemoglobin–Albumin–Lymphocyte–Platelet (HALP) score, a novel composite parameter reflecting both systemic inflammation and hematological status [ 6 ]. Although the number of studies evaluating its prognostic value in geriatric fracture populations is limited, the individual components of the HALP score, particularly albumin, hemoglobin, and lymphocyte count, are well-known predictors of mortality [ 7 – 9 ]. Recent findings in oncology and cardiovascular disease populations suggest that the HALP score may be a promising biomarker for predicting clinical outcomes [ 10 ]. However, its prognostic predictive value in the elderly population with hip fractures has not yet been adequately investigated. Another important parameter for assessing nutritional and immunological status is the Prognostic Nutrition Index (PNI). PNI is a simple indicator commonly used to assess perioperative nutritional status. It is calculated from the preoperative serum albumin level and total lymphocyte count [ 11 ]. Low PNI levels have been reported to be associated with poor clinical outcomes in various diseases such as gastrointestinal cancer [ 12 ], colorectal cancer [ 13 ], lung cancer [ 14 ], chronic obstructive pulmonary disease (COPD) [ 15 ], diabetic nephropathy [ 16 ], and cardiovascular diseases [ 17 ]. Moreover, research indicates that diminished preoperative PNI values correlate with elevated mortality rates and increased postoperative complications in patients with hip fractures [ 18 – 22 ]. Additionally, research indicates that decreased preoperative PNI values are linked with raised mortality rates and heightened postoperative complications in patients with hip fractures [ 18 – 22 ]. These results indicate that strengthening nutritional status may be a potentially adjustable factor in lowering mortality risk. This study aimed to investigate the relationship between HALP score, PNI, and one-year all-cause mortality in elderly patients admitted to the intensive care unit (ICU) following surgical intervention for hip fractures. 2. Materials and Methods Study design and patient population This retrospective observational study was conducted to examine the relationship between nutritional assessment scores and mortality in elderly patients admitted to the intensive care unit (ICU) after surgical treatment for hip fractures. Patients who underwent surgery for hip fractures at the Orthopedics and Traumatology Clinic of Gazi Yaşargil Training and Research Hospital, University of Health Sciences, between January 2020 and December 2025 were included in the study. Patients aged 65 years and older with femoral neck, intertrochanteric, and subtrochanteric fractures were included. Patients with pathological fractures, a history of multiple traumas, active infections, and incomplete laboratory data were excluded from the study. After excluding patients who met the exclusion criteria, a total of 257 patients were included in the study. Based on medical records, patients who died during follow-up were classified as non-survivors (n = 40), while those who remained alive were assigned to the survivor group (n = 217). All patients were managed in the ICU during the postoperative period. The study was approved by Gazi Yaşargil Training and Research Hospital, University of Health Sciences Ethics Committee (Date: January 27, 2026, Decision Number: 43). All procedures were carried out in accordance with the principles of the Helsinki Declaration. Due to the retrospective nature of the study, no additional informed consent was obtained from the participants. Data collection Patients' demographic characteristics (age, gender), clinical characteristics and comorbidities, fracture type, surgical treatment method applied, length of hospital stay, and need for intensive care were retrospectively recorded through the hospital's electronic record system. Preoperative laboratory parameters (hemoglobin, serum albumin, lymphocyte, and platelet counts) were obtained within 24 hours before surgery. Comorbidities were assessed using the Charlson Comorbidity Index (CCI). HALP score and PNI calculation The HALP score was calculated using the following formula: HALP = Hemoglobin (g/L) × Albumin (g/L) × Lymphocyte count (/L) ÷ Platelet count (/L) The Prognostic Nutrition Index (PNI) was calculated using the following formula: PNI = (10 × serum albumin [g/dL]) + (0.005 × total lymphocyte count [/mm³]) All calculations were performed using the initial preoperative laboratory values. The primary aim of our study was to investigate the association of HALP score and PNI with 1-year all-cause mortality. 3. Statistical Analysis The statistical analyses were conducted using SPSS version 27.0 for Windows (Armonk, NY, USA: IBM Corp.). The normality of distribution for continuous variables was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests, in conjunction with visual inspection of histograms. Continuous variables demonstrating normal distribution were expressed as mean ± standard deviation (SD), whereas those not conforming to normal distribution were reported as median and interquartile range (IQR). Comparisons of normally distributed continuous variables were performed using the Student’s t-test, while the Mann–Whitney U test was applied for variables without normal distribution. Categorical variables were compared using the chi-square test or Fisher’s exact test when appropriate, and were presented as percentages. To identify independent predictors of 1-year all-cause mortality, univariable and multivariable Cox regression analyses were conducted. Variables with a p-value < 0.10 in univariable analysis were entered into the multivariable model. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the association between 1-year all-cause mortality and PNI and HALP scores. The optimal cutoff values were determined based on the Youden index. A two-tailed p-value of < 0.05 was considered statistically significant. 4. Results A total of 257 patients were included in the study after the exclusion criteria; 40 of these (15.6%) were deceased, and 217 (84.4%) were in the surviving patient group. The demographic and clinical characteristics of the patients are summarized in Table 1 . No significant difference was found between the groups in terms of age (p = 0.678). The female gender ratio was higher in the survivor group (survivor 58.5%; non-survivor 35%), while the male gender was significantly associated with mortality (p = 0.006). The presence of malignancy was found to be significantly higher in the non-survivor group (12.9% vs. 1.6%; p < 0.001). ASA score and CCI were significantly higher in the non-survivor group (p = 0.020 and p = 0.006, respectively). The duration of intensive care monitoring was significantly longer in the non-survivor group (median 13 days; IQR 21.5; p < 0.001). When laboratory parameters were evaluated, albumin levels were significantly lower in the non-survivor group (28.5 ± 7.2 vs. 31.5 ± 5.1 mg/dl; p = 0.042). Similarly, the PNI value was found to be significantly lower in the non-survivor group (34.1 ± 7.7 vs. 37.9 ± 6.6; p = 0.004). The HALP score was also significantly lower in the non-survivor group (median 11.3 vs. 17.3; p = 0.003). No significant differences were observed between the groups in terms of hemoglobin, hematocrit, white blood cell, and platelet values ​​(p > 0.05 for each). Figure 1 represents box-plot comparisons of critical care duration, PNI, CCI, and HALP scores across the groups. Table 1 Baseline characteristics of the total population Parameters Non-survivors (n = 40) Survivors (n = 217) Total population (n = 257) p-value Age (years) 81.9 ± 9.6 77.9 ± 10.2 78.5 ± 10.1 0.678 Female gender, n (%) 14 (35) 127 (58.5) 141 (54.9) 0.006 Chronic kidney disease, n (%) 7 (17.5) 17 (7.8) 24 (9.3) 0.054 Hypertension, n (%) 17 (42.5) 109 (50.2) 126 (49) 0.369 Diabetes mellitus, n (%) 9 (22.5) 29 (13.4) 38 (14.8) 0.135 Cerebrovascular disease n (%) 2 (5) 13 (6) 15 (5.8) 0.806 Malignancy, n (%) 5 (12.9) 4 (1.6) 9 (3.5) < 0.001 Coronary artery disease, n (%) 8 (20) 50 (23) 58 (22.6) 0.672 Demantia, n (%) 2 (5) 11 (5.1) 13 (5.1) 0.985 ASA 3 ± 0.4 2.6 ± 0.6 2.7 ± 0.6 0.020 CCI 5.6 ± 1.1 4.4 ± 1.2 4.6 ± 1.3 0.006 Critical care duration (IQR) 13 (21.5) 2 (3) 2 (4) < 0.001 Laboratory findings Hemoglobin, (mg/dl) 10.9 ± 2 11.6 ± 2 11.5 ± 2 0.139 Hematocrit, (mg/dl) 34 ± 5.8 36.1 ± 6 35.8 ± 6 0.215 WBC (×10³/µL) 11.1 ± 3.9 10.5 ± 4 10.6 ± 4 0.404 Lymphocyte (×10³/µL) 1.12 ± 0.6 1.3 ± 0.6 1.26 ± 0.6 0.925 Platelet (×10³/µL) 233 (164) 239 (102) 237 (113) 0.284 Albumin (g/dl) 28.5 ± 7.2 31.5 ± 5.1 31 ± 5.6 0.042 PNI 34.1 ± 7.7 37.9 ± 6.6 37.3 ± 6.9 0.004 HALP score (IQR) 11.3 (11.1) 17.3 (16.2) 16.5 (16.6) 0.003 WBC: White blood cell, ASA: American Society of Anesthesiologists, CCI: Charlson Comorbidity Index, IQR: Interquartile range, In the univariable Cox regression analysis performed to determine the predictors of 1-year all-cause mortality, age (HR: 1.044; 95% CI: 1.006–1.083; p = 0.023), low PNI (HR: 0.919; 95% CI: 0.871–0.969; p = 0.002), male gender (HR: 2.621; 95% CI: 1.297–5.296; p = 0.007), presence of malignancy (HR: 7.607; 95% CI: 1.948–29.714; p = 0.004), high CCI (HR: 2.412; 95% CI: 1.738–3.348; p < 0.001) and low albumin levels (HR: 0.909; 95% CI: 0.855–0.966, p = 0.002) were associated with 1- year all-cause mortality. In multivariable Cox regression analysis, male gender HR: 3.054; 95% CI: 1.362–6.852; p = 0.007), presence of malignancy (HR: 7.303; 95% CI: 1.491–35.779; p = 0.014), high CCI (HR: 2.404; 95% CI: 1.633–3.538; p < 0.001), and low PNI (HR: 0.910; 95% CI: 0.841–0.984; p = 0.018) were identified as independent predictors of 1-year all-cause mortality. Age, HALP score, and chronic kidney disease did not retain statistical significance in multivariable analysis (p > 0.05). Table 2 shows uniivariable and multivariable Cox regression analysis results. The results of multivariable Cox regression analysis were also schematized with Forest-Plot plots (Fig. 2 ). Table 2 Univariable and multivariable Cox regression analyses for predicting total mortality Parameters Univariable Multivariable HR 95% CI p-value HR 95% CI p-value Age 1.044 (1.006–1.083) 0.023 0.996 (0.949–1.045) 0.867 PNI 0.919 (0.871–0.969) 0.002 0.910 (0.841–0.984) 0.018 HALP score 0.973 (0.944–1.003) 0.074 1.015 (0.980–1.051) 0.405 Male gender 2.621 (1.297–5.296) 0.007 3.054 (1.362–6.852) 0.007 Chronic kidney disease 2.496 (0.961–6.479) 0.060 1.514 (0.471–4.869) 0.487 Malignancy 7.607 (1.948–29.714) 0.004 7.303 (1.491–35.779) 0.014 CCI 2.412 (1.738–3.348) < 0.001 2.404 (1.633–3.538) < 0.001 Albumin 0.909 (0.855–0.966) 0.002 Hypertension 0.732 (0.371–1.447) 0.370 DM 1.882 (0.813–4.355) 0.140 Atrial fibrillation 4.286 (1.288–14.259) 0.018 Coronary artery disease 0.835 (0.362–1.928) 0.673 In ROC curve analysis, the area under the curve (AUC) was found to be 0.642 for PNI (p = 0.001), 0.617 for albumin (p = 0.002), and 0.648 for HALP score (p = 0.003). The cutoff points, determined using the Youden index, were 33.50 for PNI, 25.00 for albumin, and 15.35 for HALP, respectively. At these thresholds, the sensitivity and specificity values were 52.5% and 75.5% for PNI, 30.0% and 90.7% for albumin, and 67.5% and 58.8% for HALP, respectively. These findings indicate that PNI and HALP scores, in particular, demonstrate moderate discriminatory power in predicting 1-year all-cause mortality in patients who underwent hip fracture surgery and were followed in the intensive care unit. The results of the multivariable regression analysis were additionally illustrated using forest plot diagrams (Fig. 3 ). 5. Discussion In this study, we evaluated the relationship between PNI and HALP scores and 1-year all-cause mortality in elderly patients who were admitted to the ICU after undergoing surgical treatment for hip fracture. It was shown that low PNI is an independent predictor of mortality, while HALP was not found to be significant in multivariate analysis. In addition, male gender, presence of malignancy, and high Charlson Comorbidity Index (CCI) are associated with mortality. ROC analyses reveal that PNI and HALP scores have moderate discriminatory power in predicting mortality and that the determined cut-off values ​​may be a potential clinical tool in identifying high-risk patients. The current literature shows that low PNI and serum albumin levels have a strong association with mortality and complications after hip fracture. In the retrospective study of Durgun et al., low albumin and PNI scores were significantly associated with length of hospital stay, 30-day and 1-year mortality in 309 patients [ 21 ]. Similarly, another study examined the relationship between PNI and mortality at 1, 3, 6, 12, and 24 months after femur fracture surgery and found that low PNI increased mortality at all time points; the overall mortality rate was found to be 20% at three years of follow-up, and low PNI levels were strongly associated with long-term mortality [ 22 ]. The prognostic value of PNI has also been confirmed in large cohort studies. In their study examining 3351 hip fracture patients, Wang et al. reported that PNI was an independent predictor of postoperative complications and two-year all-cause mortality [ 23 ]. Tunçez et al., in their study involving 124 elderly patients, showed that low PNI was an independent risk factor for mortality after hip fracture surgery [ 24 ]. Malnutrition is very common in the elderly, affecting approximately 50% [ 24 ]. A recent systematic review reported that malnutrition is an independent risk factor for functional dependence and increased mortality in hip fracture patients [ 25 ]. Better nutritional care for malnourished patients is important to improve their outcomes. Therefore, early and rapid identification of malnourished patients in a hospital setting is crucial, especially in geriatric hip fracture patients [ 26 ]. In contrast to PNI, the HALP score did not remain a significant predictor of mortality in multivariate analysis in our study. Although HALP incorporates multiple components reflecting both nutritional (albumin) and hematological/inflammatory status (hemoglobin, lymphocyte, platelet), its prognostic performance in geriatric hip fracture patients remains controversial. Recent studies have reported inconsistent findings regarding the predictive value of HALP in this population. For instance, Cakmak et al. demonstrated that while PNI was significantly associated with mortality in geriatric hip fracture patients, HALP did not show independent prognostic significance for ICU mortality, which is consistent with our findings [ 27 ]. Similarly, Balaban et al. reported that the HALP score was not significantly associated with ICU or short-term mortality in critically ill elderly patients, despite evaluating multiple nutritional indices [ 28 ]. In another comparative ICU-based study, traditional indices such as PNI and GNRI outperformed HALP in predicting adverse outcomes, suggesting that HALP may have limited discriminatory capacity in critically ill populations [ 29 ]. On the other hand, some studies suggest that HALP may still have prognostic value under certain conditions. Vural et al. reported that lower HALP scores were associated with increased early and late mortality in elderly patients with proximal femur fractures, indicating that HALP may reflect overall physiological reserve [ 30 ]. Likewise, Wang et al. found that HALP could serve as a useful biomarker for mortality prediction in older hip fracture patients, although its predictive strength varied depending on patient characteristics and study design [ 31 ]. There could be a number of explanations why the studies don't agree with others. First, HALP contains hemoglobin and platelet counts, which can be affected by things that occur during and after surgery, like bleeding, transfusion, and inflammatory response. This could make it less stable as a prognostic marker in the ICU. Second, critically ill patients frequently demonstrate swift variations in hematological parameters, potentially diminishing the predictive accuracy of composite indices like HALP in contrast to more stable markers such as albumin-based PNI. Consequently, while HALP indicates both nutritional and inflammatory status, our results imply that it may not be as reliable as PNI in forecasting long-term mortality in elderly ICU patients following hip fracture surgery. Further large-scale, prospective studies are needed to clarify the role of HALP and to determine whether dynamic or serial measurements could improve its prognostic utility. Limitations This study, like many others, has some limitations. Firstly, the study's retrospective nature and single-center design make it hard to apply the results to other situations and make it impossible to show causal connections between variables. The limited number of patients who experienced mortality may influence the statistical power of multivariate analyses and the stability of the model. The inclusion of only patients monitored in the intensive care unit may have resulted in the assessment of a population with a more severe clinical presentation, potentially restricting the generalizability of the findings to all hip fracture patients. The analysis of parameters such as PNI, HALP, and albumin based only on a single measurement at the time of admission led to the neglect of temporal variations in these biomarkers. Additionally, potential confounding variables, such as nutritional status, inflammatory response, perioperative complications, and variations in treatment protocols, may not have been fully controlled. The failure to evaluate nutritional support therapies during the hospital stay is another factor that may affect the results. The moderate discriminative power of the AUC values ​​obtained in the ROC analysis suggests that the use of these parameters as strong predictors alone in clinical practice may be limited. The cutoff points determined according to the Youden index are specific to the study sample and need to be validated with prospective and multicenter studies in different populations. Finally, only mortality was evaluated in this study; functional recovery, quality of life, and long-term clinical outcomes were not analyzed. Therefore, the findings should be interpreted cautiously and supported by further studies. 6. Conclusion Our study found a significant association between PNI and HALP scores and 1-year all-cause mortality in patients who were followed up in the intensive care unit after surgery for hip fracture. These findings suggest that simple and easily calculable nutritional indices may be useful in clinical practice for risk stratification and early prognosis assessment in high-risk patient groups such as hip fractures. However, the integration of these parameters into clinical decision-making processes needs to be validated with prospective, multicenter studies with larger sample sizes. Abbreviations ASA → American Society of Anesthesiologists score AUC → Area Under the Curve CCI → Charlson Comorbidity Index CI → Confidence Interval COPD → Chronic Obstructive Pulmonary Disease DM → Diabetes Mellitus g/dL → grams per deciliter HALP → Hemoglobin–Albumin–Lymphocyte–Platelet score HR → Hazard Ratio ICU → Intensive Care Unit IQR → Interquartile Range LOS → Length of Stay mg/dL → milligrams per deciliter n → number (sample size) PNI → Prognostic Nutritional Index ROC → Receiver Operating Characteristic SD → Standard Deviation SPSS → Statistical Package for the Social Sciences WBC → White Blood Cell /L → per liter /mm³ → per cubic millimeter Declarations Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics Committee Approval The study was approved by Gazi Yaşargil Training and Research Hospital, University of Health Sciences Ethics Committee (Date: January 27, 2026, Decision Number: 43). All procedures were carried out in accordance with the principles of the Helsinki Declaration. Funding statement No external funding. Conflict of interest The authors declare no conflict of interest. Consent to publish Not applicable Consent to participate The requirement for informed consent was waived due to the study's retrospective design. Author Contributions Conceptualization: HTS, FA Methodology: HTS, FA Data curation: HTS, FA Formal analysis: HTS Investigation: HTS Visualization: HTS, FA Writing – original draft: HTS Writing – review & editing: HTS, FA Supervision: HTS Project administration: HTS References Cooper C, Campion G, Melton LJ. Hip fractures in the elderly: A world-wide projection. 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Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12964977/ Balaban U, Yalcin N, Kaya EK, Ortac Ersoy E. Assessment of nutritional indices for predicting clinical outcomes in critically ill elderly patients: A prospective cohort study. BMC Anesthesiol. 2025. doi:10.1186/s12871-025-03232-6 Kollu K, Yerdelen EA, Duran S, Kabatas B, Karakas F, et al. Comparison of nutritional risk indices (PNI, GNRI, mNUTRIC) and HALP score in predicting adverse clinical outcomes in older ICU patients. Medicine. 2024. Available from: https://journals.lww.com/md-journal/fulltext/2024/06210/comparison_of_nutritional_risk_indices__pni,_gnri,.14.aspx Vural A, Dolanbay T, Yagar H. Hemoglobin, albumin, lymphocyte and platelet (HALP) score for predicting early and late mortality in elderly patients with proximal femur fractures. PLoS One. 2025. doi:10.1371/journal.pone.0313842 Wang Z, Liu H, Liu M. The hemoglobin, albumin, lymphocyte, and platelet score as a predictor for mortality in older patients with hip fracture. Front Med. 2025. doi:10.3389/fmed.2025.1450818 Tahak F, Yaka H, Kirilmaz A, Kekec AF. Relationship between mortality and HALP score in femoral neck fractures treated with hemiarthroplasty. Jt Dis Relat Surg. 2025. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12456328/ Lv X, Liu X, Shen Y, Li C, Shen T, Wang Y, et al. Association between HALP score and in-hospital mortality in sepsis patients: A multicenter retrospective cohort study. Front Public Health. 2025. doi:10.3389/fpubh.2025.1710118 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9271468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623498656,"identity":"2f139223-108e-4b85-b5bc-8b5e91dc4ad0","order_by":0,"name":"Hülya Tosun Söner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACAwbmBhQBOQOIuAUeLYxALQkIAWOgISBxCeK1JG4Aa2HArcVcIrFN4ucPG3uD2+0PHxdU3Evfzt5/dMOPAgkG/vbuBGxaLGcktkn2JKQlbrhzxth4xpni3J09h9lu9gAdJnHm7AasDrsBtIUn4XCCwY0cNmnetoTcDTeS2W7wALUYSOTi1CL5J+GwvcGN9GfSvP8S0g2AWm7+IaBFGmgL44YbCWbSvA0JCSAtt/HacuZhs7VMWlrizBs5xsY8xxIMN5w5bHZbxkCCB6dfjicfvPnGxsae70b6w8c8NQnyBscbn91888dGjr+9F6sWIGDBHgU8OJSDAPMHPJKjYBSMglEwChgYAL6LZTZ6ipffAAAAAElFTkSuQmCC","orcid":"","institution":"Diyarbakır Gazi Yaşargil Eğitim ve Araştırma Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Hülya","middleName":"Tosun","lastName":"Söner","suffix":""},{"id":623498659,"identity":"66c1ac73-912a-4db2-83a1-1f73741409a9","order_by":1,"name":"Fatma Acil","email":"","orcid":"","institution":"Diyarbakır Gazi Yaşargil Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"","lastName":"Acil","suffix":""}],"badges":[],"createdAt":"2026-03-30 19:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9271468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9271468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107617915,"identity":"20eacb17-b807-4573-bae8-acc851c2240e","added_by":"auto","created_at":"2026-04-23 09:23:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":372244,"visible":true,"origin":"","legend":"\u003cp\u003eBox-plot comparisons of critical care duration, PNI, CCI, and HALP scores across the groups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9271468/v1/946485625ffcc799bb67cbcf.png"},{"id":107617907,"identity":"6c12c0e9-1ade-4d5e-ae33-ea38b8235a0b","added_by":"auto","created_at":"2026-04-23 09:23:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":311377,"visible":true,"origin":"","legend":"\u003cp\u003eForest-Plot Graph of Multivariable Cox Regression Analyses\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9271468/v1/654cf97e0e4b6d7522a264a8.png"},{"id":107617908,"identity":"58f3acae-123d-4fa2-a7f5-6822419ee09e","added_by":"auto","created_at":"2026-04-23 09:23:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290963,"visible":true,"origin":"","legend":"\u003cp\u003eComparative ROC curve analysis of PNI, HALP score, and albumin\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9271468/v1/127009278da83f0c8119620e.png"},{"id":108976864,"identity":"8aa633d2-244b-4fd5-bac2-c9cf4a0d7a8e","added_by":"auto","created_at":"2026-05-11 11:29:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":869232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9271468/v1/1543cd5a-552b-4dcb-9ebe-ab8b659791b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparative Predictive Performance of HALP Score and Prognostic Nutritional Index for Mortality in ICU Patients After Hip Fracture Surgery \u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe number of hip fractures in older people has gone up a lot because the world's population is getting older. This is becoming a bigger public health issue. Hip fractures are a major public health issue for both patients and healthcare systems because they lead to longer hospital stays, more complications after surgery, and higher death rates. Epidemiological projections predict that the annual number of hip fractures worldwide will increase from approximately 1.6\u0026nbsp;million in 2000 to 6.3\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Current data show that 30-day mortality rates after hip fracture range from 1\u0026ndash;7%, while one-year mortality rates are approximately 23\u0026ndash;30% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although it has been shown that performing surgery within the first 24\u0026ndash;48 hours after admission can reduce postoperative complications [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], many patients with hip fractures continue to experience significant reductions in functional capacity and adverse clinical outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, the identification of reliable and easily applicable biomarkers that can predict mortality and adverse clinical outcomes in patients with hip fractures is becoming increasingly crucial. One indicator that has attracted attention in recent years is the Hemoglobin\u0026ndash;Albumin\u0026ndash;Lymphocyte\u0026ndash;Platelet (HALP) score, a novel composite parameter reflecting both systemic inflammation and hematological status [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the number of studies evaluating its prognostic value in geriatric fracture populations is limited, the individual components of the HALP score, particularly albumin, hemoglobin, and lymphocyte count, are well-known predictors of mortality [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent findings in oncology and cardiovascular disease populations suggest that the HALP score may be a promising biomarker for predicting clinical outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, its prognostic predictive value in the elderly population with hip fractures has not yet been adequately investigated.\u003c/p\u003e \u003cp\u003eAnother important parameter for assessing nutritional and immunological status is the Prognostic Nutrition Index (PNI). PNI is a simple indicator commonly used to assess perioperative nutritional status. It is calculated from the preoperative serum albumin level and total lymphocyte count [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Low PNI levels have been reported to be associated with poor clinical outcomes in various diseases such as gastrointestinal cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], colorectal cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], lung cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], diabetic nephropathy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and cardiovascular diseases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, research indicates that diminished preoperative PNI values correlate with elevated mortality rates and increased postoperative complications in patients with hip fractures [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, research indicates that decreased preoperative PNI values are linked with raised mortality rates and heightened postoperative complications in patients with hip fractures [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These results indicate that strengthening nutritional status may be a potentially adjustable factor in lowering mortality risk.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the relationship between HALP score, PNI, and one-year all-cause mortality in elderly patients admitted to the intensive care unit (ICU) following surgical intervention for hip fractures.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eStudy design and patient population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis retrospective observational study was conducted to examine the relationship between nutritional assessment scores and mortality in elderly patients admitted to the intensive care unit (ICU) after surgical treatment for hip fractures. Patients who underwent surgery for hip fractures at the Orthopedics and Traumatology Clinic of Gazi Yaşargil Training and Research Hospital, University of Health Sciences, between January 2020 and December 2025 were included in the study. Patients aged 65 years and older with femoral neck, intertrochanteric, and subtrochanteric fractures were included. Patients with pathological fractures, a history of multiple traumas, active infections, and incomplete laboratory data were excluded from the study. After excluding patients who met the exclusion criteria, a total of 257 patients were included in the study. Based on medical records, patients who died during follow-up were classified as non-survivors (n\u0026thinsp;=\u0026thinsp;40), while those who remained alive were assigned to the survivor group (n\u0026thinsp;=\u0026thinsp;217). All patients were managed in the ICU during the postoperative period. The study was approved by Gazi Yaşargil Training and Research Hospital, University of Health Sciences Ethics Committee (Date: January 27, 2026, Decision Number: 43). All procedures were carried out in accordance with the principles of the Helsinki Declaration. Due to the retrospective nature of the study, no additional informed consent was obtained from the participants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003e Patients' demographic characteristics (age, gender), clinical characteristics and comorbidities, fracture type, surgical treatment method applied, length of hospital stay, and need for intensive care were retrospectively recorded through the hospital's electronic record system. Preoperative laboratory parameters (hemoglobin, serum albumin, lymphocyte, and platelet counts) were obtained within 24 hours before surgery. Comorbidities were assessed using the Charlson Comorbidity Index (CCI).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHALP score and PNI calculation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe HALP score was calculated using the following formula:\u003c/p\u003e \u003cp\u003eHALP\u0026thinsp;=\u0026thinsp;Hemoglobin (g/L) \u0026times; Albumin (g/L) \u0026times; Lymphocyte count (/L) \u0026divide; Platelet count (/L)\u003c/p\u003e \u003cp\u003eThe Prognostic Nutrition Index (PNI) was calculated using the following formula:\u003c/p\u003e \u003cp\u003ePNI = (10 \u0026times; serum albumin [g/dL]) + (0.005 \u0026times; total lymphocyte count [/mm\u0026sup3;])\u003c/p\u003e \u003cp\u003eAll calculations were performed using the initial preoperative laboratory values.\u003c/p\u003e \u003cp\u003eThe primary aim of our study was to investigate the association of HALP score and PNI with 1-year all-cause mortality.\u003c/p\u003e"},{"header":"3. Statistical Analysis","content":"\u003cp\u003eThe statistical analyses were conducted using SPSS version 27.0 for Windows (Armonk, NY, USA: IBM Corp.). The normality of distribution for continuous variables was assessed using the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests, in conjunction with visual inspection of histograms. Continuous variables demonstrating normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas those not conforming to normal distribution were reported as median and interquartile range (IQR). Comparisons of normally distributed continuous variables were performed using the Student\u0026rsquo;s t-test, while the Mann\u0026ndash;Whitney U test was applied for variables without normal distribution. Categorical variables were compared using the chi-square test or Fisher\u0026rsquo;s exact test when appropriate, and were presented as percentages. To identify independent predictors of 1-year all-cause mortality, univariable and multivariable Cox regression analyses were conducted. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariable analysis were entered into the multivariable model. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the association between 1-year all-cause mortality and PNI and HALP scores. The optimal cutoff values were determined based on the Youden index. A two-tailed p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eA total of 257 patients were included in the study after the exclusion criteria; 40 of these (15.6%) were deceased, and 217 (84.4%) were in the surviving patient group. The demographic and clinical characteristics of the patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant difference was found between the groups in terms of age (p\u0026thinsp;=\u0026thinsp;0.678). The female gender ratio was higher in the survivor group (survivor 58.5%; non-survivor 35%), while the male gender was significantly associated with mortality (p\u0026thinsp;=\u0026thinsp;0.006). The presence of malignancy was found to be significantly higher in the non-survivor group (12.9% vs. 1.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ASA score and CCI were significantly higher in the non-survivor group (p\u0026thinsp;=\u0026thinsp;0.020 and p\u0026thinsp;=\u0026thinsp;0.006, respectively). The duration of intensive care monitoring was significantly longer in the non-survivor group (median 13 days; IQR 21.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When laboratory parameters were evaluated, albumin levels were significantly lower in the non-survivor group (28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 vs. 31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 mg/dl; p\u0026thinsp;=\u0026thinsp;0.042). Similarly, the PNI value was found to be significantly lower in the non-survivor group (34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 vs. 37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6; p\u0026thinsp;=\u0026thinsp;0.004). The HALP score was also significantly lower in the non-survivor group (median 11.3 vs. 17.3; p\u0026thinsp;=\u0026thinsp;0.003). No significant differences were observed between the groups in terms of hemoglobin, hematocrit, white blood cell, and platelet values ​​(p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for each). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents box-plot comparisons of critical care duration, PNI, CCI, and HALP scores across the groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the total population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;217)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale gender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemantia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical care duration (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit, (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233 (164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237 (113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALP score (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eWBC: White blood cell, ASA: American Society of Anesthesiologists, CCI: Charlson Comorbidity Index, IQR: Interquartile range,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the univariable Cox regression analysis performed to determine the predictors of 1-year all-cause mortality, age (HR: 1.044; 95% CI: 1.006\u0026ndash;1.083; p\u0026thinsp;=\u0026thinsp;0.023), low PNI (HR: 0.919; 95% CI: 0.871\u0026ndash;0.969; p\u0026thinsp;=\u0026thinsp;0.002), male gender (HR: 2.621; 95% CI: 1.297\u0026ndash;5.296; p\u0026thinsp;=\u0026thinsp;0.007), presence of malignancy (HR: 7.607; 95% CI: 1.948\u0026ndash;29.714; p\u0026thinsp;=\u0026thinsp;0.004), high CCI (HR: 2.412; 95% CI: 1.738\u0026ndash;3.348; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and low albumin levels (HR: 0.909; 95% CI: 0.855\u0026ndash;0.966, p\u0026thinsp;=\u0026thinsp;0.002) were associated with 1- year all-cause mortality.\u003c/p\u003e \u003cp\u003eIn multivariable Cox regression analysis, male gender HR: 3.054; 95% CI: 1.362\u0026ndash;6.852; p\u0026thinsp;=\u0026thinsp;0.007), presence of malignancy (HR: 7.303; 95% CI: 1.491\u0026ndash;35.779; p\u0026thinsp;=\u0026thinsp;0.014), high CCI (HR: 2.404; 95% CI: 1.633\u0026ndash;3.538; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and low PNI (HR: 0.910; 95% CI: 0.841\u0026ndash;0.984; p\u0026thinsp;=\u0026thinsp;0.018) were identified as independent predictors of 1-year all-cause mortality. Age, HALP score, and chronic kidney disease did not retain statistical significance in multivariable analysis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows uniivariable and multivariable Cox regression analysis results. The results of multivariable Cox regression analysis were also schematized with Forest-Plot plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable Cox regression analyses for predicting total mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.044 (1.006\u0026ndash;1.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996 (0.949\u0026ndash;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.919 (0.871\u0026ndash;0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910 (0.841\u0026ndash;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALP score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.973 (0.944\u0026ndash;1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.015 (0.980\u0026ndash;1.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.621 (1.297\u0026ndash;5.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.054 (1.362\u0026ndash;6.852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.496 (0.961\u0026ndash;6.479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.514 (0.471\u0026ndash;4.869)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.607 (1.948\u0026ndash;29.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.303 (1.491\u0026ndash;35.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.412 (1.738\u0026ndash;3.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.404 (1.633\u0026ndash;3.538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.909 (0.855\u0026ndash;0.966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.732 (0.371\u0026ndash;1.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.882 (0.813\u0026ndash;4.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.286 (1.288\u0026ndash;14.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.835 (0.362\u0026ndash;1.928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn ROC curve analysis, the area under the curve (AUC) was found to be 0.642 for PNI (p\u0026thinsp;=\u0026thinsp;0.001), 0.617 for albumin (p\u0026thinsp;=\u0026thinsp;0.002), and 0.648 for HALP score (p\u0026thinsp;=\u0026thinsp;0.003). The cutoff points, determined using the Youden index, were 33.50 for PNI, 25.00 for albumin, and 15.35 for HALP, respectively. At these thresholds, the sensitivity and specificity values were 52.5% and 75.5% for PNI, 30.0% and 90.7% for albumin, and 67.5% and 58.8% for HALP, respectively. These findings indicate that PNI and HALP scores, in particular, demonstrate moderate discriminatory power in predicting 1-year all-cause mortality in patients who underwent hip fracture surgery and were followed in the intensive care unit. The results of the multivariable regression analysis were additionally illustrated using forest plot diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eIn this study, we evaluated the relationship between PNI and HALP scores and 1-year all-cause mortality in elderly patients who were admitted to the ICU after undergoing surgical treatment for hip fracture. It was shown that low PNI is an independent predictor of mortality, while HALP was not found to be significant in multivariate analysis. In addition, male gender, presence of malignancy, and high Charlson Comorbidity Index (CCI) are associated with mortality. ROC analyses reveal that PNI and HALP scores have moderate discriminatory power in predicting mortality and that the determined cut-off values ​​may be a potential clinical tool in identifying high-risk patients.\u003c/p\u003e \u003cp\u003eThe current literature shows that low PNI and serum albumin levels have a strong association with mortality and complications after hip fracture. In the retrospective study of Durgun et al., low albumin and PNI scores were significantly associated with length of hospital stay, 30-day and 1-year mortality in 309 patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, another study examined the relationship between PNI and mortality at 1, 3, 6, 12, and 24 months after femur fracture surgery and found that low PNI increased mortality at all time points; the overall mortality rate was found to be 20% at three years of follow-up, and low PNI levels were strongly associated with long-term mortality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prognostic value of PNI has also been confirmed in large cohort studies. In their study examining 3351 hip fracture patients, Wang et al. reported that PNI was an independent predictor of postoperative complications and two-year all-cause mortality [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Tun\u0026ccedil;ez et al., in their study involving 124 elderly patients, showed that low PNI was an independent risk factor for mortality after hip fracture surgery [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMalnutrition is very common in the elderly, affecting approximately 50% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A recent systematic review reported that malnutrition is an independent risk factor for functional dependence and increased mortality in hip fracture patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Better nutritional care for malnourished patients is important to improve their outcomes. Therefore, early and rapid identification of malnourished patients in a hospital setting is crucial, especially in geriatric hip fracture patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to PNI, the HALP score did not remain a significant predictor of mortality in multivariate analysis in our study. Although HALP incorporates multiple components reflecting both nutritional (albumin) and hematological/inflammatory status (hemoglobin, lymphocyte, platelet), its prognostic performance in geriatric hip fracture patients remains controversial. Recent studies have reported inconsistent findings regarding the predictive value of HALP in this population.\u003c/p\u003e \u003cp\u003eFor instance, Cakmak et al. demonstrated that while PNI was significantly associated with mortality in geriatric hip fracture patients, HALP did not show independent prognostic significance for ICU mortality, which is consistent with our findings [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, Balaban et al. reported that the HALP score was not significantly associated with ICU or short-term mortality in critically ill elderly patients, despite evaluating multiple nutritional indices [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In another comparative ICU-based study, traditional indices such as PNI and GNRI outperformed HALP in predicting adverse outcomes, suggesting that HALP may have limited discriminatory capacity in critically ill populations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, some studies suggest that HALP may still have prognostic value under certain conditions. Vural et al. reported that lower HALP scores were associated with increased early and late mortality in elderly patients with proximal femur fractures, indicating that HALP may reflect overall physiological reserve [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Likewise, Wang et al. found that HALP could serve as a useful biomarker for mortality prediction in older hip fracture patients, although its predictive strength varied depending on patient characteristics and study design [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere could be a number of explanations why the studies don't agree with others. First, HALP contains hemoglobin and platelet counts, which can be affected by things that occur during and after surgery, like bleeding, transfusion, and inflammatory response. This could make it less stable as a prognostic marker in the ICU. Second, critically ill patients frequently demonstrate swift variations in hematological parameters, potentially diminishing the predictive accuracy of composite indices like HALP in contrast to more stable markers such as albumin-based PNI.\u003c/p\u003e \u003cp\u003eConsequently, while HALP indicates both nutritional and inflammatory status, our results imply that it may not be as reliable as PNI in forecasting long-term mortality in elderly ICU patients following hip fracture surgery. Further large-scale, prospective studies are needed to clarify the role of HALP and to determine whether dynamic or serial measurements could improve its prognostic utility.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study, like many others, has some limitations. Firstly, the study's retrospective nature and single-center design make it hard to apply the results to other situations and make it impossible to show causal connections between variables. The limited number of patients who experienced mortality may influence the statistical power of multivariate analyses and the stability of the model. The inclusion of only patients monitored in the intensive care unit may have resulted in the assessment of a population with a more severe clinical presentation, potentially restricting the generalizability of the findings to all hip fracture patients. The analysis of parameters such as PNI, HALP, and albumin based only on a single measurement at the time of admission led to the neglect of temporal variations in these biomarkers. Additionally, potential confounding variables, such as nutritional status, inflammatory response, perioperative complications, and variations in treatment protocols, may not have been fully controlled. The failure to evaluate nutritional support therapies during the hospital stay is another factor that may affect the results. The moderate discriminative power of the AUC values ​​obtained in the ROC analysis suggests that the use of these parameters as strong predictors alone in clinical practice may be limited. The cutoff points determined according to the Youden index are specific to the study sample and need to be validated with prospective and multicenter studies in different populations. Finally, only mortality was evaluated in this study; functional recovery, quality of life, and long-term clinical outcomes were not analyzed. Therefore, the findings should be interpreted cautiously and supported by further studies.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eOur study found a significant association between PNI and HALP scores and 1-year all-cause mortality in patients who were followed up in the intensive care unit after surgery for hip fracture. These findings suggest that simple and easily calculable nutritional indices may be useful in clinical practice for risk stratification and early prognosis assessment in high-risk patient groups such as hip fractures. However, the integration of these parameters into clinical decision-making processes needs to be validated with prospective, multicenter studies with larger sample sizes.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASA \u0026rarr; American Society of Anesthesiologists score\u003c/p\u003e\n\u003cp\u003eAUC \u0026rarr; Area Under the Curve\u003c/p\u003e\n\u003cp\u003eCCI \u0026rarr; Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eCI \u0026rarr; Confidence Interval\u003c/p\u003e\n\u003cp\u003eCOPD \u0026rarr; Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eDM \u0026rarr; Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eg/dL \u0026rarr; grams per deciliter\u003c/p\u003e\n\u003cp\u003eHALP \u0026rarr; Hemoglobin\u0026ndash;Albumin\u0026ndash;Lymphocyte\u0026ndash;Platelet score\u003c/p\u003e\n\u003cp\u003eHR \u0026rarr; Hazard Ratio\u003c/p\u003e\n\u003cp\u003eICU \u0026rarr; Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eIQR \u0026rarr; Interquartile Range\u003c/p\u003e\n\u003cp\u003eLOS \u0026rarr; Length of Stay\u003c/p\u003e\n\u003cp\u003emg/dL \u0026rarr; milligrams per deciliter\u003c/p\u003e\n\u003cp\u003en \u0026rarr; number (sample size)\u003c/p\u003e\n\u003cp\u003ePNI \u0026rarr; Prognostic Nutritional Index\u003c/p\u003e\n\u003cp\u003eROC \u0026rarr; Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eSD \u0026rarr; Standard Deviation\u003c/p\u003e\n\u003cp\u003eSPSS \u0026rarr; Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eWBC \u0026rarr; White Blood Cell\u003c/p\u003e\n\u003cp\u003e/L \u0026rarr; per liter\u003c/p\u003e\n\u003cp\u003e/mm\u0026sup3; \u0026rarr; per cubic millimeter\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Committee Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Gazi Yaşargil Training and Research Hospital, University of Health Sciences Ethics Committee (Date: January 27, 2026, Decision Number: 43). All procedures were carried out in accordance with the principles of the Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement for informed consent was waived due to the study\u0026apos;s retrospective design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: HTS, FA\u003c/p\u003e\n\u003cp\u003eMethodology: HTS, FA\u003c/p\u003e\n\u003cp\u003eData curation: HTS, FA\u003c/p\u003e\n\u003cp\u003eFormal analysis: HTS\u003c/p\u003e\n\u003cp\u003eInvestigation: HTS\u003c/p\u003e\n\u003cp\u003eVisualization: HTS, FA\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: HTS\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: HTS, FA\u003c/p\u003e\n\u003cp\u003eSupervision: HTS\u003c/p\u003e\n\u003cp\u003eProject administration: HTS\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCooper C, Campion G, Melton LJ. 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Retrospective analysis of geriatric patients undergoing hip fracture surgery: Delaying surgery is associated with increased morbidity, mortality, and length of stay. Geriatr Orthop Surg Rehabil. 2018;9:1-7. doi:10.1177/2151459318795260\u003c/li\u003e\n \u003cli\u003eChen XL, Xue L, Wang W, et al. Prognostic significance of the combination of preoperative hemoglobin, albumin, lymphocyte and platelet in patients with gastric carcinoma: a retrospective cohort study. Oncotarget. 2015;6(38):41370-41382. doi:10.18632/oncotarget.5629).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang Z, Liu H, Liu M. The hemoglobin, albumin, lymphocyte, and platelet score as a useful predictor for mortality in older patients with hip fracture. Front Med. 2025;12:1-10. doi:10.3389/fmed.2025.1450818\u003c/li\u003e\n \u003cli\u003eZhang BF, Wei X, Huang H, et al. The association between hemoglobin at admission and mortality of older patients with hip fracture: A mean 3-year follow-up cohort study. Eur Geriatr Med. 2023;14(2):275-284. doi:10.1007/s41999-023-00759-0\u003c/li\u003e\n \u003cli\u003ePan L, Zhang W, Li Y, et al. Prognostic nomogram for risk of mortality after hip fracture surgery in geriatrics. Injury. 2022;53(4):1484-1489. doi:10.1016/j.injury.2022.01.029\u003c/li\u003e\n \u003cli\u003eS\u0026ouml;ner S, G\u0026uuml;zel T, Aktan A, et al. Prognostic value of hemoglobin, albumin, lymphocyte, platelet (HALP) scores in patients with non-valvular atrial fibrillation: insights from the AFTER-2 study. BMC Cardiovasc Disord. 2025;25(1):528. Published 2025 Jul 19. doi:10.1186/s12872-025-04993-1\u003c/li\u003e\n \u003cli\u003eBuzby GP, Mullen JL, Matthews DC, Hobbs CL, Rosato EF. Prognostic nutritional index in gastrointestinal surgery. Am J Surg. 1980;139(1):160-167. doi:10.1016/0002-9610(80)90246-9).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOnodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 1984;85(9):1001-1005.\u003c/li\u003e\n \u003cli\u003eTokunaga R, Sakamoto Y, Nakagawa S, et al. Prognostic nutritional index predicts severe complications, recurrence, and poor prognosis in patients with colorectal cancer undergoing primary tumor resection. Dis Colon Rectum. 2015;58(11):1048-1057. doi:10.1097/DCR.0000000000000458\u003c/li\u003e\n \u003cli\u003eShoji F, Matsubara T, Kozuma Y, et al. Pretreatment prognostic nutritional index as a novel biomarker in non-small cell lung cancer patients treated with immune checkpoint inhibitors. Lung Cancer. 2019;136:45-51. doi:10.1016/j.lungcan.2019.08.006\u003c/li\u003e\n \u003cli\u003eSuzuki E, Yoshikawa M, Iwasaki T, et al. Prognostic nutritional index as a potential prognostic tool for exacerbation of COPD in elderly patients. Int J Chron Obstruct Pulmon Dis. 2023;18:1077-1090. doi:10.2147/COPD.S385374\u003c/li\u003e\n \u003cli\u003eChen Y, Zhang X, Liu Y, et al. Prognostic nutritional index (PNI) is an independent predictor for functional outcome after hip fracture in the elderly: A prospective cohort study. Arch Osteoporos. 2024;19(1). doi:10.1007/s11657-024-01469-1\u003c/li\u003e\n \u003cli\u003eS\u0026ouml;ner S, G\u0026uuml;zel T, Aktan A, et al. Predictive value of nutritional scores in non-valvular atrial fibrillation patients: Insights from the AFTER-2 study. Nutr Metab Cardiovasc Dis. 2025;35(3):103794. doi:10.1016/j.numecd.2024.103794\u003c/li\u003e\n \u003cli\u003eWang Y, Li J, Zhang X, et al. Prognostic nutritional index with postoperative complications and 2-year mortality in hip fracture patients: An observational cohort study. Int J Surg. 2023;109(11):3395-3406. doi:10.1097/JS9.0000000000000614\u003c/li\u003e\n \u003cli\u003eKilic CY, Gursan O, Acan AE, Gultac E. Prognostic nutritional index predicts perioperative adverse events in patients undergoing hemiarthroplasty after a hip fracture. J Exp Clin Med. 2022;39(1):24-27. doi:10.52142/omujecm.39.1.5\u003c/li\u003e\n \u003cli\u003eMi X, Jia Y, Song Y, Liu K, Liu T, Han D, et al. Preoperative prognostic nutritional index value as a predictive factor for postoperative delirium in older adult patients with hip fractures: A secondary analysis. BMC Geriatr. 2024;24(1):1-9. doi:10.1186/s12877-023-04629-z\u003c/li\u003e\n \u003cli\u003eDurgun HM, Ozkul E, Yaman M, Sen A. Prognostic value of HALP score, PNI, and SII in predicting 1-year mortality in geriatric femoral fractures: A 5-year emergency department cohort study. Med Sci Monit. 2026;32:e950481. doi:10.12659/MSM.950481\u003c/li\u003e\n \u003cli\u003eChen Y, Zhang X, Liu Y, et al. Prognostic nutritional index as an independent predictor of 3-year postoperative mortality in elderly patients with hip fracture: A post hoc analysis of a prospective cohort study. Orthop Surg. 2024;16(11):2761-2770. doi:10.1111/os.14200\u003c/li\u003e\n \u003cli\u003eWang Y, Li J, Zhang X, et al. Prognostic nutritional index with postoperative complications and 2-year mortality in hip fracture patients: An observational cohort study. Int J Surg. 2023;109(11):3395-3406. doi:10.1097/JS9.0000000000000614\u003c/li\u003e\n \u003cli\u003eTuncez M, Bulut T, Suner U, Onder Y, Kazimoglu C. Prognostic nutritional index (PNI) is an independent risk factor for postoperative mortality in geriatric patients undergoing hip arthroplasty for femoral neck fracture: A prospective controlled study. Arch Orthop Trauma Surg. 2024;144(3):1289-1295. doi:10.1007/s00402-024-05201-z\u003c/li\u003e\n \u003cli\u003eCakmak G, Eyyupkoca E, Turan S. Prognostic value of HALP and PNI scores in predicting 6-month mortality among geriatric hip fracture patients. Clin Res. 2026. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12964977/\u003c/li\u003e\n \u003cli\u003eBalaban U, Yalcin N, Kaya EK, Ortac Ersoy E. Assessment of nutritional indices for predicting clinical outcomes in critically ill elderly patients: A prospective cohort study. BMC Anesthesiol. 2025. doi:10.1186/s12871-025-03232-6\u003c/li\u003e\n \u003cli\u003eKollu K, Yerdelen EA, Duran S, Kabatas B, Karakas F, et al. Comparison of nutritional risk indices (PNI, GNRI, mNUTRIC) and HALP score in predicting adverse clinical outcomes in older ICU patients. Medicine. 2024. Available from: https://journals.lww.com/md-journal/fulltext/2024/06210/comparison_of_nutritional_risk_indices__pni,_gnri,.14.aspx\u003c/li\u003e\n \u003cli\u003eVural A, Dolanbay T, Yagar H. Hemoglobin, albumin, lymphocyte and platelet (HALP) score for predicting early and late mortality in elderly patients with proximal femur fractures. PLoS One. 2025. doi:10.1371/journal.pone.0313842\u003c/li\u003e\n \u003cli\u003eWang Z, Liu H, Liu M. The hemoglobin, albumin, lymphocyte, and platelet score as a predictor for mortality in older patients with hip fracture. Front Med. 2025. doi:10.3389/fmed.2025.1450818\u003c/li\u003e\n \u003cli\u003eTahak F, Yaka H, Kirilmaz A, Kekec AF. Relationship between mortality and HALP score in femoral neck fractures treated with hemiarthroplasty. Jt Dis Relat Surg. 2025. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12456328/\u003c/li\u003e\n \u003cli\u003eLv X, Liu X, Shen Y, Li C, Shen T, Wang Y, et al. Association between HALP score and in-hospital mortality in sepsis patients: A multicenter retrospective cohort study. Front Public Health. 2025. doi:10.3389/fpubh.2025.1710118\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hip fracture surgery, HALP score, prognostic nutritional index, intensive care unit, all-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-9271468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9271468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHip fractures in elderly patients are associated with high morbidity and mortality. Simple and reliable prognostic biomarkers are needed to guide perioperative management. Hemoglobin\u0026ndash;Albumin\u0026ndash;Lymphocyte\u0026ndash;Platelet (HALP) score and Prognostic Nutritional Index (PNI) reflect nutritional and immunological status and may predict postoperative outcomes. We aimed to investigate the association of preoperative HALP score and PNI with 1-year all-cause mortality in elderly patients admitted to the intensive care unit (ICU) after surgical treatment for hip fractures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study was performed on 257 patients aged 65 years and older who were admitted to the ICU following surgical intervention for hip fractures. Demographic information, comorbidities, and laboratory results were all gathered. The patients were categorized into two groups: non-survivors (n\u0026thinsp;=\u0026thinsp;40) and survivors (n\u0026thinsp;=\u0026thinsp;217). Univariable and multivariable Cox regression analyses were performed to identify independent predictors of 1-year all-cause mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNon-survivors had significantly lower PNI (34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 vs. 37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6; p\u0026thinsp;=\u0026thinsp;0.004) and HALP scores (median 11.3 vs. 17.3; p\u0026thinsp;=\u0026thinsp;0.003). Multivariable analysis identified low PNI (HR 0.910; 95% CI 0.841\u0026ndash;0.984; p\u0026thinsp;=\u0026thinsp;0.018), male gender (HR 3.054; p\u0026thinsp;=\u0026thinsp;0.007), malignancy (HR 7.303; p\u0026thinsp;=\u0026thinsp;0.014), and high Charlson Comorbidity Index (HR 2.404; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent predictors of mortality. ROC analysis demonstrated moderate discriminative power for PNI (AUC 0.642) and HALP (AUC 0.648).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative PNI is an independent predictor of mortality in elderly hip fracture patients, whereas HALP shows moderate prognostic value. These simple nutritional indices may aid early risk stratification and guide perioperative care.\u003c/p\u003e","manuscriptTitle":"Comparative Predictive Performance of HALP Score and Prognostic Nutritional Index for Mortality in ICU Patients After Hip Fracture Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:23:03","doi":"10.21203/rs.3.rs-9271468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac0e40d0-80cd-4f3c-ab35-fd0b77e63234","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-02T16:05:31+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-02T16:09:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:23:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9271468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9271468","identity":"rs-9271468","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}