Evaluation of Mortality Prediction Scales in Extracapsular Hip Fractures: A Retrospective Case-Control Study

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Methods: A retrospective case-control study was designed including patients older than 65 years with extracapsular hip fracture, and matching patients who died within 12 months postoperatively with surviving patients. The Orthopaedic Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (O-POSSUM), Nottingham Hip Fracture Score (NHFS), Almelo Hip Fracture Score (AHFS) and SERNBO score were applied to the two groups, analysing the reliability, discriminative power and calibration of each of them. Results: 57 patients with 12-month mortality and 57 survivors were included, with a mean age of 86 years, 80.7% women. For 30-day postoperative mortality, the NHFS had the highest mean sensitivity (7.83%), while the AHFS showed the best specificity (95.23%). For 12-month postoperative mortality, the AHFS scale had the highest mean sensitivity (54.39%), and the NHFS had the best specificity (94.23%). Regarding calibration, the O-POSSUM, NHFS and AHFS scales overestimated 30-day and 12-month postoperative mortality. The scale with the best discriminative ability for 30-day mortality was SERNBO and AHFS (AUC 0.62-0.60), while NHFS had the best AUC for 12-month mortality (0.66). Conclusion: The AHFS and SERNBO scales are useful for predicting 30-day postoperative mortality, while the NHFS scale is a better choice for predicting 12-month mortality. extracapsular hip fracture mortality O-POSSUM NHFS AHFS SERNBO Figures Figure 1 Figure 2 INTRODUCTION Predicting the risk of mortality in elderly patients with hip fracture is important for decision making and adequate information for patients and families. Several predictor scales have been developed, but all of them included patients without distinction of fracture type or only included intracapsular fractures [ 1 ]. Studies have found differences between fracture types in factors influencing mortality and complications, such as surgery within 48 hours [ 2 ]. Among hip fractures, extracapsular fractures are the most frequent [ 3 ] and because of their anatomical characteristics and surgical treatment different from intracapsular fractures, they could present specific differences in postoperative mortality. The aim of the present study was to evaluate the effectiveness of some of the most widely used predictors of postoperative mortality in patients with extracapsular hip fractures. MATERIAL AND METHODS A case-control study was designed and approved by the Institutional Ethics Committee. Informed consent was not required as it was retrospective, and all data was obtained from the computerized clinical history. The case-control design is considered the most appropriate, as it allows us to match cases and controls by age, sex and date of surgery, thus minimizing confounding variables, ensuring that the differences observed in the mortality prediction scales are more attributable to their predictive accuracy than to population differences. Patients treated at our institution between 2013 and 2018 were identified from the departmental hip fracture database. Inclusion criteria were patients aged 65 years or older who underwent surgery for extracapsular hip fractures (trochanteric and subtrochanteric), with a postoperative follow-up of 12 months unless death occurred earlier. Exclusion criteria included ASA V on the American Society of Anesthesiologists (ASA) scale [ 4 ], pathological tumor fractures, polytrauma or high-energy accidents, and periprosthetic fractures. A random sample of 57 patients who died within 12 months postoperatively was selected, with each case patient matched to a control patient who survived at least 12 months, based on gender, age (± 2 years), and a proximal surgery date. Patient selection was performed using the hospital’s electronic medical record system and hip fracture registry, with cases and controls identified through documented discharge summaries and mortality records. Evaluations All patient data were obtained from computerized medical records. Mortality was also checked in the institutional registry of our center which was linked to the national mortality registry. For each patient, data were collected to determine the 30-day and 12-month postoperative mortality risk on the Orthopaedic Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (O-POSSUM) [ 5 ], Nottingham Hip Fracture Score (NHFS) [ 6 ], Almelo Hip Fracture Score (AHFS) [ 7 ] and SERNBO Score [ 8 ]. On admission, all patients were assessed for anesthetic risk ASA, and the level of physical-social dependency was assessed using the Barthel scale [ 9 ]. Statistical analysis The required sample size was calculated by estimating the relative risk in a cohort. Using an estimated 12-month mortality prevalence of 0.15 (15%) in our study population and assuming a relative risk of 2.0 mortality, a confidence level of 0.95 and a statistical power of 0.80, 57 patients were required in each group. Statistical calculations were performed with the Epidat v. 4.1. software (Pan American Health Organization, WHO). For each scale, reliability was analyzed by precision and discrimination for 30-day and 12-month mortality. For accuracy, reliability (sensitivity and specificity) was analyzed. Calibration was estimated by the ratio between the number of observed (O) and expected (E) cases, where O/E = 1 was an excellent estimate; 1 overestimation (higher mortality than expected). The discriminative ability of the scale to identify patients in relation to 30-day and 12-month postoperative mortality was analyzed by Receiver Operating Characteristics (ROC) analysis. The area under the curve (AUC) was shown with its 95% CI. In biomedical practice, an AUC value close to 0.70 was considered adequate. To account for potential confounding factors, the following additional covariates were included in the analysis: comorbidities, preoperative functional status, surgical factors, postoperative complications, and medication use. Comorbidities were assessed using the Charlson Comorbidity Index and included a history of cardiovascular disease, diabetes mellitus, chronic kidney disease, and chronic obstructive pulmonary disease. Preoperative functional status was evaluated using the Barthel Index prior to the fracture. The surgical factors considered were the type of fracture (pertrochanteric or subtrochanteric fracture), surgical delay (> 48h), duration of surgery over 150 minutes and the need for intensive care unit (ICU) admission. Postoperative complications analyzed included: medical complications (acute kidney injury, pneumonia, sepsis, thromboembolic events…), surgical and wound complications. Additionally, preoperative use of anticoagulants or antiplatelet therapy, which may impact surgical risk and postoperative outcomes, was included as a covariate. These covariates were incorporated to improve the robustness of the analysis and ensure that observed differences in mortality were not due to unmeasured confounding. RESULTS Table 1 Patient characteristics and univariant analysis Variable Deceased Survival p (n = 57) (n = 57) Age (years) 85 (5.62) 86 (5.83) 0.45 > 80 years 47 (82.5%) 44 (77.2%) 0.48 Gender (F/M) 42/15 42/15 1.00 Barthel index score 54 (19.79) 79 (21.19) < 0.01 Barthel II 46 (80.7%) 40 (70.2%) 0.19 Charlson Index > 4 54 (94.7%) 54 (94.7%) 1.00 Dementia 33 (57.9%) 9 (15.8%) < 0.01 Neurological disease 23 (40.4%) 23 (40.4%) 1.00 Heart disease 49 (86%) 53 (93%) 0.22 Lung disease 15 (26.3%) 9 (15.8%) 0.17 Kidney disease 12 (21.1%) 10 (17.5%) 0.64 Diabetes Mellitus 13 (22.8%) 14 (25.9%) 0.70 Inadequate BMI (Overweight-Obesity) 27 (47.4%) 40 (70.2%) 0.01 Lymphopenia 34 (59.6%) 37 (64.9%) 0.56 Antiplatelet or anticoagulant therapy 26 (45.6%) 29 (51.8%) 0.51 Early surgery ( 150min 2 (3.5%) 1 (1.8%) 0.56 Admision to the intensive care unit (ICU) 4 (7%) 4 (7%) 1.00 Pre-surgical transfusion 26 (45.6%) 10 (17.5%) < 0.01 Post-surgical transfusion 25 (43.9%) 16 (28.1%) 0.08 Post-surgical medical complication 28 (49.1%) 21 (36.8%) 0.19 Surgical wound complications 8 (14%) 5 (8.8%) 0.38 Post-surgical complication requiring surgery 4 (7%) 2 (3.5%) 0.40 30–90 day hospital readmission 27 (47.3%) 6 (10.5%) < 0.01 Readmissions for medical reasons 25 (43.9%) 5 (8.8%) < 0.01 Readmissions for surgical reasons 3 (5.3%) 1 (1.8%) 0.31 Quantitative data as mean (standard deviation) A total of 114 patients operated on for extracapsular hip fracture with a mean age of 86 years (range 65–95 years), 84 females (73.6%) and 30 males (26.3%) were included in the study. In the group of those who died within 12 months postoperatively (n = 57), 6 patients (10.5%) died within 30 days postoperatively. Baseline data in both groups are shown in Table 1 . Table 2 Accuracy and discrimination of 30-day and 12-month postoperative mortality scales Scale O-POSSUM NHFS AHFS SERNBO 30 d 12 m 30 d 12 m 30 d 12 m 30 d 12 m Sensitivity (%) 6.71 48.88 5.88 51.28 7.83 54.39 5.26 52.83 Specificity (%) 94.56 47.33 94.23 48.58 95.23 48.19 94.53 47.15 Calibration (O:E) 1.30 1.01 1.14 0.99 1.52 1.05 0.97 0.99 Regarding 30-day postoperative mortality (Table 2 ), the AHFS scale showed the best accuracy with a mean sensitivity of 7.83%, and mean specificity of 95.23%. Regarding calibration, an overestimation of 30-day postoperative mortality was observed for the O-POSSUM, NHFS and AHFS scales. In contrast, the SERNBO scale underestimated mortality, although with a value close to one. Regarding the discriminative ability for 30-day postoperative mortality (Fig. 1 ), all scales had poor discrimination. The SERNBO and AHFS scales had the highest discriminations with AUC values of 0.63 (95% CI 0.41–0.85) and 0.60 (95% CI 0.40–0.81), respectively (Table 3 ). Table 3 Area under the curve (AUC) for 30-day and 12-months postoperative mortality Scale 30-day Mortality 12-months Mortality AUC (95% CI) p AUC (95% CI) p O-POSSUM 0.48 (0.25–0.72) 0.914 0.65 (0.55–0.75) 0.006 NHFS 0.50 (0.26–0.74) 0.990 0.61 (0.50–0.71) 0.044 AHFS 0.60 (0.4–0.81) 0.388 0.63 (0.53–0.73) 0.018 SERNBO 0.63 (0.41–0.85) 0.284 0.36 (0.26–0.46) 0.009 Regarding 12-month postoperative mortality (Table 2 ), the AHFS scale also had the best accuracy with a mean sensitivity of 54.39% and mean specificity of 48.19%. For calibration, the AHFS, NHFS and O-POSSUM scales slightly overestimated mortality, while the SERNBO scale underestimated mortality, but in all scales the values were close to one. Regarding the discriminative ability for 12-month postoperative mortality (Fig. 2 ), all scales had poor discrimination. The O-POSSUM and NHFS scales had the highest discriminations with AUC values of 0.65 (95% CI 0.55–0.75) and 0.61 (95% CI 0.50–0.71), respectively (Table 3 ). A logistic regression model was performed to identify independent predictors of 12-month mortality in extracapsular hip fractures, adjusting for potential confounders. The model included age, sex, comorbidities (Charlson Comorbidity Index, diabetes, chronic kidney disease, cardiovascular disease), pre-fracture functional status (Barthel Index), surgical factors (fracture type, surgical delay, operative time over 150minutes), and postoperative medical, surgical or wound complications. Adjusted odds ratios (OR) with 95% confidence intervals (CI) were reported for each predictor. The analysis confirmed that dementia (OR = 10.04, p < 0.001) were the most significant predictors of mortality at 12 months. In contrast, Barthel Index, pre-surgical transfusion and readmission, despite being significantly different in univariate comparisons, were not retained as independent predictors after adjusting for confounders. DISCUSSION Our results indicated that the NHFS and AHFS scales presented the best performance in predicting mortality in extracapsular fractures. AHFS and SERNBO scales are useful for predicting 30-day postoperative mortality, while the NHFS is the best choice for predicting 12-month postoperative mortality. All scales, except the O-POSSUM, take dementia into account among the factors that determine their score. Authors, such as Jeong et al [ 10 ], in recent studies have observed that patients with hip fracture and dementia have a higher risk of mortality at 30 days and 12 months. This finding was confirmed in our analysis of 12-month mortality (OR = 10.04, p < 0.001). The AHFS scale showed the best predictive ability at 30 days, (AUC 0.60), outperforming NHFS and O-POSSUM, probably because it includes the Barthel Index among its variables. Previous studies [ 11 ] have shown that a Barthel < 55 on admission is associated with higher early mortality, which may explain why the AHFS more accurately identified high-risk patients in our study. However, in comparison to other series [ 12 ], where AHFS had an AUC of 0.70 for 30 days, our value is lower. This finding may be because, in their series, 63.7% of patients had an ASA I-II, in ours only 25%, suggesting that its performance may depend on characteristics of the population assessed. The NHFS scale showed adequate discriminative ability for 12-month mortality (AUC 0.61), which is consistent with the findings of previous studies [ 13 ]. However, in our study, its performance was lower for 30-day mortality compared to the study by Karres et al [ 1 ], where the NHFS achieved an AUC of 0.77. This difference could be explained by the fact that the NHFS includes fracture type (intracapsular/extracapsular) as one of its predictor variables. Since all patients in our study had extracapsular fractures, this variable lost its discriminative value, which could justify its lower accuracy at 30 days (AUC 0.50). However, the focus of the NHFS on comorbidities and mental status allowed it to maintain a better performance at 12 months (AUC 0.61), as these factors are key determinants of long-term survival. Okur et al [ 14 ], in their series of intra- and extracapsular hip fractures, found that patients with extracapsular fractures have higher AHFS values, indicating that this scale correctly identifies these patients as being at higher baseline risk. However, the calibration of the AHFS in our series was poor (O:E = 1.52). This suggests that the AHFS may be good at identifying high-risk patients (discrimination) but needs adjustments in its calibration to better predict actual mortality in extracapsular fractures. The SERNBO scale, initially developed for intracapsular fractures [ 15 ], performed acceptably at 30 days (AUC 0.63), probably because it assesses previous ambulation ability, which is a factor in Barthel. Pre-fracture mobility influences postoperative recovery, which justifies its predictive ability in the short term. However, it is not useful in the long term (AUC 0.36), probably because it does not include comorbidities and postoperative functional status, key factors in 12-month mortality. O-POSSUM showed very low discrimination in our study (AUC 0.48), which is consistent with previous studies that have reported overestimation of mortality in high-risk patients and underestimation in low-risk patients [ 16 – 18 ]. O-POSSUM is based on physiological and surgical parameters (blood pressure, hemoglobin, leukocytes, cardiac status, operative time, among others). This may explain its overestimation of mortality, as it does not consider chronic comorbidities or the patient's baseline functionality. Our 30-day mortality (5.26%) is comparable with that reported in previous studies, such as that of Karres et al [ 1 ] (8.2%), or Coto Caramés et al [ 19 ] (5.3%), which confirms the overall validity of our data. The authors of this series of extracapsular fractures highlight that age, comorbidities (Charlson index > 2) and medical complications are the main factors associated with mortality. This finding is in line with the results of our series, given that the scales that best predicted mortality (AHFS and NHFS) are precisely those that include variables related to comorbidities (Charlson and ASA, respectively). However, our 12-month mortality (50%) is higher than that reported in previous studies (31–36%) [ 20 , 17 ]. This is due to our study design, in which a group of deceased patients (cases) was selected and compared with an age- and sex-matched control group. This methodology ensures a more homogeneous comparison between the groups but biases the overall mortality rates by increasing the proportion of deceased patients in the sample analyzed. Our study has certain limitations that should be considered when interpreting the results. As a retrospective study based on data obtained from medical records, it is susceptible to selection and recording biases. To mitigate these, cases and controls were matched by age, sex, and surgery date, ensuring comparability and reducing confounding effects. Additionally, the use of electronic medical records and standardized hospital documentation minimized recall bias and the risk of misclassification. Furthermore, important covariates, such as the Charlson Comorbidity Index, pre-fracture mobility, ASA score, and post-surgical complications, were included in the analysis to adjust for potential confounders, thereby enhancing the validity of the findings. Secondly, although patients with extracapsular fractures were included, the sample is limited to a single institution, so generalization of the findings to other populations should be made with caution. These aspects highlight the need for prospective studies that analyze in greater depth the impact of baseline function and other determinants on mortality in patients with extracapsular fractures, as well as the validation of these models in larger and more diverse cohorts. There is recent interest [ 2 , 14 , 19 ] in analyzing specific fracture types, so as not to generalize data. This approach is relevant because mortality prediction models developed for hip fractures in general may not adequately fit the particularities of extracapsular fractures, which explains the variability in the performance of the scales evaluated in our study. Our findings show the importance of selecting the appropriate scale according to the clinical objective. For extracapsular fractures, the AHFS and SERNBO scales are useful for predicting 30-day postoperative mortality, although the AHFS needs adjustments in its calibration, while the NHFS is the best choice for predicting 12-month postoperative mortality. Declarations Competing Interests Statement : The authors declare that they have no financial or non-financial interests, either directly or indirectly related to the work submitted for publication. The authors declare that they have no financial or non-financial interests, either directly or indirectly related to the work submitted for publication. Ethics statement This study was conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments. The protocol was approved by the Institutional Ethics Committee of Hospital ‘‘Vega Baja’’ Orihuela. Informed consent was waived due to the retrospective nature of the study and the use of anonymized data extracted from electronic medical records. Author Contribution ‘E.B., B.M. and S.C. devised and compiled the database, F.L. and A.L. participated in the analysis of the data and the focus of the results. E.B. and F.L. drafted the main text of the manuscript and prepared the figures and tables. All authors revised the manuscript’. Data Availability Data is provided within the manuscript or supplementary information files References Karres J, Heesakkers NA, Ultee JM, Vrouenraets BC. Predicting 30-day mortality following hip fracture surgery: Evaluation of six risk prediction models. 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SAGE Open Med. 2020;8:2050312120918268. https://doi.org/10.1177/2050312120918268 . Additional Declarations No competing interests reported. Supplementary Files Libro1DATAEJTES1.xlsx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6659039","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459797645,"identity":"4f8c8763-e97a-4d2a-8f1b-2e252946ffa4","order_by":0,"name":"Elena Blay-Domínguez","email":"","orcid":"","institution":"Hospital Vega Baja","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Blay-Domínguez","suffix":""},{"id":459797646,"identity":"06bd87b2-b881-49f0-a00b-9b475c9b0807","order_by":1,"name":"Francisco Lajara-Marco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDCCA8wNDBJwXgUQMwNF8GthRNZyBqSFkQgtcMDYBibxa+G7fbDxg0XNPXnd9rMPP3ycVxvN3w7U8qNiG04tkucSmyUkjhUbbjuTbiw5c9vx3BmHGRsYe87cxqnF4Axjg4QEWwLjtgNpbMy8247lNgC1MDO24dXS/EPiX4L9tvPPgFrmHMudT4SWNgnJtoTEbTdAtjTU5G4gpEUSqMVCsi8heduNZ8ySM44dyN0I1HIQn1/4zjAfvi3xLcF22/k0xg8faupy550/fPDBjwrcWkCAGRGVDIfB5AG86oGA8QOCXUdI8SgYBaNgFIxAAAAd/V9zuKdYMwAAAABJRU5ErkJggg==","orcid":"","institution":"Hospital Universitario Reina Sofía","correspondingAuthor":true,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Lajara-Marco","suffix":""},{"id":459797647,"identity":"f121269e-1e52-4426-af02-8414c35a9a20","order_by":2,"name":"Beatriz Muela-Pérez","email":"","orcid":"","institution":"Hospital Vega Baja","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Muela-Pérez","suffix":""},{"id":459797648,"identity":"52859fd3-b5cc-42a7-b042-9c73ea6f3c6a","order_by":3,"name":"Silvia Correoso-Castellanos","email":"","orcid":"","institution":"Hospital Vega Baja","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Correoso-Castellanos","suffix":""},{"id":459797649,"identity":"68446feb-6823-4924-aef1-b7d96c5e170e","order_by":4,"name":"Alejandro Lizaur-Utrilla","email":"","orcid":"","institution":"Miguel Hernandez University","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Lizaur-Utrilla","suffix":""}],"badges":[],"createdAt":"2025-05-13 23:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6659039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6659039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83328069,"identity":"e5c068fe-ffdb-4fb5-bbbb-d0504eae3762","added_by":"auto","created_at":"2025-05-23 07:07:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205276,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves. Area under the curve for 30-day postoperative mortality.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6659039/v1/0e62d3bbf164759b79e6d222.jpeg"},{"id":83328066,"identity":"b2fa2e3d-9ce7-4890-a7bf-8301d18b76bd","added_by":"auto","created_at":"2025-05-23 07:07:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79689,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves. Area under the curve for 12-month postoperative mortality.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6659039/v1/074cd623ab3e56dfe4c6876a.png"},{"id":84344422,"identity":"9a2ab831-e13d-43c7-88e7-a9ff11f72341","added_by":"auto","created_at":"2025-06-10 19:46:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6659039/v1/c7c85ed8-dfa6-4e1d-b516-fd48dcea7551.pdf"},{"id":83328065,"identity":"7be43594-1b76-4223-a48a-67cd79f50255","added_by":"auto","created_at":"2025-05-23 07:07:10","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":49589,"visible":true,"origin":"","legend":"","description":"","filename":"Libro1DATAEJTES1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6659039/v1/e34816acbc7bd1ff44179c79.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Mortality Prediction Scales in Extracapsular Hip Fractures: A Retrospective Case-Control Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePredicting the risk of mortality in elderly patients with hip fracture is important for decision making and adequate information for patients and families. Several predictor scales have been developed, but all of them included patients without distinction of fracture type or only included intracapsular fractures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Studies have found differences between fracture types in factors influencing mortality and complications, such as surgery within 48 hours [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among hip fractures, extracapsular fractures are the most frequent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and because of their anatomical characteristics and surgical treatment different from intracapsular fractures, they could present specific differences in postoperative mortality.\u003c/p\u003e \u003cp\u003eThe aim of the present study was to evaluate the effectiveness of some of the most widely used predictors of postoperative mortality in patients with extracapsular hip fractures.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e A case-control study was designed and approved by the Institutional Ethics Committee. Informed consent was not required as it was retrospective, and all data was obtained from the computerized clinical history. The case-control design is considered the most appropriate, as it allows us to match cases and controls by age, sex and date of surgery, thus minimizing confounding variables, ensuring that the differences observed in the mortality prediction scales are more attributable to their predictive accuracy than to population differences.\u003c/p\u003e \u003cp\u003ePatients treated at our institution between 2013 and 2018 were identified from the departmental hip fracture database. Inclusion criteria were patients aged 65 years or older who underwent surgery for extracapsular hip fractures (trochanteric and subtrochanteric), with a postoperative follow-up of 12 months unless death occurred earlier. Exclusion criteria included ASA V on the American Society of Anesthesiologists (ASA) scale [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], pathological tumor fractures, polytrauma or high-energy accidents, and periprosthetic fractures. A random sample of 57 patients who died within 12 months postoperatively was selected, with each case patient matched to a control patient who survived at least 12 months, based on gender, age (\u0026plusmn;\u0026thinsp;2 years), and a proximal surgery date. Patient selection was performed using the hospital\u0026rsquo;s electronic medical record system and hip fracture registry, with cases and controls identified through documented discharge summaries and mortality records.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEvaluations\u003c/h2\u003e \u003cp\u003eAll patient data were obtained from computerized medical records. Mortality was also checked in the institutional registry of our center which was linked to the national mortality registry. For each patient, data were collected to determine the 30-day and 12-month postoperative mortality risk on the Orthopaedic Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (O-POSSUM) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Nottingham Hip Fracture Score (NHFS) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Almelo Hip Fracture Score (AHFS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and SERNBO Score [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On admission, all patients were assessed for anesthetic risk ASA, and the level of physical-social dependency was assessed using the Barthel scale [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe required sample size was calculated by estimating the relative risk in a cohort. Using an estimated 12-month mortality prevalence of 0.15 (15%) in our study population and assuming a relative risk of 2.0 mortality, a confidence level of 0.95 and a statistical power of 0.80, 57 patients were required in each group.\u003c/p\u003e \u003cp\u003eStatistical calculations were performed with the Epidat v. 4.1. software (Pan American Health Organization, WHO). For each scale, reliability was analyzed by precision and discrimination for 30-day and 12-month mortality. For accuracy, reliability (sensitivity and specificity) was analyzed. Calibration was estimated by the ratio between the number of observed (O) and expected (E) cases, where O/E\u0026thinsp;=\u0026thinsp;1 was an excellent estimate; \u0026lt;1 underestimation (lower mortality than expected), \u0026gt;\u0026thinsp;1 overestimation (higher mortality than expected). The discriminative ability of the scale to identify patients in relation to 30-day and 12-month postoperative mortality was analyzed by Receiver Operating Characteristics (ROC) analysis. The area under the curve (AUC) was shown with its 95% CI. In biomedical practice, an AUC value close to 0.70 was considered adequate.\u003c/p\u003e \u003cp\u003eTo account for potential confounding factors, the following additional covariates were included in the analysis: comorbidities, preoperative functional status, surgical factors, postoperative complications, and medication use. Comorbidities were assessed using the Charlson Comorbidity Index and included a history of cardiovascular disease, diabetes mellitus, chronic kidney disease, and chronic obstructive pulmonary disease. Preoperative functional status was evaluated using the Barthel Index prior to the fracture. The surgical factors considered were the type of fracture (pertrochanteric or subtrochanteric fracture), surgical delay (\u0026gt;\u0026thinsp;48h), duration of surgery over 150 minutes and the need for intensive care unit (ICU) admission. Postoperative complications analyzed included: medical complications (acute kidney injury, pneumonia, sepsis, thromboembolic events\u0026hellip;), surgical and wound complications. Additionally, preoperative use of anticoagulants or antiplatelet therapy, which may impact surgical risk and postoperative outcomes, was included as a covariate. These covariates were incorporated to improve the robustness of the analysis and ensure that observed differences in mortality were not due to unmeasured confounding.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" style=\"width: 439.333px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient characteristics and univariant analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 242px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 36px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e85 (5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e86 (5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;80 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e47 (82.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e44 (77.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eGender (F/M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e42/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e42/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eBarthel index score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e54 (19.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e79 (21.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eBarthel\u0026thinsp;\u0026lt;\u0026thinsp;60 (Severe dependence)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e19 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e15 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eFracture type (Subtrochanteric)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e6 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eSide (Left/Right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e28/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e32/25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eASA\u0026thinsp;\u0026gt;\u0026thinsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e46 (80.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e40 (70.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eCharlson Index\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e54 (94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e54 (94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e33 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e9 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eNeurological disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e23 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e23 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e49 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e53 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eLung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e15 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e9 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eKidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e12 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e10 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e13 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e14 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eInadequate BMI (Overweight-Obesity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e27 (47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e40 (70.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eLymphopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e34 (59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e37 (64.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eAntiplatelet or anticoagulant therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e26 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e29 (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eEarly surgery (\u0026lt;\u0026thinsp;48h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e32 (56.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e22 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eOpen reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e7 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e7 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eSurgery\u0026thinsp;\u0026gt;\u0026thinsp;150min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e2 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eAdmision to the intensive care unit (ICU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003ePre-surgical transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e26 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e10 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003ePost-surgical transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e25 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e16 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003ePost-surgical medical complication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e28 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e21 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eSurgical wound complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e8 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e5 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003ePost-surgical complication requiring surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e2 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;90 day hospital readmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e27 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e6 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eReadmissions for medical reasons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e25 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e5 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\" align=\"left\"\u003e\n \u003cp\u003eReadmissions for surgical reasons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e3 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\" align=\"left\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 409.333px;\" colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eQuantitative data as mean (standard deviation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA total of 114 patients operated on for extracapsular hip fracture with a mean age of 86 years (range 65\u0026ndash;95 years), 84 females (73.6%) and 30 males (26.3%) were included in the study. In the group of those who died within 12 months postoperatively (n\u0026thinsp;=\u0026thinsp;57), 6 patients (10.5%) died within 30 days postoperatively. Baseline data in both groups are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAccuracy and discrimination of 30-day and 12-month postoperative mortality scales\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eO-POSSUM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNHFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAHFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSERNBO\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalibration (O:E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding 30-day postoperative mortality (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the AHFS scale showed the best accuracy with a mean sensitivity of 7.83%, and mean specificity of 95.23%. Regarding calibration, an overestimation of 30-day postoperative mortality was observed for the O-POSSUM, NHFS and AHFS scales. In contrast, the SERNBO scale underestimated mortality, although with a value close to one.\u003c/p\u003e\n\u003cp\u003eRegarding the discriminative ability for 30-day postoperative mortality (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), all scales had poor discrimination. The SERNBO and AHFS scales had the highest discriminations with AUC values of 0.63 (95% CI 0.41\u0026ndash;0.85) and 0.60 (95% CI 0.40\u0026ndash;0.81), respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea under the curve (AUC) for 30-day and 12-months postoperative mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e30-day Mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e12-months Mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO-POSSUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.25\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 (0.55\u0026ndash;0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNHFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50 (0.26\u0026ndash;0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (0.50\u0026ndash;0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 (0.4\u0026ndash;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63 (0.53\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSERNBO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63 (0.41\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36 (0.26\u0026ndash;0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding 12-month postoperative mortality (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the AHFS scale also had the best accuracy with a mean sensitivity of 54.39% and mean specificity of 48.19%. For calibration, the AHFS, NHFS and O-POSSUM scales slightly overestimated mortality, while the SERNBO scale underestimated mortality, but in all scales the values were close to one.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the discriminative ability for 12-month postoperative mortality (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), all scales had poor discrimination. The O-POSSUM and NHFS scales had the highest discriminations with AUC values of 0.65 (95% CI 0.55\u0026ndash;0.75) and 0.61 (95% CI 0.50\u0026ndash;0.71), respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA logistic regression model was performed to identify independent predictors of 12-month mortality in extracapsular hip fractures, adjusting for potential confounders. The model included age, sex, comorbidities (Charlson Comorbidity Index, diabetes, chronic kidney disease, cardiovascular disease), pre-fracture functional status (Barthel Index), surgical factors (fracture type, surgical delay, operative time over 150minutes), and postoperative medical, surgical or wound complications. Adjusted odds ratios (OR) with 95% confidence intervals (CI) were reported for each predictor. The analysis confirmed that dementia (OR\u0026thinsp;=\u0026thinsp;10.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the most significant predictors of mortality at 12 months. In contrast, Barthel Index, pre-surgical transfusion and readmission, despite being significantly different in univariate comparisons, were not retained as independent predictors after adjusting for confounders.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur results indicated that the NHFS and AHFS scales presented the best performance in predicting mortality in extracapsular fractures. AHFS and SERNBO scales are useful for predicting 30-day postoperative mortality, while the NHFS is the best choice for predicting 12-month postoperative mortality. All scales, except the O-POSSUM, take dementia into account among the factors that determine their score. Authors, such as Jeong et al [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], in recent studies have observed that patients with hip fracture and dementia have a higher risk of mortality at 30 days and 12 months. This finding was confirmed in our analysis of 12-month mortality (OR\u0026thinsp;=\u0026thinsp;10.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe AHFS scale showed the best predictive ability at 30 days, (AUC 0.60), outperforming NHFS and O-POSSUM, probably because it includes the Barthel Index among its variables. Previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] have shown that a Barthel\u0026thinsp;\u0026lt;\u0026thinsp;55 on admission is associated with higher early mortality, which may explain why the AHFS more accurately identified high-risk patients in our study. However, in comparison to other series [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], where AHFS had an AUC of 0.70 for 30 days, our value is lower. This finding may be because, in their series, 63.7% of patients had an ASA I-II, in ours only 25%, suggesting that its performance may depend on characteristics of the population assessed.\u003c/p\u003e \u003cp\u003eThe NHFS scale showed adequate discriminative ability for 12-month mortality (AUC 0.61), which is consistent with the findings of previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, in our study, its performance was lower for 30-day mortality compared to the study by Karres et al [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], where the NHFS achieved an AUC of 0.77. This difference could be explained by the fact that the NHFS includes fracture type (intracapsular/extracapsular) as one of its predictor variables. Since all patients in our study had extracapsular fractures, this variable lost its discriminative value, which could justify its lower accuracy at 30 days (AUC 0.50). However, the focus of the NHFS on comorbidities and mental status allowed it to maintain a better performance at 12 months (AUC 0.61), as these factors are key determinants of long-term survival.\u003c/p\u003e \u003cp\u003eOkur et al [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], in their series of intra- and extracapsular hip fractures, found that patients with extracapsular fractures have higher AHFS values, indicating that this scale correctly identifies these patients as being at higher baseline risk. However, the calibration of the AHFS in our series was poor (O:E\u0026thinsp;=\u0026thinsp;1.52). This suggests that the AHFS may be good at identifying high-risk patients (discrimination) but needs adjustments in its calibration to better predict actual mortality in extracapsular fractures.\u003c/p\u003e \u003cp\u003eThe SERNBO scale, initially developed for intracapsular fractures [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], performed acceptably at 30 days (AUC 0.63), probably because it assesses previous ambulation ability, which is a factor in Barthel. Pre-fracture mobility influences postoperative recovery, which justifies its predictive ability in the short term. However, it is not useful in the long term (AUC 0.36), probably because it does not include comorbidities and postoperative functional status, key factors in 12-month mortality.\u003c/p\u003e \u003cp\u003eO-POSSUM showed very low discrimination in our study (AUC 0.48), which is consistent with previous studies that have reported overestimation of mortality in high-risk patients and underestimation in low-risk patients [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. O-POSSUM is based on physiological and surgical parameters (blood pressure, hemoglobin, leukocytes, cardiac status, operative time, among others). This may explain its overestimation of mortality, as it does not consider chronic comorbidities or the patient's baseline functionality.\u003c/p\u003e \u003cp\u003eOur 30-day mortality (5.26%) is comparable with that reported in previous studies, such as that of Karres et al [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (8.2%), or Coto Caram\u0026eacute;s et al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (5.3%), which confirms the overall validity of our data. The authors of this series of extracapsular fractures highlight that age, comorbidities (Charlson index\u0026thinsp;\u0026gt;\u0026thinsp;2) and medical complications are the main factors associated with mortality. This finding is in line with the results of our series, given that the scales that best predicted mortality (AHFS and NHFS) are precisely those that include variables related to comorbidities (Charlson and ASA, respectively).\u003c/p\u003e \u003cp\u003eHowever, our 12-month mortality (50%) is higher than that reported in previous studies (31\u0026ndash;36%) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This is due to our study design, in which a group of deceased patients (cases) was selected and compared with an age- and sex-matched control group. This methodology ensures a more homogeneous comparison between the groups but biases the overall mortality rates by increasing the proportion of deceased patients in the sample analyzed.\u003c/p\u003e \u003cp\u003eOur study has certain limitations that should be considered when interpreting the results. As a retrospective study based on data obtained from medical records, it is susceptible to selection and recording biases. To mitigate these, cases and controls were matched by age, sex, and surgery date, ensuring comparability and reducing confounding effects. Additionally, the use of electronic medical records and standardized hospital documentation minimized recall bias and the risk of misclassification. Furthermore, important covariates, such as the Charlson Comorbidity Index, pre-fracture mobility, ASA score, and post-surgical complications, were included in the analysis to adjust for potential confounders, thereby enhancing the validity of the findings. Secondly, although patients with extracapsular fractures were included, the sample is limited to a single institution, so generalization of the findings to other populations should be made with caution.\u003c/p\u003e \u003cp\u003eThese aspects highlight the need for prospective studies that analyze in greater depth the impact of baseline function and other determinants on mortality in patients with extracapsular fractures, as well as the validation of these models in larger and more diverse cohorts.\u003c/p\u003e \u003cp\u003eThere is recent interest [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in analyzing specific fracture types, so as not to generalize data. This approach is relevant because mortality prediction models developed for hip fractures in general may not adequately fit the particularities of extracapsular fractures, which explains the variability in the performance of the scales evaluated in our study.\u003c/p\u003e \u003cp\u003eOur findings show the importance of selecting the appropriate scale according to the clinical objective. For extracapsular fractures, the AHFS and SERNBO scales are useful for predicting 30-day postoperative mortality, although the AHFS needs adjustments in its calibration, while the NHFS is the best choice for predicting 12-month postoperative mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no financial or non-financial interests, either directly or indirectly related to the work submitted for publication.\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or non-financial interests, either directly or indirectly related to the work submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments. The protocol was approved by the Institutional Ethics Committee of Hospital \u0026lsquo;\u0026lsquo;Vega Baja\u0026rsquo;\u0026rsquo; Orihuela. Informed consent was waived due to the retrospective nature of the study and the use of anonymized data extracted from electronic medical records.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e\u0026lsquo;E.B., B.M. and S.C. devised and compiled the database, F.L. and A.L. participated in the analysis of the data and the focus of the results. E.B. and F.L. drafted the main text of the manuscript and prepared the figures and tables. All authors revised the manuscript\u0026rsquo;.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKarres J, Heesakkers NA, Ultee JM, Vrouenraets BC. Predicting 30-day mortality following hip fracture surgery: Evaluation of six risk prediction models. Injury. 2015;46:371\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.injury.2014.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2014.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinberg EL, Sternheim A, Kadar A, Sagi Y, Sherer Y, Chechik O. Early operative intervention is associated with better patient survival in patients with intracapsular femur fractures but not extracapsular fractures. 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Rev Esp Cir Ortop Traumatol (Engl Ed). 2020;64:342\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.recot.2020.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.recot.2020.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson MJ, Scott J, Sivalingam P. Evaluation of Nottingham Hip Fracture Score, Age-Adjusted Charlson Comorbidity Index and the Physiological and Operative Severity Score for the enumeration of Mortality and morbidity as predictors of mortality in elderly neck of femur fracture patients. SAGE Open Med. 2020;8:2050312120918268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2050312120918268\u003c/span\u003e\u003cspan address=\"10.1177/2050312120918268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"extracapsular hip fracture, mortality, O-POSSUM, NHFS, AHFS, SERNBO","lastPublishedDoi":"10.21203/rs.3.rs-6659039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6659039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eThe aim of this study was to assess the reliability of various scales predictive of 30-day and 12-month postoperative mortality for patients with extracapsular hip fracture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective case-control study was designed including patients older than 65 years with extracapsular hip fracture, and matching patients who died within 12 months postoperatively with surviving patients. The Orthopaedic Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (O-POSSUM), Nottingham Hip Fracture Score (NHFS), Almelo Hip Fracture Score (AHFS) and SERNBO score were applied to the two groups, analysing the reliability, discriminative power and calibration of each of them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e57 patients with 12-month mortality and 57 survivors were included, with a mean age of 86 years, 80.7% women. For 30-day postoperative mortality, the NHFS had the highest mean sensitivity (7.83%), while the AHFS showed the best specificity (95.23%). For 12-month postoperative mortality, the AHFS scale had the highest mean sensitivity (54.39%), and the NHFS had the best specificity (94.23%). Regarding calibration, the O-POSSUM, NHFS and AHFS scales overestimated 30-day and 12-month postoperative mortality. The scale with the best discriminative ability for 30-day mortality was SERNBO and AHFS (AUC 0.62-0.60), while NHFS had the best AUC for 12-month mortality (0.66).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe AHFS and SERNBO scales are useful for predicting 30-day postoperative mortality, while the NHFS scale is a better choice for predicting 12-month mortality.\u003c/p\u003e","manuscriptTitle":"Evaluation of Mortality Prediction Scales in Extracapsular Hip Fractures: A Retrospective Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 07:07:05","doi":"10.21203/rs.3.rs-6659039/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":"3f0ac718-4250-43c5-ad74-29cd369988eb","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T19:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 07:07:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6659039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6659039","identity":"rs-6659039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}