Prediction of mortality at one year after surgery for pertrochanteric fracture in the elderly via a Bayesian belief network

Death of the P Value? Bayesian Statistics for Orthopaedic Surgeons

Statistical interpretation is foundational to evidence-based medicine. Frequentist ( P value testing) and Bayesian statistics are two major approaches for hypothesis testing. Studies analyzed with Bayesian methods are increasingly common with a 4-fold increase in the past 10 years. The Bayesian approach can align with clinical decision making by interpreting smaller differences that are not limited by P values and misleading claims of "trends toward significance." Both methods follow a workflow that includes sampling, hypothesis testing, interpretation, and iteration. Frequentist methodology is familiar and common. However, the limitations are the misunderstanding, misuse, and deceptively simple utility of interpreting dichotomous P values. Bayesian approaches are relatively less common and provide an alternative approach to trial design and data interpretation. Marginal differences elucidated by Bayesian methods may be perceived as less decisive than a P value that may reject a null hypothesis. The purposes of this review are to introduce Bayesian principles and Bayes theorem, define how pretest probability and known information may inform diagnostic testing using an example from prosthetic joint infection, contrast Bayesian and frequentist approaches using an example from the VANCO orthopaedic prospective trial, and describe the criteria for critically reviewing Bayesian studies.

Effect of oral trehalose supplementation on inflammation and wound healing in patients with peri-trochanteric fractures: study protocol for a randomised clinical trial

INTRODUCTION

Peri-trochanteric fractures, common among the elderly with osteoporosis, pose significant morbidity and mortality risks. These fractures are increasing due to the ageing population, with Nordic countries seeing a high incidence. They present challenges for orthopaedic surgeons and can lead to functional decline and high medical costs. Hip fractures have severe consequences, including pain, immobility and increased mortality. Managing wound care is complex, involving various healing phases. Trehalose, a disaccharide with antioxidant and anti-inflammatory properties, has shown promise in wound healing and other health conditions. Studies suggest its potential benefits in reducing inflammation and aiding wound healing in peri-trochanteric fracture patients, but more research is needed to confirm its clinical effectiveness in humans.

METHODS AND ANALYSIS

The study is a double-blind, placebo-controlled randomised clinical trial aiming to evaluate the effect of trehalose consumption on patients with peri-trochanteric fractures. The study will include 64 patients meeting specific inclusion criteria and will assess inflammatory markers and wound healing at different time points. Patients will be divided into two groups, one receiving trehalose and the other receiving a placebo for 12 weeks. Various measurements and assessments will be conducted, including biochemical assays, wound assessments, anthropometric measurements and dietary intake evaluations. Data analysis will be performed using SPSS software, and statistical tests will be used to compare outcomes between the intervention and control groups.

ETHICS AND DISSEMINATION

The Ethics Committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.RETECH.REC.1403.191).

TRIAL REGISTRATION NUMBER

Iranian Registry of Clinical Trials. IRCT20180404039188N5. URL of trial registry record: https://irct.behdasht.gov.ir/trial/77572. Registration date: 7 July 2024.

Predictors for 1-year mortality in geriatric patients following fragile intertrochanteric fracture surgery

OBJECTIVE

To investigate the risk factors influencing 1-year mortality after intramedullary nail fixation for fragile intertrochanteric fracture in elderly individuals.

METHODS

The medical records of 622 consecutive elderly patients (aged ≥ 65 years) with fragile intertrochanteric fractures treated with proximal femoral nail anti-rotation (PFNA) and followed-up were retrospectively analyzed. The patients were divided into death and survival groups according to their survival status within 1 year after surgery, and the differences in age, sex, region of residence, tobacco use, alcohol use, body mass index (BMI), comorbidities (hypertension, diabetes mellitus, coronary heart disease, stroke, dementia, chronic obstructive pulmonary disease, pneumonia), preoperative hemoglobin, preoperative albumin, deep vein thrombosis, fracture type (AO classification), injury-to-surgery time, American Society of Anesthesiologists (ASA) score, anesthesia modality, duration of surgery, intraoperative blood loss, and blood transfusion were compared. The Kaplan-Meier method was used for univariate analysis to screen for statistically significant differences between the two groups, and the data were entered into the Cox proportional hazards model for multivariate analysis to determine independent risk factors affecting 1-year postoperative mortality. For subgroup analysis, we explored the varying effects of hypoproteinemia and being underweight in patients of different genders, as well as the effects of different age ranges, different injury-to-surgery times, and different blood transfusion volumes on 1-year postoperative mortality.

RESULTS

The mortality rates at 1, 3, and 6 months, and 1 year after surgery were 3.9%, 7.2%, 10.1%, and 15.3%, respectively. Univariate analysis showed that advanced age, male sex, tobacco use, underweight (BMI < 18.5), coronary heart disease, stroke, dementia, pneumonia, number of comorbidities ≥ 3, hypoproteinemia and injury-to-surgery time ≤ 2 days were associated with the 1-year postoperative survival status (P < 0.1). Multivariate analysis revealed that advanced age, male sex, dementia, number of comorbidities ≥ 3, hypoalbuminemia, and being underweight were independent risk factors for 1-year postoperative mortality. Subgroup analysis showed that being underweight was associated with 1-year postoperative mortality only in male patients but not in female patients, whereas hypoproteinemia was associated with 1-year postoperative mortality in both male and female patients. Furthermore, an injury-to-surgery time of less than 2 days improved patient survival, and patients more than 80 years old showed an elevated risk of postoperative mortality.

CONCLUSIONS

Preoperative health status is a critical predictor of postoperative outcomes in elderly patients with fragile intertrochanteric fractures. Priority care should be given to the patients who are elderly, male, have dementia, have comorbidities, or are malnourished. Prompt nutritional reinforcement should be provided to patients with intertrochanteric fractures with comorbid hypoproteinemia and underweight. Furthermore, surgery should be performed as early as possible in patients with fewer comorbidities.

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases

BACKGROUND

In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality.

METHODS

A total of 52 707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application.

FINDINGS

The eXGBM model exhibited the best prediction performance, with an area under the curve (AUC) of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001).

CONCLUSIONS

The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Systematic review of multivariable prognostic models for outcomes at least 30 days after hip fracture finds 18 mortality models but no nonmortality models warranting validation

OBJECTIVES

Prognostic models have the potential to aid clinical decision-making after hip fracture. This systematic review aimed to identify, critically appraise, and summarize multivariable prediction models for mortality or other long-term recovery outcomes occurring at least 30 days after hip fracture.

STUDY DESIGN AND SETTING

MEDLINE, Embase, Scopus, Web of Science, and CINAHL databases were searched up to May 2023. Studies were included that aimed to develop multivariable models to make predictions for individuals at least 30 days after hip fracture. Risk of bias (ROB) was dual-assessed using the Prediction model Risk Of Bias ASsessment Tool. Study and model details were extracted and summarized.

RESULTS

From 5571 records, 80 eligible studies were identified. They predicted mortality in n = 55 studies/81 models and nonmortality outcomes (mobility, function, residence, medical, and surgical complications) in n = 30 studies/45 models. Most (n = 46; 58%) studies were published since 2020. A quarter of studies (n = 19; 24%) reported using 'machine-learning methods', while the remainder used logistic regression (n = 54; 68%) and other statistical methods (n = 11; 14%) to build models. Overall, 15 studies (19%) presented 18 low ROB models, all predicting mortality. Common concerns were sample size, missing data handling, inadequate internal validation, and calibration assessment. Many studies with nonmortality outcomes (n = 11; 37%) had clear data complexities that were not correctly modeled.

CONCLUSION

This review has comprehensively summarized and appraised multivariable prediction models for long-term outcomes after hip fracture. Only 15 studies of 55 predicting mortality were rated as low ROB, warranting further development of their models. All studies predicting nonmortality outcomes were high or unclear ROB. Careful consideration is required for both the methods used and justification for developing further nonmortality prediction models for this clinical population.

Systematic review of machine-learning models in orthopaedic trauma

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Methods

A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.

Results

A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures.

Conclusion

The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

Artificial intelligence applications in bone fractures: A bibliometric and science mapping analysis

Background

Bone fractures are a common medical issue worldwide, causing a serious economic burden on society. In recent years, the application of artificial intelligence (AI) in the field of fracture has developed rapidly, especially in fracture diagnosis, where AI has shown significant capabilities comparable to those of professional orthopedic surgeons. This study aimed to review the development process and applications of AI in the field of fracture using bibliometric analysis, while analyzing the research hotspots and future trends in the field.

Materials and methods

Studies on AI and fracture were retrieved from the Web of Science Core Collections since 1990, a retrospective bibliometric and visualized study of the filtered data was conducted through CiteSpace and Bibliometrix R package.

Results

A total of 1063 publications were included in the analysis, with the annual publication rapidly growing since 2017. China had the most publications, and the United States had the most citations. Technical University of Munich, Germany, had the most publications. Doornberg JN was the most productive author. Most research in this field was published in Scientific Reports. Doi K's 2007 review in Computerized Medical Imaging and Graphics was the most influential paper.

Conclusion

AI application in fracture has achieved outstanding results and will continue to progress. In this study, we used a bibliometric analysis to assist researchers in understanding the basic knowledge structure, research hotspots, and future trends in this field, to further promote the development of AI applications in fracture.

Predictive Value of Machine Learning Models in Postoperative Mortality of Older Adults Patients with Hip Fracture: A Systematic Review and Meta-analysis

BACKGROUND

Some researchers have used machine learning to predict mortality in old patients with hip fracture, but its application value lacks an evidence-based basis. Hence, we conducted this meta-analysis to explore the predictive accuracy of machine learning for mortality in old patients with hip fracture.

METHODS

We systematically retrieved PubMed, Cochrane, Embase, and Web of Science for relevant studies published before July 15, 2022. The PROBAST assessment tool was used to assess the risk of bias in the included studies. A random-effects model was used for the meta-analysis of C-index, whereas a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. The meta-analysis was performed on R and Stata.

RESULTS

Eighteen studies were included, involving 8 machine learning models and 398,422 old patients undergoing hip joint surgery, of whom 60,457 died. According to the meta-analysis, the pooled C-index for machine learning models was 0.762 (95% CI: 0.691 ∼ 0.833) in the training set and 0.838 (95% CI: 0.783 ∼ 0.892) in the validation set, which is better than the C-index of the main clinical scale (Nottingham Hip Fracture Score), that is, 0.702 (95% CI: 0.681 ∼ 0.723). Among different machine learning models, ANN and Bayesian belief network had the best predictive performance.

CONCLUSION

Machine learning models are more accurate in predicting mortality in old patients after hip joint surgery than current mainstream clinical scoring systems. Subsequent research could focus on updating clinical scoring systems and improving their predictive performance by relying on machine learning models.

Application of artificial intelligence in trauma orthopedics: Limitation and prospects

The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.

Pre- and Postoperative Exercise Effectiveness in Mobility, Hemostatic Balance, and Prognostic Biomarkers in Hip Fracture Patients: A Study Protocol for a Randomized Controlled Trial

Hip fractures are a major health concern, particularly for older adults, as they can reduce life quality, mobility loss, and even death. Current evidence reveals that early intervention is recommended for endurance in patients with hip fractures. To our knowledge, preoperative exercise intervention in patients with hip fractures remains poorly researched, and no study has yet applied aerobic exercise preoperatively. This study aims to investigate the short-term benefits of a supervised preoperative aerobic moderate-intensity interval training (MIIT) program and the added effect of an 8-week postoperative MIIT aerobic exercise program with a portable upper extremity cycle ergometer. The work-to-recovery ratio will be 1-to-1, consisting of 120 s for each bout and four and eight rounds for the pre- and postoperative programs, respectively. The preoperative program will be delivered twice a day. A parallel group, single-blinded, randomized controlled trial (RCT) was planned to be conducted with 58 patients each in the intervention and control groups. This study has two primary purposes. First, to study the effect of a preoperative aerobic exercise program with a portable upper extremity cycle ergometer on immediate postoperative mobility. Second, to investigate the additional effect of an 8-week postoperative aerobic exercise program with a portable upper extremity cycle ergometer on the walking distance at eight weeks after surgery. This study also has several secondary objectives, such as ameliorating surgical and keeping hemostatic balance throughout exercise. This study may expand our knowledge of preoperative exercise effectiveness in hip fracture patients and enhance the current literature about early intervention benefits.

Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making.

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis

IMPORTANCE

Artificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.

OBJECTIVE

To evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.

DATA SOURCES

A systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.

STUDY SELECTION

Studies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.

DATA EXTRACTION AND SYNTHESIS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.

MAIN OUTCOMES AND MEASURES

Diagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.

RESULTS

Of 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).

CONCLUSIONS AND RELEVANCE

The findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables

OBJECTIVES

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

METHODS

We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.

RESULTS

We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).

CONCLUSIONS

The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Admission lactate concentration has predictive value for death or severe complications within 30 days after admission in cattle with long-bone fractures

OBJECTIVE

To determine the prognostic value of lactate concentration measurements at admission in cattle with long-bone fractures.

ANIMALS

43 cattle with long-bone fractures between July 2016 and Dec 2018.

PROCEDURES

In this prospective cohort study, lactate concentration was measured in systemic venous blood and locally in capillary blood sampled from the fractured and contralateral limbs of cattle and assessed for outcome prediction. The cutoff value was determined by maximizing the Youden index from receiver-operating characteristic curves. Multivariable logistic regression was employed to verify whether higher lactate concentrations compared with the cutoff value were an independent risk factor for poor outcomes at 30 days or 3 years after admission.

RESULTS

Poor outcome was associated with higher capillary lactate concentration in the fractured limb (P < .001) and greater difference with systemic blood (P = .005). A cutoff value of lactate difference ≥ 2.4 mmol/L (sensitivity = 0.80; specificity = 0.965) between capillary lactate in the fractured limb and systemic blood was the best predictor of death ≤ 30 days after admission (P < .001). Multivariable analysis revealed that this cutoff value was an independent risk factor for 30-day and 3-year outcomes (P < .001).

CLINICAL RELEVANCE

Admission blood lactate concentration difference ≥ 2.4 mmol/L between the fractured limb and systemic blood was a robust and independent predictor of outcome for cattle of the present report. Lactate metabolism is locally impaired in fractured limbs of nonsurviving or at higher complication risk cattle, which may help identify patients at high risk for poor outcomes.

Management of pertrochanteric fractures in patients over 90 years: In-hospital mortality rate, complications and associated risk factors

PURPOSE

This study aims primarily to investigate the outcome following surgical management of pertrochanteric fractures of patients over 90 years compared to the outcome of a control group below 90 years under special consideration of the timing of surgery. The second aim was to analyze potential risk factors for early deaths in very old patients. This study allows us to draw conclusions to minimize complications linked to this particular age segment.

METHODS

The study group consisted of very old patients aged 90 years and older. Geriatric patients aged between 60 and 89 years of age were part of the control group. Type A1 pertrochanteric fractures were typically treated by dynamic hip crews, type A2 and A3 fractures by femoral nails. Full weight bearing physiotherapy was initiated on the day after surgery to improve mobility and muscle strength.

RESULTS

A total of 71 patients belonged to the study group (mean age: 92.5 years ±2.3 years), whereas 223 patients formed the control group (mean age: 79.9 ± 7.4 years). The mortality rate and the number of detected and documented complications were significantly higher in the study group (p = 0.001; p = 0.009, respectively). Despite the significantly higher complication rate in the > 90-year-old patients, there was no significant difference in the mean length of in-hospital-stay between the both groups (> 90 yrs.: 12.1d; < 90 yrs.: 13.1 d) and the timing of surgery.

CONCLUSION

The number of co-morbidities, number of daily-administered medications and the time between admission and surgery have no impact on the outcome. We noticed a longer period between admission and surgery in very old patients who survived. Patients with pertrochanteric fractures should be screened for multimorbidity and cognitive disorders in a standardized manner.