Background Hip fracture is associated with high mortality. Identification of individual risk informs anesthetic and surgical decision-making and can reduce the risk of death. However, interpreting mathematical models and applying them in clinical practice can be difficult. There is a need to simplify risk indices for clinicians and laypeople alike. Objective Our primary objective was to develop a web-based nomogram for prediction of survival up to 365 days after hip fracture surgery. Methods We collected data from 329 patients. Our variables included sex; age; BMI; white cell count; levels of lactate, creatinine, hemoglobin, and C-reactive protein; physical status according to the American Society of Anesthesiologists Physical Status Classification System; socioeconomic status; duration of surgery; total time in the operating room; side of surgery; and procedure urgency. Thereafter, we internally calibrated and validated a Cox proportional hazards model of survival 365 days after hip fracture surgery; logistic regression models of survival 30, 120, and 365 days after surgery; and a binomial model. To present the models on a laptop, tablet, or mobile phone in a user-friendly way, we built an app using Shiny (RStudio). The app showed a drop-down box for model selection and horizontal sliders for data entry, model summaries, and prediction and survival plots. A slider represented patient follow-up over 365 days. Results Of the 329 patients, 24 (7.3%) died within 30 days of surgery, 65 (19.8%) within 120 days, and 94 (28.6%) within 365 days. In all models, the independent predictors of mortality were age, BMI, creatinine level, and lactate level. The logistic model also incorporated white cell count as a predictor. The Cox proportional hazards model showed that mortality differed as follows: age 80 vs 60 years had a hazard ratio (HR) of 0.6 (95% CI 0.3-1.1), a plasma lactate level of 2 vs 1 mmol/L had an HR of 2.4 (95% CI 1.5-3.9), and a plasma creatinine level of 60 vs 90 mol/L had an HR of 2.3 (95% CI 1.3-3.9). Conclusions In conclusion, we provide an easy-to-read web-based nomogram that predicts survival up to 365 days after hip fracture. The Cox proportional hazards model and logistic models showed good discrimination, with concordance index values of 0.732 and 0.781, respectively.

Previous research has described the importance of debriefing in Simulation-Based Medical Education; it is considered the most critical part of the teaching experience The aim of this work was to develop an inter-professional faculty with a variety of backgrounds to assist on an inter-professional nursing-medical simulation course in Emergency Medicine. A further aim was to develop a novel formal debrief for the debriefer to help improve confidence in this skill.A variety of professionals were invited to attend the course as faculty. Following their debrief of the scenario, the debriefer was invited to discuss their opinion on how they managed the debrief, from room set up to structure used. Troubleshooting advice was offered and an action plan was put in place for next steps of development. Faculty members were asked to complete a formal feedback form at the end of the session.Inter-professional faculty members included Emergency Medicine consultants, trainees and clinical fellows, simulation technicians, emergency medicine nursing staff and resuscitation officers. 75% of faculty members had attended <5 simulation courses as faculty prior to this session. 81% of faculty members scored 4 and 5/5 for feeling confident at debriefing as a result of the session. 100% scored 4 and 5/5 for feeling supported during their debrief. 100% felt that the session had improved their debriefing skills. 87.5% felt appropriately challenged as a faculty member. 100% were willing to attend the course again in the future. Free-text comments included the best part of the day was ‘Personally observing and practicing debrief, brief and debrief of my debrief’, ‘Supportive atmosphere for faculty’ and ‘Debrief learning points’.Overall, faculty members from varying clinical and simulation backgrounds were supported throughout the day and as a result were more confident in their debriefing abilities following the session. Future work aims to continue this incremental learning to allow all faculty members to feel confident and able to ‘debrief the debriefer’. This will ensure the quality of the debrief for learners, maximizing the impact of simulation-based medical education.

BACKGROUND Hip fracture is associated with high mortality. Identification of individual risk informs anesthetic and surgical decision making and can reduce the risk of death. However, interpretation of data, and application of research findings can be difficult, and there is a need to simplify risk indices for clinicians and lay-people alike. Results Twenty-four (7.3%) patients died within 30 days, 65 (19.8%) within 120 days and 94 (28.6%) within 365 days of surgery. Independent predictors of mortality common to all models were admission Age, BMI, and creatinine, lactate and their combination. Age and BMI inversely correlated with mortality. Presentation with a creatinine level of 90 mol.L-1 increased the odds of death OR 2.9 (1.4 - 6.0) 365 days after surgery compared to an admission level of 60 mol. L-1 Presentation with a plasma lactate level of 2 mmol. L-1 increased the odds of death OR 2.2 (1.1 - 4.5) 365 days after surgery compared to a plasma lactate level of 1 mmol. L-1. Patients presenting to hospital with a BMI of 30 kg.m-2 were less likely to die within 365 days OR 0.41 (0.17 - 0.99) after surgery compared to patients with a BMI of 20 kg.m-2. We presented four models in Shiny. Data entry created Kaplan-Meier graphs and outcome measures (95%CI). Conclusion We developed easy to read and interpretable web-based nomograms for prediction of survival after hip fracture surgery. OBJECTIVE Our primary objective was to develop a web-based nomogram for prediction of survival 365 days after fracture hip surgery. METHODS We collected data from 329 patients up to 365 days after hip fracture surgery and built four models using packages in RStudio. A global Cox Proportional Hazards Model was developed from all covariates. Covariates included sex, age, BMI, white cell count, lactate, creatinine, hemoglobin, C-reactive protein, ASA status, socio-economic status, duration of surgery, total time in the operating room, side of surgery and procedure urgency. We also developed a Cox proportional hazards model (CPH). a logistic regression model (LRM), and a generalized linear model (GLM) for binomial response data using iterative data reduction and elimination. We wrote an app in Shiny in order to present the models in a user-friendly way. The app consists of a drop-down box for model selection, horizontal sliders for data entry, model summaries, and prediction and survival plots. A slider selects patient follow-up over 365 days. RESULTS Twenty-four (7.3%) patients died within 30 days, 65 (19.8%) within 120 days and 94 (28.6%) within 365 days of surgery. Independent predictors of mortality common to all models were admission Age, BMI, and creatinine, lactate and their combination. Age and BMI inversely correlated with mortality. Presentation with a creatinine level of 90 mol.L-1 increased the odds of death OR 2.9 (1.4 - 6.0) 365 days after surgery compared to an admission level of 60 mol. L-1 Presentation with a plasma lactate level of 2 mmol. L-1 increased the odds of death OR 2.2 (1.1 - 4.5) 365 days after surgery compared to a plasma lactate level of 1 mmol. L-1. Patients presenting to hospital with a BMI of 30 kg.m-2 were less likely to die within 365 days OR 0.41 (0.17 - 0.99) after surgery compared to patients with a BMI of 20 kg.m-2. We presented four models in Shiny. Data entry created Kaplan-Meier graphs and outcome measures (95%CI). CONCLUSIONS We developed easy to read and interpretable web-based nomograms for prediction of survival after hip fracture surgery. CLINICALTRIAL Nil