McLeod Graeme, Kennedy Iain, Simpson Eilidh, Joss Judith, Goldmann Katriona
Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom.
School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom.
Interact J Med Res. 2022 Mar 30;11(1):e34096. doi: 10.2196/34096.
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.
Our primary objective was to develop a web-based nomogram for prediction of survival up to 365 days after hip fracture surgery.
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.
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).
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.
髋部骨折与高死亡率相关。识别个体风险有助于麻醉和手术决策，并可降低死亡风险。然而，解读数学模型并将其应用于临床实践可能具有挑战性。有必要为临床医生和普通民众简化风险指数。
我们的主要目的是开发一种基于网络的列线图，用于预测髋部骨折手术后365天内的生存率。
我们收集了329例患者的数据。我们的变量包括性别、年龄、体重指数、白细胞计数、乳酸、肌酐、血红蛋白和C反应蛋白水平；根据美国麻醉医师协会身体状况分类系统的身体状况；社会经济状况；手术持续时间；在手术室的总时间；手术侧别；以及手术紧迫性。此后，我们对髋部骨折手术后365天生存的Cox比例风险模型、手术后30、120和365天生存的逻辑回归模型以及二项式模型进行了内部校准和验证。为了以用户友好的方式在笔记本电脑、平板电脑或手机上展示这些模型，我们使用Shiny（RStudio）构建了一个应用程序。该应用程序显示一个用于模型选择的下拉框以及用于数据输入、模型总结、预测和生存图的水平滑块。一个滑块代表患者365天的随访情况。
在329例患者中，24例（7.3%）在手术后30天内死亡，65例（19.8%）在120天内死亡，94例（28.6%）在365天内死亡。在所有模型中，死亡率的独立预测因素是年龄、体重指数、肌酐水平和乳酸水平。逻辑模型还将白细胞计数纳入预测因素。Cox比例风险模型显示死亡率差异如下：年龄80岁与60岁相比，风险比（HR）为0.6（95%CI 0.3 - 1.1），血浆乳酸水平2 mmol/L与1 mmol/L相比，HR为2.4（95%CI 1.5 - 3.9），血浆肌酐水平60 μmol/L与90 μmol/L相比，HR为2.3（95%CI 1.3 - 3.9）。
总之，我们提供了一种易于阅读的基于网络的列线图，可预测髋部骨折后365天内的生存率。Cox比例风险模型和逻辑模型显示出良好的区分度，一致性指数值分别为0.732和0.781。
