The aim of this study was to develop and internally validate a clinical prediction model that can predict 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above to aid the challenging treatment decision-making. The developed and internally validated models show promise in estimating mortality in this frail patient population.
Limitations
The results of this study should be viewed in light of several limitations. First, the study was a retrospective study beholden to limitations inherent to such research design and prospective validation remains to be evaluated. Second, the mortality rate in our cohort was relatively low compared to other populations of hip fracture patients [
38]. This resulted in predicted probabilities as shown in the calibration plots, up to 50% and 80% risk for respectively 90 day and 2 year mortality. This means that our model is likely more accurate in healthier hip fracture patients. To ensure external validation, our model should be validated in a cohort with representative rates, and future studies should assess the transportability of the developed algorithm to datasets with patients with higher mortality rates. Third, for this study, we chose a 80/20 ratio for data splitting into training and test set, which has been mostly used in previous literature [
20‐
22,
39]. There is no fixed rule for the ratio of data splitting but a different ratio for algorithm training may have led to different model performances. Fourth, preoperative risk stratification for mortality is needed to guide the difficult treatment decision-making, although intraoperative and postoperative factors associated with complications, such as reoperation or postoperative infection, may be confounding with mortality after surgery. Future research may estimate this influence looking at causality for confounding factors [
40]. Fifth, patients were included in the study undergoing femoral neck fracture surgery. However, patients who were suspected by the clinician of a very short survival prediction (e.g. 30 day) were chosen to be treated conservatively and were not investigated in this study. In future studies, both conservative and surgical treated patients should be included to optimize mortality prediction in all patients sustaining a femoral neck fracture to guide the challenging treatment decision-making (i.e. whether to operate or not?). Sixth, evaluating possible co-injuries occurring during trauma, some of which may cause significant disability, may influence survival outcome. Evaluating these co-injuries and calculating their injury severity score may have had an influence as candidate input variable on the model performance. In addition, we did not investigate the influence of the presence of advanced directives, which may influence the decision-making process in patients aged 65 years or above. In future research, when comparing treatment effects in conservatively and operatively treated patients, we recommend these influences to be investigated. Lastly, the 2 year mortality was chosen on the basis of endpoints in prior prospective randomized controlled trials [
5,
6]. The 90 days was chosen to predict short-term mortality and accounts for a possible underestimation in outcomes seen with only a 30 day mortality. From a patient and provider perspective, a death 90 days post hip fracture is just as significant as one within 30 days. It takes in to account not just acute in-hospital complications but also short-term complications that may occur in skilled nursing facility and discharge to the community. There is growing evidence in other specialties that 30-day mortality underestimates short-term mortality [
41,
42]. Future studies may additionally investigate earlier time points, such as 30 days or 1 year.
Findings
In the ranges of risk where we think clinical utility of the model is to be expected, the 2 year model clearly adds clinical utility over treating everyone or none with total hip arthroplasty. However, we assumed a more simplified scenario, since there are multiple treatment options available, namely nonoperative management, surgical fixation and arthroplasty surgery. The 90 day mortality model might add clinical utility for decisions between these tiered treatment options, which are more subtle and complex to assume. Moreover, clinical utility should be reassessed after external validation, and with input from multiple institutions from different countries. If found to be externally valid (generalizable to independent populations), future studies should prospectively evaluate the developed and validated tool. In patients with limited life expectancy, patients predicted with a high risk of short-term mortality, nonoperative management might be a viable option in the shared decision-making process compared to surgical fixation [
8]. If patients have a high chance of surviving beyond the 90 day endpoint, surgical management would be in place [
43]. Frail patients with a nondisplaced hip fracture may be favored to surgical fixation compared to arthroplasty surgery [
6,
18]. However, arthroplasty is associated with a lower risk of reoperation and better long-term functional outcomes, at the cost of greater infection rates, blood loss, and operative time and possibly an increase in early mortality rates and may be recommended in patients with a longer-term life expectancy (e.g., high probability of surviving beyond the 2 year endpoint) [
44].
When aiming to develop a prediction model that is applicable in daily practice, variables should be included in the trained algorithm that are readily available and use of definitions that are in line with daily practice should be followed. In this study, variables derived from variable selection are clinically readily available and in line with daily practice. It is important to emphasize that treatment decision-making should not be solely based on the outcome of an individualized probability calculator. The orthopaedic surgeon should discuss the available treatment options and reach a treatment decision following a shared decision-making process. Prediction of mortality is only one of the aspects to be considered in treatment decision-making.
The most important factors associated with a greater risk of 90 day mortality included in the SGB algorithm were INR, age, creatinine level, absolute neutrophil, CHF, male gender, hemoglobin level, displaced fracture, hemiplegia and COPD. For 2 year mortality, the most important factors were age, male gender, absolute neutrophil, CHF, use of beta-blocker, COPD, CVA, hemoglobin, creatinine level and INR. Our findings are in line with previous research on proximal femoral neck fractures in general and broader populations. Regarding age and sex, prior studies revealed a higher risk for higher age and the male gender [
45‐
47]. The effect of CHF, CVA and COPD is in line with the high risk reported for a higher ASA classification in earlier studies [
48,
49]. A possible explanation for this effect might be a lower physical condition of the patient at baseline and therefore a less adequate recovery after complications (e.g. pneumonia). Another explanation for comorbidities in general could be a lower life expectancy as a result of the comorbidity itself. In regard to displacement of the fracture, a reasonable explanation for the higher risk might be the disruption of the vascularization of the femoral head and the tendency that a displaced fracture comes from a frailer patient to start with where more displacement occurred compared to a younger patient (with the same level energy of trauma). This could lead to multiple complications and secondary surgery eventually resulting in death [
50]. The prognostic value of laboratory characteristics in predicting mortality after hip surgery is a less explored subject. But the elevation of creatinine and absolute neutrophil count reflects respectively declined renal function and inflammation [
51]. Which again is linked to a higher ASA score and a lower baseline physical condition. Whereas a higher INR is reflecting the inability to coagulate and most likely the use of anticoagulants, resulting in a higher risk for bleeding and as a result of this a higher risk for morbidity and mortality [
46,
51]. On the contrary a lower hemoglobin is related to chronic comorbidities, which might reflect in a lower odds for mortality for higher hemoglobin levels [
51].
Over the recent years, a lot of research has been done predicting mortality in femoral neck fracture patients. The greater part of these tools developed made an estimation of risk based on age, gender and in general the presence of comorbidity [
52,
53], whereas the other part looked at postoperative factors, such as early ambulation after surgery and postoperative lab values [
54,
55]. In contrast to the broader presence of comorbidity, our study used the ability of ML algorithms to differ between the effects of different types of comorbidity in a large database to estimate the individual value of each factor. This resulted in a more patient centered prediction tool.