Creation of a Patient-Specific Total Hip Arthroplasty Periprosthetic Fracture Risk Calculator

Abstract

Background:

Many risk factors have been described for periprosthetic femur fracture (PPFFx) following total hip arthroplasty (THA), yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to develop a high-dimensional, patient-specific risk-stratification nomogram that allows dynamic risk modification based on operative decisions.

Methods:

We evaluated 16,696 primary non-oncologic THAs performed between 1998 and 2018. During mean 6-year follow-up, 558 patients (3.3%) sustained PPFFx. Patients were characterized by individual natural language processing-assisted chart review on non-modifiable factors (demographics, THA indication, comorbidities), and modifiable operative decisions (femoral fixation [cemented/uncemented], surgical approach [direct anterior, lateral, posterior], implant type [collared/collarless]). Multivariable Cox regression models and nomograms were developed with PPFFx as a binary outcome at 90-days, 1-year, and 5-years postoperatively.

Results:

Patient-specific PPFFx risk based on comorbid profile was wide-ranging from 0.4–18% at 90-days, 0.4–20% at 1-year, and 0.5–25% at 5-years. Among 18 evaluated patient factors, 7 were retained in multivariable analyses. The 4 significant non-modifiable factors included: women (Hazard Ratio (HR)=1.6), older age (HR=1.2 per 10 years), diagnosis of osteoporosis or use of osteoporosis medications (HR=1.7), and indication for surgery other than osteoarthritis (HR=2.2 for fracture, HR=1.8 for inflammatory arthritis, HR=1.7 for osteonecrosis). The 3 modifiable surgical factors were included: uncemented femoral fixation (HR=2.5), collarless femoral implants (HR=1.3), and surgical approach other than direct anterior (lateral HR=2.9, posterior HR=1.9).

Conclusion:

This patient-specific PPFFx risk calculator demonstrated a wide-ranging risk based on comorbid profile and enables surgeons to quantify risk mitigation based on operative decisions.

LEVEL OF EVIDENCE:

Level III, Prognostic

INTRODUCTION

Periprosthetic femur fracture (PPFFx) remains one of the most common and challenging problems associated with total hip arthroplasty (THA) [1]. Indeed, recent data from the American Joint Replacement Registry (AJRR) indicates it is the second most frequent indication for early revision THA (behind infection) [2], which is corroborated by institutional [3] and international registry data [4,5]. A myriad of non-modifiable patient characteristics have been identified as risk factors for PPFFx, including demographics and various comorbidities [6,7]. Concomitantly, modifiable operative decisions are known to influence risk.

Classically, the most profound factor has been fixation technique for the femoral component with series consistently documenting lower rates of PPFFx with cemented fixation [8]. Implications of this relationship are especially profound for practices with low rates of cemented femoral fixation, which includes most surgeons in the United States. The 2022 American Joint Replacement Registry (AJRR) data indicate cemented fixation is used in only 19% of THAs for femoral neck fracture, and 49% of hemiarthroplasty for femoral neck fracture; rates are considerably lower (<5%) for elective arthroplasty [2]. Furthermore, other operative variables have been associated with differential PPFFx risk including operative approach [9,10] and whether or not the femoral component has a collar [11].

Although substantial literature has evaluated the impact of individual characteristics, achieving patient-specific risk prediction has remained elusive due to small datasets, or more commonly, cohorts with insufficient characterization [6,12]. An ideal risk prediction model would evaluate patients on a wide spectrum of covariates, while remaining parsimonious to include only causal factors with strong associations. Perhaps more importantly, a risk prediction clinical tool should be adaptive to decisions within control of the surgeon. Importantly, similar work has proven successful with this approach for determining patient-specific dislocation risk [13,14].

The purpose of this study was to develop a high-dimensional, patient-specific risk prediction model for PPFFx that allows for dynamic risk modification based on operative decisions. The goal of the model was to create a usable clinical nomogram that could calculate individual risk for any patient. The nomograms presented in this work could further serve as the basis for a user-friendly electronic calculator of patient-specific PPFFx risk.

PATIENTS AND METHODS

Following Institutional Review Board approval, 16,696 primary non-oncologic THAs were evaluated from a single institutional total joint registry (TJR) from January 1, 1998 to December 31, 2018 with a mean 6 years of follow-up (range, 0 to 21 years). Patients were characterized using a prospectively-collected total joint registry with augmentation to determine specific comorbidities and medication exposures of interest (e.g., osteoporosis, diabetes mellitus, end stage renal disease [ESRD], malnutrition, liver disease, bariatric surgery, oral or intravenous steroids, chemotherapy, alcoholism, smoking) using diagnosis/procedure codes and natural language processing (NLP)-assisted chart review of the medical record with individual manual review of all diagnoses.

Analyzed surgical variables included: 1. femoral fixation (cemented/uncemented); 2. femoral implant type (collared/collarless); and 3. surgical approach (direct anterior, lateral, posterior). Ultimately, this enabled determination of patient profiles with non-modifiable preoperative variables and modifiable intraoperative variables (Table 1). All cases were assumed to be at risk of PPFFx during or after THA and were followed until fracture, last follow-up, or death. All fractures were considered equivalent regardless of timing, location, or subsequent treatment. Univariable and multivariable Cox regression analyses determined hazard ratios (HRs) for variables associated with differential PPFFx risk. Since the study focused on PPFFx events within five years after surgery, follow-up was censored at a maximum of 6 years from THA. Variables that remained significant in multivariable analyses or improved the model fit based on the Akaike information criterion (AIC) [15] were included in the final model.

Table 1.

Characterization of Patients with Preoperative Non-Modifiable Variables and Intraoperative Modifiable Variables

Preoperative Non-Modifiable Factors
Category	Variables
Demographics	Age, Sex, Body Mass Index
Surgical Indication	Osteoarthritis, Osteonecrosis, Inflammatory, Post-Traumatic/Non-Union
Comorbidities	Osteoporosis, Diabetes Mellitus, End Stage Renal Disease, Malnutrition, Liver Disease, Bariatric Surgery
Medications	Oral or Intravenous Steroids, Chemotherapy, Alcoholism, Smoking
Intraoperative Modifiable Factors
Category	Variables
Approach	Posterior, Lateral, Direct Anterior
Femoral Fixation	Cemented or Uncemented
Femoral Implant	Collared or Collarless

A patient-specific PPFFx risk calculator was created with nomograms from multivariable modeling such that the individual risk for a patient who has any combination of non-modifiable factors could be calculated and would determine differential risk based on modifiable operative decisions. These nomograms were built separately for 90-day, 1-year, and 5-year timepoints. Discrimination was assessed using the concordance statistic (c-statistic) for the Cox models [16]. Calibration was assessed by comparing observed versus expected events in deciles of predicted risk using goodness-of-fit tests, which included standardized incidence ratios (SIR) [17]. All hazard ratios (HRs) reported below are statistically significant, with confidence intervals (CI) and P-values reported in the accompanying tables.

Patient Characteristics and Operative Management

Overall, mean patient age was 66 years (range, 12 to 100 years), mean body mass index (BMI) was 30 (range, 14 to 75 kg/m²), and 50% patients were women(Table 2). Mean follow-up was 6 years (range, 2 to 21 years). History of evaluated comorbidities and medication exposures present at the time of THA was as follows: history of smoking (56%); alcoholism (38%); diabetes mellitus (31%); osteoporosis (20%); ESRD (14%); liver disease (11%); oral or intravenous corticosteroids (11%); malnutrition (5%); bariatric surgery (3%); and chemotherapy (1%) (Table 2). Primary THA surgery was performed for osteoarthritis (79%), post-traumatic or fracture (9%), osteonecrosis (9%), and inflammatory arthritis (3%) (Table 2).

Table 2.

Patient Characteristics

	Total (N=16696)
Age at Surgery
Mean (SD)	66 (13)
Median	68
Range	11, 100
Sex, n (%)
Female	8338 (50%)
Male	8358 (50%)
BMI kg/m2
Mean (SD)	16696
Median	30 (6)
Range	29
	14, 75
BMI kg/m2, n (%)
BMI <= 18	90 (1%)
19–24.9	3247 (19%)
25–29.9	5847 (35%)
30–34.4	4312 (26%)
35–39.9	1981 (12%)
>=40	1219 (7%)
Follow-up (years)
Mean (SD)	6 (5)
Median	5
Range	0.0, 21
Follow-up (years) – censored at 6
Mean (SD)	4 (2)
Median	5
Range	0, 6
Indication for THA, n (%)
OA	13116 (79%)
Fracture/Posttraumatic	1552 (9%)
AVN	1505 (9%)
Inflammatory	523 (3%)
Approach, n (%)
Lateral	5818 (35%)
Direct Anterior	1756 (11%)
Posterior	9122 (55%)
Osteoporosis, n (%)
No	13342 (80%)
Yes	3354 (20%)
Steroids, n (%)
No	14863 (89%)
Yes	1833 (11%)
DMARDs, n (%)
No	15485 (93%)
Yes	1211 (7%)
Chemotherapy, n (%)
No	16513 (99%)
Yes	183 (1%)
Any History of Smoking, n (%)
No	7299 (44%)
Yes	9397 (56%)
Any History of Alcohol Use, n (%)
No	10370 (62%)
Yes	6326 (38%)
Diabetes, n (%)
No	11504 (69%)
Yes	5192 (31%)
Renal-ESRD, n (%)
No	14372 (86%)
Yes	2324 (14%)
Liver Disease, n (%)
No	14928 (89%)
Yes	1768 (11%)
Bariatric, n (%)
No	16283 (97%)
Yes	413 (3%)
Immunodeficiency, n (%)
No	16696 (100%)
Malnutrition, n (%)
No	15861 (95%)
Yes	835 (5%)
Collared, n (%)
No	13864 (83%)
Yes	2832 (17%)
Cemented, n (%)
No	12746 (76%)
Yes	3950 (24%)

SD=standard deviation; BMI=body mass index; THA=total hip arthroplasty; OA=osteoarthritis; AVN=avascular necrosis; DMARDs=disease modifying antirheumatic drugs; ESRD=end stage renal disease

Primary THA was performed with a posterior approach in 55%, lateral approach in 35%, and direct anterior approach with 11%. Cemented femoral fixation was performed in 24% and collared femoral implants were used in 17% (Table 2).

RESULTS

Among the 16,696 primary non-oncologic THAs, 558 patients sustained a PPFFx (5-year Kaplan-Meier survivorship rate of 3.7%). Among the 18 evaluated patient factors, 7 were included in the final multivariable model (Table 3). The 4 significant non-modifiable factors included: women (HR=1.6), older age (HR=1.2 per 10 years), diagnosis of osteoporosis or use of osteoporosis medications (HR=1.7), and indication for surgery other than osteoarthritis (HR=2.2 for fracture, HR=1.8 for inflammatory arthritis, HR=1.7 for osteonecrosis). All 3 analyzed modifiable surgical factors were included following multivariable analyses as follows: uncemented femoral fixation (HR=2.5), collarless femoral implants (HR=1.3), and surgical approach other than direct anterior (lateral HR=2.9, posterior HR=1.9).

Table 3.

Univariable and Multivariable Analysis of Risk of Periprosthetic Fracture

Patient Factor	Unadjusted HR (95% CI)	Adjusted HR (95% CI)*	Adjusted P-Value*
Age per 10 Years	1.1 (1.1 – 1.2)	1.2 (1.1 – 1.3)	< 0.001
Sex (ref=Male)
Female	1.7 (1.4 – 2.0)	1.6 (1.3 – 1.9)	< 0.001
BMI kg/m2 (ref=19–24.9)
≤18.0	0.8 (0.2 – 2.4)
25.0 – 29.9	0.7 (0.6 – 0.9)
30.0 – 34.4	0.6 (0.5 – 0.8)
35.0 – 39.9	0.7 (0.5 – 1.0)
≥40.0	0.8 (0.6 – 1.1)
Indication for THA (ref=Osteoarthritis)
Fracture/Post-traumatic	2.3 (1.8 – 2.9)	2.2 (1.7 – 2.8)	< 0.001
AVN	1.5 (1.2 – 2.0)	1.7 (1.3 – 2.2)	< 0.001
Inflammatory	1.7 (1.1 – 2.6)	1.8 (1.2 – 2.8)	0.007
Steroids (ref=No)
Yes	1.4 (1.1 – 1.8)
DMARDS (ref=No)
Yes	1.5 (1.1 – 2.0)
Chemotherapy (ref=No)
Yes	1.4 (0.7 – 2.9)
Osteoporosis Diagnosis or Drugs (ref=No)
Yes	2.1 (1.8 – 2.5)	1.7 (1.4 – 2.0)	< 0.001
Smoking (ref=No)
Yes	1.2 (1.0 – 1.4)
Alcohol (ref=No)
Yes	1.0 (0.9 – 1.2)
Diabetes (ref=No)
Yes	1.1 (0.9 – 1.3)
Renal Disease (ref=No)
Yes	1.2 (0.9 – 1.5)
Liver Disease (ref=No)
Yes	1.3 (1.0 – 1.6)
Bariatric (ref=No)
Yes	1.7 (1.1 – 2.6)
Malnutrition (ref=No)
Yes	1.9 (1.4 – 2.6)
Cemented Fixation (ref=Yes)
No	1.4 (1.1 – 1.7)	2.5 (2.0 – 3.2)	< 0.001
Collared Femoral Implant (ref=Yes)
No	1.3 (1.0 – 1.7)	1.3 (1.0 – 1.7)	0.059
Operative Approach (ref=Direct Anterior)
Lateral	3.2 (2.1, 4.9)	2.9 (1.9 – 4.6)	< 0.001
Posterior	2.0 (1.3, 3.1)	1.9 (1.2 – 2.9)	0.006

HR=hazard ratio, CI=confidence interval, BMI=body mass index, THA=total hip arthroplasty, AVN=avascular necrosis, DMARDS=disease modifying anti-rheumatic drugs

^*Adjusted HR and CI only shown for variables significant in multivariable analysis. Exception for collared femoral implant type that trended toward significance in the final model.

The multivariable models demonstrated calibration SIR values of 0.98 to 0.99, consistent with “excellent” calibration. The model discrimination (concordance or C-statistic) values ranged from 0.68 to 0.69, consistent with “very good” discrimination.

Nomogram Risk Calculator and Spectrum of Individual Patient Risk

Nomograms of individual patient PPFFx risk were created from Cox proportional hazard models (Figures 1A to C). Each patient factor is calibrated to be worth a certain number of points. Total points are calculated to obtain projected risk of PPFFx at 90 days, 1 year, and 5 years. The final data input line in the nomograms is approach, fixation method, and implant type. The combination of these 3 factors yields the greatest differential in possible point total, underscoring the power surgeons have to modify risk.

Figure 1.

Patient-specific periprosthetic femur fracture (PPFFx) absolute risk nomograms for primary total hip arthroplasty at 90-days, (A), 1-year (B) and 5-years (C). Risk is calculated by selecting the patient factor and drawing a vertical line upwards to the top row “Points” to calculate the number of points for that factor. The sum of the points for all factors is applied to the row “Total Points” and a vertical line is draw downward to obtain the result for absolute risk.

To understand the range of risk associated with non-modifiable patient factors, as well as the impact of modifiable operative decisions, a series of patient scenarios were created to define the upper and lower boundaries of the nomogram using best and worst possible patient scenarios (Table 4). Patient-specific PPFFx risk based on comorbid profile was wide-ranging from 0.4 to 18% at 90-days, 0.4 to 20% at 1-year, and 0.5 to 25% at 5-years.

Table 4.

Periprosthetic Fracture Risk Based on Non-Modifiable Patient Factors and Modifiable Operative Decisions

Operative Approach	Direct Anterior				Lateral				Posterior
Collar	No		Yes		No		Yes		No		Yes
Cement	Yes n=68	No n=477	Yes n=3	No n=1208	Yes n=1833	No n= 3111	Yes n=106	No n=768	Yes n= 1901	No n=6474	Yes n=39	No n=708
PPFx (HR)	-	0.29	-	0.48	0.59	1.65	-	1.33	0.47	1.00 (ref)	-	0.71
Absolute Risk at 90 Days (%)
Worst Host *	-	3.5	-	5.6	6.8	18	-	14.8	5.5	11.3	-	8.2
Best Host **	-	0.4	-	0.6	0.7	2.0	-	1.6	0.6	1.2	-	0.9
Absolute Risk at 1 Year (%)
Worst Host *	-	4.0	-	6.3	7.7	20.3	-	16.6	6.2	12.8	-	9.3
Best Host **	-	0.4	-	0.7	0.8	2.3	-	1.9	0.7	1.4	-	1.0
Absolute Risk at 5 Years (%)
Worst Host *	-	5.1	-	8.1	10.0	25.5	-	21.1	8.0	16.3	-	11.9
Best Host **	-	0.5	-	0.9	1.1	3.0	-	2.4	0.9	1.8	-	1.3

Dashes represent combinations with insufficient sample size to calculate reliable hazard ratios or absolute risks

HR, hazard ratio (reference set to 1.00 individually for highest risk group)

^*Worst possible host: female, age=85 years, indication for THA=Fracture/Post-traumatic, Osteoporosis present

^**Best possible host: male, age=55 years, indication for THA = OA, Osteoporosis not present.

Case Examples

An 85-year-old woman underwent a THA for a fracture, and she had a history of osteoporosis. Her absolute risk of PPFFx at 90-days ranged from 3.5 to 18% based on her comorbid profile, and final risk within that spectrum was based on choices within control of the surgeon. If the performing surgeon preferred a posterior approach, an uncemented collarless stem would yield the highest risk at 11.3%, which could be decreased to 8.2% with use of a collared implant or decreased to 5.5% with cemented fixation (Figure 2A). Furthermore, risk for this patient using a lateral or direct anterior approach can be calculated in Table 4 or Figure 2A.

Figure 2.

Example of how to use the nomogram for a hypothetical patient and various risk estimates in the setting of a primary THA for fracture (A) and osteoarthritis (B). In these examples, blue dots are assigned to the non-modifiable patient factors. Blue arrows then point upward to indicate the number of points assigned to that calibrated data point. The row indicating “Approach/Collar/Cement” has a possible data point for all combinations included in this study. Three example combinations have been chosen for illustration, all with a posterior approach. The three collar/cement choices are: 1) no collar/no cement, 2) collar/no cement, 3) no collar/cement. For illustration, the combination portending the highest risk is assigned a red dot pointing upward to indicate the number of points assigned to that calibrated data point. There is also a corresponding red dot shown for the row indicating “Total Points” and an arrow pointing downward to show the 90-day risk of PPFFx for this specific patient. The exact same method is applied for the combination with intermediate risk (yellow dots and arrows) and the combination with lowest risk (green dots and arrows).

Presume the same patient presents for a posterior approach THA, but the indication is for routine osteoarthritis as opposed to fracture. Her absolute risk of PPFFx at 90 days would range from 1.6 to 8.7%. An uncemented collarless stem would yield the highest risk at 5.4%, which could be decreased to 3.9% with use of a collared implant or decreased to 2.6% with cemented fixation (Figure 2B).

DISCUSSION

Periprosthetic femur fracture remains one of the most frequent complications and reasons for revision following THA. Ultimately, individual patient risk is a complex amalgamation of non-modifiable characteristics and modifiable operative decisions. This study leveraged a large cohort of patients who were meticulously characterized across a broad range of important PPFFx risk comorbidities to derive risk prediction nomograms that are patient-specific and responsive to operative decisions. Surgeons can use these prediction tools to forecast 90-day, 1-year, and 5-year probability of PPFFx and determine the impact of fixation technique, implant type, and operative approach for risk mitigation.

It should be emphasized that this cohort included thorough characterization of patients beyond traditional demographic and operative factors by including evaluation of several comorbidities and medication exposures potentially related to bone quality. These data were all individually and manually validated. The aforementioned diagnoses then supplemented our TJR that already tracks patient demographic, operative, and complication data with >98% capture. An ideal model should balance the competing demands of 1) providing optimal prediction (which often means more variables), and 2) being parsimonious and user-friendly (which often means only keeping the most influential variables). Serendipitously, among the broad array of assessed factors with peer-reviewed literature support, the most robust final model was quite parsimonious. Only 4 of 15 non-modifiable factors were included in the final model following multivariable analyses and all are readily acertained in routine workup: age, sex, history of osteoporosis, and indication for THA. The additional 3 factors in the model are those under control by the surgeon: fixation technique, implant type, and operative approach. It should be noted that implant type (collared vs collarless) only trended toward significance in the final model. In various evaluated models, this parameter was significant in some and trended toward significance in others, but it improved model fit based on the Akaike information criterion (AIC). Furthermore, there is increasing evidence on the protective nature of collars in PPFFx prevention, thus the decision was made to include it as a modifiable factor [4,6,11].

Baseline risk of PPFFx was shown to be highly variable based on non-modifiable comorbidities and risk factors, a fact most poignantly demonstrated by comparing “worst case” and “best case” patients in Table 4. This underscores the importance of carefully considering comorbid status to accurately classify patients. Nevertheless, an encouraging message for surgeons in the presented work centers on the considerable control to influence outcomes based on operative decisions. Indeed, operative covariates were the most impactful nomogram variables by a wide margin. Operative approach demonstrated a marked influence on PPFFx risk. Thus, for surgeons that perform some combination of approaches in practice, this may afford an opportunity to more selectively employ one approach versus another. However, for the many surgeons who default to a specific approach, the data provides highly actionable information as well. Fixation technique was the single most important factor in the model with implant type also being highly influential. For example, a predominantly posterior approach surgeon can see in Table 4 that using a collared implant can decrease risk by approximately 30% and cementing the femoral component can decrease risk by >50%.

It should be noted that absolute risk is important to consider in addition to relative risk. The overall impact of a mitigation strategy is quite different for a patient who has a baseline 90-day PPFFx risk of 1 versus 10%. In this example, undertaking an operative decision that reduces relative risk by 50% changes the absolute risk from 1 to 0.5% vs. 10 to 5% in these hypothetical patients. This point underscores the actionable nature of this calculator, especially as it pertains to selective cementing of femoral components.

Realistically, cementing the femoral component is likely to remain a minority practice in the United States as currently <6% of elective THA and <17% of THA for acute fracture are cemented [2]. However, a calculator of this nature that shows comparative absolute risk may facilitate appropriate selective use of cementing to best serve high risk patients in accordance with evidence applied as individualized medicine [13]. Advancing as a specialty in this area is critical. The proportion of patients undergoing THA who have relevant risk factors is increasing [3,12]. A PPFFx is a devastating complication with a high 1-year mortality and is quite morbid and demanding of scarce healthcare resources [18–20]. It is currently the second most common reason for early revision in the AJRR and data clearly indicates the readily-available, facile, and cost-effective strategies to address this issue are underutilized [1,8,21]. The National Health Service of the United Kingdom has recently undertaken an initiative known as Get It Right the First Time (GIRFT). Driving this program is the idea that performing the right surgery, on the right patient, at the right time, and in the right place avoids many otherwise preventable complications. GIRFT has been quite successful in the first few years in decreasing revision and re-revision rates, demonstrating a real-world proof-of-concept that employing patient-specific evidence-based decisions can improve quality and cost of THA care [22,23].

This study must be interpreted in light of potential limitations. These results represent the experience of single center and thus may not be representative of other practices. In particular, certain combinations of approach, fixation method, and implant type were too rare for inclusion, but may be common at other centers. Comparative analysis from data at other institutions and external validation will be helpful to create a more generalizable model. Furthermore, PPFFx is a relatively rare event with multifactorial etiology. That combination, for any clinic prediction problem, makes model discrimination and calibration difficult. Our model had “very good” discrimination, despite the aforementioned challenges inherent to modeling problems like PPFFx and achieved “excellent” calibration, which is a testament to model fine-tuning. Moreover, all fractures in this series were treated as equivalent regardless of timing, pattern, or subsequent treatment. Subclassifying on these variables would have been prohibitive to meaningful risk modeling. In addition, this surgeons performing DAA in this study also selectively perform posterior or lateral THA in some patients. The most common reasons are poor anterior skin quality, substantially elevated BMI, or challenging proximal femoral morphology. As such, there is some level of subjective selection bias from these specific surgeons, although they performed the vast minority of posterior and lateral THA in the cohort. Also, this study identified that protectiveness of a collared implant against PPFFx. It should be noted there is a potential downside to use of these types of implants, as they may be more susceptible to aseptic loosening if final broaching and implant insertion is performed with poor technique.

This study is the first to our knowledge yielding a patient-specific PPFFx risk calculator. Modeling accounted for myriad potentially important comorbidities, yet the final model is quite parsimonious and actionable with factors that can be ascertained routinely. The resultant nomograms are responsive to fixation method, implant type, and operative approach decisions, and thus can be used as a screening tool to identify and individualize recommendations and treatment for THA patients. This is especially important given the wide range of individual patient risk identified in this study, and the degree of risk mitigation portended by various operative strategies. Nomograms from this work will serve as the underlying foundation for a digital clinical tool to calculate patient risk in a streamlined fashion.