2.1 Study design

3.1 Descriptive characteristics

Table 1 delineates the descriptive population statistics of patients with and without active cancer diagnoses that are pertinent to this study. A comparative analysis was conducted between patients diagnosed with active cancer (n = 456) and those without any history of cancer (n = 9342) (Table 1). Patient demographics included predominantly female (55.5 %), white (81.4 %), and non-Hispanic (96.4 %) patients followed by black individuals (16.9 %) with 43.7 % of patients being smokers (Table 1). Patients with private insurance had lower active cancer diagnoses (3.4 % vs 4.8 %, p = .024), as did females (4.1 % vs 5.4 %, p = .002) and elective surgery recipients (4.3 % vs 8.6 %, p < .001) (Table 1). Cancer patients had higher adjusted CCI (1.45 vs 1.02, p < .001), lower BMI (28.77 vs 30.08, p < .001), and higher age (68.23 vs 64.83, p < .001) (Table 1). They did not have a differential length of stay (1.73 vs 1.71, p = .753) (Table 1). Cancer patients did have a higher complication rate (2.6 % vs 1.7 %), but this difference was not significant (p = .139) (Table 1).

Demographics and descriptive characteristics of patients with active cancer diagnosis suffering from acute surgical complications are demonstrated in Table 2. Out of 456 patients with an active cancer diagnosis, 12 patients (2.6 %) suffered from acute complications (Table 2). Cancer patients suffering from complications had higher CCI values (2.5 vs. 1.42, p = .074) (Table 2). Nonelective surgeries were also associated with higher acute complication rates (8.2 % vs. 1.8 %, p = .014) (Table 2). Black patients had higher acute complication rates than white patients (4.2 % vs. 2.4 %), and patients undergoing active cancer treatment were at higher risk for surgical complication (4.4 % vs. 2.2 %); however, these differences were not significant (p = .503, p = .265) (Table 2). Complication rates decreased significantly with year (Table 2).

3.2 Logistic regression

Following adjustment, cancer patients still had a higher acute complication rate than those without cancer, but this difference was also not significant (aOR = 1.32, 95 % CI = 0.71–2.43, p = .377) (Fig. 1). Additionally, increased CCI (aOR = 1.44, 95 % CI = 1.34–1.55, p < .001), white race (aOR = 1.62, 95 % CI = 1.03–2.56, p = .038), and non-elective surgery (aOR = 2.07, 95 % CI = 1.33–3.21, p = .001) were significant contributors to complication risk after adjustment (Fig. 1). Increased surgery year was associated with a decrease in acute complication rate (aOR = 0.73, 95 % CI = 0.67–0.80, p < .001) (Fig. 1).

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Fig. 1 Multivariable Logistic regression results predicting acute complication in cancer and non-cancer patients.

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Cancer patients (HR = 2.4, 95 % CI = 1.66–3.48) and patients undergoing non-elective surgery (HR = 2.14, 95 % CI = 1.52–3, p=<0.001) were at higher risk for mortality. (Fig. 2). The estimated 5 year survival probability from Kaplan-Meieter analyses was 95.4 % (94.7%–96.1 %) for non-cancer patients and 86.6 % (95 % CI = 81.8%–91.6 %) for active cancer patients. Smoking history (HR = 1.6, 95 % CI = 1.24–2.06, p < .001), increased CCI (HR = 1.34, 95 % CI = 1.26–1.42, p < .001), private insurance (HR = 1.62, 95 % CI = 1.07–2.46, p = .037), and older age (HR = 1.05, 95 % CI = 1.03–1.06, p < .001) were also significant risk factors for mortality in all patients (Fig. 2). Additionally, risk factors for increased morality in a subgroup of patients with active cancer diagnosis included private insurance status (HR = 3.01, 95 % CI = 1.14–7.99, p = .061) and non-elective surgery (HR = 3.09, 95 % CI = 1.34–7.15, p = .009) (Fig. 3).

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Fig. 2 Multivariable Cox regression results predicting survival rate in cancer and non-cancer patients.

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Fig. 3 Multivariable Cox regression results predicting survival rate limited to patients with an active cancer diagnosis.

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4.1 Key results

Our study aimed to investigate the rates of acute complications and postoperative mortality in a specific subgroup of patients undergoing total joint arthroplasty procedures—those with a history of cancer. Understanding the impact of cancer on surgical outcomes is crucial for informing clinical decision-making and improving patient care. In this manuscript, we offer a comprehensive examination of patient demographics, acute complication rates, and mortality risk factors within this population. Our findings present compelling evidence opposing the notion of precluding these patients from undergoing elective TJA based solely on a cancer diagnosis.

The demographic profile is essential for contextualizing our findings and recognizing potential disparities in surgical outcomes. Comparative analyses revealed that patients with public insurance, males, and those undergoing nonelective surgery had higher rates of active cancer diagnoses (Table 1). Notably, cancer patients exhibited distinct features, including a higher adjusted Charlson Comorbidity Index (CCI), lower BMI, and advanced age, all of which contribute to the complexity of their surgical management (Table 1). However, while cancer patients had higher adjusted CCI, our study did not find a differential length of stay or statistically significant higher acute complication rate among cancer patients when compared to those without cancer, highlighting the potential feasibility of these procedures within the cancer population (Table 1).

A significant decrease in 30-day complication rates over the study period was also observed, underscoring the importance of ongoing advancements in surgical techniques, perioperative care, and patient management (Table 2). Additionally, while cancer patients undergoing active cancer treatment were found to be at an increased risk for acute surgical complications (Table 2), it is crucial to dispel the historic stereotype surrounding chemotherapy. Modern cancer medications, tailored to specific cancer subtypes with minimized side effects, can be safely navigated during elective surgeries.^22,23 Surgeons should consider the refined landscape of contemporary cancer treatments, enabling informed decisions to proceed with elective procedures for patients undergoing active cancer treatment, thus improving overall care and outcomes.

Logistic regression analysis revealed that increased CCI, white race, and non-elective surgery were significant contributors to complication risk in all patients (Fig. 1). The association between cancer and acute complication rates was not statistically significant (Fig. 1). These findings highlight the importance of carefully considering patient comorbidity, racial disparities, and surgical urgency when assessing and managing complication risks in diverse populations.

Increased mortality risk was identified in cancer patients, and in all patients undergoing non-elective surgery (Figs. 2 and 3). Furthermore, cancer patients with private insurance status demonstrated higher mortality rates (Fig. 3). This data emphasizes the need for careful consideration and operative planning, rather than increasing selection bias against these patient populations (Fig. 2). Smoking history, increased CCI, and older age were also significant risk factors for mortality in the overall patient population (Fig. 2). Surgeons routinely encounter patients with modifiable risk factors such as smoking, as demonstrated by a substantial proportion of our patient population (43.7 %) being tobacco users. This level of adaptability in approaching cases should also be applied to patients with active cancer diagnoses, underlining the need for a nuanced and inclusive perspective in surgical decision-making.

Current literature demonstrates that postoperative complications and mortality following total joint arthroplasty (TJA) are common in patients with a diagnosis of cancer. Patients with an active diagnosis or history or cancer have increased morbidity and mortality following TJA.¹⁸ Furthermore, our study revealed elevated mortality rates among patients with an active cancer diagnosis. However, the observed increase in complication rates in this group did not reach statistical significance, suggesting that surgeons can safely perform operations on these individuals. These findings contribute to the growing body of knowledge guiding personalized care strategies for this vulnerable patient population and may help to prevent the stigma of decreased joint replacement utility in patients with lower life expectancy.

Preoperative patient optimization, with the goal of improved outcomes and financial performance, is a common phenomenon in the world of arthroplasty. However, patient optimization can often result in selection bias against patients with modifiable risk factors such as diabetes, smoking, increased BMI, blood pressure and cancer who are not capable of achieving maximal optimization due to health disparities or low socioeconomic status.²⁴ Optimizing cancer patients for TJA may involve perioperative plans similar to other high-risk patients with additional considerations beyond standard protocols. Emphasis should be placed on evaluating kidney and liver function, as these are often impacted by cancer treatments. Additionally, it is important to address nutritional deficiencies and potential immunosuppression by ensuring adequate protein levels and appropriate absolute neutrophil counts. Close collaboration with the patient's oncologist is essential, including obtaining a clearance letter to guide individual treatment plans. This partnership enables any concerns from the orthopedic surgeon to be thoroughly assessed and resolved in coordination with the oncologist, ensuring a comprehensive, patient-specific approach to optimization.

4.2 Limitations & strength

Our study is not without limitations. Most importantly, this investigation was potentially limited by its retrospective nature. While supported by a large sample size and patients from four different institutions, all patients are from the southern region of the United States and this data gathered from this patient population may not extrapolate to other regions. Additionally, this study was limited by its reliance on database ICD coding, which may result in inaccurate identification of patient characteristics, including cancer diagnoses and the presence of complications. However, the study team took significant steps to mitigate this limitation, spending months meticulously reviewing and refining ICD codes to ensure inclusivity and accurate identification of relevant patient data. Further, our definition of “active cancer” being specific ICD codes tagged within 365 days before surgery or 30 days after surgery may not encompass potential influences on surgical outcomes such as specific cancer stages, cancer types, or treatment modalities. This study may not have fully accounted for other confounding factors, including socioeconomic status, comorbidities, and functional status, which may also impact both cancer diagnoses and surgical outcomes. While our study offers significant insights, further research is needed to validate our findings and explore extended follow-up periods. Additionally, assessing long-term outcomes—such as functional recovery, quality of life, long-term complications, and postoperative mortality—would provide a more comprehensive evaluation of total joint arthroplasty in cancer patients.