Risk Factors for Infectious Disease Mortality in Breast Cancer: A Retrospective Cohort Study and Nomogram Development

Risk Factors for Infectious Disease Mortality in Breast Cancer: A Retrospective Cohort Study and Nomogram Development | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk Factors for Infectious Disease Mortality in Breast Cancer: A Retrospective Cohort Study and Nomogram Development Ali Hemade, Maria Akiki, Rabih Hallit, Chebli Dagher, Souheil Hallit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6209450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Breast cancer patients face an elevated risk of infectious disease-related mortality due to immunosuppressive treatments and disease-related immune dysfunction. However, the specific risk factors influencing infection-related mortality in this population remain poorly understood. This study aims to identify key clinical and demographic factors associated with infectious disease mortality in breast cancer patients and develop a predictive nomogram for individualized risk estimation. Methods: A retrospective cohort study was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). Patients diagnosed with breast cancer and with complete clinical and survival data were included. The primary outcome was infectious disease-related mortality. Descriptive statistics, Kaplan-Meier survival analysis, and Cox proportional hazards regression were performed to identify significant predictors. A nomogram was developed based on multivariable Cox regression to estimate mortality risk at 1, 3, and 5 years. Results: A total of 43,483 breast cancer patients were analyzed, with 482 experiencing infectious disease-related mortality. Significant predictors of mortality included increasing age (HR = 1.017, p < 0.001), Medullary breast cancer subtype (HR = 4.778, p = 0.0129), tumor stage (T2: HR = 0.7079, p = 0.0017), and presence of a single primary tumor (HR = 1.574, p = 0.0018). Chemotherapy and radiotherapy were associated with improved survival outcomes. A predictive nomogram was constructed with a concordance index (C-index) of 0.868, demonstrating strong predictive accuracy. Conclusion: This study identifies key clinical and demographic risk factors associated with infectious disease mortality in breast cancer patients. The predictive nomogram provides a useful tool for individualized risk assessment, aiding in targeted infection prevention strategies and optimizing clinical decision-making. Further validation in external cohorts is necessary to confirm its clinical utility. Infectious Diseases Oncology Breast cancer infectious disease mortality risk factors SEER database nomogram survival analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Infectious diseases remain a significant concern in cancer patients due to their heightened susceptibility stemming from compromised immunity and treatment-related side effects [1]. Breast cancer, one of the most prevalent malignancies worldwide [2], presents unique challenges in this regard. The rising life expectancy of individuals diagnosed with breast cancer has brought greater attention to the challenges associated with post-diagnosis morbidity, including infectious causes, underscoring its significance in clinical care and public health efforts [3]. The risk of infection-related mortality in breast cancer patients is heightened in the early post-diagnosis period, when systemic chemotherapy and other aggressive treatments are most frequently administered [4]. These treatments can lead to neutropenia, disruption of the skin and mucosal barriers, and immune system dysregulation, which increases susceptibility to infections [5]. Advancements in early detection and therapeutic strategies have significantly improved overall survival rates for breast cancer patients. The American Cancer Society reports a 43% decline in breast cancer death rates from 1989 to 2020, attributing this decrease to earlier detection through screenings and enhanced treatments [2]. However, these treatments, particularly chemotherapy, can compromise the immune system, increasing susceptibility to infections. A study found that breast cancer patients undergoing chemotherapy are at a heightened risk for infection-related hospitalizations, which can adversely affect survival outcomes [3]. An analysis of data from the Surveillance, Epidemiology, and End Results (SEER) program revealed that infectious diseases accounted for a notable proportion of deaths in cancer patients, underscoring the critical need for effective infection prevention and management strategies in this population [6]. To our knowledge, no studies have specifically focused on infection-related mortality in breast cancer patients, despite the well-established risks posed by immunosuppressive treatments such as chemotherapy, and HER2-targeted therapies. Infections are a potentially preventable cause of mortality, unlike cancer progression, and understanding the key risk factors specific to breast cancer patients is crucial for developing targeted prevention strategies. By identifying high-risk subgroups and integrating risk prediction tools into clinical practice, this study can help reduce infection-related mortality and improve overall patient outcomes. The variables chosen for this study are based on their established or potential associations with mortality and infection outcomes in cancer patients. Demographic factors, including age, race, and sequence number of primary cancers, were studied due to their potential impact on infection risk and cancer outcomes. Older patients are particularly vulnerable due to age-related immunosenescence and comorbidities [7], while racial disparities have been linked to differences in access to care and biological susceptibility [8]. Hormone receptor statuses, such as Estrogen Receptors (ER), Progesterone Receptors (PR), and Human Epidermal Growth Factor Receptor 2 (HER2), influence both the biological behavior of breast cancer and the choice of treatment modalities, which can alter immune responses and susceptibility to infections [9, 10]. Tumor characteristics, including stage and subtype, are similarly critical due to their role in driving cancer progression [11]. The type of treatment, including chemotherapy, radiotherapy, and surgery, was also examined given its varying effects on immune function, with chemotherapy frequently linked to immunosuppression and opportunistic infections [3]. By incorporating these clinical, demographic, biological, and treatment-related variables, this study aims to comprehensively analyze infection-related mortality in breast cancer patients and address gaps in the current literature. Methods 1. Study Design and Population This study was a retrospective cohort analysis using data from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2015. The study population included patients diagnosed with breast cancer during this time. Patients were included if they had complete clinical and survival data and were followed until the occurrence of death or the end of the study period. The primary outcome of interest was infectious disease-related mortality, defined as death attributed to an infectious cause as coded in the SEER database (parasite infections including HIV-included together by SEER to protect patient confidentiality, tuberculosis, septicemia, and pneumonia and influenza). Patients were excluded if they had missing or incomplete clinical information or were lost to follow-up. Key variables extracted from SEER included demographic characteristics (age, race), tumor characteristics (subtype, stage), treatment details (chemotherapy, radiotherapy, surgery, and survival outcomes (survival months, infectious mortality status). 2. Ethical Considerations This study utilized de-identified patient data from SEER, which is publicly available and does not require institutional review board (IRB) approval. The analysis complied with SEER data usage guidelines and adhered to ethical principles outlined in the Declaration of Helsinki. As SEER data does not contain personal identifiers, patient confidentiality was maintained, and no informed consent was required. 3. Data Collection Data were extracted from the SEER database using SEER*Stat software for statistical analysis. The dataset included patients diagnosed between 2010 and 2015, with follow-up available through the latest SEER data release. Demographic variables included age and race, while clinical characteristics included tumor subtype, staging (T, N, M classification), and biomarker status (e.g., PR, HER2 positivity). Treatment variables encompassed surgical intervention, chemotherapy, and radiotherapy, and mortality outcomes were classified based on cause of death, survival time (in months), and follow-up status. 4. Statistical Analysis Statistical analysis was conducted using SPSS v. 27 and R. Continuous variables were summarized using mean, standard deviation (SD), median, and interquartile range (IQR). Categorical variables were presented as frequencies and percentages. Chi-square tests were performed to examine associations between categorical predictor variables and infectious disease mortality. Cross-tabulations were generated for each categorical variable with infectious disease mortality as the column variable, displaying absolute frequencies, row percentages, and total sample size (n). The Pearson’s chi-square test was applied to determine statistical significance. When applicable, Fisher’s exact test was used for small sample sizes. Kaplan-Meier survival curves were used to estimate overall survival probability for patients with infectious disease-related mortality. Survival curves were stratified by key clinical and treatment factors, including chemotherapy, radiotherapy, and tumor subtype. The log-rank test was applied to compare survival distributions between groups. Cox proportional hazards regression models were used to identify factors associated with infectious disease-related mortality. Initially, univariable Cox regression was performed for each predictor variable, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. Variables with p < 0.25 in univariable analysis were included in the multivariable Cox regression model. The final adjusted model accounted for clinically relevant covariates, and the proportional hazards assumption was tested using Schoenfeld residuals. All tests were two-sided and a p-value < 0.05 was considered statistically significant. A nomogram was constructed based on the multivariable Cox proportional hazards model to provide a visual representation of predicted infectious disease-related mortality risk at 1, 3, and 5 years. The nomogram incorporated clinically relevant variables selected through the multivariable Cox model. The rms package in R was used to construct the nomogram, with survival probabilities converted into mortality risks. The nomogram was evaluated for discrimination using Harrell’s C-index. Results Descriptive statistics The study included a cohort of 43,483 individuals, of whom 482 experienced infectious disease-related mortality. Descriptive analyses were conducted to explore the associations between demographic, clinical, and treatment-related variables and infectious disease mortality and summarized in Table 1 . Table 1 – Cohort summary of clinical and demographic characteristics Variable Infectious-cause of death Non-infectious cause of death/Alive Chi-Square Test p-value Race American Indian/Alaska Native 294 (98.33%) 5 (1.67%) 13.17 0.004 Asian or Pacific Islander 3351 (99.26%) 25 (0.74%) Black 6213 (98.53%) 93 (1.47%) White 33143 (98.93%) 359 (1.07%) Total 43001 (98.89%) 482 (1.11%) Subtype Adenocarcinoma 1623 (98.90%) 18 (1.10%) 7.66 0.363 Ductal 170 (99.42%) 1 (0.58%) Infiltrating ductal 36203 (98.90%) 403 (1.10%) Inflammatory breast cancer 432 (99.54%) 2 (0.46%) Lobular 3961 (98.75%) 50 (1.25%) Medullary 81 (96.43%) 3 (3.57%) Metaplastic breast cancer 400 (99.01%) 4 (0.99%) Paget's disease of the breast 131 (99.24%) 1 (0.76%) Total 43001 (98.89%) 482 (1.11%) Laterality Left 22098 (98.89%) 249 (1.11%) 0.01 0.942 Right 20903 (98.90%) 233 (1.10%) Total 43001 (98.89%) 482 (1.11%) T T1 15130 (98.55%) 222 (1.45%) 25.27 < 0.001 T2 16479 (99.12%) 147 (0.88%) T3 5324 (99.01%) 53 (0.99%) T4 6068 (99.02%) 60 (0.98%) Total 43001 (98.89%) 482 (1.11%) N N0 21118 (98.74%) 270 (1.26%) 9.35 0.024 N1 13571 (99.02%) 135 (0.98%) N2 4321 (99.11%) 39 (0.89%) N3 3991 (99.06%) 38 (0.94%) Total 43001 (98.89%) 482 (1.11%) M M0 35422 (98.81%) 426 (1.19%) 11.47 < 0.001 M1 7579 (99.27%) 56 (0.73%) Total 43001 (98.89%) 482 (1.11%) Chemotherapy No 23697 (98.70%) 312 (1.30%) 17.46 < 0.001 Yes 19304 (99.13%) 170 (0.87%) Total 43001 (98.89%) 482 (1.11%) Radiotherapy No 26360 (98.85%) 307 (1.15%) 1.05 0.305 Yes 16641 (98.96%) 175 (1.04%) Total 43001 (98.89%) 482 (1.11%) Surgery No 9603 (99.09%) 88 (0.91%) 4.34 0.037 Yes 33398 (98.83%) 394 (1.17%) Total 43001 (98.89%) 482 (1.11%) Sequence number 1st of 2 or more primaries 5885 (99.09%) 54 (0.91%) 2.28 0.130 One primary only 37116 (98.86%) 428 (1.14%) Total 43001 (98.89%) 482 (1.11%) ER Status Negative 11576 (99.17%) 97 (0.83%) 10.87 < 0.001 Positive 31425 (98.79%) 385 (1.21%) Total 43001 (98.89%) 482 (1.11%) PR Status Negative 17103 (99.12%) 151 (0.88%) 13.85 < 0.001 Positive 25898 (98.74%) 331 (1.26%) Total 43001 (98.89%) 482 (1.11%) HER2 Negative 35745 (98.85%) 416 (1.15%) 3.22 0.072 Positive 7256 (99.10%) 66 (0.90%) Total 43001 (98.89%) 482 (1.11%) Kaplan Meier Curves Kaplan-Meier survival analysis was conducted to estimate the probability of survival over time for infectious disease mortality. The overall survival curve, as shown in Fig. 1 , depicts the probability of survival in the entire cohort of 43,483 individuals. The survival probability remained high throughout the follow-up period, with a gradual decline observed over time. By the end of the 60-month period, the survival probability was slightly below 0.92. To examine the impact of chemotherapy on infectious disease mortality, survival curves were stratified based on chemotherapy treatment status (Fig. 2 ). The results indicate a statistically significant difference in survival probabilities between individuals who received chemotherapy and those who did not (log-rank p < 0.0001). Similarly, survival curves were generated to compare outcomes between individuals who received radiotherapy and those who did not (Fig. 3 ). The survival probability was consistently higher in the radiotherapy group, with a statistically significant difference observed (log-rank p = 0.0022). Cox regression models Age was identified as a significant factor in both univariable and multivariable Cox regression analyses. For every one-year increase in age, the hazard of infectious disease mortality increased (HR = 1.0171, 95% CI: 1.0098–1.0244, p < 0.001). Race was categorized into four groups: American Indian/Alaska Native, Asian or Pacific Islander, Black, and White. While the Asian or Pacific Islander group demonstrated a reduced risk of infectious disease mortality in univariable analysis (HR = 0.4181, 95% CI: 0.1600–1.092, p = 0.0751) and multivariable analysis (HR = 0.4382, 95% CI: 0.1677–1.1452, p = 0.0923), the findings did not reach conventional statistical significance. No significant associations were observed for the Black or White groups. Regarding tumor subtypes, the Medullary subtype was associated with a significantly increased hazard of mortality in both univariable analysis (HR = 3.3804, 95% CI: 0.9954–11.479, p = 0.0509) and multivariable analysis (HR = 4.7784, 95% CI: 1.3926–16.3955, p = 0.0129). Other tumor subtypes, including ductal, lobular, and metaplastic breast cancer, did not show significant associations with infectious disease mortality. Tumor stage (T) was assessed for its association with mortality risk. The T2 stage was significantly protective in both univariable analysis (HR = 0.6822, 95% CI: 0.5537–0.8404, p = 0.0003) and multivariable analysis (HR = 0.7079, 95% CI: 0.5708–0.8779, p = 0.0017). No significant associations were observed for T3 or T4 stages. Chemotherapy, surgery, and radiotherapy were also evaluated. Surgery was not a significant predictor of infectious-cause mortality in either model. Chemotherapy demonstrated a protective effect in univariable analysis (HR = 0.6781, 95% CI: 0.5625–0.8175, p < 0.001); however, this effect was not statistically significant in multivariable analysis. Radiotherapy showed a consistent protective effect, with significance observed in univariable analysis (HR = 0.7491, 95% CI: 0.6222–0.902, p = 0.0023) but not in multivariable analysis (HR = 0.8504, 95% CI: 0.6985–1.0353, p = 0.1064). Sequence number, indicating whether the individual had one primary cancer or multiple primaries, was significantly associated with mortality risk. Those with only one primary cancer had a higher hazard of infectious disease mortality in univariable analysis (HR = 1.5141, 95% CI: 1.141–2.01, p = 0.0041) and multivariable analysis (HR = 1.5737, 95% CI: 1.1841–2.0917, p = 0.0018). Hormone receptor status (PR and HER2) was also examined. Positive PR status and positive HER2 status were not statistically significant in either univariable or multivariable analyses. Table 2 Cox regression analysis (univariable and multivariable) predicting infectious cause mortality free survival. Univariable cox Multivariable cox Variable HR 95% CI p-value HR 95% CI p-value Age 1.0186 1.013–1.025 < 0.001 1.0171 1.0098–1.0244 < 0.001 Race: American Indian 1 - - 1 - - Race: Asian or Pacific Islander 0.4181 0.1600–1.092 0.075 0.4382 0.1677–1.1452 0.092 Race: Black 0.9318 0.3789–2.291 0.877 0.9811 0.3986–2.4149 0.966 Race: White 0.6133 0.2537–1.482 0.277 0.5862 0.2423–1.4182 0.236 Subtype: Ductal 0.4764 0.0636–3.569 0.470 0.5847 0.0780–4.3843 0.601 Subtype: Infiltrating ductal 1.0040 0.6261–1.610 0.986 1.1954 0.7434–1.9222 0.461 Subtype: Inflammatory breast cancer 0.6430 0.1491–2.772 0.553 0.7066 0.1600–3.1216 0.646 Subtype: Lobular 1.0394 0.6065–1.781 0.888 1.1606 0.6757–1.9935 0.589 Subtype: Medullary 3.3804 0.9954–11.479 0.050 4.7784 1.3926–16.3955 0.012 Subtype: Metaplastic breast cancer 1.2665 0.4285–3.744 0.669 1.6556 0.5545–4.9428 0.366 Subtype: Paget’s disease of the breast 0.8127 0.1085–6.088 0.840 0.9593 0.1271–7.2432 0.967 Laterality: Right 0.9796 0.8193–1.171 0.821 - - - T: T2 0.6822 0.5537–0.8404 < 0.001 0.7079 0.5708–0.8779 0.001 T: T3 0.8428 0.6243–1.1376 0.263 0.9262 0.6759–1.2691 0.633 T: T4 1.0894 0.8179–1.4511 0.558 1.1478 0.8366–1.5748 0.392 N: N1 0.8841 0.7189–1.087 0.243 - - - N: N2 0.8051 0.5754–1.127 0.206 - - - N: N3 0.9270 0.6598–1.302 0.662 - - - M: M1 0.9540 0.7215–1.262 0.741 - - - Chemotherapy: Yes 0.6781 0.5625–0.8175 < 0.001 0.9224 0.7264–1.1712 0.507 Radiotherapy: Yes 0.7491 0.6222–0.9020 0.002 0.8504 0.6985–1.0353 0.106 Surgery: Yes 0.8049 0.6382–1.015 0.066 0.9159 0.7120–1.1782 0.494 Sequence number: 1st of 2 or more primaries 1 - - 1 - - Sequence number: One primary only 1.5141 1.141–2.010 0.004 1.5737 1.1841–2.0917 0.001 ER Status: Positive 1.1043 0.8831–1.381 0.384 - - - PR Status: Positive 1.1384 0.9384–1.381 0.188 1.0924 0.8896–1.3414 0.398 HER2: Positive 0.8375 0.6459–1.086 0.181 0.9256 0.7065–1.2126 0.574 Nomogram Discussion Our study aimed to identify independent prognostic factors associated with infectious-cause survival in breast cancer patients using the SEER database. Age was a significant predictor of mortality due to infectious causes, with the hazard of infectious disease mortality increasing by approximately 1.7% for every additional year of age (HR = 1.017, p < 0.001). This finding aligns with the known age-related decline in immune function and the increased vulnerability of older patients to treatment-related complications, such as neutropenia and infections [7]. Race was evaluated as a potential predictor, but no statistically significant associations were observed for Black or White groups. Interestingly, the Asian or Pacific Islander group demonstrated a reduced risk of infectious disease mortality. However, this trend did not reach conventional significance, which reflect residual confounding from unmeasured variables such as socioeconomic status, access to timely care, or differences in comorbidities, all of which may impact the risk of infections. In contrast to our findings, Tao et al. reported that African-American patients had an 18% overall increased risk of mortality from breast cancer compared to White patients [8]. While their study focused on overall breast cancer mortality, the lack of significant associations for Black and White patients in our study suggests that factors driving infectious disease mortality may differ from those driving overall cancer mortality. This highlights the need for future research examining race-specific differences in immune responses, comorbidities, and access to infection-related preventive care within the context of breast cancer. Regarding tumor characteristics, the Medullary subtype was significantly associated with increased infectious disease mortality. Medullary breast cancer, while relatively rare, is histologically characterized by poorly differentiated, high-grade tumor cells, which may contribute to its aggressive behavior [12]. Despite its aggressive histology, most studies suggest that Medullary breast cancer generally has a favorable prognosis [12]. However, atypical forms of Medullary carcinoma, which tend to be more invasive, are associated with significantly worse outcomes [13]. Medullary subtypes, characterized by lymphocytic infiltration, may predispose patients to immune dysregulation, thereby increasing the likelihood of infection [14]. In our study, Medullary breast cancer was significantly associated with increased infection-related mortality, suggesting that multiple factors may contribute to this association, including tumor biology, immune response, and treatment-related variables. Other subtypes, including ductal, lobular, and metaplastic breast cancer, did not exhibit significant associations with infectious mortality, emphasizing the unique risk profile of Medullary breast cancer and its treatments. Tumor stage was another critical factor. Patients with T2 tumors had a protective association with infectious disease mortality, while advanced stages (T3 and T4) showed no significant impact. This protective effect of T2 could reflect differences in tumor biology or treatment responsiveness. Notably, prior studies have indicated that earlier-stage breast cancers often respond better to curative interventions, which might indirectly reduce infectious complications[3]. However, the lack of significance in advanced-stage tumors may be due to confounding factors like treatment regimens and immune compromise, which require further exploration. Interestingly, patients with only one primary cancer exhibited a higher risk of infectious disease mortality, contrasting with studies that typically associate multiple primaries with worse outcomes [15]. This finding might be explained by survivor bias, where individuals with multiple cancers often undergo closer monitoring and follow-up, potentially leading to earlier detection and management of infections. Treatment-related factors were also examined. Chemotherapy demonstrated a protective effect in univariable analysis but lost significance in multivariable analysis, suggesting that other confounding factors may influence this relationship. Radiotherapy showed a similar pattern, with a protective effect in univariable analysis that was attenuated after adjustment. These findings suggest that while these treatments are essential for cancer control, their impact on infection risk is complex and may be influenced by other factors such as patient age, comorbidities, and overall immune status[3]. Our study found that hormone receptor status (PR and HER2) was not significantly associated with infectious disease mortality in either univariable or multivariable analyses. These results indicate that hormone receptor-positive (PR-positive) breast cancer, as well as HER2-positive breast cancer, may not independently influence infection-related outcomes. However, it is essential to consider the impact of treatments associated with these subtypes. In PR-positive breast cancer, endocrine therapies are not typically associated with increased infection risk, as they do not cause significant immunosuppression or neutropenia. This aligns with our findings, suggesting that the absence of infection risk may be linked to the relatively mild side effects of hormone therapy [16]. In contrast, HER2-positive breast cancer is often treated with targeted therapies such as trastuzumab and pertuzumab [17]. Prior studies have demonstrated that these regimens can lead to increased risk of infection and severe neutropenia [18]. Despite this, our study did not find a statistically significant association between HER2 status and infectious mortality. One possible explanation is that while HER2-targeted therapy may contribute to short-term infectious complications, these effects may not translate to long-term infection-related mortality in our cohort. The survival analysis further supports the importance of treatment interventions in reducing infectious disease mortality. The Kaplan-Meier survival curve (Fig. 1 ) demonstrates that the overall probability of survival remained high throughout the follow-up period, with only a gradual decline observed. By the end of the 60-month period, the survival probability was slightly below 0.92, highlighting that although infectious disease-related mortality is present, it affects a relatively small subset of the overall cohort. When survival curves were stratified by chemotherapy and radiotherapy status, patients who received either treatment showed significantly improved survival probabilities compared to those who did not. Although chemotherapy is often associated with immunosuppression and transient neutropenia [19], its protective long-term effect is likely due to its ability to reduce tumor burden and systemic cancer-related complications [20], indirectly lowering the risk of infections. Radiotherapy similarly contributed to improved survival, possibly due to its role in controlling localized disease and preventing metastasis [21], which are key contributors to infection susceptibility. Moreover, patients undergoing these treatments often receive more comprehensive clinical monitoring, which could further mitigate infection-related risks. These results highlight the importance of optimizing cancer treatment while incorporating infection prevention strategies to enhance long-term outcomes. Balancing effective treatment with proper management of short-term immunosuppressive effects remains essential in reducing infectious disease mortality. To further enhance clinical applicability, we developed a nomogram to predict the probability of infectious disease mortality in breast cancer patients (Fig. 4 ). The nomogram integrates key prognostic factors identified in the multivariable Cox regression model, including age, tumor subtype, tumor stage, and sequence number, all of which demonstrated significant contributions to mortality risk. Each variable is assigned a corresponding number of points based on its relative impact, with age and the Medullary subtype having the highest point contributions due to their strong association with mortality risk. Sequence number, which differentiates between patients with one primary cancer and those with multiple primaries, also significantly influences risk, as seen by its contribution on the nomogram scale. Patients with one primary cancer accumulate more points, reflecting a much higher risk of infectious disease mortality compared to those with multiple primary cancers. The total points derived from these predictors are mapped onto a linear predictor scale, which is then used to estimate the probability of mortality at 1, 3, and 5 years. The C-index of 0.868 reflects strong predictive accuracy, indicating that the model effectively discriminates between high- and low-risk patients. For example, a patient with advanced age, a Medullary subtype, and one primary cancer will accumulate a higher total score, translating to a higher estimated probability of mortality over time. The design of the nomogram allows clinicians to easily visualize how individual factors contribute to risk, facilitating personalized decision-making. For instance, high-risk patients identified through the nomogram could benefit from intensified infection prevention strategies, closer monitoring, and tailored treatment adjustments to reduce their overall mortality risk. Additionally, the ability to estimate mortality at 1, 3, and 5 years provides flexibility in planning both short- and long-term interventions. Clinical implications The findings of this study have important clinical implications for improving the care of breast cancer patients by enabling proactive management of infection-related risks. The identification of key prognostic factors, such as age, tumor subtype, tumor stage, and sequence number, provides clinicians with a basis for stratifying patients according to their risk of infectious disease mortality. The development of the predictive nomogram further enhances this by offering an accessible tool to estimate individual mortality risk at 1, 3, and 5 years. This allows for personalized interventions, such as closer monitoring, prophylactic antibiotic use, or growth factor support, particularly for high-risk patients undergoing immunosuppressive treatments like chemotherapy or HER2-targeted therapies. Integrating this risk assessment into clinical workflows could help reduce preventable infections, improve survival outcomes, and optimize resource allocation by targeting preventive measures where they are needed most. Limitations This study has several limitations that should be considered. First, as a retrospective study, it is subject to selection bias and may lack key information on confounding variables that were not routinely collected in the original dataset, such as immune status, prior infections, or comorbidities. This can lead to an incomplete understanding of the full range of factors affecting infectious disease mortality. Second, some variables did not reach statistical significance in predicting infectious disease mortality. This may suggest that their influence on infection outcomes is mediated through their associated treatments or interactions with other variables not included in the analysis, rather than directly contributing to mortality. Additionally, while our nomogram demonstrated strong predictive performance, it was developed based on a specific patient population, and external validation is required to assess its generalizability across diverse settings and populations. Future research should aim to address these limitations by integrating prospective studies with comprehensive clinical data and external validation to enhance the robustness and clinical utility of the findings. Conclusion Our study highlights critical risk factors associated with infectious disease mortality in breast cancer patients, including age, tumor subtype, tumor stage, sequence number and treatment options. The survival analysis demonstrated that chemotherapy and radiotherapy, when appropriately administered and monitored, can improve long-term survival by reducing cancer-related complications that contribute to infection risk. The development of a predictive nomogram further enhances the clinical utility of these findings, offering a practical tool to estimate the probability of infectious disease mortality at 1, 3, and 5 years. With a strong predictive performance (C-index = 0.868), this nomogram can aid clinicians in identifying high-risk patients and tailoring personalized interventions. Moving forward, external validation studies and further exploration of risk-specific prevention strategies will be essential in refining the application of these findings to diverse clinical settings, ultimately improving outcomes for breast cancer patients. Declarations Ethical approval and consent to participate : This study utilized the SEER database, which contains publicly available decoded data. As such, ethical approval was not required. Consent for publication : not applicable. Availability of data and materials : All data used in this study is available as part of the SEER database at https://seer.cancer.gov/. Conflict of interests: The authors have nothing to declare. Funding : This study did not receive any funding. Author contributions : Conceptualization: Ali Hemade; Data curation: Ali Hemade; Formal analysis: Ali Hemade and Souheil Hallit; Methodology: Ali Hemade; Supervision: Ali Hemade and Souheil Hallit; Writing – original draft: Maria Akiki, Chebli Dagher, and Ali Hemade; Writing – review and editing: Ali Hemade, Souheil Hallit, and Rabih Hallit. References Safdar A, Bodey G, Armstrong D: Infections in patients with cancer: overview . Principles and practice of cancer infectious diseases 2011:3-15. Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, Jemal A, Siegel RL: Breast cancer statistics, 2022 . CA: a cancer journal for clinicians 2022, 72 (6):524-541. Brand JS, Colzani E, Johansson AL, Giesecke J, Clements M, Bergh J, Hall P, Czene K: Infection-related hospitalizations in breast cancer patients: risk and impact on prognosis . 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Zangouri V, Akrami M, Tahmasebi S, Talei A, Hesarooeih AG: Medullary breast carcinoma and invasive ductal carcinoma: a review study . Iranian Journal of Medical Sciences 2018, 43 (4):365. Zhao Y, Huang T, Jin X, Gong X-m, Lu Y-z: Clinicopathologic Features and Immune Cell Subtypes Analysis of Tumor-infiltrating Lymphocytes Rich Invasive Breast Carcinoma of No Special Type . Applied Immunohistochemistry & Molecular Morphology 2023, 31 (6):354-362. Amer MH: Multiple neoplasms, single primaries, and patient survival . Cancer Management and research 2014:119-134. Cella D, Fallowfield LJ: Recognition and management of treatment-related side effects for breast cancer patients receiving adjuvant endocrine therapy . Breast cancer research and treatment 2008, 107 :167-180. Oh D-Y, Bang Y-J: HER2-targeted therapies—a role beyond breast cancer . Nature reviews Clinical oncology 2020, 17 (1):33-48. Funakoshi T, Suzuki M, Muss HB: Infection risk in breast cancer patients treated with trastuzumab: a systematic review and meta-analysis . Breast cancer research and treatment 2015, 149 :321-330. Partridge AH, Burstein HJ, Winer EP: Side effects of chemotherapy and combined chemohormonal therapy in women with early-stage breast cancer . JNCI monographs 2001, 2001 (30):135-142. Hassan M, Ansari J, Spooner D, Hussain S: Chemotherapy for breast cancer . Oncology reports 2010, 24 (5):1121-1131. Haussmann J, Corradini S, Nestle-Kraemling C, Bölke E, Njanang FJD, Tamaskovics B, Orth K, Ruckhaeberle E, Fehm T, Mohrmann S: Recent advances in radiotherapy of breast cancer . Radiation oncology 2020, 15 :1-10. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6209450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427650325,"identity":"9bf78e4c-8e22-4d14-9185-e64f36fc773c","order_by":0,"name":"Ali Hemade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdsY2EMXYwN98AEhLyBDWwgzTInEsAaSFhwgtDGwQLQw5BiAGYS38zcxtDz7m2Mn2M5z5/OpGjQUPA/vhoxvwaZE4zNhuOHNbsvHM5t5t1jnHgA7jSUu7gdeaw4xt0rzbmBM3HDi7zTiHDahFgscMrxZ5kJa/2+oT9x/IeWac848ILQYgLYzbDiduYMhhfpzbRoQWQ6AWyd5tx41n3DhmxpzbJ8HDRsgvcsfbn0n83FYt29/f/Phzzrc6OX72w8fwex8JsEmASWKVgwDzB1JUj4JRMApGwcgBACr6SekvNCM5AAAAAElFTkSuQmCC","orcid":"","institution":"Lebanese University Faculty of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Hemade","suffix":""},{"id":427650420,"identity":"dea9201f-dec9-42ed-95e0-186b7281b4b2","order_by":1,"name":"Maria Akiki","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Akiki","suffix":""},{"id":427650421,"identity":"7f9feadd-8163-4f46-b112-6529a978f2e0","order_by":2,"name":"Rabih 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07:09:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6209450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6209450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78427315,"identity":"18180e1a-1bad-4655-b52e-5958e64b897c","added_by":"auto","created_at":"2025-03-13 06:39:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179035,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier curve for infectious mortality (all cases)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6209450/v1/e88319f0f2ad80ec92abf52e.png"},{"id":78426420,"identity":"d758ac12-7d7d-4782-bf92-a262e09dd408","added_by":"auto","created_at":"2025-03-13 06:31:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223233,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves stratified by chemotherapy predicting infectious cause mortality\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6209450/v1/1a45f7ace2469cc32561fdf8.png"},{"id":78427545,"identity":"04f4423f-b0f7-4aa3-ab12-a04e8898a054","added_by":"auto","created_at":"2025-03-13 06:47:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":221382,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves stratified by radiotherapy predicting infectious cause mortality\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6209450/v1/d35ffd6e1175a5a30cbc4ce4.png"},{"id":78426410,"identity":"d2e9a0b0-710b-4cc9-975f-25f215b3fcc3","added_by":"auto","created_at":"2025-03-13 06:31:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96493,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting risk for death from infectious causes in breast cancer patients.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6209450/v1/51100c4a76833aa7ddd3f519.png"},{"id":78429095,"identity":"f30cb7a0-55bf-4534-8e83-60a20b3f9f10","added_by":"auto","created_at":"2025-03-13 07:03:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2518091,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6209450/v1/9d235912-360c-4bc9-9b22-53780ab08a8c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRisk Factors for Infectious Disease Mortality in Breast Cancer: A Retrospective Cohort Study and Nomogram Development\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfectious diseases remain a significant concern in cancer patients due to their heightened susceptibility stemming from compromised immunity and treatment-related side effects [1]. Breast cancer, one of the most prevalent malignancies worldwide [2], presents unique challenges in this regard. The rising life expectancy of individuals diagnosed with breast cancer has brought greater attention to the challenges associated with post-diagnosis morbidity, including infectious causes, underscoring its significance in clinical care and public health efforts [3]. The risk of infection-related mortality in breast cancer patients is heightened in the early post-diagnosis period, when systemic chemotherapy and other aggressive treatments are most frequently administered [4]. These treatments can lead to neutropenia, disruption of the skin and mucosal barriers, and immune system dysregulation, which increases susceptibility to infections [5]. Advancements in early detection and therapeutic strategies have significantly improved overall survival rates for breast cancer patients. The American Cancer Society reports a 43% decline in breast cancer death rates from 1989 to 2020, attributing this decrease to earlier detection through screenings and enhanced treatments [2]. However, these treatments, particularly chemotherapy, can compromise the immune system, increasing susceptibility to infections. A study found that breast cancer patients undergoing chemotherapy are at a heightened risk for infection-related hospitalizations, which can adversely affect survival outcomes [3]. An analysis of data from the Surveillance, Epidemiology, and End Results (SEER) program revealed that infectious diseases accounted for a notable proportion of deaths in cancer patients, underscoring the critical need for effective infection prevention and management strategies in this population [6]. To our knowledge, no studies have specifically focused on infection-related mortality in breast cancer patients, despite the well-established risks posed by immunosuppressive treatments such as chemotherapy, and HER2-targeted therapies. Infections are a potentially preventable cause of mortality, unlike cancer progression, and understanding the key risk factors specific to breast cancer patients is crucial for developing targeted prevention strategies. By identifying high-risk subgroups and integrating risk prediction tools into clinical practice, this study can help reduce infection-related mortality and improve overall patient outcomes.\u003c/p\u003e \u003cp\u003eThe variables chosen for this study are based on their established or potential associations with mortality and infection outcomes in cancer patients. Demographic factors, including age, race, and sequence number of primary cancers, were studied due to their potential impact on infection risk and cancer outcomes. Older patients are particularly vulnerable due to age-related immunosenescence and comorbidities [7], while racial disparities have been linked to differences in access to care and biological susceptibility [8]. Hormone receptor statuses, such as Estrogen Receptors (ER), Progesterone Receptors (PR), and Human Epidermal Growth Factor Receptor 2 (HER2), influence both the biological behavior of breast cancer and the choice of treatment modalities, which can alter immune responses and susceptibility to infections [9, 10]. Tumor characteristics, including stage and subtype, are similarly critical due to their role in driving cancer progression [11]. The type of treatment, including chemotherapy, radiotherapy, and surgery, was also examined given its varying effects on immune function, with chemotherapy frequently linked to immunosuppression and opportunistic infections [3]. By incorporating these clinical, demographic, biological, and treatment-related variables, this study aims to comprehensively analyze infection-related mortality in breast cancer patients and address gaps in the current literature.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch3\u003e1. Study Design and Population\u003c/h3\u003e\n\u003cp\u003eThis study was a retrospective cohort analysis using data from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2015. The study population included patients diagnosed with breast cancer during this time. Patients were included if they had complete clinical and survival data and were followed until the occurrence of death or the end of the study period. The primary outcome of interest was infectious disease-related mortality, defined as death attributed to an infectious cause as coded in the SEER database (parasite infections including HIV-included together by SEER to protect patient confidentiality, tuberculosis, septicemia, and pneumonia and influenza).\u003c/p\u003e \u003cp\u003ePatients were excluded if they had missing or incomplete clinical information or were lost to follow-up. Key variables extracted from SEER included demographic characteristics (age, race), tumor characteristics (subtype, stage), treatment details (chemotherapy, radiotherapy, surgery, and survival outcomes (survival months, infectious mortality status).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2. Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study utilized de-identified patient data from SEER, which is publicly available and does not require institutional review board (IRB) approval. The analysis complied with SEER data usage guidelines and adhered to ethical principles outlined in the Declaration of Helsinki. As SEER data does not contain personal identifiers, patient confidentiality was maintained, and no informed consent was required.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. Data Collection\u003c/h3\u003e\n\u003cp\u003eData were extracted from the SEER database using SEER*Stat software for statistical analysis. The dataset included patients diagnosed between 2010 and 2015, with follow-up available through the latest SEER data release. Demographic variables included age and race, while clinical characteristics included tumor subtype, staging (T, N, M classification), and biomarker status (e.g., PR, HER2 positivity). Treatment variables encompassed surgical intervention, chemotherapy, and radiotherapy, and mortality outcomes were classified based on cause of death, survival time (in months), and follow-up status.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS v. 27 and R. Continuous variables were summarized using mean, standard deviation (SD), median, and interquartile range (IQR). Categorical variables were presented as frequencies and percentages. Chi-square tests were performed to examine associations between categorical predictor variables and infectious disease mortality. Cross-tabulations were generated for each categorical variable with infectious disease mortality as the column variable, displaying absolute frequencies, row percentages, and total sample size (n). The Pearson’s chi-square test was applied to determine statistical significance. When applicable, Fisher’s exact test was used for small sample sizes.\u003c/p\u003e \u003cp\u003eKaplan-Meier survival curves were used to estimate overall survival probability for patients with infectious disease-related mortality. Survival curves were stratified by key clinical and treatment factors, including chemotherapy, radiotherapy, and tumor subtype. The log-rank test was applied to compare survival distributions between groups.\u003c/p\u003e \u003cp\u003eCox proportional hazards regression models were used to identify factors associated with infectious disease-related mortality. Initially, univariable Cox regression was performed for each predictor variable, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. Variables with p \u0026lt; 0.25 in univariable analysis were included in the multivariable Cox regression model. The final adjusted model accounted for clinically relevant covariates, and the proportional hazards assumption was tested using Schoenfeld residuals. All tests were two-sided and a p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eA nomogram was constructed based on the multivariable Cox proportional hazards model to provide a visual representation of predicted infectious disease-related mortality risk at 1, 3, and 5 years. The nomogram incorporated clinically relevant variables selected through the multivariable Cox model. The rms package in R was used to construct the nomogram, with survival probabilities converted into mortality risks. The nomogram was evaluated for discrimination using Harrell’s C-index.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\u003cp\u003eThe study included a cohort of 43,483 individuals, of whom 482 experienced infectious disease-related mortality. Descriptive analyses were conducted to explore the associations between demographic, clinical, and treatment-related variables and infectious disease mortality and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e– Cohort summary of clinical and demographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfectious-cause of death\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-infectious cause of death/Alive\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-Square Test\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294 (98.33%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (1.67%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3351 (99.26%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (0.74%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6213 (98.53%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93 (1.47%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33143 (98.93%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e359 (1.07%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1623 (98.90%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (1.10%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170 (99.42%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.58%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfiltrating ductal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36203 (98.90%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e403 (1.10%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory breast cancer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432 (99.54%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.46%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3961 (98.75%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (1.25%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedullary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81 (96.43%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (3.57%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetaplastic breast cancer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400 (99.01%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (0.99%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaget's disease of the breast\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131 (99.24%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.76%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22098 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20903 (98.90%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233 (1.10%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15130 (98.55%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222 (1.45%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16479 (99.12%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147 (0.88%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5324 (99.01%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (0.99%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6068 (99.02%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (0.98%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21118 (98.74%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270 (1.26%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13571 (99.02%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135 (0.98%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4321 (99.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (0.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3991 (99.06%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (0.94%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35422 (98.81%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e426 (1.19%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7579 (99.27%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (0.73%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23697 (98.70%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e312 (1.30%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19304 (99.13%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170 (0.87%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26360 (98.85%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e307 (1.15%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16641 (98.96%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e175 (1.04%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9603 (99.09%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (0.91%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33398 (98.83%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e394 (1.17%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence number\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st of 2 or more primaries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5885 (99.09%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (0.91%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne primary only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37116 (98.86%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e428 (1.14%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER Status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11576 (99.17%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (0.83%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31425 (98.79%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e385 (1.21%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR Status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17103 (99.12%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151 (0.88%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25898 (98.74%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331 (1.26%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35745 (98.85%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e416 (1.15%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7256 (99.10%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66 (0.90%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43001 (98.89%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482 (1.11%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eKaplan Meier Curves\u003c/h2\u003e\u003cp\u003eKaplan-Meier survival analysis was conducted to estimate the probability of survival over time for infectious disease mortality. The overall survival curve, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, depicts the probability of survival in the entire cohort of 43,483 individuals. The survival probability remained high throughout the follow-up period, with a gradual decline observed over time. By the end of the 60-month period, the survival probability was slightly below 0.92.\u003c/p\u003e\u003cp\u003eTo examine the impact of chemotherapy on infectious disease mortality, survival curves were stratified based on chemotherapy treatment status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results indicate a statistically significant difference in survival probabilities between individuals who received chemotherapy and those who did not (log-rank p \u0026lt; 0.0001).\u003c/p\u003e\u003cp\u003eSimilarly, survival curves were generated to compare outcomes between individuals who received radiotherapy and those who did not (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The survival probability was consistently higher in the radiotherapy group, with a statistically significant difference observed (log-rank p = 0.0022).\u003c/p\u003e\u003ch3\u003eCox regression models\u003c/h3\u003e\u003cp\u003eAge was identified as a significant factor in both univariable and multivariable Cox regression analyses. For every one-year increase in age, the hazard of infectious disease mortality increased (HR = 1.0171, 95% CI: 1.0098–1.0244, p \u0026lt; 0.001). Race was categorized into four groups: American Indian/Alaska Native, Asian or Pacific Islander, Black, and White. While the Asian or Pacific Islander group demonstrated a reduced risk of infectious disease mortality in univariable analysis (HR = 0.4181, 95% CI: 0.1600–1.092, p = 0.0751) and multivariable analysis (HR = 0.4382, 95% CI: 0.1677–1.1452, p = 0.0923), the findings did not reach conventional statistical significance. No significant associations were observed for the Black or White groups. Regarding tumor subtypes, the Medullary subtype was associated with a significantly increased hazard of mortality in both univariable analysis (HR = 3.3804, 95% CI: 0.9954–11.479, p = 0.0509) and multivariable analysis (HR = 4.7784, 95% CI: 1.3926–16.3955, p = 0.0129). Other tumor subtypes, including ductal, lobular, and metaplastic breast cancer, did not show significant associations with infectious disease mortality. Tumor stage (T) was assessed for its association with mortality risk. The T2 stage was significantly protective in both univariable analysis (HR = 0.6822, 95% CI: 0.5537–0.8404, p = 0.0003) and multivariable analysis (HR = 0.7079, 95% CI: 0.5708–0.8779, p = 0.0017). No significant associations were observed for T3 or T4 stages. Chemotherapy, surgery, and radiotherapy were also evaluated. Surgery was not a significant predictor of infectious-cause mortality in either model. Chemotherapy demonstrated a protective effect in univariable analysis (HR = 0.6781, 95% CI: 0.5625–0.8175, p \u0026lt; 0.001); however, this effect was not statistically significant in multivariable analysis. Radiotherapy showed a consistent protective effect, with significance observed in univariable analysis (HR = 0.7491, 95% CI: 0.6222–0.902, p = 0.0023) but not in multivariable analysis (HR = 0.8504, 95% CI: 0.6985–1.0353, p = 0.1064). Sequence number, indicating whether the individual had one primary cancer or multiple primaries, was significantly associated with mortality risk. Those with only one primary cancer had a higher hazard of infectious disease mortality in univariable analysis (HR = 1.5141, 95% CI: 1.141–2.01, p = 0.0041) and multivariable analysis (HR = 1.5737, 95% CI: 1.1841–2.0917, p = 0.0018). Hormone receptor status (PR and HER2) was also examined. Positive PR status and positive HER2 status were not statistically significant in either univariable or multivariable analyses.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression analysis (univariable and multivariable) predicting infectious cause mortality free survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable cox\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable cox\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0186\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.013–1.025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0098–1.0244\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace: American Indian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace: Asian or Pacific Islander\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4181\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1600–1.092\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4382\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1677–1.1452\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace: Black\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9318\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3789–2.291\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9811\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3986–2.4149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace: White\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6133\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2537–1.482\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5862\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2423–1.4182\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Ductal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4764\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0636–3.569\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5847\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0780–4.3843\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Infiltrating ductal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6261–1.610\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1954\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7434–1.9222\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Inflammatory breast cancer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6430\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1491–2.772\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7066\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1600–3.1216\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Lobular\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0394\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6065–1.781\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1606\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6757–1.9935\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Medullary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3804\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9954–11.479\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7784\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3926–16.3955\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Metaplastic breast cancer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2665\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4285–3.744\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6556\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5545–4.9428\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype: Paget’s disease of the breast\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8127\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1085–6.088\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9593\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1271–7.2432\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality: Right\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9796\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8193–1.171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT: T2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6822\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5537–0.8404\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7079\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5708–0.8779\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT: T3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8428\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6243–1.1376\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9262\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6759–1.2691\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT: T4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0894\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8179–1.4511\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1478\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8366–1.5748\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN: N1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8841\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7189–1.087\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN: N2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8051\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5754–1.127\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN: N3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9270\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6598–1.302\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM: M1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9540\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7215–1.262\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy: Yes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6781\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5625–0.8175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9224\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7264–1.1712\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy: Yes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7491\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6222–0.9020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8504\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6985–1.0353\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery: Yes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6382–1.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7120–1.1782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence number: 1st of 2 or more primaries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence number: One primary only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5141\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.141–2.010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5737\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1841–2.0917\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER Status: Positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1043\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8831–1.381\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR Status: Positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9384–1.381\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8896–1.3414\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2: Positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8375\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6459–1.086\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7065–1.2126\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch3\u003eNomogram\u003c/h3\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study aimed to identify independent prognostic factors associated with infectious-cause survival in breast cancer patients using the SEER database. Age was a significant predictor of mortality due to infectious causes, with the hazard of infectious disease mortality increasing by approximately 1.7% for every additional year of age (HR\u0026thinsp;=\u0026thinsp;1.017, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding aligns with the known age-related decline in immune function and the increased vulnerability of older patients to treatment-related complications, such as neutropenia and infections [7].\u003c/p\u003e \u003cp\u003eRace was evaluated as a potential predictor, but no statistically significant associations were observed for Black or White groups. Interestingly, the Asian or Pacific Islander group demonstrated a reduced risk of infectious disease mortality. However, this trend did not reach conventional significance, which reflect residual confounding from unmeasured variables such as socioeconomic status, access to timely care, or differences in comorbidities, all of which may impact the risk of infections. In contrast to our findings, Tao et al. reported that African-American patients had an 18% overall increased risk of mortality from breast cancer compared to White patients [8]. While their study focused on overall breast cancer mortality, the lack of significant associations for Black and White patients in our study suggests that factors driving infectious disease mortality may differ from those driving overall cancer mortality. This highlights the need for future research examining race-specific differences in immune responses, comorbidities, and access to infection-related preventive care within the context of breast cancer.\u003c/p\u003e \u003cp\u003eRegarding tumor characteristics, the Medullary subtype was significantly associated with increased infectious disease mortality. Medullary breast cancer, while relatively rare, is histologically characterized by poorly differentiated, high-grade tumor cells, which may contribute to its aggressive behavior [12]. Despite its aggressive histology, most studies suggest that Medullary breast cancer generally has a favorable prognosis [12]. However, atypical forms of Medullary carcinoma, which tend to be more invasive, are associated with significantly worse outcomes [13]. Medullary subtypes, characterized by lymphocytic infiltration, may predispose patients to immune dysregulation, thereby increasing the likelihood of infection [14]. In our study, Medullary breast cancer was significantly associated with increased infection-related mortality, suggesting that multiple factors may contribute to this association, including tumor biology, immune response, and treatment-related variables. Other subtypes, including ductal, lobular, and metaplastic breast cancer, did not exhibit significant associations with infectious mortality, emphasizing the unique risk profile of Medullary breast cancer and its treatments.\u003c/p\u003e \u003cp\u003eTumor stage was another critical factor. Patients with T2 tumors had a protective association with infectious disease mortality, while advanced stages (T3 and T4) showed no significant impact. This protective effect of T2 could reflect differences in tumor biology or treatment responsiveness. Notably, prior studies have indicated that earlier-stage breast cancers often respond better to curative interventions, which might indirectly reduce infectious complications[3]. However, the lack of significance in advanced-stage tumors may be due to confounding factors like treatment regimens and immune compromise, which require further exploration.\u003c/p\u003e \u003cp\u003eInterestingly, patients with only one primary cancer exhibited a higher risk of infectious disease mortality, contrasting with studies that typically associate multiple primaries with worse outcomes [15]. This finding might be explained by survivor bias, where individuals with multiple cancers often undergo closer monitoring and follow-up, potentially leading to earlier detection and management of infections.\u003c/p\u003e \u003cp\u003eTreatment-related factors were also examined. Chemotherapy demonstrated a protective effect in univariable analysis but lost significance in multivariable analysis, suggesting that other confounding factors may influence this relationship. Radiotherapy showed a similar pattern, with a protective effect in univariable analysis that was attenuated after adjustment. These findings suggest that while these treatments are essential for cancer control, their impact on infection risk is complex and may be influenced by other factors such as patient age, comorbidities, and overall immune status[3].\u003c/p\u003e \u003cp\u003eOur study found that hormone receptor status (PR and HER2) was not significantly associated with infectious disease mortality in either univariable or multivariable analyses. These results indicate that hormone receptor-positive (PR-positive) breast cancer, as well as HER2-positive breast cancer, may not independently influence infection-related outcomes. However, it is essential to consider the impact of treatments associated with these subtypes. In PR-positive breast cancer, endocrine therapies are not typically associated with increased infection risk, as they do not cause significant immunosuppression or neutropenia. This aligns with our findings, suggesting that the absence of infection risk may be linked to the relatively mild side effects of hormone therapy [16]. In contrast, HER2-positive breast cancer is often treated with targeted therapies such as trastuzumab and pertuzumab [17]. Prior studies have demonstrated that these regimens can lead to increased risk of infection and severe neutropenia [18]. Despite this, our study did not find a statistically significant association between HER2 status and infectious mortality. One possible explanation is that while HER2-targeted therapy may contribute to short-term infectious complications, these effects may not translate to long-term infection-related mortality in our cohort.\u003c/p\u003e \u003cp\u003eThe survival analysis further supports the importance of treatment interventions in reducing infectious disease mortality. The Kaplan-Meier survival curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrates that the overall probability of survival remained high throughout the follow-up period, with only a gradual decline observed. By the end of the 60-month period, the survival probability was slightly below 0.92, highlighting that although infectious disease-related mortality is present, it affects a relatively small subset of the overall cohort. When survival curves were stratified by chemotherapy and radiotherapy status, patients who received either treatment showed significantly improved survival probabilities compared to those who did not. Although chemotherapy is often associated with immunosuppression and transient neutropenia [19], its protective long-term effect is likely due to its ability to reduce tumor burden and systemic cancer-related complications [20], indirectly lowering the risk of infections. Radiotherapy similarly contributed to improved survival, possibly due to its role in controlling localized disease and preventing metastasis [21], which are key contributors to infection susceptibility. Moreover, patients undergoing these treatments often receive more comprehensive clinical monitoring, which could further mitigate infection-related risks. These results highlight the importance of optimizing cancer treatment while incorporating infection prevention strategies to enhance long-term outcomes. Balancing effective treatment with proper management of short-term immunosuppressive effects remains essential in reducing infectious disease mortality.\u003c/p\u003e \u003cp\u003eTo further enhance clinical applicability, we developed a nomogram to predict the probability of infectious disease mortality in breast cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nomogram integrates key prognostic factors identified in the multivariable Cox regression model, including age, tumor subtype, tumor stage, and sequence number, all of which demonstrated significant contributions to mortality risk. Each variable is assigned a corresponding number of points based on its relative impact, with age and the Medullary subtype having the highest point contributions due to their strong association with mortality risk. Sequence number, which differentiates between patients with one primary cancer and those with multiple primaries, also significantly influences risk, as seen by its contribution on the nomogram scale. Patients with one primary cancer accumulate more points, reflecting a much higher risk of infectious disease mortality compared to those with multiple primary cancers. The total points derived from these predictors are mapped onto a linear predictor scale, which is then used to estimate the probability of mortality at 1, 3, and 5 years. The C-index of 0.868 reflects strong predictive accuracy, indicating that the model effectively discriminates between high- and low-risk patients. For example, a patient with advanced age, a Medullary subtype, and one primary cancer will accumulate a higher total score, translating to a higher estimated probability of mortality over time.\u003c/p\u003e \u003cp\u003eThe design of the nomogram allows clinicians to easily visualize how individual factors contribute to risk, facilitating personalized decision-making. For instance, high-risk patients identified through the nomogram could benefit from intensified infection prevention strategies, closer monitoring, and tailored treatment adjustments to reduce their overall mortality risk. Additionally, the ability to estimate mortality at 1, 3, and 5 years provides flexibility in planning both short- and long-term interventions.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eThe findings of this study have important clinical implications for improving the care of breast cancer patients by enabling proactive management of infection-related risks. The identification of key prognostic factors, such as age, tumor subtype, tumor stage, and sequence number, provides clinicians with a basis for stratifying patients according to their risk of infectious disease mortality. The development of the predictive nomogram further enhances this by offering an accessible tool to estimate individual mortality risk at 1, 3, and 5 years. This allows for personalized interventions, such as closer monitoring, prophylactic antibiotic use, or growth factor support, particularly for high-risk patients undergoing immunosuppressive treatments like chemotherapy or HER2-targeted therapies. Integrating this risk assessment into clinical workflows could help reduce preventable infections, improve survival outcomes, and optimize resource allocation by targeting preventive measures where they are needed most.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be considered. First, as a retrospective study, it is subject to selection bias and may lack key information on confounding variables that were not routinely collected in the original dataset, such as immune status, prior infections, or comorbidities. This can lead to an incomplete understanding of the full range of factors affecting infectious disease mortality. Second, some variables did not reach statistical significance in predicting infectious disease mortality. This may suggest that their influence on infection outcomes is mediated through their associated treatments or interactions with other variables not included in the analysis, rather than directly contributing to mortality. Additionally, while our nomogram demonstrated strong predictive performance, it was developed based on a specific patient population, and external validation is required to assess its generalizability across diverse settings and populations. Future research should aim to address these limitations by integrating prospective studies with comprehensive clinical data and external validation to enhance the robustness and clinical utility of the findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights critical risk factors associated with infectious disease mortality in breast cancer patients, including age, tumor subtype, tumor stage, sequence number and treatment options. The survival analysis demonstrated that chemotherapy and radiotherapy, when appropriately administered and monitored, can improve long-term survival by reducing cancer-related complications that contribute to infection risk. The development of a predictive nomogram further enhances the clinical utility of these findings, offering a practical tool to estimate the probability of infectious disease mortality at 1, 3, and 5 years. With a strong predictive performance (C-index\u0026thinsp;=\u0026thinsp;0.868), this nomogram can aid clinicians in identifying high-risk patients and tailoring personalized interventions. Moving forward, external validation studies and further exploration of risk-specific prevention strategies will be essential in refining the application of these findings to diverse clinical settings, ultimately improving outcomes for breast cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e: This study utilized the SEER database, which contains publicly available decoded data. As such, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: All data used in this study is available as part of the SEER database at https://seer.cancer.gov/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests:\u003c/strong\u003e The authors have nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: Conceptualization: Ali Hemade; Data curation: Ali Hemade; Formal analysis: Ali Hemade and Souheil Hallit; Methodology: Ali Hemade; Supervision: Ali Hemade and Souheil Hallit; Writing \u0026ndash; original draft: Maria Akiki, Chebli Dagher, and Ali Hemade; Writing \u0026ndash; review and editing: Ali Hemade, Souheil Hallit, and Rabih Hallit.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSafdar A, Bodey G, Armstrong D: \u003cstrong\u003eInfections in patients with cancer: overview\u003c/strong\u003e. \u003cem\u003ePrinciples and practice of cancer infectious diseases \u003c/em\u003e2011:3-15.\u003c/li\u003e\n\u003cli\u003eGiaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, Jemal A, Siegel RL: \u003cstrong\u003eBreast cancer statistics, 2022\u003c/strong\u003e. \u003cem\u003eCA: a cancer journal for clinicians \u003c/em\u003e2022, \u003cstrong\u003e72\u003c/strong\u003e(6):524-541.\u003c/li\u003e\n\u003cli\u003eBrand JS, Colzani E, Johansson AL, Giesecke J, Clements M, Bergh J, Hall P, Czene K: \u003cstrong\u003eInfection-related hospitalizations in breast cancer patients: risk and impact on prognosis\u003c/strong\u003e. \u003cem\u003eJournal of Infection \u003c/em\u003e2016, \u003cstrong\u003e72\u003c/strong\u003e(6):650-658.\u003c/li\u003e\n\u003cli\u003eEngelhardt EG, Garvelink MM, de Haes JC, van der Hoeven JJ, Smets EM, Pieterse AH, Stiggelbout AM: \u003cstrong\u003ePredicting and communicating the risk of recurrence and death in women with early-stage breast cancer: a systematic review of risk prediction models\u003c/strong\u003e. \u003cem\u003eJournal of Clinical Oncology \u003c/em\u003e2014, \u003cstrong\u003e32\u003c/strong\u003e(3):238-250.\u003c/li\u003e\n\u003cli\u003eFontanella C, Bolzonello S, Lederer B, Aprile G: \u003cstrong\u003eManagement of breast cancer patients with chemotherapy-induced neutropenia or febrile neutropenia\u003c/strong\u003e. \u003cem\u003eBreast care \u003c/em\u003e2014, \u003cstrong\u003e9\u003c/strong\u003e(4):239-245.\u003c/li\u003e\n\u003cli\u003eElhadi M, Khaled A, Msherghi A: \u003cstrong\u003eInfectious diseases as a cause of death among cancer patients: a trend analysis and population-based study of outcome in the United States based on the Surveillance, Epidemiology, and End Results database\u003c/strong\u003e. \u003cem\u003eInfectious agents and cancer \u003c/em\u003e2021, \u003cstrong\u003e16\u003c/strong\u003e:1-11.\u003c/li\u003e\n\u003cli\u003eSung WWY, Sharma K, Chan AW, Al Khaifi M, Oldenburger E, Chuk E: \u003cstrong\u003eA narrative review of the challenges and impact of breast cancer treatment in older adults beyond cancer diagnosis\u003c/strong\u003e. \u003cem\u003eAnnals of Palliative Medicine \u003c/em\u003e2024, \u003cstrong\u003e13\u003c/strong\u003e(6):1521-1529.\u003c/li\u003e\n\u003cli\u003eTao L, Gomez SL, Keegan TH, Kurian AW, Clarke CA: \u003cstrong\u003eBreast cancer mortality in African-American and non-Hispanic white women by molecular subtype and stage at diagnosis: a population-based study\u003c/strong\u003e. \u003cem\u003eCancer epidemiology, biomarkers \u0026amp; 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Molecular Morphology \u003c/em\u003e2023, \u003cstrong\u003e31\u003c/strong\u003e(6):354-362.\u003c/li\u003e\n\u003cli\u003eAmer MH: \u003cstrong\u003eMultiple neoplasms, single primaries, and patient survival\u003c/strong\u003e. \u003cem\u003eCancer Management and research \u003c/em\u003e2014:119-134.\u003c/li\u003e\n\u003cli\u003eCella D, Fallowfield LJ: \u003cstrong\u003eRecognition and management of treatment-related side effects for breast cancer patients receiving adjuvant endocrine therapy\u003c/strong\u003e. \u003cem\u003eBreast cancer research and treatment \u003c/em\u003e2008, \u003cstrong\u003e107\u003c/strong\u003e:167-180.\u003c/li\u003e\n\u003cli\u003eOh D-Y, Bang Y-J: \u003cstrong\u003eHER2-targeted therapies\u0026mdash;a role beyond breast cancer\u003c/strong\u003e. \u003cem\u003eNature reviews Clinical oncology \u003c/em\u003e2020, \u003cstrong\u003e17\u003c/strong\u003e(1):33-48.\u003c/li\u003e\n\u003cli\u003eFunakoshi T, Suzuki M, Muss HB: \u003cstrong\u003eInfection risk in breast cancer patients treated with trastuzumab: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eBreast cancer research and treatment \u003c/em\u003e2015, \u003cstrong\u003e149\u003c/strong\u003e:321-330.\u003c/li\u003e\n\u003cli\u003ePartridge AH, Burstein HJ, Winer EP: \u003cstrong\u003eSide effects of chemotherapy and combined chemohormonal therapy in women with early-stage breast cancer\u003c/strong\u003e. \u003cem\u003eJNCI monographs \u003c/em\u003e2001, \u003cstrong\u003e2001\u003c/strong\u003e(30):135-142.\u003c/li\u003e\n\u003cli\u003eHassan M, Ansari J, Spooner D, Hussain S: \u003cstrong\u003eChemotherapy for breast cancer\u003c/strong\u003e. \u003cem\u003eOncology reports \u003c/em\u003e2010, \u003cstrong\u003e24\u003c/strong\u003e(5):1121-1131.\u003c/li\u003e\n\u003cli\u003eHaussmann J, Corradini S, Nestle-Kraemling C, B\u0026ouml;lke E, Njanang FJD, Tamaskovics B, Orth K, Ruckhaeberle E, Fehm T, Mohrmann S: \u003cstrong\u003eRecent advances in radiotherapy of breast cancer\u003c/strong\u003e. \u003cem\u003eRadiation oncology \u003c/em\u003e2020, \u003cstrong\u003e15\u003c/strong\u003e:1-10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, infectious disease mortality, risk factors, SEER database, nomogram, survival analysis","lastPublishedDoi":"10.21203/rs.3.rs-6209450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6209450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Breast cancer patients face an elevated risk of infectious disease-related mortality due to immunosuppressive treatments and disease-related immune dysfunction. However, the specific risk factors influencing infection-related mortality in this population remain poorly understood. This study aims to identify key clinical and demographic factors associated with infectious disease mortality in breast cancer patients and develop a predictive nomogram for individualized risk estimation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective cohort study was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). Patients diagnosed with breast cancer and with complete clinical and survival data were included. The primary outcome was infectious disease-related mortality. Descriptive statistics, Kaplan-Meier survival analysis, and Cox proportional hazards regression were performed to identify significant predictors. A nomogram was developed based on multivariable Cox regression to estimate mortality risk at 1, 3, and 5 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eA total of 43,483 breast cancer patients were analyzed, with 482 experiencing infectious disease-related mortality. Significant predictors of mortality included increasing age (HR = 1.017, p \u0026lt; 0.001), Medullary breast cancer subtype (HR = 4.778, p = 0.0129), tumor stage (T2: HR = 0.7079, p = 0.0017), and presence of a single primary tumor (HR = 1.574, p = 0.0018). Chemotherapy and radiotherapy were associated with improved survival outcomes. A predictive nomogram was constructed with a concordance index (C-index) of 0.868, demonstrating strong predictive accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study identifies key clinical and demographic risk factors associated with infectious disease mortality in breast cancer patients. The predictive nomogram provides a useful tool for individualized risk assessment, aiding in targeted infection prevention strategies and optimizing clinical decision-making. Further validation in external cohorts is necessary to confirm its clinical utility.\u003c/p\u003e","manuscriptTitle":"Risk Factors for Infectious Disease Mortality in Breast Cancer: A Retrospective Cohort Study and Nomogram Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 06:31:16","doi":"10.21203/rs.3.rs-6209450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d77a0ba5-2f67-464b-b205-d3642a9eaaf7","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45562597,"name":"Infectious Diseases"},{"id":45562598,"name":"Oncology"}],"tags":[],"updatedAt":"2025-03-13T06:31:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 06:31:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6209450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6209450","identity":"rs-6209450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}