Competing Risk Analysis of Cardiovascular/Cerebrovascular Death in T1/2 Kidney Cancer: A SEER Database Analysis

doi:10.21203/rs.3.rs-94460/v1

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Competing Risk Analysis of Cardiovascular/Cerebrovascular Death in T1/2 Kidney Cancer: A SEER Database Analysis

https://doi.org/10.21203/rs.3.rs-94460/v1

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published 05 Jan, 2021

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Background: Kidney cancer (KC) is associated with cardiovascular regulation disorder, which easily leads to cardiovascular and cerebrovascular death (CCD), and CCD is one of the major causes of death in patients with KC, especially in T1/2 status. However, there are few studies treated CCD as an independent outcome and analyzed the risk factors related to this outcome. We aimed to evaluate the key factors associated with CCD or kidney cancer-specific death (KCD) in patients with T1/2 KC by competing risk analysis, and compared these two kinds of risk factors to offer some information for clinical management.

Methods: A total of 45117 patients diagnosed with first primary KC in T1/2 status between 2004-2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. According to their outcomes at the end of follow-up, all patients were divided into CCD group (n=3087), KCD group (n=3212), Other Events group (n=6312) and Alive group (n=32506). Patients’ clinical characteristics were estimated their association with CCD and KCD by Fine-Gray’s competing risk model. Factors significantly correlating with CCD or KCD were used to create forest plots to compare their differences.

Results: The Fine-Gray’s competing risk analysis showed that age at diagnosis, race, marital status, tumor size, AJCC T stage, chemotherapy, kind of surgery of primary site and scope of lymph node were correlated significantly with CCD. Moreover, age at diagnosis, sex, marital status, tumor size, AJCC T/N status, radiation therapy, chemotherapy, kind of surgery of primary site and scope of lymph node were correlated significantly with KCD. Then the forest plots of these two kinds factors were established to compare their difference. It was found that age at diagnose, race, AJCC T/N status and therapy methods represented significantly different risks for patients with T1/2 KC developing to CCD or KCD.

Conclusion: We firstly separated CCD and KCD as two independent outcomes to analysis the risk factors related them, and found that age at diagnose, race, AJCC T/N status and therapy methods differently affected patients with T1/2 KC developing to CCD or KCD.

Cancer Biology

Kidney cancer

Cardiovascular and cerebrovascular death

SEER database

Competing risk analysis

Kidney cancer (KC) is commonly diagnosed in older adults by chance. As one of the most common malignant cancers, KC represents the sixth most frequently diagnosed in male and the 10th in female worldwide¹. According to World Health Organization, the newest data show there are more than 140,000 KC-related deaths every year. Especially in Europe and North America, the lifetime risk for developing KC ranges from 1.3–1.8%². Moreover, KC has ranked as the 13th leading cause of cancer death worldwide³.

In respect of pathology of KC, hypertension and intracranial pressure increasing are the common paraneoplastic disorders of KC⁴. These two paraneoplastic disorders as main reasons that cause KC patients cardiovascular and cerebrovascular death (CCD).

CCD is one of the most prevalent causes of death in patients with KC, which occupies nearly 41.96% of all in elder patients, even in long-term survival follow-up study, cardiovascular related death is still a main cause of death⁵. In addition, nearly 70% KC patients were diagnosed at American Joint Committee on Cancer Staging (AJCC) status T1/2, which didn’t show a relatively high incidence of cancer-specific death. Considering most of all KC patients were in T1/2 status, it is necessary to aim at these patients to analyze the risk factors related to CCD.

To make an appropriated clinical plan, it is necessary to identify those patients who have high risks of CCD. Unfortunately, many studies have attempted to quantitate the risks of kidney cancer-specific death (KCD)^6–9, but few reports analyzed those patients who faced high risk of CCD so that the precise clinical management to those patients was difficult to carry out.

We aimed to analyze the risk factors associated with CCD in patients with T1/2 KC, but CCD is one of the outcomes in KC. The outcomes of KC are various and all of them are competing events. In the respect of traditional survival analysis, all of other events are treated as censored events that may generate bias¹⁰. To analyze the event with competing events, Fine-Gray competing risk regression analysis is more suitable^11,12. Therefore, we carried out a competing risk model to identify risk factors of CCD and KCD in patients with T1/2 KC. We also curved forest plots to compare those two kinds of factors.

Data source

Surveillance, Epidemiology, and End Results (SEER) database (http://seer.cancer.gov/seerstat) covers approximately 30% of the US population and provides complete cancer patients data including demographic, clinical information and follow-up data. This database is updated annually by the National Center for Health Statistics¹³. We chose 18 Registry Research Dataset (2000-2016, with additional treatment field, Nov 2018 submit) in the SEER database to identify cases for this study.

Inclusion and exclusion criteria

We fetched cases when meeting the following criteria: (1) The ICD-O-3 codes C64.9, C65.9 and C66.9, (2) KC is the first primary cancer, (3) Diagnosed between 2004 and 2015.

Cases were excluded according to the following criteria: (1) Unknown demographic information including race and marital status; (2) The autopsy/death certification reports, which are lack of survival periods; (3) Unknown clinical information including American Joint Committee on Cancer (ACJJ) stage, T/N/M status and tumor size; (4) Cases without histological confirmed; (5) Indefinite surgery of primary site information; (6) patients were diagnosed with KC at T3/4 status.

Then the patients were divided into four group according to their outcomes at the end of follow-up: (1) Alive; (2) Cardiovascular and cerebrovascular death (CCD), which includes diseases of heart, cerebrovascular diseases and hypertension without heart disease; (3) Kidney cancer-specific death (KCD); (4)Other events (OE), which includes developing a secondary primary cancer, and other non-CCD/KCD cause of death. The inclusion and exclusion procedure mentioned above was shown in Figure 1.

Statistical analysis

Firstly, we depicted cumulative incidence function (CIF) plot, which showed the cumulative incidence of different outcomes in all included KC patients as time went by. The incidence rate of KCD and CCD after diagnosed 36 months, 60 months and 120months were calculated and compared.

Then, We used the univariate and multivariate Fine-Gray’s competing risk models to analyze the hazard ratios (HRs) of each variable that significantly correlated to CCD and KCD¹⁴. Demographic, pathological characteristics and treatment information were considered into the analysis. After identifying the key variables, we curved forest plots to represent the significant risk factors and their HRs.

The competing risk analyses and chi-square test in this study were conducted using R software(version:3.6.3).The Fine-Gray’s competing risk model was conducted by R package of cmprsk¹⁵, the forest plots were depicted by R package of forestplot.

To find the best cutoff value of continuous variables, such as age at diagnose, the X-tile tool (Yale University, New Haven, Connecticut, USA) were used to estimate the most suitable cutoff points, which was widely used in many cancer-related researches. This tool is via enumeration method to calculate the chi-square test results of different cutoff values according the outcomes, and the minimum values represent the best cutoff points. The optimal cut-off values for age were 1-50, 51-58, 59-65, 66-73 and over 75 years (Figure 1);

Differences in the reported variables were evaluated by the chi-square test and p < 0.05 was considered statistically significant.

Patients characteristics

As shown in figure 1, a total of 45117 patients met the eligibility criteria, and among them, there were 32506(72.05%) patients in Alive group, 3087 (6.84%) in CCD group, 3212 (7.12%) in KCD group and 6312 (13.99%) in OE group.

As Table 1 showed, in CCD group, the majority patients were married (n=1645, 53.29%), white race (n=2482, 80.40%), male (n=1928, 62.46%) and diagnosed with KC at age≥75 (n=1400, 45.35%). There were more patients stayed as AJCC T1 (n=2668, 86.43%), N0 status (n=3063, 99.22%) and with tumor size＞3cm (n=2030, 65.76%). As for treatment situation, the minority of patients received radiation therapy (n=23, 0.75%), chemotherapy (n=16, 0.52%) and scope lymph node (n=177, 5.73%), but most of them performed surgery (n=2570, 83.25%). In KCD group, patients’ characteristics were similar to the patients in CCD group. There were 1093(34.03%) patients diagnosing at age>75, 1890 (58.84%) married, 2670 (83.13%) White people and 2035 (63.36%) males. There were 2039 (63.48%) patients in AJCC T1 status, 2992 (93.15%) in N0 status. As for treatment situation, there were 86 (2.68%) patients received radiation therapy, 164 (5.11%) received chemotherapy, 453 (14.10%) received scoping lymph node and 2606 (81.13%) operated surgery. In Alive group, the patients’ characteristics were different from KCD and CCD group. There were 3280 (10.09%) patients diagnosed at age >75, 22042 (67.81%) married, 26784 (82.40%) White people and 19568 males. There were 28286 (87.02%) patients in AJCC T1 status, 32389 (99.64%) in N0 status. As for treatment situation, there were 49 (0.15%) patients received radiation therapy, 139 (0.43%) received chemotherapy and 2252 (6.93%) received scoping lymph node and 31733 (97.62%) operated surgery.

Cumulative incidence function of CCD, KCD and OE

We estimated the cumulative incidence ratios of each outcomes via competing risks model. As figure 2 showed, KCD and CCD represented almost the same cumulative incidences in patients after diagnosed with T1/2 KC. On 36 months after diagnosed, the cumulative incidences of CCD, KCD and OE were 2.50%, 3.50% and 4.83%, respectively. On 60 months after diagnosed, the cumulative incidences were 4.03 %, 5.08% and 7.97%, respectively. On 120 months after diagnosed, the cumulative incidences were 8.13%, 8.04% and 16.60%, respectively (Table 2).

Univariate and multivariate analysis by Fine-Gray’s competing risk model

Firstly, we used the univariate Fine-Gray’s competing risk model to analysis all factors listed above. In CCD group, as Table 3 showed, except American Indian/Alaska native, sex and AJCC N status, all factors were significant associated with CCD (p<0.05). Then we estimated those factors by multivariate Fine-Gray’s competing risk model, and it was found that age at diagnosis, race, marital status, AJCC T status, chemotherapy and surgery of primary site stayed as significant risk factors of CCD. In detail, elder patients showed a higher risk of CCD, the respective HRs were 2.117 (95% CI:1.756-2.552), 3.200 (95% CI:2.672-3.832), 4.981 (95% CI:4.187-5.925) and 9.525 (95% CI:8.049-11.273) at age 51-58, 59-65, 66-73 and over 75 versus 1-50. Black patients faced higher risk (black versus white: HR=1.475, 95% CI:1.334-1.632), but Asian/Pacific Islander patients had less risk of CCD(Asian/Pacific Islander versus white: HR=0.826, 95% CI:0.683-0.998), married patients also showed a lower risk (married versus single/divorce/widow: HR=0.677, 95% CI:0.628-0.730). As for AJCC T status, T2 status had less risks of CCD than T1 (HR=0.847, 95% CI:0.758-0.946) , tumor size>3 cm group had higher risk (HR=1.111, 95% CI:0.1.023-1.206); patients received chemotherapy and surgery of primary site also reduced risks of CCD, in detail, the respective HRs were 0.574 (95% CI:0.347-0.949) with chemotherapy versus without; 0.631 (95% CI:0.532-0.747), 0.526(95% CI:0.466-0.594), 0.607 (95% CI:0.543-0.680) with local tumor destruction/excision, with partly nephrectomy and with radical nephrectomy versus no operation.

When it came to KCD group, as Table 4 showed, it was found that age at diagnosis, marital status, sex, AJCC TN status, tumor size, radiation therapy, chemotherapy, scope lymph node and surgery of primary site were significantly associated with this outcome by univariate competing risk model. Then we estimated these factors by multivariate competing risk model, these factors were still significant associated with KCD. In detail, elder patients still showed a higher risk of KCD, the respective HRs were 1.578 (95% CI:1.376-1.810), 1.933 (95% CI:1.688-2.215), 2.176 (95% CI:1.899-2.494) and 3.205 (95% CI:2.814-3.150) at age 51-58, 59-65, 66-73 and over 75 versus 1-50. Married patients showed a lower risk of KCD (married versus single/divorce/widow: HR=0.867, 95% CI:804-0.935). As for AJCC T status, T2 status had more risks of KCD than T1 (HR=2.259, 95% CI:2.081-2.451), advanced N status had more risks of KCD, the respective HRs were 3.347 (95% CI:2.698-4.152), 4.004 (95% CI: 2.837-5.650) stayed as N1, N2 versus N0 status; tumor size>3 cm group had higher risk (HR=2.319, 95% CI:2.086-2.579). Patients received chemotherapy, radiation therapy and scope of lymph node still had high risks of KCD, the respective HRs were 2.896 (95% CI:2.342-3.581) with chemotherapy versus without; 2.552 (95% CI:1.946-3.346) with radiation therapy versus without, 1.378(95% CI:1.206-1.575) with 1-3 regional lymph nodes removed, 1.230 (95% CI:1.022-1.480), 4 or more regional lymph nodes removed than no scoping of lymph node. Patients received surgery of primary site faced lower risks of KCD, the respective HRs were 0.356 (95% CI:0.286-0.443), 0.275 (95% CI:0.242-0.312) and 0.393 (95% CI: 0.352-0.438) with local tumor destruction/excision, with partly nephrectomy and with radical nephrectomy versus no operation.

Forest plots of the risk factors and hazard ratios of CCD and KCD

We summarized the multivariate competing risk analysis results of CCD and KCD in patients with T1/2 kidney cancer, and curved forest plots to visualized these results, which were shown in Figure 3 and 4. Comparing these results, we found that there were some differences in the risk factors that significant associated with CCD or KCD. For CCD, age was the biggest risk factor that represented the highest HRs, race was also associated with CCD, but N status and scope of lymph node didn’t show the relationship with CCD. However, for KCD, age didn’t represent the comparatively high HRs to KCD, race was not associated with KCD. Moreover, N status was the biggest risk factors associated with KCD, and T status showed the opposite HRs to KCD comparing with CCD. Radiation therapy, chemotherapy and scope of lymph node also showed different HRs to CCR and KCD, but surgery of primary site represented the similar HRs.

In this study, we aimed to analyze CCD in patients with T1/2 KC based on the SEER database. A total of 45117 patients with first primary T1/2 KC were screened out to curve the CIF of CCD and RCD, then the competing risk model was used to estimate the risk factors associated with CCD and RCD. It was found that age at diagnose, marital status, race, T status, tumor size, chemotherapy and surgery of primary site were significantly associated with CCD; age at diagnose, marital status, sex, T and N status, tumor size, chemotherapy, radiation therapy, scope of lymph node and surgery of primary site were significantly associated with KCD. The HRs of these factors were estimated and then visualized by forest plots to compare these risk factors.

Although there were many previous studies about survival analysis for patients with KC have reported, which included postoperative overall survival, early stage KC-specific survival, metastatic KC-specific survival and so on, it was still lack of CCD-related analysis in patients with KC, especially in T1/2 status, which occupied nearly 70% of KC diagnosis and had relatively high survival probability. Moreover, most of the previous works analyzed the overall survival by treating all outcomes as one, which might not be suitable to identify KC patients with different risk of various outcomes. In the present study, we used competing risk model to analyze CCD and KCD in patients with T1/2 KC, which was as possible as it can to get more precise results and help clinical management to make some decision.

CCD is one of the main outcomes in patients with cancer, and the incidence rate of CCD is greater than cancer-free people. In the present work, the CIF we plotted showed many characteristics of T1/2 KC. Firstly, the incidence rate of CCD was similar as KCD from the beginning of diagnosis to even 10 year later. This feature indicated CCD was as important as KCD in T1/2 KC. Secondly, the cumulative incidence rate of CCD increased smoothly, which indicated after diagnosed with KC, the risk of CCD didn’t change year by year. This result was different with other cancer-specific CCD which reported previously by Fang, they used large cohort study to get the conclusion that the incidence rate of cardiovascular death after diagnosis was highest at first year, and decreased year by year. This feature indicated that the management of patients with KC was a long-term period to prevent cardiovascular events.

As for the risk factors of CCD and KCD, in the present study we found that Age was the predominant risk factors associated with CCD that the elder patients faced higher hazard risk of CCD, but this factor didn’t show such predominant effect to KCD, which was also proved by other previous studies. Black patients faced highest risk of CCD than other races, Asians faced lowest risk of CCD, but race was not a significant factor associated with KC-specific death, which was also reported by Kun-Chi¹⁶ et al and Yuan Z et al¹⁷. Marry status show the similar hazard risk of CCD and KCD that married patients faced lower risk than those who were single/divorce/widow.

As for the pathological characteristics of patients, it was found that patients with KC stayed as T2 status showed a lower risk of CCD but higher risk of KCD than T1 status. N status was the predominant factors associated with KCD, but didn’t show the significant association with CCD. As we known, advanced AJCC status represents giant tumor size, distant metastasis and lymph nodes invasion, which are significantly associated with cancer specific death and development of secondary cancer¹⁸. But the results in the present study showed AJCC status had different relationship with CCD, which indicated patients with high risk of CCD was not the same group of patients with high risk of KCD. Unfortunately, it was lack of other related research about AJCC status with CCD in KC cancer to prove this result, so this conclusion should be verified by further studies.

As for surgery of KC patients, with tumor excision surgery or nephrectomy showed lower risks of CCD in our study. This result indicated surgery could reduce the risk of CCD in KC patients. But in terms of different surgery types, there is no consensus about the long-term cardiovascular events after surgery. Some reports suggested that KC patient with radical nephrectomy had a higher risk of cardiovascular events than those with partly nephrectomy surgery, which was similar to our results. Huang et al analyzed the SEER-Medicare, and made a conclusion that nearly 20% cardiovascular events rate increased after KC patients treating with radical nephrectomy than nephron-sparing surgery²¹. Umberto et al reported that nephron-sparing surgery showed an independent protective effect on hypertension in KC patients without preoperative cardiovascular disease, but not on other major cardiovascular events²². One of the possible pathophysiological mechanisms of this finding is the acute loss of half of nephrons may induce a compensatory function of remaining kidney that increases the arterial blood inflow by activation of the renin-angiotensin-aldosterone system^23,24. Although there are many reports showing us radical nephrectomy brings more risk of cardiovascular events for KC patients than nephron-sparing surgery²⁵, the surgery plan should be made based on the situation of patient to benefit most, the risk of cardiovascular events after surgery is one respect should be considered. It is also necessary to pay attention to post-operation care to prevent acute hypertension. Additionally, it is expected to find out the relationship between different surgery types and post-operation cardiovascular events in KC patients, which could help clinicians to make an optimal treatment decision.

As for radiation therapy and chemotherapy, both of these two therapy methods were not popular in KC therapy, it was found that a little of patients (nearly 1%) with KC received these therapy methods. In our study, it was found that chemotherapy made KC patients decrease risk of CCD but increased risk of RCD, which was contrary to common sense owing to the bias existing in our study, one of the possible explanation was the patients received chemotherapy was with poor prognosis, but those who didn’t received chemotherapy was with good prognosis that didn’t need chemotherapy. Radiation therapy and chemotherapy were confirmed useful for KC treatment in many respects, such as reducing the risk of local recurrence²⁶, delaying the metastasis of cancer ²⁷and so on²⁸, but there were rare studies about the effect of radiation therapy and chemotherapy on CCD in KC patients. Further studies are expected to conduct in the clinic and laboratory to provide more information on the relationship between these two therapy methods and cardiovascular/cerebrovascular events in KC patients.

There are several limitations existing in this study. Firstly, our analysis quantifies hazard risk of many variables associated with CCD and KCD, but didn’t take patients’ comorbidities into account due to limitations of SEER. Nevertheless, even comorbidities were not taken into the analysis, the results in this study were still meaningful and just should be improved. Secondly, because the demographic and clinical information provided by the SEER database is not complete, more than 20,000 individuals were excluded, and it may lead to some bias. Thirdly, retrospective study has its instinct limitations, such as selection bias, experimental bias and so on. Although several limitations exist in this study, the analysis in the present study was still meaningful and could offer some information to clinical management.

We made competing risk analyses of CCD and KCD in patients with first primary T1/2 kidney cancer based on the SEER database. The significant factors of CCD and KCD were identified and calculated their hazard ratios. It was found that age, race, marry status, sex, AJCC T status, chemotherapy and surgery of primary site were associated with CCD. Among these factors, age, race, AJCC TN status and chemotherapy show different relationships to CCD compared with KCD. Further studies about CCD in patients with KC were expected to establish a practical prognostic model for clinical use.

KC: kidney cancer; CCD: cardiovascular and cerebrovascular death; OE: other events; SEER: Surveillance, Epidemiology, and End Results; CIF: cumulative incidence function; AJCC: American Joint Committee on Cancer; HR: hazard ratio; CI: confidence interval.

Acknowledgements

The authors acknowledge the efforts of the SEER Program tumor registries in providing high-quality open resources for researches.

Funding

This work was supported by the Changzhou Science and Technology Program (Key Research Laboratory of High Technology, Grant No. CM20193010);

Youth Talent Science and Technology Project of Changzhou Commission of Health (Innovative and Entrepreneurial Talents Plan of Jiangsu Province, Grant No. QN201921)

Author contributions

(I) Conception and design: Xiaofei Mo and Mingge Zhou
(II) Administrative support: Yuetao Wang
(III) Collection and assembly of data: Hui Yan
(V) Data analysis and interpretation: Xueqin Chen and Xiaofei Mo
(VI) Manuscript writing: All authors
(VII) Final approval of manuscript: All authors

Footnote: The authors have completed the STROBEreporting checklist

Ethical statement:

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Competing Risk Analysis of Cardiovascular/Cerebrovascular Death in T1/2 Kidney Cancer: A SEER Database Analysis

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