乳腺癌患者新发心血管疾病预测模型的建立与验证：基于内蒙古区域医疗数据

doi:10.19723/j.issn.1671-167X.2023.03.013

Abstract

Abstract:

Objective: To develop and validate a three-year risk prediction model for new-onset cardiovascular diseases (CVD) among female patients with breast cancer. Methods: Based on the data from Inner Mongolia Regional Healthcare Information Platform, female breast cancer patients over 18 years old who had received anti-tumor treatments were included. The candidate predictors were selected by Lasso regression after being included according to the results of the multivariate Fine & Gray model. Cox proportional hazard model, Logistic regression model, Fine & Gray model, random forest model, and XGBoost model were trained on the training set, and the model performance was evaluated on the testing set. The discrimination was evaluated by the area under the curve (AUC) of the receiver operator characteristic curve (ROC), and the calibration was evaluated by the calibration curve. Results: A total of 19 325 breast cancer patients were identified, with an average age of (52.76±10.44) years. The median follow-up was 1.18 [interquartile range (IQR): 2.71] years. In the study, 7 856 patients (40.65%) developed CVD within 3 years after the diagnosis of breast cancer. The final selected variables included age at diagnosis of breast cancer, gross domestic product (GDP) of residence, tumor stage, history of hypertension, ischemic heart disease, and cerebrovascular disease, type of surgery, type of chemotherapy and radiotherapy. In terms of model discrimination, when not considering survival time, the AUC of the XGBoost model was significantly higher than that of the random forest model [0.660 (95%CI: 0.644-0.675) vs. 0.608 (95%CI: 0.591-0.624), P < 0.001] and Logistic regression model [0.609 (95%CI: 0.593-0.625), P < 0.001]. The Logistic regression model and the XGBoost model showed better calibration. When considering survival time, Cox proportional hazard model and Fine & Gray model showed no significant difference for AUC [0.600 (95%CI: 0.584-0.616) vs. 0.615 (95%CI: 0.599-0.631), P=0.188], but Fine & Gray model showed better calibration. Conclusion: It is feasible to develop a risk prediction model for new-onset CVD of breast cancer based on regional medical data in China. When not considering survival time, the XGBoost model and the Logistic regression model both showed better performance; Fine & Gray model showed better performance in consideration of survival time.

Key words: Breast neoplasms, Cardiovascular disease, Risk prediction model, Risk assessment, Computerized medical records systems

CLC Number:

R737.9

Yun-jing ZHANG,Li-ying QIAO,Meng QI,Ying YAN,Wei-wei KANG,Guo-zhen LIU,Ming-yuan WANG,Yun-feng XI,Sheng-feng WANG. Development and validation of risk prediction model for new-onset cardiovascular diseases among breast cancer patients: Based on regional medical data of Inner Mongolia[J].Journal of Peking University (Health Sciences), 2023, 55(3): 471-479.

Figures/Tables 6

Table 1

The information of variables included in the prediction model for CVD among breast cancer patients in the Inner Mongolia Autonomous Region from 2012 to 2021"

Variables	Measurement	Variable assignments
Age on set	Age at diagnosis of breast cancer according to birthday and date of diagnosis	Years
Type of medical insurance	Type of medical insurance	0=employee, 1=residence
Ethnicity	Ethnicity according to medical insurance data	0=Han, 1=Mongolian, 2=others
GDP of residence	GDP of district where patients lived according to Inner Mongolia Bureau of Statistics	100 million yuan
Tumor stage	According to clinical TNM stage and treatment patterns. Stage=0, 1, 2, 3 was defined as “early” while stage=4 was defined as “advanced”	0=early, 1=unknown, 2=advanced
Type of surgery	According to records of treatments from medical insurance data	0=none, 1=breast-conserving surgery, 2=mastectomy
Type of chemotherapy	According to records of treatments from medical insurance data	0=none, 1=anthracyclines, 2=non-anthracyclines
Endocrine therapy	According to records of treatments from medical insurance data	0=no, 1=yes
Type of targeted therapy	According to records of treatments from medical insurance data	0=none, 1=trastuzumab, 2=non-trastuzumab
Radiotherapy	According to records of treatments from medical insurance data	0=no, 1=yes
History of diabetes	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of renal diseases	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of hypertension	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of ischemic heart diseases	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of cerebrovascular diseases	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of dyslipidemia	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of hypothyroidism	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
History of hyperthyroidism	According to records of medical insurance before breast cancer diagnosis	0=no, 1=yes
Charlson comorbidity index	According to records of medical insurance	Points
Hospitalization before diagnosis within 1 year	According to records of medical insurance before breast cancer diagnosis within 1 year	0=no, 1=yes
Length of stay before diagnosis within 1 year	According to the duration of hospitalization records from medical insurance before breast cancer diagnosis within 1 year	Days

Table 1

Table 2

Demographic characteristics of breast cancer patients receiving anti-tumor treatment in the Inner Mongolia Autonomous Region from 2012 to 2021"

Items	CVD (n=7 856)	No CVD (n=11 469)	P value
Age onset/years, ${\bar x}$±s	53.48±10.38	52.28±10.45	< 0.001
Type of medical insurance, n (%)			< 0.001
Employee	4 291 (54.62)	6 829 (59.54)
Resident	3 565 (45.38)	4 640 (40.46)
Ethnicity, n (%)			0.002
Han	6 727 (85.63)	9 620 (83.88)
Mongolian	865 (11.01)	1 372 (11.96)
Others	263 (3.35)	473 (4.12)
GDP of residence, median (IQR)	156.21 (180.36)	174.46 (312.93)	< 0.001
Charlson comorbidity index, ${\bar x}$±s	1.89±3.32	1.84±3.24	0.254
Tumor stage, n (%)			< 0.001
Early	3 619 (46.07)	4 807 (41.91)
Advanced	3 114 (39.64)	4 680 (40.81)
Unknown	1 123 (14.29)	1 982 (17.28)
Previous disease history, n (%)
Dyslipidemia	943 (12.00)	2 202 (19.20)	< 0.001
Diabetes	943 (12.00)	2 202 (19.20)	< 0.001
Renal diseases	81 (1.03)	181 (1.58)	0.002
Hypertension	1 325 (16.87)	3 535 (30.82)	< 0.001
Ischemic heart diseases	435 (5.54)	1 313 (11.45)	< 0.001
Cerebrovascular diseases	547 (6.96)	1 372 (11.96)	< 0.001
Hyperthyroidism	10 (0.13)	26 (0.23)	0.160
Hypothyroidism	9 (0.11)	24 (0.21)	0.165
Hospitalization before diagnosis within 1 year, n (%)	1 743 (22.19)	3 309 (28.85)	< 0.001
Length of stay before diagnosis within 1 year, ${\bar x}$±s	2.88±9.15	3.67±8.99	< 0.001
Type of surgery, n (%)			< 0.001
Mastectomy	3 262 (41.52)	3 910 (34.09)
Breast-conserving	247 (3.14)	495 (4.32)
None	4 347 (55.33)	7 064 (61.59)
Type of chemotherapy, n (%)			< 0.001
Anthracyclines	3 399 (43.27)	4 299 (37.48)
Non-anthracyclines	2 394 (30.47)	3 593 (31.33)
None	2 063 (26.26)	3 577 (31.19)
Endocrine therapy, n (%)	4 669 (59.43)	7 114 (62.03)	< 0.001
Type of targeted therapy, n (%)			0.018
Trastuzumab	1 023 (13.02)	1 649 (14.38)
Non-trastuzumab	67 (0.85)	111 (0.97)
None	6 766 (86.13)	9 709 (84.65)
Radiotherapy, n (%)	587 (7.47)	533 (4.65)	< 0.001

Table 2

Table 3

Results of multivariate competing risk models for breast cancer patients receiving anti-tumor treatment in the Inner Mongolia Autonomous Region from 2012 to 2021"

Factor	β	SE	Wald χ²	HR (95%CI)	P value
Age onset/years	0.02	0.00	14.17	1.02 (1.01-1.02)	< 0.001
Type of medical insurance
Employee				1.00
Resident	-0.03	0.03	-1.05	0.97 (0.92-1.03)	0.290
Ethnicity
Han				1.00
Mongolian	0.00	0.04	0.12	1.00 (0.94-1.08)	0.910
Others	-0.13	0.06	-2.02	0.88 (0.78-1.00)	0.043
GDP of residence	0.00	0.00	-8.36	1.00 (1.00-1.00)	< 0.001
Charlson comorbidity index	0.00	0.00	-0.40	1.00 (0.99-1.01)	0.690
Tumor stage
Early				1.00
Advanced	-0.14	0.04	-3.44	0.87 (0.80-0.94)	0.001
Unknown	-0.06	0.03	-2.15	0.94 (0.88-0.99)	0.031
Type of surgery
None				1.00
Breast-conserving	0.06	0.07	0.83	1.06 (0.92-1.22)	0.410
Mastectomy	0.27	0.03	8.75	1.31 (1.23-1.39)	< 0.001
Type of chemotherapy
None				1.00
Anthracyclines	0.19	0.03	6.22	1.21 (1.14-1.29)	< 0.001
Non-anthracyclines	0.11	0.03	3.48	1.11 (1.05-1.18)	< 0.001
Endocrine therapy
No				1.00
Yes	-0.10	0.02	-4.07	0.90 (0.86-0.95)	< 0.001
Type of targeted therapy
None				1.00
Trastuzumab	-0.01	0.03	-0.26	0.99 (0.93-1.06)	0.790
Non-trastuzumab	-0.04	0.12	-0.33	0.96 (0.76-1.21)	0.740
Radiotherapy
No				1.00
Yes	0.26	0.05	5.35	1.30 (1.18-1.42)	< 0.001
History of hypertension
No				1.00
Yes	-0.44	0.04	-11.52	0.64 (0.60-0.69)	< 0.001
History of dyslipidemia
No				1.00
Yes	-0.02	0.04	-0.53	0.98 (0.90-1.06)	0.590
History of diabetes
No				1.00
Yes	0.03	0.04	0.83	1.03 (0.96-1.12)	0.410
History of ischemic heart diseases
No				1.00
Yes	-0.35	0.06	-6.31	0.71 (0.63-0.79)	< 0.001
History of cerebrovascular diseases
No				1.00
Yes	-0.05	0.05	-1.07	0.95 (0.86-1.05)	0.280
History of renal diseases
No				1.00
Yes	0.08	0.12	0.72	1.09 (0.87-1.36)	0.470
History of hyperthyroidism
No				1.00
Yes	-0.07	0.32	-0.22	0.93 (0.50-1.75)	0.830
History of hypothyroidism
No				1.00
Yes	0.01	0.32	0.02	1.01 (0.53-1.90)	0.980
Hospitalization before diagnosis within 1 year
No				1.00
Yes	-0.07	0.04	-1.75	0.93 (0.86-1.01)	0.081
Length of stay before diagnosis within 1 year	0.00	0.00	-1.20	1.00 (0.99-1.00)	0.230

Table 3

Figure 1

Figure 2

Figure 3

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Development and validation of risk prediction model for new-onset cardiovascular diseases among breast cancer patients: Based on regional medical data of Inner Mongolia

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