Analysis of risk factors and construction of risk prediction model for anemia in patients with chronic obstructive pulmonary disease

doi:10.19983/j.issn.2096-8493.2024003

Abstract

Abstract:

Objective: To explore the risk factors of anemia in patients with chronic obstructive pulmonary disease (COPD), and construct a nomograph prediction model. Methods: COPD patients admitted to the Respiratory Department of the Second Affiliated Hospital of Shenyang Medical College from December 2019 to March 2023 were retrospectively selected as the study objects (492 patients). LASSO regression was used to screen risk factors, and logistic regression analysis was used to construct a prediction model of anemia in COPD patients, and a nomogram prediction model was constructed. The model was validated internally by Bootstrap resample method. The calibration curve and its C-index were used to evaluate the differentiation of the model. The area under receiver operating characteristic (ROC) curve and clinical decision curve (DCA) were used to evaluate the prediction ability and clinical applicability of the nomogram prediction model, respectively. Results: A total of 492 COPD patients were included, 19.51% (96/492) of them had anemia. Nine candidate predictors were identified by LASSO regression analysis: gender, creatinine, hypoproteinemia, diabetes, hypertension, COVID-19 infection, red blood cells (RBC), hemoglobin (Hb), body mass index (BMI). They were included in logistic regression analysis, and the results showed that gender being female (OR=3.353, 95%CI: 1.530-7.349), elevated creatinine levels (OR=1.024, 95%CI: 1.010-1.037), elevated Hb levels (OR=0.928, 95%CI: 0.905-0.951), hypoproteinemia (OR=6.239, 95%CI: 2.845-13.678), diabetes mellitus (OR=0.198, 95%CI: 0.056-0.703) were all independent influencing factors for anemia. Calibration curve of the nomogram prediction model showed good fitness, with a C-index of 0.933 (95%CI: 0.910-1.848), indicating that the model was well distinguished. The area under the curve was 0.933 (95%CI: 0.910-0.957), and DCA curve showed good clinical applicability of the model. Conclusion: The prediction model of COPD combined with anemia is simple and accurate, has certain value in early clinical screening of high-risk groups of anemia and the formulation of individualized precise prevention and treatment plans.

Key words: Pulmonary disease, chronic obstructive, Anemia, Factor analysis, statistical, Forecasting, Nomograms

CLC Number:

R563

Fu Yiting, Liu Lei, Zhao Qian, Meng Jixian, Zhen Ziyi, Wang Yang, Li Rongmei. Analysis of risk factors and construction of risk prediction model for anemia in patients with chronic obstructive pulmonary disease[J]. Journal of Tuberculosis and Lung Disease , 2024, 5(1): 20-27. doi: 10.19983/j.issn.2096-8493.2024003

Figures/Tables 8

变量^a	贫血组(96例)	无贫血组(396例)	统计检验值	P值
性别[例(构成比,%)]			χ²=2.139	0.168
男性(1)	61(63.50)	219(55.30)
女性(0)	35(36.50)	177(44.70)
年龄(岁, $\bar{x} \pm s$)	80.24±8.25	74.47±9.94	t=-5.889	<0.001
体质量指数($\bar{x} \pm s$)	21.22±3.64	23.00±5.17	t=3.196	0.001
吸烟指数[支/年,M(Q₁,Q₃)]	360.00(0.00,800.00)	205.00(0.00,800.00)	Z=-0.815	0.415
病程[年,M(Q₁,Q₃)]	2.00(0.10,6.00)	3.00(0.10,7.00)	Z=-0.872	0.383
住院次数[次,M(Q₁,Q₃)]	3.00(2.00,7.00)	4.00(2.00,8.00)	Z=-1.372	0.170
红细胞计数(×10¹²/L, $\bar{x} \pm s$)	3.77±0.56	4.53±0.63	t=10.784	<0.001
血红蛋白(g/L, $\bar{x} \pm s$)	108.43±20.77	137.80±18.66	t=13.530	<0.001
红细胞体积分布宽度[%,M(Q₁,Q₃)]	13.70(13.00,15.00)	13.00(12.50,14.00)	Z=-5.311	<0.001
二氧化碳分压[mmHg,M(Q₁,Q₃)]	43.00(36.50,47.00)	45.00(40.00,50.00)	Z=-3.394	0.001
血氧分压[mmHg,M(Q₁,Q₃)]	72.00(63.50,85.00)	72.00(60.50,81.00)	Z=-0.694	0.488
血氧饱和度[%,M(Q₁,Q₃)]	93.00(91.00,97.00)	93.90(91.00,96.00)	Z=-0.903	0.367
白蛋白(g/L, $\bar{x} \pm s$)	37.03±5.37	40.69±4.33	t=6.201	<0.001
C反应蛋白[mg/L,M(Q₁,Q₃)]	28.65(8.50,60.74)	9.12(2.51,28.65)	Z=-4.872	<0.001
肌酐[μmol/L,M(Q₁,Q₃)]	72.70(68.95,102.00)	70.20(58.30,77.00)	Z=-4.667	<0.001
中性粒细胞与淋巴细胞比值[M(Q₁,Q₃)]	5.21(3.26,8.63)	3.58(2.33,5.21)	Z=-4.391	<0.001
糖尿病[例(构成比,%)]			χ²=6.711	0.009
无(0)	90(93.75)	330(83.33)
有(1)	6(6.25)	66(16.67)
冠心病[例(构成比,%)]			χ²=2.892	0.110
无(0)	38(39.58)	195(49.24)
有(1)	58(60.42)	201(50.76)
低氧血症[例(构成比,%)]			χ²=1.573	0.239
无(0)	66(68.75)	245(61.87)
有(1)	30(31.25)	151(38.13)
低蛋白血症[例(构成比,%)]			χ²=64.620	<0.001
无(0)	58(60.42)	365(92.17)
有(1)	38(39.58)	31(7.83)
高脂血症[例(构成比,%)]			χ²=3.039	0.094
无(0)	95(98.96)	376(94.95)
有(1)	1(1.04)	20(5.05)
新型冠状病毒感染[例(构成比,%)]			χ²=21.017	<0.001
无(0)	87(90.62)	392(98.99)
有(1)	9(9.38)	4(1.01)
高血压[例(构成比,%)]			χ²=10.380	0.001
无(0)	60(62.50)	175(44.19)
有(1)	36(37.50)	221(55.81)

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变量	β值	s $\bar{x}$值	Wald χ²值	P值	OR值	95%CI值
变量	β值	s $\bar{x}$值	Wald χ²值	P值	OR值	下限	上限
性别(女性)	1.210	0.400	9.133	0.003	3.353	1.530	7.349
肌酐(连续变量)	0.023	0.007	11.443	0.001	1.024	1.010	1.037
血红蛋白(连续变量)	-0.075	0.013	34.447	<0.001	0.928	0.905	0.951
低蛋白血症(是)	1.831	0.401	20.892	<0.001	6.239	2.845	13.678
糖尿病(是)	-1.620	0.647	6.275	0.012	0.198	0.056	0.703