Establishment and validation of an in-hospital mortality risk prediction model for coronary heart disease and chronic kidney disease

Zhirui QI; Yunfei PU; Shiquan YU; Boli RAN

doi:10.16016/j.2097-0927.202311017

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Establishment and validation of an in-hospital mortality risk prediction model for coronary heart disease and chronic kidney disease

Clinical Medicine | 更新时间：2026-01-09

- Establishment and validation of an in-hospital mortality risk prediction model for coronary heart disease and chronic kidney disease
- Journal of Army Medical University Vol. 46, Issue 6, Pages: 630-636(2024)
- 作者机构：
  
  1.637000 四川南充，川北医学院临床医学系
  2.401120 重庆，重庆市人民医院心血管内科
- 作者简介：
- 基金信息：
  
  the General Program of National Natural Science Foundation of China(81400351);the General Program of Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX1563)
- DOI：10.16016/j.2097-0927.202311017
  CLC： R195.1;R541.4;R692
- Received：04 November 2023，
  
  Revised：2024-01-02，
  
  Published：30 March 2024
- 稿件说明：
移动端阅览
Zhirui QI, Yunfei PU, Shiquan YU, et al. Establishment and validation of an in-hospital mortality risk prediction model for coronary heart disease and chronic kidney disease[J]. Journal of Army Medical University, 2024, 46(6): 630-636.
DOI：

Zhirui QI, Yunfei PU, Shiquan YU, et al. Establishment and validation of an in-hospital mortality risk prediction model for coronary heart disease and chronic kidney disease[J]. Journal of Army Medical University, 2024, 46(6): 630-636. DOI： 10.16016/j.2097-0927.202311017.

摘要

目的

探讨冠心病(coronary artery disease，CAD)合并慢性肾脏病(chronic kidney disease，CKD)患者院内死亡的危险因素，建立死亡风险预测模型并验证其预测效果。

方法

回顾性分析2008-2019年重症监护医学信息数据库-Ⅳ(medical information mart for intensive care-Ⅳ，MIMIC-Ⅳ)诊断为CAD合并CKD患者数据。将1 609例患者数据按照7 ∶3比例分为构建模型的建模组(1 126例)和内部验证组(483例)，以院内死亡为结局事件，采用单因素及多因素Logistic回归分析筛选出变量，绘制预测院内死亡风险列线图，并通过受试者工作特征曲线下面积(area under curve，AUC)、敏感度、特异度、Hosmer-Lemeshow检验以及校正曲线对模型进行检验，对模型进行内部验证；收集2023年1-10月重庆市人民医院CAD合并CKD患者287例作为外部验证组

对模型进行外部验证。

结果

多因素Logistic回归结果表明，年龄(

=1.031

95%

: 1.015~1.049)、抗血小板药物(

=0.520

95%

: 0.342~0.792)、他汀类药物(

=0.312

95%

: 0.212~0.460)、白细胞计数(

=1.035

95%

: 1.016~1.057)、主要不良心血管事件(major adverse cardiovascular events，MACE)(

=2.417

95%

: 1.330~4.643)、eGFR(

=0.986

95%

: 0.978~0.995)、磷酸盐(

=1.226

95%

: 1.104~1.362)、碳酸氢盐(

=0.938

95%

: 0.900~0.977)、氯化物(

=0.

969

95%

: 0.942~0.997)、PCI或CABG(

=0.362

95%

: 0.174~0.685)均与CAD合并CKD患者发生院内死亡事件相关。由此绘制列线图，建模组AUC为0.800(95%

: 0.768~0.832)，敏感度为0.693，特异度为0.760；内部验证组预测模型的AUC为0.724(95%

: 0.663~0.785)，敏感度为0.689，特异度为0.682；外部验证组AUC为0.858(95%

: 0.809~0.907)、敏感度为0.800，特异度为0.787，表明该模型具有较好预测能力。建模组与外部验证组Hosmer-Lemeshow检验校准曲线显示模型校准能力良好(χ

=5.975

=0.650；χ

=7.891

=0.444)。

结论

CAD合并CKD患者院内死亡与年龄、抗血小板药物、他汀类药物、白细胞计数、MACE、eGFR、磷酸盐、碳酸氢盐、PCI或CABG及氯化物有关，构建的列线图对临床CAD合并CKD患者的死亡风险具有良好的预测价值。

Abstract

Objective

To explore the risk factors of in-hospital death in patients with coronary artery disease (CAD) complicated with chronic kidney disease (CKD)

and establish a mortality risk prediction model and verify its predictive efficacy.

Methods

The data of CAD patients with CKD diagnosed by Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) from 2008 to 2019 were collected and retrospectively analyzed. The data of 1 609 patients were divided into the modeling group (1 126 cases) and the internal validation group (483 cases) in a ratio of 7 ∶3. The in-hospital death was used as the outcome event. The variables were screened by univariate and multivariate logistic regression analyses

and a nomogram for predicting the risk of in-hospital death was drawn. The model was then assessed with receiver operating characteristic (ROC) curve analysis for area under curve (AUC)

sensitivity and specificity

Hosmer-Lemeshow test

and calibration curve analysis

and the model was internally validated. A total of 287 patients with CAD and CKD admitted in Chongqing General Hospital from January to October 2023 were recruited as the external validation group

and the model was externally validated.

Results

Multiva

riate logistic regression analysis showed that age (

=1.031

95%

: 1.015~1.049)

antiplatelet drugs (

=0.520

95%

: 0.342~0.792)

statins (

=0.312

95%

: 0.212~0.460)

WBC count (

=1.035

95%

: 1.016~1.057)

major adverse cardiovascular events (MACE) (

=2.417

95%

: 1.330~4.643)

eGFR (

=0.986

95%

: 0.978~0.995)

phosphate (

=1.226

95%

: 1.104~1.362)

bicarbonate (

=0.938

95%

: 900~0.977)

chloride (

=0.969

95%

: 0.942~0.997)

and PCI or CABG (

=0.362

95%

: 0.174~0.685) were significantly associated with in-hospital mortality in patients with CAD and CKD. The established nomogram showed an AUC value of 0.800 (95%

: 0.768~0.832)

a sensitivity of 0.693

and a specificity of 0.760 in prediction of in-hospital death. In the internal validation

the AUC value was 0.724 (95%

: 0.663~0.785)

the sensitivity was 0.689

and the specificity was 0.682; and the above values were 0.858 (95%

: 0.809~0.907)

0.800

and 0.787

respectively in the external validation

indicating that the model had good predictive ability. Hosmer-Lemeshow test of the modeling group and the external validation group showed that the model had good calibration ability (

Chi

-square=5.975

=0.650;

Chi

-square=7.891

=0.444).

Conclusion

The in-hospital mortality of CAD patients with CKD is related to age

antiplatelet drugs

statins

WBC count

MACE incidence

eGFR

phosphate

bicarbonate

PCI or CABG

and chloride. Our established nomogram has a good predictive value for the risk of death in CAD patients with CKD.

关键词

Keywords

references

TSAO C W, ADAY A W, ALMARZOOQ Z I, et al . Heart disease and stroke statistics-2023 update: a report from the American heart association[J ] . Circulation, 2023, 147(8): e93-e621. DOI: 10.1161/CIR.0000000000001123.

MATSUSHITA K, BALLEW S H, WANG A Y M, et al . Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease[J ] . Nat Rev Nephrol, 2022, 18(11): 696-707. DOI: 10.1038/s41581-022-00616-6.

MA J L, BIAN S Y, GAO M. Prediction of outcomes through cystatin C and cTnI in elderly type 2 myocardial infarction patients[J]. Clin Interv Aging, 2023, 1(5)8: 1415-1422. DOI: 10.2147/CIA.S416372.

SIDDIQUI M U, JUNARTA J, MARHEFKA G D. Coronaryrevascularization versus optimal medical therapy in renal transplant candidates with coronary artery disease: a systematic review and meta-analysis[J]. J Am Heart Assoc, 2022, 11(4): e023548. DOI: 10.1161/JAHA.121.023548.

CHEN X, ZHANG X H, YAN Y F, et al . The better option of revascularization in complex coronary artery disease patients complicate with chronic kidney disease: a review and meta-analysis[J ] . Curr Probl Cardiol, 2021, 46(9): 100886. DOI: 10.1016/j.cpcardiol.2021.100886.

COLLINS G S, REITSMA J B, ALTMAN D G, et al . Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement[J ] . Eur J Clin Invest, 2015, 45(2): 204-214. DOI: 10.1111/eci.12376.

VIRANI S S, NEWBY L K, ARNOLD S V, et al . 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA guideline for the management of patients with chronic coronary disease: a report of the American heart association/american college of cardiology joint committee on clinical practice guidelines[J ] . Circulation, 2023, 148(9): e9-e119. DOI : 10.1161/CIR.0000000000001168.

LEVEY A S, DE JONG P E, CORESH J, et al . The definition, classification, and prognosis of chronic kidney disease: a KDIGO controversies conference report[J ] . Kidney Int, 2011, 80(1): 17-28. DOI: 10.1038/ki.2010.483.

LEVEY A S, STEVENS L A, SCHMID C H, et al . A new equation to estimate glomerular filtration rate[J ] . Ann Intern Med, 2009, 150(9): 604-612. DOI: 10.7326/0003-4819-150-9-200905050-00006.

FU Y, SUN H, GUO Z S, et al . A risk score model to predict in-hospital mortality of patients with end-stage renal disease and acute myocardial infarction[J ] . Intern Emerg Med, 2021, 16(4): 905-912. DOI: 10.1007/s11739-020-02529-3.

FU S H, YI S Y, ZHU B, et al . Prevalence, clinical predictors, and prognostic impact of chronic renal insufficiency in very old Chinese patients with coronary artery disease[J ] . Aging Clin Exp Res, 2013, 25(4): 385-391. DOI: 10.1007/s40520-013-0059-0.

MAJMUNDAR M, IBARRA G, KUMAR A, et al . Invasive versus medical management in patients with chronic kidney disease and non-ST-segment-el evation myocardial infarction[J ] . J Am Heart Assoc, 2022, 11(12): e025205. DOI: 10.1161/JAHA.121.025205.

ZHENG S J, YANG J R, TAN T C, et al . Dialysis therapy and mortality in older adults with heart failure and advanced chronic kidney disease: a high-dimensional propensity-matched cohort study[J ] . PLoS One, 2022, 17(1): e0262706. DOI: 10.1371/journal.pone.0262706.

NIMMO C, WRIGHT M, GOLDSMITH D. Management of atrial fibrillation in chronic kidney disease: double trouble[J]. Am Heart J, 2013, 166(2): 230-239. DOI: 10.1016/j.ahj.2013.05.010.

VIEGAS C, ARAÚJO N, MARREIROS C, et al . The interplay between mineral metabolism, vascular calcification and inflammation in Chronic Kidney Disease (CKD): challenging old concepts with new facts[J ] . Aging, 2019, 11(12): 4274-4299. DOI: 10.18632/aging.102046.

TONELLI M, SACKS F, PFEFFER M, et al . Relation between serum phosphate level and cardiovascular event rate in people with coronary disease[J ] . Circulation, 2005, 112(17): 2627-2633. DOI: 10.1161/CIRCULATIONAHA.105.553198.

DRVEKE T B, MASSY Z A. Atherosclerosis in CKD: differences from the general population[J]. Nat Rev Nephrol, 2010, 6(12): 723-735. DOI: 10.1038/nrneph.2010.143.

Writing Group for the CKD Prognosis Consortium, GRAMS M E, CORESH J, et al . Estimated glomerular filtration rate, albuminuria, and adverse outcomes: an individual-participant data meta-analysis[J ] . JAMA, 2023, 330(13): 1266-1277. DOI: 10.1001/jama.2023.17002.

DE LUCA L, CAPPADONA F, TEMPORELLI P L, et al . Impact of eGFR rate on 1-year all-cause mortality in patients with stable coronary artery disease[J ] . Eur J Intern Med, 2022, 101: 98-105. DOI: 10.1016/j.ejim.2022.04.021.

CHENG Y L, HUANG S C, HO M Y, et al . Effect of sodium bicarbonate on cardiovascular outcome and mortality in patients with advanced chronic kidney disease[J ] . Front Pharmacol, 2023, 14: 1146668. DOI: 10.3389/fphar.2023.1146668.

DI IORIO B R, BELLASI A, RAPHAEL K L, et al . Treatment of metabolic acidosis with sodium bicarbonate delays progression of chronic kidney disease: the UBI Study[J ] . J Nephrol, 2019, 32(6): 989-1001. DOI: 10.1007/s40620-019-00656-5.

KHATRI M, ZITOVSK Y J, LEE D L, et al . The association between serum chloride levels and chronic kidney disease progression: a cohort study[J ] . BMC Nephrol, 2020, 21(1): 165. DOI: 10.1186/s12882-020-01828-3.

ARAI Y, KANDA E, IIMORI S, et al . Low white blood cell count is independently associated with chronic kidney disease progression in the elderly: the CKD-ROUTE study[J ] . Clin Exp Nephrol, 2018, 22(2): 291-298. DOI: 10.1007/s10157-017-1441-6.

CHEN T, DENG Y L, GONG R. Cardiovascular protection of aspirin in chronic kidney disease patients: an updated systematic review and meta-analysis[J]. Curr Vasc Pharmacol, 2023. DOI: 10.2174/15701611216662305301 54647.

CARRERO J J, VARENHORST C, JENSEVIK K, et al . Long-term versus short-term dual antiplatelet therapy was similarly associated with a lower risk of death, stroke, or infarction in patients with acute coronary syndrome regardless of underlying kidney disease[J ] . Kidney Int, 2017, 91(1): 216-226. DOI: 10.1016/j.kint.2016.09.014.

KANNAN, POONGKUNRAN C, MEDINA R, et al . Coronary revascularization in chronic and end-stage renal disease: a systematic review and meta-analysis[J ] . Am J Ther, 2016, 23(1): e16-e28. DOI: 10.1097/MJT.0000000000000089.

HSIAO C C, YEH J K, LI Y R, et al . Statin uses in adults with non-dialysis advanced chronic kidney disease: focus on clinical outcomes of infectious and cardiovascular diseases[J ] . Front Pharmacol, 2022, 13: 996237. DOI: 10.3389/fphar.2022.996237.

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