Establishment and evaluation of a predictive model for death risk in patients with acute kidney injury secondary to sepsis

doi:10.3877/cma.j.issn.1674-6880.2023.03.004

Abstract

Abstract:

Objective

To establish a nomogram model for prediction of 28-day death in patients with acute kidney injury (AKI) secondary to sepsis.

Methods

This study was a secondary analysis on the data of AKI patients undergoing continuous renal replacement therapy in the Dryad Free Open Database. The predictive factors were selected by Lasso regression, and the risk factors for 28-day death were analyzed by logistic regression. A nomogram predictive model was established using R language software based on the predictive factors. The efficacy and clinical value of the nomogram predictive model were evaluated using the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) and were compared with existing models. Kaplan-Meier curves were used to assess the survival of different stratification groups at 28 days and 90 days.

Results

A total of 798 patients with AKI secondary to sepsis were enrolled, and eight predictive factors were selected by the Lasso regression, including albumin, 24-hour phosphate ion concentration, sequential organ failure assessment (SOFA) score, serum creatinine, mean arterial pressure, urea nitrogen-creatinine ratio, systolic blood pressure and 2-hour urine output. Logistic regression analysis showed that albumin [odds ratio (OR) = 0.589, 95% confidence interval (CI) (0.393, 0.883), P = 0.010], 24-hour phosphate ion concentration [OR = 1.406, 95%CI (1.225, 1.613), P < 0.001], SOFA score [OR = 1.234, 95%CI (1.152, 1.321), P < 0.001], serum creatinine [OR = 0.773, 95%CI (0.658, 0.908), P = 0.002], urea nitrogen-creatinine ratio [OR = 1.017, 95%CI (1.001, 1.034), P = 0.037], systolic blood pressure [OR = 0.982, 95%CI (0.967, 0.998), P = 0.023], and 2-hour urine output [OR = 0.997, 95%CI (0.995, 0.999), P = 0.011] were independent risk factors for 28-day death in patients with AKI secondary to sepsis. A nomogram predictive model was established using the eight predictive factors. The area under ROC curves (0.839, 0.809, 0.618), goodness-of-fit of calibration curves and high threshold probability range of DCA curves (0.25-1.00, 0.35-1.00, 0.00-0.75) were performed on the nomogram model, Jung model and acute physiology and chronic health evaluation II model, and the nomogram model was superior to the other two models. The difference of overall survival among three stratification groups at 28 days and 90 days based on the nomogram predictive model was statistically significant (χ² = 178.847, 180.665; both P < 0.001), and there were also significant differences between groups (all P < 0.001).

Conclusions

The nomogram predictive model shows good predictive efficacy on the risk of 28-day death in patients with AKI secondary to sepsis. Compared with the existing predictive tools, this model relies on fewer variables and shows better performance. Besides, the nomogram model has advantages in individualized, visualized and graphical prediction, which can provide assistance for early identification and early treatment to improve the outcomes of patients.

Key words: Sepsis, Acute kidney injury, Death risk, Predictive model

Xiaoqiao Mo, Zheying Hu, Donghua Liao, Tian Xie. Establishment and evaluation of a predictive model for death risk in patients with acute kidney injury secondary to sepsis[J]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2023, 16(03): 198-206.

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Figures/Tables 5

References 29

1	Uchino S, Kellum JA, Bellomo R, et al. Acute renal failure in critically ill patients: a multinational, multicenter study[J]. JAMA, 2005, 294 (7): 813-818.
2	Bagshaw SM, Uchino S, Bellomo R, et al. Septic acute kidney injury in critically ill patients: clinical characteristics and outcomes[J]. Clin J Am Soc Nephrol, 2007, 2 (3): 431-439.
3	Morgera S, Slowinski T, Melzer C, et al. Renal replacement therapy with high-cutoff hemofilters: impact of convection and diffusion on cytokine clearances and protein status[J]. Am J Kidney Dis, 2004, 43 (3): 444-453.
4	Lins RL, Elseviers MM, Van der Niepen P, et al. Intermittent versus continuous renal replacement therapy for acute kidney injury patients admitted to the intensive care unit: results of a randomized clinical trial[J]. Nephrol Dial Transplant, 2009, 24 (2): 512-518.
5	Miao H, Shi J, Wang C, et al. Continuous renal replacement therapy in pediatric severe sepsis: a propensity score-matched prospective multicenter cohort study in the PICU[J]. Crit Care Med, 2019, 47 (10): e806-e813.
6	Jung SY, Kwon J, Park S, et al. Phosphate is a potential biomarker of disease severity and predicts adverse outcomes in acute kidney injury patients undergoing continuous renal replacement therapy[J]. PLoS One, 2018, 13 (2): e0191290.
7	Mehta RL, Kellum JA, Shah SV, et al. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury[J]. Crit Care, 2007, 11 (2): R31.
8	Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315 (8): 801-810.
9	Mayeux PR, MacMillan-Crow LA. Pharmacological targets in the renal peritubular microenvironment: implications for therapy for sepsis-induced acute kidney injury[J]. Pharmacol Ther, 2012, 134 (2): 139-155.
10	Leedahl DD, Frazee EN, Schramm GE, et al. Derivation of urine output thresholds that identify a very high risk of AKI in patients with septic shock[J]. Clin J Am Soc Nephrol, 2014, 9 (7): 1168-1174.
11	Jaques DA, Spahr L, Berra G, et al. Biomarkers for acute kidney injury in decompensated cirrhosis: a prospective study[J]. Nephrology (Carlton), 2019, 24 (2): 170-180.
12	Priyanka P, Zarbock A, Izawa J, et al. The impact of acute kidney injury by serum creatinine or urine output criteria on major adverse kidney events in cardiac surgery patients[J]. J Thorac Cardiovasc Surg, 2021, 162 (1): 143-151.e7.
13	Burgess LG, Goyal N, Jones GM, et al. Evaluation of acute kidney injury and mortality after intensive blood pressure control in patients with intracerebral hemorrhage[J]. J Am Heart Assoc, 2018, 7 (8): e008439.
14	Fitzgerald JC, Ross ME, Thomas NJ, et al. Association of early hypotension in pediatric sepsis with development of new or persistent acute kidney injury[J]. Pediatr Nephrol, 2021, 36 (2): 451-461.
15	Walsh M, Devereaux PJ, Garg AX, et al. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension[J]. Anesthesiology, 2013, 119 (3): 507-515.
16	Monk TG, Bronsert MR, Henderson WG, et al. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery [published correction appears in Anesthesiology, 2016, 124 (3): 741-742][J]. Anesthesiology, 2015, 123 (2): 307-319.
17	Sun LY, Wijeysundera DN, Tait GA, et al. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery[J]. Anesthesiology, 2015, 123 (3): 515-523.
18	van Waes JA, van Klei WA, Wijeysundera DN, et al. Association between intraoperative hypotension and myocardial injury after vascular surgery[J]. Anesthesiology, 2016, 124 (1): 35-44.
19	Bae SJ, Lee SH, Yun SJ, et al. Comparison of IVC diameter ratio, BUN/creatinine ratio and BUN/albumin ratio for risk prediction in emergency department patients[J]. Am J Emerg Med, 2021 (47): 198-204.
20	Deng L, Wang C, Qiu S, et al. Association between blood urea nitrogen-to-creatinine ratio and three-month outcome in patients with acute ischemic stroke[J]. Curr Neurovasc Res, 2019, 16 (2): 166-172.
21	Wu KH, Shih HA, Hung MS, et al. The association between blood urea nitrogen to creatinine ratio and mortality in patients with upper gastrointestinal bleeding[J]. Arab J Gastroenterol, 2018, 19 (4): 143-147.
22	Qian H, Tang C, Yan G. Predictive value of blood urea nitrogen/creatinine ratio in the long-term prognosis of patients with acute myocardial infarction complicated with acute heart failure[J]. Medicine (Baltimore), 2019, 98 (11): e14845.
23	Uchino S, Bellomo R, Goldsmith D. The meaning of the blood urea nitrogen/creatinine ratio in acute kidney injury[J]. Clin Kidney J, 2012, 5 (2): 187-191.
24	陈敏华，朱峥，金君，等. B型利钠肽在预测重症患者急性肾损伤发生发展中的临床价值[J/CD].中华危重症医学杂志（电子版），2022，15（4）：279-284.
25	Patel A, Laffan MA, Waheed U, et al. Randomised trials of human albumin for adults with sepsis: systematic review and meta-analysis with trial sequential analysis of all-cause mortality [published correction appears in BMJ, 2014, (349): g4850][J]. BMJ, 2014 (349): g4561.
26	Padkins M, Breen T, Anavekar N, et al. Association between albumin level and mortality among cardiac intensive care unit patients[J]. J Intensive Care Med, 2021, 36 (12): 1475-1482.
27	Zhou H, Wang A, Meng X, et al. Low serum albumin levels predict poor outcome in patients with acute ischaemic stroke or transient ischaemic attack[J]. Stroke Vasc Neurol, 2021, 6 (3): 458-466.
28	Foley RN. Phosphate levels and cardiovascular disease in the general population[J]. Clin J Am Soc Nephrol, 2009, 4 (6): 1136-1139.
29	de Fornasari ML, Dos Santos Sens YA. Replacing phosphorus-containing food additives with foods without additives reduces phosphatemia in end-stage renal disease patients: a randomized clinical trial[J]. J Ren Nutr, 2017, 27 (2): 97-105.

组别	例数	年龄（岁）	性别（例，男/女）	体质量指数（kg/m²）	收缩压（mmHg， ± s）	舒张压（mmHg， ± s）	平均动脉压（mmHg， ± s）	Charlson合并症指数（ ± s）	APACHEⅡ评分（分， ± s）	SOFA评分（分， ± s）	2 h尿量（mL）
28 d生存组	300	65（54，74）	187/113	23.7（21.6，26.5）	117 ± 22	63 ± 15	80 ± 16	2.7 ± 2.1	26 ± 7	10 ± 4	54（15，130）
28 d死亡组	498	66（56，74）	307/191	23.2（20.4，25.6）	108 ± 20	60 ± 14	76 ± 14	3.4 ± 2.3	28 ± 8	13 ± 3	25（0，80）
χ²/t/Z值		0.925	0.014	2.633	6.011	2.927	4.343	4.143	4.676	10.110	5.973
P值		0.355	0.906	0.008	< 0.001	0.004	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
组别	例数	CRRT病因[例（%）]						CRRT剂量（mL/kg）	钾离子（mmol/L）	碳酸氢盐（mmol/L， ± s）	0 h磷酸根离子（mg/L）
组别	例数	少尿	代谢性酸中毒	容量超负荷	血尿	高钾血症	其他	CRRT剂量（mL/kg）	钾离子（mmol/L）	碳酸氢盐（mmol/L， ± s）	0 h磷酸根离子（mg/L）
28 d生存组	300	15（5.0）	53（17.7）	91（30.3）	63（21.0）	33（11.0）	45（15.0）	36.4（34.5，38.9）	4.50（4.10，5.20）	17 ± 5	155（117，196）
28 d死亡组	498	25（5.0）	131（26.3）	116（23.3）	121（24.3）	48（9.6）	57（11.4）	36.8（33.9，39.5）	4.50（4.00，5.25）	17 ± 6	177（130，228）
χ²/t/Z值		12.711						0.530	0.494	1.287	4.146
P值		0.026						0.596	0.621	0.199	< 0.001
组别	例数	24 h磷酸根离子（mg/L）	白细胞计数（× 10⁶/L）	血红蛋白（g/L）	尿素氮（mg/L）	血肌酐（mg/L）	白蛋白（g/L， ± s）	C反应蛋白（mg/L）	肾小球滤过率（mL/min）	尿素氮血肌酐比	C反应蛋白白蛋白比
28 d生存组	300	111（89，139）	12.19（8.87，19.29）	94（84，111）	470（340，700）	25.4（16.7，35.5）	27 ± 6	54（18，146）	23.2（15.6，38.5）	18.0（13.7，26.6）	18.6（6.6，54.2）
28 d死亡组	498	143（108，190）	10.69（4.79，18.30）	94（82，106）	530（360，750）	22.5（16.3，31.1）	25 ± 5	74（20，176）	28.3（18.7，39.2）	22.2（16.5，75.3）	29.4（8.0，75.3）
χ²/t/Z值		7.994	3.618	1.663	1.905	2.638	5.375	1.857	2.788	4.823	2.727
P值		< 0.001	< 0.001	0.096	0.057	0.008	< 0.001	0.063	0.005	< 0.001	0.006

组别	例数	年龄（岁）	性别（例，男/女）	体质量指数（kg/m²）	收缩压（mmHg， ± s）	舒张压（mmHg， ± s）	平均动脉压（mmHg， ± s）	Charlson合并症指数（ ± s）	APACHEⅡ评分（分， ± s）	SOFA评分（分， ± s）	2 h尿量（mL）
28 d生存组	300	65（54，74）	187/113	23.7（21.6，26.5）	117 ± 22	63 ± 15	80 ± 16	2.7 ± 2.1	26 ± 7	10 ± 4	54（15，130）
28 d死亡组	498	66（56，74）	307/191	23.2（20.4，25.6）	108 ± 20	60 ± 14	76 ± 14	3.4 ± 2.3	28 ± 8	13 ± 3	25（0，80）
χ²/t/Z值		0.925	0.014	2.633	6.011	2.927	4.343	4.143	4.676	10.110	5.973
P值		0.355	0.906	0.008	< 0.001	0.004	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
组别	例数	CRRT病因[例（%）]						CRRT剂量（mL/kg）	钾离子（mmol/L）	碳酸氢盐（mmol/L， ± s）	0 h磷酸根离子（mg/L）
组别	例数	少尿	代谢性酸中毒	容量超负荷	血尿	高钾血症	其他	CRRT剂量（mL/kg）	钾离子（mmol/L）	碳酸氢盐（mmol/L， ± s）	0 h磷酸根离子（mg/L）
28 d生存组	300	15（5.0）	53（17.7）	91（30.3）	63（21.0）	33（11.0）	45（15.0）	36.4（34.5，38.9）	4.50（4.10，5.20）	17 ± 5	155（117，196）
28 d死亡组	498	25（5.0）	131（26.3）	116（23.3）	121（24.3）	48（9.6）	57（11.4）	36.8（33.9，39.5）	4.50（4.00，5.25）	17 ± 6	177（130，228）
χ²/t/Z值		12.711						0.530	0.494	1.287	4.146
P值		0.026						0.596	0.621	0.199	< 0.001
组别	例数	24 h磷酸根离子（mg/L）	白细胞计数（× 10⁶/L）	血红蛋白（g/L）	尿素氮（mg/L）	血肌酐（mg/L）	白蛋白（g/L， ± s）	C反应蛋白（mg/L）	肾小球滤过率（mL/min）	尿素氮血肌酐比	C反应蛋白白蛋白比
28 d生存组	300	111（89，139）	12.19（8.87，19.29）	94（84，111）	470（340，700）	25.4（16.7，35.5）	27 ± 6	54（18，146）	23.2（15.6，38.5）	18.0（13.7，26.6）	18.6（6.6，54.2）
28 d死亡组	498	143（108，190）	10.69（4.79，18.30）	94（82，106）	530（360，750）	22.5（16.3，31.1）	25 ± 5	74（20，176）	28.3（18.7，39.2）	22.2（16.5，75.3）	29.4（8.0，75.3）
χ²/t/Z值		7.994	3.618	1.663	1.905	2.638	5.375	1.857	2.788	4.823	2.727
P值		< 0.001	< 0.001	0.096	0.057	0.008	< 0.001	0.063	0.005	< 0.001	0.006

变量	回归系数	标准误	Wald值	OR值	95%CI	P值
白蛋白（g/L）	-0.529	0.206	6.572	0.589	0.393 ~ 0.883	0.010
24 h磷酸根离子（mg/L）	0.341	0.070	23.526	1.406	1.225 ~ 1.613	< 0.001
SOFA评分（分）	0.210	0.035	36.207	1.234	1.152 ~ 1.321	< 0.001
血肌酐（mg/L）	-0.257	0.082	9.797	0.773	0.658 ~ 0.908	0.002
平均动脉压（mmHg）	-0.007	0.012	0.348	0.993	0.971 ~ 1.016	0.555
尿素氮肌酐比	0.017	0.008	4.368	1.017	1.001 ~ 1.034	0.037
收缩压（mmHg）	-0.018	0.008	5.142	0.982	0.967 ~ 0.998	0.023
2 h尿量（mL）	-0.003	0.001	6.400	0.997	0.995 ~ 0.999	0.011
常量	1.017	1.017	0.945	2.766	-	0.331

变量	回归系数	标准误	Wald值	OR值	95%CI	P值
白蛋白（g/L）	-0.529	0.206	6.572	0.589	0.393 ~ 0.883	0.010
24 h磷酸根离子（mg/L）	0.341	0.070	23.526	1.406	1.225 ~ 1.613	< 0.001
SOFA评分（分）	0.210	0.035	36.207	1.234	1.152 ~ 1.321	< 0.001
血肌酐（mg/L）	-0.257	0.082	9.797	0.773	0.658 ~ 0.908	0.002
平均动脉压（mmHg）	-0.007	0.012	0.348	0.993	0.971 ~ 1.016	0.555
尿素氮肌酐比	0.017	0.008	4.368	1.017	1.001 ~ 1.034	0.037
收缩压（mmHg）	-0.018	0.008	5.142	0.982	0.967 ~ 0.998	0.023
2 h尿量（mL）	-0.003	0.001	6.400	0.997	0.995 ~ 0.999	0.011
常量	1.017	1.017	0.945	2.766	-	0.331

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