A Prediction Nomogram for Acute Kidney Injury in Very-Low-Birth-Weight Infants: A Retrospective Study

Qian Hu; Yuan Shi; Zi-Yu Hua; Lei Bao; Fang Li; Hong Wei; Ping Song; He-Jia Ou-Yang; Qiu Li; Mo Wang

doi:10.3389/fped.2020.575097

A Prediction Nomogram for Acute Kidney Injury in Very-Low-Birth-Weight Infants: A Retrospective Study

Front Pediatr. 2021 Jan 15:8:575097. doi: 10.3389/fped.2020.575097. eCollection 2020.

Authors

Qian Hu¹, Yuan Shi², Zi-Yu Hua², Lei Bao², Fang Li², Hong Wei², Ping Song³, He-Jia Ou-Yang⁴, Qiu Li¹, Mo Wang¹

Affiliations

¹ Department of Nephrology, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Infection and Immunity, Chongqing, China.
² Department of Neonatology, Children's Hospital of Chongqing Medical University, Chongqing, China.
³ Children's Medical Large Data Intelligent Application as University Engineering Research Center in Chongqing, Chongqing, China.
⁴ Information Center, Children's Hospital of Chongqing Medical University, Chongqing, China.

Abstract

Background and objective: Acute kidney injury (AKI) is recognized as an independent predictor for mortality in very-low-birth-weight (VLBW) infants and is reported to have a high incidence. In this study, we sought to identify the predictors for AKI in VLBW infants and thereby develop a prediction nomogram for the early detection and management of VLBW infants at high risk of developing AKI. Methods: We designed a retrospective study wherein we investigated the baseline hospitalization data of VLBW infants treated at our hospital between January 2012 and October 2018. Independent predictors of AKI in VLBW infants, as identified by multivariate logistic regression, were incorporated into a model. Hosmer-Lemeshow test was used to test the goodness of fit of the model, and a receiver operating characteristic (ROC) curve was plotted to assess the discriminative ability of the model. The model was internally validated using the 10-fold cross-validation method. A nomogram was plotted to predict the risk of AKI in VLBW infants on the basis of the results of multivariate logistic regression analysis. Results: We investigated the data of 604 VLBW infants, of which 144 (23.8%) developed AKI; in 111 (77.1%) of these infants, AKI occurred within 7 days of birth. Multivariate logistic regression analysis identified the following as predictive factors for AKI in VLBW infants: gestational age, red blood cell count within 3 days of birth, serum calcium concentration within 3 days of birth, maternal age of ≥35 years, and pulmonary arterial hypertension or myocardial injury. Furthermore, the nomogram was found to be effective in estimating the risk of AKI in VLBW infants, with an area under the curve (AUC) of 0.794 [95% confidence interval (CI): 0.754-0.834; P < 0.001]. Internal validation done by cross-validation showed that the average AUC was 0.788. Conclusion: The nomogram developed in this study was found to be sensitive and specific for the preoperative prediction of AKI in VLBW infants, as per the Kidney Disease: Improving Global Outcomes (KDIGO) criteria modified for neonates.

Keywords: acute kidney injury; prediction model; prediction nomogram; predictor; very-low-birth-weight infants.