A Novel Algorithm With Paired Predictive Indexes to Stratify the Risk Levels of Neonates With Invasive Bacterial Infections: A Multicenter Cohort Study

• Published in: The Pediatric infectious disease journal Vol. 41; no. 4; p. e149
• Main Authors: Yin, Zhanghua, Chen, Yan, Zhong, Wenhua, Shan, Liqin, Zhang, Qian, Gong, Xiaohui, Li, Jing, Lei, Xiaoping, Zhou, Qin, Zhao, Youyan, Chen, Chao, Zhang, Yongjun
• Format: Journal Article
• Language: English
• Published: United States 01.04.2022
• Subjects: Algorithms; Bacterial Infections - diagnosis; C-Reactive Protein - analysis; Humans; Infant, Newborn; Predictive Value of Tests; Procalcitonin; Retrospective Studies
• ISSN: 1532-0987
• DOI: 10.1097/INF.0000000000003437

Our aim was to develop a predictive model comprising clinical and laboratory parameters for early identification of full-term neonates with different risks of invasive bacterial infections (IBIs). We conducted a retrospective study including 1053 neonates presenting in 9 tertiary hospitals in China from January 2010 to August 2019. An algorithm with paired predictive indexes (PPIs) for risk stratification of neonatal IBIs was developed. Predictive performance was validated using k-fold cross-validation. Overall, 166 neonates were diagnosed with IBIs (15.8%). White blood cell count, C-reactive protein level, procalcitonin level, neutrophil percentage, age at admission, neurologic signs, and ill-appearances showed independent associations with IBIs from stepwise regression analysis and combined into 23 PPIs. Using 10-fold cross-validation, a combination of 7 PPIs with the highest predictive performance was picked out to construct an algorithm. Finally, 58.1% (612/1053) patients were classified as low-risk cases. The sensitivity and negative predictive value of the algorithm were 95.3% (95% confidence interval: 91.7-98.3) and 98.7% (95% confidence interval: 97.8-99.6), respectively. An online calculator based on this algorithm was developed for clinical use. The new algorithm constructed for this study was a valuable tool to screen neonates with suspected infection. It stratified risk levels of IBIs and had an excellent predictive performance.