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预测新生儿和婴儿持续肾脏替代治疗的死亡率和短期预后。

Predicting mortality and short-term outcomes of continuous kidney replacement therapies in neonates and infants.

作者信息

Deja Anna, Deja Kamil, Cappoli Andrea, Labbadia Raffaella, Baptista Rute Baeta, Arslan Zainab, Oh Jun, Bayazit Aysun Karabay, Yildizdas Dincer, Schmitt Claus Peter, Tkaczyk Marcin, Cvetkovic Mirjana, Kostic Mirjana, Jankauskiene Augustina, Virsilas Ernestas, Longo Germana, Vidal Enrico, Mir Sevgi, Bulut Ipek Kaplan, Pasini Andrea, Paglialonga Fabio, Montini Giovanni, Yilmaz Ebru, Correia-Costa Liane, Teixeira Ana, Schaefer Franz, Guzzo Isabella

机构信息

Department of Pediatrics and Nephrology, Medical University of Warsaw, Warsaw, Poland.

Doctoral School, Medical University of Warsaw, Warsaw, Poland.

出版信息

Nephrol Dial Transplant. 2025 Aug 27. doi: 10.1093/ndt/gfaf173.

Abstract

BACKGROUND

Continuous kidney replacement therapy (CKRT) has emerged as a valuable treatment option in critically ill neonates and infants with acute kidney injury (AKI) requiring dialysis. In this population, we apply Artificial Intelligence (AI) to identify factors influencing mortality and short-term adverse kidney outcomes.

METHODS

The study involved neonates and infants included in the EurAKId registry (NCT02960867), who underwent CKRT treatment. Using the AI XGBoost models, we identified key clinical factors associated with short-term outcomes: mortality before hospital discharge, as well as proteinuria at discharge. We considered the patients' clinical characteristics, anthropometric features, and CKRT technical settings.

RESULTS

The study comprised 95 patients, 31.6% neonates and 68.4% infants with a median age at hospital admission of 1 month (interquartile range, IQR 0-7 months). Ten children were born prematurely. The overall mortality rate was 47.3% and did not differ significantly between neonates and infants (53.3% vs 44.4% respectively, p = 0.422). The XGBoost model for predicting mortality had the accuracy of 59.53 ± 0.96% and AUC of 0.64 ± 0.11. Lower urine output at CKRT initiation, larger serum creatinine (SCr) rise, longer time to dialysis initiation, and lower blood pressure were associated with increased risk of mortality. Proteinuria at hospital discharge was present in 30.6% of survivors. The XGBoost model for predicting proteinuria had the accuracy of 79.11 ± 2.46% and AUC (0.74 ± 0.04). Higher SCr concentrations at hospital admission and at CKRT start, as well as primary kidney disease were the most important risk factors for proteinuria.

CONCLUSION

We propose the XGBoost models for identifying factors associated with short-term outcomes of CKRT in neonates and infants. Lower urine output at CKRT start, more severe AKI progression and longer time to CKRT initiation might be important risk factors for mortality in infants and neonates. Primary kidney disease and related biochemical parameters are strong predictors of proteinuria at hospital discharge.

摘要

背景

连续性肾脏替代治疗(CKRT)已成为患有急性肾损伤(AKI)且需要透析的危重新生儿和婴儿的一种有价值的治疗选择。在这一人群中,我们应用人工智能(AI)来识别影响死亡率和短期不良肾脏结局的因素。

方法

该研究纳入了EurAKId注册研究(NCT02960867)中接受CKRT治疗的新生儿和婴儿。使用AI XGBoost模型,我们确定了与短期结局相关的关键临床因素:出院前死亡率以及出院时蛋白尿。我们考虑了患者的临床特征、人体测量特征和CKRT技术参数。

结果

该研究包括95例患者,其中31.6%为新生儿,68.4%为婴儿,入院时中位年龄为1个月(四分位间距,IQR 0 - 7个月)。10名儿童为早产儿。总体死亡率为47.3%,新生儿和婴儿之间无显著差异(分别为53.3%和44.�%,p = 0.422)。预测死亡率的XGBoost模型准确率为59.53 ± 0.96%,曲线下面积(AUC)为0.64 ± 0.11。CKRT开始时尿量较低、血清肌酐(SCr)升高幅度较大、开始透析时间较长以及血压较低与死亡风险增加相关。30.6%的幸存者出院时存在蛋白尿。预测蛋白尿的XGBoost模型准确率为79.11 ± 2.46%,AUC为(0.74 ± 0.04)。入院时和CKRT开始时较高的SCr浓度以及原发性肾脏疾病是蛋白尿最重要的危险因素。

结论

我们提出了XGBoost模型来识别与新生儿和婴儿CKRT短期结局相关的因素。CKRT开始时尿量较低、AKI进展更严重以及开始CKRT的时间较长可能是婴儿和新生儿死亡的重要危险因素。原发性肾脏疾病和相关生化参数是出院时蛋白尿的强有力预测指标。

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