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Role of Intra-abdominal Pressure in Early Acute Kidney Injury: A Prospective Cohort Study in Critically Ill Obstetric Patients
AIMS: This prospective cohort study evaluated intra-abdominal pressure (IAP) and its role in causing acute kidney injury (AKI) in critically ill obstetric patients and utility of urinary neutrophil gelatinase-associated lipocalin (NGAL) to predict AKI. METHODS: A total of 50 eligible obstetric patie...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6108294/ https://www.ncbi.nlm.nih.gov/pubmed/30186012 http://dx.doi.org/10.4103/ijccm.IJCCM_170_18 |
Sumario: | AIMS: This prospective cohort study evaluated intra-abdominal pressure (IAP) and its role in causing acute kidney injury (AKI) in critically ill obstetric patients and utility of urinary neutrophil gelatinase-associated lipocalin (NGAL) to predict AKI. METHODS: A total of 50 eligible obstetric patients admitted to our Intensive Care Unit were enrolled and daily IAP measured using indwelling Foley catheter. Early AKI was diagnosed as per the KDIGO criteria and urine assessed for NGAL using ELISA. RESULTS: AKI was seen in 54% and intra-abdominal hypertension (IAH) in 21% patients. In patients with and without AKI, there was statistically similar IAP on day 1 (P = 0.542) and day 2 (P = 0.907) as well as incidence of IAH (19% vs. 23%) (P = 0.766). Area under receiver operating characteristic curve (AUC) for IAP to predict early AKI was 0.499 (95% confidence interval [CI]: 0.325–0.673) (P = 0.992). Urinary NGAL concentration was significantly greater in patients with early AKI compared to those without (P = 0.006); AUC for urinary NGAL to detect early AKI was 0.734 (95% CI: 0.583–0.884) (P = 0.006) and optimal cutoff was 53.7 ng/ml. CONCLUSIONS: IAH and AKI are common in critically ill obstetric patients. While IAP does not correlate with early AKI, NGAL is useful to predict AKI. |
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