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Development and Validation of a Model to Predict Acute Kidney Injury in Hospitalized Patients With Cirrhosis.

ABSTRACT: OBJECTIVES:Acute kidney injury (AKI) is a common complication in hospitalized patients with cirrhosis which contributes to morbidity and mortality. Improved prediction of AKI in this population is needed for prevention and early intervention. We developed a model to identify hospitalized patients at risk for AKI. METHODS:Admission data from a prospective cohort of hospitalized patients with cirrhosis without AKI on admission (n = 397) was used for derivation. AKI development in the first week of admission was captured. Independent predictors of AKI on multivariate logistic regression were used to develop the prediction model. External validation was performed on a separate multicenter cohort (n = 308). RESULTS:In the derivation cohort, the mean age was 57 years, the Model for End-Stage Liver Disease score was 17, and 59 patients (15%) developed AKI after a median of 4 days. Admission creatinine (OR: 2.38 per 1 mg/dL increase [95% CI: 1.47-3.85]), international normalized ratio (OR: 1.92 per 1 unit increase [95% CI: 1.92-3.10]), and white blood cell count (OR: 1.09 per 1 × 10/L increase [95% CI: 1.04-1.15]) were independently associated with AKI. These variables were used to develop a prediction model (area underneath the receiver operator curve: 0.77 [95% CI: 0.70-0.83]). In the validation cohort (mean age of 53 years, Model for End-Stage Liver Disease score of 16, and AKI development of 13%), the area underneath the receiver operator curve for the model was 0.70 (95% CI: 0.61-0.78). DISCUSSION:A model consisting of admission creatinine, international normalized ratio, and white blood cell count can identify patients with cirrhosis at risk for in-hospital AKI development. On further validation, our model can be used to apply novel interventions to reduce the incidence of AKI among patients with cirrhosis who are hospitalized.

SUBMITTER: Patidar KR

PROVIDER: S-EPMC6775340 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Development and Validation of a Model to Predict Acute Kidney Injury in Hospitalized Patients With Cirrhosis.

Patidar Kavish R KR Xu Chenjia C Shamseddeen Hani H Cheng Yao-Wen YW Ghabril Marwan S MS Mukthinuthalapati V V Pavan K VVPK Fricker Zachary P ZP Akinyeye Samuel S Nephew Lauren D LD Desai Archita P AP Anderson Melissa M El-Achkar Tarek M TM Chalasani Naga P NP Orman Eric S ES

Clinical and translational gastroenterology 20190901 9

<h4>Objectives</h4>Acute kidney injury (AKI) is a common complication in hospitalized patients with cirrhosis which contributes to morbidity and mortality. Improved prediction of AKI in this population is needed for prevention and early intervention. We developed a model to identify hospitalized patients at risk for AKI.<h4>Methods</h4>Admission data from a prospective cohort of hospitalized patients with cirrhosis without AKI on admission (n = 397) was used for derivation. AKI development in th ...[more]

PMID: 31478958

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Project description:The significance of minimal increases in serum creatinine below the levels indicative of the acute kidney injury (AKI) stage is not well established. We aimed to investigate the influence of pre-stage AKI (pre-AKI) on clinical outcomes. We enrolled a total of 21,261 patients who were admitted to the Seoul National University Bundang Hospital from January 1, 2013 to December 31, 2013. Pre-AKI was defined as a 25-50% increase in peak serum creatinine levels from baseline levels during the hospital stay. In total, 5.4% of the patients had pre-AKI during admission. The patients with pre-AKI were predominantly female (55.0%) and had a lower body weight and lower baseline levels of serum creatinine (0.63 ± 0.18 mg/dl) than the patients with AKI and the patients without AKI (P < 0.001). The patients with pre-AKI had a higher prevalence of diabetes mellitus (25.1%) and malignancy (32.6%). The adjusted hazard ratio of in-hospital mortality for pre-AKI was 2.112 [95% confidence interval (CI), 1.143 to 3.903]. In addition, patients with pre-AKI had an increased length of stay (7.7 ± 9.7 days in patients without AKI, 11.4 ± 11.4 days in patients with pre-AKI, P < 0.001) and increased medical costs (4,061 ± 4,318 USD in patients without AKI, 4,966 ± 5,099 USD in patients with pre-AKI, P < 0.001) during admission. The adjusted hazard ratio of all-cause mortality for pre-AKI during the follow-up period of 2.0 ± 0.6 years was 1.473 (95% CI, 1.228 to 1.684). Although the adjusted hazard ratio of pre-AKI for overall mortality was not significant among the patients admitted to the surgery department or who underwent surgery, pre-AKI was significantly associated with mortality among the non-surgical patients (adjusted HR 1.542 [95% CI, 1.330 to 1.787]) and the patients admitted to the medical department (adjusted HR 1.384 [95% CI, 1.153 to 1.662]). Pre-AKI is associated with increased mortality, longer hospital stay, and increased medical costs during admission. More attention should be paid to the clinical significance of pre-AKI.

Project description:IntroductionAcute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients.MethodsData from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined.ResultsA predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774-0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737-0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and validation sets. DCA suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score <0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, p < 0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6 vs. 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52).ConclusionsThe newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.

Project description:ObjectiveWe aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients.MethodsClinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model.ResultsThe development cohort enrolled 730 patients with a median follow-up time of 87 (40-98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043-1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951-0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678-13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930-0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287-3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p < 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839-0.921) and 0.798 (95% CI = 0.720-0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability.ConclusionWe developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.

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Development and Validation of a Model to Predict Acute Kidney Injury in Hospitalized Patients With Cirrhosis.

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Development and Validation of a Model to Predict Acute Kidney Injury in Hospitalized Patients With Cirrhosis.

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