Dataset Information

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.

ABSTRACT:

Background

This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA).

Methods

A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n?=?532) and a validation set (30%, n?=?228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P?ResultsA multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706-0.789) in the training dataset, and 0.712 (95% CI, 0.642-0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P?>?0.05). DCA showed that our model was clinically useful.

Conclusion

The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis.

SUBMITTER: Fan T

PROVIDER: S-EPMC7931351 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.

Fan Tingting T Wang Haosheng H Wang Jiaxin J Wang Wenrui W Guan Haifei H Zhang Chuan C

BMC endocrine disorders 20210304 1

<h4>Background</h4>This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA).<h4>Methods</h4>A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to ...[more]

PMID: 33663489

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Project description:BackgroundAcute kidney injury (AKI) due to Diabetic Ketoacidosis (DKA) is rather common. Novel biomarkers to diagnose AKI are being increasingly used in different settings. The use of urinary Neutrophil Gelatinase-Associated Lipocalin (uNGAL) in predicting persistent AKI in pediatric DKA cases is still not thoroughly investigated.MethodsThis was a secondary analysis of Saline versus Plasma-Lyte in Ketoacidosis (SPinK) trial data; 66 children (> 1 month-12 years) with DKA, defined by the International Society for Pediatric and Adolescent Diabetes (ISPAD), were analyzed. Children with cerebral edema, chronic kidney disease and those who received pre-referral fluids and/or insulin were excluded. uNGAL and urine NGAL-creatinine ratio (uNCR) at 0 and 24 h were measured in all. Persistent AKI was defined as a composite outcome of continuance of AKI defined by the Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3 beyond 48 h from AKI onset, progression of AKI from either KDIGO stage 0 or 1 to a worse stage, need of renal replacement therapy or death.Main outcomesThirty-five (53%) children had AKI at admission; 32 (91.4%) resolved within 48 h. uNGAL was significantly higher in the AKI group at admission [79.8 ± 27.2 vs 54.6 ± 22.0, p = 0.0002] and at 24 h [61.4 ± 28.3 vs 20.2 ± 14.5, p = 0.0003]. Similar trend was observed with uNCR at admission [6.7 ± 3.7 vs 4.1 ± 2.6, p = 0.002] and at 24 h [6.3 ± 2.5 vs 1.2 ± 1.0, p = 0.01]. Furthermore, uNGAL at admission showed a moderate positive linear correlation with serum creatinine. Additionally, elevated uNGAL at 0 and 24 h correlated with corresponding KDIGO stages. Admission uNGAL >88 ng/ml and uNCR of >11.3 ng/mg had a sensitivity of 66% and 67%, specificity of 76% and 95%, and Area under the receiver operating characteristic curve (AUC) of 0.78 and 0.89 respectively for predicting persistent AKI at 48 h.ConclusionsMajority of AKI resolved with fluid therapy. While uNGAL and uNCR both correlated with serum creatinine and AKI stages, serial uNCR was a better predictor of persistent AKI than uNGAL alone. However, feasibility of routine uNGAL measurement to predict persistent AKI in DKA needs further elucidation.Trial registrationThis was a secondary analysis of the data of SPinK trial [CTRI/2018/05/014042 ( ctri.nic.in )].

Project description:BACKGROUND:We aimed to develop a nomogram based on preprocedural features for early prediction of acute kidney injury (AKI) and to assess the prognosis in patients after radical and partial nephrectomy. METHODS:The study included a development cohort of 1111 patients who were treated between June 2012 and June 2017 and an additional validation cohort of 356 patients who were treated between July 2017 and June 2018. Stepwise regression and logistic regression analyses were used to evaluate the association between predictors and AKI. Incorporating all independent predictors, a nomogram for postoperative AKI was developed and externally validated. Patients were followed up for 5 years to assess renal function, acute kidney disease (AKD), chronic kidney disease (CKD), hospital readmission and mortality were key prognosis we focused on. RESULTS:After multivariate logistic regression, radical nephrectomy (odds ratio (OR)?=?3.57, p?<?0.001), aspirin (OR?=?1.79, p?=?0.008), systolic blood pressure (OR?=?1.41, p?=?0.004), triglyceride (OR?=?1.26, p?=?0.024), and alkaline phosphatase (OR?=?1.75, p?=?0.034) were independent risk factors for postoperative AKI, while albumin (OR?=?0.72, p?=?0.031) was a protective factor for postoperative AKI. Patients with a higher estimated glomerular filtration rate (eGFR) (60-90?ml/min/1.73?m2, OR?=?0.41, p?=?0.004; ? 90?ml/min/1.73?m2, OR?=?0.37, p?<?0.001) were less prone to AKI than those with a lower eGFR (<?15?ml/min/1.73?m2). These predictors were all included in the final nomogram. The area under the receiver operating characteristics curve for the model were 0.77 (p?<?0.001) in the development cohort and 0.72 (p?<?0.001) in the validation cohort. The incidence of AKD and CKD were 27.12 and 18.64% in AKI group, which were much higher than those in no AKI group (p?<?0.001). CONCLUSIONS:The nomogram had excellent predictive ability and might have significant clinical implications for the early detection of AKI in patients undergoing nephrectomy.

Dataset Information

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.

Background

Methods

Conclusion

Publications

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.

Similar Datasets

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