PMID- 33663489
OWN - NLM
STAT- MEDLINE
DCOM- 20211102
LR  - 20211102
IS  - 1472-6823 (Electronic)
IS  - 1472-6823 (Linking)
VI  - 21
IP  - 1
DP  - 2021 Mar 4
TI  - Nomogram to predict the risk of acute kidney injury in patients with diabetic 
      ketoacidosis: an analysis of the MIMIC-III database.
PG  - 37
LID - 10.1186/s12902-021-00696-8 [doi]
LID - 37
AB  - 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 < 0.1 were included in the final model. Then, a nomogram was 
      constructed applying these significant risk predictors based on a multivariate 
      logistic regression model. The discriminatory ability of the model was determined 
      by illustrating a receiver operating curve (ROC) and calculating the area under 
      the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow 
      goodness-of-fit test (HL test) were conducted to evaluate the performance of our 
      newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate 
      the clinical net benefit. RESULTS: A 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.
FAU - Fan, Tingting
AU  - Fan T
AD  - Department of Endocrinology, Second Affiliated Hospital of Jilin University, 
      Ziqiang Street 218, Changchun, 130041, Jilin, China.
FAU - Wang, Haosheng
AU  - Wang H
AD  - Department of Orthopedics, Second Affiliated Hospital of Jilin University, 
      Changchun, China.
FAU - Wang, Jiaxin
AU  - Wang J
AD  - Department of Endocrinology, Second Affiliated Hospital of Jilin University, 
      Ziqiang Street 218, Changchun, 130041, Jilin, China.
FAU - Wang, Wenrui
AU  - Wang W
AD  - Department of Endocrinology, Second Affiliated Hospital of Jilin University, 
      Ziqiang Street 218, Changchun, 130041, Jilin, China.
FAU - Guan, Haifei
AU  - Guan H
AD  - Department of Endocrinology, Second Affiliated Hospital of Jilin University, 
      Ziqiang Street 218, Changchun, 130041, Jilin, China.
FAU - Zhang, Chuan
AU  - Zhang C
AUID- ORCID: 0000-0002-9884-699X
AD  - Department of Endocrinology, Second Affiliated Hospital of Jilin University, 
      Ziqiang Street 218, Changchun, 130041, Jilin, China. wangs93@sina.com.
LA  - eng
PT  - Journal Article
DEP - 20210304
PL  - England
TA  - BMC Endocr Disord
JT  - BMC endocrine disorders
JID - 101088676
SB  - IM
MH  - Acute Kidney Injury/*diagnosis/epidemiology
MH  - Adult
MH  - *Data Analysis
MH  - Databases, Factual/statistics & numerical data
MH  - Diabetes Mellitus, Type 2/diagnosis/epidemiology
MH  - Diabetic Ketoacidosis/*diagnosis/epidemiology
MH  - Female
MH  - Humans
MH  - *Intensive Care Units/statistics & numerical data
MH  - Male
MH  - Medical Informatics/*methods/statistics & numerical data
MH  - Middle Aged
MH  - *Nomograms
MH  - Predictive Value of Tests
PMC - PMC7931351
OTO - NOTNLM
OT  - Acute kidney injury
OT  - Diabetes mellitus
OT  - Diabetic ketoacidosis
OT  - Nomogram
COIS- The authors declare that they have no competing interests.
EDAT- 2021/03/06 06:00
MHDA- 2021/11/03 06:00
PMCR- 2021/03/04
CRDT- 2021/03/05 05:39
PHST- 2020/09/04 00:00 [received]
PHST- 2021/02/10 00:00 [accepted]
PHST- 2021/03/05 05:39 [entrez]
PHST- 2021/03/06 06:00 [pubmed]
PHST- 2021/11/03 06:00 [medline]
PHST- 2021/03/04 00:00 [pmc-release]
AID - 10.1186/s12902-021-00696-8 [pii]
AID - 696 [pii]
AID - 10.1186/s12902-021-00696-8 [doi]
PST - epublish
SO  - BMC Endocr Disord. 2021 Mar 4;21(1):37. doi: 10.1186/s12902-021-00696-8.