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Xiao W, Huang L, Guo H, Liu W, Zhang J, Liu Y, Hua T, Yang M. Development and validation of potential phenotypes of serum electrolyte disturbances in critically ill patients and a Web-based application. J Crit Care 2024; 82:154793. [PMID: 38548515 DOI: 10.1016/j.jcrc.2024.154793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 06/01/2024]
Abstract
BACKGROUND Electrolyte disturbances are highly heterogeneous and severely affect the prognosis of critically ill patients. Our study was to determine whether data-driven phenotypes of seven electrolytes have prognostic relevance in critically ill patients. METHODS We extracted patient information from three large independent public databases, and clustered the electrolyte distribution of ICU patients based on the extreme value, median value and coefficient of variation of electrolytes. Three plausible clinical phenotypes were calculated using K-means clustering algorithm as the basic clustering method. MIMIC-IV was considered a training set, and two others have been designated as verification set. The robustness of the model was then validated from different angles, providing dynamic and interactive visual charts for more detailed characterization of phenotypes. RESULTS 15,340, 12,445 and 2147 ICU patients with electrolyte records during early ICU stay in MIMIC-IV, eICU-CRD and AmsterdamUMCdb were enrolled. After clustering, three reasonable and interpretable phenotypes are defined as α, β and γ according to the order of clusters. The α and γ phenotype, with significant differences in electrolyte distribution and clinical variables, higher 28-day mortality and longer length of ICU stay (p < 0.001), was further demonstrated by robustness analysis. The α phenotype has significant kidney injury, while the β phenotype has the best prognosis. In addition, the assignment methods of the three phenotypes were developed into a web-based tool for further verification and application. CONCLUSIONS Three different clinical phenotypes were identified that correlated with electrolyte distribution and clinical outcomes. Further validation and characterization of these phenotypes is warranted.
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Affiliation(s)
- Wenyan Xiao
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China
| | - Lisha Huang
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China
| | - Heng Guo
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China
| | - Wanjun Liu
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China
| | - Jin Zhang
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China
| | - Yu Liu
- Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Ministry of Education, Hefei, Anhui 230601, PR China; School of Integrated Circuits, Anhui University, Anhui, Hefei 230601, PR China
| | - Tianfeng Hua
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China
| | - Min Yang
- The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China; The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei 230601, PR China.
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Wang X, Fu X. Predicting AKI in patients with AMI: Development and assessment of a new predictive nomogram. Medicine (Baltimore) 2023; 102:e33991. [PMID: 37327276 PMCID: PMC10270522 DOI: 10.1097/md.0000000000033991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 05/23/2023] [Indexed: 06/18/2023] Open
Abstract
Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI) and is associated with both long- and short-term consequences. This study aimed to investigate relevant risk variables and create a nomogram that predicts the probability of AKI in patients with AMI, so that prophylaxis could be initiated as early as possible. Data were gathered from the medical information mart for the intensive care IV database. We included 1520 patients with AMI who were admitted to the coronary care unit or the cardiac vascular intensive care unit. The primary outcome was AKI during hospitalization. Independent risk factors for AKI were identified by applying least absolute shrinkage and selection operator regression models and multivariate logistic regression analyses. A multivariate logistic regression analysis was used to build a predictive model. The discrimination, calibration, and clinical usefulness of the prediction model were assessed using C-index, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. Of 1520 patients, 731 (48.09%) developed AKI during hospitalization. Hemoglobin, estimated glomerular filtration rate, sodium, bicarbonate, total bilirubin, age, heart failure, and diabetes were identified as predictive factors for the nomogram construction (P < .01). The model displayed good discrimination, with a C-index of 0.857 (95% CI:0.807-0.907), and good calibration. A high C-index value of 0.847 could still be reached during interval validation. Decision curve analysis showed that the AKI nomogram was clinically useful when the intervention was determined at an AKI possibility threshold of 10%. The nomogram constructed herein can successfully predict the risk of AKI in patients with AMI early and provide critical information that can facilitate prompt and efficient interventions.
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Affiliation(s)
- Xun Wang
- Department of Cardiology. The Second Hospital of Hebei Medical University, Shijiazhuang, China
- Department of Cardiology. The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Xianghua Fu
- Department of Cardiology. The Second Hospital of Hebei Medical University, Shijiazhuang, China
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Ang Y, Li S, Ong MEH, Xie F, Teo SH, Choong L, Koniman R, Chakraborty B, Ho AFW, Liu N. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Sci Rep 2022; 12:7111. [PMID: 35501411 PMCID: PMC9061747 DOI: 10.1038/s41598-022-11129-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/12/2022] [Indexed: 12/24/2022] Open
Abstract
Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on the Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. A machine learning-based framework AutoScore was used to generate clinical scores from the study sample which was randomly divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, diastolic blood pressure, and age. AUC of AKI-RiSc was 0.730 (95% CI 0.714–0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI 0.646–0.679) on the testing cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.6% and specificity of 46.7%. AKI-RiSc is a simple clinical score that can be easily implemented on the ground for early identification of AKI and potentially be applied in international settings.
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Zhang X, Chen S, Lai K, Chen Z, Wan J, Xu Y. Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease. Ren Fail 2022; 44:43-53. [PMID: 35166177 PMCID: PMC8856083 DOI: 10.1080/0886022x.2022.2036619] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Purpose Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with acute cerebrovascular disease. Methods This study was a retrospective study based on two different cohorts. Five machine learning methods were used to develop AKI risk prediction models. We used six popular metrics (AUROC, F2-Score, accuracy, sensitivity, specificity and precision) to evaluate the performance of these models. Results We identified 2935 patients in the MIMIC-III database and 499 patients in our local database to develop and validate the AKI risk prediction model. The incidence of AKI in these two different cohorts was 18.3% and 61.7%, respectively. Analysis showed that several laboratory parameters (serum creatinine, hemoglobin, white blood cell count, bicarbonate, blood urea nitrogen, sodium, albumin, and platelet count), age, and length of hospital stay, were the top ten important factors associated with AKI. The analysis demonstrated that the XGBoost had higher AUROC (0.880, 95%CI: 0.831–0.929), indicating that the XGBoost model was better at predicting AKI risk in patients with acute cerebrovascular disease than other models. Conclusions This study developed machine learning methods to identify critically ill patients with acute cerebrovascular disease who are at a high risk of developing AKI. This result suggested that machine learning techniques had the potential to improve the prediction of AKI risk models in critical care.
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Affiliation(s)
- Xiaohong Zhang
- Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Siying Chen
- Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Kunmei Lai
- Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Zhimin Chen
- Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Jianxin Wan
- Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Yanfang Xu
- Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
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Fan T, Wang H, Wang J, Wang W, Guan H, Zhang C. Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database. BMC Endocr Disord 2021; 21:37. [PMID: 33663489 PMCID: PMC7931351 DOI: 10.1186/s12902-021-00696-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 02/10/2021] [Indexed: 12/28/2022] Open
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 < 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.
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Affiliation(s)
- Tingting Fan
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Haosheng Wang
- Department of Orthopedics, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Jiaxin Wang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Wenrui Wang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Haifei Guan
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Chuan Zhang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China.
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