Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database
Abstract To develop and validate a dynamic nomogram for predicting the need for continuous renal replacement therapy (CRRT) in septic patients in the intensive care unit (ICU). Data were extracted from the MIMIC-IV 3.0 database and divided into a training set and a validation set in a 7:3 ratio. Rel...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-07647-6 |
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| author | Binglin Song Ping Liu Chun Liu Kangrui Fu Xiangde Zheng Ying Liu |
| author_facet | Binglin Song Ping Liu Chun Liu Kangrui Fu Xiangde Zheng Ying Liu |
| author_sort | Binglin Song |
| collection | DOAJ |
| description | Abstract To develop and validate a dynamic nomogram for predicting the need for continuous renal replacement therapy (CRRT) in septic patients in the intensive care unit (ICU). Data were extracted from the MIMIC-IV 3.0 database and divided into a training set and a validation set in a 7:3 ratio. Relevant risk factors were identified through LASSO regression, and a binary logistic regression model was subsequently developed. The CRRT risk nomogram was visualized using R language, with the DynNom package employed to create a dynamic nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Harrell’s C-index, and calibration curves. The clinical utility of the model was evaluated via decision curve analysis (DCA). A total of 7361 septic patients were included in this study, of which 525 required CRRT. The study identified several predictive factors for CRRT, including respiratory rate, oxygen saturation, international normalized ratio (INR), activated partial thromboplastin time (APTT), creatinine, lactate, pH, body weight, renal disease, and severe liver disease. The C-index was 0.871. The AUCs for the training and validation sets were 0.87 (95% CI: 0.8535–0.8883) and 0.86 (95% CI: 0.8282–0.8887), respectively. The calibration curves demonstrated good predictive consistency. DCA confirmed the model’s significant clinical value. The dynamic nomogram is available for visualization at: https://zhong-hua-min-zu-wan-sui.shinyapps.io/CRRT_prediction_nomogram/ . We have developed a dynamic nomogram based on the MIMIC-IV database, incorporating 10 clinical features, to predict the probability of CRRT requirement in septic patients. Internal validation showed that this model exhibits robust predictive performance. |
| format | Article |
| id | doaj-art-9e1221146a4d422eaea9b6729b24b8d2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9e1221146a4d422eaea9b6729b24b8d22025-08-20T03:37:29ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-07647-6Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV databaseBinglin Song0Ping Liu1Chun Liu2Kangrui Fu3Xiangde Zheng4Ying Liu5Clinical Medical College of North Sichuan Medical CollegeSouthwest Medical UniversityEmergency Department of Dazhou Central HospitalClinical Medical College of North Sichuan Medical CollegeEmergency Department of Dazhou Central HospitalSouthwest Medical UniversityAbstract To develop and validate a dynamic nomogram for predicting the need for continuous renal replacement therapy (CRRT) in septic patients in the intensive care unit (ICU). Data were extracted from the MIMIC-IV 3.0 database and divided into a training set and a validation set in a 7:3 ratio. Relevant risk factors were identified through LASSO regression, and a binary logistic regression model was subsequently developed. The CRRT risk nomogram was visualized using R language, with the DynNom package employed to create a dynamic nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Harrell’s C-index, and calibration curves. The clinical utility of the model was evaluated via decision curve analysis (DCA). A total of 7361 septic patients were included in this study, of which 525 required CRRT. The study identified several predictive factors for CRRT, including respiratory rate, oxygen saturation, international normalized ratio (INR), activated partial thromboplastin time (APTT), creatinine, lactate, pH, body weight, renal disease, and severe liver disease. The C-index was 0.871. The AUCs for the training and validation sets were 0.87 (95% CI: 0.8535–0.8883) and 0.86 (95% CI: 0.8282–0.8887), respectively. The calibration curves demonstrated good predictive consistency. DCA confirmed the model’s significant clinical value. The dynamic nomogram is available for visualization at: https://zhong-hua-min-zu-wan-sui.shinyapps.io/CRRT_prediction_nomogram/ . We have developed a dynamic nomogram based on the MIMIC-IV database, incorporating 10 clinical features, to predict the probability of CRRT requirement in septic patients. Internal validation showed that this model exhibits robust predictive performance.https://doi.org/10.1038/s41598-025-07647-6Intensive care unitSepsisContinuous renal replacement therapyPredictionDynamic nomogram |
| spellingShingle | Binglin Song Ping Liu Chun Liu Kangrui Fu Xiangde Zheng Ying Liu Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database Scientific Reports Intensive care unit Sepsis Continuous renal replacement therapy Prediction Dynamic nomogram |
| title | Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database |
| title_full | Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database |
| title_fullStr | Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database |
| title_full_unstemmed | Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database |
| title_short | Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database |
| title_sort | development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the mimic iv database |
| topic | Intensive care unit Sepsis Continuous renal replacement therapy Prediction Dynamic nomogram |
| url | https://doi.org/10.1038/s41598-025-07647-6 |
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