Data mining in clinical big data: the frequently used databases, steps, and methodological models

Wu, Wentao; Li, Yuan-Jie; Feng, Aozi; Li, Li; Huang, Tao; Xu, Anding; Lyu, Jun

doi:10.1186/s40779-021-00338-z

Cited by 298 publications

(275 citation statements)

References 95 publications

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“…It contains a comprehensive information on more than 250,000 electronic admission records of the Beth Israel Deaconess Medical Center in Boston, Massachusetts from 2008 to 2019. These records include the diagnosis, vital signs, laboratory tests, medication, and surgical information [ 18 , 19 ]. The data used in this study were from the latest version of MIMI-IV 1.0, which was released in March 2021.…”

Section: Methodsmentioning

confidence: 99%

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit

Zhang

Han

et al. 2021

J Transl Med

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Background Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI. Methods Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine–Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes. Results A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137–1.875), 1.532 (1.197–1.961), and 2.232 (1.795–2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results. Conclusions The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

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Section: Methodsmentioning

confidence: 99%

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit

Zhang

Han

et al. 2021

J Transl Med

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“…The continuous variable was expressed as median and quartile spacing, while the categorical variable was expressed as percentage. The predictive variables were selected using stepwise selection method in the Cox regression model in the training cohort, 19 and the 3-, 5-, and 8-year OS rates for patients with ALM were constructed on the basis of the identified variables.…”

Section: Methodsmentioning

confidence: 99%

Nomogram for Predicting Overall Survival in Acral Lentiginous Melanoma: A Population‐based Study

Yin

Zhao

Yang

et al. 2021

IJGM

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Background The objective of this study was to establish a nomogram for predicting the overall survival (OS) of patients with acral lentiginous melanoma (ALM). Materials and Methods The study sample was selected from 1785 patients diagnosed with ALM from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database, and R software was used to divide patients into the training cohort and validation cohort at a ratio of 7: 3. Stepwise selection method in the Cox regression model was used in the training cohort to select predictive variables to construct the nomogram, and model validation parameters were used in the validation cohort to evaluate the performance of the nomogram. Results The nomogram showed that age at diagnosis had the greatest impact on OS in patients with ALM, followed by AJCC stage, surgical treatment, SEER stage, sex, race, and marital status. The index of concordance, area under the receiver operating characteristic curve, calibration plots, net reclassification improvement, integrated discrimination improvement, and decision curve analysis demonstrate the good performance of this nomogram. Conclusion The prognostic value of the nomogram is superior to that of the AJCC staging system alone, and it helps clinicians to better predict 3-, 5-, and 8-year OS in patients with ALM.

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“…MIMIC is a large, single-center, open-access database. MIMIC-III includes data on more than 58,000 admissions to Beth Israel Deaconess Medical Center in Boston from 2001 to 2012, comprising 38,645 adults and 7,875 newborns ( 14 – 16 )And MIMIC-IV covers 524,740 admissions for 382,278 patients to this center from 2008 to 2019 ( 17 , 18 ). The relevant records include demographic data, hourly vital signs, laboratory test results, microbial culture results, imaging data, treatment procedures, medication records, and survival information.…”

Section: Methodsmentioning

confidence: 99%

“…LCEV patient data were obtained from Medical Information Mart for Intensive Care III and IV (MIMIC-III v1. 4 (17,18). The relevant records include demographic data, hourly vital signs, laboratory test results, microbial culture results, imaging data, treatment procedures, medication records, and survival information.…”

Section: Data Sourcementioning

confidence: 99%

A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices

Zhang

Wang

et al. 2021

Front. Med.

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Introduction: Liver cirrhosis is caused by the development of various acute and chronic liver diseases. Esophageal varices is a common and serious complication of liver cirrhosis during decompensation. Despite the development of various treatments, the prognosis for liver cirrhosis with esophageal varices (LCEV) remains poor. We aimed to establish and validate a nomogram for predicting in-hospital death in LCEV patients.Methods: Data on LCEV patients were extracted from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV) database. The patients from MIMIC-III were randomly divided into training and validation cohorts. Training cohort was used for establishing the model, validation and MIMIC-IV cohorts were used for validation. The independent prognostic factors for LCEV patients were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression. We then constructed a nomogram to predict the in-hospital death of LCEV patients. Multiple indicators were used to validate the nomogram, including the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA).Results: Nine independent prognostic factors were identified by using LASSO and stepwise regressions: age, Elixhauser score, anion gap, sodium, albumin, bilirubin, international normalized ratio, vasopressor use, and bleeding. The nomogram was then constructed and validated. The AUC value of the nomogram was 0.867 (95% CI = 0.832–0.904) in the training cohort, 0.846 (95% CI = 0.790–0.896) in the validation cohort and 0.840 (95% CI = 0.807–0.872) in the MIMIC-IV cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and the Hosmer-Lemeshow test results demonstrated that the nomogram was well-calibrated. Improvements in NRI and IDI values suggested that our nomogram was superior to MELD-Na, CAGIB, and OASIS scoring system. DCA curves indicated that the nomogram had good value in clinical applications.Conclusion: We have established the first prognostic nomogram for predicting the in-hospital death of LCEV patients. The nomogram is easy to use, performs well, and can be used to guide clinical practice, but further external prospective validation is still required.

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Data mining in clinical big data: the frequently used databases, steps, and methodological models

Cited by 298 publications

References 95 publications

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit

Nomogram for Predicting Overall Survival in Acral Lentiginous Melanoma: A Population‐based Study

A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices

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