Dataset Information

MIMIC-III, a freely accessible critical care database.

ABSTRACT: MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.

SUBMITTER: Johnson AE

PROVIDER: S-EPMC4878278 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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MIMIC-III, a freely accessible critical care database.

Johnson Alistair E W AE Pollard Tom J TJ Shen Lu L Lehman Li-Wei H LW Feng Mengling M Ghassemi Mohammad M Moody Benjamin B Szolovits Peter P Celi Leo Anthony LA Mark Roger G RG

Scientific data 20160524

MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and indust ...[more]

PMID: 27219127

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Project description:Background: Mechanically ventilated patients in the intensive care unit (ICU) have high mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology Score II (SAPS II), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA), widely used in the general ICU population. We aimed to establish prediction scores on mechanically ventilated patients with the combination of these disease severity scores and other features available on the first day of admission. Methods: A retrospective administrative database study from the Medical Information Mart for Intensive Care (MIMIC-III) database was conducted. The exposures of interest consisted of the demographics, pre-ICU comorbidity, ICU diagnosis, disease severity scores, vital signs, and laboratory test results on the first day of ICU admission. Hospital mortality was used as the outcome. We used the machine learning methods of k-nearest neighbors (KNN), logistic regression, bagging, decision tree, random forest, Extreme Gradient Boosting (XGBoost), and neural network for model establishment. A sample of 70% of the cohort was used for the training set; the remaining 30% was applied for testing. Areas under the receiver operating characteristic curves (AUCs) and calibration plots would be constructed for the evaluation and comparison of the models' performance. The significance of the risk factors was identified through models and the top factors were reported. Results: A total of 28,530 subjects were enrolled through the screening of the MIMIC-III database. After data preprocessing, 25,659 adult patients with 66 predictors were included in the model analyses. With the training set, the models of KNN, logistic regression, decision tree, random forest, neural network, bagging, and XGBoost were established and the testing set obtained AUCs of 0.806, 0.818, 0.743, 0.819, 0.780, 0.803, and 0.821, respectively. The calibration curves of all the models, except for the neural network, performed well. The XGBoost model performed best among the seven models. The top five predictors were age, respiratory dysfunction, SAPS II score, maximum hemoglobin, and minimum lactate. Conclusion: The current study indicates that models with the risk of factors on the first day could be successfully established for predicting mortality in ventilated patients. The XGBoost model performs best among the seven machine learning models.

Project description:Low heart rate is a risk factor of mortality in many cardiovascular diseases. However, the relationship of minimum heart rate (MHR) with outcomes after cardiac surgery is still unclear, and the association between optimum MHR and risk of mortality in patients receiving cardiac surgery remains unknown. In this retrospective study using the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC-III) database, 8243 adult patients who underwent cardiac surgery were included. The association between MHR and the 30-day, 90-day, 180-day, and 1-year mortality of patients undergoing cardiac surgery was analyzed using multivariate Cox proportional hazard analysis. As a continuous variable, MHR was evaluated using restricted cubic regression splines, and appropriate cut-off points were determined. Kaplan-Meier curve was used to further explore the relationship between MHR and prognosis. Subgroup analyses were performed based on age, sex, hypertension, diabetes, and ethnicity. The rates of the 30-day, 90-day, 180-day, and 1-year mortalities of patients in the low MHR group were higher than those in the high MHR group (4.1% vs. 2.9%, P < 0.05; 6.8% vs. 5.3%, P < 0.05; 8.9% vs. 7.0%, P < 0.05, and 10.9% vs. 8.8%, P < 0.05, respectively). Low MHR significantly correlated with the 30-day, 90-day, 180-day, and 1-year mortality after adjusting for confounders. A U-shaped relationship was observed between the 30-day, 90-day, 180-day, and 1-year mortality and MHR, and the mortality was lowest when the MHR was 69 bpm. Kaplan-Meier curve analysis also indicated that low MHR had poor prognosis in patients undergoing cardiac surgery. According to subgroup analyses, the effect of low MHR on post-cardiac surgery survival was restricted to patients who were < 75 years old, male, without hypertension and diabetes, and of White ethnicity. MHR (69 bpm) was associated with better 30-day, 90-day, 180-day, and 1-year survival in patients after cardiac surgery. Therefore, effective HR control strategies are required in this high-risk population.

Dataset Information

MIMIC-III, a freely accessible critical care database.

Publications

MIMIC-III, a freely accessible critical care database.

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