Prediction of mortality in an Indian intensive care unit

Nimgaonkar, Ashish; Karnad, Dilip R.; Sudarshan, S.; Ohno-Machado, Lucila; Kohane, Isaac S.

doi:10.1007/s00134-003-2105-4

Cited by 45 publications

(15 citation statements)

References 34 publications

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“…The use of small data sets has been identified as a major limitation in previous studies [17,30,31]. Considering the fact that the UKH is a largest and most comprehensive academic hospital within the state of Kentucky, the study findings are representative of public health observation in critical care services.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

2011

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ObjectivesThe intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.MethodsThe models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.ResultsOnly 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).ConclusionsWith fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

2011

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“…Moreover, developers of the SAPS 3 model could take advantage of computer-intensive methods of data selection and analysis, such as the use of additive partition trees and logistic regression with random effects. Several new statistical techniques have been used in recent years to allow a more stable prediction of outcome, such as genetic algorithms and artificial neural networks [20, 21], dynamic microsimulation techniques [22], and first- and second-level customization strategies [23–25]. However, the value of these techniques is for the moment limited, usually because they are based on regional databases [24–26] that prevent extrapolation to other settings; moreover, their superiority in even the regional setting still needs to be established.…”

Section: Discussionmentioning

confidence: 99%

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

et al. 2005

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ObjectiveTo develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data.DesignProspective multicentre, multinational cohort study.Patients and settingA total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002.Measurements and resultsICU admission data (recorded within ±1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test Ĥ=10.56, p=0.39, Ĉ=14.29, p=0.16). Customised equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit.ConclusionsThe SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels.Electronic Supplementary MaterialElectronic supplementary material is included in the online fulltext version of this article and accessible for authorised users: http://dx.doi.org/10.1007/s00134-005-2763-5

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“…Most Indian studies have addressed specific subgroups like respiratory, obstetric, surgical, cancer, and so on. [1011121314151617181920] To our knowledge, there are no published studies on validation of these models in mixed ICUs in Maharashtra, India.…”

Section: Introductionmentioning

confidence: 99%

Assessment of performance and utility of mortality prediction models in a single Indian mixed tertiary intensive care unit

Sathe

Bapat

2014

Int J Crit Illn Inj Sci

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Objectives:To assess the performance and utility of two mortality prediction models viz. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in a single Indian mixed tertiary intensive care unit (ICU). Secondary objectives were bench-marking and setting a base line for research.Materials and Methods:In this observational cohort, data needed for calculation of both scores were prospectively collected for all consecutive admissions to 28-bedded ICU in the year 2011. After excluding readmissions, discharges within 24 h and age <18 years, the records of 1543 patients were analyzed using appropriate statistical methods.Results:Both models overpredicted mortality in this cohort [standardized mortality ratio (SMR) 0.88 ± 0.05 and 0.95 ± 0.06 using APACHE II and SAPS II respectively]. Patterns of predicted mortality had strong association with true mortality (R2 = 0.98 for APACHE II and R2 = 0.99 for SAPS II). Both models performed poorly in formal Hosmer-Lemeshow goodness-of-fit testing (Chi-square = 12.8 (P = 0.03) for APACHE II, Chi-square = 26.6 (P = 0.001) for SAPS II) but showed good discrimination (area under receiver operating characteristic curve 0.86 ± 0.013 SE (P < 0.001) and 0.83 ± 0.013 SE (P < 0.001) for APACHE II and SAPS II, respectively). There were wide variations in SMRs calculated for subgroups based on International Classification of Disease, 10th edition (standard deviation ± 0.27 for APACHE II and 0.30 for SAPS II).Interpretation and Conclusion:Lack of fit of data to the models and wide variation in SMRs in subgroups put a limitation on utility of these models as tools for assessing quality of care and comparing performances of different units without customization. Considering comparable performance and simplicity of use, efforts should be made to adapt SAPS II.

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Prediction of mortality in an Indian intensive care unit

Abstract: Artificial neural networks, trained on Indian patient data, used fewer variables and yet outperformed the APACHE II system in predicting hospital outcome.

Cited by 45 publications

References 34 publications

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

Assessment of performance and utility of mortality prediction models in a single Indian mixed tertiary intensive care unit

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