Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit

Dólera-Moreno, Cristina; Palazón‐Bru, Antonio; Colomina-Climent, Francisco; Gil-Guillén, Vicente Francisco

doi:10.1111/ijcp.12851

Cited by 26 publications

(48 citation statements)

References 25 publications

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“…Consequently, if others plan to conduct studies to externally validate the scoring system to predict mortality in the ICU [10], the sample size calculation for these studies is available to them.…”

Section: Discussionmentioning

confidence: 99%

“…The minimum score of this system is x min = 0 points and the maximum score is x max = 15 points, the coefficients of the logistic regression model associated with the system are β 0 = −5.92252114678228 and β 1 = 0.6, the proportion of events is p event = 0.10781990521327014218009478672986 and the probability distribution for each of the associated scores (ordered from x min = 0 to x max = 15) is (0.29023508137432200,0.03887884267631100,0.09222423146473780, 0.18625678119349000,0.05967450271247740,0.08318264014466550, 0.06057866184448460,0.02893309222423150,0.02441229656419530, 0.02622061482820980,0.03345388788426760,0.00994575045207957, 0.03526220614828210,0.02893309222423150,0,0.00180831826401447). These data were obtained from the original publication [10]. The established range of possible values for n event was between 25 and 1000.…”

Section: Methodsmentioning

confidence: 99%

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Sample size calculation to externally validate scoring systems based on logistic regression models

et al. 2017

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BackgroundA sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model.ObjectiveThe aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.MethodsThe algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.ResultsIn the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.ConclusionAn algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Sample size calculation to externally validate scoring systems based on logistic regression models

et al. 2017

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“…In a recent paper, we described the construction and internal validation of a scoring system for mortality in intensive care units . The performance of our model has been discussed in a letter, and in this report, we aim to clarify the highlighted points and update some of our results.…”

Section: Introductionmentioning

confidence: 94%

An update to the internal validation of the new mortality risk score for patients admitted to the intensive care unit

et al. 2016

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“…The Sequential Organ Failure Assessment (SOFA) score17 is somewhat simpler than SAPS II, and although it was not developed for this purpose, it appears to be only slightly inferior to SAPS II at predicting mortality 1819 Recently, Dólera-Moreno et al 20 presented a simple score that predicts in-ICU mortality, based on data from a single ICU.…”

Section: Introductionmentioning

confidence: 99%

Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule

et al. 2017

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IntroductionMortality prediction scores are widely used in intensive care units (ICUs) and in research, but their predictive value deteriorates as scores age. Existing mortality prediction scores are imprecise and complex, which increases the risk of missing data and decreases the applicability bedside in daily clinical practice. We propose the development and validation of a new, simple and updated clinical prediction rule: the Simplified Mortality Score for use in the Intensive Care Unit (SMS-ICU).Methods and analysisDuring the first phase of the study, we will develop and internally validate a clinical prediction rule that predicts 90-day mortality on ICU admission. The development sample will comprise 4247 adult critically ill patients acutely admitted to the ICU, enrolled in 5 contemporary high-quality ICU studies/trials. The score will be developed using binary logistic regression analysis with backward stepwise elimination of candidate variables, and subsequently be converted into a point-based clinical prediction rule. The general performance, discrimination and calibration of the score will be evaluated, and the score will be internally validated using bootstrapping. During the second phase of the study, the score will be externally validated in a fully independent sample consisting of 3350 patients included in the ongoing Stress Ulcer Prophylaxis in the Intensive Care Unit trial. We will compare the performance of the SMS-ICU to that of existing scores.Ethics and disseminationWe will use data from patients enrolled in studies/trials already approved by the relevant ethical committees and this study requires no further permissions. The results will be reported in accordance with the Transparent Reporting of multivariate prediction models for Individual Prognosis Or Diagnosis (TRIPOD) statement, and submitted to a peer-reviewed journal.

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Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit

Abstract: A predictive model of mortality in the ICU has been constructed and internally validated. This model improves on the previous models through its simplicity, its discriminating power and free use. External validation studies are needed in other geographical areas.

Cited by 26 publications

References 25 publications

Sample size calculation to externally validate scoring systems based on logistic regression models

Sample size calculation to externally validate scoring systems based on logistic regression models

An update to the internal validation of the new mortality risk score for patients admitted to the intensive care unit

Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule

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