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

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Simplified Mortality Score for the Intensive Care Unit (SMS-ICU) : protocol for the development and validation of a bedside clinical prediction rule. / Granholm, Anders; Perner, Anders; Krag, Mette; Hjortrup, Peter Buhl; Haase, Nicolai; Holst, Lars Broksø; Marker, Søren; Collet, Marie Oxenbøll; Jensen, Aksel Karl Georg; Møller, Morten Hylander.

In: B M J Open, Vol. 7, No. 3, e015339, 2017.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Granholm, A, Perner, A, Krag, M, Hjortrup, PB, Haase, N, Holst, LB, Marker, S, Collet, MO, Jensen, AKG & Møller, MH 2017, 'Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule', B M J Open, vol. 7, no. 3, e015339. https://doi.org/10.1136/bmjopen-2016-015339

APA

Granholm, A., Perner, A., Krag, M., Hjortrup, P. B., Haase, N., Holst, L. B., ... Møller, M. H. (2017). Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule. B M J Open, 7(3), [e015339]. https://doi.org/10.1136/bmjopen-2016-015339

Vancouver

Granholm A, Perner A, Krag M, Hjortrup PB, Haase N, Holst LB et al. Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule. B M J Open. 2017;7(3). e015339. https://doi.org/10.1136/bmjopen-2016-015339

Author

Granholm, Anders ; Perner, Anders ; Krag, Mette ; Hjortrup, Peter Buhl ; Haase, Nicolai ; Holst, Lars Broksø ; Marker, Søren ; Collet, Marie Oxenbøll ; Jensen, Aksel Karl Georg ; Møller, Morten Hylander. / Simplified Mortality Score for the Intensive Care Unit (SMS-ICU) : protocol for the development and validation of a bedside clinical prediction rule. In: B M J Open. 2017 ; Vol. 7, No. 3.

Bibtex

@article{f50a724504dc411eb07c45b9e82edc9f,
title = "Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule",
abstract = "INTRODUCTION: Mortality 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 ANALYSIS: During 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 DISSEMINATION: We 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.",
keywords = "Adolescent, Adult, Aged, Cohort Studies, Critical Care, Critical Illness, Decision Support Techniques, Female, Hospital Mortality, Humans, Intensive Care Units, Male, Middle Aged, Point-of-Care Systems, Prognosis, Severity of Illness Index, Young Adult, Journal Article, Multicenter Study, Observational Study, Validation Studies",
author = "Anders Granholm and Anders Perner and Mette Krag and Hjortrup, {Peter Buhl} and Nicolai Haase and Holst, {Lars Broks{\o}} and S{\o}ren Marker and Collet, {Marie Oxenb{\o}ll} and Jensen, {Aksel Karl Georg} and M{\o}ller, {Morten Hylander}",
note = "Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.",
year = "2017",
doi = "10.1136/bmjopen-2016-015339",
language = "English",
volume = "7",
journal = "B M J Open",
issn = "2044-6055",
publisher = "BMJ Publishing Group",
number = "3",

}

RIS

TY - JOUR

T1 - Simplified Mortality Score for the Intensive Care Unit (SMS-ICU)

T2 - protocol for the development and validation of a bedside clinical prediction rule

AU - Granholm, Anders

AU - Perner, Anders

AU - Krag, Mette

AU - Hjortrup, Peter Buhl

AU - Haase, Nicolai

AU - Holst, Lars Broksø

AU - Marker, Søren

AU - Collet, Marie Oxenbøll

AU - Jensen, Aksel Karl Georg

AU - Møller, Morten Hylander

N1 - Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

PY - 2017

Y1 - 2017

N2 - INTRODUCTION: Mortality 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 ANALYSIS: During 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 DISSEMINATION: We 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.

AB - INTRODUCTION: Mortality 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 ANALYSIS: During 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 DISSEMINATION: We 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.

KW - Adolescent

KW - Adult

KW - Aged

KW - Cohort Studies

KW - Critical Care

KW - Critical Illness

KW - Decision Support Techniques

KW - Female

KW - Hospital Mortality

KW - Humans

KW - Intensive Care Units

KW - Male

KW - Middle Aged

KW - Point-of-Care Systems

KW - Prognosis

KW - Severity of Illness Index

KW - Young Adult

KW - Journal Article

KW - Multicenter Study

KW - Observational Study

KW - Validation Studies

U2 - 10.1136/bmjopen-2016-015339

DO - 10.1136/bmjopen-2016-015339

M3 - Journal article

VL - 7

JO - B M J Open

JF - B M J Open

SN - 2044-6055

IS - 3

M1 - e015339

ER -

ID: 188191738