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Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues
Accepted Manuscript Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues
Saeid Safiri, Erfan Ayubi PII: DOI: Reference:
S0883-9441(17)31043-2 doi: 10.1016/j.jcrc.2017.07.043 YJCRC 52616
To appear in: Revised date: Accepted date:
###REVISEDDATE### ###ACCEPTEDDATE###
Please cite this article as: Saeid Safiri, Erfan Ayubi , Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues, (2017), doi: 10.1016/j.jcrc.2017.07.043
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues Saeid Safiri 1,2 and Erfan Ayubi3 ,* 1
T
Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran 2
CR
IP
Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 3
US
Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AN
*Correspondence: Erfan Ayubi, Assistant Professor of Epidemiology, Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran,
ED
M
Iran; E-mail: [email protected]
PT
Acknowledgment
CE
None.
AC
Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article. 1
ACCEPTED MANUSCRIPT Dear Editor-in-Chief, We read the paper authored by Aziz Al Khawaja and colleagues that was published in the Journal of Critical Care, with great interest (1). The authors purposed to describe the epidemiological data of sickle cell disease (SCD) patients admitted to the ICU and to determine
IP
T
predictors of mortality in order to help intensivists identify patients at most risk. They
CR
concluded that older age, less frequent hospitalization, shorter stays in the ICU, and higher need for renal replacement therapy were found to predictors of high mortality rate. Although
US
the valuable study was conducted, some methodological issues need to be noticed.
AN
First, authors determined the predictor of mortality in the univariable model, where the multivariable analysis has not been conducted and confounding variables have not been
ED
M
controlled. Hence, the reported associations may be biased due to confounding effect (2). Second, Aziz Al Khawaja et al. gathered information on the association between various
PT
independent variables with time until death (survival status); but unfortunately the survival
CE
analysis methods such as Cox regression models were not applied to use the data completely (3). The authors used logistic regression model in their study which is not suitable for their data
AC
and leads to considerable information loss (4). In fact, survival analysis is usually used when the outcome studies is time to event, whereas the logistic regression model is used when the studied outcome is event (3). Third, in their Table 3, Aziz Al Khawaja and colleagues did not report odds ratio (OR) for the quantitative independent variables and Z values have been reported instead of ORs. The point
2
ACCEPTED MANUSCRIPT is that the OR can be calculated for both quantitative and qualitative independent variables and reporting Z values is not common (2). Finally, authors constructed prediction model in their study but they have not validated their model. Hence, their interpretations of findings is optimistic and internal or external validation
US
CR
IP
T
should be done through using appropriate statistical models such as Bootstrapping (5).
AN
References
AC
CE
PT
ED
M
1. Aziz Al Khawaja SA, Humood ZM, Al Hammam RA. Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC. Journal of Critical Care. 2. Rothman KJ, Greenland S, Lash TL. Modern epidemiology: Lippincott Williams & Wilkins; 2008. 3. Kleinbaum DG, Klein M. Survival analysis: a self-learning text: Springer Science & Business Media; 2006. 4. Lim J-H, Lee K-E, Hahn K-S, Park K-W. Analyzing Survival Data as Binary Outcomes with Logistic Regression. Communications for Statistical Applications and Methods. 2010;17(1):117-26. 5. Steyerberg E. Clinical prediction models: a practical approach to development, validation, and updating: Springer Science & Business Media; 2008.
3
Saeid Safiri, Erfan Ayubi PII: DOI: Reference:
S0883-9441(17)31043-2 doi: 10.1016/j.jcrc.2017.07.043 YJCRC 52616
To appear in: Revised date: Accepted date:
###REVISEDDATE### ###ACCEPTEDDATE###
Please cite this article as: Saeid Safiri, Erfan Ayubi , Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues, (2017), doi: 10.1016/j.jcrc.2017.07.043
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues Saeid Safiri 1,2 and Erfan Ayubi3 ,* 1
T
Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran 2
CR
IP
Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 3
US
Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AN
*Correspondence: Erfan Ayubi, Assistant Professor of Epidemiology, Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran,
ED
M
Iran; E-mail: [email protected]
PT
Acknowledgment
CE
None.
AC
Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article. 1
ACCEPTED MANUSCRIPT Dear Editor-in-Chief, We read the paper authored by Aziz Al Khawaja and colleagues that was published in the Journal of Critical Care, with great interest (1). The authors purposed to describe the epidemiological data of sickle cell disease (SCD) patients admitted to the ICU and to determine
IP
T
predictors of mortality in order to help intensivists identify patients at most risk. They
CR
concluded that older age, less frequent hospitalization, shorter stays in the ICU, and higher need for renal replacement therapy were found to predictors of high mortality rate. Although
US
the valuable study was conducted, some methodological issues need to be noticed.
AN
First, authors determined the predictor of mortality in the univariable model, where the multivariable analysis has not been conducted and confounding variables have not been
ED
M
controlled. Hence, the reported associations may be biased due to confounding effect (2). Second, Aziz Al Khawaja et al. gathered information on the association between various
PT
independent variables with time until death (survival status); but unfortunately the survival
CE
analysis methods such as Cox regression models were not applied to use the data completely (3). The authors used logistic regression model in their study which is not suitable for their data
AC
and leads to considerable information loss (4). In fact, survival analysis is usually used when the outcome studies is time to event, whereas the logistic regression model is used when the studied outcome is event (3). Third, in their Table 3, Aziz Al Khawaja and colleagues did not report odds ratio (OR) for the quantitative independent variables and Z values have been reported instead of ORs. The point
2
ACCEPTED MANUSCRIPT is that the OR can be calculated for both quantitative and qualitative independent variables and reporting Z values is not common (2). Finally, authors constructed prediction model in their study but they have not validated their model. Hence, their interpretations of findings is optimistic and internal or external validation
US
CR
IP
T
should be done through using appropriate statistical models such as Bootstrapping (5).
AN
References
AC
CE
PT
ED
M
1. Aziz Al Khawaja SA, Humood ZM, Al Hammam RA. Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC. Journal of Critical Care. 2. Rothman KJ, Greenland S, Lash TL. Modern epidemiology: Lippincott Williams & Wilkins; 2008. 3. Kleinbaum DG, Klein M. Survival analysis: a self-learning text: Springer Science & Business Media; 2006. 4. Lim J-H, Lee K-E, Hahn K-S, Park K-W. Analyzing Survival Data as Binary Outcomes with Logistic Regression. Communications for Statistical Applications and Methods. 2010;17(1):117-26. 5. Steyerberg E. Clinical prediction models: a practical approach to development, validation, and updating: Springer Science & Business Media; 2008.
3