<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03270345</url>
  </required_header>
  <id_info>
    <org_study_id>DecubICUs</org_study_id>
    <nct_id>NCT03270345</nct_id>
  </id_info>
  <brief_title>One-Day Prevalence Study on Pressure Injuries in Intensive Care Units</brief_title>
  <acronym>DecubICUs</acronym>
  <official_title>Multicentre International One-Day Prevalence Study on Pressure Injuries in Intensive Care Units</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>European Society of Intensive Care Medicine</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>European Society of Intensive Care Medicine</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The objective of this study is to provide an up-to-date, global picture of the extent and&#xD;
      patterns of pressure injuries in ICUs. Point prevalence studies are only of value when&#xD;
      performed on a vast scale. To sample a representative cohort, it is the intention to recruit&#xD;
      about 1200 ICUs with all continents covered and as many countries as possible within each&#xD;
      continent.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The objective is to provide an up-to-date, international &quot;global&quot; picture of the extent and&#xD;
      patterns of pressure injuries in ICUs. Thereto , the plan is to perform a 1-day, prospective,&#xD;
      multicenter point-prevalence study. The large scale of the project should allow thorough&#xD;
      epidemiological analyses. More precisely the study will enable to identify:&#xD;
&#xD;
        -  major risk factors for pressure ulcer development;&#xD;
&#xD;
        -  preventive measures used in distinct ICU populations and countries;&#xD;
&#xD;
        -  shortages in the availability of evidence-based measures to prevent pressure sores;&#xD;
&#xD;
        -  malpractice pressure sore prevention in particular regions or countries;&#xD;
&#xD;
        -  occurrence rates of pressure ulcers with/without accurate adjustment for risk profile&#xD;
           and preventive measures taken;&#xD;
&#xD;
        -  benchmarking between regions/countries; clinical outcomes associated with pressure&#xD;
           ulcers (major organ derangements and mortality);&#xD;
&#xD;
        -  economic outcomes associated with pressure ulcers (length of ICU stay) and linking these&#xD;
           outcomes with local practice regarding prevention measures applied/available.&#xD;
&#xD;
        -  country and regional differences in prevalence of pressure ulcers and outcome.&#xD;
&#xD;
      Pressures ulcer stages will be graded following the classification system jointly developed&#xD;
      by the National Pressure Ulcer Advisory Panel and European Pressure Ulcer Advisory Panel.&#xD;
&#xD;
      Data to be recorded include patient demographics, data on severity of underlying disease and&#xD;
      acute illness, organ failure, pressure ulcers, major risk factors for pressure ulcers, and&#xD;
      measures taken to prevent pressure ulcers.&#xD;
&#xD;
      Statistical Plan&#xD;
&#xD;
      Power Calculation. For a risk factor with a prevalence in the study cohort of only 10% (for&#xD;
      example patients with a BMI&lt;20) and an outcome difference of only 5% to be statistically&#xD;
      significant(15% vs. 20% in decubitus occurrence rate), a sample size of 5255 patients is&#xD;
      required (478 patients with the index risk factor and 4777 without) (alpha=0.05; Beta&gt;0.80).&#xD;
&#xD;
      Data cleaning &amp;missing data. Exceptional values will be traced through distribution plotting.&#xD;
      In case of uncertainties, the individual investigators will be contacted. Missing data will&#xD;
      be handled by imputation (1, 2). Data quality checks will be performed, such as checks on&#xD;
      completeness, consistency, correctness, and uniqueness.&#xD;
&#xD;
      Descriptives. A single final analysis is planned at the end of the study; no interim analyses&#xD;
      are planned. Socio-demographic study cohort characteristics will be described as proportions&#xD;
      for categorical variables and for continuous variables as mean and standard deviation if&#xD;
      normally distributed or median and inter-quartile range if not normally distributed&#xD;
      (according to the Kolmogorov-Smirnov test for normality).&#xD;
&#xD;
      The proportion of patients with decubitus (percentage, %, and their 95% confidence intervals)&#xD;
      will be reported overall and according to geographic region (continent), country&#xD;
      classification by income as defined by The World Bank (high-, upper-middle-, lower-middle-,&#xD;
      and low-income countries), percentage of the gross domestic product spent on healthcare&#xD;
      (obtained from the World Health Organization), and according to theEducation and Health Human&#xD;
      Development Report of the United Nations Development Program. Subsequently,&#xD;
      potentialdifferences in prevalence might offer the opportunity to evaluate variances in&#xD;
      prevention measures on a large scale.&#xD;
&#xD;
      Modelling. Covariates that will be evaluated on their relationship with the presence of&#xD;
      decubitus encompass various organizational aspects of the ICU (e.g. nurse-to-patient ratio),&#xD;
      decubitus prevention measures (e.g. type of matrasses used), and patient-specific&#xD;
      characteristics (e.g. age, underlying conditions, severity of acute illness, body morphology,&#xD;
      BMI,length of ICU stay etc.). Covariates will be considered for adjusted analysis when they&#xD;
      have an association with pressure injuries at a statistical level &lt;0.25 in unadjusted&#xD;
      (univariate) analysis or because of their clinical relevance. A stepwise approach willbe used&#xD;
      to eliminate terms from the regression model; p&lt;0.15 or p&lt;0.10 will beset as the limit to&#xD;
      keep covariates in the model.&#xD;
&#xD;
      Relationships with binary outcome variables (e.g. decubitus, mortality) will be assessed by&#xD;
      means of unadjusted statistical tests and multivariate logistic regression. The value of&#xD;
      additional propensity score correction in the regression model will be assessed. Multinomial&#xD;
      logistic regression will be performed to assess independent relationships with decubitus&#xD;
      stages. Likewise, linear mixed-effect modelling will be used to assess unadjusted and&#xD;
      adjusted relationships with continuous outcome variables (e.g. length of ICU stay and length&#xD;
      of hospitalization). Results of logistic regression will be reported as adjusted odds ratios&#xD;
      with 95% confidence intervals.&#xD;
&#xD;
      To develop a decubitus prediction model for distinct ICU populations (e.g., trauma, surgical&#xD;
      or medical patients) models will be build using machine learning techniques (e.g. Random&#xD;
      Forest, Gradient Boosting Machine). In the process different techniques will be applied in&#xD;
      order to receive the optimal accuracy. In order to gain insight in the correlation between&#xD;
      predictors and variables, regression techniques will be applied, as state above.&#xD;
&#xD;
      For validation of our models the study cohort will be split into a training, test and&#xD;
      validation set. As such this gives a fair interpretation of the results. Alternatively&#xD;
      ten-fold cross-validation can be applied to prevent overfitting.&#xD;
&#xD;
      Benchmarking for individual centers based on decubitus will be performed by providing&#xD;
      directly or indirectly standardized risks based on fixed center effects in a logistic&#xD;
      regression model (3, 4). Besides the presence of the binary quality outcome (i.e. decubitus)&#xD;
      and the center code, this model also includes patient-specific baseline co-variates to adjust&#xD;
      for differential case-mix. The Firth correction will be applied to the logistic regression&#xD;
      model to maintain convergence in the presence of very small centers (5).&#xD;
&#xD;
      Statistical analysis will be performed using SPSS and R. The head investigator (SB) is&#xD;
      responsible for all statistical analysis. Advanced statistical methods will be executed&#xD;
      byMiekeDeschepper (Strategic Policy Cell at Ghent University Hospital).&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">May 15, 2018</start_date>
  <completion_date type="Actual">December 31, 2018</completion_date>
  <primary_completion_date type="Actual">December 31, 2018</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>major risk factors for pressure injury development.</measure>
    <time_frame>on the data collection day of 18 May 2018 (84 days follow-up)</time_frame>
    <description>Pressures injury stages will be graded following the classification system jointly developed by the National Pressure Ulcer Advisory Panel, European Pressure Ulcer Advisory Panel, and Pan Pacific Pressure Injury Alliance. Data to be recorded include patient demographics, data on severity of underlying disease and acute illness, organ failure, pressure ulcers, major risk factors for pressure ulcers, and measures taken to prevent pressure injuries.</description>
  </primary_outcome>
  <enrollment type="Actual">13340</enrollment>
  <condition>Pressure Injury</condition>
  <eligibility>
    <study_pop>
      <textblock>
        All , adult ICU patients present in the ICU.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All adult patients (&gt;18 years of age) present on the date of data collection&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  There are no exclusion criteria. All patients should be included. Patients with severe&#xD;
             clinical conditions not allowing safe pressure ulcer identification should not be&#xD;
             evaluated for the respective risk zones. If it is known that the patient has a&#xD;
             pressure ulcer at the body sites that cannot be safely evaluated, the stage of the&#xD;
             pressure ulcer should be recorded as previously known. If it is unknown whether the&#xD;
             patient has a pressure ulcer at these body sites, this should be indicated with a '?'&#xD;
             in the Case Report Form&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Stijn BLOT</last_name>
    <role>Principal Investigator</role>
    <affiliation>Full Professor, Dept. of Internal Medicine, Faculty of Medicine &amp; Health Science, Ghent University, Belgium</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Sonia LABEAU</last_name>
    <role>Principal Investigator</role>
    <affiliation>Lecturer at University College Ghent, Belgium</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>All Centers From All Over the World Willing to Contribute Are Welcome</name>
      <address>
        <city>Brussels</city>
        <country>Belgium</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Belgium</country>
  </location_countries>
  <reference>
    <citation>Bennett G, Dealey C, Posnett J. The cost of pressure ulcers in the UK. Age Ageing. 2004 May;33(3):230-5. doi: 10.1093/ageing/afh086.</citation>
    <PMID>15082426</PMID>
  </reference>
  <reference>
    <citation>Keller BP, Wille J, van Ramshorst B, van der Werken C. Pressure ulcers in intensive care patients: a review of risks and prevention. Intensive Care Med. 2002 Oct;28(10):1379-88. doi: 10.1007/s00134-002-1487-z. Epub 2002 Sep 7.</citation>
    <PMID>12373461</PMID>
  </reference>
  <reference>
    <citation>Blot S, Cankurtaran M, Petrovic M, Vandijck D, Lizy C, Decruyenaere J, Danneels C, Vandewoude K, Piette A, Vershraegen G, Van Den Noortgate N, Peleman R, Vogelaers D. Epidemiology and outcome of nosocomial bloodstream infection in elderly critically ill patients: a comparison between middle-aged, old, and very old patients. Crit Care Med. 2009 May;37(5):1634-41. doi: 10.1097/CCM.0b013e31819da98e.</citation>
    <PMID>19325489</PMID>
  </reference>
  <reference>
    <citation>Iranmanesh S, Rafiei H, Sabzevari S. Relationship between Braden scale score and pressure ulcer development in patients admitted in trauma intensive care unit. Int Wound J. 2012 Jun;9(3):248-52. doi: 10.1111/j.1742-481X.2011.00852.x. Epub 2011 Sep 13.</citation>
    <PMID>21910830</PMID>
  </reference>
  <reference>
    <citation>Manzano F, Navarro MJ, Roldan D, Moral MA, Leyva I, Guerrero C, Sanchez MA, Colmenero M, Fernandez-Mondejar E; Granada UPP Group. Pressure ulcer incidence and risk factors in ventilated intensive care patients. J Crit Care. 2010 Sep;25(3):469-76. doi: 10.1016/j.jcrc.2009.09.002. Epub 2009 Oct 30.</citation>
    <PMID>19879730</PMID>
  </reference>
  <reference>
    <citation>Nijs N, Toppets A, Defloor T, Bernaerts K, Milisen K, Van Den Berghe G. Incidence and risk factors for pressure ulcers in the intensive care unit. J Clin Nurs. 2009 May;18(9):1258-66. doi: 10.1111/j.1365-2702.2008.02554.x. Epub 2008 Dec 11.</citation>
    <PMID>19077028</PMID>
  </reference>
  <reference>
    <citation>Terekeci H, Kucukardali Y, Top C, Onem Y, Celik S, Oktenli C. Risk assessment study of the pressure ulcers in intensive care unit patients. Eur J Intern Med. 2009 Jul;20(4):394-7. doi: 10.1016/j.ejim.2008.11.001. Epub 2008 Dec 6.</citation>
    <PMID>19524181</PMID>
  </reference>
  <reference>
    <citation>Harvey SE, Parrott F, Harrison DA, Bear DE, Segaran E, Beale R, Bellingan G, Leonard R, Mythen MG, Rowan KM; CALORIES Trial Investigators. Trial of the route of early nutritional support in critically ill adults. N Engl J Med. 2014 Oct 30;371(18):1673-84. doi: 10.1056/NEJMoa1409860. Epub 2014 Oct 1.</citation>
    <PMID>25271389</PMID>
  </reference>
  <reference>
    <citation>Matos LS, Duarte NLV, Minetto RdCs, (2010) Incidence and prevalence of ulcer for pressure in CTI of a Public Hospital of DF. Revista Eletronica de Enfermagem 12: 719-726</citation>
  </reference>
  <reference>
    <citation>European Pressure Ulcer Advisory Panel and National Pressure Ulcer Advisory Panel (2009) Prevention and treatment of pressure ulcers: quick reference guide. National Pressure Ulcer Advisory Panel, Washington DC</citation>
  </reference>
  <reference>
    <citation>Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999 Mar;8(1):3-15. doi: 10.1177/096228029900800102.</citation>
    <PMID>10347857</PMID>
  </reference>
  <reference>
    <citation>Schafer JL, Olsen MK. Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective. Multivariate Behav Res. 1998 Oct 1;33(4):545-71. doi: 10.1207/s15327906mbr3304_5.</citation>
    <PMID>26753828</PMID>
  </reference>
  <reference>
    <citation>Van Messem M, Varewyck M: Evaluating hospital peformance Risk Standard package. Ghent: Ghent University; 2015</citation>
  </reference>
  <reference>
    <citation>Varewyck M, Vansteelandt S, Eriksson M, Goetghebeur E. On the practice of ignoring center-patient interactions in evaluating hospital performance. Stat Med. 2016 Jan 30;35(2):227-38. doi: 10.1002/sim.6634. Epub 2015 Aug 24.</citation>
    <PMID>26303843</PMID>
  </reference>
  <reference>
    <citation>Firth D: Bias Reduction of Maximum Likelihood Estimates.Biometrika Vol. 80, No. 1 (Mar., 1993), pp. 27-38</citation>
  </reference>
  <verification_date>February 2019</verification_date>
  <study_first_submitted>August 29, 2017</study_first_submitted>
  <study_first_submitted_qc>August 30, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">September 1, 2017</study_first_posted>
  <last_update_submitted>February 11, 2019</last_update_submitted>
  <last_update_submitted_qc>February 11, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">February 12, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Decubitus</keyword>
  <keyword>Pressure</keyword>
  <keyword>Ulcer</keyword>
  <keyword>Injuries</keyword>
  <keyword>Sores</keyword>
  <keyword>Intensive care</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Wounds and Injuries</mesh_term>
    <mesh_term>Crush Injuries</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03270345
org study id: DecubICUs
nct id: NCT03270345
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The objective of this study is to provide an up-to-date, global picture of the extent and
      patterns of pressure injuries in ICUs. Point prevalence studies are only of value when
      performed on a vast scale. To sample a representative cohort, it is the intention to recruit
      about 1200 ICUs with all continents covered and as many countries as possible within each
      continent.
The objective is to provide an up-to-date, international "global" picture of the extent and
      patterns of pressure injuries in ICUs. Thereto , the plan is to perform a 1-day, prospective,
      multicenter point-prevalence study. The large scale of the project should allow thorough
      epidemiological analyses. More precisely the study will enable to identify:

        -  major risk factors for pressure ulcer development;

        -  preventive measures used in distinct ICU populations and countries;

        -  shortages in the availability of evidence-based measures to prevent pressure sores;

        -  malpractice pressure sore prevention in particular regions or countries;

        -  occurrence rates of pressure ulcers with/without accurate adjustment for risk profile
           and preventive measures taken;

        -  benchmarking between regions/countries; clinical outcomes associated with pressure
           ulcers (major organ derangements and mortality);

        -  economic outcomes associated with pressure ulcers (length of ICU stay) and linking these
           outcomes with local practice regarding prevention measures applied/available.

        -  country and regional differences in prevalence of pressure ulcers and outcome.

      Pressures ulcer stages will be graded following the classification system jointly developed
      by the National Pressure Ulcer Advisory Panel and European Pressure Ulcer Advisory Panel.

      Data to be recorded include patient demographics, data on severity of underlying disease and
      acute illness, organ failure, pressure ulcers, major risk factors for pressure ulcers, and
      measures taken to prevent pressure ulcers.

      Statistical Plan

      Power Calculation. For a risk factor with a prevalence in the study cohort of only 10% (for
      example patients with a BMI<20) and an outcome difference of only 5% to be statistically
      significant(15% vs. 20% in decubitus occurrence rate), a sample size of 5255 patients is
      required (478 patients with the index risk factor and 4777 without) (alpha=0.05; Beta>0.80).

      Data cleaning &missing data. Exceptional values will be traced through distribution plotting.
      In case of uncertainties, the individual investigators will be contacted. Missing data will
      be handled by imputation (1, 2). Data quality checks will be performed, such as checks on
      completeness, consistency, correctness, and uniqueness.

      Descriptives. A single final analysis is planned at the end of the study; no interim analyses
      are planned. Socio-demographic study cohort characteristics will be described as proportions
      for categorical variables and for continuous variables as mean and standard deviation if
      normally distributed or median and inter-quartile range if not normally distributed
      (according to the Kolmogorov-Smirnov test for normality).

      The proportion of patients with decubitus (percentage, %, and their 95% confidence intervals)
      will be reported overall and according to geographic region (continent), country
      classification by income as defined by The World Bank (high-, upper-middle-, lower-middle-,
      and low-income countries), percentage of the gross domestic product spent on healthcare
      (obtained from the World Health Organization), and according to theEducation and Health Human
      Development Report of the United Nations Development Program. Subsequently,
      potentialdifferences in prevalence might offer the opportunity to evaluate variances in
      prevention measures on a large scale.

      Modelling. Covariates that will be evaluated on their relationship with the presence of
      decubitus encompass various organizational aspects of the ICU (e.g. nurse-to-patient ratio),
      decubitus prevention measures (e.g. type of matrasses used), and patient-specific
      characteristics (e.g. age, underlying conditions, severity of acute illness, body morphology,
      BMI,length of ICU stay etc.). Covariates will be considered for adjusted analysis when they
      have an association with pressure injuries at a statistical level <0.25 in unadjusted
      (univariate) analysis or because of their clinical relevance. A stepwise approach willbe used
      to eliminate terms from the regression model; p<0.15 or p<0.10 will beset as the limit to
      keep covariates in the model.

      Relationships with binary outcome variables (e.g. decubitus, mortality) will be assessed by
      means of unadjusted statistical tests and multivariate logistic regression. The value of
      additional propensity score correction in the regression model will be assessed. Multinomial
      logistic regression will be performed to assess independent relationships with decubitus
      stages. Likewise, linear mixed-effect modelling will be used to assess unadjusted and
      adjusted relationships with continuous outcome variables (e.g. length of ICU stay and length
      of hospitalization). Results of logistic regression will be reported as adjusted odds ratios
      with 95% confidence intervals.

      To develop a decubitus prediction model for distinct ICU populations (e.g., trauma, surgical
      or medical patients) models will be build using machine learning techniques (e.g. Random
      Forest, Gradient Boosting Machine). In the process different techniques will be applied in
      order to receive the optimal accuracy. In order to gain insight in the correlation between
      predictors and variables, regression techniques will be applied, as state above.

      For validation of our models the study cohort will be split into a training, test and
      validation set. As such this gives a fair interpretation of the results. Alternatively
      ten-fold cross-validation can be applied to prevent overfitting.

      Benchmarking for individual centers based on decubitus will be performed by providing
      directly or indirectly standardized risks based on fixed center effects in a logistic
      regression model (3, 4). Besides the presence of the binary quality outcome (i.e. decubitus)
      and the center code, this model also includes patient-specific baseline co-variates to adjust
      for differential case-mix. The Firth correction will be applied to the logistic regression
      model to maintain convergence in the presence of very small centers (5).

      Statistical analysis will be performed using SPSS and R. The head investigator (SB) is
      responsible for all statistical analysis. Advanced statistical methods will be executed
      byMiekeDeschepper (Strategic Policy Cell at Ghent University Hospital).
observational model: Cohort
time perspective: Cross-Sectional
measure: major risk factors for pressure injury development.
time frame: on the data collection day of 18 May 2018 (84 days follow-up)
description: Pressures injury stages will be graded following the classification system jointly developed by the National Pressure Ulcer Advisory Panel, European Pressure Ulcer Advisory Panel, and Pan Pacific Pressure Injury Alliance. Data to be recorded include patient demographics, data on severity of underlying disease and acute illness, organ failure, pressure ulcers, major risk factors for pressure ulcers, and measures taken to prevent pressure injuries.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Stijn BLOT
role: Principal Investigator
affiliation: Full Professor, Dept. of Internal Medicine, Faculty of Medicine & Health Science, Ghent University, Belgium
last name: Sonia LABEAU
role: Principal Investigator
affiliation: Lecturer at University College Ghent, Belgium
facility: 
country: Belgium
citation: Bennett G, Dealey C, Posnett J. The cost of pressure ulcers in the UK. Age Ageing. 2004 May;33(3):230-5. doi: 10.1093/ageing/afh086.
PMID: 15082426
citation: Keller BP, Wille J, van Ramshorst B, van der Werken C. Pressure ulcers in intensive care patients: a review of risks and prevention. Intensive Care Med. 2002 Oct;28(10):1379-88. doi: 10.1007/s00134-002-1487-z. Epub 2002 Sep 7.
PMID: 12373461
citation: Blot S, Cankurtaran M, Petrovic M, Vandijck D, Lizy C, Decruyenaere J, Danneels C, Vandewoude K, Piette A, Vershraegen G, Van Den Noortgate N, Peleman R, Vogelaers D. Epidemiology and outcome of nosocomial bloodstream infection in elderly critically ill patients: a comparison between middle-aged, old, and very old patients. Crit Care Med. 2009 May;37(5):1634-41. doi: 10.1097/CCM.0b013e31819da98e.
PMID: 19325489
citation: Iranmanesh S, Rafiei H, Sabzevari S. Relationship between Braden scale score and pressure ulcer development in patients admitted in trauma intensive care unit. Int Wound J. 2012 Jun;9(3):248-52. doi: 10.1111/j.1742-481X.2011.00852.x. Epub 2011 Sep 13.
PMID: 21910830
citation: Manzano F, Navarro MJ, Roldan D, Moral MA, Leyva I, Guerrero C, Sanchez MA, Colmenero M, Fernandez-Mondejar E; Granada UPP Group. Pressure ulcer incidence and risk factors in ventilated intensive care patients. J Crit Care. 2010 Sep;25(3):469-76. doi: 10.1016/j.jcrc.2009.09.002. Epub 2009 Oct 30.
PMID: 19879730
citation: Nijs N, Toppets A, Defloor T, Bernaerts K, Milisen K, Van Den Berghe G. Incidence and risk factors for pressure ulcers in the intensive care unit. J Clin Nurs. 2009 May;18(9):1258-66. doi: 10.1111/j.1365-2702.2008.02554.x. Epub 2008 Dec 11.
PMID: 19077028
citation: Terekeci H, Kucukardali Y, Top C, Onem Y, Celik S, Oktenli C. Risk assessment study of the pressure ulcers in intensive care unit patients. Eur J Intern Med. 2009 Jul;20(4):394-7. doi: 10.1016/j.ejim.2008.11.001. Epub 2008 Dec 6.
PMID: 19524181
citation: Harvey SE, Parrott F, Harrison DA, Bear DE, Segaran E, Beale R, Bellingan G, Leonard R, Mythen MG, Rowan KM; CALORIES Trial Investigators. Trial of the route of early nutritional support in critically ill adults. N Engl J Med. 2014 Oct 30;371(18):1673-84. doi: 10.1056/NEJMoa1409860. Epub 2014 Oct 1.
PMID: 25271389
citation: Matos LS, Duarte NLV, Minetto RdCs, (2010) Incidence and prevalence of ulcer for pressure in CTI of a Public Hospital of DF. Revista Eletronica de Enfermagem 12: 719-726
citation: European Pressure Ulcer Advisory Panel and National Pressure Ulcer Advisory Panel (2009) Prevention and treatment of pressure ulcers: quick reference guide. National Pressure Ulcer Advisory Panel, Washington DC
citation: Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999 Mar;8(1):3-15. doi: 10.1177/096228029900800102.
PMID: 10347857
citation: Schafer JL, Olsen MK. Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective. Multivariate Behav Res. 1998 Oct 1;33(4):545-71. doi: 10.1207/s15327906mbr3304_5.
PMID: 26753828
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responsible party type: Sponsor
mesh term: Wounds and Injuries
mesh term: Crush Injuries