- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03270345
One-Day Prevalence Study on Pressure Injuries in Intensive Care Units (DecubICUs)
Multicentre International One-Day Prevalence Study on Pressure Injuries in Intensive Care Units
Study Overview
Status
Conditions
Detailed Description
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).
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Brussels, Belgium
- All Centers From All Over the World Willing to Contribute Are Welcome
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- All adult patients (>18 years of age) present on the date of data collection
Exclusion Criteria:
- There are no exclusion criteria. All patients should be included. Patients with severe clinical conditions not allowing safe pressure ulcer identification should not be evaluated for the respective risk zones. If it is known that the patient has a pressure ulcer at the body sites that cannot be safely evaluated, the stage of the pressure ulcer should be recorded as previously known. If it is unknown whether the patient has a pressure ulcer at these body sites, this should be indicated with a '?' in the Case Report Form
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Cross-Sectional
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
major risk factors for pressure injury development.
Time Frame: on the data collection day of 18 May 2018 (84 days follow-up)
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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.
|
on the data collection day of 18 May 2018 (84 days follow-up)
|
Collaborators and Investigators
Investigators
- Principal Investigator: Stijn BLOT, Full Professor, Dept. of Internal Medicine, Faculty of Medicine & Health Science, Ghent University, Belgium
- Principal Investigator: Sonia LABEAU, Lecturer at University College Ghent, Belgium
Publications and helpful links
General Publications
- 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.
- 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.
- 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.
- Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999 Mar;8(1):3-15. doi: 10.1177/096228029900800102.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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
- Van Messem M, Varewyck M: Evaluating hospital peformance Risk Standard package. Ghent: Ghent University; 2015
- 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.
- Firth D: Bias Reduction of Maximum Likelihood Estimates.Biometrika Vol. 80, No. 1 (Mar., 1993), pp. 27-38
- De Pascale G, Antonelli M, Deschepper M, Arvaniti K, Blot K, Brown BC, de Lange D, De Waele J, Dikmen Y, Dimopoulos G, Eckmann C, Francois G, Girardis M, Koulenti D, Labeau S, Lipman J, Lipovetsky F, Maseda E, Montravers P, Mikstacki A, Paiva JA, Pereyra C, Rello J, Timsit JF, Vogelaers D, Blot S; Abdominal Sepsis Study (AbSeS) group and the Trials Group of the European Society of Intensive Care Medicine. Poor timing and failure of source control are risk factors for mortality in critically ill patients with secondary peritonitis. Intensive Care Med. 2022 Nov;48(11):1593-1606. doi: 10.1007/s00134-022-06883-y. Epub 2022 Sep 23.
- Arvaniti K, Dimopoulos G, Antonelli M, Blot K, Creagh-Brown B, Deschepper M, de Lange D, De Waele J, Dikmen Y, Eckmann C, Einav S, Francois G, Fjeldsoee-Nielsen H, Girardis M, Jovanovic B, Lindner M, Koulenti D, Labeau S, Lipman J, Lipovestky F, Makikado LDU, Maseda E, Mikstacki A, Montravers P, Paiva JA, Pereyra C, Rello J, Timsit JF, Tomescu D, Vogelaers D, Blot S; Abdominal Sepsis Study (AbSeS) Group on behalf of the Trials Group of the European Society of Intensive Care Medicine. Epidemiology and age-related mortality in critically ill patients with intra-abdominal infection or sepsis: an international cohort study. Int J Antimicrob Agents. 2022 Jul;60(1):106591. doi: 10.1016/j.ijantimicag.2022.106591. Epub 2022 Apr 20.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- DecubICUs
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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