<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/NCT03781713</url>
  </required_header>
  <id_info>
    <org_study_id>CONEP 1.487.682</org_study_id>
    <nct_id>NCT03781713</nct_id>
  </id_info>
  <brief_title>Prospective Clinical Surveillance With Application of Trigger Tools in Critically Ill Patients</brief_title>
  <official_title>Prospective Clinical Surveillance With Application of Trigger Tools in Critically Ill Patients</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Hospital Sao Domingos</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Hospital Sao Domingos</source>
  <oversight_info>
    <has_dmc>Yes</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>
      This study evaluates the impact of prospective clinical surveillance with the use of triggers&#xD;
      to identify risk of adverse events with prompt adoption of interventions on the stabilization&#xD;
      time of critically ill patients.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      In the 1999 landmark report, &quot;To Err is Human: Building a Safe Health System,&quot; the Institute&#xD;
      of Medicine estimated that avoidable errors in health contributed to 44 to 98,000 deaths and&#xD;
      more than 1 million injuries annually in the United States (1). Several years after the&#xD;
      publication of this study, numerous initiatives have emerged to improve patient safety in the&#xD;
      USA and the world (2).&#xD;
&#xD;
      An important advance in the detection of adverse events is the use of triggers, algorithms&#xD;
      that use patient data to look for consistent patterns that predict the onset of an adverse&#xD;
      event (3).&#xD;
&#xD;
      The Institute for Health Care Improvement (IHI) has developed several tools with the use of&#xD;
      triggers. The Global Trigger Tool (GTT), developed in 2009, is a tool applied retrospectively&#xD;
      and proved to be effective in the detection of adverse events (4). It is an easily applicable&#xD;
      method for quantifying damage. Countries outside USA did not have the same result with their&#xD;
      application and questioned their benefit (5,6,7).&#xD;
&#xD;
      Using a prospective clinical surveillance tool, which consisted of direct observation by a&#xD;
      trained nurse, Forster et al (8) identified a high risk of adverse events and a significant&#xD;
      variation of risks and sub types between services. These results have suggested that&#xD;
      institutions will have to assess service-specific safety issues to define priorities and&#xD;
      improvement strategies in the design of care. This model was later used by Wong et al.(9),&#xD;
      who identified a wide range of factors contributing to adverse events. Despite the&#xD;
      prospective methodology, the impact of the actions instituted to prevent the events was not&#xD;
      identified in these studies.&#xD;
&#xD;
      Prospective clinical surveillance with the use of triggers as a tool to identify the risk of&#xD;
      adverse events, with the prompt adoption of interventions and evaluation of the evidence of&#xD;
      the expected outcome may be the answer to improving patient safety, which remains a major&#xD;
      problem of public health 20 years after the publication of the reference &quot;To err is human&quot;.&#xD;
&#xD;
      OBJECTIVE To evaluate the impact of prospective trigger tools and near real time&#xD;
      interventions on the stabilization time of critically ill patients.&#xD;
&#xD;
      METHOD This is a retrospective cohort study conducted at a surgical intensive care unit (13&#xD;
      beds), a medical intensive care unit (19 beds) and a surgical and trauma high complexity unit&#xD;
      (12 beds) of a tertiary hospital. The hospital São Domingos Ethics in Research Committee&#xD;
      approved the study (number 1.487.682).&#xD;
&#xD;
      All adult patients (18 years of age or older) who were admitted to one of the four intensive&#xD;
      care units were included in the study, in the period from November 1, 2017 to October 30,&#xD;
      2018.&#xD;
&#xD;
      Patients who are readmitted and those with length of ICU stay less than 48 hours were&#xD;
      excluded, since this is the minimum time required for the triggering of the trigger and its&#xD;
      corresponding intervention.&#xD;
&#xD;
      Once admitted to the ICU and meeting the inclusion criteria, patients were followed by the&#xD;
      multidisciplinary surveillance team composed of physicians, nurses, physiotherapists and&#xD;
      pharmacists. They conducted a daily active search for the triggers in the medical records. To&#xD;
      ensure the reliability of the data collected, each professional is responsible for a specific&#xD;
      trigger. If one of them is activated, the team member approaches the care team responsible&#xD;
      for the patient within 24 hours, and the data is also presented in the daily&#xD;
      multidisciplinary round. This moment is still used to solve all doubts regarding the&#xD;
      triggering of the trigger and its respective conduct or change in the therapeutic plan in&#xD;
      order to reach the expected result of each trigger.&#xD;
&#xD;
      The prospective trigger tool uses indicators that are capable of predicting serious&#xD;
      complications that increase the morbidity and mortality of ICU patients that could be&#xD;
      preventable. The triggers used were:&#xD;
&#xD;
        1. Acute kidney injury (KDIGO) (10) - When triggered, the patient is classified as at risk&#xD;
           (1.5 to 1.9 x baseline creatinine or increase of 0.3 mg / dl), injury (2.0 to 2.9 x&#xD;
           baseline creatinine) or insufficiency (3 x basal creatinine). It is expected that the&#xD;
           risk can be identified early and that therapeutic interventions be undertaken in order&#xD;
           to prevent progression to renal replacement therapy.&#xD;
&#xD;
        2. Delta SOFA (11)- The SOFA (Sequential Organ Failure Assessment) of all patients are&#xD;
           calculated on the first and third days. The increase in this score on the third day&#xD;
           identify clinical worsening, triggering the trigger and requiring revision of the&#xD;
           initial therapeutic plan. The impact of this change is analyzed by calculating the fifth&#xD;
           day SOFA, which should be lower.&#xD;
&#xD;
        3. Hypoglycaemia (glycemia &lt;60 mg / dl) - Once this trigger is activated, it is expected&#xD;
           that the clinical intervention will be effective to prevent the recurrence of&#xD;
           hypoglycemia in the next 24 hours.&#xD;
&#xD;
        4. Drug interaction risk D or X - When identifying a D or X risk interaction in the&#xD;
           patient's prescription, the assistant team is advised to make alterations in the&#xD;
           therapeutic plan in order to avoid adverse drug reactions.&#xD;
&#xD;
        5. Antimicrobial stewardship - Whenever an antimicrobial regimen is started or modified it&#xD;
           is assessed for its suitability through MALDI-TOF and sensitivity test. If there is no&#xD;
           microbiological identification, the clinical improvement, represented by at least two of&#xD;
           the three following parameters (leukocyte drop, absence of fever and improvement of CRP)&#xD;
           is taken into account.&#xD;
&#xD;
      Demographic data also were collected including age, gender, hospitalization category&#xD;
      (clinical or surgical), Charlson comorbidities index and score and SAPS 3 (Simplified Acute&#xD;
      Physiology Score 3) with its respective risk of death.&#xD;
&#xD;
      To evaluate the impact of the tool on the time of stabilization of patients, two groups will&#xD;
      be compared: the first will be composed of patients who did not triggered triggers and the&#xD;
      second by patients who triggered triggers and had interventions.&#xD;
&#xD;
      For the calculation of the stabilization time, the long-term risk rating of the Epimed&#xD;
      Performance software (Epimed solutions) will be used, which allows us to estimate the length&#xD;
      of ICU stay of the patients individually using more than 60 variables.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">November 1, 2017</start_date>
  <completion_date type="Actual">December 10, 2018</completion_date>
  <primary_completion_date type="Actual">October 31, 2018</primary_completion_date>
  <phase>Phase 2</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Non-Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <intervention_model_description>To evaluate the impact of the tool on the time of stabilization of patients, two groups will be compared: the first will be composed of patients who did not triggered triggers and the second by patients who triggered triggers and had interventions.&#xD;
For the calculation of the stabilization time, the long-term risk rating of the Epimed Performance software will be used, which allows us to estimate the length of ICU stay of the patients individually using more than 60 variables.</intervention_model_description>
    <primary_purpose>Prevention</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>To evaluate the impact of prospective trigger tools and near real time interventions on the stabilization time of critically ill patients.</measure>
    <time_frame>3 months</time_frame>
    <description>For the calculation of the stabilization time, the long-term risk rating of the Epimed Performance software (Epimed solutions) will be used, which allows us to estimate the length of ICU stay of the patients individually using more than 60 variables.</description>
  </primary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">1200</enrollment>
  <condition>Critical Illness</condition>
  <arm_group>
    <arm_group_label>STUDY GROUP</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>patients who triggered triggers and had interventions. Kdigo: interventions to prevent renal replacement therapy Delta SOFA: interventions to improve SOFA score Hypoglycemia: Interventions to prevent new episodes of hypoglycemia in the next 24 hours Drug interaction risk D or X - Intervention in the therapeutic plan in order to avoid adverse drug reactions. Antimicrobial stewardship: optimization of antimicrobial therapy based on Gram stain, MALDI TOF, MIC, antimicrobial susceptibility</description>
  </arm_group>
  <arm_group>
    <arm_group_label>CONTROL GROUP</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>patients who did not triggered triggers</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>KDIGO</intervention_name>
    <description>Interventions to prevent renal replacement therapy</description>
    <arm_group_label>STUDY GROUP</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Delta SOFA</intervention_name>
    <description>interventions to improve SOFA score</description>
    <arm_group_label>STUDY GROUP</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Combination Product</intervention_type>
    <intervention_name>Hypoglycemia</intervention_name>
    <description>Interventions to prevent new episodes of hypoglycemia in the next 24 hours</description>
    <arm_group_label>STUDY GROUP</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Drug</intervention_type>
    <intervention_name>Drug interaction risk D or X</intervention_name>
    <description>Intervention in the therapeutic plan in order to avoid adverse drug reactions.</description>
    <arm_group_label>STUDY GROUP</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Drug</intervention_type>
    <intervention_name>Antimicrobial stewardship</intervention_name>
    <description>Optimization of anticrobial therapy based on Gram stain, MALDI TOF, MIC and antimicrobial susceptibility</description>
    <arm_group_label>STUDY GROUP</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria: All adult patients admitted to the ICU with expexted length of stay of&#xD;
        at least 48 hours -&#xD;
&#xD;
        Exclusion Criteria: End of life and exclusive palliative care&#xD;
&#xD;
        -&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>JOSE R AZEVEDO, MD. PhD</last_name>
    <role>Study Chair</role>
    <affiliation>Hospital Sao Domingos</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Hospital São Domingos</name>
      <address>
        <city>Sao Luis</city>
        <state>Maranhão</state>
        <zip>65060-645</zip>
        <country>Brazil</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Hospital Sao Domingos</name>
      <address>
        <city>Sao Luis</city>
        <state>MA</state>
        <zip>65060-642</zip>
        <country>Brazil</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Brazil</country>
  </location_countries>
  <reference>
    <citation>Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010 Nov 25;363(22):2124-34. doi: 10.1056/NEJMsa1004404. Erratum In: N Engl J Med. 2010 Dec 23;363(26):2573.</citation>
    <PMID>21105794</PMID>
  </reference>
  <results_reference>
    <citation>Institute of Medicine (US) Committee on Quality of Health Care in America; Kohn LT, Corrigan JM, Donaldson MS, editors. To Err is Human: Building a Safer Health System. Washington (DC): National Academies Press (US); 2000. Available from http://www.ncbi.nlm.nih.gov/books/NBK225182/</citation>
    <PMID>25077248</PMID>
  </results_reference>
  <results_reference>
    <citation>Griffin FA, Classen DC. Detection of adverse events in surgical patients using the Trigger Tool approach. Qual Saf Health Care. 2008 Aug;17(4):253-8. doi: 10.1136/qshc.2007.025080.</citation>
    <PMID>18678721</PMID>
  </results_reference>
  <results_reference>
    <citation>4. Griffin FA, Resar RK. IHI global trigger tool for measuring adverse events. 2nd edn. Cambridge, Massachusetts Institute for Healthcare Improvement, 2009.</citation>
  </results_reference>
  <results_reference>
    <citation>5. Shimada S, Rivard PE, Mull J, et al. Triggers and Targeted Injury Detection Systems: Aiming for the Right Target With the Appropriate Tool. 2009.</citation>
  </results_reference>
  <results_reference>
    <citation>Franklin BD, Birch S, Schachter M, Barber N. Testing a trigger tool as a method of detecting harm from medication errors in a UK hospital: a pilot study. Int J Pharm Pract. 2010 Oct;18(5):305-11. doi: 10.1111/j.2042-7174.2010.00058.x.</citation>
    <PMID>20840687</PMID>
  </results_reference>
  <results_reference>
    <citation>Carnevali L, Krug B, Amant F, Van Pee D, Gerard V, de Bethune X, Spinewine A. Performance of the adverse drug event trigger tool and the global trigger tool for identifying adverse drug events: experience in a Belgian hospital. Ann Pharmacother. 2013 Nov;47(11):1414-9. doi: 10.1177/1060028013500939.</citation>
    <PMID>24285758</PMID>
  </results_reference>
  <results_reference>
    <citation>Forster AJ, Worthington JR, Hawken S, Bourke M, Rubens F, Shojania K, van Walraven C. Using prospective clinical surveillance to identify adverse events in hospital. BMJ Qual Saf. 2011 Sep;20(9):756-63. doi: 10.1136/bmjqs.2010.048694. Epub 2011 Mar 1.</citation>
    <PMID>21367769</PMID>
  </results_reference>
  <results_reference>
    <citation>Wong BM, Dyal S, Etchells EE, Knowles S, Gerard L, Diamantouros A, Mehta R, Liu B, Baker GR, Shojania KG. Application of a trigger tool in near real time to inform quality improvement activities: a prospective study in a general medicine ward. BMJ Qual Saf. 2015 Apr;24(4):272-81. doi: 10.1136/bmjqs-2014-003432. Epub 2015 Mar 6.</citation>
    <PMID>25749028</PMID>
  </results_reference>
  <results_reference>
    <citation>10. Kdigo Clinical Practice Guideline for acute kidney injury. Kidney International 2012; 2 9Suppl 1).</citation>
  </results_reference>
  <results_reference>
    <citation>Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, Bruining H, Reinhart CK, Suter PM, Thijs LG. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996 Jul;22(7):707-10. doi: 10.1007/BF01709751. No abstract available.</citation>
    <PMID>8844239</PMID>
  </results_reference>
  <verification_date>December 2018</verification_date>
  <study_first_submitted>December 18, 2018</study_first_submitted>
  <study_first_submitted_qc>December 19, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 20, 2018</study_first_posted>
  <last_update_submitted>December 23, 2018</last_update_submitted>
  <last_update_submitted_qc>December 23, 2018</last_update_submitted_qc>
  <last_update_posted type="Actual">December 26, 2018</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Hospital Sao Domingos</investigator_affiliation>
    <investigator_full_name>José Raimundo Araujo de Azevedo</investigator_full_name>
    <investigator_title>MD; PhD</investigator_title>
  </responsible_party>
  <keyword>prospective</keyword>
  <keyword>surveillance</keyword>
  <keyword>trigger</keyword>
  <keyword>stabilization</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Critical Illness</mesh_term>
  </condition_browse>
  <intervention_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Anti-Infective Agents</mesh_term>
    <mesh_term>Anti-Bacterial Agents</mesh_term>
  </intervention_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- 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/NCT03781713
org study id: CONEP 1.487.682
nct id: NCT03781713
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
This study evaluates the impact of prospective clinical surveillance with the use of triggers
      to identify risk of adverse events with prompt adoption of interventions on the stabilization
      time of critically ill patients.
In the 1999 landmark report, "To Err is Human: Building a Safe Health System," the Institute
      of Medicine estimated that avoidable errors in health contributed to 44 to 98,000 deaths and
      more than 1 million injuries annually in the United States (1). Several years after the
      publication of this study, numerous initiatives have emerged to improve patient safety in the
      USA and the world (2).

      An important advance in the detection of adverse events is the use of triggers, algorithms
      that use patient data to look for consistent patterns that predict the onset of an adverse
      event (3).

      The Institute for Health Care Improvement (IHI) has developed several tools with the use of
      triggers. The Global Trigger Tool (GTT), developed in 2009, is a tool applied retrospectively
      and proved to be effective in the detection of adverse events (4). It is an easily applicable
      method for quantifying damage. Countries outside USA did not have the same result with their
      application and questioned their benefit (5,6,7).

      Using a prospective clinical surveillance tool, which consisted of direct observation by a
      trained nurse, Forster et al (8) identified a high risk of adverse events and a significant
      variation of risks and sub types between services. These results have suggested that
      institutions will have to assess service-specific safety issues to define priorities and
      improvement strategies in the design of care. This model was later used by Wong et al.(9),
      who identified a wide range of factors contributing to adverse events. Despite the
      prospective methodology, the impact of the actions instituted to prevent the events was not
      identified in these studies.

      Prospective clinical surveillance with the use of triggers as a tool to identify the risk of
      adverse events, with the prompt adoption of interventions and evaluation of the evidence of
      the expected outcome may be the answer to improving patient safety, which remains a major
      problem of public health 20 years after the publication of the reference "To err is human".

      OBJECTIVE To evaluate the impact of prospective trigger tools and near real time
      interventions on the stabilization time of critically ill patients.

      METHOD This is a retrospective cohort study conducted at a surgical intensive care unit (13
      beds), a medical intensive care unit (19 beds) and a surgical and trauma high complexity unit
      (12 beds) of a tertiary hospital. The hospital São Domingos Ethics in Research Committee
      approved the study (number 1.487.682).

      All adult patients (18 years of age or older) who were admitted to one of the four intensive
      care units were included in the study, in the period from November 1, 2017 to October 30,
      2018.

      Patients who are readmitted and those with length of ICU stay less than 48 hours were
      excluded, since this is the minimum time required for the triggering of the trigger and its
      corresponding intervention.

      Once admitted to the ICU and meeting the inclusion criteria, patients were followed by the
      multidisciplinary surveillance team composed of physicians, nurses, physiotherapists and
      pharmacists. They conducted a daily active search for the triggers in the medical records. To
      ensure the reliability of the data collected, each professional is responsible for a specific
      trigger. If one of them is activated, the team member approaches the care team responsible
      for the patient within 24 hours, and the data is also presented in the daily
      multidisciplinary round. This moment is still used to solve all doubts regarding the
      triggering of the trigger and its respective conduct or change in the therapeutic plan in
      order to reach the expected result of each trigger.

      The prospective trigger tool uses indicators that are capable of predicting serious
      complications that increase the morbidity and mortality of ICU patients that could be
      preventable. The triggers used were:

        1. Acute kidney injury (KDIGO) (10) - When triggered, the patient is classified as at risk
           (1.5 to 1.9 x baseline creatinine or increase of 0.3 mg / dl), injury (2.0 to 2.9 x
           baseline creatinine) or insufficiency (3 x basal creatinine). It is expected that the
           risk can be identified early and that therapeutic interventions be undertaken in order
           to prevent progression to renal replacement therapy.

        2. Delta SOFA (11)- The SOFA (Sequential Organ Failure Assessment) of all patients are
           calculated on the first and third days. The increase in this score on the third day
           identify clinical worsening, triggering the trigger and requiring revision of the
           initial therapeutic plan. The impact of this change is analyzed by calculating the fifth
           day SOFA, which should be lower.

        3. Hypoglycaemia (glycemia <60 mg / dl) - Once this trigger is activated, it is expected
           that the clinical intervention will be effective to prevent the recurrence of
           hypoglycemia in the next 24 hours.

        4. Drug interaction risk D or X - When identifying a D or X risk interaction in the
           patient's prescription, the assistant team is advised to make alterations in the
           therapeutic plan in order to avoid adverse drug reactions.

        5. Antimicrobial stewardship - Whenever an antimicrobial regimen is started or modified it
           is assessed for its suitability through MALDI-TOF and sensitivity test. If there is no
           microbiological identification, the clinical improvement, represented by at least two of
           the three following parameters (leukocyte drop, absence of fever and improvement of CRP)
           is taken into account.

      Demographic data also were collected including age, gender, hospitalization category
      (clinical or surgical), Charlson comorbidities index and score and SAPS 3 (Simplified Acute
      Physiology Score 3) with its respective risk of death.

      To evaluate the impact of the tool on the time of stabilization of patients, two groups will
      be compared: the first will be composed of patients who did not triggered triggers and the
      second by patients who triggered triggers and had interventions.

      For the calculation of the stabilization time, the long-term risk rating of the Epimed
      Performance software (Epimed solutions) will be used, which allows us to estimate the length
      of ICU stay of the patients individually using more than 60 variables.
allocation: Non-Randomized
intervention model: Parallel Assignment
intervention model description: To evaluate the impact of the tool on the time of stabilization of patients, two groups will be compared: the first will be composed of patients who did not triggered triggers and the second by patients who triggered triggers and had interventions.
For the calculation of the stabilization time, the long-term risk rating of the Epimed Performance software will be used, which allows us to estimate the length of ICU stay of the patients individually using more than 60 variables.
primary purpose: Prevention
masking: None (Open Label)
measure: To evaluate the impact of prospective trigger tools and near real time interventions on the stabilization time of critically ill patients.
time frame: 3 months
description: For the calculation of the stabilization time, the long-term risk rating of the Epimed Performance software (Epimed solutions) will be used, which allows us to estimate the length of ICU stay of the patients individually using more than 60 variables.
arm group label: STUDY GROUP
arm group type: Active Comparator
description: patients who triggered triggers and had interventions. Kdigo: interventions to prevent renal replacement therapy Delta SOFA: interventions to improve SOFA score Hypoglycemia: Interventions to prevent new episodes of hypoglycemia in the next 24 hours Drug interaction risk D or X - Intervention in the therapeutic plan in order to avoid adverse drug reactions. Antimicrobial stewardship: optimization of antimicrobial therapy based on Gram stain, MALDI TOF, MIC, antimicrobial susceptibility
arm group label: CONTROL GROUP
arm group type: No Intervention
description: patients who did not triggered triggers
intervention type: Other
intervention name: KDIGO
description: Interventions to prevent renal replacement therapy
arm group label: STUDY GROUP
intervention type: Other
intervention name: Delta SOFA
description: interventions to improve SOFA score
arm group label: STUDY GROUP
intervention type: Combination Product
intervention name: Hypoglycemia
description: Interventions to prevent new episodes of hypoglycemia in the next 24 hours
arm group label: STUDY GROUP
intervention type: Drug
intervention name: Drug interaction risk D or X
description: Intervention in the therapeutic plan in order to avoid adverse drug reactions.
arm group label: STUDY GROUP
intervention type: Drug
intervention name: Antimicrobial stewardship
description: Optimization of anticrobial therapy based on Gram stain, MALDI TOF, MIC and antimicrobial susceptibility
arm group label: STUDY GROUP
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: JOSE R AZEVEDO, MD. PhD
role: Study Chair
affiliation: Hospital Sao Domingos
facility: 
facility: 
country: Brazil
citation: Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010 Nov 25;363(22):2124-34. doi: 10.1056/NEJMsa1004404. Erratum In: N Engl J Med. 2010 Dec 23;363(26):2573.
PMID: 21105794
citation: Institute of Medicine (US) Committee on Quality of Health Care in America; Kohn LT, Corrigan JM, Donaldson MS, editors. To Err is Human: Building a Safer Health System. Washington (DC): National Academies Press (US); 2000. Available from http://www.ncbi.nlm.nih.gov/books/NBK225182/
PMID: 25077248
citation: Griffin FA, Classen DC. Detection of adverse events in surgical patients using the Trigger Tool approach. Qual Saf Health Care. 2008 Aug;17(4):253-8. doi: 10.1136/qshc.2007.025080.
PMID: 18678721
citation: 4. Griffin FA, Resar RK. IHI global trigger tool for measuring adverse events. 2nd edn. Cambridge, Massachusetts Institute for Healthcare Improvement, 2009.
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PMID: 8844239
responsible party type: Principal Investigator
investigator affiliation: Hospital Sao Domingos
investigator full name: José Raimundo Araujo de Azevedo
investigator title: MD; PhD
mesh term: Critical Illness
mesh term: Anti-Infective Agents
mesh term: Anti-Bacterial Agents
sharing ipd: No