Prospective Clinical Surveillance With Application of Trigger Tools in Critically Ill Patients

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: 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. 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. 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. 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. 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.

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.

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

Optimization of anticrobial therapy based on Gram stain, MALDI TOF, MIC and antimicrobial susceptibility

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

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.

Inclusion Criteria: All adult patients admitted to the ICU with expexted length of stay of at least 48 hours - Exclusion Criteria: End of life and exclusive palliative care -

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.

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/

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.

4. Griffin FA, Resar RK. IHI global trigger tool for measuring adverse events. 2nd edn. Cambridge, Massachusetts Institute for Healthcare Improvement, 2009.

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.

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.

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.

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.

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.

10. Kdigo Clinical Practice Guideline for acute kidney injury. Kidney International 2012; 2 9Suppl 1).

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.