Failure to Rescue in the Intensive Care Unit: A Multi-Disciplinary Approach to Reducing Preventable Cardiac Arrest
Michael Donnino, MD
Status of Study:
Purpose of Protocol:
Our overall hypothesis is that implementation of the Clinical 'Trigger and Response' System (CTRS) will result in a reduction in ICU cardiac arrest events and specifically those events deemed to be preventable
Specific Aim #1: Investigate the incidence/cause(s) of cardiac arrest in the ICU. To achieve this Aim, we will have multiple independent providers review all ICU cardiac arrests since 2010. The likelihood that each arrest was preventable will be assessed on a Likert scale; contributing factors leading to the arrest will be identified. This cohort will be utilized both to optimize our intervention and serve as a historical control group for comparative outcomes (Aim #2).
Specific Aim #2a: Development of a comprehensive, ICU-specific CTRS to reduce preventable cardiac arrests in the ICU. Based on themes identified and lessons learned in Aim #1, we will develop the CTRS. We will accomplish this through the current team working with all ICU leadership to develop the final product.
Specific Aim #2b: Implementation of the CTRS in BIDMC ICUs and assessment of CTRS effectiveness in reducing preventable cardiac arrest. The CTRS will be implemented in BIDMC ICUs. The effectiveness of the CTRS in forestalling preventable cardiac arrest will be determined based on comparison to historical controls.
Specific Aim #3: Assessment of nursing comfort level in caring for decompensating ICU patients and alerting physicians to clinical deterioration: To achieve this aim, ICU nurses at BIDMC will be surveyed with regards to the above both prior to implementation of the CTRS and one year afterwards.
In-Hospital Cardiac Arrest (IHCA) is a relatively common event amongst hospitalized patients and carries a significant morbidity/mortality burden. Patients in the ICU are often at increased risk for IHCA given hemodynamic instability, respiratory compromise, organ failure, and electrolyte abnormalities. Unfortunately, cardiac arrest in the ICU is often seen as the inevitable result of the underlying disease process and therefore not preventable. However, as described in the preliminary data section below, a number of cardiac arrests in the ICU may in fact represent ‘failure to rescue’ events wherein a patient in a decompensated state may not progress to cardiac arrest if the appropriate preventative actions are taken. This is important as cardiac arrest carries an additional morbidity/mortality burden beyond the pre-existing critical illness. Patients with acute respiratory compromise, for example, who are successfully intubated prior to arrest have a mortality rate of approximately 30%, whereas patients with acute respiratory compromise who suffer cardiac arrest have a mortality rate approaching 80% (see prelim data). Therefore, prevention of these cardiac arrests may be the single best intervention to improve mortality in these critically ill patients. In addition to cardiac arrest being a devastating event for the patient, preventable cardiac arrests in the ICU represents a substantial medical-legal risk for providers and the overall health care system. Many of the reasons for preventability (e.g. failure to diagnose, delayed recognition of clinical deterioration, procedural complication) would be just cause for medical-legal action. Therefore, reducing preventable cardiac arrests in the ICU is essential to improving patient safety while reducing malpractice exposure.
1) Adult patient in ICU at BIDMC
2) Cardiac Arrest (defined by loss of pulse requiring chest compressions)
Strategies used for data truncated due to early mortality in adult trials in the ICU with SOFA score as an outcome: a scoping review protocol
Objective: The objective of this scoping review is categorizing the different methods used to handle data that is truncated due to early mortality prior to outcome assessment, in interventional randomized clinical trials in critically ill patients that use a primary or secondary outcome of SOFA score. Specifically, the review questions are:
- What is the universe of methods used to handle data that is truncated due to early mortality, and how frequently is each method used?
- What is the degree of data truncated due to early mortality in each trial?
Sepsis is defined as “life-threatening organ dysfunction caused by a dysregulated host response to infection”1. Globally, there are estimated to be 30 million episodes of sepsis with six million attributable deaths2. In adults in the United States, there are estimated to be 1.7 million hospitalizations for sepsis with more than 250,000 deaths per year3. In the latest consensus statement on defining sepsis, use of the Sequential (or Sepsis-related) Organ Failure Assessment (SOFA) score to quantify organ dysfunction as an increase of ≥2 points is recommended1.
The SOFA score (sepsis-related organ failure assessment score or sequential organ failure assessment score), developed in 19944, describes organ failure based on laboratory results and clinical data5 and is associated with ICU morbidity4 and mortality6. The score is a summary of six different scores from individual organ systems: respiratory, cardiovascular, hepatic, coagulation, renal and neurological. It is often used as a primary or secondary outcome in trials of critical illness6 as a surrogate outcome measure for mortality in interventional studies of critically ill patients7. However, SOFA is complicated by missing information and patients who expire prior to the time when the primary outcome is measured. Methods on how to handle these issues vary from study to study. To the best of our knowledge, no systematic accounting for these methods and the degree of data truncated due to early mortality in these trials exists. Our scoping review will address this knowledge gap.
This is not only a problem in clinical trials of critical illness. A panel created and funded by the FDA, the Panel on the Handling of Missing Data in Clinical Trials, which developed 18 recommendations on missing data in clinical trials “advocates (recommendation 16) the development of new research on the issue of missing data and on analysis of existing…clinical trials to help define best practices for dealing with missing data. In particular, it seems especially important to learn how often results change when diﬀerent methods of analyzing the data…are applied and whether this can be predicted”8. The goal of this panel and its report was to improve the conduct and analysis of clinical trials8. With this scoping review, we aim to classify one area of missing data, that truncated due to early mortality prior to outcome assessment, in hopes that this will ultimately lead to a standardized way of reporting and handling this type of data.
Data Missingness: In the context of clinical research, data missingness occurs when there is no information available for a certain variable of interest in one or more patients. Missing data are common, although we do not know how common in trials of critical illness, and can affect results profoundly9. There are a few reasons for missingness: the value is not available (for example, a patient did not have a blood pressure measured at the time of interest, a subject did not respond to a question on a survey, or a patient did not return for a follow-up visit), the value is unable to be recorded (for example, a male subject does not have a value for last menstrual period or a patient does not have a lab result if they have expired), or the value is able to be recorded but was missed through human error (for example, if data was entered incorrectly as a value that is not physiologically possible, such as an age of 300).
Early Mortality: For the purpose of this scoping review, we define early mortality as mortality that occurs before the time the outcome of SOFA score was intended to be measured (for example, if the primary outcome was SOFA score 72 hours after study enrollment and the patient died 48 hours after enrollment).
Interventional Randomized Clinical Trials: A randomized clinical trial is one in which investigators seek to test out the performance of an intervention by using random allocation of the interventional treatment versus another treatment, usually a control treatment, once patients have been screened for eligibility. The goal of randomization is to balance both measured and unmeasured factors in patient groups10.
ICU: An intensive care unit (ICU) is a hospital department that treats patients with severe and/or life-threatening injuries. Patients often have conditions such as acute respiratory distress syndrome, trauma, multiple organ failure and sepsis11.
SOFA score: The SOFA score (sepsis-related organ failure assessment score or sequential organ failure assessment score) was developed to predict ICU morbidity and mortality based on laboratory results and clinical data. The score is a summary of six different scores from individual organ systems: respiratory, cardiovascular, hepatic, coagulation, renal and neurological. Each individual score ranges from 0-4, with 0 indicating normal organ function and 4 indicating severe organ dysfunction/failure. The SOFA score can therefore range from 0-24 once summed and the worst values from each day are used in calculating the score5. Due to difficulties with collection of some of the laboratory results and clinical data, especially those that may not be collected /documented routinely, some trials use a modified version of one or more components of the SOFA score. For example, the respiratory component of SOFA was originally measured using PaO2/FiO2 but has also been validated using SpO2/FiO2 for cases in which daily arterial blood gas data are unavailable12. How frequently modifications like this occur is unknown but will be documented in this scoping review.
This review will include studies that take place in an ICU or Emergency Department (prior to presumed admission to the ICU) that are testing an intervention in adult patients and use randomization to determine treatment allocation. The studies must have SOFA score as a primary or secondary outcome, and can include any disease states that are being primarily treated in the ICU. Only human studies will be included.
In this scoping review, the key concept is the method used for handling data truncated due to early mortality in statistical analysis of the outcome of SOFA score. A secondary concept is the degree of data truncated due to early mortality due to early mortality in each study.
In this scoping review, the context is the critical care setting. This scoping review will include only studies pertinent to patients requiring hospitalization in the ICU or critically ill patients in the ED. Studies related to trials in other care settings will be excluded.
Types of studies
In this review, only peer-reviewed literature on clinical trials will be considered, based on the stated inclusion and exclusion criteria. Reviews, conference abstracts, dissertations, and opinion papers will not be included. Only studies published in English will be considered.
Our search strategy involves looking at other systematic reviews and meta-analyses for comparable search terms and meeting with a librarian at our affiliated university. Together, we will come up with the appropriate search terms and refine as necessary in an iterative fashion. The databases to be searched will include: PubMed, Ovid, MEDLINE, Cochrane Library, CINAHL, EMBASE, and Web of Science. We will not search for unpublished data. Due to limitations of time and resources, only studies published in English will be included in this review. The search will not be limited to a specific time period, as both recent and older RCTs could be relevant and much scientific research builds on prior studies. Reviewers will contact authors for further information, if required.
Following the search process, identified citations will be uploaded into Endnote (Clarivate Analytics, PA, USA) and duplicates removed. Titles and abstracts will then be screened by two independent reviewers to assess if they meet inclusion criteria via Covidence13. Studies that meet/potentially meet inclusion criteria will be retrieved in full and assessed in detail against inclusion criteria. Full text studies that do not meet the inclusion criteria will be excluded; reasons for exclusion will be provided. Search results will be presented in a PRISMA flow diagram14. Any disagreements that arise between the reviewers will be resolved by a third reviewer.
Data extracted will include specific details about the trial, study population, and mortality and SOFA score outcomes. Any disagreements that arise between the reviewers will be resolved by a third reviewer. Authors of papers will be contacted to request missing or additional data. A data extraction form has been developed to record information about the study such as author, journal, and results or findings relevant to the review question/s. This charting table may be refined iteratively according to study needs and will be reported as an appendix when the scoping review is published.
The results extracted using the data extraction form will be listed in a table with a row for each individual study and also presented as counts and frequencies as well as in a narrative format. This will provide a comprehensive overview of the universe of strategies used to handle data truncated due to early mortality in randomized clinical trials in critically ill patients and the frequency with which each strategy is used. This will provide valuable information in guiding interpretation of trial results.
With this scoping review, we hope to describe the frequency and proportion of data that is truncated due to death as well as the universe of strategies used to handle this issue. By doing this, we will be able to discern the potential impact that this may have on the results and interpretability of randomized controlled trials in critical illness with SOFA score as an outcome.
- Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016 Feb 23;315(8):801-10.
- Reinhart K, Daniels R, Kissoon N, Machado FR, Schachter RD, Finfer S. Recognizing Sepsis as a Global Health Priority - A WHO Resolution. N Engl J Med. 2017 Aug 3;377(5):414-417.
- Rhee C, Dantes R, Epstein L, Murphy DJ, Seymour CW, Iwashyna TJ, Kadri SS, Angus DC, Danner RL, Fiore AE, Jernigan JA, Martin GS, Septimus E, Warren DK, Karcz A, Chan C, Menchaca JT, Wang R, Gruber S, Klompas M; CDC Prevention Epicenter Program. Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014. JAMA. 2017 Oct 3;318(13):1241-1249.
- Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, Sprung CL, Colardyn F, Blecher S. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Crit Care Med. 1998 Nov;26(11):1793-800.
- Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça 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.
- de Grooth HJ, Geenen IL, Girbes AR, Vincent JL, Parienti JJ, Oudemans-van Straaten HM. SOFA and mortality endpoints in randomized controlled trials: a systematic review and meta-regression analysis. Crit Care. 2017 Feb 24;21(1):38.
- Minne L, Abu-Hanna A, de Jonge E. Evaluation of SOFA-based models for predicting mortality in the ICU: a systematic review. Crit Care. 2008;12:R161.
- O'Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Ther. 2012 Mar;91(3):550-4. doi: 10.1038/clpt.2011.340.
- Little RJ, D'Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, Frangakis C, Hogan JW, Molenberghs G, Murphy SA, Neaton JD, Rotnitzky A, Scharfstein D, Shih WJ, Siegel JP, Stern H. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012 Oct 4;367(14):1355-60.
- Altman DG and Bland JM. Treatment allocation in controlled trials: why randomise? BMJ. 1999 May 1; 318(7192): 1209.
- The Intensive Care Society. 2009. What is Intensive Care? Retrieved from https://web.archive.org/web/20091226214851/http://www.ics.ac.uk/patients___relatives/what_is_intensive_care_
- Pandharipande PP, Shintani AK, Hagerman HE, et al. Derivation and validation of Spo2/Fio2 ratio to impute for Pao2/Fio2 ratio in the respiratory component of the Sequential Organ Failure Assessment score. Critical care medicine. 2009;37(4):1317-1321.
- Covidence [Computer program]. Version accessed 11 July 2018. Melbourne, Australia: Veritas Health Innovation. Available at www.covidence.org.
- Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009; 339: b2535. Published online 2009 Jul 21. doi: 10.1136/bmj.b2535
Long-Term Outcomes in Critical Illness
Anne Grossestreuer, PhD
Status of Study:
Purpose of Protocol:
Critically ill patients who survive hospitalization face significant challenges in achieving premorbid levels of function. To date, predictors of long term recovery following critical illness remain incompletely characterized. Nevertheless, clinicians are often asked to “predict” recovery to inform patients and families in decision making, despite lack of scientific evidence to guide predictions. Therefore, clinicians frequently resort to clinical gestalt to gauge expectations for individual patients. However, these impressions are typically not based on quantitative data as this area is under-studied and alignment of physician expectations with patient experience is unknown. Nonetheless, physician gestalt impression is often utilized to make important healthcare decisions, including withdrawal of life-sustaining therapies.
There presently is not uniform methodology for tracking outcomes after patients leave the hospital following critical illness. Existing techniques are time-consuming, incompletely validated, and costly, yielding sparse and inconsistent data. As such, we propose a novel methodology enabled by new technology. We will track patient- and caregiver-reported recovery in a serial, longitudinal, and granular manner using weekly smartphone-based surveys and “passive data” collection; these records will be compared with clinician predictions regarding patient recovery. Through this process, we will determine whether patient experience reflects clinician assessment and enhance patient-centered decision-making processes.
The overall hypothesis is (1) clinician predictions will differ from patient and caregiver reports of recovery and (2) patient assessments will reflect smartphone “passive data.” We will test this hypothesis in patients discharged from the Intensive Care Unit with the following aims:
Aim 1. To assess feasibility, response rate, and barriers to implementation of smartphone-based surveys on recovery and passive data collection in patients admitted to ICU and their caregivers after hospital discharge.
Aim 2: To compare clinician prediction/expectation of recovery for patients to reported recovery garnered from patient and caregiver assessments.
Aim 3: To compare patient- and caregiver-reported recovery to activity data collected by the patient’s smartphone GPS and accelerometer.
Significance and Background:
This study utilizes methodology not previously applied in the critically ill and furnishes longitudinal data on recovery, measuring change over time, which is not available through existing longitudinal data. We anticipate that this smartphone-based methodology will efficiently capture long-term outcomes in the critically ill.
Whether clinician expectation of recovery in critically ill patients aligns with reported experience or “passive data” is unknown, although discordance has been reported in other populations such as brain injury and ARDS. We therefore hypothesize that clinician predictions of long-term outcome will not be concordant with patient-reported recovery, but that passive data will be. If successful, our proposal will identify a simple method for capturing long-term outcomes which then can be used in future studies of post-discharge recovery and ultimately allow for more accurate clinician prognostication and improved discharge planning.
Prospective observational cohort study
Patient Inclusion Criteria:
- Adult (≥18 years)
- Experienced cardiac arrest, sepsis, acute respiratory distress syndrome, traumatic brain injury, or were otherwise mechanically ventilated for ³1 day while in the ICU
- Intensive Care Unit stay ≥48 hours
- Pre-existing dementia, severe brain injury, or dependence on others for activities of daily living (i.e. a modified Rankin scale score of 4 or higher)
- Not comatose (i.e., not following commands) at hospital discharge
- Does not speak English
- Does not have a smartphone
- Protected population (pregnant, prisoner)
Patients and Caregivers
Subjects will receive a text message once/week for 6 months opening into a smartphone application asking if the patient had changed location, seen a clinician (doctor, nurse, or other healthcare professional), or experienced any changes in their emotional, physical, or mental status since their last response. If there has been a change in emotional, physical, or mental status, the subject will be prompted to complete the SF-12.
At six months post-discharge, we will call patients and caregivers for feedback on study methodology and recovery assessment using the SF-36.
De-identified GPS and accelerometer data will be recorded by the patient’s phone and periodically uploaded.
At hospital discharge, the clinician completing the patient’s discharge summary will predict when patient will achieve maximal recovery over next 6 months and predict their SF-12 score at this time.
The primary outcome will be mean SF-12 score at the time the clinician predicted maximal recovery over six months. Secondary outcomes include factors influencing participation, concordance of patient and caregiver assessments, response rates, and correlation between smartphone-measured activity to patient- and caregiver-reported recovery.
SF-12 and SF-36: The Short Form-12 (SF-12) is a modified and shorter version of the Short Form Health Survey (SF-36), a validated set of generic, easily administered quality-of-life measures designed for patient self-reporting and frequently used in studies of functional outcomes in cardiac arrest and other critical illnesses.
“Passive” smartphone data: “Passive” smartphone data is data collected on a smartphone that is generated without any participation or action from the subject. In this study, we will collect de-identified GPS and accelerometer data recorded by the patient’s phone to estimate activity patterns.