Risk factors for unplanned transfer to intensive care within 24 hours of admission from the emergency department in an integrated healthcare system

The Role of Intermediate Care in Supporting Critically Ill Patients and Critical Care Infrastructure

Intermediate care (IC) is used for patients who do not require the human and technological support of the intensive care unit (ICU) yet require more care and monitoring than can be provided on general wards. Though prevalent in many countries, there is marked variability in models of organization and staffing, as well as monitoring and interventions provided. In this article, the authors will discuss the historical background of IC, review the impact of IC on ICU and IC patient outcomes, and highlight where future studies can shed light on how to optimize IC organization and outcomes.

Association between emergency department disposition and mortality in patients with COVID-19 acute respiratory distress syndrome

Objectives

Patients hospitalized for COVID-19 frequently develop hypoxemia and acute respiratory distress syndrome (ARDS) after admission. In non-COVID-19 ARDS studies, admission to hospital wards with subsequent transfer to intensive care unit (ICU) is associated with worse outcomes. We hypothesized that initial admission to the ward may affect outcomes in patient with COVID-19 ARDS.

Methods

This was a retrospective study of consecutive adults admitted for COVID-19 ARDS between March 2020 and March 2021 at Stanford Health Care. Mortality scores at hospital admission (Coronavirus Clinical Characterization Consortium Mortality Score [4C score]) and ICU admission (Simplified Acute Physiology Score III [SAPS-III]) were calculated, as well as ROX index for patients on high flow nasal oxygen. Patients were classified by emergency department (ED) disposition (ward-first vs. ICU-direct), and 28- and 60-day mortality and highest level of respiratory support within 1 day of ICU admission were compared. A second cohort (April 2021‒July 2022, n = 129) was phenotyped to validate mortality outcome.

Results

A total of 157 patients were included, 48% of whom were first admitted to the ward (n = 75). Ward-first patients had more comorbidities, including lung disease. Ward-first patients had lower 4C and similar SAPS-III score, yet increased mortality at 28 days (32% vs. 17%, hazard ratio [HR] 2.0, 95% confidence interval [95% CI] 1.0‒3.7, p = 0.039) and 60 days (39% vs. 23%, HR 1.83, 95% CI 1.04‒3.22, p = 0.037) compared to ICU-direct patients. More ward-first patients escalated to mechanical ventilation on day 1 of ICU admission (36% vs. 14%, p = 0.002) despite similar ROX index. Ward-first patients who upgraded to ICU within 48 h of ED presentation had the highest mortality. Mortality findings were replicated in a sensitivity analysis.

Conclusion

Despite similar baseline risk scores, ward-first patients with COVID-19 ARDS had increased mortality and escalation to mechanical ventilation compared to ICU-direct patients. Ward-first patients requiring ICU upgrade within 48 h were at highest risk, highlighting a need for improved identification of this group at ED admission.

Effectiveness of Implementing Modified Early Warning System and Rapid Response Team for General Ward Inpatients

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GWnon-MEWS and 71,023 GWMEWS. The numbers of inpatients who underwent an unplanned ICU admission in GWnon-MEWS and GWMEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GWMEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

Recognition of Critically Ill Patients by Acute Health Care Providers: A Multicenter Observational Study

OBJECTIVES

Although the Modified Early Warning Score (MEWS) is increasingly being used in the acute care chain to recognize disease severity, its superiority compared with clinical gestalt remains unproven. Therefore, the aim of this study was to compare the accuracy of medical caregivers and MEWS in predicting the development of critical illness.

DESIGN

This was a multicenter observational prospective study.

SETTING

It was performed in a level-1 trauma center with two different sites and emergency departments (EDs) with a combined capacity of about 50.000 patients annually.

PATIENTS

It included all adult patients presented to the ED by Emergency Medical Services (EMS).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

For all patients, the acute caregivers were asked several standardized questions regarding clinical predicted outcome (clinical gestalt), and the MEWS was calculated. The primary outcome was the occurrence of critical illness, defined as ICU admission, serious adverse events, and mortality within 72 hours. The sensitivity, specificity, and discriminative power of both clinical gestalt and MEWS for the occurrence of critical illness were calculated as the area under the receiver operating characteristic curve (AUROC). Among the total of 800 included patients, 113 patients (14.1%) suffered from critical illness. The specificity for predicting three-day critical illness for all caregivers (for EMS nurses, ED nurses, and physicians) was 93.2%; 97.3%, and 96.8%, respectively, and was significantly (p < 0.01) better than an MEWS score of 3 or higher (70.4%). The sensitivity was significantly lower for EMS and ED nurses, but not significantly different for physicians compared with MEWS. The AUROCs for prediction of 3-day critical illness by both the ED nurses (AUROC = 0.809) and the physicians (AUROC = 0.848) were significantly higher (p = 0.032 and p = 0.010, respectively) compared with MEWS (AUROC = 0.731).

CONCLUSIONS

For patients admitted to the ED by EMS, medical professionals can predict the development of critical illness within 3 days significantly better than the MEWS. Although MEWS is able to correctly predict those patients that become critically ill, its use leads to overestimation due to a substantial number of false positives.

Unexpected ICU Transfer and Mortality in COVID-19 Related to Hospital Volume

Introduction: Coronavirus 2019 (COVID-19) illness continues to affect national and global hospital systems, with a particularly high burden to intensive care unit (ICU) beds and resources. It is critical to identify patients who initially do not require ICU resources but subsequently rapidly deteriorate. We investigated patient populations during COVID-19 at times of full or near-full (surge) and non-full (non-surge) hospital capacity to determine the effect on those who may need a higher level of care or deteriorate quickly, defined as requiring a transfer to ICU within 24 hours of admission to a non-ICU level of care, and to provide further knowledge on this high-risk group of patients. Methods: This was a retrospective cohort study of a single health system comprising four emergency departments and three tertiary hospitals in New York, NY, across two different time periods (during surge and non-surge inpatient volume times during the COVID-19 pandemic). We queried the electronic health record for all patients admitted to a non-ICU setting with unexpected ICU transfer (UIT) within 24 hours of admission. We then made a comparison between adult patients with confirmed coronavirus 2019 and without during surge and non-surge time periods. Results: During the surge period, there was a total of 86 UITs in a one-month period. Of those, 60 were COVID-19 positive patients who had a mortality rate of 63.3%, and 26 were COVID-19 negative with a 30.8 % mortality rate. During the non-surge period, there was a total of 112 UITs; of those, 24 were COVID-19 positive with a 37.5% mortality rate, and 90 were COVID-19 negative with a 11.1% mortality rate. Conclusion: During the surge, the mortality rate for both COVID-19 positive and COVID-19 negative patients experiencing an unexpected ICU transfer was significantly higher.

Predicting Unplanned Intensive Care Unit Admission for Trauma Patients: The CRASH Score

INTRODUCTION

Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an associated increase in mortality, hospital length of stay, and cost. Trauma teams need to determine which patients necessitate ICU admission on presentation rather than waiting to intervene on deteriorating patients. This study sought to develop a novel Clinical Risk of Acute ICU Status during Hospitalization (CRASH) score to predict the risk of unplanned ICU admission.

METHODS

The 2017 Trauma Quality Improvement Program database was queried for patients admitted to nonICU locations. The group was randomly divided into two equal sets (derivation and validation). Multiple logistic regression models were created to determine the risk of unplanned ICU admission using patient demographics, comorbidities, and injuries. The weighted average and relative impact of each independent predictor were used to derive a CRASH score. The score was validated using area under the curve.

RESULTS

A total of 624,786 trauma patients were admitted to nonICU locations. From 312,393 patients in the derivation-set, 3769 (1.2%) had an unplanned ICU admission. A total of 24 independent predictors of unplanned ICU admission were identified and the CRASH score was derived with scores ranging from 0 to 32. The unplanned ICU admission rate increased steadily from 0.1% to 3.9% then 12.9% at scores of 0, 6, and 14, respectively. The area under the curve for was 0.78.

CONCLUSIONS

The CRASH score is a novel and validated tool to predict unplanned ICU admission for trauma patients. This tool may help providers admit patients to the appropriate level of care or identify patients at-risk for decompensation.

Capillary Refill Technology to Enhance the Accuracy of Peripheral Perfusion Evaluation in Sepsis

Background: Monitoring of capillary refill time (CRT) is a common bedside assessment used to ascertain peripheral perfusion in a patient for a vast array of conditions. The literature has shown that a change in CRT can be used to recognize life-threatening conditions that cause decreased perfusion, such as sepsis, and aid in resuscitation. The current practice for calculating CRT invites subjectivity and produces a highly variable result. Innovative technology may be able to standardize this process and provide a reliable and accurate value for use in diagnostics and treatment. This study aimed to assess a new technology (DCR by ProMedix Inc.) for rapid, bedside, and noninvasive detection of CRT. Methods: This was a secondary analysis of a prospective observational study evaluating the accuracy of new technology towards CRT-guided diagnosis of sepsis. It was carried out in the adult emergency departments (ED) of an academic tertiary care medical center. Patients seeking care in the ED were determined eligible if they were > 18 years in age and exhibited chief complaints suggestive of possible sepsis. The CRT produced by the technology was compared to the gold standard manual waveform assessment. Results: 218 consecutive subject enrollments were included and multiple measurements were made on each patient. Data with irregular waveforms were excluded. A total of 692 waveforms were evaluated for CRT values by a pair of trained PhD biomedical engineers. The average age of the cohort was 50.62 and 51.4% female. Results showed a Pearson correlation coefficient of 0.91 for the device CRT compared to the CRT gold standard. The Pearson correlation coefficient for the two independent engineering review of the waveform data was 0.98. This device produces accurate, consistent results and eliminates the subjectivity of CRT measurements that is in practice currently.

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

Delays to Initiate Interfacility Transfer for Patients Transported by a Critical Care Transport Organization

OBJECTIVE

The time to initiate an interfacility transfer is an important and understudied cause of delay to definitive management. This study identifies characteristics associated with delays to initiate interfacility transfer of critically ill patients.

METHODS

We performed a retrospective cohort study of adult patients who underwent interfacility transfer by a provincial critical care transport organization over a 3-year period. The primary outcome was the time to initiate interfacility transfer. Quantile regression explored the impact of patient, environmental, and institutional characteristics.

RESULTS

In total 11,231 patients were included. Cardiac (+1.45 hours), gastrointestinal (+3.28 hours), respiratory (+4.90 hours), or sepsis (+3.03 hours) reasons for transfer; vasopressor requirements (+2.31 hours); and evening time (+3.67 hours) were associated with longer times to initiate interfacility transfer at the 90th quantile. Neurologic (-1.45 hours), obstetric (-1.56 hours), or trauma (-3.14 hours) reasons for transfer; Glasgow Coma Scale < 8 (-0.98 hours); blood transfusion requirement (-1.47 hours); and smaller sending sites were associated with shorter times to initiate transfer.

CONCLUSION

The time to initiate interfacility transfer represents a modifiable delay in a patient's transport journey. This study highlights important patient, environmental, and institutional characteristics associated with increased time to initiate transfer. Collaboration between transport organizations and hospitals in developing regional bypass criteria and prearranged transfer agreements may help facilitate timely patient transfer.

Does Timing Matter?: Timing and Outcomes Among Early Unplanned PICU Transfers

BACKGROUND

Many institutions track early ICU transfers (transfer from an inpatient floor to an ICU within 24 hours of admission) as a marker of quality of emergency department (ED) care. There are limited data evaluating whether patient characteristics or clinical outcomes differ on the basis of timing of ICU transfer within this 24-hour window.

METHODS

We conducted a retrospective cohort study examining all patients ≤21 years old admitted to an inpatient pediatric floor from the ED and subsequently transferred to an ICU within 24 hours of hospitalization. Patient characteristics and clinical outcomes were compared on the basis of timing (0-6 hours, 6-12 hours, 12-24 hours) of ICU transfer. Outcomes assessed included receipt of critical intervention, timing of intervention with respect to transfer, type of intervention received, hospital and ICU length of stay, and mortality at 72 hours and during hospitalization.

RESULTS

A total of 841 patients were transferred to an ICU within 24 hours from admission to a pediatric ward from the ED; 266 patients (32%) transferred within 6 hours of admission, 269 patients (32%) transferred between 6 and 12 hours, and 306 patients (36%) transferred between 12 and 24 hours. Patient characteristics did not materially differ on the basis of timing of ICU transfer, nor did clinical outcomes.

CONCLUSIONS

Among children transferred to an ICU within 24 hours of hospitalization, patient characteristics and clinical outcomes did not materially differ based on the timing of transfer relative to admission from the ED.

Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial

BACKGROUND

Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided early warning of potentially catastrophic clinical events. We present a dynamic, visual, predictive analytics monitoring tool that integrates real-time bedside telemetric physiologic data into robust clinical models to estimate and communicate risk of imminent events. This tool, Continuous Monitoring of Event Trajectories (CoMET), has been shown in retrospective observational studies to predict clinical decompensation on the acute care ward. There is a need to more definitively study this advanced predictive analytics or AI monitoring system in a prospective, randomized controlled, clinical trial.

OBJECTIVE

The goal of this trial is to determine the impact of an AI-based visual risk analytic, CoMET, on improving patient outcomes related to clinical deterioration, response time to proactive clinical action, and costs to the health care system.

METHODS

We propose a cluster randomized controlled trial to test the impact of using the CoMET display in an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster randomization was estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number will occur every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group will receive standard of care only.

RESULTS

The primary outcome will be hours free from events of clinical deterioration. Hours of acute clinical events are defined as time when one or more of the following occur: emergent ICU transfer, emergent surgery prior to ICU transfer, cardiac arrest prior to ICU transfer, emergent intubation, or death. The clinical trial began randomization in January 2021.

CONCLUSIONS

Very few AI-based health analytics have been translated from algorithm to real-world use. This study will use robust, prospective, randomized controlled, clinical trial methodology to assess the effectiveness of an advanced AI predictive analytics monitoring system in incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of health care organizations to evolve as learning health systems, in which bioinformatics data are applied to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by health care providers.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/29631.

Characteristics and outcomes of diabetic patients with acute exacerbation of COPD

Rationale aims and objectives

Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and diabetes mellitus form a special population due to an increased risk of hyperglycemia from the use of corticosteroids. There is limited data regarding specific outcomes in diabetic patients with AECOPD.

Methods

A retrospective data analysis of adult patients admitted to North Florida Division of the Hospital Corporation of America (HCA Healthcare) with a primary or secondary diagnosis of AECOPD from January 1, 2018, to December 31, 2018. We excluded patients who needed intensive care unit (ICU) care on day 0. Outcomes assessed included length of stay, mortality, and need for ICU transfer after 48 h from admission. Characteristics included age, sex, and race, comorbidities such as diabetes mellitus, chronic kidney disease, acute kidney injury, congestive heart failure, and anemia were analyzed. Comparisons were analyzed via binary and multivariate logistic regression models.

Results

A total of 3788 patients admitted for AECOPD were included; amongst them, 1356 patients (~36%) had diabetes mellitus. This subset of patients had higher rates of comorbidities. A significant portion of diabetic patients (72%) received intravenous rather than oral steroids, similar to non-diabetic patients. In addition, diabetic patients were more likely to develop acute kidney injury (14.2% vs 8.0%, p < 0.004) and decompensated heart failure (9.2% vs 4.6%, p < 0.001). Diabetic patients had higher length of stay and increased need for ICU transfer. However, diabetes itself did not independently affect length of stay (CI -0.028, 0.479, p = 0.081) when adjusted to comorbidities and patient's characteristics. Moreover, diabetes was independently associated with an increased need for transfer to ICU (Odds ratio 1.9, p = 0.031). The oral route of steroid use was associated with decreased LOS (β coefficient - 0.9, p < 0.001).

Conclusion

Diabetes mellitus is independently associated with increased ICU transfers amongst patients hospitalized with AECOPD. The use of oral steroids rather than intravenous steroids was independently associated with decreased length of stay in diabetic and non-diabetic patients. Despite no difference in intravenous vs. oral corticosteroids demonstrated in previous COPD trials, a significant portion of diabetic patients continue to receive intravenous corticosteroids. Further investigation is required to explore these findings.

A pre-hospital risk score predicts critical illness in non-trauma patients transported by ambulance to a Dutch tertiary referral hospital

Background

Early pre-hospital identification of critically ill patients reduces morbidity and mortality. To identify critically ill non-traumatic and non-cardiac arrest patients, a pre-hospital risk stratification tool was previously developed in the United States. The aim of this study was to investigate the accuracy of this tool in a Dutch Emergency Department.

Methods

This retrospective study included all patients of 18 years and older transported by ambulance to the Emergency Department of a tertiary referral hospital between January 1st 2017 and December 31st 2017. Documentation of pre-hospital vital parameters had to be available. The tool included a full set of vital parameters, which were categorized by predetermined thresholds.

Study outcome was the accuracy of the tool in predicting critical illness, defined as admittance to the Intensive Care Unit for delivery of vital organ support or death within 28 days. Accuracy of the risk stratification tool was measured with the Area Under the Receiver Operating Characteristics (AUROC) curve.

Results

Nearly 3000 patients were included in the study, of whom 356 patients (12.2%) developed critical illness. We observed moderate discrimination of the pre-hospital risk score with an AUROC of 0.74 (95%-CI 0.71–0.77). Using a threshold of 3 to identify critical illness, we observed a sensitivity of 45.0% (95%-CI 44.8–45.2) and a specificity of 86.0% (95%-CI 85.9–86.0).

Conclusion

These data show that this pre-hospital risk stratification tool is a moderately effective tool to predict which patients are likely to become critically ill in a Dutch non-trauma and non-cardiac arrest population.

The occurrence of and risk factors for developing acute critical illness during quarantine as a response to the COVID-19 pandemic

BACKGROUND/PURPOSE

Early detection and timely quarantine measures are necessary to control disease spread and prevent nosocomial outbreaks of Coronavirus disease 2019 (COVID-19). In this study, we aimed to investigate the impact of a quarantine strategy on patient safety and quality of care.

METHODS

This retrospective cohort study enrolled patients admitted to the quarantine ward in a tertiary hospital in southern Taiwan. The incidence and causes of acute critical illness, including clinical deterioration and unexpected complications during the quarantine period, were reviewed. Further investigation was performed to identify risk factors for acute critical illness during quarantine.

RESULTS

Of 320 patients admitted to the quarantine ward, more than two-thirds were elderly, and 37.8% were bedridden. During the quarantine period, 68 (21.2%) developed acute critical illness, which more commonly occurred among patients older than 80 years and with a bedridden status, nasogastric tube feeding, or dyspnea symptoms. Bedridden status was an independent predictor of acute critical illness. Through optimization of sampling for COVID-19 and laboratory schedules, both the duration of quarantine and the proportion of acute critical illness among bedridden patients during quarantine exhibited a decreasing trend. There was no COVID-19 nosocomial transmission during the study period.

CONCLUSION

The quarantine ward is a key measure to prevent nosocomial transmission of COVID-19 but may carry a potential negative impact on patient care and safety. For patients with multiple comorbidities and a bedridden status, healthcare workers should remain alert to rapid deterioration and unexpected adverse events during quarantine.

Risk factors for unplanned ICU admission after emergency department holding orders

STUDY HYPOTHESIS

Emergency department (ED) holding orders are used in an effort to streamline patient flow. Little research exists on the safety of this practice. Here, we report on prevalence and risk factors for upgrade of medical admissions to ICU for whom holding orders were written.

METHODS

Retrospective review of holding order admissions through our ED for years 2013-2018. Pregnancy, prisoner, pediatric, surgical, and ICU admissions were excluded, as were transfers from other hospitals. Risk factors of interest included vital signs, physiologic data, laboratory markers, sequential organ failure assessment (SOFA), Quick SOFA (qSOFA), modified early warning (MEWS) scores, and Charlson Comorbidity Index (CCI). Primary outcome was ICU transfer within 24 hours of admission. Analysis was completed using multivariable logistic regression.

RESULTS

Between 2013 and 2018, the ED had 203,374 visits. Approximately 20% (N = 54,915) were admitted, 23% of whom had holding orders (N = 12,680). A minority of those with a holding order were transferred to the ICU within 24 hours (N = 79; 0.62%). Those transferred to ICU had increased heart and respiratory rate, P/F ratio, and increased oxygen need. They also had higher MEWS, quick SOFA (qSOFA), and SOFA scores. Multivariable logistic regression demonstrated a significant association between ICU admission and FiO2 (odds ratio [OR] 1.47; 95% confidence interval [CI] 1.25-1.74), MEWS (OR 1.31; 95% CI 1.14-1.52), SOFA Score (OR 1.19; 95% CI 1.05-1.35), and gastrointestinal (OR 3.25; 95% CI: 1.50-7.03) or other combined diagnosis (OR 2.19; CI: 1.07-4.48) (P = 0.0017).

CONCLUSION

Holding orders are used for >20% of all admissions and <1% of those admissions required transfer to ICU within 24 hours.

Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration

BACKGROUND

Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported.

METHODS

On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient's condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions.

RESULTS

The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001).

CONCLUSIONS

The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality. (Funded by the Gordon and Betty Moore Foundation and others.).

Identifying 'at-risk' critically ill patients who present to the emergency department and require intensive care unit admission: A retrospective observational cohort study

BACKGROUND

Emergency department (ED) triage is the process of prioritising patients by medical urgency. Delays in intensive care unit (ICU) admission can adversely affect patients.

OBJECTIVES

This study aimed to identify characteristics associated with ICU admission for patients triaged as Australasian Triage Scale (ATS) 3 but subsequently admitted to the ICU within 24 h of triage.

METHODS

This retrospective, observational cohort study was conducted in a public teaching hospital in Queensland, Australia. Patients older than 18 y triaged with an ATS 3 and admitted to the ICU within 24 h of triage or admitted to the ward between January 1, 2012, and December 31, 2012, were included. The demographic and clinical profiles of ICU admissions vs. all other ward admissions for patients triaged an ATS of 3 were compared. Multivariable regression analysis compared characteristics of patients triaged with an ATS of 3 who did and did not require ICU transfer. Descriptive data are reported as n (%) and median and interquartile range (IQR). Regression analysis is reported as adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs).

RESULTS

Of the 27 454 adult ED presentations triaged with an ATS of 3, 22.4% (n = 6138) required hospital admission, comprising 5302 individuals, 2.1% of whom (n = 110) were admitted to the ICU within 24 h of triage. Age- and sex-adjusted predictors of ICU admission for patients triaged with an ATS of 3 included infectious (aOR: 3.7; 95% CI: 2.0-6.9), neurological (aOR: 2.8; 95% CI: 1.6-5.0), and gastrointestinal disorders (aOR: 2.2; 95% CI 1.2-3.5); arriving by ambulance; arriving after hours; or arriving on weekends. Regardless of diagnosis or sex, persons older than 80 y were less likely to be admitted to the ICU (aOR: 0.4; 95% CI: 0.2-0.8).

CONCLUSIONS

Patients triaged as ATS 3 presenting on weekends or after hours, and those with infectious, gastrointestinal, or neurological conditions warrant careful attention as these factors were associated with higher odds of ICU admission. Ongoing staff education regarding triage and signs of deterioration are important to prevent avoidable outcomes.

Clinical deterioration and hospital-acquired complications in adult patients with isolation precautions for infection control: A systematic review

AIM

To review and synthesize literature examining clinical deterioration and hospital-acquired complications in adult patients with isolation precautions for infection control.

BACKGROUND

Isolation precautions are a common infection prevention and control strategy which may have impact on safety and quality of care.

DESIGN

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided this systematic review, which was registered with PROSPERO [CRD42019131573].

DATA SOURCES

A search of Medline, Embase, and Cumulative Index to Nursing and Allied Health Literature was conducted for studies published in English up to 5 April 2019.

REVIEW METHODS

Risk of bias was determined using Critical Appraisal Skills Program tools. Quality appraisal was performed using the Grades of Recommendation, Assessment, Development, and Evaluation approach. The primary outcomes of interest were clinical deterioration events and hospital-acquired complications. In-hospital death and hospital length of stay were secondary outcomes. Data were synthesized using a narrative approach.

RESULTS

The search yielded 785 citations after removal of duplicates, of which, six studies were relevant. Certainty of evidence for outcomes of interest was low to very low.

CONCLUSION

There is no strong evidence that adult medical and surgical ward patients in isolation precautions for infection control are more or less likely to experience clinical deterioration or hospital-acquired complications.

IMPACT

What problem did the study address? Are patients in isolation precautions more likely to experience clinical deterioration or hospital-acquired complications than non-isolated patients? What were the main findings? There is no strong evidence that clinical deterioration and hospital-acquired complications are more likely to occur to patients in isolation precautions for infection control. This research is of relevance to acute care nurses.

Quantitative and Qualitative Assessments of Cholesterol Association With Bacterial Infection Type in Sepsis and Septic Shock

BACKGROUND

Reduced cholesterol levels are associated with increased organ failure and mortality in sepsis. Cholesterol levels may vary by infection type (gram negative vs positive), possibly reflecting differences in cholesterol-mediated bacterial clearance.

METHODS

This was a secondary analysis of a combined data set of 2 prospective cohort studies of adult patients meeting Sepsis-3 criteria. Infection types were classified as gram negative, gram positive, or culture negative. We investigated quantitative (levels) and qualitative (dysfunctional high-density lipoprotein [HDL]) cholesterol differences. We used multivariable logistic regression to control for disease severity.

RESULTS

Among 171 patients with sepsis, infections were gram negative in 67, gram positive in 46, and culture negative in 47. Both gram-negative and gram-positive infections occurred in 11 patients. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and HDL cholesterol (HDL-C) levels were lower for culture-positive sepsis at enrollment (TC, P < .001; LDL-C, P < .001; HDL-C, P = .011) and persisted after controlling for disease severity. Similarly, cholesterol levels were lower among culture-positive patients at 48 hours (TC, P = .012; LDL-C, P = .029; HDL-C, P = .002). Triglyceride (TG) levels were lower at enrollment (P =.033) but not at 48 hours (P = .212). There were no differences in dysfunctional HDL. Among bacteremic patients, cholesterol levels were lower at enrollment (TC, P = .010; LDL-C, P = .010; HDL-C, P ≤ .001; TG, P = .005) and at 48 hours (LDL-C, P = .027; HDL-C, P < .001; TG, P = .020), except for 48 hour TC (P = .051). In the bacteremia subgroup, enrollment TC and LDL-C were lower for gram-negative versus gram-positive infections (TC, P = .039; LDL-C, P = .023).

CONCLUSION

Cholesterol levels are significantly lower among patients with culture-positive sepsis and bacteremia.

Unplanned Admission to the ICU: A Qualitative Study Examining Family Member Experiences

BACKGROUND

Transfers to the ICU from acute care are common, and it is essential to understand how family members of critically ill patients experience these transitions of care.

RESEARCH QUESTION

Can we enhance our understanding of family members' experiences during hospital stays complicated by a patient's unplanned admission to the ICU?

STUDY DESIGN AND METHODS

Qualitative interviews were conducted with family members of patients were transferred from acute care to the ICU at a level I trauma center in Seattle, WA (n = 17). To organize data, we used thematic analysis, coupled with a validated conceptual model of clinician-surrogate communication.

RESULTS

Drawing from a validated conceptual model, we used two domains to frame our coding: "information processing" and "relationship building." Within information processing, we coded information disclosure, sensemaking, and expectations; within relationship building, we coded emotional support, trust, and consensus and conflict. Family members wanted timely, accurate information about the patient's condition both during and after transfer. An unplanned ICU admission was a stressful event for family members, who looked to clinicians for emotional support. Developing trust was challenging, because family members struggled to feel like integrated members of the medical team when patients transitioned from one setting to another.

INTERPRETATION

Family of patients who experience an unplanned ICU admission want high-quality communication both during and after a patient's transfer to the ICU. This communication should help family members make sense of the situation, address unmet expectations, and provide emotional support. In addition, interventions that foster family-clinician trust can help family members feel like integrated members of the care team as they face the challenge of navigating multiple different environments within the hospital.

Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All

Supplemental Digital Content is available in the text.

Objectives:

Early detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models.

Design:

Individual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; and logistic regression modeling for the outcome of ICU transfer for a specific clinical reason.

Setting:

Cardiac medical-surgical ward; tertiary care academic hospital.

Patients:

Eight-thousand one-hundred eleven adult patients, 457 of whom were transferred to an ICU for clinical deterioration.

Interventions:

None.

Measurements and Main Results:

We calculated the contributing relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate receiver operating characteristic areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons—respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy—had distinct signatures of illness. Statistical models trained to target-specific reasons for ICU transfer performed better than one model targeting combined events.

Conclusions:

A single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer.

Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p < 0.001) and 3.4 days longer hospital stays (5.9 vs. 2.5, p < 0.001) than those without an early transfer. Our predictive analytic model had a cross-validated area under the receiver operating characteristic of 0.70 (95% CI 0.67–0.72) and identified 10% of early ICU transfers with an alert rate of 1.6 per week (162.2 acute care admits per week, 1.9 early ICU transfers). Predictive analytic monitoring based on data available in the emergency department can identify patients that will require upgrade to ICU or IMU if admitted to acute care. Incorporating this tool into ED practice may draw attention to high-risk patients before acute care admit and allow early intervention.

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Most existing prediction studies were based on univariate analysis or multiple logistic regression to provide one-size-fit-all results. However, patient condition varying from case to case may not be accurately examined by such a simplistic judgment. In this study, we present a new decision tool using a mathematical optimization approach aiming to automatically discover rules associating diagnostic features with high-risk outcome (i.e., unplanned transfers) in different deterioration scenarios. We consider four mutually exclusive patient subgroups based on the principal reasons of ED visits: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases at a suburban teaching hospital. The analysis results demonstrate significant rules associated with unplanned transfer outcome for each subgroups and also show comparable prediction accuracy (>70%) compared to state-of-the-art machine learning methods while providing easy-to-interpret symptom-outcome information.

What Do We Do After the Pilot Is Done? Implementation of a Hospital Early Warning System at Scale

BACKGROUND

Adults who deteriorate outside the ICU have high mortality. Most rapid response systems (RRSs) have employed manual detection processes that rapid response teams (RRTs) use to identify patients at risk. This project piloted the use of an automated early warning system (EWS), based on a very large database, that provides RRTs with 12 hours lead time to mount a response. Results from a 2-hospital pilot were encouraging, so leadership decided to deploy the Advance Alert Monitor (AAM) program in 19 more hospitals.

CHALLENGE

How can one deploy an RRS using an automated EWS at scale?

SOLUTION

EWS displays were removed from frontline clinicians' hospital electronic dashboards, and a Virtual Quality Team (VQT) RN was interposed between the EWS and the RRT. VQT RNs monitor the EWS remotely-when alerts are issued, they conduct a preliminary chart review and contact hospital RRT RNs. VQT and RRT RNs review the cases jointly. The RRT RNs then consult with hospitalists regarding clinical rescue and/or palliative care workflows. Subsequently, VQT RNs monitor patient charts, ensuring adherence to RRS practice standards. To enable this process, the project team developed a governance structure, clinical workflows, palliative care workflows, and documentation standards.

RESULTS

The AAM Program now functions in 21 Kaiser Permanente Northern California hospitals. VQT RNs monitor EWS alerts 24 hours a day, 7 days a week. The AAM Program handles ∼16,000 alerts per year. Its implementation has resulted in standardization of RRT staffing, clinical rescue workflows, and in-hospital palliative care.

Predictive factors for secondary intensive care unit admission within 48 hours after hospitalization in a medical ward from the emergency room

BACKGROUND

Unplanned transfer to an ICU within 48 hours of admission from the emergency department (ED) can be considered an adverse event. Screening at risk for such an event is a challenge for ED staff. Our purpose was to identify the clinical and biological variables which may be identified in the ED setting and can predict short-term unplanned secondary transfer to the intensive care setting.

METHODS

This was a three-year retrospective case controlled monocentric study. The cases were patients transferred to a medical ICU within 48 hours of admission to the general wards from the ED. Each case was matched to two controls (patients not transferred to the ICU) based on age, gender, year of admission, and hospital unit. A conditional logistic regression was performed.

RESULTS

Three hundred nineteen patients, including 107 cases and 212 controls, were studied. Community-acquired pneumonia (CAP) was the most frequent diagnosis (23% of cases) followed by sepsis (16%). We identified six predictive factors of an unplanned short-term transfer to the ICU. Former smoking status, fever between 38°C and 40°C, dyspnea as the chief complaint in the ED, a lower MEDS score, an elevated acute physiology age chronic health evaluation score, and the ordering of an arterial blood gas each correlate with secondary transfer to an intensive care setting.

CONCLUSION

We report a higher risk of short-term unscheduled ICU transfer in patients meeting these criteria. These patients should be closely monitored and frequently re-evaluated before being transferred to a general ward.

Shock Index Predicts Outcome in Patients with Suspected Sepsis or Community-Acquired Pneumonia: A Systematic Review

Background: To improve outcomes for patients who present to hospital with suspected sepsis, it is necessary to accurately identify those at high risk of adverse outcomes as early and swiftly as possible. To assess the prognostic accuracy of shock index (heart rate divided by systolic blood pressure) and its modifications in patients with sepsis or community-acquired pneumonia. Methods: An electronic search of MEDLINE, EMBASE, Allie and Complementary Medicine Database (AMED), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Open Grey, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (WHO ITRP) was conducted from conception to 26th March 2019. Eligible studies were required to assess the prognostic accuracy of shock index or its modifications for outcomes of death or requirement for organ support either in sepsis or pneumonia. The methodological appraisal was carried out using the Downs and Black checklist. Evidence was synthesised using a narrative approach due to heterogeneity. Results: Of 759 records screened, 15 studies (8697 patients) were included in this review. Shock index ≥ 1 at time of hospital presentation was a moderately accurate predictor of mortality in patients with sepsis or community-acquired pneumonia, with high specificity and low sensitivity. Only one study reported outcomes related to organ support. Conclusions: Elevated shock index at time of hospital presentation predicts mortality in sepsis with high specificity. Shock index may offer benefits over existing sepsis scoring systems due to its simplicity.

Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring

Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.

A Retrospective Case-Control Study to Identify Predictors of Unplanned Admission to Pediatric Intensive Care Within 24 Hours of Hospitalization

OBJECTIVES

To identify the clinical findings available at the time of hospitalization from the emergency department that are associated with deterioration within 24 hours.

DESIGN

A retrospective case-control study.

SETTING

A pediatric hospital in Ottawa, ON, Canada.

PATIENTS

Children less than 18 years old who were hospitalized via the emergency department between January 1, 2008, and December 31, 2012. Cases (n = 98) had an unplanned admission to the PICU or unexpected death on the hospital ward within 24 hours of hospitalization and controls (n = 196) did not.

INTERVENTIONS

None.

MAIN RESULTS

Ninety-eight children (53% boys; mean age 63.2 mo) required early unplanned admission to the PICU. Multivariable conditional logistic regression resulted in a model with five predictors reaching statistical significance: higher triage acuity score (odds ratio, 4.1; 95% CI, 1.7-10.2), tachypnea in the emergency department (odds ratio, 4.6; 95% CI, 1.8-11.8), tachycardia in the emergency department (odds ratio, 2.6; 95% CI, 1.1-6.5), PICU consultation in the emergency department (odds ratio, 8.0; 95% CI, 1.1-57.7), and admission to a ward not typical for age and/or diagnosis (odds ratio, 4.5; 95% CI, 1.7-11.6).

CONCLUSIONS

We have identified risk factors that should be included as potential predictor variables in future large, prospective studies to derive and validate a weighted scoring system to identify hospitalized children at high risk of early clinical deterioration.

Risk Factors for Mortality in Pediatric Postsurgical versus Medical Severe Sepsis

BACKGROUND

Sepsis is a leading cause of morbidity and mortality after surgery. Most studies regarding sepsis do not differentiate between patients who have had recent surgery and those without. Few data exist regarding the risk factors for poor outcomes in pediatric postsurgical sepsis. Our hypothesis is pediatric postsurgical, and medical patients with severe sepsis have unique risk factors for mortality.

METHODS

Data were extracted from a secondary analysis of an international point prevalence study of pediatric severe sepsis. Sites included 128 pediatric intensive care units from 26 countries. Pediatric patients with severe sepsis were categorized into those who had recent surgery (postsurgical sepsis) versus those that did not (medical sepsis) before sepsis onset. Multivariable logistic regression models were used to determine risk factors for mortality.

RESULTS

A total of 556 patients were included: 138 with postsurgical and 418 with medical sepsis. In postsurgical sepsis, older age, admission from the hospital ward, multiple organ dysfunction syndrome at sepsis recognition, and cardiovascular and respiratory comorbidities were independent risk factors for death. In medical sepsis, resource-limited region, hospital-acquired infection, multiple organ dysfunction syndrome at sepsis recognition, higher Pediatric Index of Mortality-3 score, and malignancy were independent risk factors for death.

CONCLUSIONS

Pediatric patients with postsurgical sepsis had different risk factors for mortality compared with medical sepsis. This included a higher mortality risk in postsurgical patients presenting to the intensive care unit from the hospital ward. These data suggest an opportunity to develop and test early warning systems specific to pediatric sepsis in the postsurgical population.

Patients in prehospital transport to the emergency department: a cohort study of risk factors for 7-day mortality

BACKGROUND

Ambulance transfer is the first contact with the healthcare system for many patients in emergency conditions.We aimed to identify prognostic risk factors accessible in the prehospital phase that indicate an increased risk of 7-day mortality.

PATIENTS AND METHODS

We included patients aged 18 years or older, transferred by ambulance to the emergency department at Odense University Hospital, from 1 April 2012 to 30 September 2014. We carried out multivariate logistic regressions, adjusted for age and sex, to describe the relationship between vital sign values recorded in the prehospital setting and 7-day mortality.

RESULTS

A total of 32 076 ambulance transfers were recorded. Of these, 20 328 were first-time transfers, including 2692 that received assistance from a physician-staffed mobile emergency care unit (MECU). The 7-day mortality was 5.3% [95% confidence interval (CI): 5.0-5.6]. The risk of death increased with age. The odds ratios (ORs) were 2.0 (95% CI: 1.1-3.5) for ages 30-44 years and 7.3 (95% CI: 4.5-11) for ages 45-69 years compared with the 18-29-year-olds. All abnormal vital sign values were associated with increased 7-day mortality. Glasgow Coma Score of less than 14 had the strongest association (OR: 17, 95% CI: 14.7-19.7). MECU assistance showed an adjusted OR of 5.3 (95% CI: 4.6-6.1).

CONCLUSION

The overall 7-day mortality was 5.3%, but differed in the two subgroups, with 15.4% in the MECU-assisted ambulance transfers and 3.8% in non-MECU-assisted transfers. Older age and Glasgow Coma Scores below 14 were the strongest of factors associated significantly with 7-day mortality.

Emergency Room Safer Transfer of Patients (ER-STOP): a quality improvement initiative at a community-based hospital to improve the safety of emergency room patient handovers

OBJECTIVES

Ensure early identification and timely management of patient deterioration as essential components of safe effective healthcare. Prompted by analyses of incident reports and deterioration events, a multicomponent organisational rescue from danger system was redesigned to decrease unexpected inpatient deterioration.

DESIGN

Quality improvement before-after unblinded trial.

SETTING

430-bed Canadian community teaching hospital.

PARTICIPANTS

All admitted adult medical-surgical patients in a before-after 12-month interventional study.

INTERVENTION

Locally validated checklist (Modified Early Warning Score+urinary catheter in situ+nurse concern) with an intentional pause and explicit management options was deployed as a modification of an existing ward transfer of accountability fax report in the emergency department (ED).

RESULTS

Following deployment of Emergency Room Safer Transfer of Patients (ER-STOP), the risk of an unexpected CCRT (critical care response team) response within 24 hours of admission from ED to adult medical and surgical wards was significantly decreased (OR 4.1, 95% CI 2.17 to 7.77). Mean (±SD) ED wait times (5.66±1.54vs 5.74±1.04 hours, p=0.30), intensive care unit admission rate (3.84%, n=233vs 4.61%, n=278, p=0.06) and cardiac care unit admission rate (9.51%, n=577vs 9.60%, n=579, p=0.198) were unchanged.

CONCLUSIONS

ER-STOP improvement was out of proportion to the predictive value of the checklist component suggesting that effectiveness of this low-cost sustainable tool was related to increased situational awareness, empowering a culture of patient safety and repurposing of an adjacent ED medical short-stay unit use. Local adaptation within existing processes is essential to successful safety outcomes.

The Boarding Patient: Effects of ICU and Hospital Occupancy Surges on Patient Flow

Patients admitted to a hospital's intensive care unit (ICU) often endure prolonged boarding within the ICU following receipt of care, unnecessarily occupying a critical care bed, and thereby delaying admission for other incoming patients due to bed shortage. Using patient-level data over two years at two major academic medical centers, we estimate the impact of ICU and ward occupancy levels on ICU length of stay (LOS), and test whether simultaneous "surge occupancy" in both areas impacts overall ICU length of stay. In contrast to prior studies that only measure total LOS, we split LOS into two individual periods based on physician requests for bed transfers. We find that "service time" (when critically ill patients are stabilized and treated) is unaffected by occupancy levels. However, the less essential "boarding time" (when patients wait to exit the ICU) is accelerated during periods of high ICU occupancy and, conversely, prolonged when hospital ward occupancy levels are high. When the ICU and wards simultaneously encounter bed occupancies in the top quartile of historical levels-which occurs 5% of the time-ICU boarding increases by 22% compared to when both areas experience their lowest utilization, suggesting that ward bed availability dominates efforts to accelerate ICU discharges to free up ICU beds. We find no adverse effects of high occupancy levels on ICU bouncebacks, in-hospital deaths, or 30-day hospital readmissions, which supports our finding that the largely discretionary boarding period fluctuates with changing bed occupancy levels.

Emergency department risk factors for serious clinical deterioration in a paediatric hospital in Peru

AIM

Identification of critically ill children upon presentation to the emergency department (ED) is challenging, especially when resources are limited. The objective of this study was to identify ED risk factors associated with serious clinical deterioration (SCD) during hospitalisation in a resource-limited setting.

METHODS

A retrospective case-control study of children less than 18 years of age presenting to the ED in a large, freestanding children's hospital in Peru was performed. Cases had SCD during the first 7 days of hospitalisation whereas controls did not. Information collected during initial ED evaluation was used to identify risk factors for SCD.

RESULTS

A total of 120 cases and 974 controls were included. In univariate analysis, young age, residence outside Lima, evaluation at another facility prior to ED presentation, congenital malformations, abnormal neurologic baseline, co-morbidities and a prior paediatric intensive care unit admission were associated with SCD. In multivariate analysis, age < 12 months, residence outside Lima and evaluation at another facility prior to ED presentation remained associated with SCD. In addition, comatose neurological status, hypoxaemia, tachycardia, tachypnoea and temperature were also associated with SCD.

CONCLUSIONS

Many risk factors for SCD during hospitalisation can be identified upon presentation to the ED. Using these factors to adjust monitoring during and after the ED stay has the potential to decrease SCD events. Further studies are needed to determine whether this holds true in other resource-limited settings.

Early in-hospital clinical deterioration is not predicted by severity of illness, functional status, or comorbidity

BACKGROUND

Prior studies concentrated on unplanned intensive care unit (ICU) transfer to gauge deterioration occurring shortly following hospital admission. However, examining only ICU transfers is not ideal since patients could stabilize with treatment, refuse ICU admission, or not require ICU evaluation. To further explore etiologies of early clinical deterioration, we used rapid response team (RRT) activation within 48 hours of admission as an index of early clinical worsening.

METHODS

A retrospective analysis of prospectively gathered admissions from the emergency department in an academic medical center was done. Data were reviewed independently by two physicians. We assessed severity of illness, functional status, comorbidity, the frequency of ICU and palliative care consultations, and changes in advance health care directives.

RESULTS

Of 655 rapid responses (RRs) within the study period, 24.6% occurred within 48 hours of admission. Disease trajectory was the most frequent perceived reason for RRs (55.6% and 58.9%, reviewer 1 and 2, respectively) followed by medical error (15.6% and 15.2%). Acute physiology and chronic health evaluation II (APACHE-II) and modified early warning scores (MEWS) were higher at the time of RR compared to admission (p<0.0001). However, admission APACHE-II, MEWS, functional status, and comorbidity scores did not predict early RRs. One third of RRs resulted in ICU consultation and 95% were accepted. Palliative care consults were requested for 15%, the majority (65%) after RR and all resulting in advance directive change.

CONCLUSION

Disease trajectory accounted for most clinical deterioration and medical error contributed to 15%. Our data suggest that it is difficult to predict early clinical deterioration as none of the measured parameters were associated with RRT activation.

Intermediate care to intensive care triage: A quality improvement project to reduce mortality

PURPOSE

Medical patients whose care needs exceed what is feasible on a general ward, but who do not clearly require critical care, may be admitted to an intermediate care unit (IMCU). Some IMCU patients deteriorate and require medical intensive care unit (MICU) admission. In 2012, staff in the Johns Hopkins IMCU expressed concern that patient acuity and the threshold for MICU admission were too high. Further, shared triage decision-making between residents and supervising physicians did not consistently occur.

METHODS

To improve our triage process, we used a 4Es quality improvement framework (engage, educate, execute, evaluate) to (1) educate residents and fellows regarding principles of triage and (2) facilitate real-time communication between MICU residents conducting triage and supervising physicians.

RESULTS

Among patients transferred from the IMCU to the MICU during baseline (n=83;July-December 2012) and intervention phases (n=94;July-December 2013), unadjusted mortality decreased from 34% to 21% (p=0.06). After adjusting for severity of illness, admitting diagnosis, and bed availability, the odds of death were lower during the intervention vs. baseline phase (OR 0.33; 95%CI 0.11-0.98).

CONCLUSIONS

Using a structured quality improvement process targeting triage education and increased resident/supervisor communication, we demonstrated reduced mortality among patients transferred from the IMCU to the MICU.

Admission lactate predicts poor prognosis independently of the CRB/CURB-65 scores in community-acquired pneumonia

OBJECTIVES

Community-acquired pneumonia (CAP) is associated with a high risk of respiratory failure or septic organ dysfunction. Lactate is an established early marker of prognosis and sepsis severity, but few data exist in patients with CAP.

METHODS

We performed a retrospective cohort study of consecutive adult CAP patients without treatment restrictions or direct intensive care unit admission. Lactate was measured as a point-of-care test within the capillary admission blood gas analysis, and its prognostic value was compared to the CRB/CURB-65 criteria by multivariate and receiver operating characteristic (ROC) curve analysis. The primary endpoint was the combination of need for mechanical ventilation, vasopressors, intensive care unit admission or hospital mortality.

RESULTS

Of 303 included patients, 75 (25%) met the primary endpoint. After ROC analysis, lactate predicted the primary endpoint (area under the curve 0.67) with an optimal cutoff of >1.8 mmol/L. Of the 76 patients with lactate above this threshold, 35 (46%) met the primary endpoint. After multivariate analysis, the predictive value of lactate was independent of the CRB/CURB-65 scores. The addition of lactate >1.8 mmol/L to the CRB/CURB-65 scores resulted in significantly improved area under the curves (0.69 to 0.74, p 0.005 and 0.71 to 0.75, p 0.008 respectively). Fourteen (42%) of 33 and 11 (39%) of 28 patients meeting the endpoint despite presenting with 0 or 1 CRB/CURB-65 criteria had lactate >1.8 mmol/L.

CONCLUSIONS

Admission lactate levels significantly improved the prognostic value of the CRB/CURB-65 scores in CAP patients. Lactate may therefore be considered a rapid, cheap and broadly available additional criterion for the assessment of risk in patients with CAP.

Unexpected intensive care transfer of admitted patients with severe sepsis

Background

Patients with severe sepsis generally respond well to initial therapy administered in the emergency department (ED), but a subset later decompensate and require unexpected transfer to the intensive care unit (ICU). This study aimed to identify clinical factors that can predict patients at increased risk for delayed transfer to the ICU and the association of delayed ICU transfer with mortality.

Methods

This is a nested case-control study in a prospectively collected registry of patients with severe sepsis and septic shock at two EDs. Cases had severe sepsis and unexpected ICU transfer within 48 h of admission from the ED; controls had severe sepsis but remained in a non-ICU level of care. Univariate and multivariate regression analyses were used to identify predictors of unexpected transfer to the ICU, which was the primary outcome. Differences in mortality between these two groups as well as a cohort of patients directly admitted to the ICU were also calculated.

Results

Of the 914 patients in our registry, 358 patients with severe sepsis were admitted from the ED to non-ICU level of care; 84 (23.5%) had unexpected ICU transfer within 48 h. Demographics and baseline co-morbidity burden were similar for patients requiring versus not requiring delayed ICU transfer. In unadjusted analysis, lactate ≥4 mmol/L and infection site were significantly associated with unexpected ICU upgrade. In forward selection multivariate logistic regression analysis, lactate ≥4 mmol/L (OR 2.0, 95% CI 1.03, 3.73; p = 0.041) and night (5 PM to 7 AM) admission (OR 1.9, 95% CI 1.07, 3.33; p = 0.029) were independent predictors of unexpected ICU transfer. Mortality of patients who were not upgraded to the ICU was 8.0%. Patients with unexpected ICU upgrade had similar mortality (25.0%) to those patients with severe sepsis/septic shock (24.6%) who were initially admitted to the ICU, despite less severe indices of illness at presentation.

Conclusions

Serum lactate ≥4 mmol/L and nighttime admissions are associated with unexpected ICU transfer in patients with severe sepsis. Mortality among patients with delayed ICU upgrade was similar to that for patients initially admitted directly to the ICU.

Pitfalls in the Treatment of Sepsis

Sepsis is a challenging, dynamic, pathophysiology requiring expertise in diagnosis and management. Controversy exists as to the most sensitive early indicators of sepsis and sepsis severity. Patients presenting to the emergency department often lack complete history or clinical data that would point to optimal management. Awareness of these potential knowledge gaps is important for the emergency provider managing the septic patient. Specific areas of management including the initiation and management of mechanical ventilation, the appropriate disposition of the patient, and consideration of transfer to higher levels of care are reviewed.

Hospital ward transfer to intensive care unit as a quality marker in emergency medicine

BACKGROUND

Minimizing and preventing adverse events and medical errors in the emergency department (ED) is an ongoing area of quality improvement. Identifying these events remains challenging.

OBJECTIVE

To investigate the utility of tracking patients transferred to the ICU within 24h of admission from the ED as a marker of preventable errors and adverse events.

METHODS

From November 2011 through June 2016, we prospectively collected data for all patients presenting to an urban, tertiary care academic ED. We utilized an automated electronic tracking system to identify ED patients who were admitted to a hospital ward and then transferred to the ICU within 24h. Reviewers screened for possible error or adverse event and if discovered the case was referred to the departmental Quality Assurance (QA) committee for deliberations and consensus agreement.

RESULTS

Of 96,377 ward admissions, 921 (1%) patients were subsequently transferred to the ICU within 24h of ED presentation. Of these 165 (19%) were then referred to the QA committee for review. Total rate of adverse events regardless of whether or not an error occurred was 2.1%, 19/921 (95% CI 1.4% to 3.0%). Medical error on the part of the ED was 2.2%, 20/921 (95% CI 1.5% to 3.1%) and ED Preventable Error in 1.1%, 10/921 (95% CI 0.6% to 1.8%).

CONCLUSION

Tracking patients admitted to the hospital from the ED who are transferred to the ICU <24h after admission may be a valuable marker for adverse events and preventable errors in the ED.

Outcomes of Emergency Medical Patients Admitted to an Intermediate Care Unit With Detailed Admission Guidelines

BACKGROUND

An important, but not well characterized, population receiving intermediate care is that of medical patients admitted directly from the emergency department.

OBJECTIVE

To characterize emergency medical patients and their outcomes when admitted to an intermediate care unit with clearly defined admission guidelines.

METHODS

Demographic data, admitting diagnoses, illness severity, comorbid conditions, lengths of stay, and hospital mortality were characterized for all emergency medical patients admitted directly to an intermediate care unit from July through December 2012.

RESULTS

A total of 317 unique patients were admitted (mean age, 54 [SD, 16] years). Most patients were admitted with respiratory (26.5%) or cardiac (17.0%) syndromes. The mean (SD) Acute Physiology and Chronic Health Evaluation score version II, Simplified Acute Physiology Score version II, and Charlson Comorbidity Index were 15.6 (6.5), 20.7 (11.8), and 2.7 (2.3), respectively. Severity of illness and length of stay were significantly different for patients who required intensive care within 24 hours of admission (n = 16) or later (n = 25), patients who continued with inter mediate care for more than 24 hours (n = 247), and patients who were downgraded or discharged in less than 24 hours (n = 29). Overall hospital mortality was 4.4% (14 deaths).

CONCLUSIONS

Emergency medical patients with moderate severity of illness and comorbidity can be admitted to an intermediate level of care with relatively infrequent transfer to intensive care and relatively low mortality.

Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals

Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun. Journal of Hospital Medicine 2016;11:S18-S24. © 2016 Society of Hospital Medicine.

Incorporating an Early Detection System Into Routine Clinical Practice in Two Community Hospitals

Efforts to improve outcomes of patients who deteriorate outside the intensive care unit have included the use of rapid response teams (RRTs) as well as manual and automated prognostic scores. Although automated early warning systems (EWSs) are starting to enter clinical practice, there are few reports describing implementation and the processes required to integrate early warning approaches into hospitalists' workflows. We describe the implementation process at 2 community hospitals that deployed an EWS. We employed the Institute for Healthcare Improvement's iterative Plan-Do-Study-Act approach. Our basic workflow, which relies on having an RRT nurse and the EWS's 12-hour outcome time frame, has been accepted by clinicians and has not been associated with patient complaints. Whereas our main objective was to develop a set of workflows for integrating the electronic medical record EWS into clinical practice, we also uncovered issues that must be addressed prior to disseminating this intervention to other hospitals. One problematic area is that of documentation following an alert. Other areas that must be addressed prior to disseminating the intervention include the need for educating clinicians on the rationale for deploying the EWS, careful consideration of interdepartment service agreements, clear definition of clinician responsibilities, pragmatic documentation standards, and how to communicate with patients. In addition to the deployment of the EWS to other hospitals, a future direction for our teams will be to characterize process-outcomes relationships in the clinical response itself. Journal of Hospital Medicine 2016;11:S25-S31. © 2016 Society of Hospital Medicine.

Unplanned Transfers from Hospital Wards to the Neurological Intensive Care Unit

BACKGROUND

The aim of this study is to evaluate the characteristics of unplanned transfers of adult patients from hospital wards to a neurological intensive care unit (NICU).

METHODS

We retrospectively reviewed consecutive unplanned transfers from hospital wards to the NICU at our institution over a 3-year period. In-hospital mortality rates were compared between patients readmitted to the NICU ("bounce-back transfers") and patients admitted to hospital wards from sources other than the NICU who were then transferred to the NICU ("incident transfers"). We also measured clinical characteristics of transfers, including source of admission and indication for transfer.

RESULTS

A total of 446 unplanned transfers from hospital wards to the NICU occurred, of which 39% were bounce-back transfers. The in-hospital mortality rate associated with all unplanned transfers to the NICU was 17% and did not differ significantly between bounce-back transfers and incident transfers. Transfers to the NICU within 24 h of admission to a floor service accounted for 32% of all transfers and were significantly more common for incident transfers than bounce-back transfers (39 vs. 21%, p = .0002). Of patients admitted via the emergency department who had subsequent incident transfers to the NICU, 50% were transferred within 24 h of admission.

CONCLUSIONS

Unplanned transfers to an NICU were common and were associated with a high in-hospital mortality rate. Quality improvement projects should target the triage process and transitions of care to the hospital wards in order to decrease unplanned transfers of high-risk patients to the NICU.

A critical analysis of unplanned ICU transfer within 48 hours from ED admission as a quality measure

HYPOTHESIS

Unplanned intensive care unit (ICU) transfer (UIT) within 48 hours of emergency department (ED) admission increases morbidity and mortality. We hypothesized that a majority of UITs do not have critical interventions (CrIs) and that CrI is associated with worse outcomes.

OBJECTIVE

The objective of the study is to characterize all UITs (including patients who died before ICU transfer), the proportion with CrI, and the effect of having CrI on mortality.

DESIGN

This is a single-center, retrospective cohort study of UITs within 48 hours from 2008 to 2013 at an urban academic medical center and included patients 18 years or older without advanced directives (ADs). Critical intervention was defined by modified Delphi process. Data included demographics, comorbidities, reasons for UIT, length of stay, CrIs, and mortality. We calculated descriptive statistics with 95% confidence intervals (CIs).

RESULTS

A total of 837 (0.76%) of 108 732 floor admissions from the ED had a UIT within 48 hours; 86 admitted patients died before ICU. We excluded 23 ADs, 117 postoperative transfers, 177 planned ICU transfers, and 4 with missing data. Of the 516 remaining, 65% (95% CI, 61%-69%) received a CrI. Unplanned ICU transfer reasons are as follows: 33 medical errors, 90 disease processes not present on arrival, and 393 clinical deteriorations. Mortality was 10.5% (95% CI, 8%-14%), and mean length of stay was 258 hours (95% CI, 233-283) for those with CrI, whereas the mortality was 2.8% (95% CI, 1%-6%) and mean length of stay was 177 hours (95% CI, 157-197) for those without CrI.

CONCLUSIONS

Unplanned ICU transfer is rare, and only 65% had a CrI. Those with CrI had increased morbidity and mortality.

A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

RATIONALE

High demand for intensive care unit (ICU) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine ICU bed assignment policies, accounting for patient acuity, to reduce ICU admission delays.

OBJECTIVES

To provide a framework for data-driven modeling of ICU patient flow, identify key measurable outcomes, and present illustrative analysis demonstrating the impact of various bed allocation scenarios on outcomes.

METHODS

A description of key inputs for constructing a queuing model was outlined, and an illustrative simulation model was developed to reflect current triage protocol within the medical ICU and step-down unit (SDU) at a single tertiary-care hospital. Patient acuity, arrival rate, and unit length of stay, consisting of a "service time" and "time to transfer," were estimated from 12 months of retrospective data (n = 2,710 adult patients) for 36 ICU and 15 SDU staffed beds. Patient priority was based on acuity and whether the patient originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied ICU/SDU sizes, (2) reserved ICU beds as a triage strategy, (3) lower targets for time to transfer out of the ICU, and (4) ICU expansion by up to four beds. Outcomes included ICU admission wait times and unit occupancy.

MEASUREMENTS AND MAIN RESULTS

With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as ICU decreased overall wait times by 7.2% to 1.06 (SD, 1.39) hours and increased bed occupancy from 80 to 84%. Reserving the last available bed for acute patients reduced wait times for acute patients from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute patients' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all ICU/SDU patients to 1 hour decreased wait times for incoming ICU patients, comparable to building one to two additional ICU beds.

CONCLUSIONS

Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in ICU bed assignment could impact unit occupancy levels and patient wait times. Trade-offs associated with dedicating resources for acute patients versus expanding capacity for all patients can be examined.

ICAT: a simple score predicting critical care needs after thrombolysis in stroke patients

Background

Patients receiving intravenous thrombolysis (IVT) for acute ischemic stroke are at risk of developing complications, commonly necessitating admission to an intensive care unit (ICU). At present, most IVT is administered in the Emergency Department or in dedicated stroke units, but no evidence-based criteria exist that allow for early identification of patients at increased risk of developing ICU needs. The present study describes a novel prediction score aiming to identify a subpopulation of post-IVT patients at high risk for critical care interventions.

Methods

We retrospectively analyzed data from 301 patients undergoing IVT at our institutions during a 5-year period. Two hundred and ninety patients met inclusion criteria. The sample was randomly divided into a development and a validation cohort. Logistic regression was used to develop a risk score by weighting predictors of critical care needs based on strength of association.

Results

Seventy-two patients (24.8 %) required critical care interventions. Black race (odds ratio [OR] 3.81, p =0.006), male sex (OR 3.79, p =0.008), systolic blood pressure (SBP; OR 1.45 per 10 mm Hg increase in SBP, p <0.001), and NIH stroke scale (NIHSS; OR 1.09 per 1 point increase in NIHSS, p =0.071) were independent predictors of critical care needs. The optimal model for score development, predicting critical care needs, achieved an AUC of 0.782 in the validation group. The score was named the ICAT (Intensive Care After Thrombolysis) score, assigning the following points: black race (1 point), male sex (1 point), SBP (2 points if 160–200 mm Hg; 4 points if >200 mm Hg), and NIHSS (1 point if 7–12; 2 points if >12). Each 1-point increase in the score was associated with 2.22-fold increased odds for critical care needs (95 % CI 1.78–2.76, p <0.001). A score ≥2 was associated with over 13 times higher odds of critical care needs compared to a score <2 (OR 13.60, 95 % CI 3.23–57.19), predicting critical care with 97.2 % sensitivity and 28.0 % specificity.

Conclusion

The ICAT score, combining information about race, sex, SBP, and NIHSS, predicts critical care needs in post-IVT patients and may be helpful when triaging post-IVT patients to the appropriate monitoring environment.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-016-1195-7) contains supplementary material, which is available to authorized users.