Relationship between ICU bed availability, ICU readmission, and cardiac arrest in the general wards

In-Hospital Triage

The intensive care unit (ICU) is a finite and expensive resource with demand not infrequently exceeding capacity. Understanding ICU capacity strain is essential to gain situational awareness. Increased capacity strain can influence ICU triage decisions, which rely heavily on clinical judgment. Having an admission and triage protocol with which clinicians are very familiar can mitigate difficult, inappropriate admissions. This article reviews these concepts and methods of in-hospital triage.

Unit-to-unit transfer due to shortage of intensive care beds in Sweden 2015-2019 was associated with a lower risk of death but a longer intensive care stay compared to no transfer: a registry study

BACKGROUND

Intensive care unit-to-unit transfer due to temporary shortage of beds is increasing in Sweden. Transportation induces practical hazards, and the change of health care provider may prolong the length of stay in intensive care. We previously showed that the risk of death at 90 days did not differ between patients transferred due to a shortage of beds and non-transferred patients with a similar burden of illness in a tertiary intensive care unit. The aim of this study was to widen the analysis to a nation-wide cohort of critically ill patients transferred to another intensive care unit in Sweden due to shortage of intensive care beds.

METHODS

Retrospective comparison between capacity transferred and non-transferred patients, based on data from the Swedish Intensive Care Registry during a 5-year period before the COVID-19 pandemic. Patients with insufficient data entries or a recurring capacity transfer within 90 days were excluded. To assess the association between capacity transfer and death as well as intensive care stay within 90 days after ICU admission, logistic regression models with step-wise adjustment for SAPS3 score, primary ICD-10 ICU diagnosis and the number of days in the intensive care unit before transfer were applied.

RESULTS

From 161,140 eligible intensive care admissions, 2912 capacity transfers were compared to 135,641 discharges or deaths in the intensive care unit. Ninety days after ICU admission, 28% of transferred and 21% of non-transferred patients were deceased. In the fully adjusted model, capacity transfer was associated with a lower risk of death within 90 days than no transfer; OR (95% CI) 0.71 (0.65-0.69) and the number of days spent in intensive care was longer: 12.4 [95% CI 12.2-12.5] vs 3.3 [3.3-3.3].

CONCLUSIONS

Intensive care unit-to-unit transfer due to shortage of bed capacity as compared to no transfer during a 5-year period preceding the COVID-19 pandemic in Sweden was associated with lower risk of death within 90 days but with longer stay in intensive care.

Reduced time to imaging, length of stay, and hospital charges following implementation of a novel postoperative pathway for craniotomy

OBJECTIVE

The authors created a postoperative postanesthesia care unit (PACU) pathway to bypass routine intensive care unit (ICU) admissions of patients undergoing routine craniotomies, to improve ICU resource utilization and reduce overall hospital costs and lengths of stay while maintaining quality of care and patient satisfaction. In the present study, the authors evaluated this novel PACU-to-floor clinical pathway for a subset of patients undergoing craniotomy with a case time under 5 hours and blood loss under 500 ml.

METHODS

A single-institution retrospective cohort study was performed to compare 202 patients enrolled in the PACU-to-floor pathway and 193 historical controls who would have met pathway inclusion criteria. The pathway cohort consisted of all adult supratentorial brain tumor cases from the second half of January 2021 to the end of January 2022 that met the study inclusion criteria. Control cases were selected from the beginning of January 2020 to halfway through January 2021. The authors also discuss common themes of similar previously published pathways and the logistical and clinical barriers overcome for successful PACU pathway implementation.

RESULTS

Pathway enrollees had a median age of 61 years (IQR 49-69 years) and 53% were female. Age, sex, pathology, and American Society of Anesthesiologists physical status distributions were similar between pathway and control patients (p > 0.05). Most of the pathway cases (96%) were performed on weekdays, and 31% had start times before noon. Nineteen percent of pathway patients had 30-day readmissions, most frequently for headache (16%) and syncope (10%), whereas 18% of control patients had 30-day readmissions (p = 0.897). The average time to MRI was 6 hours faster for pathway patients (p < 0.001) and the time to inpatient physical therapy and/or occupational therapy evaluation was 4.1 hours faster (p = 0.046). The average total length of stay was 0.7 days shorter for pathway patients (p = 0.02). A home discharge occurred in 86% of pathway cases compared to 81% of controls (p = 0.225). The average total hospitalization charges were $13,448 lower for pathway patients, representing a 7.4% decrease (p = 0.0012, adjusted model). Seven pathway cases were escalated to the ICU postoperatively because of attending physician preference (2 cases), agitation (1 case), and new postoperative neurological deficits (4 cases), resulting in a 96.5% rate of successful discharge from the pathway. In bypassing the ICU, critical care resource utilization was improved by releasing 0.95 ICU days per patient, or 185 ICU days across the cohort.

CONCLUSIONS

The featured PACU-to-floor pathway reduces the stay of postoperative craniotomy patients and does not increase the risk of early hospital readmission.

Factors that influence intensive care admission decisions for older people: A systematic review

BACKGROUND

The population worldwide is rapidly ageing, and demand for intensive care is increasing. People aged 85 years and above, known as the oldest old, are particularly vulnerable to critical illness owing to the physiological effects of ageing. Evidence surrounding admission of the oldest old to the intensive care is limited.

OBJECTIVE

The objective of this study was to systematically and comprehensively review and synthesise the published research investigating factors that influence decisions to admit the oldest old to the intensive care unit.

METHOD

This was a systematic review and narrative synthesis. Following a comprehensive search of CINAHL, Embase, and Medline databases, peer-reviewed primary research articles examining factors associated with admission or refusal to admit the oldest old to intensive care were selected. Data were extracted into tables and narratively synthesised.

RESULTS

Six studies met the inclusion criteria. Three studies identified factors associated with admission such as greater premorbid self-sufficiency, patient preferences, alignment between patient and physicians' goals of treatment, age less than 85 years, and absence of cancer, or previous intensive care admission. Factors associated with refusal to admit were identified in all six studies and included limited or no bed availability, level of ICU physician experience, patients being deemed too ill or too well to benefit, and older age.

CONCLUSIONS

Published research investigating decision-making about admission or refusal to admit the oldest old to the intensive care unit is scant. The ageing population and increasing demand for intensive care unit resources has amplified the need for greater understanding of factors that influence decisions to admit or refuse admission of the oldest old to the intensive care unit. Such knowledge may inform guidelines regarding complex practice decisions about admission of the oldest old to an intensive care unit. Such guidelines would ensure the specialty needs of this population are considered and would reduce admission decisions that might disadvantage older people.

Time-Frequency Analysis of Somatosensory Evoked High-Frequency (600 Hz) Oscillations as an Early Indicator of Arousal Recovery after Hypoxic-Ischemic Brain Injury

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery phase, where the injury is reversible, has not been tested. We hypothesize that time-frequency (TF) analysis of HFOs can determine arousal recovery in the acute recovery phase. To test our hypothesis, eleven adult male Wistar rats were subjected to asphyxial CA (five with 3-min mild and six with 7-min moderate to severe CA) and SSEPs were recorded for 60 min post-resuscitation. Arousal level was quantified by the neurological deficit scale (NDS) at 4 h. Our results demonstrated that continuous wavelet transform (CWT) of SSEPs localizes HFOs in the TF domain under baseline conditions. The energy dispersed immediately after injury and gradually recovered. We proposed a novel TF-domain measure of HFO: the total power in the normal time-frequency space (NTFS) of HFO. We found that the NTFS power significantly separated the favorable and unfavorable outcome groups. We conclude that the NTFS power of HFOs provides earlier and objective determination of arousal recovery after CA.

Critical Care Unit Organizational and Personnel Factors Impact Cardiac Arrest Prevention and Rescue in the Pediatric Cardiac Population

OBJECTIVES

Patient-level factors related to cardiac arrest in the pediatric cardiac population are well understood but may be unmodifiable. The impact of cardiac ICU organizational and personnel factors on cardiac arrest rates and outcomes remains unknown. We sought to better understand the association between these potentially modifiable organizational and personnel factors on cardiac arrest prevention and rescue.

DESIGN

Retrospective analysis of the Pediatric Cardiac Critical Care Consortium registry.

SETTING

Pediatric cardiac ICUs.

PATIENTS

All cardiac ICU admissions were evaluated for cardiac arrest and survival outcomes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Successful prevention was defined as the proportion of admissions with no cardiac arrest (inverse of cardiac arrest incidence). Rescue was the proportion of patients surviving to cardiac ICU discharge after cardiac arrest. Cardiac ICU organizational and personnel factors were captured via site questionnaires. The associations between organizational and personnel factors and prevention/rescue were analyzed using Fine-Gray and multinomial regression, respectively, accounting for clustering within hospitals. We analyzed 54,521 cardiac ICU admissions (29 hospitals) with 1,398 cardiac arrest events (2.5%) between August 1, 2014, and March 5, 2019. For both surgical and medical admissions, lower average daily cardiac ICU occupancy was associated with better cardiac arrest prevention. Better rescue for medical admissions was observed for higher registered nursing hours per patient day and lower proportions of "part time" cardiac ICU physician staff (< 6 service weeks/yr). Increased registered nurse experience was associated with better rescue for surgical admissions. Increased proportion of critical care certified nurses, full-time intensivists with critical care fellowship training, dedicated respiratory therapists, quality/safety resources, and annual cardiac ICU admission volume were not associated with improved prevention or rescue.

CONCLUSIONS

Our multi-institutional analysis identified cardiac ICU bed occupancy, registered nurse experience, and physician staffing as potentially important factors associated with cardiac arrest prevention and rescue. Recognizing the limitations of measuring these variables cross-sectionally, additional studies are needed to further investigate these organizational and personnel factors, their interrelationships, and how hospitals can modify structure to improve cardiac arrest outcomes.

Increased 30-day mortality in very old ICU patients with COVID-19 compared to patients with respiratory failure without COVID-19

Purpose

The number of patients ≥ 80 years admitted into critical care is increasing. Coronavirus disease 2019 (COVID-19) added another challenge for clinical decisions for both admission and limitation of life-sustaining treatments (LLST). We aimed to compare the characteristics and mortality of very old critically ill patients with or without COVID-19 with a focus on LLST.

Methods

Patients 80 years or older with acute respiratory failure were recruited from the VIP2 and COVIP studies. Baseline patient characteristics, interventions in intensive care unit (ICU) and outcomes (30-day survival) were recorded. COVID patients were matched to non-COVID patients based on the following factors: age (± 2 years), Sequential Organ Failure Assessment (SOFA) score (± 2 points), clinical frailty scale (± 1 point), gender and region on a 1:2 ratio. Specific ICU procedures and LLST were compared between the cohorts by means of cumulative incidence curves taking into account the competing risk of discharge and death.

Results

693 COVID patients were compared to 1393 non-COVID patients. COVID patients were younger, less frail, less severely ill with lower SOFA score, but were treated more often with invasive mechanical ventilation (MV) and had a lower 30-day survival. 404 COVID patients could be matched to 666 non-COVID patients. For COVID patients, withholding and withdrawing of LST were more frequent than for non-COVID and the 30-day survival was almost half compared to non-COVID patients.

Conclusion

Very old COVID patients have a different trajectory than non-COVID patients. Whether this finding is due to a decision policy with more active treatment limitation or to an inherent higher risk of death due to COVID-19 is unclear.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06642-z.

Does Unprecedented ICU Capacity Strain, As Experienced During the COVID-19 Pandemic, Impact Patient Outcome?

OBJECTIVES

To determine whether patients admitted to an ICU during times of unprecedented ICU capacity strain, during the COVID-19 pandemic in the United Kingdom, experienced a higher risk of death.

DESIGN

Multicenter, observational cohort study using routine clinical audit data.

SETTING

Adult general ICUs participating the Intensive Care National Audit & Research Centre Case Mix Programme in England, Wales, and Northern Ireland.

PATIENTS

One-hundred thirty-thousand six-hundred eighty-nine patients admitted to 210 adult general ICUs in 207 hospitals.

INTERVENTIONS

Multilevel, mixed effects, logistic regression models were used to examine the relationship between levels of ICU capacity strain on the day of admission (typical low, typical, typical high, pandemic high, and pandemic extreme) and risk-adjusted hospital mortality.

MEASUREMENTS AND MAIN RESULTS

In adjusted analyses, compared with patients admitted during periods of typical ICU capacity strain, we found that COVID-19 patients admitted during periods of pandemic high or pandemic extreme ICU capacity strain during the first wave had no difference in hospital mortality, whereas those admitted during the pandemic high or pandemic extreme ICU capacity strain in the second wave had a 17% (odds ratio [OR], 1.17; 95% CI, 1.05-1.30) and 15% (OR, 1.15; 95% CI, 1.00-1.31) higher odds of hospital mortality, respectively. For non-COVID-19 patients, there was little difference in trend between waves, with those admitted during periods of pandemic high and pandemic extreme ICU capacity strain having 16% (OR, 1.16; 95% CI, 1.08-1.25) and 30% (OR, 1.30; 95% CI, 1.14-1.48) higher overall odds of acute hospital mortality, respectively.

CONCLUSIONS

For patients admitted to ICU during the pandemic, unprecedented levels of ICU capacity strain were significantly associated with higher acute hospital mortality, after accounting for differences in baseline characteristics. Further study into possible differences in the provision of care and outcome for COVID-19 and non-COVID-19 patients is needed.

Evaluation of time to death after admission to an intensive care unit and factors associated with mortality: A retrospective longitudinal study

Background

Among nonsurvivors admitted to the intensive care unit (ICU), some present early mortality while other patients, despite having a favorable evolution regarding the initial disease, die later due to complications related to hospitalization. This study aims to identify factors associated with the time until death after admission to an ICU of a university hospital.

Methods

Retrospective longitudinal study that included adult patients admitted to the ICU between January 1, 2008, and December 31, 2017. Nonsurviving patients were divided into groups according to the length of time from admission to the ICU until death: Early (0-5 days), intermediate (6-28 days), and late (>28 days). Patients were considered septic if they had this diagnosis on admission to the ICU. Simple linear regression analysis was performed to evaluate the association between time to death over the years of the study. Multivariate cox regression was used to assess risk factors for the outcome in the ICU.

Results

In total, 6596 patients were analyzed. Mortality rate was 32.9% in the ICU. Most deaths occurred in the early (42.8%) and intermediate periods (47.9%). Patients with three or more dysfunctions on admission were more likely to die early (P < 0.001). The diagnosis of sepsis was associated with a higher mortality rate. The multivariate analysis identified age >60 years (hazard ratio [HR] 1.009), male (HR 1.192), mechanical ventilation (HR 1.476), dialysis (HR 2.297), and sequential organ failure assessment >6 (HR 1.319) as risk factors for mortality.

Conclusion

We found a higher proportion of early and intermediate deaths in the study period. The presence of three or more organ dysfunctions at ICU admission was associated with early death. The diagnosis of sepsis evident on ICU admission was associated with higher mortality.

Vitamin D supplementation to treat SARS-CoV-2 positive patients. Evidence from meta-analysis

BACKGROUND

Vitamin D is a likely candidate for treatment as its immune modulating characteristics have effects on coronavirus disease 2019 (COVID-19) patients. It was sought herein, to summarize the studies published to date regarding the vitamin D supplementation to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients.

METHODS

A systematic review and meta-analysis were performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The primary outcome were 14-day and in-hospital mortality reported as an odds ratio (OR) with the associated 95% confidence interval (CI).

RESULTS

Eight articles were included in the review with a combined total of 2,322 individual patients, 786 in the vitamin D supplementation group and 1,536 in the control group. The use of vitamin D compared to the group without vitamin D supplementation was associated with a lower 14-day mortality (18.8% vs. 31.3%, respectively; OR = 0.51; 95% CI: 0.12-2.19; p = 0.36), a lower in-hospital mortality (5.6% vs. 16.1%; OR = 0.56; 95% CI: 0.23-1.37; I2 = 74%; p = 0.20), the rarer intensive care unit admission (6.4% vs. 23.4%; OR = 0.19; 95% CI: 0.06-0.54; I2 = 77%; p = 0.002) as well as rarer mechanical ventilation (6.5% vs. 18.9%; OR = 0.36; 95% CI: 0.16-0.80; I2 = 0.48; p = 0.01).

CONCLUSIONS

Vitamin D supplementation in SARS-CoV-2 positive patients has the potential to positively impact patients with both mild and severe symptoms. As several high-quality randomized control studies have demonstrated a benefit in hospital mortality, vitamin D should be considered a supplemental therapy of strong interest. Should vitamin D prove to reduce hospitalization rates and symptoms outside of the hospital setting, the cost and benefit to global pandemic mitigation efforts would be substantial.

A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

Background

Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to predict the critical care outcomes in patients with chest pain, and simultaneously compare its performance with HEART, GRACE, and TIMI scores.

Methods

This was a retrospective, case-control study in patients with acute non-traumatic chest pain who presented to the emergency department (ED) between January 2017 and December 2019. The outcomes included cardiac arrest, transfer to ICU, and death during treatment in ED. In the randomly sampled training set (70%), a LASSO regression model was developed, and presented with nomogram. The performance was measured in both training set (70% participants) and testing set (30% participants), and findings were compared with the three widely used scores.

Results

We proposed a LASSO regression model incorporating mode of arrival, reperfusion therapy, Killip class, systolic BP, serum creatinine, creatine kinase-MB, and brain natriuretic peptide as independent predictors of critical care outcomes in patients with chest pain. Our model significantly outperformed the HEART, GRACE, TIMI score with AUC of 0.953 (95%CI: 0.922–0.984), 0.754 (95%CI: 0.675–0.832), 0.747 (95%CI: 0.664–0.829), 0.735 (95%CI: 0.655–0.815), respectively. Consistently, our model demonstrated better outcomes regarding the metrics of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Similarly, the decision curve analysis elucidated a greater net benefit of our model over the full ranges of clinical thresholds.

Conclusion

We present an accurate model for predicting the critical care outcomes in patients with chest pain, and provide substantial support to its application as a decision-making tool in ED.

Relationship between the Clinical Frailty Scale and short-term mortality in patients ≥ 80 years old acutely admitted to the ICU: a prospective cohort study

Background

The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context.

Methods

We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for both ICU and 30-day mortality included the CFS score as either a categorical, continuous or dichotomous variable and were adjusted for patient’s age, sex, reason for admission to the ICU, and admission Sequential Organ Failure Assessment score.

Results

The median age in the sample of 7487 consecutive patients was 84 years (IQR 81–87). The highest fraction of new prognostic information from frailty in the context of 30-day mortality was observed when the CFS score was treated as either a categorical variable using all original levels of frailty or a nonlinear continuous variable and was equal to 9% using these modelling approaches (p < 0.001). The relationship between the CFS score and mortality was nonlinear (p < 0.01).

Conclusion

Knowledge about a patient’s frailty status adds a substantial amount of new prognostic information at the moment of admission to the ICU. Arbitrary simplification of the CFS score into fewer groups than originally intended leads to a loss of information and should be avoided.

Trial registration NCT03134807 (VIP1), NCT03370692 (VIP2)

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03632-3.

Adverse Events After Transition From ICU to Hospital Ward: A Multicenter Cohort Study

OBJECTIVES

To examine adverse events and associated factors and outcomes during transition from ICU to hospital ward (after ICU discharge).

DESIGN

Multicenter cohort study.

SETTING

Ten adult medical-surgical Canadian ICUs.

PATIENTS

Patients were those admitted to one of the 10 ICUs from July 2014 to January 2016.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Two ICU physicians independently reviewed progress and consultation notes documented in the medical record within 7 days of patient's ICU discharge date to identify and classify adverse events. The adverse event data were linked to patient characteristics and ICU and ward physician surveys collected during the larger prospective cohort study. Analyses were conducted using multivariable logistic regression. Of the 451 patients included in the study, 84 (19%) experienced an adverse event, the majority (62%) within 3 days of transfer from ICU to hospital ward. Most adverse events resulted only in symptoms (77%) and 36% were judged to be preventable. Patients with adverse events were more likely to be readmitted to the ICU (odds ratio, 5.5; 95% CI, 2.4-13.0), have a longer hospital stay (mean difference, 16.1 d; 95% CI, 8.4-23.7) or die in hospital (odds ratio, 4.6; 95% CI, 1.8-11.8) than those without an adverse event. ICU and ward physician predictions at the time of ICU discharge had low sensitivity and specificity for predicting adverse events, ICU readmissions, and hospital death.

CONCLUSIONS

Adverse events are common after ICU discharge to hospital ward and are associated with ICU readmission, increased hospital length of stay and death and are not predicted by ICU or ward physicians.

Changes in cardiac arrest profiles after the implementation of a Rapid Response Team

OBJECTIVE

To evaluate changes in the characteristics of in-hospital cardiac arrest after the implementation of a Rapid Response Team.

METHODS

This was a prospective observational study of in-hospital cardiac arrest that occurred from January 2013 to December 2017. The exclusion criterion was in-hospital cardiac arrest in the intensive care unit, emergency room or operating room. The Rapid Response Team was implemented in July 2014 in the study hospital. Patients were classified into two groups: a Pre-Rapid Response Team (in-hospital cardiac arrest before Rapid Response Team implementation) and a Post-Rapid Response Team (in-hospital cardiac arrest after Rapid Response Team implementation). Patients were followed until hospital discharge or death.

RESULTS

We had a total of 308 cardiac arrests (64.6 ± 15.2 years, 60.3% men, 13.9% with initial shockable rhythm). There was a decrease from 4.2 to 2.5 in-hospital cardiac arrest/1000 admissions after implementation of the Rapid Response Team, and we had approximately 124 calls/1000 admissions. Pre-Rapid Response Team cardiac arrest was associated with more hypoxia (29.4 versus 14.3%; p = 0.006) and an altered respiratory rate (14.7 versus 4.2%; p = 0.004) compared with post-Rapid Response Team cardiac arrest. Cardiac arrest due to hypoxia was more common before Rapid Response Team implementation (61.2 versus 38.1%, p < 0.001). In multivariate analysis, return of spontaneous circulation was associated with shockable rhythm (OR 2.97; IC95% 1.04 - 8.43) and witnessed cardiac arrest (OR 2.52; IC95% 1.39 - 4.59) but not with Rapid Response Team implementation (OR 1.40; IC95% 0.70 - 2.81) or premonitory signs (OR 0.71; IC95% 0.39 - 1.28). In multivariate analysis, in-hospital mortality was associated with non-shockable rhythm (OR 5.34; IC95% 2.28 - 12.53) and age (OR 1.03; IC95% 1.01 - 1.05) but not with Rapid Response Team implementation (OR 0.89; IC95% 0.40 - 2.02).

CONCLUSION

Even though Rapid Response Team implementation is associated with a reduction in in-hospital cardiac arrest, it was not associated with the mortality of in-hospital cardiac arrest victims. A significant decrease in cardiac arrests due to respiratory causes was noted after Rapid Response Team implementation.

The mortality of critically ill patients was not associated with inter-hospital transfer due to a shortage of ICU beds - a single-centre retrospective analysis

Background

Patients in the intensive care unit (ICU) are increasingly being transferred between ICUs due to a shortage of ICU beds, although this practice is potentially harmful. However, in tertiary units, the transfer of patients who are not in need of highly specialized care is often necessary. The aim of this study was to assess the association between a 90-day mortality and inter-hospital transfer due to a shortage of ICU beds in a tertiary centre.

Methods

Data were retrieved from the local ICU database from December 2011 to September 2019. The primary analysis was a risk-adjusted logistic regression model. Secondary analyses comprised case/control (transfer/non-transfer) matching.

Results

A total of 573 patients were transferred due to a shortage of ICU beds, and 8106 patients were not transferred. Crude 90-day mortality was higher in patients transferred due to a shortage of beds (189 patients (33%) vs 2188 patients (27%), p = 0.002). In the primary, risk-adjusted analysis, the risk of death at 90 days was similar between the groups (odds ratio 0.923, 95% confidence interval 0.75–1.14, p = 0.461). In the secondary analyses, a 90-day mortality was similar in transferred and non-transferred patients matched according to SAPS 3-score, age, days in the ICU and ICU diagnosis (p = 0.407); SOFA score on the day of discharge, ICU diagnosis and age (p = 0.634); or in a propensity score model (p = 0.229).

Conclusion

Mortality at 90 days in critically ill patients treated in a tertiary centre was not affected by transfer to another intensive care units due to a shortage of beds. We found this conclusion to be valid under the assumption that patients are carefully selected and that the transports are safely performed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-020-00501-z.

Early mortality in critical illness - A descriptive analysis of patients who died within 24 hours of ICU admission

PURPOSE

To describe patients who die within 24 h of ICU admission in order to better optimize care delivery.

METHODS

This was a retrospective cohort study of patients ≥18 years old admitted to 17 adult ICUs in Alberta, Canada from January 1, 2016 and June 30, 2017. Data were obtained from a provincial clinical information system and data repository. The primary outcome was incidence of ICU death within 24 h of admission. Secondary outcomes were patient and system factors associated with early death. Variables of interest were identified a priori and examined using multivariable logistic regression.

RESULTS

Of 19,556 patients admitted to ICU in an 18-month period, 3.3% died within 24 h, representing 29.8% of ICU deaths. Factors associated with early death were age (adjusted-OR 0.99, 95% CI, 0.9-1.0), acuity (adjusted-OR 1.3, 95% CI, 1.3-1.4), admission from the Emergency Department (ED; adjusted-OR 1.5, 95% CI, 1.1-1.9) and surgical (adjusted-OR 2.27, 95% CI, 1.4-3.6), neurologic (adjusted-OR 4.6, 95% CI, 3.1-6.9) or trauma diagnosis (adjusted-OR 6.1, 95% CI, 2.4-15.6).

CONCLUSION

Patients who die within 24 h constitute one third of ICU deaths. Age, acuity, admission from the ED and surgical, neurologic or trauma-related admission diagnosis correlate with early death.

Assessing Frequency of Respiratory Complications in Children Undergoing Adenotonsillectomy

OBJECTIVES

To determine the frequency of respiratory complications in children admitted to the ICU after adenotonsillectomy and to identify factors associated with the risk of respiratory complications in this cohort.

DESIGN

Retrospective observational study.

SETTING

PICU.

PATIENT POPULATION

All children admitted to the ICU following adenotonsillectomy from September 30, 2009, to March 30, 2014.

MEASUREMENTS AND MAIN RESULTS

Of the 165 children included in the study, 150 (91%) received no respiratory support other than oxygen in the first 2 hours postoperatively. Of the 15 who required support following 2 hours, 14 required nasopharyngeal airways, one required invasive mechanical ventilation, and seven required supplemental oxygen for more than 2 hours. None of the children who received respiratory support for less than 2 hours required subsequent ICU level care. When comparing those who received support for more than 2 hours to those who did not, there were no differences in clinical characteristics except that those who received support were more likely to have chronic neurologic disease including autism, seizures, or cerebral palsy (odds ratio, 3.7; 95% CI, 1.1-11.9; p = 0.04). Intraoperative events were not predictive of need for respiratory support. Most of the children (n = 117/165 or 71%) had sleep studies preoperatively. Abnormal sleep studies (apnea-hypopnea index > 20 [n = 68] or oxygen saturation nadir < 80% [n = 48]) were not associated with need for postoperative respiratory support.

CONCLUSIONS

Most children admitted to the ICU following adenotonsillectomy in this population required no support after 2 hours. Preoperative factors such as obesity and abnormal sleep studies were not predictive of need for postoperative respiratory support. Need for respiratory support at 2 hours may be a useful criterion for need for ICU level care in this population.

Further Validation of a Novel Acute Myocardial Infarction Risk Stratification (nARS) System for Patients with Acute Myocardial Infarction

Recently, we developed a novel acute myocardial infarction (AMI) risk stratification system (nARS), which stratifies AMI patients into low- (L), intermediate- (I), and high- (H) risk groups. We have shown that the nARS shortened the length of intensive care unit (ICU) stay as well as that of hospitalization. However, the incidence of AMI-related adverse outcomes has not been fully investigated. The purpose of this study was to investigate the incidence of severe complications requiring ICU care among the 3 risk groups stratified by nARS. We retrospectively reviewed AMI patients between October 2016 and December 2018. A total of 592 patients were divided into the L- (n = 285), I- (n = 124), and H- (n = 183) risk groups. The primary endpoint was in-hospital complications requiring ICU care defined as death/cardiopulmonary arrest, shock, stroke, atrioventricular block, and respiratory failure. Among 592 patients, 239 (40.4%) developed at least 1 complication requiring ICU care, but only 28 (11.7%) developed complications in general wards. Complications requiring ICU care were most frequently observed in the H-risk group (68.9%), followed by the I-risk group (50.8%), and least in the L-risk group (17.5%) (P < 0.001). Complications requiring ICU care that occurred in the general wards were more frequently observed in the H-risk group (8.7%) compared to the I-risk (3.2%) and L-risk (2.8%) groups (P = 0.009). In conclusion, complications requiring ICU care rarely happened in the general wards, and were less in the I- and L-risk groups than in the H-risk group. These results validated the nARS, and might support the widespread use of nARS.

Prediction of mortality in Intensive Care Units: a multivariate feature selection

CONTEXT

The critical nature of patients in Intensive Care Units (ICUs) demands intensive monitoring of their vital signs as well as highly qualified professional assistance. The combination of these needs makes ICUs very expensive, which requires investment to be prioritized. Administrative issues emerge, and health institutions face dilemmas such as: "How many beds should an ICU provide to serve the population, at the lowest costs" and "Which is the most critical body information to monitor in an ICU?". Due to financial and ethical implications, these judgments require technical and precise knowledge. Decisions have usually relied on clinical scores, like the APACHE (Acute Physiology And Chronic Health Evaluation) and SOFA (Sequential Organ Failure Assessment) scores, which are imprecise and outdated. The popularization of machine learning techniques has shed some light on the topic as a way to renew score purposes. In 2012, the PhysioNet/Computing in Cardiology launched the Challenge - ICU Patients. This Challenge aimed to stimulate the development of techniques to predict mortality in ICUs. Based on biometric and physiological features collected from patients, the participants predicted the patient's death risk by using their classifiers. Several participants achieved results that were better than the results produced by the SOFA and the APACHE scores; the prediction levels were ≈54%, which is weak.

OBJECTIVES

Here, we investigate the reasons that led to these results as a means to ground our solution. Then, we propose alternative practices in an attempt to improve the results. Our main goal is to improve the prediction of mortality in ICUs by using the same data employed during the 2012 PhysioNet Challenge. Our specific objectives are (i) to simplify the problem by reducing the dimensionality; (ii) to reduce the uncontrolled variance, and (iii) to make classifiers less dependent on the training set.

METHODS

Accordingly, we propose a methodology based on extensive steps, including sample filter and data normalization. To select features and to reduce the intra-group variance, we employ multivariate data analysis by using Principal Component Analysis, Factor Analysis, Spectral Clustering, and Tukey's HSD Test, recursively. After that, we use machine learning techniques to create classifiers according to different methods. We evaluate our results with the same metrics proposed by the 2012 PhysioNet Challenge.

RESULTS

For classifiers constructed and tested by using independent datasets, our best classifier was a linear SVM, which provided results of ≈0.73. These results were significantly better than the ≈0.54 achieved in previous work at >99% confidence interval. Furthermore, our approach only demanded twelve features, which was consistently smaller than the number of features required by the previous approaches.

CONCLUSION

Our results indicated that our approach presented: (a) higher performance to predict death risks (+20%); (b) smaller dependence on the training set; and (c) lower costs for ICU monitoring (few features). Besides the better prediction power, our approach also demanded lower costs for implementation and a more extensive range of potential ICUs. Future studies should employ our proposal to investigate the possibility of including some physiological features that were not available for the 2012 PhysioNet Challenge.

The Effect of Outcome Selection on the Performance of Prediction Models in Patients at Risk for Sepsis

Supplemental Digital Content is available in the text.

Risk prediction models for patients with suspected sepsis have been derived on and applied to various outcomes, including readily available outcomes such as hospital mortality and ICU admission as well as longer-term mortality outcomes that may be more important to patients. It is unknown how selecting different outcomes influences model performance in patients at risk for sepsis.

Determining the Cost and Length of Stay at Intensive Care Units and the Factors Influencing Them in a Teaching Hospital in Malaysia

OBJECTIVES

Escalating healthcare costs calls for the efficiency of health services, especially in the intensive care unit (ICU) where the bulk of resources are used. This study aims to identify the length of stay (LOS) and cost of care at ICUs, which are proxy indicators of efficiency and the factors determining them.

METHODS

A cross-sectional study of patients requiring ICU admissions in a teaching hospital in Malaysia from 2013 to 2015 was conducted. The cost at the ICU was estimated using the step down approach. Factors that determined the cost and LOS at the ICU were also explored by using multivariate regression analysis.

RESULTS

Each day of stay cost $427 (USD) at the pediatric intensive care unit and $1324 at the general intensive care unit. The mean LOS at the ICU was 5.7 days (standard deviation [SD]: 8.4) with a median of 4 days (95% confidence interval [CI] 1-16.7 days). Average cost of care at the ICU per episode of care was $5473 (SD $6499), and the median was $3463. ICU patients spent 29.3% of the total stay and 47.2% of the cost at ICU units. Upon multivariate regression analysis, severity, case base-group, and type of ICU that the patient was admitted to were associated with the cost and LOS at ICU.

CONCLUSIONS

Compared with critical care practices in hospitals from more developed nations, a Malaysian teaching hospital required a longer length of ICU stay. Hence, implementations of strategies that can reduce the length of stay and hospital costs without compromising healthcare quality are required.

A Comprehensive Review of the Outcome for Patients Readmitted to the ICU Following Trauma and Strategies to Decrease Readmission Rates

In recent years, there has been an emphasis on evaluating the outcomes of patients who have experienced an intensive care unit (ICU) readmission. This may in part be due to the Patient Protection and Affordable Care Act's Hospital Readmission Reduction Program which imposes financial sanctions on hospitals who have excessive readmission rates, informally known as bounceback rates. The financial cost associated with avoidable bounceback combined with the potentially preventable expenses can result in unnecessary financial strain. Within the hospital readmissions, there is a subset pertaining to unplanned readmission to the ICU. Although there have been studies regarding ICU bounceback, there are limited studies regarding ICU bounceback of trauma patients and even fewer proven strategies. Although many studies have concluded that respiratory complications were the most common factor influencing ICU readmissions, there is inconclusive evidence in terms of a broadly applicable strategy that would facilitate management of these patients. The purpose of this review is to highlight the outcomes of patients readmitted to the ICU and to provide an overview of possible strategies to aid in decreasing ICU readmission rates.

Intensive care unit occupancy and premature discharge rates: A cohort study assessing the reporting of quality indicators

PURPOSE

ICU occupancy fluctuates. High levels may disadvantage patients. Currently, occupancy is benchmarked annually which may inaccurately reflect strained units. Outcomes potentially sensitive to occupancy include premature (early) ICU discharge and non-clinical transfer (NCT). This study assesses the association between daily occupancy and these outcomes, and evaluates benchmarking care across Scotland using daily occupancy.

MATERIALS AND METHODS

Population: all Scottish ICU patients, 2006-2014.

EXPOSURE

bed occupancy per unit-day; Outcomes: proportion of early discharges and NCTs.

DESIGN

Retrospective cohort study. Outcome rates were calculated above various occupancy thresholds. Polynomial regression visualised associations, and inflection points between occupancy and outcomes. Spearman's rho correlations between occupancy measures and outcomes were reported.

RESULTS

65,472 discharges occurred over 57,812 unit-days. 1954(3.0%) discharges were early; 429 (0.7%) were NCTs. Early discharge rates above 70%, 80% and 90% occupancy were 3.9%, 5.0% and 7.5% respectively. Occupancies at which outcome rates greatly increased were near 80% for early discharge, and 90% for NCT. Mean annual occupancy was not correlated with outcomes; annual proportion of days ≥90% occupancy correlated most strongly (early discharge rho = 0.46,p < .001; NCT rho = 0.31, p < .001).

CONCLUSIONS

We demonstrate a clear association between daily ICU occupancy and early discharge/NCT. Daily occupancy may better benchmark care quality than mean annual occupancy.

Association of Standardized Tracheostomy Care Protocol Implementation and Reinforcement With the Prevention of Life-Threatening Respiratory Events

Importance

Mucus plugging after tracheostomy is a preventable cause of respiratory distress. Implementation of standardized tracheostomy care guidelines may reduce the occurrence of fatal respiratory compromise.

Objective

To determine the effect of implementing and reinforcing a standardized tracheostomy care protocol on the occurrence of acute life-threatening respiratory events.

Design, Setting, and Participants

Retrospective cohort study of adult patients who received a tracheostomy between May 2014 and August 2016 at a tertiary care center.

Main Outcomes and Measures

Patient demographics, tracheostomy indication, rapid response for mucus plugging and other acute events, duration of hospital stay, and levels of care that the patients received were recorded through examination of clinical logs. Statistical analysis was conducted between patients before protocol implementation and patients after protocol implementation in terms of rapid-response use, and intragroup comparison of the mean length of stay in various hospital units was also analyzed.

Results

A total of 247 patients (89 women [36%]; mean [SD] age, 58.5 [12.3] years), 117 preprotocol and 130 postprotocol, met inclusion criteria. Of the 130 patients in the postprotocol cohort, 123 (93%) were on the new tracheostomy care protocol. Preprotocol rapid-response rate was 21 of 117 patients (17.9%) and postprotocol response rate was 12 of 130 patients (9.2%) for a difference of 8.7% (95% CI, 0.2%-18.0%). In terms of mucus plugging, preprotocol rate was 8 of 117 patients (6.8%) and the postprotocol rate was 1 of 130 patients (0.8%) for a difference of 6.0% (95% CI, 1.3%-12.2%). Intragroup difference of the mean time spent (days) in various care units between patients in the no rapid-response group vs rapid-response group demonstrated clinically meaningful longer stay for rapid responses in both preprotocol and postprotocol groups for the intensive care unit (preprotocol, 2.03; 95% CI, 1.03-3.03 vs postprotocol, 3.02; 95% CI, 1.49-4.45) and step down units (preprotocol, 1.40; 95% CI, 0.77-2.02 vs postprotocol, 2.11; 95% CI, 0.78 to 3.44).

Conclusions and Relevance

Implementation and reinforcement of a standardized tracheostomy care protocol was associated with a reduction in the occurrences of rapid-response calls for life-threatening mucus plugging and is recommended for clinical practice. In addition, length of stay in the intensive care unit and intermediate surgical care unit was increased in a clinically meaningful way for patients who experienced a rapid-response event.

Association between strained ICU capacity and healthcare costs in Canada: A population-based cohort study

BACKGROUND

Intensive care is resource intensive, with costs representing a substantial quantity of total hospitalization costs. Strained ICU capacity compromises care quality and adversely impacts outcomes; however, the association between strain and healthcare costs has not been explored.

MATERIALS AND METHODS

Population-based cohort study performed in 17 adult ICUs in Alberta, Canada. Data were captured on hospitalizations, ambulatory care, physician services and drug dispenses occurring 1-year before and 1-year after index ICU admission. Strain was defined as occupancy ≥90%; with 21 additional definitions evaluated. Patients were categorized as strain and non-strain admissions. Costs attributable to strain, were calculated as difference-in-difference costs using propensity-score matching.

RESULTS

30,557 patients were included (strain: 11,830 [38.7%]; non-strain: 18,727 [61.3%]). At 1-year, strain admissions had adjusted-incremental per-patient cost of CA$9406 (95%CI, $5654-13,157) compared to non-strain admissions, due to hospitalization costs (CA$7930; 95%CI, $4553-11,307) and physician claims (CA$844; 95%CI, $430-1259). This equated to CA$111.3 million (95%CI, $66.9-155.6 million) in excess attributable costs. Strain portended longer hospitalization (3.3 days; 95%CI, 1.1-5.5); and more ambulatory visits (1.0; 95%CI, 0.1-2.0) and physician claims (9.5; 95%CI, 6.2-12.7). Incremental costs were robust across strain definitions.

CONCLUSIONS

Admissions to ICUs experiencing strain incur incremental costs, attributed to longer hospitalization and physician services.

Readmission and hospital mortality after ICU discharge of critically ill cancer patients

Background

Intensive care unit (ICU) readmission is generally associated with increased hospital stays and increased mortality. However, there are limited data on ICU readmission in critically ill cancer patients.

Method

We conducted a retrospective cohort study based on the prospective registry of all critically ill cancer patients admitted to the oncology medical ICU between January 2012 and December 2013. After excluding patients who were discharged to another hospital or decided to end-of-life care, we divided the enrolled patients into four groups according to the time period from ICU discharge to unexpected events (ICU readmission or ward death) as follows: no (without ICU readmission or death, n = 456), early (within 2 days, n = 42), intermediate (between 2 and 7 days, n = 64), and late event groups (after 7 days of index ICU discharge, n = 129). The independent risk factors associated with ICU readmission or unexpected death after ICU discharge were also analyzed using multinomial logistic regression model.

Results

There were no differences in the reasons for ICU readmission across the groups. ICU mortality did not differ among the groups, but hospital mortality was significantly higher in the late event group than in the early event group. Mechanical ventilation during ICU stay, tachycardia, decreased mental status, and thrombocytopenia on the day of index ICU discharge increased the risk of early ICU readmission or unexpected ward death, while admission through the emergency room and sepsis and respiratory failure as the reasons for index ICU admission were associated with increased risk of late readmission or unexpected ward death. Interestingly, recent chemotherapy within 4 weeks before index ICU admission was inversely associated with the risk of late readmission or unexpected ward death.

Conclusion

In critically ill cancer patients, patient characteristics predicting ICU readmission or unexpected ward death were different according to the time period between index ICU discharge and the events.

Resource use, characteristics and outcomes of prolonged non-invasive ventilation: a single-centre observational study in China

OBJECTIVE

To report the resource use, characteristics and outcomes of patients with prolonged non-invasive ventilation (NIV).

DESIGN

A single-centre observational study.

SETTING

An intensive care unit of a teaching hospital.

PARTICIPANTS

Patients who only received NIV because of acute respiratory failure were enrolled. Prolonged NIV was defined as subjects who received NIV ≥14 days. A total of 1539 subjects were enrolled in this study; 69 (4.5%) underwent prolonged NIV.

MAIN OUTCOME MEASURES

Predictors of prolonged NIV and hospital mortality.

RESULTS

The rate of do-not-intubate (DNI) orders was 9.1% (140/1539). At the beginning of NIV, a DNI order (OR 3.95, 95% CI 2.25 to 6.95) and pH ≥7.35 (2.20, 1.27 to 3.82) were independently associated with prolonged NIV. At days 1 and 7 of NIV, heart rate (1.01 (1.00 to 1.03) and 1.02 (1.00 to 1.03], respectively) and PaO₂/FiO₂<150 (2.19 (1.25 to 3.85) and 2.05 (1.04 to 4.04], respectively) were other independent risk factors for prolonged NIV. When patients who died after starting NIV but prior to 14 days were excluded, the association was strengthened. Regarding resource use, 77.1% of subjects received NIV<7 days and only accounted for 47.0% of NIV-days. However, 18.4% of subjects received NIV 7-13.9 days and accounted for 33.4% of NIV-days, 2.9% of subjects received NIV 14-20.9 days and accounted for 9.5% of NIV-days, and 1.6% of subjects received NIV≥21 days and accounted for 10.1% of NIV-days.

CONCLUSIONS

Our results indicate the resource use, characteristics and outcomes of a prolonged NIV population with a relatively high proportion of DNI orders. Subjects with prolonged NIV make up a high proportion of NIV-days and are at high risk for in-hospital mortality.

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.

Caring for the critically ill patients over 80: a narrative review

Background

There is currently no international recommendation for the admission or treatment of the critically ill older patients over 80 years of age in the intensive care unit (ICU), and there is no valid prognostic severity score that includes specific geriatric assessments.

Main body

In this review, we report recent literature focusing on older critically ill patients in order to help physicians in the multiple-step decision-making process. It is unclear under what conditions older patients may benefit from ICU admission. Consequently, there is a wide variation in triage practices, treatment intensity levels, end-of-life practices, discharge practices and frequency of geriatrician’s involvement among institutions and clinicians. In this review, we discuss important steps in caring for critically ill older patients, from the triage to long-term outcome, with a focus on specific conditions in the very old patients.

Conclusion

According to previous considerations, we provide an algorithm presented as a guide to aid in the decision-making process for the caring of the critically ill older patients.

Electronic supplementary material

The online version of this article (10.1186/s13613-018-0458-7) contains supplementary material, which is available to authorized users.

Role of Transitional Care Measures in the Prevention of Readmission After Critical Illness

Transitioning from the critical care unit to the medical-surgical care area is vital to patients' recovery and resolution of critical illness. Such transitions are necessary to optimize use of available hospital resources to meet patient care needs. One in 10 patients discharged from the intensive care unit are readmitted to the unit during their hospitalization. Critical care readmission is associated with significant increases in illness acuity, overall length of stay, and health care costs as well as a potential 4-fold increased risk of mortality. Patients with complex illness, multiple comorbid conditions, and a prolonged initial stay in the critical care unit are at an increased risk of being readmitted to the critical care unit and experiencing poor outcomes. Implementing nurse-driven measures that support continuity of care and consistent communication practices such as critical care outreach services, transitional communication tools, discharge planning, and transitional care units improves transitions of patients from the critical care environment and reduces readmission rates.

Indicators of intensive care unit capacity strain: a systematic review

BACKGROUND

Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity.

METHODS

We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive.

RESULTS

Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies.

CONCLUSIONS

We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain.

SYSTEMATIC REVIEW REGISTRATION

This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).

Elevated red blood cell distribution width at ICU discharge is associated with readmission to the intensive care unit

BACKGROUND

Red blood cell distribution width (RDW) is a predictor of mortality in critically ill patients. Our objective was to investigate the association between the RDW at ICU discharge and the risk of ICU readmission or unexpected death in the ward.

METHODS

A secondary analysis of prospectively collected data study was conducted including patients discharged alive from the ICU to the ward. The target variable was the RDW collected at ICU discharge. Elevated RDW was defined as an RDW > 16%. Outcomes of interest included readmission to the ICU, unexpected death in the ward and in-hospital death. Variables with a p-value <0.1 in the univariate analysis or with biological plausibility for the occurrence of the outcome were included in the Cox proportional hazards model for adjustment.

RESULTS

We included 813 patients. A total of 138 readmissions to the ICU and 44 unexpected deaths in the ward occurred. Elevated RDW at ICU discharge was independently associated with readmission to the ICU or unexpected death in the ward after multivariable adjustment (HR: 1.901; 95% CI 1.357-2.662). Other variables associated with this outcome included age, tracheostomy and mean corpuscular volume (MCV) at ICU discharge. Similar results were obtained after the exclusion of unexpected deaths in the ward (HR 1.940; CI 1.312-2.871) and for in-hospital deaths (HR 1.716; 95% CI 1.141-2.580).

CONCLUSIONS

Elevated RDW at ICU discharge is independently associated with ICU readmission and in-hospital death.

Overflow models for the admission of intensive care patients

An earlier article, inspired by overflow models in telecommunication systems with multiple streams of telephone calls, proposed a new analytical model for a network of intensive care units (ICUs), and a new patient referral policy for such networks to reduce the blocking probability of external emergency patients without degrading the quality of service (QoS) of canceled elective operations, due to the more efficient use of ICU capacity overall. In this work, we use additional concepts and insights from traditional teletraffic theory, including resource sharing, trunk reservation, and mutual overflow, to design a new patient referral policy to further improve ICU network efficiency. Numerical results based on the analytical model demonstrate that our proposed policy can achieve a higher acceptance level than the original policy with a smaller number of beds, resulting in improved service for all patients. In particular, our proposed policy can always achieve much lower blocking probabilities for external emergency patients while still providing sufficient service for internal emergency and elective patients. In addition, we provide new accurate and computationally efficient analytical approximations for QoS evaluation of ICU networks using our proposed policy. We demonstrate numerically that our new approximation method yields more accurate, robust and conservative results overall than the traditional approximation. Finally, we demonstrate how our proposed approximation method can be applied to solve resource planning and optimization problems for ICU networks in a scalable and computationally efficient manner.

Variation in rates of ICU readmissions and post-ICU in-hospital mortality and their association with ICU discharge practices

BACKGROUND

Variation in intensive care unit (ICU) readmissions and in-hospital mortality after ICU discharge may indicate potential for improvement and could be explained by ICU discharge practices. Our objective was threefold: (1) describe variation in rates of ICU readmissions within 48 h and post-ICU in-hospital mortality, (2) describe ICU discharge practices in Dutch hospitals, and (3) study the association between rates of ICU readmissions within 48 h and post-ICU in-hospital mortality and ICU discharge practices.

METHODS

We analysed data on 42,040 admissions to 82 (91.1%) Dutch ICUs in 2011 from the Dutch National Intensive Care Evaluation (NICE) registry to describe variation in standardized ICU readmission and post-ICU mortality rates using funnel-plots. We send a questionnaire to all Dutch ICUs. 75 ICUs responded and their questionnaire data could be linked to 38,498 admissions in the NICE registry. Generalized estimation equations analyses were used to study the association between ICU readmissions and post-ICU mortality rates and the identified discharge practices, i.e. (1) ICU discharge criteria; (2) bed managers; (3) early discharge planning; (4) step-down facilities; (5) medication reconciliation; (6) verbal and written handover; (7) monitoring of post-ICU patients; and (8) consulting ICU nurses. In all analyses, the outcomes were corrected for patient-related confounding factors.

RESULTS

The standardized rate of ICU readmissions varied between 0.14 and 2.67 and 20.8% of the hospitals fell outside the 95% control limits and 3.6% outside the 99.8% control limits. The standardized rate of post-ICU mortality varied between 0.07 and 2.07 and 17.1% of the hospitals fell outside the 95% control limits and 4.9% outside the 99.8% control limits. We could not demonstrate an association between the eight ICU discharge practices and rates of ICU readmissions or post-ICU in-hospital mortality. Implementing a higher number of ICU discharge practices was also not associated with better patient outcomes.

CONCLUSIONS

We found both variation in patient outcomes and variation in ICU discharge practices between ICUs. However, we found no association between discharge practices and rates of ICU readmissions or post-ICU mortality. Further research is necessary to find factors, which may influence these patient outcomes, in order to improve quality of care.

Association between intensive care unit transfer delay and hospital mortality: A multicenter investigation

BACKGROUND

Previous research investigating the impact of delayed intensive care unit (ICU) transfer on outcomes has utilized subjective criteria for defining critical illness.

OBJECTIVE

To investigate the impact of delayed ICU transfer using the electronic Cardiac Arrest Risk Triage (eCART) score, a previously published early warning score, as an objective marker of critical illness.

DESIGN

Observational cohort study.

SETTING

Medical-surgical wards at 5 hospitals between November 2008 and January 2013.

PATIENTS

Ward patients.

INTERVENTION

None.

MEASUREMENTS

eCART scores were calculated for all patients. The threshold with a specificity of 95% for ICU transfer (eCART ≥ 60) denoted critical illness. A logistic regression model adjusting for age, sex, and surgical status was used to calculate the association between time to ICU transfer from first critical eCART value and in-hospital mortality.

RESULTS

A total of 3789 patients met the critical eCART threshold before ICU transfer, and the median time to ICU transfer was 5.4 hours. Delayed transfer (>6 hours) occurred in 46% of patients (n = 1734) and was associated with increased mortality compared to patients transferred early (33.2% vs 24.5%, P < 0.001). Each 1-hour increase in delay was associated with an adjusted 3% increase in odds of mortality (P < 0.001). In patients who survived to discharge, delayed transfer was associated with longer hospital length of stay (median 13 vs 11 days, P < 0.001).

CONCLUSIONS

Delayed ICU transfer is associated with increased hospital length of stay and mortality. Use of an evidence-based early warning score, such as eCART, could lead to timely ICU transfer and reduced preventable death. Journal of Hospital Medicine 2016;11:757-762. © 2016 Society of Hospital Medicine.

Triage decisions for ICU admission: Report from the Task Force of the World Federation of Societies of Intensive and Critical Care Medicine

Demand for intensive care unit (ICU) resources often exceeds supply, and shortages of ICU beds and staff are likely to persist. Triage requires careful weighing of the benefits and risks involved in ICU admission while striving to guarantee fair distribution of available resources. We must ensure that the patients who occupy ICU beds are those most likely to benefit from the ICU's specialized technology and professionals. Although prognosticating is not an exact science, preference should be given to patients who are more likely to survive if admitted to the ICU but unlikely to survive or likely to have more significant morbidity if not admitted. To provide general guidance for intensivists in ICU triage decisions, a task force of the World Federation of Societies of Intensive and Critical Care Medicine addressed 4 basic questions regarding this process. The team made recommendations and concluded that triage should be led by intensivists considering input from nurses, emergency medicine professionals, hospitalists, surgeons, and allied professionals. Triage algorithms and protocols can be useful but can never supplant the role of skilled intensivists basing their decisions on input from multidisciplinary teams. Infrastructures need to be organized efficiently both within individual hospitals and at the regional level. When resources are critically limited, patients may be refused ICU admission if others may benefit more on the basis of the principle of distributive justice.

Effects of a medical emergency team follow-up programme on patients discharged from the medical intensive care unit to the general ward: a single-centre experience

RATIONALE, AIMS AND OBJECTIVES

The aim of this study was to analyse the effects of the follow-up programme implemented by the Asan Medical Center Medical Emergency Team (MET).

METHOD

A quasi-experimental pre-post intervention design was used, retrospectively reviewed. The follow-up programme includes respiratory care, regular visits and communication between the attending doctors and MET nurse for patients discharged from the medical intensive care unit (MICU) to the general ward. This programme has been implemented since February 2013. Outcomes of patients before and at 1 year after the introduction of the programme were retrospectively reviewed.

RESULTS

A total of 1229 patients were enrolled and divided two groups (Before, n = 624; After the introduction of the programme, n = 625). Forty-six patients (3.7%) were readmitted to the ICU within 72 hours, and there was no significant difference found between the two groups (3.7% versus 3.7%, P = 0.996). Respiratory distress was the most common reason for readmission (67.4%). Cardiac arrest developed in four (0.6%) Before patients; whereas, no cardiac arrest occurred in the After group (0.0%, P = 0.062) cases. A total of 223 patients were discharged to the step-down units. The SOFA (sequential organ failure assessment) score was significantly higher in the step-down unit patients than general ward patients (4.9 ± 2.8 versus 6.2 ± 3.1, P = 0.000). In the analysis restricted to patients discharged to step-down units, unplanned ICU readmissions significantly decreased in the After group (9.3% versus 2.6%, P = 0.034).

CONCLUSIONS

The implementation of the MET follow-up programme did not change the rate of ICU readmission and cardiac arrest; however, its introduction was associated with the reduced ICU readmission of the high-risk patient populations discharged to the step-down unit.