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.

A look at the past to draw lessons for the future: how the case of an urgent ICU transfer taught us to always be ready with a plan B.

Objective: The urgent transfer of an intensive care unit (ICU) is particularly challenging because it carries a high clinical and infectious risk and is a critical node in a hospital's patient flow. In early 2017, exceptional rainfall damaged the roof of the tertiary hospital in Udine, necessitating the relocation of one of the three ICUs for six months. We decided to assess the impact of this transfer on quality of care and patient safety using a set of indicators, primarily considering the incidence of healthcare-associated infections (HAIs) and mortality rates. Methods: We performed a retrospective, observational analysis of structural, process, and outcome indicators comparing the pre- and posttransfer phases. Specifically, we analyzed data between July 2016 and June 2017 for the transferred ICU and examined mortality and the incidence of HAI. Results: Despite significant changes in structural and organizational aspects of the unit, no differences in mortality rates or cumulative incidence of HAIs were observed before/after transfer. We collected data for all 393 patients (133 women, 260 men) admitted to the ICU before (49.4%) and after transfer (50.6%). The mortality rate for 100 days in the ICU was 1.90 (34/1791) before and 2.88 (37/1258) after transfer (p = 0.063). The evaluation of the occurrence of at least one HAI included 304 patients (102 women and 202 men), as 89 of them were excluded due to a length of stay in the ICU of less than 48 h; again, there was no statistical difference between the two cumulative incidences (13.1% vs. 6.9%, p = 0.075). Conclusion: In the case studied, no adverse effects on patient outcomes were observed after urgent transfer of the injured ICU. The indicators used in this study may be an initial suggestion for further discussion.

Transitions and empowerment theory: A framework for nursing interventions during intensive care unit patient transition.

OBJECTIVES: 1) To explore the main characteristics of intensive care unit transition according to patients' lived experience and 2) To identify nursing therapeutics to facilitate patients' transition from the intensive care unit to the inpatient unit. METHODOLOGY: Secondary Analysis (SA) of the findings of a descriptive qualitative study on the experience of patients admitted to an ICU during the transition to the inpatient unit, based on the Nursing Transitions Theory. Data for the primary study were generated from 48 semi-structured interviews of patients who had survived critical illness in 3 tertiary university hospitals. RESULTS: Three main themes were identified during the transition of patients from the intensive care unit to the inpatient unit: 1) nature of ICU transition, 2) response patterns and 3) nursing therapeutics. Nurse therapeutics incorporates information, education and promotion of patient autonomy; in addition to psychological and emotional support. CONCLUSIONS: Transitions Theory as a theoretical framework helps to understand patients' experience during ICU transition. Empowerment nursing therapeutics integrates the dimensions aimed at meeting patients' needs and expectations during ICU discharge.

Unplanned Post-Anesthesia Care Unit to ICU Transfer Following Cerebral Surgery: A Retrospective Study.

Background: Unplanned transfer to intensive care unit (ICU) lead to reduced trust of patients and their families in medical staff and challenge medical staff to allocate scarce ICU resources. This study aimed to explore the incidence and risk factors of unplanned transfer to ICU during emergence from general anesthesia after cerebral surgery, and to provide guidelines for preventing unplanned transfer from post-anesthesia care unit (PACU) to ICU following cerebral surgery. Methods: This was a retrospective case-control study and included patients with unplanned transfer from PACU to ICU following cerebral surgery between January 2016 and December 2020. The control group comprised patients matched (2:1) for age (±5 years), sex, and operation date (±48 hours) as those in the case group. Stata14.0 was used for statistical analysis, and p < .05 indicated statistical significance. Results: A total of 11,807 patients following cerebral surgery operations were cared in PACU during the study period. Of the 11,807 operations, 81 unscheduled ICU transfer occurred (0.686%). Finally, 76 patients were included in the case group, and 152 in the control group. The following factors were identified as independent risk factors for unplanned ICU admission after neurosurgery: low mean blood oxygen (OR = 1.57, 95%CI: 1.20-2.04), low mean albumin (OR = 1.14, 95%CI: 1.03-1.25), slow mean heart rate (OR = 1.04, 95%CI: 1.00-1.08), blood transfusion (OR = 2.78, 95%CI: 1.02-7.58), emergency surgery (OR = 3.08, 95%CI: 1.07-8.87), lung disease (OR = 2.64, 95%CI: 1.06-6.60), and high mean blood glucose (OR = 1.71, 95%CI: 1.21-2.41). Conclusion: We identified independent risk factors for unplanned transfer from PACU to ICU after cerebral surgery based on electronic medical records. Early identification of patients who may undergo unplanned ICU transfer after cerebral surgery is important to provide guidance for accurately implementing a patient's level of care.

Patient experiences of an intensive care Liaison Nurse support service.

OBJECTIVES: To illuminate patients' experiences of being a part of an liaison nurse support service focused on supporting recently transferred intensive care unit patients. RESEARCH METHODOLOGY/DESIGN: A qualitative inductive descriptive design including in-depth interviews was chosen. SETTING: A project including an liaison nurse support service-intervention was undertaken during a 16-week period at a University hospital in Sweden. The liaison nurse support service was available Monday-Friday 10 am - 6 pm and nurses visited the patient 1-4 times after transfer to the ward. MAIN OUTCOME MEASURES: Of the 109 patients who were visited by the liaison nurse support service, 14 agreed to be interviewed about their experiences of the transfer. Data was analysed by inductive content analysis. FINDINGS: One overall theme, An advocate in a vulnerable situation emerged from the data. Four subthemes were identified: Ensures transfer of information between the intensive care unit and the general ward, Makes the circumstances understandable and coordinates between the care levels and Offers emotional support and stability in an uncertain situation. CONCLUSION: The liaison nurse support service contributed to ensuring accurate transfer of information, solved problems when the patient themselves did not have control or strength and provided emotional support.

Patient outcomes following transfer between intensive care units during the COVID-19 pandemic.

Transferring critically ill patients between intensive care units (ICU) is often required in the UK, particularly during the COVID-19 pandemic. However, there is a paucity of data examining clinical outcomes following transfer of patients with COVID-19 and whether this strategy affects their acute physiology or outcome. We investigated all transfers of critically ill patients with COVID-19 between three different hospital ICUs, between March 2020 and March 2021. We focused on inter-hospital ICU transfers (those patients transferred between ICUs from different hospitals) and compared this cohort with intra-hospital ICU transfers (patients moved between different ICUs within the same hospital). A total of 507 transfers were assessed, of which 137 met the inclusion criteria. Forty-five patients underwent inter-hospital transfers compared with 92 intra-hospital transfers. There was no significant change in median compliance 6 h pre-transfer, immediately post-transfer and 24 h post-transfer in patients who underwent either intra-hospital or inter-hospital transfers. For inter-hospital transfers, there was an initial drop in median PaO2 /FI O2 ratio: from median (IQR [range]) 25.1 (17.8-33.7 [12.1-78.0]) kPa 6 h pre-transfer to 19.5 (14.6-28.9 [9.8-52.0]) kPa immediately post-transfer (p < 0.05). However, this had resolved at 24 h post-transfer: 25.4 (16.2-32.9 [9.4-51.9]) kPa. For intra-hospital transfers, there was no significant change in PaO2 /FI O2 ratio. We also found no meaningful difference in pH; PaCO2 ;, base excess; bicarbonate; or norepinephrine requirements. Our data demonstrate that patients with COVID-19 undergoing mechanical ventilation of the lungs may have short-term physiological deterioration when transferred between nearby hospitals but this resolves within 24 h. This finding is relevant to the UK critical care strategy in the face of unprecedented demand during the COVID-19 pandemic.

Patients' experience while transitioning from the intensive care unit to a ward.

BACKGROUND: Intensive care unit (ICU) patients can experience emotional distress and post-traumatic stress disorder when they leave the ICU, also referred to as post-intensive care syndrome. A deeper understanding of what patients go through and what they need while they are transitioning from the ICU to the general ward may provide input on how to strengthen patient-centred care and, ultimately, contribute to a positive experience. AIM: To describe the patients' experience while transitioning from the ICU to a general ward. DESIGN: A descriptive qualitative study. METHOD: Data were gathered through in-depth interviews and analysed using a qualitative content analysis. The qualitative study was reported in accordance with the Consolidated Criteria for Reporting Qualitative Research guidelines. FINDINGS: Forty-eight interviews were conducted. Impact on emotional well-being emerged as a main theme, comprising four categories with six subcategories. CONCLUSION: Transition from the ICU can be a shock for the patient, leading to the emergence of a need for information, and an impact on emotional well-being that has to be planned for carefully and addressed prior to, during, and following transition from the ICU to the general ward. RELEVANCE TO CLINICAL PRACTICE: It is essential that nurses understand patients' experiences during transfer, identifying needs and concerns to be able to develop and implement new practices such as ICU Liaison Nurse or Nurse Outreach for the follow-up of these patients, the inclusion of a consultant mental health nurse, and the application of patient empowerment during ICU discharge.

The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis.

BACKGROUND: Timely recognition of the deteriorating inpatient remains challenging. Wearable monitoring systems (WMS) may augment current monitoring practices. However, there are many barriers to implementation in the hospital environment, and evidence describing the clinical impact of WMS on deterioration detection and patient outcome remains unclear. OBJECTIVE: To assess the impact of vital-sign monitoring on detection of deterioration and related clinical outcomes in hospitalised patients using WMS, in comparison with standard care. METHODS: A systematic search was conducted in August 2020 using MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL, Health Technology Assessment databases and grey literature. Studies comparing the use of WMS against standard care for deterioration detection and related clinical outcomes in hospitalised patients were included. Deterioration related outcomes (primary) included unplanned intensive care admissions, rapid response team or cardiac arrest activation, total and major complications rate. Other clinical outcomes (secondary) included in-hospital mortality and hospital length of stay. Exploratory outcomes included alerting system parameters and clinical trial registry information. RESULTS: Of 8706 citations, 10 studies with different designs met the inclusion criteria, of which 7 were included in the meta-analyses. Overall study quality was moderate. The meta-analysis indicated that the WMS, when compared with standard care, was not associated with significant reductions in intensive care transfers (risk ratio, RR 0.87; 95% confidence interval, CI 0.66-1.15), rapid response or cardiac arrest team activation (RR 0.84; 95% CI 0.69-1.01), total (RR 0.77; 95% CI 0.44-1.32) and major (RR 0.55; 95% CI 0.24-1.30) complications prevalence. There was also no statistically significant association with reduced mortality (RR 0.48; 95% CI 0.18-1.29) and hospital length of stay (mean difference, MD - 0.09; 95% CI - 0.43 to 0.44). CONCLUSION: This systematic review indicates that there is no current evidence that implementation of WMS impacts early deterioration detection and associated clinical outcomes, as differing design/quality of available studies and diversity of outcome measures make it difficult to reach a definite conclusion. Our narrative findings suggested that alarms should be adjusted to minimise false alarms and promote rapid clinical action in response to deterioration. PROSPERO Registration number: CRD42020188633 .

Risk factors for transfer from Respiratory Intermediate Care Unit to Intensive Care Unit in COVID-19.

BACKGROUND: Patients hospitalized for COVID-19-related pneumonia often need several degrees of ventilatory support, which are performed between Respiratory Intermediate Care Units (RICUs) and Intensive Care Units (ICUs), and which depend on the severity of acute respiratory distress syndrome. There is no firm consensus on transfer predictors from the RICU to the ICU. METHODS: In this retrospective observational single center study, we evaluated 96 COVID-19 patients referred to the RICU for acute respiratory failure (ARF) according to their transferal to the ICU or their stay at the RICU. We compared demographic data, baseline laboratory profile, and final clinical outcomes to identify early risk factors for transfer. RESULTS: The best predictors for transfer to the ICU were elevated C-reactive protein and lymphopenia. The mortality rate was lower in the RICU than in the ICU, where transferred patients who died were mostly younger men and with less comorbidities than those in the RICU. CONCLUSIONS: Few inflammatory markers can predict the need for transfer from the RICU to the ICU. Due to the ongoing COVID-19 pandemic, we urge better clinical stratification by early and meaningful profiles in patients admitted to the RICU who are at risk of transferal to the ICU.

D-Dimer Level and Neutrophils Count as Predictive and Prognostic Factors of Pulmonary Embolism in Severe Non-ICU COVID-19 Patients.

The incidence of pulmonary embolism (PE) is high during severe Coronavirus Disease 2019 (COVID-19). We aimed to identify predictive and prognostic factors of PE in non-ICU hospitalized COVID-19 patients. In the retrospective multicenter observational CLOTVID cohort, we enrolled patients with confirmed RT-PCR COVID-19 who were hospitalized in a medicine ward and also underwent a CT pulmonary angiography for a PE suspicion. Baseline data, laboratory biomarkers, treatments, and outcomes were collected. Predictive and prognostics factors of PE were identified by using logistic multivariate and by Cox regression models, respectively. A total of 174 patients were enrolled, among whom 86 (median [IQR] age of 66 years [55-77]) had post-admission PE suspicion, with 30/86 (34.9%) PE being confirmed. PE occurrence was independently associated with the lack of long-term anticoagulation or thromboprophylaxis (OR [95%CI], 72.3 [3.6-4384.8]) D-dimers ≥ 2000 ng/mL (26.3 [4.1-537.8]) and neutrophils ≥ 7.0 G/L (5.8 [1.4-29.5]). The presence of these two biomarkers was associated with a higher risk of PE (p = 0.0002) and death or ICU transfer (HR [95%CI], 12.9 [2.5-67.8], p < 0.01). In hospitalized non-ICU severe COVID-19 patients with clinical PE suspicion, the lack of anticoagulation, D-dimers ≥ 2000 ng/mL, neutrophils ≥ 7.0 G/L, and these two biomarkers combined might be useful predictive markers of PE and prognosis, respectively.

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.

Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study.

This retrospective, multicenter study investigated the risk factors associated with intensive care unit (ICU) admission and transfer in 461 adult patients with confirmed coronavirus disease 2019 (COVID-19) hospitalized from 22 January to 14 March 2020 in Hunan, China. Outcomes of ICU and non-ICU patients were compared, and a simple nomogram for predicting the probability of ICU transfer after hospital admission was developed based on initial laboratory data using a Cox proportional hazards regression model. Differences in laboratory indices were observed between patients admitted to the ICU and those who were not admitted. Several independent predictors of ICU transfer in COVID-19 patients were identified including older age (≥65 years) (hazard ratio [HR] = 4.02), hypertension (HR = 2.65), neutrophil count (HR = 1.11), procalcitonin level (HR = 3.67), prothrombin time (HR = 1.28), and D-dimer level (HR = 1.25). The lymphocyte count and albumin level were negatively associated with mortality (HR = 0.08 and 0.86, respectively). The developed model provides a means for identifying, at hospital admission, the subset of patients with COVID-19 who are at high risk of progression and would require transfer to the ICU within 3 and 7 days after hospitalization. This method of early patient triage allows a more effective allocation of limited medical resources.

Associations between hospital occupancy, intensive care unit transfer delay and hospital mortality.

PURPOSE: Hospital occupancy (HospOcc) pressures often lead to longer intensive care unit (ICU) stay after physician recognition of discharge readiness. We evaluated the relationships between HospOcc, extended ICU stay, and patient outcomes. MATERIALS AND METHODS: 7-year retrospective cohort study of 8500 alive discharge encounters from 4 adult ICUs of a tertiary hospital. We estimated associations between i) HospOcc and ICU transfer delay; and ii) ICU transfer delay and hospital mortality. RESULTS: Median (IQR) ICU transfer delay was 4.8 h (1.6-11.7), 1.4% (119) suffered in-hospital death, and 4% (341) were readmitted. HospOcc was non-linearly related with ICU transfer delay, with a spline knot at 80% (mean transfer delay 8.8 h [95% CI: 8.24, 9.38]). Higher HospOcc level above 80% was associated with longer transfer delays, (mean increase 5.4% per % HospOcc increase; 95% CI, 4.7 to 6.1; P < .001). Longer ICU transfer delay was associated with increasing odds of in-hospital death or ICU readmission (odds ratio 1.01 per hour; 95% CI 1.00 to 1.01; P = .04) but not with ICU readmission alone (OR 1.01 per hour; 95% CI 1.00 to 1.01, P = .14). CONCLUSIONS: ICU transfer delay exponentially increased above a threshold hospital occupancy and may be associated with increased hospital mortality.

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.

Unrecognized fluid overload during induction therapy increases morbidity in patients with acute promyelocytic leukemia.

BACKGROUND: The combination of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) has proven to be the most effective therapy for patients with acute promyelocytic leukemia (APL). The majority of the morbidity and mortality from APL therapy occur during the induction phase. The objective of the current study was to identify the risk factors associated with transfer to the intensive care unit (ICU) and endotracheal intubation during induction therapy in patients with APL. METHODS: The authors analyzed the clinical characteristics of 187 patients with newly diagnosed APL who were treated with ATRA and ATO with or without gemtuzumab ozogamicin. The authors documented the percentage change in body weight from baseline to the maximum recorded weight during induction or to the day of ICU transfer. RESULTS: A total of 18 patients (10%) who initiated therapy with ATRA and ATO on a regular hospital floor required transfer to the ICU after a median of 12 days of induction therapy. The median volume of transfusions was 4350 mL (range, 60-30,750 mL). The volume of transfusions was the main factor associated with the risk of ICU transfer (odds ratio, 4.1; P < .001). Of the 18 patients transferred to the ICU, 10 patients (5%) required intubation. An increase in the total volume of transfusions, increase in weight ≥10% during induction therapy, and a plasma albumin level ≤3.2 g/dL at the time of diagnosis were found to be associated with an increased risk of endotracheal intubation. CONCLUSIONS: Large volumes of blood product transfusions and unrecognized fluid overload during induction are associated with ICU transfer and endotracheal intubation in patients with APL. These can be prevented by limiting the amount of transfusions, careful monitoring for subtle signs of fluid overload, and early intervention with aggressive diuretic therapy.

Inter-ICU transfer of patients with ventilator dependent respiratory failure: Qualitative analysis of family and physician perspectives.

OBJECTIVES: Ventilator dependent respiratory failure (VDRF) patients are seriously ill and often transferred between ICUs. Our objective was to obtain multi-stakeholder insights into the experiences of families during inter-ICU transfer. METHODS: We conducted a qualitative study using semi-structured interviews with family members of VDRF patients as well as clinicians that have received or transferred VDRF patients to our hospital. Interviews were transcribed and template analysis was used to identify themes within/across stakeholder groups. RESULTS: Patient, family, clinician and systems-level factors were identified as key themes during inter-ICU transfer. The main findings highlight that family members were rarely engaged in the decision to transfer as well as a lack of standardized communication between clinicians during care transitions. Family members were reassured with the care after transfer in spite of practical and financial challenges. Clinicians acknowledged the lack of a systematic approach for meeting the needs of families and suggested various resources. CONCLUSIONS: This is one of the first qualitative studies to gather a multi-stakeholder perspective and identify problems faced by families during inter-ICU transfer of VDRF patients. PRACTICE IMPLICATIONS: Our results provide a starting point for the development of family-centered support interventions which will need to be tested in future studies.

Going back to the ward-transitioning care back to the ward team.

Transition of care from the intensive care unit (ICU) to the ward is usually an indication of the patient's improving clinical status, but is also a time when patients are particularly vulnerable. The transition between care teams poses a higher risk of medical error, which can be mitigated by safe and complete patient handoff and medication reconciliation. ICU readmissions are associated with increased mortality as well as ICU and hospital length of stay (LOS); however tools to accurately predict ICU readmission risk are limited. While there are many mechanisms in place to carefully identify patients appropriate for transfer to the ward, the optimal timing of transfer can be affected by ICU strain, limited resources such as ICU beds, and overall hospital capacity and flow leading to suboptimal transfer times or delays in transfer. The patient and family perspectives should also be considered when planning for transfer from the ICU to the ward. During times of transition, families will meet a new care team, experience uncertainty of future care plans, and adjust to a different daily routine which can lead to increased stress and anxiety. Additionally, a subset of patients, such as those with new technology, require additional multidisciplinary support, education and care coordination which can contribute to longer hospital LOS if not addressed proactively early in the hospitalization while the patient remains in the ICU. In this review article, we describe key components of the transfer from ICU to the ward, discuss current strategies to optimize timing of patient transfers, explore strategies to partner with patients and families during the transfer process, highlight patient populations where additional considerations are needed, and identify future areas of exploration which could improve the care transition from the ICU to the ward.

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.

Epidemiology and outcomes in patients with severe sepsis admitted to the hospital wards.

PURPOSE: The purpose of this study was to detail the trajectory and outcomes of patients with severe sepsis admitted from the emergency department to a non-intensive care unit (ICU) setting and identify risk factors associated with adverse outcomes. MATERIAL AND METHODS: This was a single-center retrospective cohort study conducted at a tertiary, academic hospital in the United States between 2005 and 2009. The primary outcome was a composite outcome of ICU transfer within 48 hours of admission and/or 28-day mortality. RESULTS: Of 1853 patients admitted with severe sepsis, 841 (45%) were admitted to a non-ICU setting, the rate increased over time (P < .001), and 12.5% of these patients were transferred to the ICU within 48 hours and/or died within 28 days. In multivariable models, age (P < .001), an oncology diagnosis (P < .001), and illness severity as measured by Acute Physiologic and Chronic Health Evaluation II (P = .04) and high (≥4 mmol/L) initial serum lactate levels (P = .005) were associated with the primary outcome. CONCLUSIONS: Patients presenting to the emergency department with severe sepsis were frequently admitted to a non-ICU setting, and the rate increased over time. Of 8 patients admitted to the hospital ward, one was transferred to the ICU within 48 hours and/or died within 28 days of admission. Factors present at admission were identified that were associated with adverse outcomes.