Managing ICU throughput and understanding ICU census

The current state of intensive care unit discharge practices - Results of an international survey study

Background

Increasing pressure on limited intensive care capacities often requires a subjective assessment of a patient's discharge readiness in the absence of established Admission, Discharge, and Transfer (ADT) guidelines. To avoid suboptimal care transitions, it is important to define clear guidelines for the admission and discharge of intensive care patients and to optimize transfer processes between the intensive care unit (ICU) and lower care levels. To achieve these goals, structured insights into usual ICU discharge and transfer practices are essential. This study aimed to generate these insights by focusing on involved stakeholders, established processes, discharge criteria and tools, relevant performance metrics, and current barriers to a timely and safe discharge.

Method

In 2022, a structured, web-based, anonymous cross-sectional survey was conducted, aimed at practicing ICU physicians, nurses, and bed coordinators. The survey consisted of 29 questions (open, closed, multiple choice, and scales) that were divided into thematic blocks. The study was supported by several national and international societies for intensive care medicine and nursing.

Results

A total of 219 participants from 40 countries (105 from Germany) participated in the survey. An overload of acute care resources with ~90% capacity utilization in the ICU and the general ward (GW) leads to not only premature but also delayed patient transfers due to a lack of available ward and intermediate care (IMC) beds. After multidisciplinary rounds within the intensive care team, the ICU clinician on duty usually makes the final transfer decision, while one-third of the panel coordinates discharge decisions across departmental boundaries. By the end of the COVID-19 pandemic, half of the hospitals had implemented ADT policies. Among these hospitals, nearly one-third of the hospitals had specific transfer criteria established, consisting primarily of vital signs and laboratory data, patient status and autonomy, and organization-specific criteria. Liaison nurses were less common but were ranked right after the required IMC capacities to bridge the care gap between the ICU and normal wards. In this study, 80% of the participants suggested that transfer planning would be easier if there was good transparency regarding the capacity utilization of lower care levels, a standardized transfer process, and improved interdisciplinary communication.

Conclusion

To improve care transitions, transfer processes should be managed proactively across departments, and efforts should be made to identify and address care gaps.

Forecasting ICU Census by Combining Time Series and Survival Models

Capacity planning of ICUs is essential for effective management of health safety, quality of patient care, and the allocation of ICU resources. Whereas ICU length of stay (LOS) may be estimated using patient information such as severity of illness scoring systems, ICU census is impacted by both patient LOS and arrival patterns. We set out to develop and evaluate an ICU census forecasting algorithm using the Multiple Organ Dysfunction Score (MODS) and the Nine Equivalents of Nursing Manpower Use Score (NEMS) for capacity planning purposes.

DESIGN

Retrospective observational study.

SETTING

We developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada.

PATIENTS

Adult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU.

CONCLUSIONS

It is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.

Delphi study to derive expert consensus on a set of criteria to evaluate discharge readiness for adult ICU patients to be discharged to a general ward-European perspective

Background/purpose

Discharge decisions in Intensive Care Unit (ICU) patients are frequently taken under pressure to free up ICU beds. In the absence of established guidelines, the evaluation of discharge readiness commonly underlies subjective judgements. The challenge is to come to the right decision at the right time for the right patient. A premature care transition puts patients at risk of readmission to the ICU. Delayed discharge is a waste of resources and may result in over-treatment and suboptimal patient flow. More objective decision support is required to assess the individual patient’s discharge readiness but also the current care capabilities of the receiving unit.

Methods

In a modified online Delphi process, an international panel of 27 intensive care experts reached consensus on a set of 28 intensive care discharge criteria. An initial evidence-based proposal was developed further through the panelists’ edits, adding, comments and voting over a course of 5 rounds. Consensus was defined as achieved when ≥ 90% of the experts voted for a given option on the Likert scale or in a multiple-choice survey. Round 1 to 3 focused on inclusion and exclusion of the criteria based on the consensus threshold, where round 3 was a reiteration to establish stability. Round 4 and 5 focused on the exact phrasing, values, decision makers and evaluation time frames per criterion.

Results

Consensus was reached on a standard set of 28 ICU discharge criteria for adult ICU patients, that reflect the patient’s organ systems ((respiratory (7), cardiovascular (9), central nervous (1), and urogenital system (2)), pain (1), fluid loss and drainages (1), medication and nutrition (1), patient diagnosis, prognosis and preferences (2) and institution-specific criteria (4). All criteria have been specified in a binary decision metric (fit for ICU discharge vs. needs further intensive therapy/monitoring), with consented value calculation methods where applicable and a criterion importance rank with “mandatory to be met” flags and applicable exceptions.

Conclusion

For a timely identification of stable intensive care patients and safe and efficient care transitions, a standardized discharge readiness evaluation should be based on patient factors as well as organizational boundary conditions and involve multiple stakeholders.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08160-6.

Challenging weekend discharges associated with excess length of stay in surgical patients at Veterans Affairs hospitals

BACKGROUND

Challenging discharges can lead to prolonged hospital stays. We hypothesized that surgical patients discharged from Veterans Affairs hospitals on weekdays have longer hospital stays and greater excess length of stay.

METHODS

We identified inpatient general and vascular procedures at Veterans Affairs hospitals from 2007 to 2014. Expected length of stay was calculated using a stratified negative binomial model adjusted for patient/operative characteristics. Excess length of stay was defined as the difference between observed and expected length of stay.

RESULTS

We identified 135,875 patients (80.4% weekday discharges, 19.6% weekend discharges). The average length of stay was 7.5 days. Patients with weekday discharges spent on average 2.5 more days in the hospital compared with patients discharged on weekends (8.0 vs. 5.5 days, P < .001); 28.5% of patients with weekday discharges had an observed length of stay at least 1 day longer than expected, compared with 16.4% of patients with weekend discharges (P < .001).

CONCLUSION

Surgical patients are less frequently discharged from Veterans Affairs hospitals on the weekends than during the week, and this corresponds to an increased excess length of stay for patients ultimately discharged on weekdays. Exploring the opportunity to coordinate safe weekend discharges may improve efficiency of post-surgery hospital care and reduce healthcare costs.

Optimizing Throughput in Clinical Practice: Lean Management and Efficient Care in Plastic and Reconstructive Surgery

BACKGROUND

As the cost of health care continues to rise, the role of medical providers has evolved to include the duties of an operations manager. Two theories of operations management can be readily applied to health care-lean management, the process of identifying and eliminating waste; and Little's law, the idea that throughput is maximized by changing the capacity to host patients or the time they spend in the system. Equipped with theories of operations management, providers are better able to identify and address flow limitations in their own practices.

METHODS

Operations flow data were collected from three areas of care-clinic, surgical booking, and the operating room-for one provider. Variables of interest included visit or procedure characteristics and operations flow characteristics, such as different time points involved in the sector of care.

RESULTS

Clinic data were collected from 48 patients. Variables with a significant relationship to total clinic visit time included afternoon appointments (p = 0.0080) and visit type (p = 0.0114). Surgical booking data were collected for 127 patients. Shorter estimated procedure length (p = 0.0211) decreased time to surgery. Operating room data were collected for 65 cases. Variables with a significant relationship to total operating room time were patient age (p = 0.0325), Charlson Comorbidity Index (p = 0.0039), flap type (p = 0.0153), and number of flaps (p < 0.0001).

CONCLUSIONS

This brief single-provider study provides examples of how to apply operations management theories to each point of care within one's own practice. Although longitudinal data following patients through each point of care are the next step in operations flow analysis, this work lays the foundation for evaluation at each time point with the goal of developing practical strategies to improve throughput in one's practice.

Effect of emergency critical care nurses and emergency department boarding time on in-hospital mortality in critically ill patients

STUDY HYPOTHESIS

We hypothesized that establishing a program of specialized emergency critical care (ECC) nurses in the ED would improve mortality of ICU patients boarding in the ED.

METHODS

This was a retrospective before-after cohort study using electronic health record data at an academic medical center. We compared in-hospital mortality between the pre- and post-intervention periods and between non-prolonged (≤6 h) boarding time and prolonged (>6 h) boarding time. In-hospital mortality was stratified by illness severity (eccSOFA category) and adjusted using logistic regression.

RESULTS

Severity-adjusted in-hospital mortality decreased from 12.8% pre-intervention to 12.3% post-intervention (-0.5% (95% CI, -3.1% to 2.1%), which was not statistically significant. This was despite a concurrent increase in ED and hospital crowding. The proportion of ECC patients downgraded to a lower level of care while still in the ED increased from 6.4% in the pre-intervention period to 17.0% in the post-intervention period. (+10.6%, 8.2% to 13.0%, p < 0.001). Severity-adjusted mortality was 12.8% in the non-prolonged group vs. 11.3% in the prolonged group (p = 0.331).

CONCLUSIONS

During the post-intervention period, there was a significant increase in illness severity, hospital congestion, ED boarding time, and downgrades in the ED, but no significant change in mortality. These findings suggest that ECC nurses may improve the safety of boarding ICU patients in the ED. Longer ED boarding times were not associated with higher mortality in either the pre- or post-intervention periods.

Efficient Postoperative Disposition Selection in Pediatric Otolaryngology Patients: A Novel Approach

OBJECTIVE

Pediatric patients undergoing surgery on the aerodigestive tract require a wide range of postoperative airway support that may be difficult predict in the preoperative period. Inaccurate prediction of postoperative resource needs leads to care inefficiencies in the form of unanticipated intensive care unit (ICU) admissions, ICU bed request cancellations, and overutilization of ICU resources. At our hospital, inefficient utilization of pediatric intensive care unit (PICU) resources was negatively impacting safety, access, throughput, and finances. We hypothesized that actionable key drivers of inefficient ICU utilization at our hospital were operative scheduling errors and the lack of predictability of intermediate-risk patients and that improvement methodology could be used in iterative cycles to enhance efficiency of care. Through testing this hypothesis, we aimed to provide a framework for similar efforts at other hospitals.

STUDY DESIGN

Quality improvement initiative.

METHODS

Plan, Do, Study, Act methodology (PDSA) was utilized to implement two cycles of change aimed at improving level-of-care efficiency at an academic pediatric hospital. In PDSA cycle 1, we aimed to address scheduling errors with surgical order placement restriction, creation of a standardized list of surgeries requiring PICU admission, and implementation of a hard stop for postoperative location in the electronic medical record surgical order. In the PDSA cycle 2, a new model of care, called the Grey Zone model, was designed and implemented where patients at intermediate risk of airway compromise were observed for 2-5 hours in the post-anesthesia care unit. After this observation period, patients were then transferred to the level of care dictated by their current status. Measures assessed in PDSA cycle 1 were unanticipated ICU admissions and ICU bed request cancellations. In addition to continued analysis of these measures, PDSA cycle 2 measures were ICU beds avoided, safety events, and secondary transfers from extended observation to ICU.

RESULTS

In PDSA cycle 1, no significant decrease in unanticipated ICU admissions was observed; however, there was an increase in average monthly ICU bed cancellations from 36.1% to 45.6%. In PDSA cycle 2, average monthly unanticipated ICU admissions and cancelled ICU bed requests decreased from 1.3% to 0.42% and 45.6% to 33.8%, respectively. In patients observed in the Grey Zone, 229/245 (93.5%) were transferred to extended observation, avoiding admission to the ICU. Financial analysis demonstrated a charge differential to payers of $1.1 million over the study period with a charge differential opportunity to the hospital of $51,720 for each additional hospital transfer accepted due to increased PICU bed availability.

CONCLUSIONS

Implementation of the Grey Zone model of care improved efficiency of ICU resource utilization through reducing unanticipated ICU admissions and ICU bed cancellations while simultaneously avoiding overutilization of ICU resources for intermediate-risk patients. This was achieved without compromising safety of patient care, and was financially sound in both fee-for-service and value-based reimbursement models. While such a model may not be applicable in all healthcare settings, it may improve efficiency at other pediatric hospitals with high surgical volume and acuity.

LEVEL OF EVIDENCE

N/A Laryngoscope, 131:S1-S10, 2021.

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.

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

OBJECTIVE

The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.

DESIGN

We used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.

SETTING

Bristol Royal Infirmary general intensive care unit (GICU).

PATIENTS

Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.

RESULTS

In both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.

CONCLUSIONS

Our findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

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.

Update in Critical Care 2015

This review documents important progress made in 2015 in the field of critical care. Significant advances in 2015 included further evidence for early implementation of low tidal volume ventilation as well as new insights into the role of open lung biopsy, diaphragmatic dysfunction, and a potential mechanism for ventilator-induced fibroproliferation. New therapies, including a novel low-flow extracorporeal CO2 removal technique and mesenchymal stem cell-derived microparticles, have also been studied. Several studies examining the role of improved diagnosis and prevention of ventilator-associated pneumonia also showed relevant results. This review examines articles published in the American Journal of Respiratory and Critical Care Medicine and other major journals that have made significant advances in the field of critical care in 2015.

A Simulated Level Loading of Supply and Demand for Beds in a Tertiary Care Children's Hospital Reduces Overall Bed Requirements

BACKGROUND

Anticipating throughput and allocating resources effectively in children's hospitals have unique challenges relative to adult inpatient centers. The seasonal and daily variation can be difficult to anticipate in terms of impact and creating plans for adequate preparation. Discrete event simulation methodology can be helpful in determining appropriate allocation of resources and has been increasingly appropriated in health care from industry.

METHODS

A representative sample set was abstracted from the Cohen Children's Medical Center census tracking system to describe the present state. A larger data set was used to determine the appropriate level load. The total work performed each hour from 8 AM to 8 PM was evaluated against the level load plan of 11.5%. During the initial hours of the working period when the total work was low, more discharges were added. For each discharge added, an equal quantity of discharges was subtracted from the later hours of the day to bring the total work below 11.5% for each hour. Once the simulated state discharges were determined, a new aggregate bed supply line was created. These values were then added to the original visualization to show improvement.

RESULTS

Our analysis suggests that a large part of the discharge/transfer activity and bed demand activity occurs in the pediatric intensive care unit in a roughly 4- to 5-hour window. Our simulation analysis suggests that level loading of this resource-intensive activity period has a potential to reduce bed occupancy, increase bed availability in peak bed demand times, and improve efficiency and throughput throughout the hospital.

CONCLUSION

Discrete event simulation can be an effective tool for pediatric inpatient centers to determine appropriate allocation of resources to enhance patient safety and throughput without significant, costly expansion of bed capacity.

Telemedicine coverage for post-operative ICU patients

Introduction There is an increased demand for intensive care unit (ICU) beds. We sought to determine if we could create a safe surge capacity model to increase ICU capacity by treating ICU patients in the post-anaesthesia care unit (PACU) utilizing a collaborative model between an ICU service and a telemedicine service during peak ICU bed demand. Methods We evaluated patients managed by the surgical critical care service in the surgical intensive care unit (SICU) compared to patients managed in the virtual intensive care unit (VICU) located within the PACU. A retrospective review of all patients seen by the surgical critical care service from January 1st 2008 to July 31st 2011 was conducted at an urban, academic, tertiary centre and level 1 trauma centre. Results Compared to the SICU group ( n = 6652), patients in the VICU group ( n = 1037) were slightly older (median age 60 (IQR 47-69) versus 58 (IQR 44-70) years, p = 0.002) and had lower acute physiology and chronic health evaluation (APACHE) II scores (median 10 (IQR 7-14) versus 15 (IQR 11-21), p < 0.001). The average amount of time patients spent in the VICU was 13.7 + /-9.6 hours. In the VICU group, 750 (72%) of patients were able to be transferred directly to the floor; 287 (28%) required subsequent admission to the surgical intensive care unit. All patients in the VICU group were alive upon transfer out of the PACU while mortality in the surgical intensive unit cohort was 5.5%. Discussion A collaborative care model between a surgical critical care service and a telemedicine ICU service may safely provide surge capacity during peak periods of ICU bed demand. The specific patient populations for which this approach is most appropriate merits further investigation.

Sustaining critical care: using evidence-based simulation to evaluate ICU management policies

Intensive Care Units (ICU) are costly yet critical hospital departments that should be available to care for patients needing highly specialized critical care. Shortage of ICU beds in many regions of the world and the constant fire-fighting to make these beds available through various ICU management policies motivated this study. The paper discusses the application of a generic system dynamics model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to explore the dynamics of intended and unintended consequences of such ICU management policies under a natural disaster crisis scenario. ICU management policies that can be implemented by a single hospital on short notice, namely premature transfer from ICU, boarding in ward, and general ward admission control, along with their possible combinations, are modeled and their impact on managerial and health outcome measures are investigated. The main insight out of the study is that the general ward admission control policy outperforms the rest of ICU management policies under such crisis scenarios with regards to reducing total mortality, which is counter intuitive for hospital administrators as this policy is not very effective at alleviating the symptoms of the problem, namely high ED and ICU occupancy rates that are closely monitored by hospital management particularly in times of crisis. A multivariate sensitivity analysis on parameters with diverse range of values in the literature found the superiority of the general ward admission control to hold true in every scenario.

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

RATIONALE

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

OBJECTIVES

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

METHODS

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

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Intensive Care Unit Admission With Community-Acquired Pneumonia

BACKGROUND

There has been a dramatic increase in the use of intensive care units (ICUs) over the past 25 years. Greater use of validated measures of illness severity may better inform ICU admission decisions in patients with community-acquired pneumonia. This article examined predictors of ICU admission and hospitalization costs, including the pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, blood pressure, age ≥65 years) scores.

METHODS

The study identified 422 patients hospitalized for community-acquired pneumonia, ascertaining patient characteristics by chart review and extraction of administrative data. Multivariate logistic regression was performed to quantify the association of the PSI, CURB-65 and comorbidities with ICU admission. The predictors of cost were estimated using a generalized linear model.

RESULTS

Compared to 194 general medicine patients, certain clinical and radiographic findings were more common among 228 ICU patients. Compared to PSI reference group I/II/III, ICU admission was strongly associated with risk class IV (odds ratio [OR], 3.06; 95% confidence interval [CI], 1.63-5.72) and V (OR, 4.84; CI, 2.44-9.62), and also CURB-65 ≥3 (OR, 2.90; CI, 1.51-5.56). The relative increase in mortality among PSI risk class V (compared to IV) patients was 2.68 times higher in general medicine, compared with the ICU. Among ICU admissions, risk class V was associated with an additional cost of $14,548 (95% CI, $4,232 to $24,864).

CONCLUSIONS

Illness severity and chronic pulmonary disease are strong predictors of ICU admission. More extensive use of the PSI may optimize site-of-care decisions, thereby minimizing mortality and unnecessary resource utilization.

Trauma Surge Index: Advancing the Measurement of Trauma Surges and Their Influence on Mortality

BACKGROUND

Increases in trauma patient volume and acuity, such as during mass casualty events, can overwhelm hospitals, potentially worsening patient outcomes. Due to methodological limitations, the effect of trauma surges on clinical outcomes remains unclear, so hospitals have not prepared for such events in an evidence-based manner. The objective of this study was to develop a new measure of hospital capacity strain corresponding to trauma admissions and to examine the relationship between trauma surges and inpatient mortality.

STUDY DESIGN

Using trauma registry data from hospitals across the United States and Canada (2010 to 2011), we developed the Trauma Surge Index (TSI), a measure of capacity strain that controls for variation in hospital admission volume and patient acuity. Using the TSI and an established definition of mass casualty events, we quantified hospital surges and entered each measure as an exposure variable in separate risk-adjusted mortality models.

RESULTS

Using the TSI method, we observed that patients admitted during high-surge periods display significantly increased mortality compared with patients admitted during low-surge periods (odds ratio [OR] = 2.05; 95% CI, 1.36-3.10), and patients with firearms injuries were particularly at risk (OR = 7.29; 95% CI, 2.13-24.91). Using mass casualty event criteria, we found no difference between the mortality of patients admitted during trauma surges and nonsurge periods (OR = 0.94; 95% CI, 0.88-1.01).

CONCLUSIONS

We demonstrate the TSI, which is a novel method that identified periods of high-capacity strain in hospitals associated with increased trauma patient mortality. Our newly developed TSI method can be implemented by hospitals and trauma systems to examine periods of high-capacity strain retrospectively, identify specific resources that might have been needed, and better direct future investments in an evidence-based manner.

Delayed admission to intensive care unit for critically surgical patients is associated with increased mortality

BACKGROUND

Shortage of beds in intensive care units (ICUs) is an increasing common phenomenon worldwide. Consequently, many critically ill patients have to be cared for in other hospital areas without specialized staff, such as general wards, emergency department, post anesthesia care unit (PACU). However, boarding critically ill patients in general wards or emergency department has been associated with higher mortality. The purpose of this study was to evaluate if a delay in ICU admission, waiting in PACU and managed by anesthesiologists, affects their ICU outcomes for critically surgical patients.

METHODS

A retrospective cohort of adult critically surgical patients admitted to our ICU between January 2010 and June 2012 were analyzed. ICU admission was classified as either immediate or delayed (waiting in PACU). A general estimation equation was used to examine the relationship of PACU waiting hours before ICU admission with ICU outcomes by adjusting for age, patient sex, comorbidities, surgical categories, end time of operation, operation hours, and clinical conditions.

RESULTS

A total of 2,279 critically surgical patients were evaluated. Two thousand ninety-four (91.9%) patients were immediately admitted and 185 (8.1%) patients had delayed ICU admission. There was a significant increase in ICU mortality rates with a delay in ICU admission (P < .001). Prolonged waiting hours in PACU (≥ 6 hours) was associated with higher ICU mortality (adjusted odds ratio 5.32; 95% confidence interval 1.25 to 22.60, P = .024). However, longer PACU waiting times was not associated with mechanical ventilation days, ICU length of stay, and ICU cost.

CONCLUSION

Prolonged waiting hours in PACU because of ICU bed shortage was associated with higher ICU mortality for critically surgical patients.

Outcomes among patients discharged from busy intensive care units

BACKGROUND

Strains on the capacities of intensive care units (ICUs) may influence the quality of ICU-to-floor transitions.

OBJECTIVE

To determine how 3 metrics of ICU capacity strain (ICU census, new admissions, and average acuity) measured on days of patient discharges influence ICU length of stay (LOS) and post-ICU discharge outcomes.

DESIGN

Retrospective cohort study from 2001 to 2008.

SETTING

155 ICUs in the United States.

PATIENTS

200 730 adults discharged from ICUs to hospital floors.

MEASUREMENTS

Associations between ICU capacity strain metrics and discharged patient ICU LOS, 72-hour ICU readmissions, subsequent in-hospital death, post-ICU discharge LOS, and hospital discharge destination.

RESULTS

Increases in the 3 strain variables on the days of ICU discharge were associated with shorter preceding ICU LOS (all P < 0.001) and increased odds of ICU readmissions (all P < 0.050). Going from the 5th to 95th percentiles of strain was associated with a 6.3-hour reduction in ICU LOS (95% CI, 5.3 to 7.3 hours) and a 1.0% increase in the odds of ICU readmission (CI, 0.6% to 1.5%). No strain variable was associated with increased odds of subsequent death, reduced odds of being discharged home from the hospital, or longer total hospital LOS.

LIMITATION

Long-term outcomes could not be measured.

CONCLUSION

When ICUs are strained, triage decisions seem to be affected such that patients are discharged from the ICU more quickly and, perhaps consequentially, have slightly greater odds of being readmitted to the ICU. However, short-term patient outcomes are unaffected. These results suggest that bed availability pressures may encourage physicians to discharge patients from the ICU more efficiently and that ICU readmissions are unlikely to be causally related to patient outcomes.

PRIMARY FUNDING SOURCE

Agency for Healthcare Research and Quality; National Heart, Lung, and Blood Institute; and Society of Critical Care Medicine.

The incomplete infrastructure for interhospital patient transfer

OBJECTIVE

Interhospital transfer of critically ill patients is a common part of their care. This article sought to review the data on the current patterns of use of interhospital transfer and identify systematic barriers to optimal integration of transfer as a mechanism for improving patient outcomes and value of care.

DATA SOURCE

Narrative review of medical and organizational literature.

SUMMARY

Interhospital transfer of patients is common, but not optimized to improve patient outcomes. Although there is a wide variability in quality among hospitals of nominally the same capability, patients are not consistently transferred to the highest quality nearby hospital. Instead, transfer destinations are selected by organizational routines or non-patient-centered organizational priorities. Accomplishing a transfer is often quite difficult for sending hospitals. But once a transfer destination is successfully found, the mechanics of interhospital transfer now appear quite safe.

CONCLUSION

Important technological advances now make it possible to identify nearby hospitals best able to help critically ill patients, and to successfully transfer patients to those hospitals. However, organizational structures have not yet developed to insure that patients are optimally routed, resulting in potentially significant excess mortality.

Sustained effectiveness of a primary-team-based rapid response system

OBJECTIVE

Laws and regulations require many hospitals to implement rapid-response systems. However, the optimal resource intensity for such systems is unknown. We sought to determine whether a rapid-response system that relied on a patient's usual care providers, not a critical-care-trained rapid-response team, would improve patient outcomes.

DESIGN, SETTING, AND PATIENTS

An interrupted time-series analysis of over a 59-month period.

SETTING

Urban, academic hospital.

PATIENTS

One hundred seven-one thousand, three hundred forty-one consecutive adult admissions.

INTERVENTION

In the intervention period, patients were monitored for predefined, standardized, acute, vital-sign abnormalities or marked nursing concern. If these criteria were met, a team consisting of the patient's existing care providers was assembled.

MEASUREMENTS AND MAIN RESULTS

The unadjusted risk of unexpected mortality was 72% lower (95% confidence interval 55%-83%) in the intervention period (absolute risk: 0.02% vs. 0.09%, p < .0001). The unadjusted in-hospital mortality rate was not significantly lower (1.9% vs. 2.1%, p = .07). After adjustment for age, gender, race, season of admission, case mix, Charlson Comorbidity Index, and intensive care unit bed capacity, the intervention period was associated with an 80% reduction (95% confidence interval 63%-89%, p < .0001) in the odds of unexpected death, but no significant change in overall mortality [odds ratio 0.91 (95% confidence interval 0.82-1.02), p = .09]. Analyses that also adjusted for secular time trends confirmed these findings (relative risk reduction for unexpected mortality at end of intervention period: 65%, p = .0001; for in-hospital mortality, relative risk reduction = 5%, p = .2).

CONCLUSIONS

A primary-team-based implementation of a rapid response system was independently associated with reduced unexpected mortality. This system relied on the patient's usual care providers, not an intensive care unit based rapid response team, and may offer a more cost-effective approach to rapid response systems, particularly for systems with limited intensivist availability.