Impact of Intensive Care Unit Discharge Delays on Patient Outcomes: A Retrospective Cohort Study

Interventions to improve patient admission and discharge practices in adult intensive care units: A systematic review

OBJECTIVES

To identify and synthesise interventions and implementation strategies to optimise patient flow, addressing admission delays, discharge delays, and after-hours discharges in adult intensive care units.

METHODS

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Five electronic databases, including CINAHL, PubMed, Emcare, Scopus, and the Cochrane Library, were searched from 2007 to 2023 to identify articles describing interventions to enhance patient flow practices in adult intensive care units. The Critical Appraisal Skills Program (CASP) tool assessed the methodological quality of the included studies. All data was synthesised using a narrative approach.

SETTING

Adult intensive care units.

RESULTS

Eight studies met the inclusion criteria, mainly comprising quality improvement projects (n = 3) or before-and-after studies (n = 4). Intervention types included changing workflow processes, introducing decision support tools, publishing quality indicator data, utilising outreach nursing services, and promoting multidisciplinary communication. Various implementation strategies were used, including one-on-one training, in-person knowledge transfer, digital communication, and digital data synthesis and display. Most studies (n = 6) reported a significant improvement in at least one intensive care process-related outcome, although fewer studies specifically reported improvements in admission delays (0/0), discharge delays (1/2), and after-hours discharge (2/4). Two out of six studies reported significant improvements in patient-related outcomes after implementing the intervention.

CONCLUSION

Organisational-level strategies, such as protocols and alert systems, were frequently employed to improve patient flow within ICUs, while healthcare professional-level strategies to enhance communication were less commonly used. While most studies improved ICU processes, only half succeeded in significantly reducing discharge delays and/or after-hours discharges, and only a third reported improved patient outcomes, highlighting the need for more effective interventions.

IMPLICATIONS FOR CLINICAL PRACTICE

The findings of this review can guide the development of evidence-based, targeted, and tailored interventions aimed at improving patient and organisational outcomes.

Establishing critical care nursing research priorities for three Australian regional public hospitals: A mixed method priority setting study

OBJECTIVE

To determine key priorities for critical care nursing research in three Australian regional public hospitals, representing the shared priorities of healthcare professionals and patient representatives.

METHODS

A three phase priority setting study, including consensus methods (nominal group), survey, qualitative interviews and focus groups were conducted between May 2021 and March 2022. Healthcare professionals and patient representatives from critical care units in regional public hospitals in Australia participated. A patient representative contributed to research design and co-authored this paper.

RESULTS

In phase one, 29 research topics were generated. In phase two, during a nominal group ranking process, the top 5 priority areas for each site were identified. In the final phase, three themes from focus groups and interviews included patient flow through intensive care, patient care through intensive care journey and intensive care patient recovery.

CONCLUSION

Identifying context specific research priorities through a priority setting exercise provides insight into the topics that are important to healthcare professionals and to patients in critical care. The top research priorities for nursing research in critical care in regional Australian hospitals include patient flow, patient recovery, and evidence based patient care through the intensive care journey, such as delirium management, pain and sedation, and mobilisation. These shared priorities will be used to guide future nursing research in critical care over the next 3-5 years.

IMPLICATIONS FOR CLINICAL PRACTICE

The method we used in identifying the research priorities can be used by other researchers and clinicians; close collaboration among researchers and clinicians will be beneficial for practice improvement; and how we can be reassured that our practice is evidence based is worthy of attention.

Delayed Discharge From the Intensive Care Unit Is Associated With Longer Hospital Lengths of Stay

OBJECTIVE

The study authors sought to determine if delayed discharge from the intensive care unit (ICU) secondary to a lack of floor beds led to longer postoperative hospital length of stay (LOS) or more hospital readmissions.

DESIGN

A retrospective study comparing patients with delayed discharge from the ICU to patients without delayed discharge.

SETTING

At a cardiovascular ICU in a tertiary care university hospital.

PARTICIPANTS

A total of 5,777 patients that were ready for discharge from the ICU after recovering from cardiac surgery.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The authors used linear regression to measure postoperative hospital LOS and logistic regression to measure hospital readmission in patients whose transfer out of the ICU was delayed at least overnight to patients who were transferred out the same day. There were 3,903 patients transferred to the stepdown unit on the same day as the transfer order and 1,874 patients were transferred on a subsequent day. The postoperative LOS was shorter in the no delay group (9 ± 9 v 11 ± 10 days, standardized difference = 0.162), whereas the stepdown unit stay was similar (6 ± 6 v 5 ± 6 days, standardized difference = 0.076). The readmission rates were 15% in the no delay group versus 14% in the delayed discharge group (standardized difference = 0.032). After adjustment, the authors found by linear regression that delayed discharge was associated with an increase (0.72 [95% CI 0.43-1.01] days, p < 0.001) in postoperative LOS but was not associated with readmission.

CONCLUSIONS

The study authors found that patients who had their discharge from the ICU delayed had an increased hospital LOS but a similar rate of hospital readmission.

Direct Discharge to Home From the Pediatric Cardiovascular ICU

OBJECTIVES

To describe direct discharge to home from the cardiovascular ICU.

DESIGN

Mixed-methods including retrospective Pediatric Cardiac Critical Care Consortium and Pediatric Acute Care Cardiology Collaborative data and survey.

SETTING

Tertiary pediatric heart center.

PATIENTS

Patients less than 25 years old, with a cardiovascular ICU stay of greater than 24 hours and direct discharge to home from January 1, 2016, to December 8, 2020, were included. Select data describing patients discharged from acute care internally and nationally from Pediatric Acute Care Cardiology Collaborative sites were compared with the direct discharge to home cohort.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Encounter- and patient-specific characteristics. Seven-day and 30-day readmission and 30-day mortality served as surrogate safety markers. A survey of cardiovascular ICU frontline providers assessed comfort and skills related to direct discharge to home.There were 364 direct discharge to home encounters that met inclusion criteria. The majority of direct discharge to home encounters were associated with a surgery or procedure (305; 84%). There were 27 encounters (7.4%) for medical technology-dependent patients requiring direct discharge to home. Unplanned 7-day readmissions among direct discharge to home patients was 1.9% compared with 4.6% (p = 0.04) of patients discharged from acute care internally. Readmission among those discharged from acute care internally did not differ from those at Pediatric Acute Care Cardiology Collaborative sites nationally. Frontline cardiovascular ICU providers had mixed levels of confidence in technical aspects and low levels of confidence in logistics of direct discharge to home.

CONCLUSIONS

Cardiovascular ICU direct discharge to home was not associated with increased unplanned readmissions compared with patients discharged from acute care and may be safe in select patients. Frontline cardiovascular ICU providers feel time constraints challenge direct discharge to home. Further research is needed to identify patient characteristics associated with safe direct discharge to home and systems needed to support this practice.Summary statistics are described using proportions or medians with interquartile ranges (IQRs) and were performed using Microsoft Excel (Microsoft, Redmond, WA). Two-sample tests of proportions were used to compare readmission frequency of the DDH cohort compared with internal and national PAC3 data using STATA Version 15 (StataCorp, College Station, TX).

Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study

BACKGROUND

Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, has become one of the major causes of death in Intensive Care Units (ICUs). The heterogeneity and complexity of this syndrome lead to the absence of golden standards for its diagnosis, treatment, and prognosis. The early prediction of in-hospital mortality for sepsis patients is not only meaningful to medical decision making, but more importantly, relates to the well-being of patients.

METHODS

In this paper, a rule discovery and analysis (rule-based) method is used to predict the in-hospital death events of 2021 ICU patients diagnosed with sepsis using the MIMIC-III database. The method mainly includes two phases: rule discovery phase and rule analysis phase. In the rule discovery phase, the RuleFit method is employed to mine multiple hidden rules which are capable to predict individual in-hospital death events. In the rule analysis phase, survival analysis and decomposition analysis are carried out to test and justify the risk prediction ability of these rules. Then by leveraging a subset of these rules, we establish a prediction model that is both more accurate at the in-hospital death prediction task and more interpretable than most comparable methods.

RESULTS

In our experiment, RuleFit generates 77 risk prediction rules, and the average area under the curve (AUC) of the prediction model based on 62 of these rules reaches 0.781 ([Formula: see text]) which is comparable to or even better than the AUC of existing methods (i.e., commonly used medical scoring system and benchmark machine learning models). External validation of the prediction power of these 62 rules on another 1468 sepsis patients not included in MIMIC-III in ICU provides further supporting evidence for the superiority of the rule-based method. In addition, we discuss and explain in detail the rules with better risk prediction ability. Glasgow Coma Scale (GCS), serum potassium, and serum bilirubin are found to be the most important risk factors for predicting patient death.

CONCLUSION

Our study demonstrates that, with the rule-based method, we could not only make accurate prediction on in-hospital death events of sepsis patients, but also reveal the complex relationship between sepsis-related risk factors through the rules themselves, so as to improve our understanding of the complexity of sepsis as well as its population.

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning–based real-time bedside decision support tool.

The Association between Discharge Delay from Intensive Care and Patient Outcomes

Rationale: ICU discharge delay occurs when a patient is considered ready to be discharged but remains in the ICU. The effect of discharge delay on patient outcomes is uncertain.Objectives: To investigate the association between discharge delay and patient outcomes including hospital mortality, readmission to ICU, and length of hospital stay after ICU discharge.Methods: Data were accessed from the Australian and New Zealand Intensive Care Society Adult Patient Database between 2011 and 2019. Descriptive analyses and hierarchical logistic and Cox proportional hazards regression were used to examine association between discharge delay and adjusted outcomes. Patients were stratified and analyzed by categories of mortality risk at ICU admission.Measurements and Main Results: The study included 1,014,540 patients from 190 ICUs: 756,131 (75%) were discharged within 6 hours of being deemed ready, with 137,042 (13%) discharged in the next 6 hours; 17,656 (2%) were delayed 48-72 hours; 31,389 (3.1%) died in hospital; and 45,899 (4.5%) patients were readmitted to ICU. Risk-adjusted mortality declined with increasing discharge delay and was lowest at 48-72 hours (adjusted odds ratio, 0.87; 95% confidence interval, 0.79-0.94). The effect was seen in patients with predicted risk of death on admission to ICU of greater than 5% (adjusted odds ratio, 0.77; 95% confidence interval, 0.70-0.84). There was a progressive reduction in adjusted odds of readmission with increasing discharge delay.Conclusions: Increasing discharge delay in ICUs is associated with reduced likelihood of mortality and ICU readmission in high-risk patients. Consideration should be given to delay the discharge of patients with high risk of death on ICU admission.

Characteristics and Outcomes of Patients Discharged Directly Home From a Medical Intensive Care Unit: A Retrospective Cohort Study

OBJECTIVE

To evaluate the safety of directly discharging patients home from the medical intensive care unit (MICU).

MATERIALS AND METHODS

Single-center retrospective observational study of consecutive MICU direct discharges to home from an urban university hospital between June, 1, 2017, and June 30, 2019.

RESULTS

Of 1061 MICU discharges, 331 (31.2%) patients were eligible for analysis. Patients were divided into 2 groups based on duration of wait-time (< or ≥24 hours) between ward transfer order and ultimate hospital discharge. Most patients (68.2%) were discharged in <24 hours. Patients who waited for a floor bed for ≥24 hours prior to discharge had longer hospital length-of-stay (LOS, median 3.83 versus 2.00 days) and ICU LOS (median 3.51 versus 1.74 days). Overall, 44 (13.3%) direct MICU discharges were readmitted to the hospital within 30-days, but there was no difference in this outcome or in 30-day mortality when comparing the 2 wait-time groups.

CONCLUSIONS

The practice of directly discharging MICU patients home does not negatively influence patient outcomes. Patients who overstay in the ICU after being deemed transfer-ready are unlikely to be benefiting from critical care, but impact hospital throughput and resource utilization. Prospective investigation into this practice may provide further confirmation of its feasibility and safety.

Transition of care at discharge from the Intensive Care Unit: a scoping review

OBJECTIVE

to map the available evidence on the components of the transition of care, practices, strategies, and tools used in the discharge from the Intensive Care Unit (ICU) to the Inpatient Unit (IU) and its impact on the outcomes of adult patients.

METHOD

a scoping review using search strategies in six relevant health databases.

RESULTS

37 articles were included, in which 30 practices, strategies or tools were identified for organizing and executing the transfer process, with positive or negative impacts, related to factors intrinsic to the Intensive Care Unit and the Inpatient Unit and cross-sectional factors regarding the staff. The analysis of hospital readmission and mortality outcomes was prevalent in the included studies, in which trends and potential protective actions for a successful care transition are found; however, they still lack more robust evidence and consensus in the literature.

CONCLUSION

transition of care components and practices were identified, in addition to factors intrinsic to the patient, associated with worse outcomes after discharge from the Intensive Care Unit. Discharges at night or on weekends were associated with increased rates of readmission and mortality; however, the association of other practices with the patient's outcome is still inconclusive.

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.

Critical Care, Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.