Preventing Early Bouncebacks to the Neurointensive Care Unit: A Retrospective Analysis and Quality Improvement Pilot

The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

BACKGROUND

Patients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient's clinical condition and a predictive tool of clinical deterioration over time. This study was designed to test the hypothesis that the Rothman Index represents a sensitive predictor of unanticipated ICU readmissions.

METHODS

A retrospective propensity-matched cohort study was performed at a tertiary referral academic medical center in the United States from January 1, 2022, to December 31, 2022. Inclusion criteria were adult patients admitted to an ICU and readmitted within seven days of transfer to a lower level of care. The control group consisted of patients who were downgraded from ICU without a subsequent readmission. The primary outcome measure was in-hospital mortality or discharge to hospice for end-of-life care. Secondary outcome measures were overall hospital length of stay, ICU length of stay, and 30-day readmission rates. Propensity matching was used to control for differences between the study cohorts. Regression analyses were performed to determine independent risk factors of an unplanned readmission to ICU.

RESULTS

A total of 5,261 ICU patients met the inclusion criteria, of which 212 patients (4%) had an unanticipated readmission to the ICU within 7 days. The study cohort and control group were stratified by propensity matching into equal group sizes of n = 181. Lower Rothman Index scores (reflecting higher physiologic acuity) at the time of downgrade from the ICU were significantly associated with an unplanned readmission to the ICU (p < 0.0001). Patients readmitted to ICU had a lower mean Rothman Index score (p < 0.0001) and significantly increased rates of mortality (19.3% vs. 2.2%, p < 0.0001) and discharge to hospice (14.4% vs. 6.1%, p = 0.0073) compared to the control group of patients without ICU readmission. The overall length of ICU stay (mean 8.0 vs. 2.2 days, p < 0.0001) and total length of hospital stay (mean 15.8 vs. 7.3 days, p < 0.0001) were significantly increased in patients readmitted to ICU, compared to the control group.

CONCLUSION

The Rothman Index represents a sensitive predictor of unanticipated readmissions to ICU, associated with a significantly increased mortality and overall ICU and hospital length of stay. The Rothman Index should be considered as a real-time objective measure for prediction of a safe downgrade from ICU to a lower level of care.

Perioperative risk factors associated with unplanned neurological intensive care unit readmission following elective supratentorial brain tumor resection

OBJECTIVE

The aim of this study was to describe the clinical and procedural risk factors associated with the unplanned neurosurgical intensive care unit (NICU) readmission of patients after elective supratentorial brain tumor resection and serves as an exploratory analysis toward the development of a risk stratification tool that may be prospectively applied to this patient population.

METHODS

This was a retrospective observational cohort study. The electronic medical records of patients admitted to an institutional NICU between September 2018 and November 2021 after elective supratentorial brain tumor resection were reviewed. Demographic and perioperative clinical factors were recorded. A prognostic model was derived from the data of 4892 patients recruited between September 2018 and May 2021 (development cohort). A nomogram was created to display these predictor variables and their corresponding points and risks of readmission. External validation was evaluated using a series of 1118 patients recruited between June 2021 and November 2021 (validation cohort). Finally, a decision curve analysis was performed to determine the clinical usefulness of the prognostic model.

RESULTS

Of the 4892 patients in the development cohort, 220 (4.5%) had an unplanned NICU readmission. Older age, lesion type, Karnofsky Performance Status (KPS) < 70 at admission, longer duration of surgery, retention of endotracheal intubation on NICU entry, and longer NICU length of stay (LOS) after surgery were independently associated with an unplanned NICU readmission. A total of 1118 patients recruited between June 2021 and November 2021 were included for external validation, and the model's discrimination remained acceptable (C-statistic = 0.744, 95% CI 0.675-0.814). The decision curve analysis for the prognostic model in the development and validation cohorts showed that at a threshold probability between 0.05 and 0.8, the prognostic model showed a positive net benefit.

CONCLUSIONS

A predictive model that included age, lesion type, KPS < 70 at admission, duration of surgery, retention of endotracheal intubation on NICU entry, and NICU LOS after surgery had an acceptable ability to identify elective supratentorial brain tumor resection patients at high risk for an unplanned NICU readmission. These risk factors and this prediction model may facilitate better resource allocation in the NICU and improve patient outcomes.

The risk assessment tool for intensive care unit readmission: A systematic review and meta-analysis

OBJECTIVE

To review and evaluate existing risk assessment tools for intensive care unitreadmission.

METHODS

Nine electronic databases (Medline, CINAHL, Web of Science, Cochrane Library, Embase, Sino Med, CNKI, VIP, and Wan fang) were systematically searched from their inception to September 2022. Two authors independently extracted data from the literature included. Meta-analysis was performed under the bivariate modeling and summary receiver operating characteristic curve method.

RESULTS

A total of 29 studies were included in this review, among which 11 were quantitatively Meta-analyzed. The results showed Stability and Workload Index for Transfer: Sensitivity = 0.55, Specificity = 0.65, Area under curve = 0.63. And Early warning score: Sensitivity = 0.78, Specificity = 0.83, Area under curve = 0.88. The remaining tools included scores, nomograms, machine learning models, and deep learning models. These studies, with varying reports on thresholds, case selection, data preprocessing, and model performance, have a high risk of bias.

CONCLUSION

We cannot identify a tool that can be used directly in intensive care unit readmission risk assessment. Scores based on early warning score are moderately accurate in predicting readmission, but there is heterogeneity and publication bias that requires model adjustment for local factors such as resources, demographics, and case mix. Machine learning models present a promising modeling technique but have a high methodological bias and require further validation.

IMPLICATIONS FOR CLINICAL PRACTICE

Using reliable risk assessment tools is essential for the early identification of unplanned intensive care unit readmission risk in critically ill patients.  A reliable risk assessment tool must be developed, which is the focus of further research.

Cognition and practice on transitional care during the transfer from intensive care unit to a general ward among health care professionals: A qualitative study

AIM

The aim of this study is to explore the cognition and practice on transitional care during the transfer from intensive care unit to a general ward among health care professionals in China.

BACKGROUND

Due to the significant differences in the medical and humanistic environment at home and abroad, the safety of patients during the transmission from intensive care unit to the general ward is often ignored when their conditions become stable. There are few qualitative studies on the cognition and practice on transitional care during the transfer from intensive care unit to the ward among health care professionals in China.

METHODS

With a qualitative research design, 20 medical and nursing staff in the neurosurgery intensive care unit and ward were interviewed from May 2021 to August 2021. NVivo 11.0 software was utilized for Colaizzi's (1978) method of data analysis.

RESULTS

Based on data analysis, perceptions of transitional care, the influencing factors for transitional care and the recommendations for improving transitional care were obtained.

CONCLUSION

To ensure the continuity of care and improve patient safety during the period from intensive care unit to a general ward in China, we should clarify the expectation for the content of intensive care unit transitional care services, establish the transitional nursing team, guide nursing work, standardize the handover mode and process from intensive care unit to the general ward, promote the communication and coordination of health care professionals and improve the transitional nursing security system from the perspective of institutional level.

IMPLICATIONS FOR NURSING MANAGEMENT

This study can be used as a guide to help health care professionals provide a reference for the comprehensive development of transitional care services and the formulation of targeted intervention measures during the transfer from intensive care unit to a general ward in China.

Quality Improvement in Neurocritical Care: a Review of the Current Landscape and Best Practices

Purpose of Review

The field of neurocritical care (NCC) has grown such that there is now a substantial body of literature on quality improvement specific to NCC. This review will discuss the development of this literature over time and highlight current best practices with practical tips for providers.

Recent Findings

There is tremendous variability in patient care models for NCC patients, despite evidence showing that certain structural elements are associated with better outcomes. There now also exist evidence-based recommendations for neurocritical care unit (NCCU) structure and processes, as well as NCC-specific performance measure (PM) sets; however, awareness of these is variable among care providers. The evidence-based literature on NCC structure, staffing, training, standardized order sets and bundles, transitions of care including handoff, prevention of bounce backs, bed flow optimization, and inter-hospital transfers is growing and offers many examples of successful performance improvement initiatives in NCCUs.

Summary

NCC providers care for patients with life-threatening conditions like intracerebral and subarachnoid hemorrhages, ischemic stroke, and traumatic brain injury, which are associated with high morbidity, complexity of treatment, and cost. Quality improvement initiatives have been successful in improving many aspects of NCC patient care, and NCC providers should continue to update and standardize their practices with consideration of this data. More research is needed to continue to identify high-risk and high-cost NCCU structures and processes and strategies to optimize them, validate current NCC PMs, and encourage clinical adoption of those that prove to be associated with improved outcomes.

Frequency, Risk Factors, and Outcomes of Unplanned Readmission to the Neurological Intensive Care Unit after Spontaneous Intracerebral Hemorrhage

BACKGROUND

Unplanned readmission to the neurological intensive care unit (ICU) is an underinvestigated topic in patients admitted after spontaneous intracerebral hemorrhage (ICH). The purpose of this study is to investigate the frequency, clinical risk factors, and outcome of bounce back to the neurological ICU in a cohort of patients admitted after ICH.

METHODS

This is a retrospective observational study inspecting bounce back to the neurological ICU in patients admitted with spontaneous ICH over an 8-year period. For each patient, demographics, medical history, clinical presentation, length of ICU stay, unplanned readmission to neurological ICU, cause of readmission, and mortality were reviewed. Bounce back to the neurological ICU was defined as an unplanned readmission to the neurological ICU from a general floor service during the same hospitalization. A multivariable analysis was used to define independent variables associated with bounce back to the neurological ICU as well as association between bounce back to the neurological ICU and mortality. The significance level was set at p < 0.05.

RESULTS

A total of 221 patients were included. Among those, 20 (9%) had a bounce back to the neurological ICU. Respiratory complications (n = 11) was the most common reason for bounce back to the neurological ICU, followed by neurological (n = 5) and cardiological (n = 4) complications. In a multivariable logistic regression, location of hemorrhage in the basal ganglia (odds ratio [OR]: 3.0, 95% confidence interval [CI]: 1.0-8.9, p = 0.03) and dysphagia at the time of transfer (OR: 3.9, 95% CI: 1.0-15.4, p = 0.04) were significantly associated with bounce back to the neurological ICU. After we controlled for ICH score, readmission to the ICU was also independently associated with higher mortality (OR: 14.1, 95% CI: 2.8-71.7, p < 0.01).

CONCLUSIONS

Bounce back to the neurological ICU is not an infrequent complication in patients with spontaneous ICH and is associated with higher hospital length of stay and mortality. We identified relevant and potentially modifiable risk factors associated with bounce back to the neurological ICU. Future prospective studies are necessary to develop patient-centered strategies that may improve transition from the neurological ICU to the general floor.

Quality Improvement Metrics and Methods for Neurohospitalists

Measurement of clinical performance is largely driven by the requirements of the Centers for Medicare and Medicaid Services and accrediting bodies like The Joint Commission. Performance measures include length of stay, readmission rate, mortality rate, hospital-acquired complications, and stroke core measures. Hospital rankings also depend heavily on quality and patient safety indicators. Becoming facile with these measures can aid neurohospitalists in understanding their value and garnering resources to support improvement projects. Neurohospitalists can apply a structured A3-based method to define a clinical problem, perform systematic analysis, then design and test solutions to drive improved outcomes for patients with neurologic disease.

Standardized Transfer Process for a Neurointensive Care Unit and Assessment of Patient Bounceback

Background

Patients who require readmission to an intensive care unit (ICU) after transfer to a lower level of care (“bounceback”) suffer from increased mortality and longer hospital stays. We aimed to create a multifaceted standardized transfer process for patients moving from the neurointensive care unit (neuro-ICU) to a lower level of care. We hypothesized that this process would lead to improvement in provider-rated safety and a decreased rate of bouncebacks to the neuro-ICU after transfer.

Methods

The study took place at the Hospital of the University of Pennsylvania from October 2018 to October 2020. A standardized five-step transfer process was created and implemented for transferring patients from the neuro-ICU to a lower level of care. Patient care providers completed a survey before and after implementation of the protocol to assess a variety of components related to safety concerns when transferring patients. The rate of bouncebacks pre and post intervention was calculated by using a two-sample Wilcoxon rank-sum test, and disposition at discharge was calculated by using Fisher’s exact test.

Results

Of the 1176 total patient transfers out of the neuro-ICU, 29 patients bounced back within 48 h. The average age of patients who bounced back was 63.3 years old, with a similar distribution among men and women. The most common reason for bounceback was respiratory distress, followed by cardiac arrhythmia, stroke, and sepsis. Implementation of the standardized process led to a decrease in provider-rated concern of overall safety (5 to 3, p = 0.008). There was improvement in transfer delays due to bed availability (3 to 4.5, p = 0.020), identification of high-risk patients (5 to 6, p = 0.021), patient assignment to the appropriate level of care (5 to 6, p = 0.019), and use of the electronic medical record handoff indicator (5 to 6, p = 0.003). There was no statistically significant difference in terms of patient bounceback rate after implementation of the process (2.4% vs. 2.5%, p = 1.00) or patient disposition at discharge (p = 0.553).

Conclusions

Patients who bounceback to the neuro-ICU within 48 h had an increased length of hospital stay, had an increased length of ICU stay, and were more likely to be intubated for more than 96 h. Implementation of a standardized five-step transfer process from the neuro-ICU to a lower level of care resulted in improvement in multiple provider-rated safety outcomes and identification of high-risk patients but led to no difference in the patient bounceback rate or patient disposition at discharge.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12028-021-01385-z.

Novel Score for Stratifying Risk of Critical Care Needs in Patients With Intracerebral Hemorrhage

OBJECTIVE

To develop a risk prediction score identifying patients with intracerebral hemorrhage (ICH) at low risk for critical care.

METHODS

We retrospectively analyzed data of 451 patients with ICH between 2010 and 2018. The sample was randomly divided into a development and a validation cohort. Logistic regression was used to develop a risk score by weighting independent predictors of intensive care unit (ICU) needs according to strength of association. The risk score was tested in the validation cohort and externally validated in a dataset from another institution.

RESULTS

The rate of ICU interventions was 80.3%. Systolic blood pressure (SBP), Glasgow Coma Scale (GCS) score, intraventricular hemorrhage (IVH), and ICH volume were independent predictors of critical care, resulting in the following point assignments for the Intensive Care Triaging in Spontaneous Intracerebral Hemorrhage (INTRINSIC) score: SBP 160 to 190 mm Hg (1 point), SBP >190 mm Hg (3 points); GCS 8 to 13 (1 point), GCS <8 (3 points); ICH volume 16 to 40 cm3 (1 point), ICH volume >40 cm3 (2 points); and presence of IVH (1 point), with values ranging between 0 and 9. Among patients with a score of 0 and no ICU needs during their emergency department stay, 93.6% remained without critical care needs. In an external validation cohort of patients with ICH, the INTRINSIC score achieved an area under the receiver operating characteristic curve of 0.823 (95% confidence interval 0.782-0.863). A score <2 predicted the absence of critical care needs with 48.5% sensitivity and 88.5% specificity, and a score <3 predicted the absence of critical care needs with 61.7% sensitivity and 83.0% specificity.

CONCLUSION

The INTRINSIC score identifies patients with ICH who are at low risk for critical care interventions.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that the INTRINSIC score identifies patients with ICH at low risk for critical care interventions.

After-Hours/Nighttime Transfers Out of the Intensive Care Unit and Patient Outcomes: A Systematic Review and Meta-Analysis

PURPOSE

We evaluated the effects of after-hours/nighttime patient transfers out of the ICU on patient outcomes, by performing a systematic review and meta-analysis (PROSPERO CRD 42017074082).

DATA SOURCES

MEDLINE, PubMed, EMBASE, Google Scholar, CINAHL, and the Cochrane Library from 1987-November 2019. Conference abstracts from the Society of Critical Care Medicine, American Thoracic Society, CHEST, Critical Care Canada Forum, and European Society of Intensive Care Medicine from 2011-2019.

DATA EXTRACTION

Observational or randomized studies of adult ICU patients were selected if they compared after-hours transfer out of the ICU to daytime transfer on patient outcomes. Case reports, case series, letters, and reviews were excluded. Study year, country, design, co-variates for adjustment, definitions of after-hours, mortality rates, ICU readmission rates, and hospital length of stay (LOS) were extracted.

DATA SYNTHESIS

We identified 3,398 studies. Thirty-one observational studies (1,418,924 patients) were selected for the systematic review and meta-analysis. Included studies had varying definitions of after-hours, with the after-hours period starting anytime between 16:00-22:00 and ending between 06:00-09:00. Approximately 16% of transfers occurred after-hours. After-hours transfers were associated with increased in-hospital mortality for both unadjusted (odds ratio [OR] 1.51, 95% confidence interval [CI] 1.30-1.75, I² = 96%, number of studies [n] = 26, P < 0.001, low certainty) and adjusted (OR 1.32, 95% CI 1.25-1.38, I ² = 33%, n = 10, P < 0.001, low certainty) data, compared to daytime transfers. They were also associated with increased ICU readmission (pooled unadjusted OR 1.28, 95% CI 1.18-1.38, I² = 85%, n = 17, P < 0.001, low certainty) and longer hospital LOS (standardized mean difference 0.13, 95% CI 0.09-0.18, I ² = 93%, n = 9, P < 0.001, low certainty), compared to daytime transfers.

CONCLUSIONS

After-hours transfers out of the ICU are associated with increased in-hospital mortality, ICU readmission, and hospital LOS, across many settings. While the certainty of evidence is low, future research is needed to reduce the number and effects of after-hours transfers.

Communicating Neurocritical Illness: The Anatomy of Misunderstanding

We talk, text, email all day. Do we perceive things correctly? Do we need to improve the way we communicate? It is a truism that providing insufficient information about a patient results in delays and errors in management. How can we best communicate urgent triage or urgent changes in the patient condition? There is no substitute for a face-to-face conversation but what would the receiving end want to know? One starting point for those practicing acute neurology and neurocritical care is a new mnemonic TELL ME (Time course, Essence, Laboratory, Life-sustaining interventions, Management, Expectation), which will assist physicians in standardizing their communication skills before they start a conversation or pick up a phone. These include knowing the time course (new and "out of the blue" or ongoing for some time); extracting the essentials (eliminating all irrelevancies); communicating what tests are known and pending (computerized tomography and laboratory); relaying how much critical support will be needed (secretion burden, intubation, vasopressors); knowing fully which emergency drugs have been administered (e.g., mannitol, antiepileptics, tranexamic acid), when transport is anticipated, and what can be expected in the following hours. Perfect orchestration in communication may be too much to ask, but we neurointensivists strive to convey information accurately and completely. Communication must be taught, learned, and practiced. This article provides guiding principles for a number of scenarios involving communication inside and outside the hospital.

Two latent classes of diagnostic and treatment procedures among traumatic brain injury inpatients

To characterize latent classes of diagnostic and/or treatment procedures among hospitalized U.S. adults, 18-64 years, with primary diagnosis of TBI from 2004-2014 Nationwide Inpatient Samples, latent class analysis (LCA) was applied to 10 procedure groups and differences between latent classes on injury, patient, hospital and healthcare utilization outcome characteristics were modeled using multivariable regression. Using 266,586 eligible records, LCA resulted in two classes of hospitalizations, namely, class I (n = 217,988) (mostly non-surgical) and class II (n = 48,598) (mostly surgical). Whereas orthopedic procedures were equally likely among latent classes, skin-related, physical medicine and rehabilitation procedures as well as behavioral health procedures were more likely among class I, and other types of procedures were more likely among class II. Class II patients were more likely to have moderate-to-severe TBI, to be admitted on weekends, to urban, medium-to-large hospitals in Midwestern, Southern or Western regions, and less likely to be > 30 years, female or non-White. Class II patients were also less likely to be discharged home and necessitated longer hospital stays and greater hospitalization charges. Surgery appears to distinguish two classes of hospitalized patients with TBI with divergent healthcare needs, informing the planning of healthcare services in this target population.

Neuro R2 score: Predicting high-risk neurologic readmissions within 30 days

OBJECTIVE

To evaluate clinical and demographic factors of patients with neurologic disorders to determine which patient characteristics are significant for predicting 30-day hospital readmissions to develop a readmission risk predictor specific to patients with neurologic disorders.

METHODS

We performed a retrospective single-center chart review for all patients admitted to the Department of Neurology or neurologic intensive care unit from January 1, 2013, to December 31, 2017. Clinical and demographic factors were analyzed to determine the association with readmission. Multivariable logistic regression analysis was performed and validated to develop a simple tool (Neuro R² score) for predicting patients with neurologic disorders at high risk for hospital readmission.

RESULTS

After removal of planned readmissions and patients who died in the hospital, the records of 4,876 patients with 314 (6.4%) readmission events were analyzed. The strongest predictors for readmission were Charlson disease count (odds ratio [OR] 1.20, 95% confidence interval [CI] 1.06-1.35, p = 0.005), urgent or emergent admission (OR 1.50, 95% CI 1.04-2.17, p = 0.031), discharge to rehabilitation (OR 1.66, 95% CI 1.16-2.35, p = 0.005), cancer (OR 1.70, 95% CI 1.15-2.50, p = 0.007), brain tumor (OR 1.82, 95% CI 1.08-3.09, p < 0.03), cerebrovascular disease (OR 2.18, 95% CI 1.53-3.11, p < 0.001), and discharge to skilled nursing facility (OR 2.43, 95% CI 1.65-3.57, p < 0.001).

CONCLUSIONS

The Neuro R² score was developed to predict readmission risk, specifically in patients with neurologic disorders. Future research could include further validation of this readmission risk tool and strategies to reduce readmission in patients with the highest risk.

Transitions of Care for Patients with Neurologic Diagnoses Transition from the Intensive Care Unit to the Floor

Transition of care from the intensive care unit to acute care units after critical neurologic injury includes the consideration of a variety of factors to ensure safe and effective care, and promote ongoing neurologic recovery. Assessment of effectiveness of deescalation techniques, agitation management, and risk factor mitigation are important strategies to enhance the success of transitions. Clear and consistent interdisciplinary communication between teams during hand-off between units is imperative to decrease the risk of complications and errors, and to streamline discharge processes.

Evaluation of Hospital-wide Readmission Risk Calculator to Predict 30-Day Readmission in Neurocritical Care Patients

BACKGROUND AND PURPOSE

Thirty-day hospital readmissions have been shown to be a measure of quality and result in higher mortality and increased costs. Readmissions are a target for hospitals and payers; thus, several centers have developed predictive readmission scores to identify high-risk patients. The purpose of this study was to evaluate the current hospital-wide readmission risk calculator and the ability of this tool to predict 30-day readmissions in the neurocritical care population.

METHODS

A retrospective chart review was performed that included 340 consecutive patients admitted to our neuroscience critical care unit. Data including readmission scores, reason for admission, length of stay, and whether they were readmitted were recorded.

RESULTS

After removing patients without readmission scores or who died at the end of the original admission, the records of N = 279 patients were analyzed. Patients were more likely to be readmitted if they were initially emergently hospitalized or if there was a history of malignancy. Readmitted patients had a longer original hospital length of stay. Furthermore, 65.8% of the patients who were given a "low risk" for readmission were readmitted within 30 days.

CONCLUSIONS

This small set of data in a specific patient population found that the current risk prediction score was inaccurate in predicting readmission in the neuroscience intensive care unit population. Further evaluation is needed of a larger patient population to generalize these results for all neuroscience intensive care unit patients. To design an accurate readmission risk tool, centers should create unique readmission scores based on less heterogeneous patient populations.