The characteristics of very short stay ICU admissions and implications for optimizing ICU resource utilization: the Saudi experience

Characteristics of victims of trauma requiring invasive mechanical ventilation with a short stay in critical care

BACKGROUND

Many patients who are admitted to the intensive care unit (ICU) have needs which rapidly resolve and are discharged alive within 24 h. We sought to characterize the outcomes of critically ill trauma victims at our institution with a short stay in the ICU.

METHODS

We conducted a retrospective cohort study of all critically ill adult trauma victims presenting to our ED between January 1st, 2011 and December 31st, 2019. We included patients who were endotracheally intubated in either the prehospital setting or the ED and were admitted either to the operating room (OR), angiography suite, or ICU. Our primary outcome was the proportion of patients who were discharged alive from the ICU within 24 h.

RESULTS

We included 3869 patients meeting the criteria above who were alive at 24 h. This population was 78% male with a median age of 40 and 76% of patients suffered from blunt trauma. The median injury severity score (ISS) of the group was 21 [inter-quartile range (IQR) 11-30]. In-hospital mortality amongst the group was 12%. 17% of the group were discharged alive from the ICU within 24 h. Thirty-four percent of the group had an ISS ≤ 15. Of the group which left the ICU alive within 24 h, six patients (0.9%) died in the hospital, 2 % of patients were re-admitted to an ICU, and 0.6% of patients required re-intubation.

CONCLUSIONS

We found that 17% of patients who were intubated in the prehospital setting or emergency department and subsequently hospitalized were discharged alive from the ICU within 24 h.

Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan's largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development

OBJECTIVE

While clinical Artificial Intelligence (cAI) mortality prediction models and relevant studies have increased, limitations including the lack of external validation studies and inadequate model calibration leading to decreased overall accuracy have been observed. To combat this, we developed and evaluated a novel deep neural network (DNN) and a validation framework to promote transparent cAI development.

METHODS

Data from Japan's largest ICU database was used to develop the DNN model, predicting in-hospital mortality including ICU and post-ICU mortality by days since ICU discharge. The most important variables to the model were extracted with SHapley Additive exPlanations (SHAP) to examine the DNN's efficacy as well as develop models that were also externally validated.

MAIN RESULTS

The area under the receiver operating characteristic curve (AUC) for predicting ICU mortality was 0.94 [0.93-0.95], and 0.91 [0.90-0.92] for in-hospital mortality, ranging between 0.91-0.95 throughout one year since ICU discharge. An external validation using only the top 20 variables resulted with higher AUCs than traditional severity scores.

CONCLUSIONS

Our DNN model consistently generated AUCs between 0.91-0.95 regardless of days since ICU discharge. The 20 most important variables to our DNN, also generated higher AUCs than traditional severity scores regardless of days since ICU discharge. To our knowledge, this is the first study that predicts ICU and in-hospital mortality using cAI by post-ICU discharge days up to over a year. This finding could contribute to increased transparency on cAI applications.

ICU-Managed Patients' Epidemiology, Characteristics, and Outcomes: A Retrospective Single-Center Study

Background

Resources are limited, and it is exceedingly difficult to provide intensive care in developing nations. In Somalia, intensive care unit (ICU) care was introduced only a few years ago.

Purpose

In this study, we aimed to determine the epidemiology, characteristics, and outcome of ICU-managed patients in a tertiary hospital in Mogadishu.

Methods

We retrospectively evaluated the files of 1082 patients admitted to our ICU during the year 2021.

Results

The majority (39.7%) of the patients were adults (aged between 20 and 39 years), and 67.8% were male patients. The median ICU length of stay was three days (IQR = 5 days), and nonsurvivors had shorter stays, one day. The mortality rate was 45.1%. The demand for critical care services in low-income countries is high.

Conclusion

The country has a very low ICU bed capacity. Critical care remains a neglected area of health service delivery in this setting, with large numbers of patients with potentially treatable conditions not having access to such services.

Association between Mortality and Sequential Organ Failure Assessment Score during a Short Stay in the Intensive Care Unit after Non-Cardiac Surgery

Background: The sequential organ failure assessment (SOFA) score has been validated in various clinical situations. However, it has not been investigated during a short stay in the intensive care unit (ICU). This study aimed to evaluate the association between the SOFA score and outcomes in patients who were monitored for less than one day after non-cardiac surgery. Methods: From a total of 203,787 consecutive adult patients who underwent non-cardiac surgery between January 2011 and June 2019, we selected 17,714 who were transferred to the ICU immediately after surgery and stayed for less than 24 h. Patients were divided according to quartile value and change between the initial and follow-up levels of SOFA score. Results: Three-year mortality tended to increase with a higher initial SOFA score (11.7%, 11.8%, 15.1%, and 17.8%, respectively). The patients were divided according to changes in the SOFA score at the midnight postoperative follow-up check: 16,176 (91.3%) in the stable group and 1538 (8.7%) in the worsened group. The worsened group showed significantly higher three-year mortality and complications (13.2% vs. 18.6%; HR [hazard ratio]: 1.236; 95% CI [confidence interval]: 1.108−1.402; p ≤ 0.0021 for three-year mortality and 3.8% vs. 9.1%; HR: 2.13; 95% CI: 1.73−2.60; p < 0.001 for acute kidney injury). Conclusions: The SOFA score during a short stay in the ICU after non-cardiac surgery showed an association with mortality. The change in SOFA score may need to be considered at discharge from the ICU.

A retrospective cohort study of short-stay admissions to the medical intensive care unit: Defining patient characteristics and critical care resource utilization

Background

Little is known about the mortality and utilization outcomes of short-stay intensive care unit (ICU) patients who require <24 h of critical care. We aimed to define characteristics and outcomes of short-stay ICU patients whose need for ICU level-of-care is ≤24 h compared to nonshort-stay patients.

Methods

Single-center retrospective cohort study of patients admitted to the medical ICU at an academic tertiary care center in 2019. Fisher's exact test or Chi-square for descriptive categorical variables, t-test for continuous variables, and Mann-Whitney two-sample test for length of stay (LOS) outcomes.

Results

Of 819 patients, 206 (25.2%) were short-stay compared to 613 (74.8%) nonshort-stay. The severity of illness as measured by the Mortality Probability Model-III was significantly lower among short-stay compared to nonshort-stay patients (P = 0.0001). Most short-stay patients were admitted for hemodynamic monitoring not requiring vasoactive medications (77, 37.4%). Thirty-six (17.5%) of the short-stay cohort met Society of Critical Care Medicine's guidelines for ICU admission. Nonfull-ICU LOS, or time spent waiting for transfer out to a non-ICU bed, was similar between the two groups. Hospital mortality was lower among short-stay patients compared to nonshort-stay patients (P = 0.01).

Conclusions

Despite their lower illness severity and fewer ICU-level care needs, short-stay patients spend an equally substantial amount of time occupying an ICU bed while waiting for a floor bed as nonshort-stay patients. Further investigation into the factors influencing ICU triage of these subacute patients and contributors to system inefficiencies prohibiting their timely transfer may improve ICU resource allocation, hospital throughput, and patient outcomes.

Assessment of the intermediate care unit triage system

BACKGROUND

An important critique with respect to the utilization of intermediate care units (IMCU) is that they potentially admit patients who would otherwise be cared for on the regular ward. This would lead to an undesired waste of critical care resources. This article aims to (1) describe the caseload at the IMCU and (2) to assess the triage system at the IMCU to determine potentially unnecessary admissions.

METHODS

This cohort study included all admissions at the mixed-surgical IMCU from 2001 to 2015. The Therapeutic Intervention Scoring System-28 (TISS-28) was prospectively collected for all admissions to describe the caseload at the IMCU and to identify medical criteria for admission. These were combined with logistical criteria to assess the IMCU triage system.

RESULTS

A total of 8816 admissions were included in the study. The average TISS-28 was 20.19 (95% CI 18.05 to 22.33), corresponding with 3.57 (95% CI 3.19 to 3.94) hours of direct patient-related work per patient per nursing shift. Over time, this increased by an average of 0.27 points/year (p<0.001). Of all admissions, 6539 (74.2%) were medically considered to be justly admitted, and 7093 (80.4%) were logistically considered to be justly admitted. With these criteria combined, a total of 8324 (94.4%) were correctly admitted.

DISCUSSION

Most admissions to the IMCU are medically and/or logistically necessary, as the majority of admitted patients demand a higher level of nursing care than available on the general ward. Continuous triage is thereby essential. These findings support further utilization of the IMCU in our current healthcare system and has important implications for IMCU-related management decisions.

LEVEL OF EVIDENCE

Level VI.

Characteristics of Short-stay Critical Care Admissions From Emergency Departments in Maryland

OBJECTIVES

Critical care is an expensive and limited resource, and short-stay critical care admissions may be treated in alternate, less costly settings. This study objective was to determine the proportion of critical care admissions with a short critical care length of stay (LOS) and identify the clinical characteristics and diagnoses associated with high and low rates of short-stay critical care admissions.

METHODS

This study was a secondary analysis of the 2011 Maryland State Inpatient Database. The study included adult emergency department (ED) visits admitted to a critical care unit. We compared clinical data and discharge diagnoses for short- (≤1 day) versus longer- (≥2 days) stay critical care admissions.

RESULTS

A total of 30,212 critical care admissions were eligible, of which 11,494 (38.0%) were short stay. There were significant differences in age, insurance, and comorbidities between the short-stay and the longer-stay critical care admissions. Of short-stay critical care admissions, 3,404 (29.6%) also had a 1-day overall hospital LOS. The diagnoses with the highest proportion of short-stay critical care admissions were nonspecific chest pain (87.9%), syncope (70.6%), and transient cerebral ischemia (67.6%) and the lowest proportion were respiratory failure (17.9%), sepsis (19.4%), and aspiration pneumonitis (19.8%).

CONCLUSIONS

Over one-third of critical care admissions were short stay. Alternate strategies to manage these patients, including ED-based critical care units or other venues of inpatient care, may be more cost-efficient for selected patients.

Does intermediate care improve patient outcomes or reduce costs?

ICUs are an essential but expensive part of all modern hospitals. With increasingly limited healthcare funding, methods to reduce expenditure without negatively influencing patient outcomes are, therefore, of interest. One possible solution has been the development of ‘intermediate care units’, which provide more intensive monitoring and patient management with higher nurse:patient ratios than the general ward but less than is offered in the ICU. However, although such units have been introduced in many hospitals, there is relatively little published, especially prospective, evidence to support the benefits of this approach on costs or patient outcomes. We review the available data and suggest that, where possible, a larger unit with combined intermediate care and intensive care beds in one location may be preferable in terms of greater flexibility and efficiency.

Is the SAPS II score valid in surgical intensive care unit patients?

AIMS AND OBJECTIVES

We investigated the performance of the simplified acute physiology score II (SAPS II) in a large cohort of surgical intensive care unit (ICU) patients and tested the hypothesis that customization of the score would improve the uniformity of fit in subgroups of surgical ICU patients.

METHODS

Retrospective analysis of prospectively collected data from all 12,938 patients admitted to a postoperative ICU between January 2004 and January 2009. Probabilities of hospital death were calculated for original and customized (C1-SAPS II and C2-SAPS II) scores. A priori subgroups were defined according to age, probability of death according to the SAPS II score, ICU length of stay (LOS), surgical procedures and type of admission.

RESULTS

The median ICU LOS was 1 (1-3) day. ICU and hospital mortality rates were 5.8% and 10.3%, respectively. Discrimination of the SAPS II was moderate [area under receiver operating characteristic curve (aROC) = 0.76 (0.75-0.78)], but calibration was poor. This model markedly overestimated hospital mortality rates [standardized mortality rate: 0.35 (0.33-0.37)]. First-level customization (C1-SAPS II) did not improve discrimination in the whole cohort or the subgroups, but calibration improved in some subgroups. Second-level customization (C2-SAPS II) improved discrimination in the whole cohort [aROC = 0.82 (0.79-0.85)] and most of the subgroups (aROC range 0.65-86). Calibration in this model (C2-SAPS II) improved in the whole cohort and in subgroups except in patients with ICU LOS 4-14 days and those undergoing neuro- or gastrointestinal surgery.

CONCLUSIONS

In this large cohort of surgical ICU patients, performance of the original SAPS II model was generally poor. Although second-level customization improved discrimination and calibration in the whole cohort and most of the subgroups, it failed to simultaneously improve calibration in the subgroups stratified according to the type of surgery, age or ICU LOS.

Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV

BACKGROUND

To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models.

METHODS

Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models.

RESULTS

The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p <or= 0.05) for three, two, and six deciles using APACHE IVrecal, MPM(0) III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations.

CONCLUSIONS

APACHE IV and MPM(0) III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM(0) III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

The impact of a neurointensivist-led team on a semiclosed neurosciences intensive care unit*

OBJECTIVE

To evaluate the impact of a newly appointed neurointensivist on neurosciences intensive care unit (NICU) patient outcomes and quality of care variables.

DESIGN

Observational cohort with historical controls.

SETTING

Ten-bed neurointensive care unit in tertiary university hospital.

PATIENTS

Mortality, length of stay (LOS), and discharge disposition of all patients admitted to the NICU were compared between two 19-month periods, before and after the appointment of a neurointensivist. Data regarding these patients were collected using the hospital database and the University Hospitals Consortium database. Individual patient medical records were reviewed for major complications and important prognostic variable documentation.

INTERVENTIONS

Appointment of a neurointensivist.

MEASUREMENTS AND MAIN RESULTS

We analyzed 1,087 patients before and 1,279 after the neurointensivist's appointment. The unadjusted in-hospital mortality decreased from 10.1% in the before to 9.1% in the after period (95% confidence interval, -1.3 to 3%, relative mortality reduction of 9.9%), but this decrease was significantly different than the expected increase of 1.4% in University Hospitals Consortium mortality during the same period (p = .048). The unadjusted mortality in the NICU decreased from 8% to 6.3% (95% confidence interval, -0.5 to 4, relative mortality reduction 21%) and mean NICU LOS from 3.5 to 2.9 days (95% confidence interval, 0.2 to 0.9, relative NICU LOS reduction 17%). A significant 42% reduction of the risk of death during the first 3 days of NICU admission (p = .003) and a 12% greater risk for NICU discharge (p = .02) were found in the after period in multivariate proportional hazard models. Discharge home increased from 51.7% in the before to 59.7% in the after period (95% confidence interval, 4 to 12, relative increase of 15%) and discharge to a nursing home decreased from 8.1% to 6.8% (95% confidence interval, -1 to 4, relative decrease of 16%). Although a higher total number of complications occurred in the after period, fewer of them occurred in the NICU (odds ratio, 0.2; 95% confidence interval, 0.08 to 0.54, p = .001); this may possibly be due to the better documentation by the NICU team in the after period.

CONCLUSIONS

The institution of a neurointensivist-led team model was associated with an independent positive impact on patient outcomes, including a lower intensive care unit mortality, LOS, and discharge to a skilled nursing facility and a higher discharge home.