Critical care medicine in the United States 2000-2005: an analysis of bed numbers, occupancy rates, payer mix, and costs

Estimated Cost-Effectiveness of Implementing a Statewide Tranexamic Acid Protocol for the Management of Suspected Hemorrhage in the Prehospital Setting

Introduction: Hemorrhage is responsible for up to 40% of all traumatic deaths. The seminal CRASH-2 trial demonstrated a reduction in overall mortality following early tranexamic acid (TXA) administration to bleeding trauma patients. Following publication of the trial results, TXA has been incorporated into many prehospital trauma protocols. However, the cost-effectiveness of widespread TXA adoption by EMS is unknown.Objective: To estimate the cost-effectiveness of statewide implementation of a TXA protocol.Methods: The North Carolina Trauma Registry was queried to identify potential TXA patients using the a priori criteria of age ≥18 years, suspected hemorrhage, penetrating or blunt injury, and prehospital blood pressure <90 mmHg and heart rate >110 bpm. Using life tables adjusted for age, sex, and race, and the absolute risk reductions in mortality with early TXA administration reported in the literature, the life-years gained were calculated for each potential life saved. Implementation costs consisted of initial stocking, training, and replacement costs. Projected reduction in hospitalization costs were based on estimates reported in the literature. Economic analyses were conducted from societal and state EMS system perspectives. To assess the robustness of the model, univariate and bivariate sensitivity analyses were performed on selected input variables.Results: Based on the TXA inclusionary criteria, 159 patients could potentially receive TXA per year. In the base-case scenario with a projected absolute mortality reduction of 3%, an additional 4.8 lives per year in NC would be saved, with an estimated 191 total life-years gained. The statewide implementation and operation cost was $305,122 in year 1, and continued operating costs were $6,042 in years 2 and 3, yielding a cost per life saved of $63,967 in year 1 and $1,267 in years 2 and 3. The cost per life-year gained was $1,595 in year 1 and $32 in years 2 and 3. Annual hospitalization costs would potentially be reduced by $1,828,072.Conclusion: Previous studies have demonstrated the clinical effectiveness of early TXA administration to patients with hemorrhage. Our modeling of the financial implications and clinical benefits of implementing a statewide TXA protocol suggests that prehospital TXA is a cost-effective treatment.

Development and validation of a deep learning model to predict the survival of patients in ICU

BACKGROUND

Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared with traditional algorithms.

METHODS

Variables from the Early Warning Score, Sequential Organ Failure Assessment Score, Simplified Acute Physiology Score II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and APACHE IV models were selected for model development. The Cox regression, random survival forest (RSF), and DL methods were used to develop prediction models for the survival probability of ICU patients. The prediction performance was independently evaluated in the MIMIC-III Clinical Database (MIMIC-III), the eICU Collaborative Research Database (eICU), and Shanghai Pulmonary Hospital Database (SPH).

RESULTS

Forty variables were collected in total for model development. 83 943 participants from 3 databases were included in the study. The New-DL model accurately stratified patients into different survival probability groups with a C-index of >0.7 in the MIMIC-III, eICU, and SPH, performing better than the other models. The calibration curves of the models at 3 and 10 days indicated that the prediction performance was good. A user-friendly interface was developed to enable the model's convenience.

CONCLUSIONS

Compared with traditional algorithms, DL algorithms are more accurate in predicting the survival probability during ICU hospitalization. This novel model can provide reliable, individualized survival probability prediction.

Evaluation of a Standardized Protocol for Medical Management of Uncomplicated Acute Type B Aortic Dissection

OBJECTIVES

The goals of medical management for uncomplicated acute type B aortic dissection are to prevent expansion of the false lumen and malperfusion syndrome. This is accomplished with antihypertensive agents, but medication selection and titration are typically provider dependent. Given the paucity of data on evidence-based management of this population, we hypothesized that a standardized type B aortic dissection medical management protocol would reduce resource utilization and costs, without compromising patient outcomes.

METHODS

A multidisciplinary team developed a goal-directed protocol to standardize the medical management of uncomplicated acute type B aortic dissection, with an emphasis on early initiation of oral medications, weaning of anti-hypertensive infusions and frequent assessment for de-escalation of care. Implementation was in April 2018. A retrospective review of acute type B aortic dissection patients presenting to our institution from April 2016- April 2020 was performed. Patients requiring aortic or peripheral intervention were excluded. Included patients were analyzed based on treatment before or after protocol implementation. Patient demographics, systolic blood pressure, presence of acute kidney injury at presentation, length of stay, cost metrics, and 30-day mortality were compared.

RESULTS

39 patients were included, 21 pre- and 18 post-protocol implementation. Baseline demographics, systolic blood pressure, and presence of acute kidney injury at presentation were similar between the groups. Post-protocol patients had shorter total (8.6 vs 5.5 days, p=.02) and intensive care unit (3.2 vs 1.8 days, p=.002) length of stay. The protocol was associated with significantly decreased total hospital ($38,928 vs $28,066, p=.04), total variable ($23,115 vs $15,627, p=0.02), and pharmacy ($5,094 vs $1,181, p<.001) costs, while inpatient care costs ($15,152 vs $11,467, p=.09) trended down. Post-protocol patients required fewer oral antihypertensive agents at discharge (3.8 vs 2.7, p=.005). No significant difference in 30-day mortality was observed.

CONCLUSIONS

A goal directed protocol reduces resource utilization and costs without compromising early mortality rates for patients with uncomplicated acute type B aortic dissection. Such a strategy may have broader application in medical management of acute aortic syndromes.

Effects on health-related quality of life of interventions affecting survival in critically ill patients: a systematic review

Survival has been considered the cornerstone for clinical outcome evaluation in critically ill patients admitted to intensive care unit (ICU). There is evidence that ICU survivors commonly show impairments in long-term outcomes such as quality of life (QoL) considering them as the most relevant ones. In the last years, the concept of patient-important outcomes has been introduced and increasingly reported in peer-reviewed publications. In the present systematic review, we evaluated how many randomized controlled trials (RCTs) were conducted on critically ill patients and reporting a benefit on survival reported also data on QoL. All RCTs investigating nonsurgical interventions that significantly reduced mortality in critically ill patients were searched on MEDLINE/PubMed, Scopus and Embase from inception until August 2021. In a second stage, for all the included studies, the outcome QoL was investigated. The primary outcome was to evaluate how many RCTs analyzing interventions reducing mortality reported also data on QoL. The secondary endpoint was to investigate if QoL resulted improved, worsened or not modified. Data on QoL were reported as evaluated outcome in 7 of the 239 studies (2.9%). The tools to evaluate QoL and QoL time points were heterogeneous. Four interventions showed a significant impact on QoL: Two interventions improved survival and QoL (pravastatin in subarachnoid hemorrhage, dexmedetomidine in elderly patients after noncardiac surgery), while two interventions reduced mortality but negatively influenced QoL (caloric restriction in patients with refeeding syndrome and systematic ICU admission in elderly patients). In conclusion, only a minority of RCTs in which an intervention demonstrated to affect mortality in critically ill patients reported also data on QoL. Future research in critical care should include patient-important outcomes like QoL besides mortality. Data on this topic should be collected in conformity with PROs statement and core outcome sets to guarantee quality and comparability of results.

Postoperative ICU admission following major elective surgery: A nationwide inpatient database study

BACKGROUND

Whether the routine use of the ICU after major elective surgery improves postoperative outcomes is not well established.

OBJECTIVES

To describe the association between use of postoperative ICU admission and clinical outcomes for patients undergoing major elective surgery.

DESIGN

Observational study.

SETTING

Nationwide inpatient database in Japan, July 2010 to March 2018.

PATIENTS

Patients undergoing one of 15 major elective orthopaedic, gastrointestinal, neurological, thoracic or cardiovascular surgical procedures.

INTERVENTION

ICU admission on the day of surgery. ICU was defined as a separate unit providing critical care services with around-the-clock physician staffing and nursing, the equipment necessary for critical care and a nurse-to-patient ratio at least one to two.

MAIN OUTCOME

In-hospital mortality. Patient-level and hospital-level analyses were performed.

RESULTS

Overall, 2 011 265 patients from 1524 hospitals were assessed. The cohort size ranged from 38 547 patients in 467 hospitals for surgical clipping for cerebral aneurysms to 308 952 patients in 599 hospitals for spinal fixation, laminectomy or laminoplasty. In the patient-level analyses, there were no significant mortality differences among patients undergoing the 12 major noncardiovascular surgical procedures, whereas postoperative ICU admission was associated with trends towards lower in-hospital mortality among patients undergoing coronary artery bypass grafting, risk difference -1.0% (95% CI -1.8 to -0.1) open aortic aneurysm repair, risk difference -0.6% (95% CI -1.3 to 0.1), and heart valve replacement, risk difference -0.7% (95% CI - 1.6 to 0.1). In the hospital-level analyses, similar to the results of the patient-level analyses, a higher proportion of postoperative ICU admission at hospital level was associated with trends toward lower in-hospital mortality for patients undergoing the three cardiovascular surgical procedures.

CONCLUSION

This nationwide observational study showed that postoperative ICU admission was associated with improved survival outcomes among patients undergoing three types of cardiac surgery but not among patients undergoing low-risk elective surgery.

Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database

Objective: The mortality rate of critically ill patients in ICUs is relatively high. In order to evaluate patients’ mortality risk, different scoring systems are used to help clinicians assess prognosis in ICUs, such as the Acute Physiology and Chronic Health Evaluation III (APACHE III) and the Logistic Organ Dysfunction Score (LODS). In this research, we aimed to establish and compare multiple machine learning models with physiology subscores of APACHE III—namely, the Acute Physiology Score III (APS III)—and LODS scoring systems in order to obtain better performance for ICU mortality prediction. Methods: A total number of 67,748 patients from the Medical Information Database for Intensive Care (MIMIC-IV) were enrolled, including 7055 deceased patients, and the same number of surviving patients were selected by the random downsampling technique, for a total of 14,110 patients included in the study. The enrolled patients were randomly divided into a training dataset (n = 9877) and a validation dataset (n = 4233). Fivefold cross-validation and grid search procedures were used to find and evaluate the best hyperparameters in different machine learning models. Taking the subscores of LODS and the physiology subscores that are part of the APACHE III scoring systems as input variables, four machine learning methods of XGBoost, logistic regression, support vector machine, and decision tree were used to establish ICU mortality prediction models, with AUCs as metrics. AUCs, specificity, sensitivity, positive predictive value, negative predictive value, and calibration curves were used to find the best model. Results: For the prediction of mortality risk in ICU patients, the AUC of the XGBoost model was 0.918 (95%CI, 0.915–0.922), and the AUCs of logistic regression, SVM, and decision tree were 0.872 (95%CI, 0.867–0.877), 0.872 (95%CI, 0.867–0.877), and 0.852 (95%CI, 0.847–0.857), respectively. The calibration curves of logistic regression and support vector machine performed better than the other two models in the ranges 0–40% and 70%–100%, respectively, while XGBoost performed better in the range of 40–70%. Conclusions: The mortality risk of ICU patients can be better predicted by the characteristics of the Acute Physiology Score III and the Logistic Organ Dysfunction Score with XGBoost in terms of ROC curve, sensitivity, and specificity. The XGBoost model could assist clinicians in judging in-hospital outcome of critically ill patients, especially in patients with a more uncertain survival outcome.

Current stage of the intensive care unit structure in Argentina: results from the Sociedad Argentina de Terapia Intensiva self-assessment survey of intensive care units

OBJECTIVE

To describe and compare the structure of Argentinean intensive care units that completed the "self-assessment survey of intensive care units" developed by the Sociedad Argentina de Terapia Intensiva.

METHODS

An observational crosssectional study was conducted using an online voluntary survey through the Sociedad Argentina de Terapia Intensiva member database and other social media postings. Answers received between December 2018 and July 2020 were analyzed. Descriptive statistics and nonparametric tests were used.

RESULTS

A total of 392 surveys were received, and 244 were considered for the analysis. Seventy-seven percent (187/244) belonged to adult intensive care units, and 23% (57/244) belonged to pediatric intensive care units. The overall completion rate was 76%. The sample included 2,567 ICU beds (adult: 1,981; pediatric: 586). We observed a clear concentration of intensive care units in the Central and Buenos Aires regions of Argentina. The median number of beds was 10 (interquartile range 7 - 15).The median numbers of multiparameter monitors, mechanical ventilators, and pulse oximeters were 1 per bed with no regional or intensive care unit type differences (adult versus pediatric). Although our sample showed that the pediatric intensive care units had a higher mechanical ventilation/bed ratio than the adult intensive care units, this finding was not linearly correlated.

CONCLUSION

Argentina has a notable concentration of critical care beds and better structural complexity in the Buenos Aires and Centro regions for both adult and pediatric intensive care units. In addition, a lack of accurate data reported from the intensive care unit structure and resources was observed. Further improvement opportunities are required to allocate intensive care unit resources at the institutional and regional levels.

Models of Care in Geriatric Intensive Care-A Scoping Review on the Optimal Structure of Care for Critically Ill Older Adults Admitted in an ICU

A growing proportion of critically ill patients admitted in ICUs are older adults. The need for improving care provided to older adults in critical care settings to optimize functional status and quality of life for survivors is acknowledged, but the optimal model of care remains unknown. We aimed to identify and describe reported models of care.

DATA SOURCES

We conducted a scoping review on critically ill older adults hospitalized in the ICU. Medline (PubMed), Embase (OvidSP), Cumulative Index to Nursing and Allied Health Literature (Ebsco), and Web of Science (Clarivate) were searched from inception to May 5, 2020.

STUDY SELECTION

We included original articles, published abstracts, review articles, editorials, and commentaries describing or discussing the implementation of geriatric-based models of care in critical care, step-down units, and trauma centers. The organization of care had to be described. Articles only discussing geriatric syndromes and specific interventions were not included.

DATA EXTRACTION

Full texts of included studies were obtained. We collected publication and study characteristics, structures of care, human resources used, interventions done or proposed, results, and measured outcomes. Data abstraction was done by two investigators and reconciled, and disagreements were resolved by discussion.

DATA SYNTHESIS

Our search identified 3,765 articles, and we found 19 reporting on the implementation of geriatric-based models of care in the setting of critical care. Four different models of care were identified: dedicated geriatric beds, geriatric assessment by a geriatrician, geriatric assessment without geriatrician, and a fourth model called "other approaches" including geriatric checklists, bundles of care, and incremental educational strategies. We were unable to assess the superiority of any model due to limited data.

CONCLUSIONS

Multiple models have been reported in the literature with varying degrees of resource and labor intensity. More data are required on the impact of these models, their feasibility, and cost-effectiveness.

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients

Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients' length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.

Use of patient-relevant outcome measures to assess the long-term effects of care bundles in the ICU: a scoping review protocol

INTRODUCTION

There is only moderate adherence to evidence-based practice in critical care. Care bundles can be used to increase adherence to best clinical practice. Components of bundle interventions, bundle implementation rates, barriers and facilitators of bundle implementation, and the effect of care bundles on short-term patient outcomes such as intensive care unit (ICU) mortality all appear to be regularly studied. However, over the last years, critical care research has turned towards long-term patient-relevant outcomes after discharge from the ICU. To our knowledge, there is no systematic overview on the long-term effect of care bundle implementation on patient-relevant outcomes. We present a protocol for a scoping review of the available literature on the effect of the implementation of care bundles in the ICU on long-term patient-relevant outcomes.

METHODS AND ANALYSIS

This scoping review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines and the Arksey and O'Malley framework. The recommendations of the Joanna Briggs Institute for Scoping Reviews will also be followed. A systematic literature research will be performed using electronic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, CDSR and CENTRAL). A preliminary search has been conducted on 1 September 2021, yielding 1929 entries. The main search, data extraction and charting has not been started yet. This scoping review will provide an overview of the long-term patient-relevant outcomes that have been used to assess the implementation of care bundles in the ICU. It will be the first study to summarise the long-term impact of care bundles for critically ill patients and identify research gaps to inform future research.

ETHICS AND DISSEMINATION

Due to the utilisation of already published primary studies, ethical approval is dispensable. Results of this work will be published in a peer-reviewed journal.

Recent Trends in Admission Diagnosis and Related Mortality in the Medically Critically Ill

PURPOSE

With decades of declining ICU mortality, we hypothesized that the outcomes and distribution of diseases cared for in the ICU have changed and we aimed to further characterize them.

STUDY DESIGN AND METHODS

A retrospective cohort analysis of 287,154 nonsurgical-critically ill adults, from 237 U.S. ICUs, using the manually abstracted Cerner APACHE Outcomes database from 2008 to 2016 was performed. Surgical patients, rare admission diagnoses (<100 occurrences), and low volume hospitals (<100 total admissions) were excluded. Diagnoses were distributed into mutually exclusive organ system/disease-based categories based on admission diagnosis. Multi-level mixed-effects negative binomial regression was used to assess temporal trends in admission, in-hospital mortality, and length of stay (LOS).

RESULTS

The number of ICU admissions remained unchanged (IRR 0.99, 0.98-1.003) while certain organ system/disease groups increased (toxicology [25%], hematologic/oncologic [55%] while others decreased (gastrointestinal [31%], pulmonary [24%]). Overall risk-adjusted in-hospital mortality was unchanged (IRR 0.98, 0.96-1.0004). Risk-adjusted ICU LOS (Estimate -0.06 days/year, -0.07 to -0.04) decreased. Risk-adjusted mortality varied significantly by disease.

CONCLUSION

Risk-adjusted ICU mortality rate did not change over the study period, but there was evidence of shifting disease burden across the critical care population. Our data provides useful information regarding future ICU personnel and resource needs.

A Decade of Post-Intensive Care Syndrome: A Bibliometric Network Analysis

Background and Objectives: In 2012, the umbrella term post-intensive care syndrome (PICS) was introduced to capture functional long-term impairments of survivors of critical illness. We present a bibliometric network analysis of the PICS research field. Materials and Methods: The Web of Science core database was searched for articles published in 2012 or later using ‘post-intensive care syndrome’ and variant spellings. Using VOSviewer, we computed co-authorship networks of countries, institutions, and authors, as well as keyword co-occurrence networks. We determined each country’s relative research effort and Category Normalized Citation Index over time and analyzed the 100 most-cited articles with respect to article type, country of origin, and publishing journal. Results: Our search yielded 379 articles, of which 373 were analyzed. Annual PICS research output increased from 11 (2012) to 95 articles (2020). Most PICS research originates from the US, followed by England, Australia, the Netherlands, and Germany. We found various collaborations between countries, institutions, and authors, with recent collaborative networks of English and Australian institutions. Article keywords cover aspects of cognitive, mental health, and physical impairments, and more recently, COVID-19. Only a few keywords and articles pertained to PICS prevention and treatment. Conclusions: Our analysis of Web of Science-indexed PICS articles highlights the stark increase in PICS research output in recent years, primarily originating from US- and Europe-based authors and institutions. Despite the research field’s growth, knowledge gaps with respect to PICS prevention and treatment remain.

Nomogram to Predict Intensive Care Following Gastrectomy for Gastric Cancer: A Useful Clinical Tool to Guide the Decision-Making of Intensive Care Unit Admission

BACKGROUND

We aimed to generate and validate a nomogram to predict patients most likely to require intensive care unit (ICU) admission following gastric cancer surgery to improve postoperative outcomes and optimize the allocation of medical resources.

METHODS

We retrospectively analyzed 3,468 patients who underwent gastrectomy for gastric cancer from January 2009 to June 2018. Here, 70.0% of the patients were randomly assigned to the training cohort, and 30.0% were assigned to the validation cohort. Least absolute shrinkage and selection operator (LASSO) method was performed to screen out risk factors for ICU-specific care using the training cohort. Then, based on the results of LASSO regression analysis, multivariable logistic regression analysis was performed to establish the prediction nomogram. The calibration and discrimination of the nomogram were evaluated in the training cohort and validated in the validation cohort. Finally, the clinical usefulness was determined by decision curve analysis (DCA).

RESULTS

Age, the American Society of Anesthesiologists (ASA) score, chronic pulmonary disease, heart disease, hypertension, combined organ resection, and preoperative and/or intraoperative blood transfusions were selected for the model. The concordance index (C-index) of the model was 0.843 in the training cohort and 0.831 in the validation cohort. The calibration curves of the ICU-specific care risk nomogram suggested great agreement in both training and validation cohorts. The DCA showed that the nomogram was clinically useful.

CONCLUSIONS

Age, ASA score, chronic pulmonary disease, heart disease, hypertension, combined organ resection, and preoperative and/or intraoperative blood transfusions were identified as risk factors for ICU-specific care after gastric surgery. A clinically friendly model was generated to identify those most likely to require intensive care.

Integration of an ICU Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds: A Cluster Randomized Controlled Trial (Preprint)

Background

 Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU).

Objective

i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs.

Methods

A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys.

Results

Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs.

Conclusions

 i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs.

Trial Registration

ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698

Variation in severity-adjusted resource use and outcome in intensive care units

PURPOSE

Intensive care patients have increased risk of death and their care is expensive. We investigated whether risk-adjusted mortality and resources used to achieve survivors change over time and if their variation is associated with variables related to intensive care unit (ICU) organization and structure.

METHODS

Data of 207,131 patients treated in 2008-2017 in 21 ICUs in Finland, Estonia and Switzerland were extracted from a benchmarking database. Resource use was measured using ICU length of stay, daily Therapeutic Intervention Scoring System Scores (TISS) and purchasing power parity-adjusted direct costs (2015-2017; 17 ICUs). The ratio of observed to severity-adjusted expected resource use (standardized resource use ratio; SRUR) was calculated. The number of expected survivors and the ratio of observed to expected mortality (standardized mortality ratio; SMR) was based on a mortality prediction model covering 2015-2017. Fourteen a priori variables reflecting structure and organization were used as explanatory variables for SRURs in multivariable models.

RESULTS

SMR decreased over time, whereas SRUR remained unchanged, except for decreased TISS-based SRUR. Direct costs of one ICU day, TISS score and ICU admission varied between ICUs 2.5-5-fold. Differences between individual ICUs in both SRUR and SMR were up to > 3-fold, and their evolution was highly variable, without clear association between SRUR and SMR. High patient turnover was consistently associated with low SRUR but not with SMR.

CONCLUSION

The wide and independent variation in both SMR and SRUR suggests that they should be used together to compare the performance of different ICUs or an individual ICU over time.

The long-term effects of lower versus higher oxygenation levels in adult ICU patients - protocol for a systematic review

BACKGROUND

Many organs can remain impaired after discharge from the intensive care unit (ICU) leading to temporal or permanent dysfunctions. Long-term impairments may be affected by supplemental oxygen, a common treatment in ICU, having both potential beneficial and harmful long-lasting effects. This systematic review aims to assess the long-term outcomes of lower versus higher oxygen supplementation and/or oxygenation levels in adults admitted to ICU.

METHODS

We will include trials differentiating between a lower and a higher oxygen supplementation or a lower and a higher oxygenation strategy in adults admitted to the ICU. We will search major electronic databases and trial registers for randomised clinical trials. Two authors will independently screen and select references for inclusion using Covidence and predefined data will be extracted. The methodological quality and risk of bias of included trials will be evaluated using the Cochrane Risk of Bias tool 2. Meta-analysis will be performed if two or more trials with comparable outcome measures will be included. Otherwise, a narrative description of the trials' results will be presented instead. To assess the certainty of evidence, we will create a 'Summary of findings' table containing all prespecified outcomes using the GRADE system. The protocol is submitted on the PROSPERO database (ID 223630).

CONCLUSION

No systematic reviews on the impact of oxygen treatment in the ICU on long-term outcomes, other than mortality and quality of life, have been reported yet. This systematic review will provide an overview of the current evidence and will help future research in the field.

Influence on Depression, Anxiety, and Satisfaction of the Relatives' Visit to Intensive Care Units prior to Hospital Admission for Elective Cardiac Surgery: A Randomized Clinical Trial

BACKGROUND

Intensive care units (ICUs) may produce stress on the relatives of patients that have long-term physiological and psychological implications.

OBJECTIVES

This study aimed to evaluate the effects of the relatives´ visit prior to hospital admission(s) on the patient's scheduled cardiac surgery regarding depression, anxiety, and satisfaction of the patient's family in an ICU.

METHODS

A randomized clinical trial [NCT03605420] was carried out according to the CONSORT criteria. Thirty-eight relatives of ICU patients were recruited at an ICU and randomized into study groups. Experimental group participants (n = 19) consisted of relatives who received 1 ICU visit prior to the patient's admission. Control group participants (n = 19) consisted of patients' relatives who received standard care alone. A self-report test battery, including the Impact of Event Scale-Revised (IES-R) and the Hospital Anxiety and Depression Scale (HADS), was completed by the patient's relative prior to the patient's ICU admission and again three and 90 days after ICU discharge. Furthermore, the Family Satisfaction with Care in the Intensive Care Unit (FS-ICU) and Critical Care Family Needs Inventory (CCFNI) were administered to help determine the respondents' satisfaction three days after the patient's ICU discharge.

RESULTS

Statistically significant differences in FS-ICU results were found between control and experimental groups; no statistically significant differences were found in IES-R, HADS, and CCFNI results. Thus, members in the control group were more satisfied with the time elapsed to raise their concerns (p=0.005), emotional support provided (p=0.020), quality of care (p=0.035), opportunities to express concerns and ask questions (p=0.005), and general satisfaction with the ICU's decision-making (p=0.003).

CONCLUSIONS

Relatives' satisfaction during patients' ICU admission may be impaired after their prior visit to the hospital admission. Relative's anxiety and depression scores did not seem to be significantly affected. Relatives´ visit prior to elective cardiac surgery hospital admission impaired their satisfaction in an ICU and may not be advisable for healthcare practice.

Direct Medical Costs of Tetanus, Dengue, and Sepsis Patients in an Intensive Care Unit in Vietnam

Background

Critically ill patients often require complex clinical care by highly trained staff within a specialized intensive care unit (ICU) with advanced equipment. There are currently limited data on the costs of critical care in low-and middle-income countries (LMICs). This study aims to investigate the direct-medical costs of key infectious disease (tetanus, sepsis, and dengue) patients admitted to ICU in a hospital in Ho Chi Minh City (HCMC), Vietnam, and explores how the costs and cost drivers can vary between the different diseases.

Methods

We calculated the direct medical costs for patients requiring critical care for tetanus, dengue and sepsis. Costing data (stratified into different cost categories) were extracted from the bills of patients hospitalized to the adult ICU with a dengue, sepsis and tetanus diagnosis that were enrolled in three studies conducted at the Hospital for Tropical Diseases in HCMC from January 2017 to December 2019. The costs were considered from the health sector perspective. The total sample size in this study was 342 patients.

Results

ICU care was associated with significant direct medical costs. For patients that did not require mechanical ventilation, the median total ICU cost per patient varied between US$64.40 and US$675 for the different diseases. The costs were higher for patients that required mechanical ventilation, with the median total ICU cost per patient for the different diseases varying between US$2,590 and US$4,250. The main cost drivers varied according to disease and associated severity.

Conclusion

This study demonstrates the notable cost of ICU care in Vietnam and in similar LMIC settings. Future studies are needed to further evaluate the costs and economic burden incurred by ICU patients. The data also highlight the importance of evaluating novel critical care interventions that could reduce the costs of ICU care.

Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury

Recent advances in emergency medicine and the co-ordinated delivery of trauma care mean more critically-injured patients now reach the hospital alive and survive life-saving operations. Indeed, between 2008 and 2017, the odds of surviving a major traumatic injury in the UK increased by nineteen percent. However, the improved survival rates of severely-injured patients have placed an increased burden on the healthcare system, with major trauma a common cause of intensive care unit (ICU) admissions that last ≥10 days. Improved understanding of the factors influencing patient outcomes is now urgently needed. We investigated the serum metabolomic profile of fifty-five major trauma patients across three post-injury phases: acute (days 0–4), intermediate (days 5–14) and late (days 15–112). Using ICU length of stay (LOS) as a clinical outcome, we aimed to determine whether the serum metabolome measured at days 0–4 post-injury for patients with an extended (≥10 days) ICU LOS differed from that of patients with a short (<10 days) ICU LOS. In addition, we investigated whether combining metabolomic profiles with clinical scoring systems would generate a variable that would identify patients with an extended ICU LOS with a greater degree of accuracy than models built on either variable alone. The number of metabolites unique to and shared across each time segment varied across acute, intermediate and late segments. A one-way ANOVA revealed the most variation in metabolite levels across the different time-points was for the metabolites lactate, glucose, anserine and 3-hydroxybutyrate. A total of eleven features were selected to differentiate between <10 days ICU LOS vs. >10 days ICU LOS. New Injury Severity Score (NISS), testosterone, and the metabolites cadaverine, urea, isoleucine, acetoacetate, dimethyl sulfone, syringate, creatinine, xylitol, and acetone form the integrated biomarker set. Using metabolic enrichment analysis, we found valine, leucine and isoleucine biosynthesis, glutathione metabolism, and glycine, serine and threonine metabolism were the top three pathways differentiating ICU LOS with a p < 0.05. A combined model of NISS and testosterone and all nine selected metabolites achieved an AUROC of 0.824. Differences exist in the serum metabolome of major trauma patients who subsequently experience a short or prolonged ICU LOS in the acute post-injury setting. Combining metabolomic data with anatomical scoring systems allowed us to discriminate between these two groups with a greater degree of accuracy than that of either variable alone.

PHarmacist Avoidance or Reductions in Medical Costs in CRITically Ill Adults: PHARM-CRIT Study

To comprehensively classify interventions performed by ICU clinical pharmacists and quantify cost avoidance generated through their accepted interventions.

DESIGN

A multicenter, prospective, observational study was performed between August 2018 and January 2019.

SETTING

Community hospitals and academic medical centers in the United States.

PARTICIPANTS

ICU clinical pharmacists.

INTERVENTIONS

Recommendations classified into one of 38 intervention categories (divided into six unique sections) associated with cost avoidance.

MEASUREMENTS AND MAIN RESULTS

Two-hundred fifteen ICU pharmacists at 85 centers performed 55,926 interventions during 3,148 shifts that were accepted on 27,681 adult patient days and generated $23,404,089 of cost avoidance. The quantity of accepted interventions and cost avoidance generated in six established sections was adverse drug event prevention (5,777 interventions; $5,822,539 CA), resource utilization (12,630 interventions; $4,491,318), individualization of patient care (29,284 interventions; $9,680,036 cost avoidance), prophylaxis (1,639 interventions; $1,414,465 cost avoidance), hands-on care (1,828 interventions; $1,339,621 cost avoidance), and administrative/supportive tasks (4,768 interventions; $656,110 cost avoidance). Mean cost avoidance was $418 per intervention, $845 per patient day, and $7,435 per ICU pharmacist shift. The annualized cost avoidance from an ICU pharmacist is $1,784,302. The potential monetary cost avoidance to pharmacist salary ratio was between $3.3:1 and $9.6:1.

CONCLUSIONS

Pharmacist involvement in the care of critically ill patients results in significant avoidance of healthcare costs, particularly in the areas of individualization of patient care, adverse drug event prevention, and resource utilization. The potential monetary cost avoidance to pharmacist salary ratio employing an ICU clinical pharmacist is between $3.3:1 and $9.6:1.

Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries

BACKGROUND

Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set.

METHODS

We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004-2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019.

RESULTS

Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as "high risk" for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%-96.6%) but improved specificity (67.4%-81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs.

CONCLUSIONS

The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population.

Predicting Prolonged Length of ICU Stay through Machine Learning

This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes.

The impact of patient delirium in the intensive care unit: patterns of anxiety symptoms in family caregivers

BACKGROUND

The purpose of this study was to examine the association of patient delirium in the intensive care unit (ICU) with patterns of anxiety symptoms in family caregivers when delirium was determined by clinical assessment and family-administered delirium detection.

METHODS

In this cross-sectional study, consecutive adult patients anticipated to remain in the ICU for longer than 24 h were eligible for participation given at least one present family caregiver (e.g., spouse, friend) provided informed consent (to be enrolled as a dyad) and were eligible for delirium detection (i.e., Richmond Agitation-Sedation Scale score ≥ - 3). Generalized Anxiety Disorder-7 (GAD-7) was used to assess self-reported symptoms of anxiety. Clinical assessment (Confusion Assessment Method for ICU, CAM-ICU) and family-administered delirium detection (Sour Seven) were completed once daily for up to five days.

RESULTS

We included 147 family caregivers; the mean age was 54.3 years (standard deviation [SD] 14.3 years) and 74% (n = 129) were female. Fifty (34% [95% confidence interval [CI] 26.4-42.2]) caregivers experienced clinically significant symptoms of anxiety (median GAD-7 score 16.0 [interquartile range 6]). The most prevalent symptoms of anxiety were "Feeling nervous, anxious or on edge" (96.0% [95%CI 85.2-99.0]); "Not being able to stop or control worrying" (88.0% [95%CI 75.6-94.5]; "Worrying too much about different things" and "Feeling afraid as if something awful might happen" (84.0% [95%CI 71.0-91.8], for both). Family caregivers of critically ill adults with delirium were significantly more likely to report "Worrying too much about different things" more than half of the time (CAM-ICU, Odds Ratio [OR] 2.27 [95%CI 1.04-4.91]; Sour Seven, OR 2.28 [95%CI 1.00-5.23]).

CONCLUSIONS

Family caregivers of critically ill adults with delirium frequently experience clinically significant anxiety and are significantly more likely to report frequently worrying too much about different things. Future work is needed to develop mental health interventions for the diversity of anxiety symptoms experienced by family members of critically ill patients.

TRIAL REGISTRATION

This study is registered on ClinicalTrials.gov ( https://clinicaltrials.gov/ct2/show/NCT03379129 ).

Outcome Differences Between Direct Admissions to the PICU From ED and Escalations From Floor

OBJECTIVES

To compare the outcomes (mortality and ICU length of stay) of patients with direct admissions to the PICU from the emergency department [ED]) versus as an escalation of care from the floor.

METHODS

A retrospective cohort study with a secondary analysis of registry data. Patient demographics and outcome variables collected from January 1, 2015, to December 31, 2019, were obtained from the Virtual Pediatric Systems database. Patients with a source of admission other than the hospital's ED or pediatric floor were excluded. Multivariable regression analysis controlling for age groups, sex, race, diagnostic categories, and severity of illness (Pediatric Index of Mortality III), with clustering for sites, was performed.

RESULTS

A total of 209 695 patients from 121 sites were included in the analysis. A total of 154 716 (73.7%) were admitted directly from the ED, and 54 979 were admitted (26.2%) as an escalation of care from the floor. Two groups differed in age and race distribution, medical complexity, diagnostic categories, and severity of illness. After controlling for measured confounders, patients with floor escalations had higher mortality (2.78% vs 1.95%; P < .001), with an odds ratio of 1.71 (95% CI 1.5 to 1.9) and longer PICU length of stay (4.9 vs 3.6 days; P < .001). The rates of most of the common ICU procedures and their durations were also significantly higher in patients with an escalation of care.

CONCLUSIONS

Compared with direct admissions to the PICU from the ED, patients who were initially triaged to the pediatric floor and then require escalation to the PICU have worse outcomes. Further research is needed to explore the potential causes of this difference.

Risk Factors for Complications Following Lateral Skull Base Surgery and the Utility of ICU Monitoring

OBJECTIVE

To examine the role of intensive care unit (ICU) management following lateral skull base surgery for vestibular schwannoma and identify risk factors for complications warranting admission to the ICU.

STUDY DESIGN

Retrospective review.

SETTING

Tertiary referral center.

PATIENTS

Two hundred consecutive patients undergoing lateral skull base surgery for vestibular schwannomas.

INTERVENTION

Lateral skull base approach for resection of vestibular schwannoma and postoperative monitoring.

MAIN OUTCOME MEASURES

Patients were grouped if they sustained an ICU complication, a non-ICU complication, or no complication. Analysis was performed to determine patient or treatment factors that may be associated with ICU complications. Multivariate and three-way analysis of variance compared groups, and multivariate logistic regression determined adjusted odds ratios (aOR) for analyzed factors.

RESULTS

Seventeen of 200 patients sustained ICU complications (8.5%), most commonly hypertensive urgency (n = 15). Forty-six (23%) sustained non-ICU complications, and 137 (68.5%) had no complications. When controlling for age, sex, obesity, and other comorbidities, only hypertension (aOR 5.43, 95% confidence interval (CI) 1.35-21.73, p = 0.017) and tumor volume (aOR 3.29, 95% CI 1.09-9.96, p = 0.035) were independently associated with increased risk of ICU complications.

CONCLUSIONS

The necessity of intensive care following lateral skull base surgery is rare, with the primary ICU complication being hypertensive urgency. Preoperative hypertension and large tumor volume (>4500 mm3) were independently associated with increased risk for ICU complications. These findings may allow for risk stratification of patients appropriate for admission to stepdown units following resection of vestibular schwannomas. Further prospective, multi-center, randomized studies are necessary to validate these findings before systematic changes to current postoperative care practices.

Peripheral arterial tonometry as a method of measuring reactive hyperaemia correlates with organ dysfunction and prognosis in the critically ill patient: a prospective observational study

Predictions of mortality may help in the selection of patients who benefit from intensive care. Endothelial dysfunction is partially responsible for many of the organic dysfunctions in critical illness. Reactive hyperaemia is a vascular response of the endothelium that can be measured by peripheral arterial tonometry (RH-PAT). We aimed to assess if reactive hyperaemia is affected by critical illness and if it correlates with outcomes. Prospective study with a cohort of consecutive patients admitted to an Intensive Care Unit. RH-PAT was accessed on admission and on the 7th day after admission. Early and late survivors were compared to non-survivors. The effect of RH-PAT variation on late mortality was studied by a logistic regression model. The association between RH-PAT and severity scores and biomarkers of organic dysfunction was investigated by multivariate analysis. 86 patients were enrolled. Mean ln(RHI) on admission was 0.580 and was significantly lower in patients with higher severity scores (p < 0.01) and early non-survivors (0.388; p = 0.027). The model for prediction of early-mortality estimated that each 0.1 decrease in ln(RHI) increased the odds for mortality by 13%. In 39 patients, a 2nd RH-PAT measurement was performed on the 7th day. The variation of ln(RHI) was significantly different between non-survivors and survivors (- 24.2% vs. 63.9%, p = 0.026). Ln(RHI) was significantly lower in patients with renal and cardiovascular dysfunction (p < 0.01). RH-PAT is correlated with disease severity and seems to be an independent marker of early mortality, cardiovascular and renal dysfunctions. RH-PAT variation predicts late mortality. There appears to be an RH-PAT impairment in the acute phase of severe diseases that may be reversible and associated with better outcomes.

Evaluating the Balance Between Privacy and Access in Digital Information Sharing

Supplemental Digital Content is available in the text.

Access to personal health records in an ICU by persons involved in the patient’s care (referred to broadly as “family members” below) has the potential to increase engagement and reduce the negative psychologic sequelae of such hospitalizations. Currently, little is known about patient preferences for information sharing with a designated family member in the ICU. We sought to understand the information-sharing preferences of former ICU patients and their family members and to identify predictors of information-sharing preferences.

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

Cost-Effectiveness Analysis of Tisagenlecleucel for the Treatment of Patients With Relapsed or Refractory Diffuse Large B-Cell Lymphoma in the United States

PURPOSE

To assess the cost-effectiveness and cost-effective price of tisagenlecleucel, a novel, effective chimeric antigen receptor T-cell therapy, versus salvage chemotherapy (SC) for the treatment of relapsed or refractory diffuse large B-cell lymphoma (r/r DLBCL) using a willingness-to-pay (WTP) threshold of $150,000 per quality-adjusted life year (QALY) gained from a US third-party payer's perspective.

METHODS

A three-state (progression-free survival, progressive disease, and death), responder-based partitioned survival model with a lifetime horizon and 3% annual discount rate was developed. Overall survival (OS) and progression-free survival of tisagenlecleucel were estimated separately for patients with and without an overall response (OR), using data from JULIET ( Study of Efficacy and Safety of CTL019 in Adult DLBCL Patients). OS of SC was informed by SCHOLAR-1 (Retrospective Non-Hodgkin Lymphoma Research). Mixture cure models were used to inform the survival of tisagenlecleucel responders, supported by JULIET. The median OS was 11.1 months in all tisagenlecleucel-treated patients but not reached for responders; no progression or death occurred among responders since month 22 of treatment. For tisagenlecleucel nonresponders and SC, survival was based on standard parametric models until month 60and the survival of DLBCL long-term survivors thereafter. The model prediction validated well against the observed trial data. Costs and utilities were from the literature; utilities depended on health states and were used to estimate QALYs. Total costs, QALYs, and incremental cost per QALY gained were estimated. A cost-effective price range was estimated for all tisagenlecleucel-treated patients, OR responders, and complete response (CR) responders. Deterministic sensitivity and scenario analyses and a probabilistic sensitivity analysis were performed. All costs were reported in or inflated to 2020 US dollars.

FINDINGS

Tisagenlecleucel was associated with 3.35 QALYs gained versus SC.,The estimated incremental costs per QALY gained versus SC were $78,652 using the wholesale acquisition cost of $373,000 for tisagenlecleucel. The estimated cost-effective price of tisagenlecleucel in all treated patients was $612,270 at the WTP threshold of $150,000. Tisagenlecleucel OR and CR responders had an increase of 7.82 and 9.34 QALYs versus SC, with cost-effective prices estimated at $1,281,456 and $1,551,974, respectively. Sensitivity analysis results supported the base case findings.

IMPLICATIONS

Tisagenlecleucel is a cost-effective treatment versus SC for r/r DLBCL from the perspective of a US third-party payer. The estimated cost-effective prices ranged from $612,270 (all tisagenlecleucel-treated patients) to up to $1.5 million (patients achieving CR). Limitations include the use of single-arm trials due to data availability. (Clin Ther. 2021;43:XXX-XXX) © 2021 Elsevier HS Journals, Inc.

Long-Term Outcomes after Sepsis

Sepsis is one of the most serious problems in modern medicine. Long-term outcomes in septic shock patients are very discouraging: 75% individuals who survived sepsis and septic shock demonstrate signs of organ failure and experience persistent functional deficit. Acute sepsis and its management in an intensive care unit (ICU) to a great extent determine the pathogenesis of further complications. We believe that the concept of phenoptosis proposed by Prof. Skulachev deserves a special attention from anesthesiologists and ICU doctors. According to this concept, septic shock is a suicidal mechanism of programmed organism death, which protects human population from dangerously infected individuals. The article suggests a potential approach to the sepsis treatment based on the notion that septic shock can be prevented by identification and blockade of receptors involved in the processing of phenoptotic signal induced by lipopolysaccharide and other substances that initiate septic shock. In view of this, the search for agents that can block molecular mechanisms of the phenoptotic signal transmission seems very promising.

[Prediction of intensive care unit readmission for critically ill patients based on ensemble learning]

OBJECTIVE

To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms.

METHODS

A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination.

RESULTS

Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission.

CONCLUSION

The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.

Multimorbidity and its relationship with long-term outcomes following critical care discharge: a prospective cohort study

Background

Survivors of critical illness have poor long-term outcomes with subsequent increases in health care utilization. Less is known about the interplay between multimorbidity and long-term outcomes.

Research Question

How do baseline patient demographics impact mortality and health care utilization in the year after discharge from critical care?

Study Design and Methods

Using data from a prospectively collected cohort, we used propensity score matching to assess differences in outcomes between patients with a critical care encounter and patients admitted to the hospital without critical care. Long-term mortality was examined via nationally linked data as was hospital resource use in the year after hospital discharge. The cause of death was also examined.

Results

This analysis included 3,112 participants. There was no difference in long-term mortality between the critical care and hospital cohorts (adjusted hazard ratio, 1.09; 95% CI, 0.90-1.32; P = .39). Prehospitalization emotional health issues (eg, clinical diagnosis of depression) were associated with increased long-term mortality (hazard ratio, 1.49; 95% CI, 1.14-1.96; P < .004). Health care utilization was different between the two cohorts in the year after discharge with the critical care cohort experiencing a 29% increased risk of hospital readmission (OR, 1.29; 95% CI, 1.11-1.50; P = .001).

Interpretation

This national cohort study has demonstrated increased resource use for critical care survivors in the year after discharge but fails to replicate past findings of increased longer-term mortality. Multimorbidity, lifestyle factors, and socioeconomic status appear to influence long-term outcomes and should be the focus of future research.

Managing admission and discharge processes in intensive care units

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost.

Measuring the intensive care experience: A cross-sectional survey of patient and family experiences of critical care

AIMS AND OBJECTIVES

To report patient and family intensive care experiences using the Measuring the Intensive Care Experience (MICE) tool across two intensive care units (ICU).

BACKGROUND

The patient and family experience of care is an important indicator for quality improvement of ICUs, yet few studies evaluate both patient and family experiences in relation to overall care quality as well as specifically measuring quality of medical care, nursing care and organisational care as well as overall experience of the quality of intensive care.

DESIGN

A cross-sectional survey.

METHODS

A 23 item survey was administered to ICU patients and their family members across two ICUs, a regional 189-bed hospital and a metropolitan 227-bed hospital in Queensland, Australia. The response rate was 272 of 394 ICU patients (36.4%). STROBE guidelines were used in reporting this study.

RESULTS

Findings indicate a highly positive overall experience of ICU care among patients and families. However, patients reported areas of unmet needs following their stay in ICU broadly related to (1) symptom management, education and information support, and (2) improving the incorporation of patient and family care ICU-related shared decision-making.

CONCLUSIONS

Supportive interventions are needed that target improve symptom management and inform and education ICU patients.

RELEVANCE TO CLINICAL PRACTICE

The MICE survey facilitated the identification of a range of areas requiring quality improvement. Improving the integration of patients and families into shared decision-making and support is a key aspect for quality improvement.

Factors affecting adult intensive care units costs by using the bottom-up and top-down costing methodology in OECD countries: A systematic review

OBJECTIVES

To review the studies, which calculated the total intensive care unit costs and indicated the main cost drivers in the intensive care by using either top-down, bottom-up approach or the combination of them.

RESEARCH METHODOLOGY/DESIGNS

A systematic review of papers published until October 2020 was conducted. Search was performed on PubMed, Medline, Scopus and Science Direct databases.

SETTING

This review i examined costs in adult intensive care units, in countries belonging to the Organisation for Economic Co-operation and Development (OECD) (medical, surgical or general adult , paediatric and neonatal were not included).

MAIN OUTCOME MEASURES

Eighteen articles were included in the review.

RESULTS

Eight of the studies used the top-down costing methodology, six of them used the bottom-up approach and four of them used both of them. The mean total patient cost per day ranged from €200.75 to €4321.91 (all costs are presented in 2020 values for euro). Human resources were identified as the largest proportion of total costs. Length of stay, mechanical ventilation, continuous haemodialysis and severe illness are the main cost drivers of intensive care unit total costs.

CONCLUSION

There are a variety of methods and study designs used to calculate costs of an intensive care unit stay.t It is necessary to evolve standardised costing methods in order to make comparisons and succeed in cost-effective management.

Costs and Cost-Utility of Critical Care and Subsequent Health Care: A Multicenter Prospective Study

OBJECTIVES

The number of critical care survivors is growing, but their long-term outcomes and resource use are poorly characterized. Estimating the cost-utility of critical care is necessary to ensure reasonable use of resources. The objective of this study was to analyze the long-term resource use and costs, and to estimate the cost-utility, of critical care.

DESIGN

Prospective observational study.

SETTING

Seventeen ICUs providing critical care to 85% of the Finnish adult population.

PATIENTS

Adult patients admitted to any of 17 Finnish ICUs from September 2011 to February 2012, enrolled in the Finnish Acute Kidney Injury (FINNAKI) study, and matched hospitalized controls from the same time period.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We primarily assessed total 3-year healthcare costs per quality-adjusted life-years at 3 years. We also estimated predicted life-time quality-adjusted life-years and described resource use and costs. The costing year was 2016. Of 2,869 patients, 1,839 (64.1%) survived the 3-year follow-up period. During the first year, 1,290 of 2,212 (58.3%) index episode survivors were rehospitalized. Median (interquartile range) 3-year cumulative costs per patient were $49,200 ($30,000-$85,700). ICU costs constituted 21.4% of the total costs during the 3-year follow-up. Compared with matched hospital controls, costs of the critically ill remained higher throughout the follow-up. Estimated total mean (95% CI) 3-year costs per 3-year quality-adjusted life-years were $46,000 ($44,700-$48,500) and per predicted life-time quality-adjusted life-years $8,460 ($8,060-8,870). Three-year costs per 3-year quality-adjusted life-years were $61,100 ($57,900-$64,400) for those with an estimated risk of in-hospital death exceeding 15% (based on the Simplified Acute Physiology Score II).

CONCLUSIONS

Healthcare resource use was substantial after critical care and remained higher compared with matched hospital controls. Estimated cost-utility of critical care in Finland was of high value.

Acute healthcare resource utilization by age: A cohort study

Background

Granular data related to the likelihood of individuals of different ages accessing acute and critical care services over time is lacking.

Methods

We used population-based, administrative data from Ontario to identify residents of specific ages (20, 30, 40, etc. to 100) on January 1^st every year from 1995–2019. We assessed rates of emergency department (ED) visits (2003–19), hospitalizations, intensive care unit (ICU) admissions (2003–19), and mechanical ventilation.

Findings

Overall the 25-year study period, ED were the most common acute healthcare encounter with 100-year-olds having the lowest rate (138.7/1,000) and 90-year-olds the highest (378.5/1,000). Rates of hospitalization ranged from 24.2/1,000 for those age 20 up to 224.9/1,000 for those age 90. Rates of ICU admission and mechanical ventilation were lowest for those age 20 (1.0 and 0.4/1,000), more than tripled by age 50 (3.3 and 1.7/1,000) and peaked at age 80 (20.3 and 10.1/1,000). Over time rates of ED visits increased (164.3 /1,000 in 2003 vs 199.1 /1,000 in 2019) as did rates of invasive mechanical ventilation (2.0/1,000 in 1995 vs 2.9/1,000 in 2019), whereas rates of ICU admission remained stable (4.8/1,000 in 2003 vs 4.9/1,000 in 2019) and hospitalization declined (66.8/1,000 in 1995 vs 51.5/1,000 in 2019). Age stratified analysis demonstrated that rates of ED presentation increased for those age 70 and younger while hospitalization decreased for all age groups; ICU admission and mechanical ventilation rates changed variably by age, with increasing rates demonstrated primarily among people under the age of 50.

Interpretation

Rates of hospitalizations have decreased over time across all age groups, whereas rates of ED presentation, ICU admissions, and mechanical ventilation have increased, primarily driven by younger adults. These findings suggest that although the delivery of healthcare may be moving away from inpatient medicine, there is a growing population of young adults requiring significant healthcare resources.

Boarding of Critically Ill Patients in the Emergency Department

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Objectives:

Emergency department boarding is the practice of caring for admitted patients in the emergency department after hospital admission, and boarding has been a growing problem in the United States. Boarding of the critically ill has achieved specific attention because of its association with poor clinical outcomes. Accordingly, the Society of Critical Care Medicine and the American College of Emergency Physicians convened a Task Force to understand the implications of emergency department boarding of the critically ill. The objective of this article is to review the U.S. literature on (1) the frequency of emergency department boarding among the critically ill, (2) the outcomes associated with critical care patient boarding, and (3) local strategies developed to mitigate the impact of emergency department critical care boarding on patient outcomes.

Data Sources and Study Selection:

Review article.

Data Extraction and Data Synthesis:

Emergency department–based boarding of the critically ill patient is common, but no nationally representative frequency estimates has been reported. Boarding literature is limited by variation in the definitions used for boarding and variation in the facilities studied (boarding ranges from 2% to 88% of ICU admissions). Prolonged boarding in the emergency department has been associated with longer duration of mechanical ventilation, longer ICU and hospital length of stay, and higher mortality. Health systems have developed multiple mitigation strategies to address emergency department boarding of critically ill patients, including emergency department-based interventions, hospital-based interventions, and emergency department–based resuscitation care units.

Conclusions:

Emergency department boarding of critically ill patients was common and was associated with worse clinical outcomes. Health systems have generated a number of strategies to mitigate these effects. A definition for emergency department boarding is proposed. Future work should establish formal criteria for analysis and benchmarking of emergency department–based boarding overall, with subsequent efforts focused on developing and reporting innovative strategies that improve clinical outcomes of critically ill patients boarded in the emergency department.

Use of a Multidisciplinary Mechanical Ventilation Weaning Protocol to Improve Patient Outcomes and Empower Staff in a Medical Intensive Care Unit

BACKGROUND

Prolonged duration of mechanical ventilation is associated with higher mortality and increased patient complications; conventional physician-directed weaning methods are highly variable and permit significant time that weaning is inefficient and ineffective.

OBJECTIVES

The primary objective of this quality improvement project was to implement a registered nurse (RN)- and respiratory therapist (RT)-driven mechanical ventilation weaning protocol in a medical intensive care unit (ICU) at a tertiary care academic medical center.

METHODS

This quality improvement project used a quasi-experimental design with a retrospective usual care group who underwent physician-directed (conventional) weaning (n = 51) and a prospective intervention group who underwent protocol-directed weaning (n = 54). Outcomes included duration of mechanical ventilation, ICU length of stay, reintubation rates, and RN and RT satisfaction with the weaning protocol.

RESULTS

Patients in the RN- and RT-driven mechanical ventilation weaning protocol group had significantly lower duration of mechanical ventilation (74 vs 152 hours; P = .002) and ICU length of stay (6.7 vs 10.2 days; P = .031). There was no significant difference in reintubation rates between groups. Staff surveys indicate that both RN and RTs were satisfied with the process change.

DISCUSSION

Implementation of a multidisciplinary mechanical ventilation weaning protocol is a safe and effective way to improve patient outcomes and empower ICU staff.

Reduction of Intensive Care Unit Length of Stay: The Case of Early Mobilization

Bed rest or immobilization is frequently part of treatment for patients in the intensive care unit (ICU) with critical illness. The average ICU length of stay (LOS) is 3.3 days, and for every day spent in an ICU bed, the average patient spends an additional 1.5 days in a non-ICU bed. The purpose of this research study was to analyze the effects of early mobilization for patients in the ICU to determine if it has an impact on the LOS, cost of care, and medical complications. The methodology for this study was a literature review. Five electronic databases were used, with a total of 26 articles referenced for this research. Early mobilization suggested a decrease in delirium by 2 days, reduced risk of readmission or death, and reduced ventilator-assisted pneumonia, central line, and catheter infections. Length of stay in the ICU was reduced with statistical significance in several studies examining early mobilization. Limited research on cost of ICU LOS indicated potential savings with early mobilization. When implementing early mobilization in the ICU, total costs were decreased and medical complications were reduced. Early mobilization should become a standard of care for critically ill but stable patients in the ICU.

Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.

The associations between direct and delayed critical care unit admission with mortality and readmissions among patients with heart failure

BACKGROUND

Although greater than 20% of patients hospitalized with heart failure (HF) are admitted to a critical care unit, associated outcomes, and costs have not been delineated. We determined 30-day mortality, 30-day readmissions, and hospital costs associated with direct or delayed critical care unit admission.

METHODS

In a population-based analysis, we compared HF patients who were admitted to critical care directly from the emergency department (direct), after initial ward admission (delayed), or never admitted to critical care during their hospital stay (ward-only).

RESULTS

Among 178,997 HF patients (median age 80 [IQR 71-86] years, 49.6% men) 36,175 (20.2%) were admitted to critical care during their hospitalization (April 2003 to March 2018). Critical care patients were admitted directly from the emergency department (direct, 81.9%) or after initial ward admission (delayed, 18.1%). Multivariable-adjusted hazard ratios (HR) for all-cause 30-day mortality were: 1.69 for direct (95% confidence interval [CI]; 1.55, 1.84) and 4.92 for delayed (95% CI; 4.26, 5.68) critical care-admitted compared to ward-only patients. Multivariable-adjusted repeated events analysis demonstrated increased risk for all-cause 30-day readmission with both direct (HR 1.04, 95% CI; 1.01, 1.08, P = .013) and delayed critical care unit admissions (HR 1.20, 95% CI; 1.13, 1.28, P < .001). Median 30-day costs were $12,163 for direct admissions, $20,173 for delayed admissions, and $9,575 for ward-only patients (P < .001).

CONCLUSIONS

While critical care unit admission indicates increased risk of mortality and readmission at 30 days, those who experienced delayed critical care unit admission exhibited the highest risk of death and highest costs of care.

Effect of an emergency department intensive care unit on medical intensive unit admissions and care: A retrospective cohort study

OBJECTIVE

Evaluate the impact of an emergency critical care center (EC3) on the admissions of critically ill patients to a critical care medicine unit (CCMU) and their outcomes.

METHODS

This was a retrospective before/after cohort study in a tertiary university teaching hospital. To improve the care of critically ill patients in the emergency department (ED), a 9-bed EC3 was opened in the ED in February 2015. All critically ill patients in the emergency department must receive intensive support in EC3 before being considered for admission to the CCMU for further treatment. Patients from the emergency department account for a significant proportion of the patients admitted to the CCMU. The proportions of patients admitted to the CCMU from the ED were analyzed 1 year before and 1 year after the opening of the EC3. We also compared the admission data, demographic data, APACHE III scores and patient outcomes among patients admitted from ED to the CCMU in the year before and the year after the opening of the EC3.

RESULT

The establishment of the EC3 was associated with a decreased proportion of patients admitted to the CCMU from the ED (OR 0.73 95% CI 0.63-0.84, p < 0.01), a decrease in the proportion of patients with sepsis admitted from the ED (OR 0.68, 95% CI, 0.54-0.87, p < 0.01) and a decrease in the proportion of patients with gastrointestinal bleeding admitted from the ED (OR 0.49, 95% CI 0.28-0.84, p < 0.05). Following the establishment of the EC3, patients admitted to the CCMU had a higher APACHE III score in 2015 (74.85 ± 30.42 vs 72.39 ± 29.64, p = 0.015). Fewer low-risk patients were admitted to the CCMU for monitoring following the opening of the EC3 (112 [6.8%] vs. 181 [9.3%], p < 0.01). Propensity score matching analysis showed that the opening of the EC3 was associated with improved 60-day survival (HR 0.84, 95% CI 0.70-0.99, p = 0.046).

CONCLUSION

Following the opening of the EC3, the proportion of CCMU admissions from the ED decreased. The EC3 may be most effective at reducing the admission of lower-acuity patients with GI bleeding and possibly sepsis. The EC3 may be associated with improved survival in ED patients.

A clinical nomogram predicting unplanned intensive care unit admission after hip fracture surgery

BACKGROUND

Despite the improvement of surgical procedures and perioperative management, a portion of patients were still at high risk for intensive care unit admission owing to severe morbidity after hip fracture surgeries. The purpose of this study was to analyze influencing factors and to construct a clinical nomogram to predict unscheduled intensive care unit admission among inpatients after hip fracture surgeries.

METHODS

We enrolled a total of 1,234 hip fracture patients, with 40 unplanned intensive care unit admissions, from January 2011 to December 2018. Demographics, chronic coexisting conditions at admission, laboratory tests, and surgical variables were collected and compared between intensive care unit admission and nonadmission groups using univariate analysis. The optimal lasso model was refined to the whole data set, and multivariate logistic regression was used to assign relative weights. A nomogram incorporating these predictors was constructed to visualize these predictors and their corresponding points of the risk for unplanned intensive care unit admission. The model was validated temporally using an independent data set from January 2019 to December 2019 by receiver operating characteristic area under the curve analysis.

RESULTS

In the development group, we identified age, chronic heart failure, coronary heart disease, chronic obstructive pulmonary disease, Parkinson disease, and serum albumin and creatinine concentration were associated with unscheduled intensive care unit admission using multivariate analysis. The final model had an area under the curve of 0.854 (95% confidence interval, 0.742-0.966). The median calculated odds ratio of intensive care unit admission based on the nomogram was significantly higher for patients in the intensive care unit admission group than in the non-intensive care unit admission group (65.93% vs 0.02%, P < .01). The validation group proved its high predictive power with an area under the curve of 0.96 (95% confidence interval, 0.91-0.99).

CONCLUSION

In this study, we identified several independent factors that may increase the risk for unexpected intensive care unit admission after hip fracture surgery and developed a clinical nomogram based on these variables. Preoperative evaluation using this nomogram might facilitate advanced intensive care unit resource management for high-risk patients whose conditions might easily deteriorate if not closely monitored in general wards after surgeries.

Critical emergency medicine and the resuscitative care unit

Critical emergency medicine is the medical field concerned with management of critically ill patients in the emergency department (ED). Increased ED stay due to intensive care unit (ICU) overcrowding has a negative impact on patient care and outcome. It has been proposed that implementation of critical care services in the ED can negate this effect. Two main Critical Emergency Medicine models have been proposed, the "resource intensivist" and "ED-ICU" models. The resource intensivist model is based on constant presence of an intensivist in the traditional ED setting, while the ED-ICU model encompasses the notion of a separate ED-based unit, with monitoring and therapeutic capabilities similar to those of an ICU. Critical emergency medicine has the potential to improve patient care and outcome; however, establishment of evidence-based protocols and a multidisciplinary approach in patient management are of major importance.

A Systematic Review of Clinical Outcomes Associated With Intrahospital Transitions

Previous transitional care research has focused on transitions occurring between community and hospital settings. Little is known regarding intrahospital transitions and how they affect care quality. A systematic review was therefore conducted to synthesize the literature regarding clinical outcomes associated with intrahospital transitions. Literature published between January 2003 and December 2018 and indexed in Medline/PubMed, CINAHL, and PsychINFO were reviewed using PRISMA guidelines. Articles were limited to English language and peer-reviewed. Articles were excluded if they focused on transitions occurring from or to the hospital, discharge/discharge planning, or postdischarge follow-up. Data abstraction included study characteristics, sample characteristics, and reported clinical outcomes. Fourteen studies met inclusion criteria, primarily using cross-sectional, cohort, or retrospective chart review quantitative designs. Data were analyzed and synthesized based on outcomes reported. Major outcomes emerging from the articles included delirium, hospital length of stay, mortality, and adverse events. Delirium, hospital length of stay, and morbidity and mortality rates were associated with delayed transfers and transfers to inappropriate units. In addition, increased fall risk and infection rates were associated with higher rates of transfer. Intrahospital transitions represent critical periods of time where the quality of care being provided may be diminished, negatively affecting patient safety and outcomes.

Impact of Intensivist and Nursing Staff on Critically Ill Patient Mortality: A Retrospective Analysis of the Korean NHIS Cohort Data, 2011-2015

PURPOSE

Critical care medicine continues to evolve. However, critical care cases require increasing amount of medical resources. Intensive care unit (ICU) mortality significantly impacts the overall efficiency of healthcare resources within a system of limited medical resources. This study investigated the factors related to ICU mortality using long-term nationwide cohort data in South Korea.

MATERIALS AND METHODS

This retrospective cohort study used data of 14905721 patients who submitted reimbursement claims to the Korean Health Insurance Service between January 1, 2011 and December 31, 2015. A total of 1498102 patients who were admitted to all ICU types, except neonatal and long-term acute care hospitals, were enrolled.

RESULTS

Of the total 1498102 participants, 861397 (57.5%) were male and 636705 (42.5%) were female. The mean age at admission was 63.4±18.2 years; most of the subjects were aged over 60 years. During the 5-year period, in-hospital mortality rate was 12.9%. In Cox analysis, both in-hospital and 28-day mortality rates were significantly higher in male patients and those of lower socioeconomic status. As age increased and the number of nursing staff decreased, the mortality risk increased significantly by two or three times. The mortality risk was lower in patients admitted to an ICU of a tertiary university hospital and an ICU where intensivists worked.

CONCLUSION

The number of nursing staff and the presence of an intensivist in ICU were associated with the ICU mortality rate. Also, increasing the number of nursing staff and the presence of intensivist might reduce the mortality rate among ICU patients.