Examination of Impact of After-Hours Admissions on Hospital Resource Use, Patient Outcomes, and Costs.

Background: Nighttime and weekends in hospital and intensive care unit (ICU) contexts are thought to present a greater risk for adverse events than daytime admissions. Although some studies exist comparing admission time with patient outcomes, the results are contradictory. No studies currently exist comparing costs with the time of admission. We investigated the differences in-hospital mortality, ICU length of stay, ICU mortality, and cost between daytime and nighttime admissions. Methods: All adult patients (≥18 years of age) admitted to a large academic medical-surgical ICU between 2011 and 2015 were included. Admission cohorts were defined as daytime (8:00-16:59) or nighttime (17:00-07:59). Student's t-tests and chi-squared tests were used to test for associations between days spent in the ICU, days on mechanical ventilation, comorbidities, diagnoses, and cohort membership. Regression analysis was used to test for associations between patient and hospitalization characteristics and in-hospital mortality and total ICU costs. Results: The majority of admissions occurred during nighttime hours (69.5%) with no difference in the overall Elixhauser comorbidity score between groups (p=0.22). Overall ICU length of stay was 7.96 days for daytime admissions compared to 7.07 days (p=0.001) for patients admitted during nighttime hours. Overall mortality was significantly higher in daytime admissions (22.5% vs 20.6, p=0.012); however, ICU mortality was not different. The average MODS was 2.9 with those admitted during the daytime having a significantly higher MODS (3.0, p=0.046). Total ICU cost was significantly higher for daytime admissions (p=0.003). Adjusted ICU mortality was similar in both groups despite an increased rate of adverse events for nighttime admissions. Daytime admissions were associated with increased cost. There was no difference in all hospital total cost or all hospital direct cost between groups. These findings are likely due to the higher severity of illness in daytime admissions. Conclusion: Daytime admissions were associated with a higher severity of illness, mortality rate, and ICU cost. To further account for the effect of staffing differences during off-hours, it may be beneficial to compare weekday and weeknight admission times with associated mortality rates.

Delirium and Associated Length of Stay and Costs in Critically Ill Patients.

PURPOSE: Delirium frequently affects critically ill patients in the intensive care unit (ICU). The purpose of this study is to evaluate the impact of delirium on ICU and hospital length of stay (LOS) and perform a cost analysis. MATERIALS AND METHODS: Prospective studies and randomized controlled trials of patients in the ICU with delirium published between January 1, 2015, and December 31, 2020, were evaluated. Outcome variables including ICU and hospital LOS were obtained, and ICU and hospital costs were derived from the respective LOS. RESULTS: Forty-one studies met inclusion criteria. The mean difference of ICU LOS between patients with and without delirium was significant at 4.77 days (p < 0.001); for hospital LOS, this was significant at 6.67 days (p < 0.001). Cost data were extractable for 27 studies in which both ICU and hospital LOS were available. The mean difference of ICU costs between patients with and without delirium was significant at $3,921 (p < 0.001); for hospital costs, the mean difference was $5,936 (p < 0.001). CONCLUSION: ICU and hospital LOS and associated costs were significantly higher for patients with delirium, compared to those without delirium. Further research is necessary to elucidate other determinants of increased costs and cost-reducing strategies for critically ill patients with delirium. This can provide insight into the required resources for the prevention of delirium, which may contribute to decreasing healthcare expenditure while optimizing the quality of care.

Frailty Is Associated With Decreased Time Spent at Home After Critical Illness: A Population-Based Study.

BACKGROUND: Frailty is characterized by vulnerability to stressors due to an accumulation of multiple functional deficits. Frailty is increasingly recognized as a risk factor for accelerated functional decline, increasing dependency, and risk of mortality. The objective of this study was to examine the association of frailty, at the time of critical care admission, with days alive at home and health care costs post-discharge. METHODS: This retrospective cohort study used linked administrative data (2010-2016) in Ontario, Canada. We identified all patients admitted at the intensive care unit (ICU), aged 19 years and above, assessed using the Resident Assessment Instrument for Home Care (RAI-HC), within 6 months prior to index hospitalization including an ICU stay. Patients were stratified as robust, pre-frail, or frail based on a validated Frailty Index. The primary outcome was days alive at home in the year after admission. Secondary outcomes included mortality, health care-associated costs, ICU interventions, long-term care admissions, and hospital readmissions. RESULTS: Frail patients spent significantly fewer days at home within 1 year of index hospitalization (mean 159 days vs 223 days in robust cohort, P < .001). Mortality was higher among frail patients at 1 year (59.6% in the frail cohort vs 45.9% in robust patients; odds ratio for death 1.59 [1.49-1.69]). Frail patients also had higher rates of long-term care admission within 1 year (30.1% vs 10.6% in robust patients). Total health care-associated costs per person alive were $30 450 higher the year after admission in the frail cohort. CONCLUSIONS: Frailty prior to ICU admission among patients who were eligible for RAI-HC assessment was associated with higher mortality and fewer days spent at home following admission. Frail patients had markedly higher rates of long-term care admission and increased costs per life saved following critical illness. These findings add to the discussion of risk-benefit trade-offs for ICU admission.

Dosing Fluids in Early Septic Shock.

Early IV fluid administration remains one of the modern pillars of sepsis treatment; however, questions regarding amount, type, rate, mechanism of action, and even the benefits of fluid remain unanswered. Administering the optimal fluid volume is important, because overzealous fluid resuscitation can precipitate multiorgan failure, prolong mechanical ventilation, and worsen patient outcomes. After the initial resuscitation, further fluid administration should be determined by individual patient factors and measures of fluid responsiveness. This review describes various static and dynamic measures that are used to assess fluid responsiveness and summarizes the evidence addressing these metrics. Subsequently, we outline a practical approach to the evaluation of fluid responsiveness in early septic shock and explore further areas crucial to ongoing research examining this topic.

Fatal Cerebral Fat Embolism After Pelvic and Multiple Long Bone Fractures without Associated Lung Injury: A Case Report.

CASE: A 29-year-old motor vehicle collision passenger presented with fractures of the pelvic ring, bilateral femurs, and right tibia/fibula. All fractures were stabilized with external fixation and fixed definitively 48 hours later. Postoperatively, the patient suffered rapid clinical decline. Emergent head computed tomography (CT) demonstrated tonsillar herniation with loss of gray-white matter differentiation. Although respiratory status remained uncompromised throughout presentation, pathology revealed extensive cerebral fat embolism. CONCLUSION: Fat embolism can traverse the lungs without eliciting an inflammatory response, radiographic infiltrate, or impairing gas exchange. This may lead to a clinically silent, fatal cerebral fat embolism in an intubated, sedated patient.

Dynamic Assessment of Fluid Responsiveness in Surgical ICU Patients Through Stroke Volume Variation is Associated With Decreased Length of Stay and Costs: A Systematic Review and Meta-Analysis.

Static indices, such as the central venous pressure, have proven to be inaccurate predictors of fluid responsiveness. An emerging approach uses dynamic assessment of fluid responsiveness (FT-DYN), such as stroke volume variation (SVV) or surrogate dynamic variables, as more accurate measures of volume status. Recent work has demonstrated that goal-directed therapy guided by FT-DYN was associated with reduced intensive care unit (ICU) mortality; however, no study has specifically assessed this in surgical ICU patients. This study aimed to conduct a systematic review and meta-analysis on the impact of employing FT-DYN in the perioperative care of surgical ICU patients on length of stay in the ICU. As secondary objectives, we performed a cost analysis of FT-DYN and assessed the impact of FT-DYN versus standard care on hospital length of stay and mortality. We identified all randomized controlled trials (RCTs) through MEDLINE, EMBASE, and CENTRAL that examined adult patients in the ICU who were randomized to standard care or to FT-DYN from inception to September 2017. Two investigators independently reviewed search results, identified appropriate studies, and extracted data using standardized spreadsheets. A random effect meta-analysis was carried out. Eleven RCTs were included with a total of 1015 patients. The incorporation of FT-DYN through SVV in surgical patients led to shorter ICU length of stay (weighted mean difference [WMD], -1.43d; 95% confidence interval [CI], -2.09 to -0.78), shorter hospital length of stay (WMD, -1.96d; 95% CI, -2.34 to -1.59), and trended toward improved mortality (odds ratio, 0.55; 95% CI, 0.30-1.03). There was a decrease in daily ICU-related costs per patient for those who received FT-DYN in the perioperative period (WMD, US$ -1619; 95% CI, -2173.68 to -1063.26). Incorporation of FT-DYN through SVV in the perioperative care of surgical ICU patients is associated with decreased ICU length of stay, hospital length of stay, and ICU costs.

Outcomes of hospitalized hematologic oncology patients receiving rapid response system activation for acute deterioration.

BACKGROUND: Patients with hematologic malignancies who are admitted to hospital are at increased risk of deterioration and death. Rapid response systems (RRSs) respond to hospitalized patients who clinically deteriorate. We sought to describe the characteristics and outcomes of hematologic oncology inpatients requiring rapid response system (RRS) activation, and to determine the prognostic accuracy of the SIRS and qSOFA criteria for in-hospital mortality of hematologic oncology patients with suspected infection. METHODS: We used registry data from two hospitals within The Ottawa Hospital network, between 2012 and 2016. Consecutive hematologic oncology inpatients who experienced activation of the RRS were included in the study. Data was gathered at the time of RRS activation and assessment. The primary outcome was in-hospital mortality. Logistical regression was used to evaluate for predictors of in-hospital mortality. RESULTS: We included 401 patients during the study period. In-hospital mortality for all included patients was 41.9% (168 patients), and 145 patients (45%) were admitted to ICU following RRS activation. Among patients with suspected infection at the time of RRS activation, Systemic Inflammatory Response Syndrome (SIRS) criteria had a sensitivity of 86.9% (95% CI 80.9-91.6) and a specificity of 38.2% (95% CI 31.9-44.8) for predicting in-hospital mortality, while Quick Sequential Organ Failure Assessment (qSOFA) criteria had a sensitivity of 61.9% (95% CI 54.1-69.3) and a specificity of 91.4% (95% CI 87.1-94.7). Factors associated with increased in-hospital mortality included transfer to ICU after RRS activation (adjusted odds ratio [OR] 3.56, 95% CI 2.12-5.97) and a higher number of RRS activations (OR 2.45, 95% CI 1.63-3.69). Factors associated with improved survival included active malignancy treatment at the time of RRS activation (OR 0.54, 95% CI 0.34-0.86) and longer hospital length of stay (OR 0.78, 95% CI 0.70-0.87). CONCLUSIONS: Hematologic oncology inpatients requiring RRS activation have high rates of subsequent ICU admission and mortality. ICU admission and higher number of RRS activations are associated with increased risk of death, while active cancer treatment and longer hospital stay are associated with lower risk of mortality. Clinicians should consider these factors in risk-stratifying these patients during RRS assessment.

Early Renal Replacement Therapy Versus Standard Care in the ICU: A Systematic Review, Meta-Analysis, and Cost Analysis.

OBJECTIVE:: Renal replacement therapy (RRT) is the treatment of choice for severe acute kidney injury, but there are no firm guidelines as to the time of initiation of RRT in the critically ill. The primary objective of this study is to determine 1-month mortality rates of early versus late dialysis in critical care. As secondary end points, we provide a cost analysis of early versus late RRT initiation, intensive care unit (ICU) length of stay (LOS), hospital LOS, and number of patients on dialysis at day 60 postrandomization. DATA SOURCES:: We identified all randomized controlled trials (RCTs) through EMLINE and MEDBASE that examined adult patients admitted to critical care who were randomized to receiving early dialysis versus standard of care. STUDY SELECTION:: Inclusion criteria: (1) RCTs conducted after the year 2000, (2) the population evaluated had to be adults admitted to ICU, (3) the intervention had to be early RRT versus standard care, and (4) outcomes had to measure patient mortality. DATA EXTRACTION:: Two independent investigators reviewed search results and identified appropriate studies. Information was extracted using standardized case report forms. DATA SYNTHESIS:: Overall, 7 RCTs were included with a total of 1400 patients. Early RRT showed no survival benefit when compared to standard treatment (odds ratio [OR], 0.90 95% confidence interval [95% CI] 0.70-1.15, P = .39). There was no significant difference in length of hospital stay in patients with early RRT (-1.55 days [95% CI -4.75 to 1.65, P = .34]), in length of ICU stay (-0.79 days [95% CI -2.09 to 0.52], P = .24), or proportion of patients on dialysis at day 60 (OR 0.93 [95% CI 0.62 to 1.43], P = .79). Per patient, there is likely a small increase in costs (

Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call.

OBJECTIVES: Machine learning models have been used to predict mortality among patients requiring rapid response team activation. The goal of our study was to assess the impact of adding laboratory values into the model. DESIGN: A gradient boosted decision tree model was derived and internally validated to predict a primary outcome of in-hospital mortality. The base model was then augmented with laboratory values. SETTING: Two tertiary care hospitals within The Ottawa Hospital network. PATIENTS: Inpatients over the age of 18 years who experienced a rapid response team activation between January 1, 2015, and May 31, 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 2,061 rapid response team activations occurred during the study period. The in-hospital mortality rate was 29.4%. Patients who died were older (median age, 72 vs 68 yr; p < 0.001), had a longer length of stay (length of stay) prior to rapid response team activation (4 vs 2 d; p < 0.001), and more often had respiratory distress (31% vs 22%; p < 0.001). Our base model without laboratory values performed with an area under the receiver operating curve of 0.71 (95% CI, 0.71-0.72). When the base model was augmented with laboratory values, the area under the receiver operating curve improved to 0.77 (95% CI, 0.77-0.78). Important mortality predictors in the base model were age, estimated ratio of Pao2 to Fio2 (calculated using oxygen saturation and estimated Fio2), length of stay prior to rapid response team activation, and systolic blood pressure. CONCLUSIONS: Machine learning models can identify rapid response team patients at a high risk of mortality and potentially supplement clinical decision making. Incorporating laboratory values into model development significantly improved predictive performance in this study.

Cost analysis of Omega-3 supplementation in critically ill patients with sepsis.

INTRODUCTION: Nutritional supplement of omega-3 fatty acids have been proposed to improve clinical outcomes in critically ill patients. While previous work have demonstrated that omega-3 supplementation in patients with sepsis is associated with reduced ICU and hospital length of stay, the financial impact of this intervention is unknown. OBJECTIVE: Perform a cost analysis to evaluate the impact of omega-3 supplementation on ICU and hospital costs. METHODS: We extracted data related to ICU and hospital length of stay from the individual studies reported in a recent systematic review. The Cochrane Collaboration tool was used to assess the risk of bias in these studies. Average daily ICU and hospital costs per patient were obtained from a cost study by Kahn et al. We estimated the ICU and hospital costs by multiplying the mean length of stay by the average daily cost per patient in ICU or Hospital. Adjustments for inflation were made according to the USD annual consumer price index. We calculated the difference between the direct variable cost of patients with omega-3 supplementation and patients without omega-3 supplementation. 95% confidence intervals were estimated using bootstrap re-sampling procedures with 1000 iterations. RESULTS: A total of 12 RCT involving 925 patients were included in this cost analysis. Septic patients supplemented with omega-3 had both lower mean ICU costs ($15,274 vs. $18,172) resulting in $2897 in ICU savings per patient and overall hospital costs ($17,088 vs. $19,778), resulting in $2690 in hospital savings per patient. Sensitivity analyses were conducted to investigate the impact of different study methods on the LOS. The results were still consistent with the overall findings. CONCLUSION: Patients with sepsis who received omega-3 supplementation had significantly shorter LOS in the ICU and hospital, and were associated with lower direct variable costs than control patients. The 12 RCTs used in this analysis had a high risk of bias. Large-scaled, high-quality, multi-centered RCTs on the effectiveness of this intervention is recommended to improve the quality of the existing evidence.

Early vs. late tracheostomy in intensive care settings: Impact on ICU and hospital costs.

INTRODUCTION: Up to 12% of the 800,000 patients who undergo mechanical ventilation in the United States every year require tracheostomies. A recent systematic review showed that early tracheostomy was associated with better outcomes: more ventilator-free days, shorter ICU stays, less sedation and reduced long-term mortality. However, the financial impact of early tracheostomies remain unknown. OBJECTIVES: To conduct a cost-analysis on the timing of tracheostomy in mechanically ventilated patients. METHODS: We extracted individual length of hospital stay and length of ICU stay data from the studies included in the systematic review from Hosokawa et al. We also searched for any recent randomized control trials on the topic that were published after this review. The weighted length of stay was estimated using a random effects model. Average daily hospital and ICU costs per patients were obtained from a cost study by Kahn et al. We estimated hospital and ICU costs by multiplying LOS with respective average daily cost per patient. We calculated difference in costs by subtracting hospital costs, ICU costs and total direct variable costs from early tracheotomy to late tracheotomy. 95% confidence intervals were estimated using bootstrap re-sampling procedures with 1000 iterations. RESULTS: The average weighted cost of ICU stay in patients with an early tracheostomy was $4316 less when compared to patients with late tracheostomy (95% CI: 403-8229). Subgroup analysis revealed that very early tracheostomies (<4days) cost on average $3672 USD less than late tracheostomies (95% CI: -1309, 10,294) and that early tracheostomies (<10days but >4) cost on average $6385 USD less than late tracheostomies (95% CI: -4396-17,165). CONCLUSION: This study shows that early tracheostomy can significantly reduce direct variable and likely total hospital costs in the intensive care unit based on length of stay alone. This is in addition to the already shown benefits of early tracheostomy in terms of ventilator dependent days, reduced length of stays, decreased pain, and improved communication. Further prospective studies on this topic are needed to prove the cost-effectiveness of early tracheostomy in the critically ill population.

Critical care at the end of life: a population-level cohort study of cost and outcomes.

BACKGROUND: Despite the high cost associated with ICU use at the end of life, very little is known at a population level about the characteristics of users and their end of life experience. In this study, our goal was to characterize decedents who received intensive care near the end of life and examine their overall health care use prior to death. METHODS: This was a retrospective cohort study that examined all deaths in a 3-year period from April 2010 to March 2013 in Ontario, Canada. Using population-based health administrative databases, we examined healthcare use and cost in the last year of life. RESULTS: There were 264,754 individuals included in the study, of whom 18% used the ICU in the last 90 days of life; 34.5% of these ICU users were older than 80 years of age and 53.0% had more than five chronic conditions. The average cost of stay for these decedents was CA$15,511 to CA$25,526 greater than for those who were not admitted to the ICU. These individuals also died more frequently in hospital (88.7% vs 36.2%), and spent more time in acute-care settings (18.7 days vs. 10.5 days). CONCLUSIONS: We showed at a population level that a significant proportion of those with ICU use close to death are older, multi-morbid individuals who incur significantly greater costs and die largely in hospital, with higher rates of readmission, longer lengths of stay and higher rates of aggressive care.