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

Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.

Intensive care unit caseload and workload and their association with outcomes in critically unwell patients: a large registry-based cohort analysis

BACKGROUND

Too high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU).

METHODS

Retrospective cohort analysis of data from an Austrian ICU registry. Data on patients aged ≥ 18 years admitted to 144 Austrian ICUs between 2013 and 2022 were included. A Cox proportional hazards model with ICU mortality as the outcome of interest adjusted with patients' respective SAPS 3, current ICU caseload (measured by ICU occupancy rates), and current ICU workload (measured by median TISS-28 per ICU) as time-dependent covariables was constructed. Subgroup analyses were performed for types of ICUs, hospital care level, and pre-COVID or intra-COVID period.

RESULTS

415 584 patient admissions to 144 ICUs were analysed. Compared to ICU caseloads of 76 to 100%, there was no significant relationship between overuse of ICU capacity and risk of death [HR (95% CI) 1.06 (0.99-1.15), p = 0.110 for > 100%], but for lower utilisation [1.09 (1.02-1.16), p = 0.008 for ≤ 50% and 1.10 (1.05-1.15), p < 0.0001 for 51-75%]. Exceptions were significant associations for caseloads > 100% between 2020 and 2022 [1.18 (1.06-1.30), p = 0.001], i.e., the intra-COVID period. Compared to the reference category of median TISS-28 21-30, lower [0.88 (0.78-0.99), p = 0.049 for ≤ 20], but not higher workloads were significantly associated with risk of death. High workload may be associated with higher mortality in local hospitals [1.09 (1.01-1.19), p = 0.035 for 31-40, 1.28 (1.02-1.60), p = 0.033 for > 40].

CONCLUSIONS

In a system with comparably high intensive care resources and mandatory staffing levels, patients' survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death.

Three-year mortality, readmission, and medical expenses in critical care survivors: A population-based cohort study

BACKGROUND

Due to the increasing number of critical care survivors, population-based studies on the long-term outcomes after discharge are necessary to inform local decision-making.

OBJECTIVES

This study aimed to investigate mortality and its risk factors, readmissions, and medical expenses of intensive care unit survivors for 3 years after hospital discharge.

METHODS

This retrospective study analysed data from the National Health Insurance Service-National Sample Cohort in Korea. Of the 195,702 patients who survived and were discharged from hospital in 2012, 2693 intensive care unit patients were assigned to the case group for the study, and the remaining 193,009 were assigned to the comparison group. The primary outcome was all-cause mortality for 3 years after discharge. Secondary outcomes were all-cause hospital readmission and medical expenses in 3 years. We analysed risk factors for mortality using the Cox proportional hazard regression. The differences in hospital readmission and medical expenses between the case and comparison groups were analysed by multivariate logistic regression and independent t-tests.

RESULTS

The 1-year, 2-year, and 3-year cumulative mortality rates in the case group were 15.9%, 20.5%, and 24.4%, respectively, and older age, disability, medical admission, and longer hospital stay increased mortality. Almost 40% of intensive care unit survivors were readmitted to hospital within 6 months of discharge, and their odds of being readmitted were significantly higher than those of the comparison group. Medical expenses were also significantly higher in the case group, with the highest paid within 6 months.

CONCLUSIONS

Mortality, hospital readmission, and medical expenses for intensive care unit survivors were the worst within 6 months of discharge. In light of the long-term recovery trajectory of critical illness, it is necessary to investigate what factors may have contributed to the negative outcome during this period. Further research is needed to determine which services primarily contributed to the increase in medical expenses.

Association between hospital spending and in-hospital mortality of patients with sepsis based on a Japanese nationwide medical claims database study

Background

The effect of hospital spending on the mortality rate of patients with sepsis has not yet been fully elucidated. We hypothesized that hospitals that consume more medical resources would have lower mortality rates among patients with sepsis.

Methods

This retrospective study used administrative data from 2010 to 2017. The enrolled hospitals were divided into quartiles based on average daily medical cost per sepsis case. The primary and secondary outcomes were the average in-hospital mortality rate of patients with sepsis and the effective cost per survivor among the enrolled hospitals, respectively. A multiple regression model was used to determine the significance of the differences among hospital categories to adjust for baseline imbalances.

Results

Among 997 hospitals enrolled in this study, the crude in-hospital mortality rates were 15.7% and 13.2% in the lowest and highest quartiles of hospital spending, respectively. After adjusting for confounding factors, the highest hospital spending group demonstrated a significantly lower in-hospital mortality rate than the lowest hospital spending group (coefficient = -0.025, 95% confidence interval [CI] -0.034 to -0.015; p < 0.0001). Similarly, the highest hospital spending group was associated with a significantly higher effective cost per survivor than the lowest hospital spending group (coefficient = 77.7, 95% CI 73.1 to 82.3; p < 0.0001). In subgroup analyses, hospitals with a small or medium number of beds demonstrated a consistent pattern with the primary test, whereas those with a large number of beds or academic affiliations displayed no association.

Conclusions

Using a nationwide Japanese medical claims database, this study indicated that hospitals with greater expenditures were associated with a superior survival rate and a higher effective cost per survivor in patients with sepsis than those with lower expenditures. In contrast, no correlations between hospital spending and mortality were observed in hospitals with a large number of beds or academic affiliations.

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.

Intensive care unit resources and patient-centred outcomes in severe COVID-19: a prospective single-centre economic evaluation

During the COVID-19 pandemic, ICU bed shortages sparked a discussion about resource allocation. We aimed to analyse the value of ICU treatment of COVID-19 from a patient-centred health economic perspective. We prospectively included 49 patients with severe COVID-19 and calculated direct medical treatment costs. Quality of life was converted into aggregated quality-adjusted life years using the statistical remaining life expectancy. Costs for non-treatment as the comparator were estimated using the value of statistical life year approach. We used multivariable linear or logistic regression to identify predictors of treatment costs, quality of life and survival. Mean (SD) direct medical treatment costs were higher in patients in ICU with COVID-19 compared with those without (£60,866 (£42,533) vs. £8282 (£14,870), respectively; p < 0.001). This was not solely attributable to prolonged ICU length of stay, as costs per day were also higher (£3115 (£1374) vs. £1490 (£713), respectively; p < 0.001), independent of overall disease severity. We observed a beneficial cost-utility value of £7511 per quality-adjusted life-year gained, even with a more pessimistic assumption towards the remaining life expectancy. Extracorporeal membrane oxygenation therapy provided no additional quality-adjusted life-year benefit. Compared with non-treatment (costs per lost life year, £106,085), ICU treatment (costs per quality-adjusted life-year, £7511) was economically preferable, even with a pessimistic interpretation of patient preferences for survival (sensitivity analysis of the value of statistical life year, £48,848). Length of ICU stay was a positive and extracorporeal membrane oxygenation a negative predictor for quality of life, whereas costs per day were a positive predictor for mortality. These data suggest that despite high costs, ICU treatment for severe COVID-19 may be cost-effective for quality-adjusted life-years gained.

Temporal trends of medical cost and cost-effectiveness in sepsis patients: a Japanese nationwide medical claims database

Background

Sepsis is the leading cause of death worldwide. Although the mortality of sepsis patients has been decreasing over the past decade, the trend of medical costs and cost-effectiveness for sepsis treatment remains insufficiently determined.

Methods

We conducted a retrospective study using the nationwide medical claims database of sepsis patients in Japan between 2010 and 2017. After selecting sepsis patients with a combined diagnosis of presumed serious infection and organ failure, patients over the age of 20 were included in this study. We investigated the annual trend of medical costs during the study period. The primary outcome was the annual trend of the effective cost per survivor, calculated from the gross medical cost and number of survivors per year. Subsequently, we performed subgroup and multiple regression analyses to evaluate the association between the annual trend and medical costs.

Results

Among 50,490,128 adult patients with claims, a total of 1,276,678 patients with sepsis were selected from the database. Yearly gross medical costs to treat sepsis gradually increased over the decade from $3.04 billion in 2010 to $4.38 billion in 2017, whereas the total medical cost per hospitalization declined (rate = − $1075/year, p < 0.0001). While the survival rate of sepsis patients improved during the study period, the effective cost per survivor significantly decreased (rate = − $1806/year [95% CI − $2432 to − $1179], p = 0.001). In the subgroup analysis, the trend of decreasing medical cost per hospitalization remained consistent among the subpopulation of age, sex, and site of infection. After adjusting for age, sex (male), number of chronic diseases, site of infection, intensive care unit (ICU) admission, surgery, and length of hospital stay, the admission year was significantly associated with reduced medical costs.

Conclusions

We demonstrated an improvement in annual cost-effectiveness in patients with sepsis between 2010 and 2017. The annual trend of reduced costs was consistent after adjustment with the confounders altering hospital expenses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-022-00624-5.

Hospital re-admission after critical care survival: a systematic review and meta-analysis

Survivors of critical illness frequently require increased healthcare resources after hospital discharge. We undertook a systematic review and meta-analysis to assess hospital re-admission rates following critical care admission and to explore potential re-admission risk factors. We searched the MEDLINE, Embase and CINAHL databases on 05 March 2020. Our search strategy incorporated controlled vocabulary and text words for hospital re-admission and critical illness, limited to the English language. Two reviewers independently applied eligibility criteria and assessed quality using the Newcastle Ottawa Score checklist and extracted data. The primary outcome was acute hospital re-admission in the year after critical care discharge. Of the 8851 studies screened, 87 met inclusion criteria and 41 were used within the meta-analysis. The analysis incorporated data from 3,897,597 patients and 741,664 re-admission episodes. Pooled estimates for hospital re-admission after critical illness were 16.9% (95%CI: 13.3-21.2%) at 30 days; 31.0% (95%CI: 24.3-38.6%) at 90 days; 29.6% (95%CI: 24.5-35.2%) at six months; and 53.3% (95%CI: 44.4-62.0%) at 12 months. Significant heterogeneity was observed across included studies. Three risk factors were associated with excess acute care rehospitalisation one year after discharge: the presence of comorbidities; events during initial hospitalisation (e.g. the presence of delirium and duration of mechanical ventilation); and subsequent infection after hospital discharge. Hospital re-admission is common in survivors of critical illness. Careful attention to the management of pre-existing comorbidities during transitions of care may help reduce healthcare utilisation after critical care discharge. Future research should determine if targeted interventions for at-risk critical care survivors can reduce the risk of subsequent rehospitalisation.

Health-Related Quality-of-Life and Cost Utility Analyses in Critical Care: A Systematic Review

OBJECTIVES

Cost utility analyses compare the costs and health outcome of interventions, with a denominator of quality-adjusted life year, a generic health utility measure combining both quality and quantity of life. Cost utility analyses are difficult to compare when methods are not standardized. It is unclear how cost utility analyses are measured/reported in critical care and what methodologic challenges cost utility analyses pose in this setting. This may lead to differences precluding cost utility analyses comparisons. Therefore, we performed a systematic review of cost utility analyses conducted in critical care. Our objectives were to understand: 1) methodologic characteristics, 2) how health-related quality-of-life was measured/reported, and 3) what costs were reported/measured.

DESIGN

Systematic review.

DATA SOURCES

We systematically searched for cost utility analyses in critical care in MEDLINE, Embase, American College of Physicians Journal Club, CENTRAL, Evidence-Based Medicine Reviews' selected subset of archived versions of UK National Health Service Economic Evaluation Database, Database of Abstracts of Reviews of Effects, and American Economic Association electronic databases from inception to April 30, 2020.

SETTING

Adult ICUs.

PATIENTS

Adult critically ill patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 8,926 citations, 80 cost utility analyse studies were eligible. The time horizon most commonly reported was lifetime (59%). For health utility reporting, health-related quality-of-life was infrequently measured (29% reported), with only 5% of studies reporting baseline health-related quality-of-life. Indirect utility measures (generic, preference-based health utility measurement tools) were reported in 85% of studies (majority Euro-quality-of-life-5 Domains, 52%). Methods of estimating health-related quality-of-life were seldom used when the patient was incapacitated: imputation (19%), assigning fixed utilities for incapacitation (19%), and surrogates reporting on behalf of incapacitated patients (5%). For cost utility reporting transparency, separate incremental costs and quality-adjusted life years were both reported in only 76% of studies. Disaggregated quality-adjusted life years (reporting separate health utility and life years) were described in only 34% of studies.

CONCLUSIONS

We identified deficiencies which warrant recommendations (standardized measurement/reporting of resource use/unit costs/health-related quality-of-life/methodological preferences) for improved design, conduct, and reporting of future cost utility analyses in critical care.