Dataset Information

Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic.

ABSTRACT:

Background

The average length of stay (LOS) in the intensive care unit (ICU_ALOS) is a helpful parameter summarizing critical bed occupancy. During the outbreak of a novel virus, estimating early a reliable ICU_ALOS estimate of infected patients is critical to accurately parameterize models examining mitigation and preparedness scenarios.

Methods

Two estimation methods of ICU_ALOS were compared: the average LOS of already discharged patients at the date of estimation (DPE), and a standard parametric method used for analyzing time-to-event data which fits a given distribution to observed data and includes the censored stays of patients still treated in the ICU at the date of estimation (CPE). Methods were compared on a series of all COVID-19 consecutive cases (n = 59) admitted in an ICU devoted to such patients. At the last follow-up date, 99 days after the first admission, all patients but one had been discharged. A simulation study investigated the generalizability of the methods' patterns. CPE and DPE estimates were also compared to COVID-19 estimates reported to date.

Results

LOS ≥ 30 days concerned 14 out of the 59 patients (24%), including 8 of the 21 deaths observed. Two months after the first admission, 38 (64%) patients had been discharged, with corresponding DPE and CPE estimates of ICU_ALOS (95% CI) at 13.0 days (10.4-15.6) and 23.1 days (18.1-29.7), respectively. Series' true ICU_ALOS was greater than 21 days, well above reported estimates to date.

Conclusions

Discharges of short stays are more likely observed earlier during the course of an outbreak. Cautious unbiased ICU_ALOS estimates suggest parameterizing a higher burden of ICU bed occupancy than that adopted to date in COVID-19 forecasting models.

Funding

Support by the National Natural Science Foundation of China (81900097 to Dr. Zhou) and the Emergency Response Project of Hubei Science and Technology Department (2020FCA023 to Pr. Zhao).

SUBMITTER: Lapidus N

PROVIDER: S-EPMC7561433 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic.

Lapidus Nathanael N Zhou Xianlong X Carrat Fabrice F Riou Bruno B Zhao Yan Y Hejblum Gilles G

Annals of intensive care 20201016 1

<h4>Background</h4>The average length of stay (LOS) in the intensive care unit (ICU_ALOS) is a helpful parameter summarizing critical bed occupancy. During the outbreak of a novel virus, estimating early a reliable ICU_ALOS estimate of infected patients is critical to accurately parameterize models examining mitigation and preparedness scenarios.<h4>Methods</h4>Two estimation methods of ICU_ALOS were compared: the average LOS of already discharged patients at the date of estimation (DPE), and a ...[more]

PMID: 33063241

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Project description:BACKGROUND:Parents of infants in neonatal intensive care units (NICUs) are often unintentionally marginalized in pursuit of optimal clinical care. Family Integrated Care (FICare) was developed to support families as part of their infants' care team in level III NICUs. We adapted the model for level II NICUs in Alberta, Canada, and evaluated whether the new Alberta FICare™ model decreased hospital length of stay (LOS) in preterm infants without concomitant increases in readmissions and emergency department visits. METHODS:In this pragmatic cluster randomized controlled trial conducted between December 15, 2015 and July 28, 2018, 10 level II NICUs were randomized to provide Alberta FICare™ (n =?5) or standard care (n =?5). Alberta FICare™ is a psychoeducational intervention with 3 components: Relational Communication, Parent Education, and Parent Support. We enrolled mothers and their singleton or twin infants born between 32 0/7 and 34 6/7 weeks gestation. The primary outcome was infant hospital LOS. We used a linear regression model to conduct weighted site-level analysis comparing adjusted mean LOS between groups, accounting for site geographic area (urban/regional) and infant risk factors. Secondary outcomes included proportions of infants with readmissions and emergency department visits to 2?months corrected age, type of feeding at discharge, and maternal psychosocial distress and parenting self-efficacy at discharge. RESULTS:We enrolled 654 mothers and 765 infants (543 singletons/111 twin cases). Intention to treat analysis included 353 infants/308 mothers in the Alberta FICare™ group and 365 infants/306 mothers in the standard care group. The unadjusted difference between groups in infant hospital LOS (1.96?days) was not statistically significant. Accounting for site geographic area and infant risk factors, infant hospital LOS was 2.55?days shorter (95% CI, -?4.44 to -?0.66) in the Alberta FICare™ group than standard care group, P =?.02. Secondary outcomes were not significantly different between groups. CONCLUSIONS:Alberta FICare™ is effective in reducing preterm infant LOS in level II NICUs, without concomitant increases in readmissions or emergency department visits. A small number of sites in a single jurisdiction and select group infants limit generalizability of findings. TRIAL REGISTRATION:ClinicalTrials.gov Identifier NCT02879799 , retrospectively registered August 26, 2016.

Project description:IntroductionEffective planning of elective surgical procedures requiring postoperative intensive care is important in preventing cancellations and empty intensive care unit (ICU) beds. To improve planning, we constructed, validated and tested three models designed to predict length of stay (LOS) in the ICU in individual patients.MethodsRetrospective data were collected from 518 consecutive patients who underwent oesophagectomy with reconstruction for carcinoma between January 1997 and April 2005. Three multivariable linear regression models for LOS, namely preoperative, postoperative and intra-ICU, were constructed using these data. Internal validation was assessed using bootstrap sampling in order to obtain validated estimates of the explained variance (r2). To determine the potential gain of the best performing model in day-to-day clinical practice, prospective data from a second cohort of 65 consecutive patients undergoing oesophagectomy between May 2005 and April 2006 were used in the model, and the predictive performance of the model was compared with prediction based on mean LOS.ResultsThe intra-ICU model had an r2 of 45% after internal validation. Important prognostic variables for LOS included greater patient age, comorbidity, type of surgical approach, intraoperative respiratory minute volume and complications occurring within 72 hours in the ICU. The potential gain of the best model in day-to-day clinical practice was determined relative to mean LOS. Use of the model reduced the deficit number (underestimation) of ICU days by 65 and increased the excess number (overestimation) of ICU days by 23 for the cohort of 65 patients. A conservative analysis conducted in the second, prospective cohort of patients revealed that 7% more oesophagectomies could have been accommodated, and 15% of cancelled procedures could have been prevented.ConclusionPatient characteristics can be used to create models that will help in predicting LOS in the ICU. This will result in more efficient use of ICU beds and fewer cancellations.

Project description:BackgroundThis study aimed to describe the demographic characteristics and determine the risk factors associated with disease severity and length of hospital and intensive care unit (ICU) stay in a cohort of COVID-19 patients admitted into ICU in Saudi Arabia.MethodsThis was a national, multi-center, retrospective cross-sectional study of all COVID-19 cases admitted into different ICUs in Saudi Arabia between March 2020 and September 202l. Demographic, clinical features, comorbidities, and length of stay (LOS) data were retrieved from the national Health Electronic Surveillance Network (HESN) and Taqassi databases at the Saudi Ministry of Health (MOH) for subsequent analyses. We used multiple linear regression models to determine risk factors associated with critical outcomes (including LOS in ICU) among COVID-19 cases.ResultsA total of 12,436 COVID-19 patients were included in this study, with a mean age of 59.57 ± 18.30 years and 7,679 (62%) were <65 years old. COVID-19 was more common in males (N = 7,686, 61.9%) and Saudi nationals (N = 8,516, 68.5%). The clinical characteristic findings showed that 36.3% of patients required invasive ventilation whilst 65.4% received tracheostomies for ventilation, and 4% were on dialysis. Our analysis revealed that 2,978 (23.9%) patients had one comorbidity, 4,977 (47.4%) had two or more comorbidities, and diabetes (48.2%) was the most prevalent comorbidity, followed by hypertension (44.2%), and chronic cardiovascular disease (10.5%). Thirteen variables emerged as significant predictors of LOS in ICU using multiple linear regression analyses, with invasive ventilation as the strongest predictor of LOS in the ICU (beta = -0.68, p = 0.001) and hospital admission (beta = -0.65, p = 0.001).ConclusionsCOVID-19 continues to affect millions of people around the world, with a mortality rate of about 2-3% of all infected patients. Our analysis revealed that comorbidities such as chronic kidney disease, cardiovascular disease, diabetes, and older age were significant risk factors associated with a poorer prognosis and longer duration of stay in hospitals and ICU.

Project description:BackgroundGiven the variability of intensive care unit (ICU) costs in different countries and the importance of this information for guiding clinicians to effective treatment and to the organisation of ICUs at the national level, it is of value to gather data on this topic for analysis at the national level in Belgium. The objectives of the study were to assess the total cost of ICUs and the factors that influence the cost of ICUs in hospitals in Belgium.MethodsThis was a retrospective cohort study using data collected from the ICUs of 17 Belgian hospitals from January 01 to December 31, 2018. A total of 18,235 adult ICU stays were included in the study. The data set was a compilation of inpatient information from analytical cost accounting of hospitals, medical discharge summaries, and length of stay data. The costs were evaluated as the expenses related to the management of hospital stays from the hospital's point of view. The cost from the hospital perspective was calculated using a cost accounting analytical methodology in full costing. We used multivariate linear regression to evaluate factors associated with total ICU cost per stay. The ICU cost was log-transformed before regression and geometric mean ratios (GMRs) were estimated for each factor.ResultsThe proportion of ICU beds to ward beds was a median [p25-p75] of 4.7% [4.4-5.9]. The proportion of indirect costs to total costs in the ICU was 12.1% [11.4-13.3]. The cost of nurses represented 57.2% [55.4-62.2] of direct costs and this was 15.9% [12.0-18.2] of the cost of nurses in the whole hospital. The median cost per stay was €4,267 [2,050-9,658] and was €2,160 [1,545-3,221] per ICU day. The main factors associated with higher cost per stay in ICU were Charlson score, mechanical ventilation, ECMO, continuous hemofiltration, length of stay, readmission, ICU mortality, hospitalisation in an academic hospital, and diagnosis of coma/convulsions or intoxication.ConclusionsThis study demonstrated that, despite the small proportion of ICU beds in relation to all services, the ICU represented a significant cost to the hospital. In addition, this study confirms that nursing staff represent a significant proportion of the direct costs of the ICU. Finally, the total cost per stay was also important but highly variable depending on the medical factors identified in our results.

Project description:To estimate the prevalence of chronic conditions among children admitted to U.S. pediatric intensive care units and to assess whether patients with complex chronic conditions experience pediatric intensive care unit mortality and prolonged length of stay risk beyond that predicted by commonly used severity-of-illness risk-adjustment models.Retrospective cohort analysis of 52,791 pediatric admissions to 54 U.S. pediatric intensive care units that participated in the Virtual Pediatric Intensive Care Unit Systems database in 2008.Hierarchical logistic regression models, clustered by pediatric intensive care unit site, for pediatric intensive care unit mortality and length of stay >15 days. Standardized mortality ratios adjusted for severity-of-illness score alone and with complex chronic conditions.Fifty-three percent of pediatric intensive care unit admissions had complex chronic conditions, and 18.5% had noncomplex chronic conditions. The prevalence of these conditions and their organ system subcategories varied considerably across sites. The majority of complex chronic condition subcategories were associated with significantly greater odds of pediatric intensive care unit mortality (odds ratios 1.25-2.9, all p values < .02) compared to having a noncomplex chronic condition or no chronic condition, after controlling for age, gender, trauma, and severity-of-illness. Only respiratory, gastrointestinal, and rheumatologic/orthopedic/psychiatric complex chronic conditions were not associated with increased odds of pediatric intensive care unit mortality. All subcategories were significantly associated with prolonged length of stay. All noncomplex chronic condition subcategories were either not associated or were negatively associated with pediatric intensive care unit mortality, and most were not associated with prolonged length of stay, compared to having no chronic conditions. Among this group of pediatric intensive care units, adding complex chronic conditions to risk-adjustment models led to greater model accuracy but did not substantially change unit-level standardized mortality ratios.Children with complex chronic conditions were at greater risk for pediatric intensive care unit mortality and prolonged length of stay than those with no chronic conditions, but the magnitude of risk varied across subcategories. Inclusion of complex chronic conditions into models of pediatric intensive care unit mortality improved model accuracy but had little impact on standardized mortality ratios.

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Dataset Information

Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic.

Background

Methods

Results

Conclusions

Funding

Publications

Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic.

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