Dataset Information

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

ABSTRACT:

Rationale

The COVID-19 pandemic induces considerable strain on intensive care unit resources.

Objectives

We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic.

Methods

We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry.

Measurements

The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19.

Main results

The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources.

Conclusion

A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

SUBMITTER: Henzi A

PROVIDER: S-EPMC7894868 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

Henzi Alexander A Kleger Gian-Reto GR Hilty Matthias P MP Wendel Garcia Pedro D PD Ziegel Johanna F JF

PloS one 20210219 2

<h4>Rationale</h4>The COVID-19 pandemic induces considerable strain on intensive care unit resources.<h4>Objectives</h4>We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic.<h4>Methods</h4>We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a la ...[more]

PMID: 33606773

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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: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.

Dataset Information

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

Rationale

Objectives

Methods

Measurements

Main results

Conclusion

Publications

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

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