Relationship between institutional intensive care volume prior to the COVID-19 pandemic and in-hospital death in ventilated patients with severe COVID-19

Amagasa, Shunsuke; Kashiura, Masahiro; Yasuda, Hideto; Hayakawa, Mineji; Endo, Akira; Ogura, Takayuki; Hirayama, Atsushi; Yasunaga, Hideo; Tagami, Takashi

doi:10.1038/s41598-022-26893-6

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The selected covariates were based on both clinical expertise and previous literature that has examined the association between age and the Elixhauser comorbidity index 35 , as well as prior admissions 36 and their connection to severe COVID outcomes. So, the selected covariates encompassed various factors, including age, demographic characteristics (sex, LTC vs DSL resident, and specimen collection location), comorbidities (based on the Elixhauser index), the number of previous procedures (inpatient and outpatient), the number of previous admissions (hospital and ICU), symptomatic status, specimen year-month collection, and the results of the PCR test.…”

Section: Methodsmentioning

confidence: 99%

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data

Shahidi,

Rennert-May,

D’Souza

et al. 2023

Sci Rep

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In this study, we aimed to identify the factors that were associated with mortality among continuing care residents in Alberta, during the coronavirus disease 2019 (COVID-19) pandemic. We achieved this by leveraging and linking various administrative datasets together. Then, we examined pre-processing methods in terms of prediction performance. Finally, we developed several machine learning models and compared the results of these models in terms of performance. We conducted a retrospective cohort study of all continuing care residents in Alberta, Canada, from March 1, 2020, to March 31, 2021. We used a univariable and a multivariable logistic regression (LR) model to identify predictive factors of 60-day all-cause mortality by estimating odds ratios (ORs) with a 95% confidence interval. To determine the best sensitivity–specificity cut-off point, the Youden index was employed. We developed several machine learning models to determine the best model regarding performance. In this cohort study, increased age, male sex, symptoms, previous admissions, and some specific comorbidities were associated with increased mortality. Machine learning and pre-processing approaches offer a potentially valuable method for improving risk prediction for mortality, but more work is needed to show improvement beyond standard risk factors.

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Section: Methodsmentioning

confidence: 99%

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data

Shahidi,

Rennert-May,

D’Souza

et al. 2023

Sci Rep

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Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area

Qiu,

Liu,

Bai

et al. 2024

Preprint

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Background: Healthcare quality impacts patient prognosis in the intensive care unit (ICU). The healthcare quality can be indicated by the standardized mortality ratio (SMR) and is influenced by the volume of admitted patients. However, the correlation between the admission patient volume and SMR in ICUs remains unclear. Objective: This study examined SMR trends and their influencing factors and assessed the correlation between SMR and the admission patient volume across various ICU types. Methods: We analyzed data retrospectively gathered from 75 ICUs from a Quality Improvement Project from January 2011 to December 2022. It examined the correlations between SMR, admission patient volume, and other quality control indicators. We further compared SMR trends between two groups of ICUs with high or low admission volumes. The study also evaluated inter- and intra-group SMR disparities across hospital levels (secondary versus tertiary) and ICU types (general versus specialty). Results: The study encompassed 425,534 patients. A significant decline in SMR (P<0.001) was observed over the 12 years, alongside a notable negative correlation between admission patient volume and SMR (P<0.001). The low-admission group had a higher SMR than the high-admission group (P=0.010). Both the low (P=0.004) and high admission groups (P=0.001) showed a significant decreasing trend in SMR, with no significant inter-group difference (P=0.267). Moreover, the study identified distinct SMR trends between general ICUs (P=0.018) and secondary hospital ICUs (P=0.048) but not between specialtyICUs (P=0.511) and tertiary hospital ICUs (P=0.276). Conclusion: Over the past 12 years, SMR has significantly decreased. An inverse association was identified between ICU admission patient volume and SMR, with SMR exhibiting considerable variation across different ICU types. These findings underscore the importance of targeted management and healthcare quality enhancement strategies tailored to specific ICU settings.

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Relationship between institutional intensive care volume prior to the COVID-19 pandemic and in-hospital death in ventilated patients with severe COVID-19

Cited by 2 publications

References 33 publications

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data

Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area

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