Admission criteria in critically ill COVID-19 patients: A physiology-based approach

Ceruti, Samuele; Glotta, Andrea; Biggiogero, Maira; Maida, Pier Andrea; Marzano, Martino; Urso, Patrizia; Bona, G; Garzoni, Christian; Molnár, Zsolt

doi:10.1371/journal.pone.0260318

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Age and other health problems were two of the most important things that were looked at when deciding who could go to the ICU [44]. Several scientific societies have issued recommendations regarding patients' eligibility for ICU treatment in the case of the COVID-19 pandemic and limited space availability, as well as indications for undertaking resuscitation at both the pre-hospital and in-hospital stages [45][46][47][48].…”

Section: Discussionmentioning

confidence: 99%

Uncovering the effects of COVID-19 on in-hospital cardiac arrest – a living systematic review and meta-analysis

Bielski¹,

Pruc²,

Rafique³

et al. 2023

Ann Agric Environ Med.

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Introduction and Objective.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic and had a negative impact on the entire health care system. To understand the effect of COVID-19 on outcomes of in-hospital cardiac arrest (IHCA), a systematic review and meta-analysis of studies was designed to compare the pre-and intra-pandemic periods of adult patients who suffered cardiac arrest, and additionally by performing a sub-analysis related to COVID-19 positive vs. negative patients in the same group of patients. Materials and method. To evaluate the impact of COVID-19 on IHCA outcomes a systematic review and meta-analysis was performed. Pubmed (MEDLINE), Scopus, Embase, Web of Science, and Cochrane database were searched for articles published from 1 January 2020 -8 April 2023. Results. Return of spontaneous circulation events among IHCA patients in pre-COVID-19 and COVID-19 pandemic periods varied and amounted to 64.0% vs. 60.0%, respectively (OR=1.23; 95%CI: 1.19 to 1.26; p<0.001). Re-arrest occurrence was 4.5% vs. 4.9%, respectively (OR=1.24; 95%CI: 1.00 to 1.53; p=0.05). Survival to hospital discharge (SHD) was 25.1% compared to 20.9% for COVID-19 period (OR = 1.17; 95%CI: 0.96 to 1.41; p=0.12). During the COVID-19 period, SHD in COVID-19 positive patients was 14.0% compared to 25.9% for patients without 95%CI: 0.28 to 1.86; p=0.50). 30-day survival rate among COVID-19 positive vs. negative patients was 62.6% vs. 58.3%, respectively (OR =0.99; 95%CI: 0.23 to 4.24; p=0.99). Conclusions. Patients with SARS-CoV-2 infection had reduced rates of ROSC and SDH, as well as poorer neurologic outcomes and increased in hospital re-arrests during the COVID-19 period. However, the 30-day survival rate was similar in SARS-CoV-2 positive and negative patients.

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Section: Discussionmentioning

confidence: 99%

Uncovering the effects of COVID-19 on in-hospital cardiac arrest – a living systematic review and meta-analysis

Bielski¹,

Pruc²,

Rafique³

et al. 2023

Ann Agric Environ Med.

View full text Add to dashboard Cite

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“…Recent studies have revealed that different approaches were considered to manage and prioritize infected patients, varying from home isolation to ICU admission [17,18]. Thus, the effective factors regarding clinical decision-making and different approaches to managing admitted patients can be divided into six categories, comprised of (i) effective factors related to the length of stay, (ii) effective factors related to ward referrals, (iii) effective factors related to home referrals or home isolation, (iv) effective factors related to deterioration diagnosis, (v) effective factors related to identifying the discharge time, and (vi) effective factors related to ICU referrals [19][20][21][22][23][24][25]. Thus, the main objective of our study was to determine the essential and critical factors for decision-making based on case severity and disease stage through a systematic review and the opinions of clinicians.…”

Section: Introductionmentioning

confidence: 99%

Identification of the essential and critical factors of decision-making for COVID-19 patient management: a mixed-method study

Abtahi,

Kalhori,

Abooei

et al. 2024

JCTR

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Background: Understanding the contributing factors for decision-making based on the disease stage is highly crucial to improving patient care. Aim: This study aimed to identify the critical and essential factors to enhance clinical decision-making for COVID-19 patients. Methods: This mixed-method research was conducted in two phases. In the first phase, a systematic literature review was performed using defined search strategies across four databases, including PubMed, Scopus, Web of Science, and IEEE. A total of 136 studies were obtained. Next, a questionnaire-based survey was conducted to validate the findings from the review and to categorize the factors into essential and critical factors. The content validity ratio was used to categorize the factors accordingly. The identified factors were classified into six main categories based on the stages of care and the corresponding decision-making. Results: The expert panel consisted of 10 clinicians from various fields. The potential factors were categorized into six categories. A total of 293 factors were found in the literature review. The findings of the consensus survey revealed 10 factors related to the decisions on the length of stay, eight factors for ward referral decisions, one factor for decisions on home referrals, six factors for deterioration diagnosis decisions, two factors for discharge decisions, and 10 factors for decisions on intensive care unit referrals. In addition, the study identified respiratory rate, oxygen saturation at administration, arterial oxygen pressure, sequential organ failure assessment score, and glomerular filtration rate as significant decision-making factors for COVID-19 patient management. Conclusion: For medical emergencies (e.g., COVID-19 management), fewer but more significant factors may increase the efficiency of decision-making, thereby improving the quality of patient management. On this basis, this study identified the essential and critical factors for decision-making at different stages of COVID-19 patient management. Relevance for Patients: This study identified the most important factors in diagnosing the deterioration of COVID-19 patients to improve the treatment outcome of COVID-19 patients.

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“…Standardized intensive care unit admission criteria have been proven to safely reduce intensive care unit overload. However, state-of-the-art AI systems face challenges in standardizing COVID-19 severity states 1–3 …”

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confidence: 99%

A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans

Henao,

Depotter,

Bower

et al. 2023

Invest Radiol

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Objectives The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. Materials and Methods The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19–induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19–positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19–positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. Results AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. Conclusions A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.

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Admission criteria in critically ill COVID-19 patients: A physiology-based approach

Cited by 5 publications

References 26 publications

Uncovering the effects of COVID-19 on in-hospital cardiac arrest – a living systematic review and meta-analysis

Uncovering the effects of COVID-19 on in-hospital cardiac arrest – a living systematic review and meta-analysis

Identification of the essential and critical factors of decision-making for COVID-19 patient management: a mixed-method study

A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans

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