SaO2/FiO2 ratio as risk stratification for patients with sepsis

Santana, Adriell Ramalho; Sousa, Jaqueline Lima de; Amorim, Fábio Ferreira; Menezes, Bárbara Magalhães; Araújo, Fernanda Vilas Bôas; Soares, Felipe Bozi; Santos, Louise Cristhine de Carvalho; Araújo, Mariana Pinheiro Barbosa de; Rocha, Pedro Henrique Gomes; Júnior, Pedro Nery Ferreira; Paiva, Alessandra Vasconcelos da Silva; Kanhouche, Gabriel; Amorim, Alethea Patrícia Pontes; Neto, José Aires de Araújo; Moura, Edmilson Bastos de; Maia, Marcelo de Oliveira

doi:10.1186/cc12951

Cited by 4 publications

(2 citation statements)

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“…The PaO2/FIO2 ratio is a highly regarded parameter of respiratory function and serves as a predictor of ICU mortality in patients with sepsis and ARDS. [ 11 ] Moreover, SaO2/FIO2 is commonly used as an end point in clinical trials for patients with COVID-19, with success and is considered an accurate predictor of PaO2/FIO2 ratio. [ 10 ] ARDS severity when measure by PaO2/FiO2 is classified as mild if 200-300, moderate if 100-200, and severe is less than 100.…”

Section: Discussionmentioning

confidence: 99%

A randomized pilot study using calcitriol in hospitalized COVID-19 patients

Elamir

Amir

Lim

et al. 2022

Bone

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The systemic illness associated with SARS-CoV-2 infection results in hospitalization rate of 380.3 hospitalizations per 100,000 population, overwhelming health care systems. Vitamin D regulates expression of approximately 11,000 genes spanning many physiologic functions that include regulation of both innate and adaptive immune function. We investigate potential benefit of calcitriol therapy given to patients hospitalized with COVID-19. This was an open label, randomized clinical trial of calcitriol or no treatment given to hospitalized adult patients with COVID-19. Subjects were randomly assigned treatment with calcitriol 0.5 mcg daily for 14 days or hospital discharge; or no treatment [ 1 : 1 ] at time of enrollment. We enrolled 50 consecutive patients, 25 per trial arm. The change in peripheral arterial oxygen saturation to the inspired fraction of oxygen (SaO2/FIO2 ratio) on admission and discharge between the groups the control group had an average increase of +13.2 (+/- 127.7) on discharge and the calcitriol group had an increase of +91.04 (+/- 119.08) (p= .0305), suggesting an improvement in oxygenation among subjects who received calcitriol. Additionally, 12 patients in the control group required oxygen supplementation on admission and 21 of them were discharged on room air. 14 subjects needed oxygen supplementation in the calcitriol group on admission while all 25 were discharged on room air. Other clinical markers showed the average length of stay was 9.24 (± 9.4) in the control group compared to 5.5 (± 3.9) days in the calcitriol group (p= .14). The need for ICU transfer was 8 in the control group and 5 in the calcitriol group. There were 3 deaths and 4 readmissions in the control group and 0 deaths and 2 readmissions in the calcitriol group. This pilot study illustrates improvement in oxygenation among hospitalized patients with COVID-19 treated with calcitriol and suggests the need for a larger randomized trial.

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Section: Discussionmentioning

confidence: 99%

A randomized pilot study using calcitriol in hospitalized COVID-19 patients

Elamir

Amir

Lim

et al. 2022

Bone

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“…Kang et al [44] recently confirmed the strong association between serum creatinine level and mortality. Regarding respiration (PaO2), Santana and colleagues [45] recently showed how the SaO2/FiO2 ratio (a rate strongly correlated to the PaO2/FiO2 ratio) is associated with mortality from sepsis. This aspect suggests the need of additional studies and analyses in this direction.…”

Section: Discussionmentioning

confidence: 99%

Data analytics and clinical feature ranking of medical records of patients with sepsis

Chicco

Oneto

2021

BioData Mining

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Background Sepsis is a life-threatening clinical condition that happens when the patient’s body has an excessive reaction to an infection, and should be treated in one hour. Due to the urgency of sepsis, doctors and physicians often do not have enough time to perform laboratory tests and analyses to help them forecast the consequences of the sepsis episode. In this context, machine learning can provide a fast computational prediction of sepsis severity, patient survival, and sequential organ failure by just analyzing the electronic health records of the patients. Also, machine learning can be employed to understand which features in the medical records are more predictive of sepsis severity, of patient survival, and of sequential organ failure in a fast and non-invasive way. Dataset and methods In this study, we analyzed a dataset of electronic health records of 364 patients collected between 2014 and 2016. The medical record of each patient has 29 clinical features, and includes a binary value for survival, a binary value for septic shock, and a numerical value for the sequential organ failure assessment (SOFA) score. We disjointly utilized each of these three factors as an independent target, and employed several machine learning methods to predict it (binary classifiers for survival and septic shock, and regression analysis for the SOFA score). Afterwards, we used a data mining approach to identify the most important dataset features in relation to each of the three targets separately, and compared these results with the results achieved through a standard biostatistics approach. Results and conclusions Our results showed that machine learning can be employed efficiently to predict septic shock, SOFA score, and survival of patients diagnoses with sepsis, from their electronic health records data. And regarding clinical feature ranking, our results showed that Random Forests feature selection identified several unexpected symptoms and clinical components as relevant for septic shock, SOFA score, and survival. These discoveries can help doctors and physicians in understanding and predicting septic shock. We made the analyzed dataset and our developed software code publicly available online.

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Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record

Lee¹,

Kwon²,

Park³

et al. 2020

Critical Care Medicine

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Objectives: A deep learning-based early warning system is proposed to predict sepsis prior to its onset. Design: A novel algorithm was devised to detect sepsis 6 hours prior to its onset based on electronic medical records. Setting: Retrospective cohorts from three separate hospitals are used in this study. Sepsis onset was defined based on Sepsis-3. Algorithms are evaluated based on the score function used in the Physionet Challenge 2019.Patients: Over 60,000 ICU patients with 40 clinical variables (vital signs, laboratory results) for each hour of a patient’s ICU stay were used. Interventions: None. Measurements and Main Results: The proposed algorithm predicted the onset of sepsis in the preceding n hours (where n = 4, 6, 8, or 12). Furthermore, the proposed method compared how many sepsis patients can be predicted in a short time with other methods. To interpret a given result in a clinical perspective, the relationship between input variables and the probability of the proposed method were presented. The proposed method achieved superior results (area under the receiver operating characteristic curve, area under the precision-recall curve, and score) and predicted more sepsis patients in advance. In official phase, the proposed method showed the utility score of –0.101, area under the receiver operating characteristic curve 0.782, area under the precision-recall curve 0.041, accuracy 0.786, and F-measure 0.046. Conclusions: Using Physionet Challenge 2019, the proposed method can accurately and early predict the onset of sepsis. The proposed method can be a practical early warning system in the environment of real hospitals.

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SaO2/FiO2 ratio as risk stratification for patients with sepsis

Cited by 4 publications

References 1 publication

A randomized pilot study using calcitriol in hospitalized COVID-19 patients

A randomized pilot study using calcitriol in hospitalized COVID-19 patients

Data analytics and clinical feature ranking of medical records of patients with sepsis

Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record

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