The role of a noninvasive index ‘Spo2/ Fio2’ in predicting mortality among patients with COVID-19 pneumonia

SATICI, Merve OSOYDAN; İslam, Mehmet Muzaffer; Satıcı, Celal; Uygun, Cemre Nur; Ademoğlu, Enis; Altunok, İbrahim; Aksel, Gökhan; Eroğlu, Serkan Emre

doi:10.1016/j.ajem.2022.04.036

Cited by 13 publications

(7 citation statements)

References 25 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, these data are reinforced by the fact that the estimated glomerular filtration rate is also among the most relevant parameters, as it has been shown in other studies to be a good predictor of admission mortality in COVID-19. In addition, our study showed a high relevance of FiO2, closely related to acute hypoxic respiratory failure-, and potassium and pH as predictors of a poor outcome and mortality, which is compatible with the results obtained by Satici et al [48], Noori et al [49], or Liu et al [50]. Interestingly, hypokalemia has also been shown to be an indicator of a poorer prognosis and longer days of negative nucleic acid conversion.…”

Section: Discussionsupporting

confidence: 92%

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Queipo,

Barbado,

Torres

et al. 2024

Biomedicines

View full text Add to dashboard Cite

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

show abstract

Section: Discussionsupporting

confidence: 92%

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Queipo,

Barbado,

Torres

et al. 2024

Biomedicines

View full text Add to dashboard Cite

show abstract

“…For instance, no information about viral load and treatment options, which are known determinants of the duration of hospital stay and disease severity, was available for analysis [54]. Second, although previous studies reported a superior accuracy of mortality prediction in patients with COVID pneumonia by using a combination of different prediction indices compared to the use of a single indicator [31,55], our meta-analysis could not address this issue. In addition, we also did not compare the efficacy of PNI in mortality prediction with that of other predictive tools.…”

Section: Discussionmentioning

confidence: 96%

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

Hung

Wang

et al. 2022

Diagnostics

View full text Add to dashboard Cite

The associations of prognostic nutritional index (PNI) with disease severity and mortality in patients with coronavirus disease 2019 (COVID-19) remain unclear. Electronic databases, including MEDLINE, EMBASE, Google scholar, and Cochrane Library, were searched from inception to 10 May 2022. The associations of PNI with risk of mortality (primary outcome) and disease severity (secondary outcome) were investigated. Merged results from meta-analysis of 13 retrospective studies (4204 patients) published between 2020 and 2022 revealed a lower PNI among patients in the mortality group [mean difference (MD): −8.65, p < 0.001] or severity group (MD: −5.19, p < 0.001) compared to those in the non-mortality or non-severity groups. A per-point increase in PNI was associated with a reduced risk of mortality [odds ratio (OR) = 0.84, 95% CI: 0.79 to 0.9, p < 0.001, I2 = 67.3%, seven studies] and disease severity (OR = 0.84, 95% CI: 0.77 to 0.92, p < 0.001, I2 = 83%, five studies). The pooled diagnostic analysis of mortality yielded a sensitivity of 0.76, specificity of 0.71, and area under curve (AUC) of 0.79. Regarding the prediction of disease severity, the sensitivity, specificity, and AUC were 0.8, 0.61, and 0.65, respectively. In conclusion, this study demonstrated a negative association between PNI and prognosis of COVID-19. Further large-scale trials are warranted to support our findings.

show abstract

“…The results, we obtained, turned out to be interesting, regarding a higher SpO 2 /FiO 2 indicator in carriers of the 4aa genotype. After all, a high SpO 2 /FiO 2 ratio indicates the lower risk of acute respiratory distress syndrome and mortality in patients with COVID-19 [ 18 , 19 ]. It can be assumed, based indirectly on the data of Vaporidi et al and Liu et al [ 20 , 21 ], that the presence of eNOS deficiency (it should be reminded, that 4aa genotype is associated with a decrease in eNOS activity) in patients with lung damage can be a “good” factor.…”

Section: Discussionmentioning

confidence: 99%

NOS3 (rs61722009) gene variants testing in prediction of COVID-19 pneumonia severity

et al. 2023

View full text Add to dashboard Cite

The role of a noninvasive index ‘Spo2/ Fio2’ in predicting mortality among patients with COVID-19 pneumonia

Cited by 13 publications

References 25 publications

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

NOS3 (rs61722009) gene variants testing in prediction of COVID-19 pneumonia severity

Contact Info

Product

Resources

About