Prediction of an increase in oxygen requirement of SARS-CoV-2 pneumonia using three different scoring systems

Kodama, Tatsuhiko; Obinata, Hirofumi; Mori, Hitoshi; Murakami, Wakana; Suyama, Yohsuke; Sasaki, Hisashi; Kouzaki, Yuji; Kawano, Shuichi; Kawana, Akihiko; Mimura, Satoshi

doi:10.1016/j.jiac.2020.12.009

Cited by 13 publications

(12 citation statements)

References 19 publications

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“…A retrospective study done by Fan G et al, which studied 654 hospitalised patients showed ADROP as a reliable tool for risk stratification of death in COVID 19 hospitalised patients on admission [19] . Study done by T. Kodama, H. Obinata at Tokyo, Japan among 214 patients showed that the expanded CURB-65 score can be a better predictor of an increase in oxygen requirement in patients with SARS-CoV-2 pneumonia than A-DROP and CURB 65 score [20] .…”

Section: Discussionmentioning

confidence: 97%

Comparison between Curb-65 and A-Drop severity score and its correlation with clinical outcome in Covid-19 patients admitted in a tertiary care center

Vs¹,

Vikas²,

Nishchitha³

et al. 2021

Int. J. Adv. Res. Med.

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Background: Respiratory failure is the leading cause of mortality in patients with COVID-19. CURB-65 and A-DROP are widely used in predicting the severity and outcome in Community Acquired Pneumonia (CAP). However these tools have not been assessed in patients with COVID-19 infection. A simple tool to stratify patients with COVID-19 pneumonia and for predicting the outcome at the time of hospital admission would be useful. Objective: To calculate CURB-65 and A-DROP score in COVID 19 patients and to correlate them with clinical outcome, hospital stay and CT severity score. Methods: This cross sectional study included consenting adult patients who presented with COVID 19 infection confirmed by real time RT-PCR, conducted at the hospital attached to Bangalore Medical College and Research Institute involving 100 subjects. CURB-65 and A-DROP score was calculated, CT scan of thorax was done at the time of admission. Patients underwent various biochemical investigations. Based on WHO criteria patients were divided into mild, moderate and severe COVID-19 illness. We assessed the outcome with relation to measured CURB-65 /A-DROP score to know the prognosis in COVID19 patients. Results: The meanSD age of the subjects was 55.61  14.22 with 34 females and 66 males. Among 100 patients 76(76%) recovered where as death was seen in 24(24%). Maximum recovered patients had CURB-65 score 0, 1 and 2 and A-DROP score 0, 1 and 2 whereas maximum death patients had CURB-65 score 3, 4 and 5 and A-DROP score of 3,4 and 5. Chi-square test showed statistical significant association between CURB-65 score and Outcome (2=39.7; p=0.00), statistical significant association was also between A-DROP score and Outcome (2= 43.55; p=0.00). Chi-square test applied to associate the A-DROP and CURB-65 score showed statistical significant association between the two scores (2= 194.65; p=0.00). Both the scores individually showed statistically significant correlation with CT severity and duration of hospital stay. Conclusion:This study revealed significant correlation between the clinical severity of COVID 19 illness with CURB-65 and A-DROP scores. Study also showed significant correlation between the two measured scores with CT severity score and also duration of hospital stay. Hence, CURB-65 and A-DROP score can be used to assess the severity and predict the outcome in COVID 19 infection.

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Section: Discussionmentioning

confidence: 97%

Comparison between Curb-65 and A-Drop severity score and its correlation with clinical outcome in Covid-19 patients admitted in a tertiary care center

Vs¹,

Vikas²,

Nishchitha³

et al. 2021

Int. J. Adv. Res. Med.

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“…Also, inflammatory markers including CRP and D-dimer have been found to be more frequently elevated in severe COVID pneumonia, correlating inversely with respiratory function at presentation, and predicting mortality in COVID ARDS. [17] Similarly, retrospective cohorts studying patients in ICU [18] from Japan have found higher levels of D-dimer in those with HFNC failure compared with HFNC success [4.8 mg/L vs. 2.6 mg/L, p = 0.02], as well as ward settings such as Calligaro et al (1.03 mg/L vs 0.56 mg/L, p =0.002). [9] We found a D-dimer level of ≥1.7 mg/L to correctly classify 87% of HFNC failure cases and thus may help to triage patients at high likelihood of HFNC failure to early ICU transfer and a lower threshold for endotracheal intubation.…”

Section: Discussionmentioning

confidence: 97%

Use of HFNC in COVID-19 patients in non-ICU setting: Experience from a tertiary referral centre of north India and a systematic review of literature

Aggarwal

Arora

Mittal

et al. 2021

Preprint

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IntroductionThe rapid surge of cases and insufficient numbers of intensive care unit (ICU) beds have forced hospitals to utilise their general wards for administration of non-invasive respiratory support including HFNC(High Flow Nasal Cannula) in severe COVID-19. However, there is a dearth of data on the success of such advanced levels of care outside the ICU setting. Therefore, we conducted an observational study at our centre, and systematically reviewed the literature, to assess the success of HFNC in managing severe COVID-19 cases outside the ICU.MethodsA retrospective cohort study was conducted in a tertiary referral centre where records of all adult COVID-19 patients (≥18 years) requiring HFNC support were between September and December 2020 were analysed. HFNC support was adjusted to target SpO2 ≥90% and respiratory rate ≤30 per min. The clinical, demographic, laboratory, and treatment details of these patients were retrieved from the medical records and entered in pre-designed proforma. Outcome parameters included duration of oxygen during hospital stay, duration of HFNC therapy, length of hospital stay and death or discharge. HFNC success was denoted when a patient did not require escalation of therapy to NIV or invasive mechanical ventilation, or shifting to the ICU, and was eventually discharged from the hospital without oxygen therapy; otherwise, the outcome was denoted as HFNC failure. Systematic review was also performed on the available literature on the experience with HFNC in COVID-19 patients outside of ICU settings using the MEDLINE, Web of Science and Embase databases. Statistical analyses were performed with the use of STATA software, version 12, OpenMeta[Analyst], and visualization of the risk of bias plot using robvis.ResultsThirty-one patients receiving HFNC in the ward setting, had a median age of 62 (50 – 69) years including 24 (77%) males. Twenty-one (68%) patients successfully tolerated HFNC and were subsequently discharged from the wards, while 10 (32%) patients had to be shifted to ICU for non-invasive or invasive ventilation, implying HFNC failure. Patients with HFNC failure had higher median D-dimer values at baseline (2.2 mcg/ml vs 0.6 mcg/ml, p=0.001) and lower initial SpO2 on room air at admission (70% vs 80%, p=0.026) as compared to those in whom HFNC was successful .A cut-off value of 1.7 mg/L carried a high specificity (90.5%) and moderate sensitivity (80%) for the occurrence of HFNC failure. Radiographic severity scoring as per the BRIXIA score was comparable in both the groups(11 vs 10.5 out of 18, p=0.78). After screening 98 articles, total of seven studies were included for synthesis in the systematic review with a total of 820 patients, with mean age of the studies ranging from 44 to 83 years and including 62% males. After excluding 2 studies from the analysis, the pooled rates of HFNC failure were 36.3% (95% CI 31.1% – 41.5%) with no significant heterogeneity (I2 =0%, p=0.55).ConclusionsOur study demonstrated successful outcomes with use of HFNC in an outside of ICU setting among two-thirds of patients with severe COVID-19 pneumonia. Lower room air SpO2 and higher D-dimer levels at presentation were associated with failure of HFNC therapy leading to ICU transfer for endotracheal intubation or death. Also, the results from the systematic review demonstrated similar rates of successful outcomes concluding that HFNC is a viable option with failure rates similar to those of ICU settings in such patients.

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“…Fourth, the exacerbation probability in multiple patients can be objectively compared. Scoring for patients with pneumonia (using CURB-65, A-DROP, Pneumonia Severity Index, SMART-COP, NEWS2, CRB-65, or qSOFA) can also predict the severity of COVID-19 with high accuracy [22][23][24] . In addition, a new scoring system for COVID-19 has been proposed.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19

et al. 2022

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Background: The coronavirus disease poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19.Methods: This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm.Results: In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for 4 predictive capability was SpO2, followed by age, respiratory rate, and systolic blood pressure. Conclusion:In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier.Prospective validation studies are needed to verify the application of the tool in clinical practice.

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Prediction of an increase in oxygen requirement of SARS-CoV-2 pneumonia using three different scoring systems

Cited by 13 publications

References 19 publications

Comparison between Curb-65 and A-Drop severity score and its correlation with clinical outcome in Covid-19 patients admitted in a tertiary care center

Comparison between Curb-65 and A-Drop severity score and its correlation with clinical outcome in Covid-19 patients admitted in a tertiary care center

Use of HFNC in COVID-19 patients in non-ICU setting: Experience from a tertiary referral centre of north India and a systematic review of literature

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19

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