Efficacy and accuracy of qSOFA and SOFA scores as prognostic tools for community-acquired and healthcare-associated pneumonia

Asai, Nobuhiro; Watanabe, Hiroki; Shiota, Arufumi; Kato, Hideo; Sakanashi, Daisuke; Hagihara, Mao; Yamagishi, Yuka; Suematsu, Hiroyuki; Koizumi, Yusuke

doi:10.1016/j.ijid.2019.04.020

Cited by 61 publications

(80 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After a preliminary descriptive analysis of the data (SD, means, and percentages), we compared the differences between categorical variables using The PSI was initially designed to help practitioners identify which low-risk patients with CAP could be safely treated in an outpatient setting, 9 although it has subsequently been used to assess post-discharge mortality for those treated as inpatients. The capability of PSI to predict hospital mortality in our study was similar or even slightly better than that of other previous studies in CAP, in which PSI presented an AUROC of 0•778 to predict 30-day mortality or transfer to UCI 28 and 0•812 to predict 30-day mortality. 30 Our findings provide evidence that the PSI is quite a good score for assessing the risk of in-hospital mortality for patients admitted with COVID-19 pneumonia.…”

Section: Discussionsupporting

confidence: 84%

“…9 A cut-off of 91 points (Risk Class IV-V) was considered positive for a poor prognosis. 24,25,26,27,28 We considered CURB-65 to be predictive of poor prognosis when three of the five variables (confusion, BUN >7 mmol/L, respiratory rate ≥30 bpm, systolic blood pressure <90 mmHg or diastolic blood pressure ≤60 mmHg, and age ≥65 years) were positive. 21,22,23,24 The MuLBSTA score is a newly designed score for viral pneumonia 11 points or more is related with poor prognosis, as defined in the original study.…”

Section: Study Design and Participantsmentioning

confidence: 99%

“…20,21,22 In addition, qSOFA has shown good performance in predicting prognosis in pneumonia, both in the emergency department 23,24,25,26,27 and in wards. 28 The advantage of the qSOFA score is that the variables are clinical and thus laboratory tests are not required. 12…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Severity scores in COVID-19 pneumonia: a multicenter, retrospective, cohort study

Artero

Madrazo

Fernández-Garcés

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundIdentification of patients on admission to hospital with Coronavirus infectious disease 2019 (COVID-19) pneumonia who can develop poor outcomes have not yet ben comprehensively assessed.ObjectiveTo compare severity scores used for community acquired pneumonia to identify high-risk patients.DesignPSI, CURB-65, qSOFA and MuLBSTA, a new score for viral pneumonia, were calculated on admission to hospital to identify high-risk patients for in-hospital mortality. Area under receiver operating characteristics curve (AUROC), sensitivity and specificity for each score were determined and AUROC were compared among them.ParticipantsPatients with COVID-19 pneumonia included in the SEMI-COVID-19 Network.Key resultsWe examined 10,238 patients with COVID-19. Mean age of patients was 66.6 years and 57.9% were males. The most common comorbidities were: hypertension (49.2%), diabetes (18.8%) and chronic obstructive pulmonary disease (12.8%). Acute respiratory distress syndrome (34.7%) and acute kidney injury (13.9%) were the most common complications. In-hospital mortality was 20.9%. PSI and CURB-65 showed the highest AUROC (0.835 and0.825, respectively). qSOFA and MuLBSTA had a lower AUROC (0.728 and 0.715, respectively). qSOFA was the most specific score (specificity 95.7%) albeit its sensitivity was only 26.2%. PSI had the highest sensitivity (84.1%) and a specificity of 72.2%.ConclusionsPSI and CURB-65, specific severity scores for pneumonia, were the best scores for COVID-19 pneumonia and were better than qSOFA and MuLBSTA. Additionally, qSOFA, the simplest score to perform, was the most specific albeit the least sensitive.

show abstract

Section: Discussionsupporting

confidence: 84%

Section: Study Design and Participantsmentioning

confidence: 99%

See 1 more Smart Citation

Severity scores in COVID-19 pneumonia: a multicenter, retrospective, cohort study

Artero

Madrazo

Fernández-Garcés

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Several scoring systems using clinical and laboratory variables have been developed to predict the outcome of critically ill patients. The APACHE II and SOFA scores are representative of these methods and have demonstrated accurate prediction of mortality in this patient subset [13,14,[25][26][27][28]. However, these approaches showed poor performance for critically ill patients with AKI [11,12].…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Kang

Kim

et al. 2020

Crit Care

View full text Add to dashboard Cite

Background Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset. Methods We randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curves (AUCs) of several machine learning algorithms with that of the APACHE II, SOFA, and the new abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC model) results. Results For the ICU mortality, the random forest model showed the highest AUC (0.784 [0.744–0.825]), and the artificial neural network and extreme gradient boost models demonstrated the next best results (0.776 [0.735–0.818]). The AUC of the random forest model was higher than 0.611 (0.583–0.640), 0.677 (0.651–0.703), and 0.722 (0.677–0.767), as achieved by APACHE II, SOFA, and MOSAIC, respectively. The machine learning models also predicted in-hospital mortality better than APACHE II, SOFA, and MOSAIC. Conclusion Machine learning algorithms increase the accuracy of mortality prediction for patients undergoing CRRT for acute kidney injury compared with previous scoring models.

show abstract

“…The SOFA and qSOFA assessments are useful diagnostic tools for predicting hospital mortality among adults with community-acquired pneumonia (CAP) and sepsis in the ICU [19][20] . Asai's study suggested that the combination of a qSOFA score ≥2 and a SOFA score ≥4 was an independent unfavorable prognostic factor for 30-day mortality among CAP patients [21] . SOFA criteria predicting infection-related in-hospital mortality in ICU patients performed better than SIRS criteria and qSOFA score [22] .…”

Section: Discussionmentioning

confidence: 99%

Clinical characteristics and risk factors for mortality in patients with coronavirus disease 2019 in intensive care unit: a single-center, retrospective, observational study in China

Sai

Yan

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Coronavirus disease 2019 (COVID-19) is a potentially life-threatening contagious disease which has spread all over the world. Risk factors for the clinical outcomes of COVID-19 pneumonia in intensive care unit (ICU) have not yet been well determined. Methods: In this retrospective, single-centered, observational study, we consecutively included 47 patients with confirmed COVID-19 who were admitted to the ICU of Leishenshan Hospital in Wuhan, China, from February 24 to April 5, 2020. Clinical characteristics and outcomes were collected and compared between survivors and non-survivors. Multivariable logistic regression was used to explore the risk factors associated with death in patients of COVID-19.Results: The study cohort included 47 adult patients with a median age of 70.55±12.52 years, and 30 (63.8%) patients were men. Totally 15 (31.9%) patients died. Compared with survivors, non-survivors were more likely to develop septic shock (6 [40%] patients vs 3 [9.4%] patients ), disseminated intravascular coagulation (3 [21.4%] vs 0), and had higher score of APACHE II (25.07±8.03 vs 15.56±5.95), CURB-65 (3[2-4] vs 2[1-3]), Sequential Organ Failure Assessment (SOFA) (7[5-9] vs 3[1-6]), higher level of D-dimer (5.74 [2.32-18] vs 2.05 [1.09-4.00] ) and neutrophil count (9.4[7.68-14.54] vs 5.32[3.85-9.34] ). SOFA score (OR 1.47, 1.01–2.13; p=0.0042) and lymphocyte count (OR 0.02, 0.00–0.86; p=0.042) on admission were independently risk factors for mortality. Patients with higher lymphocyte count (>0.63×109/L) and lower SOFA score ≤4 on admission had a significantly well prognosis than those with lower lymphocyte count (≤0.63×109/L) and higher SOFA score >4 in overall survival.Conclusions: Higher SOFA score and lower lymphocyte count on admission were associated with poor prognosis of patients with COVID-19 in ICU. Lymphocyte count may serve as a promising prognostic biomarker.

show abstract

Efficacy and accuracy of qSOFA and SOFA scores as prognostic tools for community-acquired and healthcare-associated pneumonia

Cited by 61 publications

References 20 publications

Severity scores in COVID-19 pneumonia: a multicenter, retrospective, cohort study

Severity scores in COVID-19 pneumonia: a multicenter, retrospective, cohort study

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Clinical characteristics and risk factors for mortality in patients with coronavirus disease 2019 in intensive care unit: a single-center, retrospective, observational study in China

Contact Info

Product

Resources

About