qSOFA as predictor of mortality and prolonged ICU admission in Emergency Department patients with suspected infection

Canet, Emmanuel; Taylor, David; Khor, Richard; Krishnan, Vivek; Bellomo, Rinaldo

doi:10.1016/j.jcrc.2018.08.022

Cited by 28 publications

(32 citation statements)

References 19 publications

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“…The quick sepsis-related organ failure assessment (qSOFA) criteria [3] were introduced by the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). The qSOFA scores can be used outside of the intensive care unit (ICU) for predicting mortality or ICU stay [4][5][6][7][8][9]. However, several meta-analyses of qSOFA scores have shown that these scores had a poor sensitivity for predicting in-hospital mortality [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Kwon

Baek

2020

JCM

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The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (n = 19,353) were used as training-validation datasets and data from one (n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% (n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p < 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Kwon

Baek

2020

JCM

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“…. Similarly, in a study conducted in Australia among emergency patients with suspected sepsis, it was found that a positive qSOFA (≥ 2 points) identi ed those at high risk of in hospital mortality or longer ICU stay [26]. In the study done in India, the AUROC showed good discriminative power with qSOFA in predicting mortality (AUROC: 0.73; 95% CI, 0.69-0.77) among septic patients admitted, both in ICU and non-ICU [27].…”

Section: Discussionmentioning

confidence: 90%

High Mortality Rate of Obstetric Critically ILL Patients in Rwanda and Its Predictability

Rudakemwa

Cassidy

Twagirumugabe

2020

Preprint

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BackgroundReasons for obstetric admission in intensive care unit (ICU) vary from a setting to another. Outcomes from ICU and its prediction models are not well explored in Rwanda because of lack of appropriate scores. This study intended to assess profile and accuracy of predictive models for obstetric patients admitted in ICU in the two public tertiary hospitals in Rwanda.MethodsWe prospectively collected data from all obstetric patients admitted in the ICU of public referral hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admissions and factors for prognosis. We analysed the accuracy of mortality prediction by the quick Sequential Organ Failure Assessment (qSOFA) and Modified Early Obstetric Warning Score (MEOWS). ResultsObstetric patients represented 12.8% of all ICU admissions and 1.8% of all deliveries. Sepsis (31.9%) and haemorrhage (25.5%) were the two commonest reasons of admission. Mortality was 54.3% while the average length of stay was 6.6 days. MEOWS was an independent predictor to mortality (adjusted OR=1.25[1.07-1.46]; p=0.005). Similarly, one point of increase of the qSOFA increased odds of ICU mortality by 181% (adjusted OR=2.81[1.25-6.30]; p=0.012). The Area Under the Receiver Operating Characteristic Curve (AUROC) for MEOWS was 0.773[0.666-0.880], p=0.0001 and that of the qSOFA was 0.764[0.654-0.873]; p=0.0001.ConclusionSepsis is the most common reason for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA could accurately predict the mortality for those patients but further explorations on a larger sample size are guaranteed.

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“…For those patients who were predicted to have longer hospital stays, underlying co-morbidities and medical history were crucial. Aggressive treatment strategies including early anti-viral treatment, adequate fluid status monitoring, precautions of prolonged bed-ridden complications and strict control of blood glucose level, may increase the quality of care and speed up the recovery of this group of patients, in order to free up hospital ward space during the influenza outbreak season [1,7,8].…”

Section: Discussionmentioning

confidence: 99%

“…The qSOFA score was instead primarily designed to be a simple and prompt screening tool for identifying severe septic patients outside the ICU, who are more likely to have higher risk of death and spend more days in the ICU [6]. One study showed that ED patients with suspected infection with qSOFA ≥ 2 have a median hospital LOS of 111 h, compared to 74 h for the qSOFA b 2 group [7]. However, most of the studies discussed prolonged ICU stays and mortality instead of the entire hospital LOS.…”

Section: Discussionmentioning

confidence: 99%

“…Approximately 55,000 patients visit the study emergency department (ED) annually; about 33% of these ED patients are elderly adults [5]. The study ED patients were managed by board-certified emergency physicians [6,7]. We included geriatric patients (age ≥ 65 years) who presented to the ED between January 1, 2010 and December 31, 2015, who fulfilled the following criteria:…”

Section: Study Design Setting and Participantsmentioning

confidence: 99%

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Quick-SOFA score ≥ 2 predicts prolonged hospital stay in geriatric patients with influenza infection

Yeh

Chen

Hsu

et al. 2020

The American Journal of Emergency Medicine

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The quick Sepsis-Related Organ Failure Assessment (qSOFA) score was designed to predict mortality among sepsis patients. However, it has never been used to identify prolonged length of hospital stay (pLOS) in geriatric patients with influenza infection. We conducted this study to clarify this issue. Methods: We conducted a retrospective case-control study, including geriatric patients (aged ≥ 65 years) with influenza infection visiting the emergency department (ED) of a medical center between January 01, 2010 and December 31, 2015. The included patients were divided into two groups on the basis of their qSOFA score: qSOFA b 2, and qSOFA ≥ 2. Data regarding demographics, vital signs, qSOFA score, underlying diseases, subtypes of influenza, and outcomes were included in the analysis. We investigated the association between qSOFA score ≥ 2 and pLOS (N9 days) via logistic regression. Results: Four hundred and nine geriatric patients were included in this study with a mean age of 79.5 (standard deviation [SD], 8.3) years. The median length of stay (LOS) was 7.0 (interquartile range [IQR], 4-12) days, while the rate of pLOS (N 9 days) was 32%. The median LOS in the qSOFA ≥ 2 group, 11.0 (7-15) days, was longer than the qSOFA b 2 group, 6.0 (4-10) days (p-value b0.01). Logistic regression showed that qSOFA ≥ 2 predicts pLOS with an odds ratio of 3.78 (95% confidence interval, 2.04-6.97). Conclusion: qSOFA score ≥ 2 is a prompt and simple tool to predict pLOS in geriatric patients with influenza infection.

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qSOFA as predictor of mortality and prolonged ICU admission in Emergency Department patients with suspected infection

Cited by 28 publications

References 19 publications

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

High Mortality Rate of Obstetric Critically ILL Patients in Rwanda and Its Predictability

Quick-SOFA score ≥ 2 predicts prolonged hospital stay in geriatric patients with influenza infection

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qSOFA as predictor of mortality and prolonged ICU admission in Emergency Department patients with suspected infection

Cited by 28 publications

References 19 publications

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

High Mortality Rate of Obstetric Critically ILL Patients in Rwanda and Its Predictability

Quick-SOFA score ≥ 2 predicts prolonged hospital stay in geriatric patients with influenza infection

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Quick-SOFA score ≥ 2 predicts prolonged hospital stay in geriatric patients with influenza infection