Routine blood tests are associated with short term mortality and can improve emergency department triage: a cohort study of &gt;12,000 patients

Kristensen, Michael; Iversen, Anne Kristine Servais; Gerds, Thomas Alexander; Østervig, Rebecca; Linnet, Jakob Danker; Barfod, Charlotte; Lange, Kai Henrik Wiborg; Sölétormos, György; Forberg, Jakob Lundager; Eugen‐Olsen, Jesper; Rasmussen, Lars S.; Schou, Morten; Køber, Lars; Iversen, Kasper

doi:10.1186/s13049-017-0458-x

Cited by 24 publications

(20 citation statements)

References 29 publications

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“…Routine blood tests were determined to include the full blood count, urea and electrolytes, liver function tests, and CRP. We selected these tests because they are widely done within existing care pathways in emergency departments, and results are typically available within 1 h. 16 We computed changes in blood tests from previous laboratory samples taken at least 30 days before presentation to hospital (available from Dec 1, 2017, onwards).…”

Section: Methodsmentioning

confidence: 99%

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test

Soltan

Kouchaki

Zhu

et al. 2021

The Lancet Digital Health

113

109

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BackgroundThe early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reasons. We aimed to develop and validate two early-detection models for COVID-19, screening for the disease among patients attending the emergency department and the subset being admitted to hospital, using routinely collected health-care data (laboratory tests, blood gas measurements, and vital signs). These data are typically available within the first hour of presentation to hospitals in high-income and middle-income countries, within the existing laboratory infrastructure. MethodsWe trained linear and non-linear machine learning classifiers to distinguish patients with COVID-19 from pre-pandemic controls, using electronic health record data for patients presenting to the emergency department and admitted across a group of four teaching hospitals in Oxfordshire, UK (Oxford University Hospitals). Data extracted included presentation blood tests, blood gas testing, vital signs, and results of PCR testing for respiratory viruses. Adult patients (>18 years) presenting to hospital before Dec 1, 2019 (before the first COVID-19 outbreak), were included in the COVID-19-negative cohort; those presenting to hospital between Dec 1, 2019, and April 19, 2020, with PCR-confirmed severe acute respiratory syndrome coronavirus 2 infection were included in the COVID-19-positive cohort. Patients who were subsequently admitted to hospital were included in their respective COVID-19-negative or COVID-19-positive admissions cohorts. Models were calibrated to sensitivities of 70%, 80%, and 90% during training, and performance was initially assessed on a held-out test set generated by an 80:20 split stratified by patients with COVID-19 and balanced equally with pre-pandemic controls. To simulate real-world performance at different stages of an epidemic, we generated test sets with varying prevalences of COVID-19 and assessed predictive values for our models. We prospectively validated our 80% sensitivity models for all patients presenting or admitted to the Oxford University Hospitals between April 20 and May 6, 2020, comparing model predictions with PCR test results. FindingsWe assessed 155 689 adult patients presenting to hospital between Dec 1, 2017, and April 19, 2020. 114 957 patients were included in the COVID-negative cohort and 437 in the COVID-positive cohort, for a full study population of 115 394 patients, with 72 310 admitted to hospital. With a sensitive configuration of 80%, our emergency department (ED) model achieved 77•4% sensitivity and 95•7% specificity (area under the receiver operating characteristic curve [AUROC] 0•939) for COVID-19 among all patients attending hospital, and the admissions model achieved 77•4% sensitivity and 94•8% specificity (AUROC 0•940) for the subset of patients admitted to hospital. Both models achieved high negative predictive values (NPV; >98•5%) ac...

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Section: Methodsmentioning

confidence: 99%

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test

Soltan

Kouchaki

Zhu

et al. 2021

The Lancet Digital Health

113

109

View full text Add to dashboard Cite

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“…Most emergency departments (ED) use risk scoring systems to perform triage, [1, 2] and widely used conventional triage algorithms are 5-level scales relying on measurements of vital signs and the presenting complaint [1, 2]. Blood tests can also be included, [3, 4] and risk stratification models using various biomarkers have been shown to have high discriminative powers regarding mortality in patients arriving at the EDs [5, 6].…”

Section: Introductionmentioning

confidence: 99%

Availability of suPAR in emergency departments may improve risk stratification: a secondary analysis of the TRIAGE III trial

Rasmussen

Kallemose

Kjøller

et al. 2019

Scand J Trauma Resusc Emerg Med

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Introduction Soluble urokinase plasminogen activator receptor (suPAR) is a prognostic and nonspecific biomarker associated with short-term mortality in emergency department (ED) patients. Therefore, the blood level of suPAR might be usable for identification of patients at high- and low risk, shortly after arrival at the ED. Here, we investigate the value of adding suPAR to triage and how this may impact on risk stratification regarding mortality. Methods The analyses were performed on the TRIAGE III cohort. Patients were triaged in four groups: Red, Orange, Yellow, and Green. Outcome was all-cause mortality within seven days. Discriminative abilities of triage and suPAR on mortality were assessed using the area under the curve (AUC) for receiver operating characteristics (ROC) curves. A suPAR cut-off value was generated using the Youden’s index. Patients were subsequently reclassified one triage level up if the suPAR level was above this cut-off and one level down if the suPAR level was below that value. Results The study included 4420 patients with an available triage category and suPAR measurement. suPAR was significantly better in predicting mortality than triage; AUC (95% confidence interval): 0.85 (0.80–0.89) vs. 0.71 (0.64–0.78), P < 0.001. Combining suPAR and triage yielded an AUC of 0.87 (0.82–0-93). The Youden’s cut-off of suPAR was 5.9 ng/mL and reclassified triage using this value resulted in a more accurate risk stratification regarding hospital admission and mortality. Conclusion Addition of suPAR to triage potentially improves prediction of short-term mortality. Measurement of suPAR in relation to the triage process may allow a more accurate identification of ED patients at risk. Trial registration Clinicaltrials.gov , NCT02643459. Registered 31 December 2015. https://clinicaltrials.gov/ct2/show/NCT02643459?cond=NCT02643459&rank=1 . Electronic supplementary material The online version of this article (10.1186/s13049-019-0621-7) contains supplementary material, which is available to authorized users.

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“…Many of these biomarkers have value in risk stratification, in addition to their diagnostic value. For instance C-reactive protein (CRP) and albumin are associated with mortality, [17, 18] and a statistical model containing eight routine biomarkers has been shown to be significantly better in predicting mortality compared to traditional triage [19]. Finally, newly discovered experimental biomarkers have been found to carry prognostic information that allows for an accurate risk discrimination [20–22].…”

Section: Introductionmentioning

confidence: 99%

Risk assessment models for potential use in the emergency department have lower predictive ability in older patients compared to the middle-aged for short-term mortality – a retrospective cohort study

Rasmussen

Carlson²,

Hasselbalch

et al. 2019

BMC Geriatr

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Background Older patients is a complex group at increased risk of adverse outcomes compared to younger patients, which should be considered in the risk assessment performed in emergency departments. We evaluated whether the predictive ability of different risk assessment models for acutely admitted patients is affected by age. Methods Cohort study of middle-aged and older patients. We investigated the accuracy in discriminating between survivors and non-survivors within 7 days of different risk assessment models; a traditional triage algorithm, a triage algorithm with clinical assessment, vital signs, routine biomarkers, and the prognostic biomarker soluble urokinase plasminogen activator receptor (suPAR). Results The cohort included 22,653 (53.2%) middle-aged patients (age 40–69 years), and 19,889 (46.8%) older patients (aged 70+ years). Death within 7 days occurred in 139 patients (0.6%) in middle-aged patients and 596 (3.0%) of the older patients. The models based on vital signs and routine biomarkers had the highest area under the curve (AUC), and both were significantly better at discriminating 7-day mortality in middle-aged patients compared to older patients; AUC (95% CI): 0.88 (0.84–0.91), 0.75 (0.72–0.78), P < 0.01, and 0.86 (0.82–0.90), 0.76 (0.73–0.78), P < 0.001. In a subgroup of the total cohort (6.400 patients, 15.0%), the suPAR level was available. suPAR had the highest AUC of all individual predictors with no significant difference between the age groups, but further research in this biomarker is required before it can be used. Conclusion The predictive value was lower in older patients compared to middle-aged patients for all investigated models. Vital signs or routine biomarkers constituted the best models for predicting 7-day mortality and were better than the traditional triage model. Hence, the current risk assessment for short-term mortality can be strengthened, but modifications for age should be considered when constructing new risk assessment models in the emergency department. Electronic supplementary material The online version of this article (10.1186/s12877-019-1154-7) contains supplementary material, which is available to authorized users.

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Routine blood tests are associated with short term mortality and can improve emergency department triage: a cohort study of >12,000 patients

Cited by 24 publications

References 29 publications

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test

Availability of suPAR in emergency departments may improve risk stratification: a secondary analysis of the TRIAGE III trial

Risk assessment models for potential use in the emergency department have lower predictive ability in older patients compared to the middle-aged for short-term mortality – a retrospective cohort study

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