A prospective study of consecutive emergency medical admissions to compare a novel automated computer-aided mortality risk score and clinical judgement of patient mortality risk

ObjectivesTo compare the performance of a validated automatic computer-aided risk of mortality (CARM) score versus medical judgement in predicting the risk of in-hospital mortality for patients following emergency medical admission.DesignA prospective study.SettingConsecutive emergency medical admissions in York hospital.ParticipantsElderly medical admissions in one ward were assigned a risk of death at the first post-take ward round by consultant staff over a 2-week period. The consultant medical staff used the same variables to assign a risk of death to the patient as the CARM (age, sex, National Early Warning Score and blood test results) but also had access to the clinical history, examination findings and any immediately available investigations such as ECGs. The performance of the CARM versus consultant medical judgement was compared using the c-statistic and the positive predictive value (PPV).ResultsThe in-hospital mortality was 31.8% (130/409). For patients with complete blood test results, the c-statistic for CARM was 0.75 (95% CI: 0.69 to 0.81) versus 0.72 (95% CI: 0.66 to 0.78) for medical judgements (p=0.28). For patients with at least one missing blood test result, the c-statistics were similar (medical judgements 0.70 (95% CI: 0.60 to 0.81) vs CARM 0.70 (95% CI: 0.59 to 0.80)). At a 10% mortality risk, the PPV for CARM was higher than medical judgements in patients with complete blood test results, 62.0% (95% CI: 51.2 to 71.9) versus 49.2% (95% CI: 39.8 to 58.5) but not when blood test results were missing, 50.0% (95% CI: 24.7 to 75.3) versus 53.3% (95% CI: 34.3 to 71.7).ConclusionsCARM is comparable with medical judgements in discriminating in-hospital mortality following emergency admission to an elderly care ward. CARM may have a promising role in supporting medical judgements in determining the patient’s risk of death in hospital. Further evaluation of CARM in routine practice is required.

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Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2018-022939 ◽

2018 ◽

Vol 8 (12) ◽

pp. e022939 ◽

Cited By ~ 7

Author(s):

Muhammad Faisal ◽

Andrew J Scally ◽

Natalie Jackson ◽

Donald Richardson ◽

Kevin Beatson ◽

...

Keyword(s):

Hospital Mortality ◽

Blood Test ◽

External Validation ◽

Model Development ◽

Vital Signs ◽

Test Results ◽

Medical Admission ◽

Emergency Medical ◽

Computer Aided ◽

Acute Hospitals

ObjectivesThere are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions.DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission.ResultsThe risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00).ConclusionsWe have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

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Development and validation of automated computer aided-risk score for predicting the risk of in-hospital mortality using first electronically recorded blood test results and vital signs for COVID-19 hospital admissions: a retrospective development and validation study

10.1101/2020.11.30.20241273 ◽

2020 ◽

Author(s):

Muhammad Faisal ◽

Mohammed A Mohammed ◽

Donald Richardson ◽

Massimo Fiori ◽

Kevin Beatson

Keyword(s):

Hospital Mortality ◽

Blood Test ◽

Model Development ◽

Vital Signs ◽

Validation Dataset ◽

Test Results ◽

Medical Admission ◽

Emergency Medical ◽

Computer Aided ◽

Development And Validation

AbstractObjectivesThere are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with the novel coronavirus SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and latest version of the National Early Warning Score (NEWS2).DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admissions to hospital.SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset.ParticipantsUnplanned adult medical admissions discharged over three months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within ±24 hours and/or blood test results within ±96 hours of admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: 1) CARMc19_N: age + sex + NEWS2 including subcomponents + COVID19; 2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically), and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+.ResultsThe risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for Model CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB = 0.88 (95%CI 0.86 to 0.90) vs CARMc19_N = 0.86 (95%CI 0.83 to 0.88)). Both models had good internal and external calibration (CARMc19_NB: 1.01 (95%CI 0.88 vs 1.14) & CARMc19_N: 0.95 (95%CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model CARMc19_NB had better sensitivity and similar specificity.ConclusionsWe have developed a validated CARMc19 score with good performance characteristics for predicting the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded vital signs and blood tests results. Since the CARMc19 scores place no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

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Using routine blood test results to predict the risk of death for emergency medical admissions to hospital: an external model validation study

QJM ◽

10.1093/qjmed/hcw110 ◽

2016 ◽

Vol 110 (1) ◽

pp. 27-31 ◽

Cited By ~ 2

Author(s):

M. Faisal ◽

R. Howes ◽

E.W. Steyerberg ◽

D. Richardson ◽

M.A. Mohammed

Keyword(s):

Model Validation ◽

Validation Study ◽

Blood Test ◽

Test Results ◽

Risk Of Death ◽

Routine Blood ◽

Routine Blood Test ◽

Emergency Medical ◽

External Model

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Understanding and applying practitioner and patient views on the implementation of a novel automated Computer-Aided Risk Score (CARS) predicting the risk of death following emergency medical admission to hospital: qualitative study

BMJ Open ◽

10.1136/bmjopen-2018-026591 ◽

2019 ◽

Vol 9 (4) ◽

pp. e026591

Author(s):

Judith Dyson ◽

Claire Marsh ◽

Natalie Jackson ◽

Donald Richardson ◽

Muhammad Faisal ◽

...

Keyword(s):

Risk Score ◽

Blood Test ◽

Unintended Consequences ◽

Early Warning Score ◽

Routine Practice ◽

Successful Implementation ◽

Risk Of Death ◽

Emergency Medical ◽

Computer Aided ◽

The Impact

ObjectivesThe Computer-Aided Risk Score (CARS) estimates the risk of death following emergency admission to medical wards using routinely collected vital signs and blood test data. Our aim was to elicit the views of healthcare practitioners (staff) and service users and carers (SU/C) on (1) the potential value, unintended consequences and concerns associated with CARS and practitioner views on (2) the issues to consider before embedding CARS into routine practice.SettingThis study was conducted in two National Health Service (NHS) hospital trusts in the North of England. Both had in-house information technology (IT) development teams, mature IT infrastructure with electronic National Early Warning Score (NEWS) and were capable of integrating NEWS with blood test results. The study focused on emergency medical and elderly admissions units. There were 60 and 39 acute medical/elderly admissions beds at the two NHS hospital trusts.ParticipantsWe conducted eight focus groups with 45 healthcare practitioners and two with 11 SU/Cs in two NHS acute hospitals.ResultsStaff and SU/Cs recognised the potential of CARS but were clear that the score should not replace or undermine clinical judgments. Staff recognised that CARS could enhance clinical decision-making/judgments and aid communication with patients. They wanted to understand the components of CARS and be reassured about its accuracy but were concerned about the impact on intensive care and blood tests.ConclusionRisk scores are widely used in healthcare, but their development and implementation do not usually involve input from practitioners and SU/Cs. We contributed to the development of CARS by eliciting views of staff and SU/Cs who provided important, often complex, insights to support the development and implementation of CARS to ensure successful implementation in routine clinical practice.

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