Dataset Information

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.

ABSTRACT:

Objectives

To 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.

Design

A prospective study.

Setting

Consecutive emergency medical admissions in York hospital.

Participants

Elderly 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).

Results

The 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).

Conclusions

CARM 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.

SUBMITTER: Faisal M

PROVIDER: S-EPMC6589037 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

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.

Faisal Muhammad M Khatoon Binish B Scally Andy A Richardson Donald D Irwin Sally S Davidson Rachel R Heseltine David D Corlett Alison A Ali Javed J Hampson Rebecca R Kesavan Sandeep S McGonigal Gerry G Goodman Karen K Harkness Michael M Mohammed Mohammed M

BMJ open 20190619 6

<h4>Objectives</h4>To 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.<h4>Design</h4>A prospective study.<h4>Setting</h4>Consecutive emergency medical admissions in York hospital.<h4>Participants</h4>Elderly medical admissions in one ward were assigned a risk of death at the first post-take ward round by consultant staff over a ...[more]

PMID: 31221885

Similar Datasets

Project description:ObjectivesThere are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with 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 the National Early Warning Score (NEWS2).DesignLogistic regression model development and validation study.SettingTwo acute hospitals (York Hospital-model development data; Scarborough Hospital-external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on 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 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 calibration (CARMc19_NB=1.01 (95% CI 0.88 to 1.14) and 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 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Dataset Information

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.

Objectives

Design

Setting

Participants

Results

Conclusions

Publications

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.

Similar Datasets

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