scholarly journals 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 ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e022939 ◽  
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.

scholarly journals 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

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.

scholarly journals 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

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e027741 ◽  
Author(s):  
Muhammad Faisal ◽  
Binish Khatoon ◽  
Andy Scally ◽  
Donald Richardson ◽  
Sally Irwin ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prospective Study ◽  
Mortality Risk ◽  
Blood Test ◽  
Test Results ◽  
Risk Of Death ◽  
Emergency Medical ◽  
C Statistic ◽  
Computer Aided ◽  
A Prospective Study

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.

scholarly journals Impact of the level of sickness on higher mortality in emergency medical admissions to hospital at weekends

2017 ◽  
Vol 22 (4) ◽  
pp. 236-242 ◽  
Author(s):  
Mohammed Mohammed ◽  
Muhammad Faisal ◽  
Donald Richardson ◽  
Robin Howes ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Early Warning ◽  
Odds Ratio ◽  
Vital Signs ◽  
Early Warning Score ◽  
Emergency Admissions ◽  
Clinical Assessments ◽  
Admission Time ◽  
Emergency Medical ◽  
Acute Hospitals ◽  
National Early Warning Score

Objective Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p < 0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio = 1.10, 95% CI 1.01 to 1.20, p = 0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio = 0.99, 95% CI 0.90 to 1.09, p = 0.87). Index National Early Warning Score was recorded sooner (−0.45 h, 95% CI −0.52 to −0.38, p < 0.001) for weekend admissions. Conclusions Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed.

scholarly journals Development and validation of a prognostic model for early triage of patients diagnosed with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chansik An ◽  
Hyun Cheol Oh ◽  
Jung Hyun Chang ◽  
Seung-Jin Oh ◽  
Jung Mo Lee ◽  
...  
Keyword(s):  
Prognostic Score ◽  
External Validation ◽  
Vital Signs ◽  
Underlying Disease ◽  
Test Results ◽  
Additional Information ◽  
Patient Will ◽  
National Cohort ◽  
Evaluation Metric ◽  
Development And Validation

AbstractWe developed a tool to guide decision-making for early triage of COVID-19 patients based on a predicted prognosis, using a Korean national cohort of 5,596 patients, and validated the developed tool with an external cohort of 445 patients treated in a single institution. Predictors chosen for our model were older age, male sex, subjective fever, dyspnea, altered consciousness, temperature ≥ 37.5 °C, heart rate ≥ 100 bpm, systolic blood pressure ≥ 160 mmHg, diabetes mellitus, heart disease, chronic kidney disease, cancer, dementia, anemia, leukocytosis, lymphocytopenia, and thrombocytopenia. In the external validation, when age, sex, symptoms, and underlying disease were used as predictors, the AUC used as an evaluation metric for our model’s performance was 0.850 in predicting whether a patient will require at least oxygen therapy and 0.833 in predicting whether a patient will need critical care or die from COVID-19. The AUCs improved to 0.871 and 0.864, respectively, when additional information on vital signs and blood test results were also used. In contrast, the protocols currently recommended in Korea showed AUCs less than 0.75. An application for calculating the prognostic score in COVID-19 patients based on the results of this study is presented on our website (https://nhimc.shinyapps.io/ih-psc/), where the results of the validation ongoing in our institution are periodically updated.

scholarly journals 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 ◽  
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.

Project of Computer-aided Management of Therapeutical Apheresis

1993 ◽  
Vol 16 (5_suppl) ◽  
pp. 185-186
Author(s):  
C. Falco ◽  
N. Scarpato ◽  
G. Nappi ◽  
S. Formisano
Keyword(s):  
Data Base ◽  
Blood Test ◽  
Great Increase ◽  
Data Entry ◽  
Base System ◽  
Test Results ◽  
Blood Tests ◽  
Therapeutic Protocol ◽  
Computer Aided ◽  
Before And After

The great increase in hemapheresis units activity that occurred during the last years caused the need for a computer-aided management (1, 2). We present a project for a data base system able to manage therapeutical apheresis (3). The program consists of five sections. a) Patient's file card: it allows to record anamnesis, examination and blood test results easily and under computer's guidance. b) Choice of therapeutic protocol: Therapeutic protocol is fixed in this section (device to be used, apheretic method, plasma volume to be processed, blood tests before and after apheresis). c) Procedures: It provides procedure's data entry and guides the operator during the treatment on the ground of therapeutical protocol. d) Data processing: It allows statistics on data placed in the data base. e) Registers: It includes both a general register and the possibility of search by disease, device and method.

scholarly journals Which Is More Useful in Predicting Hospital Mortality -Dichotomised Blood Test Results or Actual Test Values? A Retrospective Study in Two Hospitals

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e46860 ◽  
Author(s):  
Mohammed A. Mohammed ◽  
Gavin Rudge ◽  
Gordon Wood ◽  
Gary Smith ◽  
Vishal Nangalia ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Hospital Mortality ◽  
Blood Test ◽  
Test Results ◽  
Actual Test
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