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Article ; Online: Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

Faisal, Muhammad / Mohammed, Mohammed Amin / Richardson, Donald / Fiori, Massimo / Beatson, Kevin

2024 Volume 17, Issue 1, Page(s) 109

Abstract: Background: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting ...

Abstract	Background: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. Methods: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). Results: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). Conclusions: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.
MeSH term(s)	Adult ; Humans ; Retrospective Studies ; State Medicine ; Risk Assessment/methods ; COVID-19/diagnosis ; COVID-19/epidemiology ; Risk Factors ; Hospital Mortality ; Computers
Language	English
Publishing date	2024-04-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2413336-X
ISSN	1756-0500 ; 1756-0500
ISSN (online)	1756-0500
ISSN	1756-0500
DOI	10.1186/s13104-024-06773-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development and validation of automated computer-aided risk scores to predict in-hospital mortality for emergency medical admissions with COVID-19: a retrospective cohort development and validation study.

Faisal, Muhammad / Mohammed, Mohammed / Richardson, Donald / Fiori, Massimo / Beatson, Kevin

BMJ open

2022 Volume 12, Issue 8, Page(s) e050274

Abstract: Objectives: There 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 ... ...

Abstract	Objectives: There 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). Design: Logistic regression model development and validation study. Setting: Two acute hospitals (York Hospital-model development data; Scarborough Hospital-external validation data). Participants: Adult (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+. Results: The 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. Conclusions: We 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.
MeSH term(s)	Adult ; COVID-19 ; Computers ; Hospital Mortality ; Humans ; Retrospective Studies ; Risk Assessment ; Risk Factors
Language	English
Publishing date	2022-08-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2021-050274
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Surgical consent during the COVID-19 pandemic.

Rotimi, O / Beatson, K / Aderombi, A / Lam, W / Bajomo, O / Kukreja, N

Annals of medicine and surgery (2012)

2020 Volume 59, Page(s) 229–233

Abstract: Background and aims: During the COVID-19 pandemic, surgical practice may deviate with operative and non-operative management considered. Appropriate discussion of options with patients is paramount to quality surgical care. Intercollegiate and EAES ... ...

Abstract	Background and aims: During the COVID-19 pandemic, surgical practice may deviate with operative and non-operative management considered. Appropriate discussion of options with patients is paramount to quality surgical care. Intercollegiate and EAES guidelines recommend discussing and documenting risk of COVID-19 exposure in the consent process for patients undergoing surgery. Materials and methods: Closed-loop audit of consent forms for patients undergoing emergency and elective surgical procedures. Interventions implemented included education of wider surgical teams. Data was collected during a one-week period for each cycle and analysed using Chi-squared test. Results: In cycle 1, 6/17 (35.3%) case notes documented discussion of COVID-19 risk. Following intervention, compliance improved to 23/29 (79.3%) cases in cycle 2 and 33/45 (73.3%) cases in cycle 3. Conclusion: Pre-intervention, our consenting practice was non-compliant. Our interventions led to significant and sustained improvements in practice. We recommend provision of wider surgical team education to facilitate good consenting practice.
Keywords	covid19
Language	English
Publishing date	2020-10-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2745440-X
ISSN	2049-0801
ISSN	2049-0801
DOI	10.1016/j.amsu.2020.10.011
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Article ; Online: Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study.

Richardson, Donald / Faisal, Muhammad / Fiori, Massimo / Beatson, Kevin / Mohammed, Mohammed

BMJ open

2021 Volume 11, Issue 2, Page(s) e043721

Abstract: Objectives: Although the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare ... ...

Abstract	Objectives: Although the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic. Design: A retrospective cross-sectional study. Setting: Two acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively. Participants: Adult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions. Results: Out of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%). Conclusions: The index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.
MeSH term(s)	Adult ; Aged ; COVID-19/mortality ; COVID-19/therapy ; Cross-Sectional Studies ; Early Warning Score ; Female ; Hospital Mortality ; Humans ; Male ; Patient Admission ; Retrospective Studies ; Risk Assessment/methods ; United Kingdom/epidemiology
Language	English
Publishing date	2021-02-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2020-043721
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predictive accuracy of enhanced versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study.

Faisal, Muhammad / Mohammed, Mohammed Amin / Richardson, Donald / Steyerberg, Ewout W / Fiori, Massimo / Beatson, Kevin

BMC health services research

2021 Volume 21, Issue 1, Page(s) 957

Abstract: Background: The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version ... ...

Abstract	Background: The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19. Methods: Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5. Results: The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5. Conclusions: Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.
MeSH term(s)	Adult ; COVID-19 ; Early Warning Score ; Hospitals ; Humans ; Patient Admission ; Retrospective Studies ; SARS-CoV-2
Language	English
Publishing date	2021-09-13
Publishing country	England
Document type	Journal Article
ISSN	1472-6963
ISSN (online)	1472-6963
DOI	10.1186/s12913-021-06951-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Surgical consent during the COVID-19 pandemic

Rotimi, O / Beatson, K / Aderombi, A / Lam, W / Bajomo, O / Kukreja, N

Ann Med Surg (Lond)

Abstract	Background and aims: During the COVID-19 pandemic, surgical practice may deviate with operative and non-operative management considered. Appropriate discussion of options with patients is paramount to quality surgical care. Intercollegiate and EAES guidelines recommend discussing and documenting risk of COVID-19 exposure in the consent process for patients undergoing surgery. Materials and methods: Closed-loop audit of consent forms for patients undergoing emergency and elective surgical procedures. Interventions implemented included education of wider surgical teams. Data was collected during a one-week period for each cycle and analysed using Chi-squared test. Results: In cycle 1, 6/17 (35.3%) case notes documented discussion of COVID-19 risk. Following intervention, compliance improved to 23/29 (79.3%) cases in cycle 2 and 33/45 (73.3%) cases in cycle 3. Conclusion: Pre-intervention, our consenting practice was non-compliant. Our interventions led to significant and sustained improvements in practice. We recommend provision of wider surgical team education to facilitate good consenting practice.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #845879
Database	COVID19

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Article ; Online: The National Early Warning Score (NEWS2) systematically underestimates the risk of in-hospital mortality in unplanned COVID-19 admissions to hospital.

Richardson, D. / Faisal, M. / Fiori, M. / Beatson, K. / A Mohammed, M.

Abstract: Background: Although the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring for deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We analysed the ... ...

Abstract	Background: Although the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring for deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We analysed the performance of National Early Warning Score (NEWS2) during the first phase of the COVID-19 pandemic. Methods: Adult non-elective admissions discharged between 11-March-2020 to 13-June-2020 with an index NEWS2 electronically recorded within 24 hours of admission are used to predict mortality at four time points (in-hospital, 24hours, 48hours, and 72hours) in COVID-19 versus non-COVID-19 admissions. Results: Out of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7yrs), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (e.g.NEWS2=5: 36% vs 9%). Interpretation: NEWS2 is a valid predictor of the mortality risk but substantially underestimates the absolute mortality risk in COVID-19 patients. Clinical staff
Keywords	covid19
Publisher	MedRxiv; WHO
Document type	Article ; Online
DOI	10.1101/2020.07.13.20144907
Database	COVID19

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Article ; Online: Surgical consent during the COVID-19 pandemic

Rotimi, O. / Beatson, K. / Aderombi, A. / Lam, W. / Bajomo, O. / Kukreja, N.

Annals of Medicine and Surgery

2020 Volume 59, Page(s) 229–233

Keywords	covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	2745440-X
ISSN	2049-0801
ISSN	2049-0801
DOI	10.1016/j.amsu.2020.10.011
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Wolff-Parkinson-White Syndrome and myocardial infarction in ventricular fibrillation arrest: a case of two one-eyed tigers.

Beatson, K / Khorsandi, M / Grubb, N

QJM : monthly journal of the Association of Physicians

2013 Volume 106, Issue 8, Page(s) 755–757

MeSH term(s)	Adult ; Arterial Occlusive Diseases/diagnosis ; Coronary Artery Disease/diagnosis ; Diagnosis, Differential ; Diagnosis, Dual (Psychiatry) ; Humans ; Magnetic Resonance Imaging ; Male ; Myocardial Infarction/diagnosis ; Myocardial Infarction/physiopathology ; Ventricular Fibrillation/diagnosis ; Ventricular Fibrillation/physiopathology ; Wolff-Parkinson-White Syndrome/diagnosis ; Wolff-Parkinson-White Syndrome/physiopathology
Language	English
Publishing date	2013-08
Publishing country	England
Document type	Case Reports ; Journal Article
ZDB-ID	1199985-8
ISSN	1460-2393 ; 0033-5622 ; 1460-2725
ISSN (online)	1460-2393
ISSN	0033-5622 ; 1460-2725
DOI	10.1093/qjmed/hcr208
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Article ; Online: Performance of externally validated enhanced computer-aided versions of the National Early Warning Score in predicting mortality following an emergency admission to hospital in England: a cross-sectional study.

Faisal, Muhammad / Richardson, Donald / Scally, Andy / Howes, Robin / Beatson, Kevin / Mohammed, Mohammed

BMJ open

2019 Volume 9, Issue 11, Page(s) e031596

Abstract: Objectives: In the English National Health Service, the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient's risk of death. ...

Abstract	Objectives: In the English National Health Service, the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient's risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS). Design: Logistic regression model development and external validation study. Setting: Two acute hospitals (YH-York Hospital for model development; NH-Northern Lincolnshire and Goole Hospital for external model validation). Participants: Adult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2). Results: The risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups. Conclusions: An externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems.
MeSH term(s)	Aged ; Aged, 80 and over ; Computers ; Cross-Sectional Studies ; Early Warning Score ; Emergency Service, Hospital ; England/epidemiology ; Female ; Hospital Mortality ; Humans ; Male ; Middle Aged ; Models, Theoretical ; Patient Admission
Language	English
Publishing date	2019-11-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2019-031596
Database	MEDical Literature Analysis and Retrieval System OnLINE

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