General surgical adverse events in a UK district general hospital–lessons to learn

Gurjar, Shashank; Roshan-Zamir, Sasha; Patel, Sandeep; Harinath, G.

doi:10.1016/j.ijsu.2010.08.008

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the definition of AEs resulting from medical treatment and the selection of codes for these could be questioned and challenged, as more ICD-10 codes may have been taken into account when estimating the burden of AEs resulting from medical treatment. The third limitation of this study is related to a bias occurring from mismanagement of the patients due to problems related to inadequate infrastructure resources, medical understaffing and ineffective communication that may often result in delays of timely treatment [ 19 , 20 ]. Such clinical events are regarded as clinical incidents, for they do have clinical consequences to the patients and to the entire medical care system [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Incidence and mortality from adverse effects of medical treatment in the UK, 1990–2013: levels, trends, patterns and comparisons

Lunevičius

Haagsma

2018

International Journal for Quality in Health Care

View full text Add to dashboard Cite

ObjectiveTo present an update on incidence and mortality from adverse effects (AEs) of medical treatment in the UK, its four countries and nine English regions between 1990 and 2013.DesignDescriptive epidemiological study on AEs of medical treatment. AEs are shown as a single cause-of-injury category from the Global Burden of Disease (GBD) 2013 study.Data sourcesThe GBD 2013 interactive data visualisation tools ‘Epi Visualisation’ and ‘GBD Compare’.Outcome measuresThe means of incidence and mortality rates with 95% uncertainty intervals (UIs). The estimates are age-standardised.ResultsIncidence rate was 175 and 176 cases per 100 000 men, 173 and 174 cases per 100 000 women in 1990 and 2013, in the UK (UI 170–180). The mortality from AEs declined from 1.33 deaths (UI 0.99–1.5) to 0.92 deaths (UI 0.75–1.2) per 100 000 individuals in the UK between 1990 and 2013 (30.8% change). Although mortality trends were descending in every region of the UK, they varied by geography and gender. Mortality rates in Scotland, North East England and West Midlands were highest. Mortality rates in South England and Northern Ireland were lowest. In 2013, age-specific mortality rates were higher in males in all 20 age groups compared with females.ConclusionsDespite gains in reducing mortality from AEs of medical treatment in the UK between 1990 and 2013, the incidence of AEs remained the same. The results of this analysis suggest revising healthcare policies and programmes aimed to reduce incidence of AEs in the UK.

show abstract

Section: Discussionmentioning

confidence: 99%

Incidence and mortality from adverse effects of medical treatment in the UK, 1990–2013: levels, trends, patterns and comparisons

Lunevičius

Haagsma

2018

International Journal for Quality in Health Care

View full text Add to dashboard Cite

show abstract

“…An adverse event (AE) refers to a patient's injury or complication caused by medical care [1]. Previous studies have shown that AEs are responsible for 44,000 to 98,000 deaths per year, an average of 31 days increase in hospital length and about US $3900 increase in the patient's hospital cost [2,3].…”

Section: Adverse Events Predictionmentioning

confidence: 99%

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury

Morid¹,

Sheng²,

Fiol³

et al. 2020

JMIR Med Inform

View full text Add to dashboard Cite

Background More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients’ data to extract the temporal features using their structural temporal patterns, that is, trends. Objective This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI). Methods Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation. Results Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001). Conclusions Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

show abstract

A Narrative Review on Intraoperative Adverse Events: Risks, Prevention, and Mitigation

Kawa,

Araji,

Kaafarani

et al. 2024

Journal of Surgical Research

View full text Add to dashboard Cite

General surgical adverse events in a UK district general hospital–lessons to learn

Cited by 5 publications

References 23 publications

Incidence and mortality from adverse effects of medical treatment in the UK, 1990–2013: levels, trends, patterns and comparisons

Incidence and mortality from adverse effects of medical treatment in the UK, 1990–2013: levels, trends, patterns and comparisons

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury

A Narrative Review on Intraoperative Adverse Events: Risks, Prevention, and Mitigation

Contact Info

Product

Resources

About