Demystifying AI in healthcare

Aims To establish if COVID-19 has worsened outcomes in patients with AO 31 A or B type hip fractures. Methods Retrospective analysis of prospectively collected data was performed for a five-week period from 20 March 2020 and the same time period in 2019. The primary outcome was mortality at 30 days. Secondary outcomes were COVID-19 infection, perioperative pulmonary complications, time to theatre, type of anaesthesia, operation, grade of surgeon, fracture type, postoperative intensive care admission, venous thromboembolism, dislocation, infection rates, and length of stay. Results In all, 76 patients with hip fractures were identified in each group. All patients had 30-day follow-up. There was no difference in age, sex, American Society of Anesthesiologists (ASA) classification or residence at time of injury. However, three in each group were not fit for surgery. No significant difference was found in 30-day mortality; ten patients (13%) in 2019 and 11 patients (14%) in 2020 (p = 0.341). In the 2020 cohort, ten patients tested positive for COVID-19, two (20%) of whom died. There was no significant increase in postoperative pulmonary complications. Median time to theatre was 20 hours (interquartile range (IQR) 16 to 25) in 2019 versus 23 hours (IQR 18 to 30) in 2020 (p = 0.130). Regional anaesthesia increased from 24 (33%) cases in 2019 to 46 (63%) cases in 2020, but ten (14%) required conversion to general anaesthesia. In both groups, 53 (70%) operations were done by trainees. Hemiarthroplasty for 31 B type fractures was the most common operation. No significant difference was found for intensive care admission or 30-day venous thromboembolism, dislocation or infection, or length of stay. Conclusion Little information exists on mortality and complications after hip fracture during the COVID-19 pandemic. At the time of writing, no other study of outcomes in the UK has been published. Cite this article: Bone Joint Open 2020;1-7:415–419.

show abstract

“…Mortality rates are highest in the older less healthy population, who are also at high risk of hip fracture. 1 …”

Section: Introductionmentioning

confidence: 99%

30-day outcomes in hip fracture patients during the COVID-19 pandemic compared to the preceding year

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) in healthcare receives an increasing amount of attention, mainly as AI-based applications are considered to have the potential for making care better, more efficient, and affordable, and hence, accessible. However, the quality of “AI for health” studies remains low, and reporting is often insufficient to fully comprehend and possibly replicate these studies ( Liu et al 2019 ; Nagendran et al 2020 ; Wynants et al 2020 ). Given the emergence of studies on AI in dentistry, action seems needed ( Schwendicke et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond

Schwendicke

Krois

2021

J Dent Res

View full text Add to dashboard Cite

An increasing number of studies on artificial intelligence (AI) are published in the dental and oral sciences. The reporting, but also further aspects of these studies, suffer from a range of limitations. Standards towards reporting, like the recently published Consolidated Standards of Reporting Trials (CONSORT)-AI extension can help to improve studies in this emerging field, and the Journal of Dental Research (JDR) encourages authors, reviewers, and readers to adhere to these standards. Notably, though, a wide range of aspects beyond reporting, located along various steps of the AI lifecycle, should be considered when conceiving, conducting, reporting, or evaluating studies on AI in dentistry.

show abstract

“…Machine learning methods such as those employed to create the model have shown potential to produce predictive models that can be applied to assist and improve clinical decisions for a broad variety of outcomes [5,6], and have recently been used in response to the COVID-19 emergency [7–9].…”

Section: Introductionmentioning

confidence: 99%

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency

Ferrari

Milić

Tonelli

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Machine learning can assist clinicians in forecasting patients with COVID-19 who develop respiratory failure requiring mechanical ventilation. This analysis aimed to determine a 48 hours prediction of moderate to severe respiratory failure, as assessed with PaO2/FiO2 < 150 mmHg, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational study that comprised all consecutive adult patients with COVID-19 pneumonia admitted to the Infectious Diseases Clinic of the University Hospital of Modena, Italy from 21 February to 6 April 2020. COVID-19 was confirmed with PCR positive nasopharyngeal swabs while the presence of pneumonia was radiologically confirmed. Patients received standard of care according to national guidelines for clinical management of SARS-CoV-2 infection. The patients' full medical history, demographic and epidemiological features, clinical data, complete blood count, coagulation, inflammatory and biochemical markers were routinely collected and aggregated in a clinically-oriented logical framework in order to build different datasets. The dataset was used to train a learning framework relying on Microsoft LightGBM and leveraging a hybrid approach, where clinical expertise is applied alongside a data-driven analysis. Shapley Additive exPlanations (SHAP) values were used to quantify the positive or negative impact of each variable included in the model on the predicted outcome. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio < 150 mmHg (≥ 13.3 kPa) in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Results A total of 198 patients contributed to generate 1068 valuable observations which allowed to build 3 prediction models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth boosted mixed model which included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84). Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine learning algorithm, with a 84% prediction accuracy, which is potentially able to assist clinicians in decision making process with therapeutic implications.

show abstract

Demystifying AI in healthcare

Abstract: Well conducted and transparently reported trials would be an excellent start

Cited by 24 publications

References 15 publications

30-day outcomes in hip fracture patients during the COVID-19 pandemic compared to the preceding year

30-day outcomes in hip fracture patients during the COVID-19 pandemic compared to the preceding year

Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency

Contact Info

Product

Resources

About