Diagnostic assistance to improve acute burn referral and triage : assessment of routine clinical tools at specialised burn centres and potential for digital health development at point of care

Background: Inappropriate referral of patients for specialised care leads to overburdened health systems and improper treatment of patients who are denied transfer due to a scarcity of resources. Burn injuries are a global health problem where specialised care is particularly important for severe cases while minor burns can be treated at point of care. Whether several solutions, existing or in development, could be used to improve the diagnosis, referral and triage of acute burns at admission to specialised burn centres remains to be evaluated. Aim: The overarching aim of this thesis is to determine the potential of diagnostic support tools for referral and triage of acute burns injuries. More specifically, sub-aims include the assessment of routine and digital health tools utilised in South Africa and Sweden: referral criteria, mortality prediction scores, image-based remote consultation and automated diagnosis. Methods: Studies I and II were two retrospective studies of patients admitted to the paediatric (I) and the adult (II) specialised burn centres of the Western Cape province in South Africa. Study I examined adherence to referral criteria at admission of 1165 patients. Logistic regression was performed to assess the associations between adherence to the referral criteria and patient management at the centre. Study II assessed mortality prediction at admission of 372 patients. Logistic regression was performed to evaluate associations between patient, injury and admission-related characteristics with mortality. The performance of an existing mortality prediction model (the ABSI score) was measured. Study III and IV were related to two image-based digital-health tools for remote diagnosis. In Study III, 26 burns experts provided a diagnosis in terms of burn size and depth for 51 images of acute burn cases using their smartphone or tablet. Diagnostic accuracy was measured with intraclass correlation coefficient. In Study IV, two deep-learning algorithms were developed using 1105 annotated acute burn images of cases collected in South Africa and Sweden. The first algorithm identifies a burn area from healthy skin, and the second classifies burn depth. Differences in performances by patient Fitzpatrick skin types were also measured. Results: Study I revealed a 93.4% adherence to the referral criteria at admission. Children older than two years (not fulfilling the age criterion) as well as those fulfilling the severity criterion were more likely to undergo surgery or stay longer than seven days at the centre. At the adult burn centre (Study II), mortality affected one in five patients and was associated with gender, burn size, and referral status after adjustments for all other variables. The ABSI score was a good estimate of mortality prediction. In Study III experts were able to accurately diagnose burn size, and to a lesser extent depth, using handheld devices. A wound identifier and a depth classifier algorithm could be developed with assessments of relatively high accuracy (Study IV). Differences were observed in performances by skin types of the patients. Conclusions: Altogether the findings inform on the use in clinical practice of four different tools that could improve the accuracy of the diagnosis, referral and triage of patients with acute burns. This would reduce inequities in access to care by improving access for both paediatric and adult patient populations in settings that are resource scarce, geographically distant or under high clinical pressure

Admission factors associated with the in-hospital mortality of burns patients in resource-constrained settings : a two-year retrospective investigation in a South African adult burns centre

CITATION: Boissin, C., et al. 2019. Admission factors associated with the in-hospital mortality of burns patients in resource-constrained settings : a two-year retrospective investigation in a South African adult burns centre. Burns, 45(6):1462-1470, doi:10.1016/j.burns.2019.03.005.The original publication is available at https://www.sciencedirect.comObjective: Little is known concerning the factors associated with in-hospital mortality of trauma patients in resource-constrained settings, not least in burns centres. We investigated this question in the adult burns centre at Tygerberg Hospital in Cape Town. We further assessed whether the Abbreviated Burn Severity Index (ABSI) is an accurate predictive score of mortality in this setting. Methods: Medical records of all patients admitted with fresh burns over a two-year period (2015 and 2016) were scrutinized to obtain data on patient, injury and admission-related characteristics. Association with in-hospital mortality was investigated for flame burns using logistic regressions and expressed as odds ratios (ORs). The mortality prediction of the ABSI score was assessed using sensitivity and specificity analyses. Results: Overall the in-hospital mortality was 20.4%. For the 263 flame burns, while crude ORs suggested gender, burn depth, burn size, inhalation injury, and referral status were all individually significantly associated with mortality, only the association with female gender, not being referred and burn size remained significant after adjustments (adjusted ORs = 3.79, 2.86 and 1.11 (per percentage increase in size) respectively). For the ABSI score, sensitivity and specificity were 84% and 86% respectively. Conclusion: In this specialised centre, mortality occurs in one in five patients. It is associated with a few clinical parameters, and can be predicted using the ABSI score.https://www.sciencedirect.com/science/article/pii/S030541791830874XPublisher's versio

ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods

Accuracy of Image-Based Automated Diagnosis in the Identification and Classification of Acute Burn Injuries. A Systematic Review

Although they are a common type of injury worldwide, burns are challenging to diagnose, not least by untrained point-of-care clinicians. Given their visual nature, developments in artificial intelligence (AI) have sparked growing interest in the automated diagnosis of burns. This review aims to appraise the state of evidence thus far, with a focus on the identification and severity classification of acute burns. Three publicly available electronic databases were searched to identify peer-reviewed studies on the automated diagnosis of acute burns, published in English since 2005. From the 20 identified, three were excluded on the grounds that they concerned animals, older burns or lacked peer review. The remaining 17 studies, from nine different countries, were classified into three AI generations, considering the type of algorithms developed and the images used. Whereas the algorithms for burn identification have not gained much in accuracy across generations, those for severity classification improved substantially (from 66.2% to 96.4%), not least in the latest generation (n = 8). Those eight studies were further assessed for methodological bias and results applicability, using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. This highlighted the feasibility nature of the studies and their detrimental dependence on online databases of poorly documented images, at the expense of a substantial risk for patient selection and limited applicability in the clinical setting. In moving past the pilot stage, future development work would benefit from greater input from clinicians, who could contribute essential point-of-care knowledge and perspectives

Clinical decision-support for acute burn referral and triage at specialized centres – Contribution from routine and digital health tools

Background Specialized care is crucial for severe burn injuries whereas minor burns should be handled at point-of-care. Misdiagnosis is common which leads to overburdening the system and to a lack of treatment for others due to resources shortage. Objectives The overarching aim was to evaluate four decision-support tools for diagnosis, referral, and triage of acute burns injuries in South Africa and Sweden: referral criteria, mortality prediction scores, image-based remote consultation and automated diagnosis. Methods Study I retrospectively assessed adherence to referral criteria of 1165 patients admitted to the paediatric burns centre of the Western Cape of South Africa. Study II assessed mortality prediction of 372 patients admitted to the adults burns centre by evaluating an existing score (ABSI), and by using logistic regression. In study III, an online survey was used to assess the diagnostic accuracy of burn experts’ image-based estimations using their smartphone or tablet. In study IV, two deep-learning algorithms were developed using 1105 acute burn images in order to identify the burn, and to classify burn depth. Results Adherence to referral criteria was of 93.4%, and the age and severity criteria were associated with patient care. In adults, the ABSI score was a good predictor of mortality which affected a fifth of the patients and which was associated with gender, burn size and referral status. Experts were able to diagnose burn size, and burn depth using handheld devices. Finally, both a wound identifier and a depth classifier algorithm could be developed with relatively high accuracy. Conclusions Altogether the findings inform on the use of four tools along the care trajectory of patients with acute burns by assisting with the diagnosis, referral and triage from point-of-care to burns centres. This will assist with reducing inequities by improving access to the most appropriate care for patients

Determinants of speeding among new generations of car drivers from the Arabian Peninsula. An investigation based among Omani drivers using the theory of planned behaviour.

In high-income countries of the Arabian Peninsula, including the Sultanate of Oman, motorization has been extremely rapid. As a result, road traffic crashes are by far the highest cause of premature mortality, and speeding is an acknowledged key risk factor. Theory-based interventions are needed to target prevention of this unsafe practice. This study sheds light on determinants of speeding among new generations of Omani drivers applying the Theory of Planned Behaviour (TPB). A questionnaire covering all five main constructs of the TPB was first contextualized and administered to two target groups: male drivers of all ages (n = 1107) approached in person when renewing their driving license and university students drivers (men and women) reached through internet contact (n = 655). Multiple, stepwise linear regression analyses were used to explore factors associated with speeding. Results indicate that driving fast and not respecting the posted speed limits was common in both groups of drivers, although rates were higher among students; 41.8% reported driving a bit faster than other drivers and 24.1% faster than the posted speed limit compared with 31.4% and 14.2% in male drivers of all ages. In both groups the TPB model predicted to a limited extent the determinants of speeding behaviour. However, the intention to speed was associated with a negative attitude towards the respect of rules for men of all ages (β = -0.30 (p<0.001)) and for students (β = -0.26 (p<0.001)); a positive view regarding subjective norms (β = 0.25 (p<0.001) and β = 0.28 (p<0.001) respectively), and behavioural control (β = 0.15 (p<0.001) and β = 0.20 (p<0.001) respectively). Intention was the only significant predictor of speeding behaviour (β = 0.48 (p<0.001); and β = 0.64 (p<0.001)). To conclude, speeding is widespread among Omani drivers of all ages and the intention to respect posted speed limits meets a range of barriers that need greater consideration in order to achieve a better safety culture in the country

Adherence to referral criteria at admission and patient management at a specialized burns centre : the case of the red cross War Memorial Children’s Hospital in Cape Town, South Africa

CITATION: Boissin, C., et al. 2017. Adherence to referral criteria at admission and patient management at a specialized burns centre : the case of the red cross War Memorial Children’s Hospital in Cape Town, South Africa. International Journal of Environmental Research and Public Health, 14(7):732, doi:10.3390/ijerph14070732.The original publication is available at http://www.mdpi.comENGLISH ABSTRACT: Referral guidelines for burn care are meant to assist in decision-making as regards patient transfer and admissions to specialized units. Little is known, however, concerning how closely they are followed and whether they are linked to patient care. This is the object of the current study, focused on the paediatric burns centre of the Red Cross War Memorial Children’s Hospital in Cape Town, South Africa. All patients admitted to the centre during the winters of 2011–2015 (n = 1165) were included. The patient files were scrutinized to clarify whether the referral criteria in place were identified (seven in total) and to compile data on patient and injury characteristics. A case was defined as adherent to the criteria when at least one criterion was fulfilled and adherence was expressed as a percentage with 95% confidence intervals, for all years aggregated as well as by year and by patient or injury characteristics. The association between adherence to any individual criterion and hospital care (surgery or longer length of stay) was measured using logistic regressions. The overall adherence was 93.4% (100% among children under 2 years of age and 86% among the others) and it did not vary remarkably over time. The two criteria of “injury sustained at a specific anatomical site” (85.2%) and “young age” (51.9%) were those most often identified. Children aged 2 years or older were more likely to undergo surgery or to stay longer than those of young age (although a referral criterion) and so were those with higher injury severity (a referral criterion). In this specialized paediatric burns centre, children are admitted mainly according to the guidelines. However, given the high prevalence of paediatric burns in the region and the limited resources at the burns centre, adherence to the guidelines need to be further studied at all healthcare levels in the province.http://www.mdpi.com/1660-4601/14/7/732Publisher's versio

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery

Abstract Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed