A proof-of-concept study on mortality prediction with machine learning algorithms using burn intensive care data

INTRODUCTION: Burn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score. METHODS: Admission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test. RESULTS: A total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decision tree model, an extreme boosting model, a random forest model, a support-vector machine (SVM) model and a generalised linear regression model (GLM) were 0.83 (95% confidence interval [CI] = 0.72-0.94), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1) and 0.84 (95% CI = 0.74-0.94), respectively. AUCs for the Baux score and revised Baux score were 0.85 (95% CI = 0.75-0.95) and 0.84 (95% CI = 0.74-0.94). No significant differences were observed when comparing ML algorithms with Baux score and revised Baux score. Secondary variable selection was made to analyse model performance. CONCLUSION: This proof-of-concept study showed initial credibility in using ML algorithms to predict mortality in burn patients. The sample size was small and future studies are needed with larger sample sizes, further variable selections and prospective testing of the algorithms. LAY SUMMARY: Burn injuries are one of the most common traumatic injuries especially in countries with limited prevention and healthcare resources. To treat a patient with large burns who has been admitted to an intensive care unit, it is often necessary to assess the risk of a fatal outcome. Physicians traditionally use simplified scores to calculate risks. One commonly used score, the Baux score, uses age of the patient and the size of the burn to predict the risk of death. Adding the factor of inhalation injury, the score is then called the revised Baux score. However, there are a number of additional causes that can influence the risk of fatal outcomes that Baux scores do not take into account. Machine learning is a method of data modelling where the system learns to predict outcomes based on previous cases and is a branch of artificial intelligence. In this study we evaluated several machine learning methods for outcome prediction in patients admitted for burn injury. We gathered data on 93 patients at admission to the intensive care unit and our experiments show that machine learning methods can reach an accuracy comparable with Baux scores in calculating the risk of fatal outcomes. This study represents a proof of principle and future studies on larger patient series are required to verify our results as well as to evaluate the methods on patients in real-life situations.Peer reviewe

Evaluating topical opioid gel on donor site pain: A small randomised double blind controlled trial

AbstractBackgroundAutologous donor skin harvested for transplantation is a common procedure in patients with burns, and patients often feel more pain at the donor site than is justified by the extent of trauma. Topical morphine gels have been thought to have an effect on peripheral opioid receptors by creating antinociceptive and anti-inflammatory effects, which could potentially reduce the systemic use of morphine-like substances and their adverse effects.MethodsWe therefore did a paired, randomised, double-blind placebo study to investigate the effect of morphine gel and placebo on dual donor sites that had been harvested in 13 patients. Pain was measured on a visual analogue scale (VAS) 15 times in a total of 5 days.ResultsThe mean (SD) VAS was 1.6 (2.3) for all sites, 1.5 (2.2) for morphine, and 2.0 (2.5) for placebo. The pain relieving effects of morphine gel were not significantly better than placebo.ConclusionThe assessment of pain at donor sites is subjective, and more systematic and objective studies are needed

Large data and machine learning in analysis, diagnostics, and clinical decision making: applications in the treatment of burn injury

Burn injury is a common trauma globally. Large burns require fluid resuscitation, infection control, and specialized intensive care. The size of the burn and infections caused by resistant microbes are correlated to mortality, and accurate mortality predictions are important. Errors are common when diagnosing burn depth, but early diagnosis is necessary to make correct surgical decisions. Machine learning (ML) is a set of mathematical algorithms with self-learning capabilities, which might make them suitable for medical applications. This thesis explores systematic large data analysis and ML algorithms for clinical applications in burn treatment by examining antibiotic resistance, improving mortality predictions, and automating diagnosis of burn depth. Paper I aims to find relevant trends and correlations on clinical outcomes such as mortality, microbial distribution, and antibiotic resistance from pooled data from a burn center. Data from 1570 patients and 15,006 microbiology cultures were systematically analyzed. Our results show a sustained low risk of harmful microbes, resistance, and a suggested low mortality rate. Paper II used clinical biomarkers from burn patients to train ML algorithms to predict mortality and compare it with Baux scores. When applying five types of ML algorithms, it showed no significant difference in mortality prediction compared with Baux scores. Paper III examines convolutional neural network (CNN) algorithms for two purposes. One to segment a burn wound and the other to classify whole wound images for surgery or conservative treatment. A total of 1105 diverse images were collected from patients at admission to burn centers in Sweden and South Africa. The algorithm was adequate for segmenting burn wounds and could be improved when categorizing images for surgery or conservative treatment. Paper IV further assesses CNN to automatically segment and diagnose a diverse set of early burn images for deep or superficial burn injury. A total of 1004 images were included. The algorithm proved adequate in segmenting superficial injuries but not deep injuries and performed similarly between darker and lighter skin patients. Future studies might incorporate infection variables in ML mortality predictions and larger sample sizes. Regarding automated burn image diagnosis, including multiple non-image variables might improve usability

Large data and machine learning in analysis, diagnostics, and clinical decision making: applications in the treatment of burn injury

Burn injury is a common trauma globally. Large burns require fluid resuscitation, infection control, and specialized intensive care. The size of the burn and infections caused by resistant microbes are correlated to mortality, and accurate mortality predictions are important. Errors are common when diagnosing burn depth, but early diagnosis is necessary to make correct surgical decisions. Machine learning (ML) is a set of mathematical algorithms with self-learning capabilities, which might make them suitable for medical applications. This thesis explores systematic large data analysis and ML algorithms for clinical applications in burn treatment by examining antibiotic resistance, improving mortality predictions, and automating diagnosis of burn depth. Paper I aims to find relevant trends and correlations on clinical outcomes such as mortality, microbial distribution, and antibiotic resistance from pooled data from a burn center. Data from 1570 patients and 15,006 microbiology cultures were systematically analyzed. Our results show a sustained low risk of harmful microbes, resistance, and a suggested low mortality rate. Paper II used clinical biomarkers from burn patients to train ML algorithms to predict mortality and compare it with Baux scores. When applying five types of ML algorithms, it showed no significant difference in mortality prediction compared with Baux scores. Paper III examines convolutional neural network (CNN) algorithms for two purposes. One to segment a burn wound and the other to classify whole wound images for surgery or conservative treatment. A total of 1105 diverse images were collected from patients at admission to burn centers in Sweden and South Africa. The algorithm was adequate for segmenting burn wounds and could be improved when categorizing images for surgery or conservative treatment. Paper IV further assesses CNN to automatically segment and diagnose a diverse set of early burn images for deep or superficial burn injury. A total of 1004 images were included. The algorithm proved adequate in segmenting superficial injuries but not deep injuries and performed similarly between darker and lighter skin patients. Future studies might incorporate infection variables in ML mortality predictions and larger sample sizes. Regarding automated burn image diagnosis, including multiple non-image variables might improve usability

Improved clinical outcome 3 months after endovascular treatment, including thrombectomy, in patients with acute ischemic stroke : a meta-analysis

BACKGROUND AND PURPOSE: Intravenous thrombolysis with tissue plasminogen activator is standard treatment in acute stroke today. The benefit of endovascular treatment has been questioned. Recently, studies evaluating endovascular treatment and intravenous thrombolysis compared with intravenous thrombolysis alone, have reported improved outcome for the intervention group. The aim of this study was to perform a meta-analysis of randomized controlled trials comparing endovascular treatment in addition to intravenous thrombolysis with intravenous thrombolysis alone. METHODS: Databases were searched for eligible randomized controlled trials. The primary outcome was a functional neurological outcome after 90 days. A secondary outcome was severe disability and death. Data were pooled in the control and intervention groups, and OR was calculated on an intention to treat basis with 95% CIs. Outcome heterogeneity was evaluated with Cochrane's Q test (significance level cut-off value at <0.10) and I(2) (significance cut-off value >50%) with the Mantel-Haenszel method for dichotomous outcomes. A p value <0.05 was regarded as statistically significant. RESULTS: Six studies met the eligibility criteria, and data from 1569 patients were analyzed. A higher probability of a functional neurological outcome after 90 days was found for the intervention group (OR 2, 95% CI 2 to 3). There was a significantly higher probability of death and severe disability in the control group compared with the intervention group. CONCLUSIONS: Endovascular treatment in addition to intravenous thrombolysis for acute ischemic stroke leads to an improved clinical outcome after 3 months, compared with patients receiving intravenous thrombolysis alone

Qualitative and Quantitative Analysis of Smile Excursion in Facial Reanimation: A Systematic Review and Meta-analysis of 1- versus 2-stage Procedures

Background:. Free functional muscle transfer has become a common treatment modality for smile restoration in long-lasting facial paralysis, but the selection of surgical strategy between a 1-stage and a 2-stage procedure has remained a matter of debate. The aim of this study was to compare the quantitative and qualitative outcomes of smile excursion between 1-stage and 2-stage free muscle transfers in the literature. Methods:. A comprehensive review of the published literature between 1975 and end of January 2017 was conducted. Results:. The abstracts or titles of 2,743 articles were screened. A total of 24 articles met our inclusion criteria of performing a quantitative or qualitative evaluation of a free-functioning muscle transfer for smile restoration. For the purpose of meta-analysis, 7 articles providing quantitative data on a total of 254 patients were included. When comparing muscle excursion between 1-stage and 2-stage procedures, the average range of smile excursion was 11.5 mm versus 6.6 mm, respectively. For the purpose of systematic review, 17 articles were included. The result of the systematic review suggested a tendency toward superior functional results for the 1-stage procedure when comparing the quality of smile. Conclusions:. The results of this review must be interpreted with great caution. Quantitative analysis suggests that 1-stage procedures produce better excursion than 2-stage procedures. Qualitative analysis suggests that 1-stage procedures might also produce superior results when based on excursion and symmetry alone, but these comparisons do not include one important variable dictating the quality of a smile—the spontaneity of the smile. The difficulty in comparing published results calls for a consensus classification system for facial palsy

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

Longitudinal Imaging Using PET/CT with Collagen-I PET-Tracer and MRI for Assessment of Fibrotic and Inflammatory Lesions in a Rat Lung Injury Model

Non-invasive imaging biomarkers (IBs) are warranted to enable improved diagnostics and follow-up monitoring of interstitial lung disease (ILD) including drug-induced ILD (DIILD). Of special interest are IB, which can characterize and differentiate acute inflammation from fibrosis. The aim of the present study was to evaluate a PET-tracer specific for Collagen-I, combined with multi-echo MRI, in a rat model of DIILD. Rats were challenged intratracheally with bleomycin, and subsequently followed by MRI and PET/CT for four weeks. PET imaging demonstrated a significantly increased uptake of the collagen tracer in the lungs of challenged rats compared to controls. This was confirmed by MRI characterization of the lesions as edema or fibrotic tissue. The uptake of tracer did not show complete spatial overlap with the lesions identified by MRI. Instead, the tracer signal appeared at the borderline between lesion and healthy tissue. Histological tissue staining, fibrosis scoring, lysyl oxidase activity measurements, and gene expression markers all confirmed establishing fibrosis over time. In conclusion, the novel PET tracer for Collagen-I combined with multi-echo MRI, were successfully able to monitor fibrotic changes in bleomycin-induced lung injury. The translational approach of using non-invasive imaging techniques show potential also from a clinical perspective