Automatic Identification and Severity Classification of Retinal Biomarkers in SD-OCT Using Dilated Depthwise Separable Convolution ResNet with SVM Classifier

PURPOSE

Diagnosis of Uveitic Macular Edema (UME) using Spectral Domain OCT (SD-OCT) is a promising method for early detection and monitoring of sight-threatening visual impairment. Viewing multiple B-scans and identifying biomarkers is challenging and time-consuming for clinical practitioners. To overcome these challenges, this paper proposes an image classification hybrid framework for predicting the presence of biomarkers such as intraretinal cysts (IRC), hyperreflective foci (HRF), hard exudates (HE) and neurosensory detachment (NSD) in OCT B-scans along with their severity.

METHODS

A dataset of 10880 B-scans from 85 Uveitic patients is collected and graded by two board-certified ophthalmologists for the presence of biomarkers. A novel image classification framework, Dilated Depthwise Separable Convolution ResNet (DDSC-RN) with SVM classifier, is developed to achieve network compression with a larger receptive field that captures both low and high-level features of the biomarkers without loss of classification accuracy. The severity level of each biomarker is predicted from the feature map, extracted by the proposed DDSC-RN network.

RESULTS

The proposed hybrid model is evaluated using ground truth labels from the hospital. The deep learning model initially, identified the presence of biomarkers in B-scans. It achieved an overall accuracy of 98.64%, which is comparable to the performance of other state-of-the-art models, such as DRN-C-42 and ResNet-34. The SVM classifier then predicted the severity of each biomarker, achieving an overall accuracy of 89.3%.

CONCLUSIONS

A new hybrid model accurately identifies four retinal biomarkers on a tissue map and predicts their severity. The model outperforms other methods for identifying multiple biomarkers in complex OCT B-scans. This helps clinicians to screen multiple B-scans of UME more effectively, leading to better treatment outcomes.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach

BACKGROUND

Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools.

OBJECTIVE

This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface.

METHODS

We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool.

RESULTS

Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity.

CONCLUSIONS

The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

Construction and Validation of a Nomogram Model to Predict the Severity of Mycoplasma pneumoniae Pneumonia in Children

Background

This study aimed to develop a nomogram model for early prediction of the severe Mycoplasma pneumoniae pneumonia (MPP) in children.

Methods

A retrospective analysis was conducted on children with MPP, classifying them into severe and general MPP groups. The risk factors for severe MPP were identified using Logistic Stepwise Regression Analysis, followed by Multivariate Regression Analysis to construct the nomogram model. The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test.

Results

Univariate analysis revealed that age, duration of fever, length of hospital-stay, decreased sounds of breathing, respiratory rate, hypokalemia, and incidence of co-infection were significantly different between severe and general MPP. Significant differences (p < 0.05) were also observed in C-reactive protein, procalcitonin, peripheral blood lymphocyte count, neutrophil-to-lymphocyte ratio, ferritin, lactate dehydrogenase, alanine aminotransferase, interleukin-6, immunoglobulin A, and CD4+ T cells between the two groups. Logistic Stepwise Regression Analysis showed that age, decreased sounds of breathing, respiratory rate, duration of fever (OR = 1.131; 95% CI: 1.060-1.207), length of hospital-stay (OR = 1.415; 95% CI: 1.287-1.555), incidence of co-infection (OR = 1.480; 95% CI: 1.001-2.189), ferritin level (OR = 1.003; 95% CI: 1.001-1.006), and LDH level (OR = 1.003; 95% CI: 1.001-1.005) were identified as risk factors for the development of severe MPP (p < 0.05 in all). The above factors were applied in constructing a nomogram model that was subsequently tested with 0.862 of the area under the ROC curve.

Conclusion

Age, decreased sound of breathing, respiratory rate, duration of fever, length of hospital-stay, co-infection with other pathogen(s), ferritin level, and LDH level were the significant contributors for the establishment of a nomogram model to predict the severity of MPP in children.

Tomato leaf disease recognition based on multi-task distillation learning

Introduction

Tomato leaf diseases can cause major yield and quality losses. Computer vision techniques for automated disease recognition show promise but face challenges like symptom variations, limited labeled data, and model complexity.

Methods

Prior works explored hand-crafted and deep learning features for tomato disease classification and multi-task severity prediction, but did not sufficiently exploit the shared and unique knowledge between these tasks. We present a novel multi-task distillation learning (MTDL) framework for comprehensive diagnosis of tomato leaf diseases. It employs knowledge disentanglement, mutual learning, and knowledge integration through a multi-stage strategy to leverage the complementary nature of classification and severity prediction.

Results

Experiments show our framework improves performance while reducing model complexity. The MTDL-optimized EfficientNet outperforms single-task ResNet101 in classification accuracy by 0.68% and severity estimation by 1.52%, using only 9.46% of its parameters.

Discussion

The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

Severe Acute Pancreatitis Prediction: A Model Derived From a Prospective Registry Cohort

Background Severe acute pancreatitis (SAP) has a mortality rate as high as 40%. Early identification of SAP is required to appropriately triage and direct initial therapies. The purpose of this study was to develop a prognostic model that identifies patients at risk for developing SAP of patients managed according to a guideline-based standardized early medical management (EMM) protocol. Methods This single-center study included all patients diagnosed with acute pancreatitis (AP) and managed with the EMM protocol Methodist Acute Pancreatitis Protocol (MAPP) between April 2017 and September 2022. Classification and regression tree (CART®; Professional Extended Edition, version 8.0; Salford Systems, San Diego, CA), univariate, and logistic regression analyses were performed to develop a scoring system for AP severity prediction. The accuracy of the scoring system was measured by the area under the receiver operating characteristic curve. Results A total of 516 patients with mild (n=436) or moderately severe and severe (n=80) AP were analyzed. CART analysis identified the cutoff values: creatinine (CR) (1.15 mg/dL), white blood cells (WBC) (10.5 × 109/L), procalcitonin (PCT) (0.155 ng/mL), and systemic inflammatory response system (SIRS). The prediction model was built with a multivariable logistic regression analysis, which identified CR, WBC, PCT, and SIRS as the main predictors of severity. When CR and only one other predictor value (WBC, PCT, or SIRS) met thresholds, then the probability of predicting SAP was >30%. The probability of predicting SAP was 72% (95%CI: 0.59-0.82) if all four of the main predictors were greater than the cutoff values. Conclusions Baseline laboratory cutoff values were identified and a logistic regression-based prognostic model was developed to identify patients treated with a standardized EMM who were at risk for SAP.

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Background

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.

Methods

The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).

Results

The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/).

Conclusions

The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.

Hypertriglyceridaemia-Induced Acute Pancreatitis: A Different Disease Phenotype

Acute pancreatitis (AP) is the most common gastrointestinal indication requiring hospitalisation. Severe hypertriglyceridaemia (HTG) is the third most common aetiology of AP (HTGAP), with a complication rate and severity that are higher than those of other aetiologies (non-HTGAP). The aim of this study was to evaluate the supposedly higher complication rate of HTGAP compared to non-HTGAP. The secondary objectives were to find different biomarkers for predicting a severe form. This was a retrospective study that included patients admitted with AP in a tertiary department of gastroenterology and hepatology. The patients were divided into two groups: HTGAP and non-HTGAP. We searched for differences regarding age, gender, the presence of diabetes mellitus (DM), the severity of the disease, the types of complications and predictive biomarkers for severity, hospital stay and mortality. A total of 262 patients were included, and 11% (30/262) of the patients had HTGAP. The mean ages were 44.4 ± 9.2 in the HTGAP group and 58.2 ± 17.1 in the non-HTGAP group, p < 0.0001. Male gender was predominant in both groups, at 76% (23/30) in the HTGAP group vs. 54% (126/232) in non-HTGAP, p = 0.02; 53% (16/30) presented with DM vs. 18% (42/232), p < 0.0001. The patients with HTG presented higher CRP 48 h after admission: 207 mg/dL ± 3 mg/dL vs. non-HTGAP 103 mg/dL ± 107 mg/dL, p < 0.0001. Among the patients with HTGAP, there were 60% (18/30) with moderately severe forms vs. 30% (71/232), p = 0.001, and 16% (5/30) SAP vs. 11% (27/232) in non-HTGAP, p = 0.4 Among the predictive markers, only haematocrit (HT) and blood urea nitrogen (BUN) had AUCs > 0.8. According to a multiple regression analysis, only BUN 48 h was independently associated with the development of SAP (p = 0.05). Diabetes mellitus increased the risk of developing severe acute pancreatitis (OR: 1.3; 95% CI: 0.1963−9.7682; p = 0.7). In our cohort, HTGAP more frequently had local complications compared with non-HTGAP. A more severe inflammatory syndrome seemed to be associated with this aetiology; the best predictive markers for complicated forms of HTGAP were BUN 48 h and HT 48 h.

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes.

SARS-CoV-2 RNAemia Predicts Clinical Deterioration and Extrapulmonary Complications from COVID-19

BACKGROUND

The determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs.

METHODS

We used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression.

RESULTS

Of SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]).

CONCLUSIONS

dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

Lung detection and severity prediction of pneumonia patients based on COVID-19 DET-PRE network

BACKGROUND

The sudden outbreak of COVID-19 pneumonia has brought a heavy disaster to individuals globally. Facing this new virus, the clinicians have no automatic tools to assess the severity of pneumonia patients.

METHODS

In the current work, a COVID-19 DET-PRE network with two pipelines was proposed. Firstly, the lungs in X-rays were detected and segmented through the improved YOLOv3 Dense network to remove redundant features. Then, the VGG16 classifier was pre-trained on the source domain, and the severity of the disease was predicted on the target domain by means of transfer learning.

RESULTS

The experiment results demonstrated that the COVID-19 DET-PRE network can effectively detect the lungs from X-rays and accurately predict the severity of the disease. The mean average precisions (mAPs) of lung detection in patients with mild and severe illness were 0.976 and 0.983 respectively. Moreover, the accuracy of severity prediction of COVID-19 pneumonia can reach 86.1%.

CONCLUSIONS

The proposed neural network has high accuracy, which is suitable for the clinical diagnosis of COVID-19 pneumonia.

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19

Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models-one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased-sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased-sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity

Due to the increase in the number of patients who died as a result of the SARS-CoV-2 virus around the world, researchers are working tirelessly to find technological solutions to help doctors in their daily work. Fast and accurate Artificial Intelligence (AI) techniques are needed to assist doctors in their decisions to predict the severity and mortality risk of a patient. Early prediction of patient severity would help in saving hospital resources and decrease the continual death of patients by providing early medication actions. Currently, X-ray images are used as early symptoms in detecting COVID-19 patients. Therefore, in this research, a prediction model has been built to predict different levels of severity risks for the COVID-19 patient based on X-ray images by applying machine learning techniques. To build the proposed model, CheXNet deep pre-trained model and hybrid handcrafted techniques were applied to extract features, two different methods: Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were integrated to select the most important features, and then, six machine learning techniques were applied. For handcrafted features, the experiments proved that merging the features that have been selected by PCA and RFE together (PCA + RFE) achieved the best results with all classifiers compared with using all features or using the features selected by PCA or RFE individually. The XGBoost classifier achieved the best performance with the merged (PCA + RFE) features, where it accomplished 97% accuracy, 98% precision, 95% recall, 96% f1-score and 100% roc-auc. Also, SVM carried out the same results with some minor differences, but overall it was a good performance where it accomplished 97% accuracy, 96% precision, 95% recall, 95% f1-score and 99% roc-auc. On the other hand, for pre-trained CheXNet features, Extra Tree and SVM classifiers with RFE achieved 99.6% for all measures.

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries

In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response. Taking into account that the nature of accidents may change over time due to several reasons, the system enables users to build an updated version of the prediction model based on the historical and newly reported accidents. A dataset of accidents that occurred over a 6-year period (2008-2013) has been used for demonstration purposes throughout this paper. The accuracy of the prediction model was 65%. The Area Under the Curve (AUC) showed that the generated classifier can reasonably predict the severity of road traffic injuries. Importantly, using the cost-sensitive learning technique, the predictor overcame the problem of imbalanced severity distributions which are inherent in traffic accident datasets.

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified “healthy” and “moderate or severe” periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.

Index C-reactive protein predicts increased severity in acute sigmoid diverticulitis

Purpose

Conservative management is successful in unperforated (Hinchey Ia) acute diverticulitis (AD) and also generally in local perforation or small abscesses (Hinchey Ib). A higher degree of radiological severity (Hinchey >Ib), ie, a larger abscess (>3-4 cm) or peritonitis, commonly requires percutaneous drainage or surgery. Retrospective studies show that high levels of C-reactive protein (CRP) distinguish Hinchey Ia from all cases of minor and major perforations (Hinchey >Ia). The current study aims to evaluate the usefulness of CRP in distinguishing AD with a higher degree of severity (Hinchey >Ib) from cases that can be treated noninvasively (Hinchey Ia/Ib).

Methods

Data from consecutive patients with AD were collected prospectively. All underwent computed tomography (CT). Index parameters obtained at the initial evaluation at the emergency unit were analyzed to assess the association with the outcome. The exclusion criteria comprised concomitant conditions that affected CRP baseline levels.

Results

Ninety-nine patients were analyzed. Eighty-eight had mild radiological grading (Hinchey Ia/Ib), and 11 had severe radiological grading (Hinchey >Ib) (median index CRP 80 mg/L vs 236 mg/L [P<0.001]). White blood cells, neutrophils/lymphocytes, serum creatinine, serum glucose, generalized peritonitis, generalized abdominal tenderness, urinary symptoms, and index CRP were related to severe disease. Index CRP was the only independent predictor for Hinchey >Ib (P=0.038). The optimal cutoff value calculated by receiver operating characteristic curve analysis was found to be 173 mg/L (sensitivity 90.9%, specificity 90.9%, P<0.001). All patients who underwent radiological drainage or surgery had an index CRP >173 mg/L and Hinchey >Ib.

Conclusion

CRP levels >173 mg/L obtained at the initial evaluation at the emergency unit predict major acute complications in AD. These patients commonly require urgent percutaneous drainage or surgical management.

Predicting Severity of Disease-Causing Variants

Most diseases, including those of genetic origin, express a continuum of severity. Clinical interventions for numerous diseases are based on the severity of the phenotype. Predicting severity due to genetic variants could facilitate diagnosis and choice of therapy. Although computational predictions have been used as evidence for classifying the disease relevance of genetic variants, special tools for predicting disease severity in large scale are missing. Here, we manually curated a dataset containing variants leading to severe and less severe phenotypes and studied the abilities of variation impact predictors to distinguish between them. We found that these tools cannot separate the two groups of variants. Then, we developed a novel machine-learning-based method, PON-PS (http://structure.bmc.lu.se/PON-PS), for the classification of amino acid substitutions associated with benign, severe, and less severe phenotypes. We tested the method using an independent test dataset and variants in four additional proteins. For distinguishing severe and nonsevere variants, PON-PS showed an accuracy of 61% in the test dataset, which is higher than for existing tolerance prediction methods. PON-PS is the first generic tool developed for this task. The tool can be used together with other evidence for improving diagnosis and prognosis and for prioritization of preventive interventions, clinical monitoring, and molecular tests.

Predicting the severity of acute bronchiolitis in infants: should we use a clinical score or a biomarker?

Krebs von den Lungen 6 antigen (KL-6) has been shown to be a useful biomarker of the severity of Respiratory syncytial virus bronchiolitis. To assess the correlation between the clinical severity of acute bronchiolitis, serum KL-6, and the causative viruses, 222 infants with acute bronchiolitis presenting at the Pediatric Emergency Department of Estaing University Hospital, Clermont-Ferrand, France, were prospectively enrolled from October 2011 to May 2012. Disease severity was assessed with a score calculated from oxygen saturation, respiratory rate, and respiratory effort. A nasopharyngeal aspirate was collected to screen for a panel of 20 respiratory viruses. Serum was assessed and compared with a control group of 38 bronchiolitis-free infants. No significant difference in KL-6 levels was found between the children with bronchiolitis (mean 231 IU/mL ± 106) and those without (230 IU/mL ± 102), or between children who were hospitalized or not, or between the types of virus. No correlation was found between serum KL-6 levels and the disease severity score. The absence of Human Rhinovirus was a predictive factor for hospitalization (OR 3.4 [1.4-7.9]; P = 0.006). Older age and a higher oxygen saturation were protective factors (OR 0.65[0.55-0.77]; P < 0.0001 and OR 0.67 [0.54-0.85] P < 0.001, respectively). These results suggest that in infants presenting with bronchiolitis for the first time, clinical outcome depends more on the adaptive capacities of the host than on epithelial dysfunction intensity. Many of the features of bronchiolitis are affected by underlying disease and by treatment.

Ranson's criteria for acute pancreatitis in high altitude: do they need to be modified?

BACKGROUND/AIM

To examine the validity of Ranson's criteria in the prediction of the severity of acute pancreatitis (as judged by the occurrence of complications) in a high-altitude area of Saudi Arabia with a predominant biliary pancreatitis.

MATERIALS AND METHODS

All consecutive cases of acute pancreatitis (AP) admitted to a tertiary care hospital over a two-and-half-year period were included in this prospective study. Ranson's criteria (RC) were used to determine the severity of the attack of AP, which was then correlated with the occurrence of complications. The validity of Ranson's score and that of each of its individual components was estimated. Using receiver operating characteristic (ROC) curve, new optimum values for these components were calculated and a new modified score was constructed.

RESULTS

Seventy-three attacks of AP in 69 patients formed the material of this study. Ranson's prediction criteria classified 43.8% of the attacks as "severe", but only 22% of those attacks were associated with complications. Calcium level (<8 mg/dl) was the only criterion that was significantly associated with complications (Kappa = 0.32, P = 0.02). Using ROC curve to determine the optimum cut-off levels for prediction identified only four criteria, which were significantly associated with complications as compared with the original Ranson's cut-off levels. Those were: a serum glucose value of >or=160 mg/dl (P < 0.05), blood urea nitrogen rise of >or=35 mg/dl (P < 0.02) and an arterial Po(2) value of <or=55 mm Hg (P < 0.01), in addition to calcium value of <8 mg/dl (P = 0.02) as originally set by Ranson. A new scoring system, ranging from 0 to 4, based on these cut-off levels, together with a calcium level of <8 mg/dl, could correctly classify the severity of AP. A total score of two or more points predicted a severe attack with a sensitivity of 88%, a specificity of 82% and a Kappa coefficient of 0.47 (P < 0.001).

CONCLUSION

This study showed that Ranson's criteria may need to be modified in high altitude with a predominant biliary pancreatitis in order to accurately predict the severity of AP.

Persistent organ failure during the first week as a marker of fatal outcome in acute pancreatitis

BACKGROUND

In predicted severe acute pancreatitis, many patients develop organ failure and recover without local complications, and mortality is only 14-30%. It has been suggested that half of patients with progressive early organ failure may die, but there are no data to relate death or local complications to duration of early (week 1) organ failure.

AIMS

To determine mortality rates in patients with transient (<48 hours) and persistent (>48 hours) early organ failure and to show whether persistent organ failure predicts death or local complications.

PATIENTS

A total of 290 patients with predicted severe acute pancreatitis previously studied in a trial of lexipafant, recruited from 78 hospitals through 18 centres in the UK.

METHOD

Manual review of trial database to determine: the presence of organ failure (Marshall score > or =2) on each of the first seven days in hospital, duration of organ failure, and outcome of pancreatitis (death, complications by Atlanta criteria).

RESULTS

Early organ failure was present in 174 (60%) patients. After transient organ failure (n = 71), outcome was good: one death and 29% local complications. Persistent organ failure (n = 103) was followed by 36 deaths and 77% local complications, irrespective of onset of organ failure on admission or later during the first week.

CONCLUSION

Duration of organ failure during the first week of predicted severe acute pancreatitis is strongly associated with the risk of death or local complications. Resolution of organ failure within 48 hours suggests a good prognosis; persistent organ failure is a marker for subsequent death or local complications.