Development and validation of a predictive model for choledocholithiasis.

BACKGROUND: Gallstone-related conditions affect a significant portion of the population, with varying prevalence among different ethnic groups. Complications such as pancreatitis and cholangitis are associated with the presence of common bile duct (CBD) stones. Existing guidelines for diagnosing choledocholithiasis lack precision, leading to excessive use of invasive procedures like endoscopic retrograde cholangiopancreatography (ERCP). METHODS: A prospective study was conducted at Hospital Central "Dr. Ignacio Morones Prieto," involving 374 patients in the development cohort and 154 patients in the validation cohort. Patients meeting inclusion criteria underwent biochemical testing and ultrasonography. A predictive scoring system was developed using logistic regression and validated in an independent cohort. Clinical and laboratory variables were collected, and model performance was assessed using receiver-operator characteristic (ROC) curves. RESULTS: The predictive model incorporated variables such as age, pancreatitis, cholangitis, bilirubin levels, and CBD stone presence on ultrasound. The model demonstrated an area under the ROC curve (AUC) of 93.81% in the validation dataset. By adjusting the threshold defining high-risk probability to 40%, the model improved specificity and sensitivity compared to existing guidelines. Notably, the model reclassified patients, leading to a more accurate risk assessment. CONCLUSIONS: The developed algorithm accurately predicts choledocholithiasis non-invasively in patients with symptomatic gallstones. This tool has the potential to reduce reliance on costly or invasive procedures like magnetic resonance cholangiopancreatography and ERCP, offering a more efficient and cost-effective approach to patient management. The user-friendly calculator developed in this study could streamline diagnostic procedures, particularly in resource-limited healthcare settings, ultimately improving patient care.

[Ameboma: possible therapeutic decisions in an amebiasis-endemic region]. / Ameboma: posibles decisiones terapéuticas en región endémica de amibiasis.

INTRODUCTION: Amebiasis can mimic cecal tumors. Unless this infection is diagnosed in a timely manner, affected individuals may undergo extensive surgery. MATERIAL AND METHODS: We carried out a retrospective analytical study of the therapeutic approach to amebiasis in a second-level hospital in an area of central Mexico with a high prevalence of this infection. Records from 2005-2011 were reviewed. There were 261 cases of amebiasis. Twenty cases were diagnosed by the histopathologist or on the basis of serological results. Sixteen patients underwent surgery due to acute abdomen, and four received medical treatment with metronidazole. Three treatment groups were analyzed: 1. hemicolectomy, 2. appendicectomy and antiamebic therapy, and 3. antiamoebic therapy alone. In the non-surgical group, imaging studies showed improvement with medical therapy. RESULTS: Length of hospital stay was higher in the group undergoing extensive surgery (p < 0.0133). There were no statistically significant differences among the remaining variables. CONCLUSIONS: The incidence of ameboma in our environment is higher (7.6%) than that reported in the literature. We believe that, in endemic regions, ameboma should be ruled out in patients with a cecal mass. As part of the therapeutic approach, patients should be tested for amebiasis or receive antiamebic therapy with monitoring of the mass to avoid extensive resective surgery.

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.

BACKGROUND: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. OBJECTIVE: This study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks. METHODS: A data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical's Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices. RESULTS: A poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. CONCLUSIONS: The interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale.

Development and validation of an algorithm to predict the treatment modality of burn wounds using thermographic scans: Prospective cohort study.

BACKGROUND: The clinical evaluation of a burn wound alone may not be adequate to predict the severity of the injury nor to guide clinical decision making. Infrared thermography provides information about soft tissue viability and has previously been used to assess burn depth. The objective of this study was to determine if temperature differences in burns assessed by infrared thermography could be used predict the treatment modality of either healing by re-epithelization, requiring skin grafts, or requiring amputations, and to validate the clinical predication algorithm in an independent cohort. METHODS AND FINDINGS: Temperature difference (ΔT) between injured and healthy skin were recorded within the first three days after injury in previously healthy burn patients. After discharge, the treatment modality was categorized as re-epithelization, skin graft or amputation. Potential confounding factors were assessed through multiple linear regression models, and a prediction algorithm based on the ΔT was developed using a predictive model using a recursive partitioning Random Forest machine learning algorithm. Finally, the prediction accuracy of the algorithm was compared in the development cohort and an independent validation cohort. Significant differences were found in the ΔT between treatment modality groups. The developed algorithm correctly predicts into which treatment category the patient will fall with 85.35% accuracy. Agreement between predicted and actual treatment for both cohorts was weighted kappa 90%. CONCLUSION: Infrared thermograms obtained at first contact with a wounded patient can be used to accurately predict the definitive treatment modality for burn patients. This method can be used to rationalize treatment and streamline early wound closure.

Effects of Local Use of Insulin on Wound Healing in Non-diabetic Patients.

BACKGROUND: Clinical trials have shown the positive effects of local insulin therapy in the formation of new vessels and fibrosis in acute and chronic diabetic wounds without major adverse effects. OBJECTIVE: The aim of this study was to investigate the effects of local insulin use on wound healing in non-diabetic patients. METHODS: A randomized, split-plot, double-blind, placebo-controlled trial was conducted. Ten non-diabetic patients with full-thickness acute wounds were recruited (5 due to trauma, 3 to burns, and 2 to pressure). All wounds received standard bedside treatment. Each wound was divided into 2 zones. One side received a standard care plus insulin, while the other received standard care plus injection of saline solution. A biopsy specimen was taken from both sites on days 0 and 14. The amount of blood vessel growth and the percentage of fibrosis were evaluated. RESULTS: A significant difference in the number of new vessels was observed on the insulin-treated site (70.6 [29.21]) compared to saline only (26.5 [34.3]; P < .04). The percentage of fibrosis (insulin 34.7 [28.02] vs saline 27.8 [29.9]) showed no significant difference. No adverse events related to the study occurred. The clinical implications of this study are considerable in terms of the formation of blood vessels but not fibrosis. CONCLUSION: We suggest that local insulin administration is a safe therapeutic option for angiogenesis in wounds of non-diabetic patients.

HISTORIQUE: Les essais cliniques démontrent les effets positifs de l'insulinothérapie localisée pour former de nouveaux vaisseaux ou une fibrose en cas de plaies aiguës ou chroniques causées par le diabète, sans entraîner de réactions indésirables majeures. OBJECTIF: La présente étude visait à évaluer les effets de l'utilisation localisée d'insuline chez des patients non diabétiques. MÉTHODOLOGIE: Les chercheurs ont réalisé un essai aléatoire et contrôlé contre placebo, en parcelles divisées et à double insu. Ils ont recruté dix patients non diabétiques ayant des plaies aiguës de pleine épaisseur (cinq à cause d'un traumatisme, trois à cause de brûlures et deux à cause de pression). Toutes les plaies ont fait l'objet de soins standards au chevet du patient et chacune a été divisée en deux zones. Une zone faisait l'objet de soins standards avec l'ajout d'insuline et l'autre zone, de soins standards avec l'injection de soluté physiologique. Les chercheurs ont prélevé une biopsie dans chaque zone les jours 0 et 14. Ils ont évalué la croissance des vaisseaux sanguins et le pourcentage de fibrose. RÉSULTATS: Les chercheurs ont observé une différence significative dans le nombre de nouveaux vaisseaux de la zone traitée à l'insuline (70,6 ± 29,21) par rapport à celle traitée à l'aide de soluté physiologique (26,5 ± 34,3; P <0,04). Ils n'ont pas constaté de différence significative dans le pourcentage de fibrose (insuline 34,7 ± 28,02 et soluté physiologique 27,8 ± 29,9) ni de réactions indésirables liées à l'étude. Les conséquences cliniques de la présente étude sont considérables à l'égard de la formation de vaisseaux sanguins, mais pas de la fibrose. CONCLUSION: Selon les chercheurs, l'administration localisée d'insuline serait sécuritaire pour l'angiogenèse des plaies des patients non diabétiques.

Local use of insulin in wounds of diabetic patients: higher temperature, fibrosis, and angiogenesis.

BACKGROUND: Clinical trials have shown the effectiveness of systemic and local insulin therapy in improving wound healing. Diabetic wounds remain a challenge for healthcare providers. Impaired angiogenesis and reduced granulation tissue formation contribute to inadequate wound healing. The aim of this study was to investigate the effect of local insulin administration in acute and chronic diabetic wounds. METHODS: Eight diabetic patients presenting with full-thickness wounds, of different causes, were included in this study. Five wounds were due to necrobiosis, one to trauma, and two to postneoplasm resection. All wounds were treated with regular bedside treatment. In addition, half of the wound surface was treated with insulin and the other half did not receive insulin. Thermographic and biopsy specimens of the two sides were obtained on days 0 and 14. The presence of fibrosis, change in temperature, and amount of blood were evaluated. RESULTS: Significant differences in the number of vessels were observed on the insulin-treated side (96 ± 47) when compared with the no-insulin side (32.88 ± 45) (p < 0.026). The percentage of fibrosis (insulin: 44.42 ± 30.42 percent versus no insulin: 12.38 ± 36.17 percent; p < 0.047) and the mean temperature (insulin: 1.27 ± 1.12°C versus no-insulin: 0.13 ± 1.22°C; p < 0.001) were also significantly different between sides. No adverse events related to the study occurred. CONCLUSION: The use of local insulin improves the formation of new blood vessels, increases fibrosis, and correlates with increased temperature. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.