Citations

Widely employed diagnostic antibody serology for Lyme disease, known as standard two-tier testing (STTT), exhibits insufficient sensitivity in early Lyme disease, yielding many thousands of false-negative test results each year. Given this problem, we applied serum antibody repertoire analysis (SERA), or next-generation sequencing (NGS)-based serology, to discover IgG and IgM antibody epitope motifs capable of detecting Lyme disease-specific antibodies with high sensitivity and specificity. Iterative motif discovery and bioinformatic analysis of epitope repertoires from subjects with Lyme disease ( n = 264) and controls ( n = 391) yielded a set of 28 epitope motifs representing 20 distinct IgG antibody epitopes and a set of 38 epitope motifs representing 21 distinct IgM epitopes, which performed equivalently in a large validation cohort of STTT-positive samples.

INTRODUCTION: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Our aim was to evaluate the abilities of multiple artificial intelligence (AI) techniques to predict clinical outcomes based on variables collected at admission, during hospitalization, and at discharge. METHODS: We used the multicenter North American Consortium for the Study of End-Stage Liver Disease (NACSELD) cohort of cirrhotic inpatients who are followed up through 90-days postdischarge for readmission and death. We used statistical methods to select variables that are significant for readmission and death and trained 3 AI models, including logistic regression (LR), kernel support vector machine (SVM), and random forest classifiers (RFC), to predict readmission and death. We used the area under the receiver operating characteristic curve (AUC) from 10-fold crossvalidation for evaluation to compare sexes. Data were compared with model for end-stage liver disease (MELD) at discharge. RESULTS: We included 2,170 patients (57 ± 11 years, MELD 18 ± 7, 61% men, 79% White, and 8% Hispanic). The 30-day and 90-day readmission rates were 28% and 47%, respectively, and 13% died at 90 days. Prediction for 30-day readmission resulted in 0.60 AUC for all patients with RFC, 0.57 AUC with LR for women-only subpopulation, and 0.61 AUC with LR for men-only subpopulation. For 90-day readmission, the highest AUC was achieved with kernel SVM and RFC (AUC = 0.62). We observed higher predictive value when training models with only women (AUC = 0.68 LR) vs men (AUC = 0.62 kernel SVM). Prediction for death resulted in 0.67 AUC for all patients, 0.72 for women-only subpopulation, and 0.69 for men-only subpopulation, all with LR. MELD-Na model AUC was similar to those from the AI models. DISCUSSION: Despite using multiple AI techniques, it is difficult to predict 30- and 90-day readmissions and death in cirrhosis. AI model accuracies were equivalent to models generated using only MELD-Na scores. Additional biomarkers are needed to improve our predictive capability (See also the visual abstract at http://links.lww.com/AJG/B710).

The burden of cirrhosis hospitalizations is increasing. The admission Model for End‐Stage Liver Disease–lactate (MELD‐lactate) was recently demonstrated to be a superior predictor of in‐hospital mortality compared with MELD in limited cohorts. We identified specific classes of hospitalizations where MELD‐lactate may be especially useful and evaluated the predictive role of lactate clearance. This was a retrospective cohort study of 1036 cirrhosis hospitalizations for gastrointestinal bleeding, infection, or other portal hypertension–related indications in the Veterans Health Administration where MELD‐lactate was measured on admission. Performance characteristics for in‐hospital mortality were compared between MELD‐lactate and MELD/MELD‐sodium (MELD‐Na), with stratified analyses of MELD categories (≤15, >15 to <25, ≥25) and reason for admission. We also incorporated day 3 lactate levels into modeling and tested for an interaction between day 1 MELD‐lactate and day 3 lactate clearance. MELD‐lactate had superior discrimination for in‐hospital mortality compared with MELD or MELD‐Na (area under the curve [AUC] 0.789 versus 0.776 versus 0.760, respectively; P < 0.001) and superior calibration. MELD‐lactate had higher discrimination among hospitalizations with MELD ≤15 (AUC 0.763 versus 0.608 for MELD, global P = 0.01) and hospitalizations for infection (AUC 0.791 versus 0.674 for MELD, global P < 0.001). We found a significant interaction between day 1 MELD‐lactate and day 3 lactate clearance; heat maps were created as clinical tools to risk‐stratify patients based on these clinical data. MELD‐lactate had significantly superior performance in predicting in‐hospital mortality among patients hospitalized for infection and/or with MELD ≤15 when compared with MELD or MELD‐Na. Incorporating day 3 lactate clearance may further improve prognostication.

AbstractInfection is a rare occurrence after revision anterior cruciate ligament reconstruction (rACLR). Because of the low rates of infection, it has been difficult to identify risk factors for infection in this patient population. The purpose of this study was to report the rate of infection following rACLR and assess whether infection is associated with patient‐ and surgeon‐dependent risk factors. We reviewed two large prospective cohorts to identify patients with postoperative infections following rACLR. Age, sex, body mass index (BMI), smoking status, history of diabetes, and graft choice were recorded for each patient. The association of these factors with postoperative infection following rACLR was assessed. There were 1423 rACLR cases in the combined cohort, with 9 (0.6%) reporting postoperative infections. Allografts had a higher risk of infection than autografts (odds ratio, 6.8; 95% CI, 0.9–54.5; p = .045). Diabetes (odds ratio, 28.6; 95% CI, 5.5–149.9; p = .004) was a risk factor for infection. Patient age, sex, BMI, and smoking status were not associated with risk of infection after rACLR.

INTRODUCTION: The purpose of this study was to find out whether 3-dimensional (3D)–printed models improved the learners' ability to identify liver segments. METHODS: A total of 116 physicians from 3 disciplines were tested in a cross-over trial at baseline and after teaching with 3D models and 2-dimensional (2D) images. Adjusted multilevel-mixed models were used to compare scores at baseline and after 3D and 2D. RESULTS: Accuracy in identifying hepatic segments was higher with 3D first than 2D (77% vs 69%; P = 0.05) and not significantly improved by a combination of 3D and 2D. Increased confidence in segment identification was highest in trainees after 3D ( P = 0.04). DISCUSSION: 3D-printed models facilitate learning hepatic segmental anatomy.