Algorithm Training and Testing for a Nonendoscopic Barrett's Esophagus Detection Test in Prospective Multicenter Cohorts.

BACKGROUND & AIMS: Endoscopic Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) detection is invasive and expensive. Nonendoscopic BE/EAC detection tools are guideline-endorsed alternatives. We previously described a 5-methylated DNA marker (MDM) panel assayed on encapsulated sponge cell collection device (CCD) specimens. We aimed to train a new algorithm using a 3-MDM panel and test its performance in an independent cohort. METHODS: Algorithm training and test samples were from 2 prospective multicenter cohorts. All BE cases had esophageal intestinal metaplasia (with or without dysplasia/EAC); control subjects had no endoscopic evidence of BE. The CCD procedure was followed by endoscopy. From CCD cell lysates, DNA was extracted, bisulfite treated, and MDMs were blindly assayed. The algorithm was set and locked using cross-validated logistic regression (training set) and its performance was assessed in an independent test set. RESULTS: Training (N = 352) and test (N = 125) set clinical characteristics were comparable. The final panel included 3 MDMs (NDRG4, VAV3, ZNF682). Overall sensitivity was 82% (95% CI, 68%-94%) at 90% (79%-98%) specificity and 88% (78%-94%) sensitivity at 84% (70%-93%) specificity in training and test sets, respectively. Sensitivity was 90% and 68% for all long- and short-segment BE, respectively. Sensitivity for BE with high-grade dysplasia and EAC was 100% in training and test sets. Overall sensitivity for nondysplastic BE was 82%. Areas under the receiver operating characteristic curves for BE detection were 0.92 and 0.94 in the training and test sets, respectively. CONCLUSIONS: A locked 3-MDM panel algorithm for BE/EAC detection using a nonendoscopic CCD demonstrated excellent sensitivity for high-risk BE cases in independent validation samples. (Clinical trials.gov: NCT02560623, NCT03060642.).

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Validation of a methylated DNA marker panel for the nonendoscopic detection of Barrett's esophagus in a multisite case-control study.

BACKGROUND AND AIMS: We previously identified a 5 methylated DNA marker (MDM) panel for the detection of nonendoscopic Barrett's esophagus (BE). In this study, we aimed to recalibrate the performance of the 5 MDM panel using a simplified assay in a training cohort, validate the panel in an independent test cohort, and explore the accuracy of an MDM panel with only 3 markers. METHODS: Participants were recruited from 3 medical centers. The sponge on a string device (EsophaCap; CapNostics, Concord, NC, USA) was swallowed and withdrawn, followed by endoscopy, in BE cases and control subjects. A 5 MDM panel was blindly assayed using a simplified assay. Random forest modeling analysis was performed, in silico cross-validated in the training set, and then locked down, before test set analysis. RESULTS: The training set had 199 patients: 110 BE cases and 89 control subjects, and the test set had 89 patients: 60 BE cases and 29 control subjects. Sensitivity of the 5 MDM panel for BE diagnosis was 93% at 90% specificity in the training set and 93% at 93% specificity in the test set. Areas under the receiver operating characteristic curves were .96 and .97 in the training and test sets, respectively. Model accuracy was not influenced by age, sex, or smoking history. Multiple 3 MDM panels achieved similar accuracy. CONCLUSIONS: A 5 MDM panel for BE is highly accurate in training and test sets in a blinded multisite case-control analysis using a simplified assay. This panel may be reduced to only 3 MDMs in the future. (Clinical trial registration number: NCT02560623.).