Patients’ subjective assessment as a decisive predictor of malignancy in pelvic masses: results of a multicentric, prospective pelvic mass study

Objective The prognosis for ovarian cancer patients remains poor. A key to maximizing survival rates is early detection and treatment. This requires an accurate prediction of malignancy. Our study seeks to improve the accuracy of prediction by focusing on early subjective assessment of malignancy. We therefore investigated the assessment of patients themselves in comparison to the assessment of physicians. Methods One thousand three hundred and thirty patients participated in a prospective and multicenter study in six hospitals in Berlin. Using univariate analysis and multivariate logistic regression models, we measured the accuracy of the early subjective assessment in comparison to the final histological outcome. Moreover, we investigated factors related to the assessment of patients and physicians. Results The patients’ assessment of malignancy is remarkably accurate. With a positive predictive value of 58%, the majority of patients correctly assessed a pelvic mass as malignant. With more information available, physicians achieved only a slightly more accurate prediction of 63%. Conclusions For the first time, our study considered subjective factors in the diagnostic process of pelvic masses. This paper demonstrates that the patients’ personal assessment should be taken seriously as it can provide a significant contribution to earlier diagnosis and thus improved therapy and overall prognosis

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451