Breast cancer risk, worry, and anxiety: Effect on patient perceptions of false-positive screening results

OBJECTIVE: The impact of mammography screening recall on quality-of-life (QOL) has been studied in women at average risk for breast cancer, but it is unknown whether these effects differ by breast cancer risk level. We used a vignette-based survey to evaluate how women across the spectrum of breast cancer risk perceive the experience of screening recall. METHODS: Women participating in mammography or breast MRI screening were recruited to complete a vignette-based survey. Using a numerical rating scale (0-100), women rated QOL for hypothetical scenarios of screening recall, both before and after benign results were known. Lifetime breast cancer risk was calculated using Gail and BRCAPRO risk models. Risk perception, trait anxiety, and breast cancer worry were assessed using validated instruments. RESULTS: The final study cohort included 162 women at low (n = 43, 26%), intermediate (n = 66, 41%), and high-risk (n = 53, 33%). Actual breast cancer risk was not a predictor of QOL for any of the presented scenarios. Across all risk levels, QOL ratings were significantly lower for the period during diagnostic uncertainty compared to after benign results were known (p \u3c 0.05). In multivariable regression analyses, breast cancer worry was a significant predictor of decreased QoL for all screening scenarios while awaiting results, including scenarios with non-invasive imaging alone or with biopsy. High trait anxiety and family history predicted lower QOL scores after receipt of benign test results (p \u3c 0.05). CONCLUSIONS: Women with high trait anxiety and family history may particularly benefit from discussions about the risk of recall when choosing a screening regimen

PI Prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections.

BackgroundPrimary immunodeficiency diseases represent an expanding set of heterogeneous conditions which are difficult to recognize clinically. Diagnostic rates outside of the newborn period have not changed appreciably. This concern underscores a need for novel methods of disease detection.ObjectiveWe built a Bayesian network to provide real-time risk assessment about primary immunodeficiency and to facilitate prescriptive analytics for initiating the most appropriate diagnostic work up. Our goal is to improve diagnostic rates for primary immunodeficiency and shorten time to diagnosis. We aimed to use readily available health record data and a small training dataset to prove utility in diagnosing patients with relatively rare features.MethodsWe extracted data from the Texas Children's Hospital electronic health record on a large population of primary immunodeficiency patients (n = 1762) and appropriately-matched set of controls (n = 1698). From the cohorts, clinically relevant prior probabilities were calculated enabling construction of a Bayesian network probabilistic model(PI Prob). Our model was constructed with clinical-immunology domain expertise, trained on a balanced cohort of 100 cases-controls and validated on an unseen balanced cohort of 150 cases-controls. Performance was measured by area under the receiver operator characteristic curve (AUROC). We also compared our network performance to classic machine learning model performance on the same dataset.ResultsPI Prob was accurate in classifying immunodeficiency patients from controls (AUROC = 0.945; pConclusionArtificial intelligence methods can classify risk for primary immunodeficiency and guide management. PI Prob enables accurate, objective decision making about risk and guides the user towards the appropriate diagnostic evaluation for patients with recurrent infections. Probabilistic models can be trained with small datasets underscoring their utility for rare disease detection given appropriate domain expertise for feature selection and network construction