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BackgroundEarly hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.AimsWe leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.MethodsA national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.ResultsWe analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.ConclusionOur model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

OBJECTIVE:Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS:A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS:The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION:This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.

Introduction The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule." Framework We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. Recommendations Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models. [ABSTRACT FROM AUTHOR]

The guiding principle behind the healing of racism is the Oneness of Humankind. This is not an old concept warmed over. It is knowledge about our collective capacity to reach deep into the human spirit and solve the most complex challenges of our time. The compound problem of race and other issues such as poverty only can be resolved with the acceptance and understanding of our human oneness. The pressing need is to be unified in all essential aspects of human life, yet infinite in our diversity. This article describes the work of the Institutes for Healing Racism, which involves intensive two-day experiences that move beyond tolerance to create deep learning. These result in new cognitive and emotional understanding among persons who had not previously experienced deep meaningful relationships across racial divides. To understand how racism distorts reality, many beliefs must be reconstructed. The stories of healing racism involve transformational, emotional experiences. Genuine human interactions are blended with specific subject matter, including: (1) Recognizing that racism is an emotional commitment to dysfunctional beliefs; (2) Understanding the pervasive impact of White privilege; (3) Recognizing the effect of institutional or structural racism; (4) Embracing the Oneness of Humankind; and (5) Taking new steps in the Healing Racism journey.

Part V. Presents the telephone service user survey which was conducted to assess the satisfaction and impact of the assistance and information provided by the Cancer Information Services (CIS) to callers in the United States. Methodology; Role of CIS cancer information specialists; Statistics on the results of the survey.

Several studies have found associations between air pollution and respiratory disease outcomes. However, there is minimal prognostic research exploring whether integrating air quality into clinical prediction models can improve accuracy and utility. In this study, we built models using both logistic regression and random forests to determine the benefits of including air quality data with meteorological and clinical data in prediction of COPD exacerbations requiring medical care. Logistic models were not improved by inclusion of air quality. However, the net benefit curves of random forest models showed greater clinical utility with the addition of air quality data. These models demonstrate a practical and relatively low-cost way to include environmental information into clinical prediction tools to improve the clinical utility of COPD prediction. Findings could be used to provide population level health warnings as well as individual-patient risk assessments., (©2023 AMIA - All rights reserved.)

In evolving clinical environments, the accuracy of prediction models deteriorates over time. Guidance on the design of model updating policies is limited, and there is limited exploration of the impact of different policies on future model performance and across different model types. We implemented a new data-driven updating strategy based on a nonparametric testing procedure and compared this strategy to two baseline approaches in which models are never updated or fully refit annually. The test-based strategy generally recommended intermittent recalibration and delivered more highly calibrated predictions than either of the baseline strategies. The test-based strategy highlighted differences in the updating requirements between logistic regression, L1-regularized logistic regression, random forest, and neural network models, both in terms of the extent and timing of updates. These findings underscore the potential improvements in using a data-driven maintenance approach over "one-size fits all" to sustain more stable and accurate model performance over time., (©2019 AMIA - All rights reserved.)

Advanced regression and machine learning models can provide personalized risk predictions to support clinical decision-making. We aimed to understand whether modeling methods impact the tendency of calibration to deteriorate as patient populations shift over time, with the goal of informing model updating practices. We developed models for 30-day hospital mortality using seven common regression and machine learning methods. Models were developed on 2006 admissions to Department of Veterans Affairs hospitals and validated on admissions in 2007-2013. All models maintained discrimination. Calibration was stable for the neural network model and declined for all other models. The L-2 penalized logistic regression and random forest models experienced smaller magnitudes of calibration drift than the other regression models. Calibration drift was linked with a changing case mix rather than shifts in predictoroutcome associations or outcome rate. Model updating protocols will need to be tailored to variations in calibration drift across methods.

Background: Patient portal adoption has increased over the last two decades. Most research about patient portals has focused on adult populations in the primary care and medical specialty settings., Objective: We describe initial and long-term portal use by pediatric patients and their caregivers in a broadly deployed patient portal at an academic medical center., Methods: We analyzed portal usage for pediatric patients and their caregivers from 2008 to 2014. We recorded usage events with time stamps; user role defined as self, surrogate (i.e., parent or guardian), or delegate; and functions accessed. Usage events were grouped into sessions to calculate descriptive statistics by patient age, user role, and active use over time., Results: From 2008 to 2014, the number of portal accounts increased from 633 to 17,128. 15.9% of pediatric patients had their own account; 93.6%, a surrogate account; and 2.2% a delegate account. During the study period, 15,711 unique users initiated 493,753 sessions and accessed 1,491,237 functions. Most commonly used functions were secure messaging (accessed in 309,204 sessions; 62.6%); test results (174,239; 35.3%) and appointments (104,830; 21.2%). Function usage was greatest for patients ages 0-2 years (136,245 functions accessed; 23.1%) and 15-17 years (109,241;18.5%). Surrogate users conducted 83.2% of logins for adolescent patients. Portal accounts were actively used for < 1 year for 9,551 patients (55.8%), 1-2 years for 2,826 patients (16.5%), 2-3 years for 1,968 patients (11.5%) and over 3 years for 2,783 patients (16.3%)., Conclusion: Pediatric patients and caregivers have avidly used messaging, test result, and appointment functions. The majority of access was done by surrogates. Adolescent portal usage increased with age. Most accounts for pediatric patients were only used actively for a few years, with peak usage for patients in early childhood and late adolescence.

Patient portal research has focused on medical outpatient settings, with little known about portal use during hospitalizations or by surgical patients. We measured portal adoption among patients admitted to surgical services over two years. Surgical services managed 37,025 admissions of 31,310 unique patients. One-fourth of admissions (9,362, 25.3%) involved patients registered for the portal. Registration rates were highest for admissions to laparoscopic/gastrointestinal (55%) and oncology/endocrine (50%) services. Portal use occurred during 1,486 surgical admissions, 4% of all and 16% of those registered at admission. Inpatient portal use was associated with patients who were white, male, and had longer lengths of stay (p < 0.01). Viewing health record data and secure messaging were the most commonly used functions, accessed in 4,836 (72.9%) and 1,626 (24.5%) user sessions. Without specific encouragement, hospitalized surgical patients are using our patient portal. The surgical inpatient setting may provide opportunities for patient engagement using patient portals.

Few studies have explored adoption of patient portals for pediatric patients outside primary care or disease-specific applications. We examined use of patient-provider messaging in a patient portal across pediatric specialties during the three years after implementation of pediatric portal accounts at Vanderbilt University Medical Center. We determined the number of patient-initiated message threads and clinic visits for pediatric specialties and percentage of these outpatient interactions (i.e., message threads + clinic visits) done through messaging. Generalized estimating equations measured the likelihood of message-based interaction. During the study period, pediatric families initiated 33,503 messages and participated in 318,386 clinic visits. The number of messages sent (and messaging percentage of outpatient interaction) increased each year from 2,860 (2.7%) to 18,772 (17%). Primary care received 4,368 messages (3.4% of outpatient interactions); pediatric subspecialties, 29,135 (13.0%). Rapid growth in messaging volume over time was seen in primary care and most pediatric specialties (OR>1.0; p<0.05).

Patient portal adoption has rapidly increased, and portal usage has been associated with patients' sociodemographics, health literacy, and education. Research on patient portals has primarily focused on the outpatient setting. We explored whether health literacy and education were associated with portal usage in an inpatient population. Among 60,159 admissions in 2012-2013, 23.3% of patients reported limited health literacy; 50.4% reported some post-secondary education; 34.4% were registered for the portal; and 23.4% of registered patients used the portal during hospitalization. Probability of registration and inpatient portal use increased with educational attainment. Health literacy was associated with registration but not inpatient use. Among admissions with inpatient use, educational attainment was associated with viewing health record data, and health literacy was associated use of appointment and health education tools. The inpatient setting may provide an opportunity to overcome barriers to patient portal adoption and reduce disparities in use of health information technologies.