Patients With Cirrhosis Rapidly Review Potentially Abnormal Hepatocellular Carcinoma Surveillance Results Through the Patient Portal.

INTRODUCTION: Test results are immediately released to patients through patient portals. We characterized patient and provider time-to-review of liver imaging results. METHODS: We identified 401 patients with cirrhosis enrolled in the portal with ≥1 liver imaging. We compared result review times for patients and providers and identified factors associated with rapid review. RESULTS: The median time-to-review for patients was shorter than providers (3.7 vs 17.6 hours, P < 0.001), with more than half of results reviewed by patients first. Rapid patient review was inversely associated with older age and Hispanic ethnicity. DISCUSSION: Patients rapidly review imaging results through the portal, often before providers.

Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury.

RATIONALE & OBJECTIVE: Risk prediction tools for assisting acute kidney injury (AKI) management have focused on AKI onset but have infrequently addressed kidney recovery. We developed clinical models for risk stratification of mortality and major adverse kidney events (MAKE) in critically ill patients with incident AKI. STUDY DESIGN: Multicenter cohort study. SETTING & PARTICIPANTS: 9,587 adult patients admitted to heterogeneous intensive care units (ICUs; March 2009 to February 2017) who experienced AKI within the first 3 days of their ICU stays. PREDICTORS: Multimodal clinical data consisting of 71 features collected in the first 3 days of ICU stay. OUTCOMES: (1) Hospital mortality and (2) MAKE, defined as the composite of death during hospitalization or within 120 days of discharge, receipt of kidney replacement therapy in the last 48 hours of hospital stay, initiation of maintenance kidney replacement therapy within 120 days, or a ≥50% decrease in estimated glomerular filtration rate from baseline to 120 days from hospital discharge. ANALYTICAL APPROACH: Four machine-learning algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) and the SHAP (Shapley Additive Explanations) framework were used for feature selection and interpretation. Model performance was evaluated by 10-fold cross-validation and external validation. RESULTS: One developed model including 15 features outperformed the SOFA (Sequential Organ Failure Assessment) score for the prediction of hospital mortality, with areas under the curve of 0.79 (95% CI, 0.79-0.80) and 0.71 (95% CI, 0.71-0.71) in the development cohort and 0.74 (95% CI, 0.73-0.74) and 0.71 (95% CI, 0.71-0.71) in the validation cohort (P < 0.001 for both). A second developed model including 14 features outperformed KDIGO (Kidney Disease: Improving Global Outcomes) AKI severity staging for the prediction of MAKE: 0.78 (95% CI, 0.78-0.78) versus 0.66 (95% CI, 0.66-0.66) in the development cohort and 0.73 (95% CI, 0.72-0.74) versus 0.67 (95% CI, 0.67-0.67) in the validation cohort (P < 0.001 for both). LIMITATIONS: The models are applicable only to critically ill adult patients with incident AKI within the first 3 days of an ICU stay. CONCLUSIONS: The reported clinical models exhibited better performance for mortality and kidney recovery prediction than standard scoring tools commonly used in critically ill patients with AKI in the ICU. Additional validation is needed to support the utility and implementation of these models. PLAIN-LANGUAGE SUMMARY: Acute kidney injury (AKI) occurs commonly in critically ill patients admitted to the intensive care unit (ICU) and is associated with high morbidity and mortality rates. Prediction of mortality and recovery after an episode of AKI may assist bedside decision making. In this report, we describe the development and validation of a clinical model using data from the first 3 days of an ICU stay to predict hospital mortality and major adverse kidney events occurring as long as 120 days after hospital discharge among critically ill adult patients who experienced AKI within the first 3 days of an ICU stay. The proposed clinical models exhibited good performance for outcome prediction and, if further validated, could enable risk stratification for timely interventions that promote kidney recovery.

Divergent prognostic effects of pre-existing and treatment-emergent thyroid dysfunction in patients treated with immune checkpoint inhibitors.

BACKGROUND: Thyroid dysfunction is among the most common autoimmune diseases and immune checkpoint inhibitor (ICI)-induced immune-related adverse events (irAE). We determined the association between longitudinal thyroid function and clinical outcomes in patients treated with ICI. METHODS: We identified all patients treated with ICI at UT Southwestern Medical Center from January 1, 2011, through December 31, 2020. We defined normal thyroid stimulating hormone (TSH) and free thyroxine (FT4) levels according to institutional reference range. We defined clinical thyroid dysfunction using established criteria incorporating labs and treatment. We determined the association between thyroid function and overall survival (OS) using Kaplan-Meier curves, log-rank tests, and multivariate Cox proportional hazards model. RESULTS: A total of 1781 patients were included in analyses, of whom 381 (21%) had abnormal baseline TSH. Patients with abnormal baseline TSH were more likely to be female, have kidney cancer, and initiate levothyroxine after ICI initiation (all P < 0.001). Patients with abnormal baseline TSH had inferior OS (median 16 vs 27 months; P < 0.001). Among patients with normal baseline TSH, those who had abnormal TSH after ICI initiation had improved OS (median 41 vs 22 months; P < 0.001). In a multivariate Cox model, abnormal baseline TSH was associated with worse OS (HR 1.62; 95% CI, 1.30-2.02; P < 0.001), while initiation of levothyroxine after ICI initiation was associated with improved OS (HR 0.62; 95% CI, 0.44-0.88; P = 0.008). CONCLUSIONS: ICI-induced thyroid dysfunction is associated with improved survival, although abnormal TSH prior to ICI initiation is associated with inferior survival. PRECIS: Thyroid abnormalities occur commonly in the general population and as immunotherapy toxicities. We found that immunotherapy-induced thyroid dysfunction is associated with better survival, but pre-existing thyroid abnormalities convey worse outcomes.

Epidemiology and risk factors for varicella zoster virus reactivation in heart transplant recipients.

Heart transplant (HT) recipients are at higher risk of varicella zoster virus (VZV) reactivation. Risk factors for VZV reactivation are currently not well defined, impeding the ability to design and implement strategies to minimize the burden of this illness in this population. Automated data extraction tools were used to retrieve data from the electronic health record (EHR) of all adult HT recipients at our center between 2010 and 2016. Information from the Organ Procurement and Transplantation Network Standard Analysis and Research Files was merged with the extracted data. Potential cases were manually reviewed and adjudicated using consensus definitions. Cumulative incidence and risk factors for VZV reactivation in HT recipients were assessed by the Kaplan-Meier method and Cox modeling, respectively. In 203 HT recipients, the cumulative incidence of VZV reactivation at 8-years post-transplantation was 26.4% (95% CI: 17.8-38.0). The median time to VZV reactivation was 2.1 years (IQR, 1.5-4.1). Half (14/28) of the cases experienced post-herpetic neuralgia (PHN). Post-transplant CMV infection (HR 9.05 [95% CI: 3.76-21.77) and post-transplant pulse-dose steroids (HR 3.19 [95% CI: 1.05-9.68]) were independently associated with a higher risk of VZV reactivation in multivariable modeling. Identification of risk factors will aid in the development of targeted preventive strategies.

Impact of the Number of Cores on the Prostate Cancer Detection Rate in Men Undergoing in-Bore Magnetic Resonance Imaging-Guided Targeted Biopsies.

OBJECTIVE: To determine the incremental detection rate of clinically significant prostate cancer (csPCa) provided by sequential cores during in-bore magnetic resonance imaging (MRI)-guided prostate biopsies. METHODS: Single-center, retrospective interpretation of prospectively acquired data in men without previous diagnosis of csPCa who underwent in-bore MRI-guided prostate biopsy between May 2017 and December 2019. Endpoints included detection of csPCa (grade group [GG] ≥ 2) and rate of GG upgrade provided by additional cores. Descriptive statistics presented as mean and standard deviation for the continuous variables, and frequency and percentage for the categorical variables. RESULTS: Four hundred and forty-three men with 747 lesions met eligibility criteria. Clinically significant prostate cancer was detected in 43.1% (322/747) of the biopsied lesions and GG 2 PCa or greater was identified by the first core in 78.3% (252/322) of them. On a per-core basis, cores 2, 3, 4, and 5 found new csPCa in 6% (42/744), 4% (26/719), 1% (2/137), and 0% (0/11) of the cases. Core biopsy 2, 3, 4, and 5 resulted in GG upgrade in 12% (91/744), 7% (49/719), 7% (9/137), and 0% (0/11) of the lesions, respectively. Each additional core was associated with a mean increase of 5 minutes in the duration of the biopsy. CONCLUSIONS: In men undergoing in-bore MRI-guided prostate biopsies, 3 targeted cores per lesion provide an optimal trade-off between detection of clinically significant tumors and biopsy duration.

Association of Megestrol Use With the Development of New Psychiatric Diagnoses.

OBJECTIVE: To analyze the risk of megestrol, a glucocorticoid and progesterone receptor agonist used to enhance appetite, on the development of a new psychiatric diagnosis. DESIGN AND PARTICIPANTS: Deidentified data of megestrol (n = 706) and propensity score-matched comparison (age, gender, and body mass index) patients (n = 2,118) from January 1, 2001 to June 30, 2018 were obtained from the UT Southwestern patient database. Data were analyzed using a series of conditional binary logistic regressions controlling for comorbidities, pre-existing psychiatric disorders, and number of patient encounters. SETTING: A large academic medical center database of megestrol-treated patients and matched comparison patients was used. MEASUREMENTS AND RESULTS: The regression model showed that megestrol was significantly associated with developing a new psychiatric diagnosis (B = 1.28, Wald χ21 = 83.12, odds ratio [OR] = 3.60, p <0.001). In subgroup analyses, development of cognitive (B = 2.42, Wald χ21 = 16.09, OR = 11.30, p <0.001), mood (B = 1.31, Wald χ21 = 40.38, OR = 3.70, p <0.001), and anxiety (B = 1.72, Wald χ21 = 45.28, OR = 5.60, p <0.001) disorders were also associated with megestrol use. CONCLUSIONS: Patients taking megestrol were significantly more likely to develop a new psychiatric diagnosis than comparison patients. Highest risks were associated with the development of cognitive diagnoses. The findings suggest that megestrol, like other glucocorticoid agonists, is associated with an increased risk of developing a psychiatric disorder. This risk should be considered when determining the risk-to-benefit ratio of megestrol use in patients.

Primary Tumor Fluorine-18 Fluorodeoxydglucose (18F-FDG) Is Associated With Cancer-Associated Weight Loss in Non-Small Cell Lung Cancer (NSCLC) and Portends Worse Survival.

Aim: To investigate the diagnostic potential of and associations between tumor 18F-FDG uptake on PET imaging and cancer-associated weight loss. Methods: 774 non-small cell lung cancer (NSCLC) patients with pre-treatment PET evaluated between 2006 and 2014 were identified. Using the international validated definition of cachexia, the presence of clinically significant pretreatment cancer-associated weight loss (WL) was retrospectively determined. Maximum Standardized Uptake Value (SUVMax) of 18F-FDG was recorded and dichotomized based on 3 experimental cutpoints for survival analyses. Each SUVMax cutpoint prioritized either survival differences, total cohort comparison sample sizes, or sample size by stage. Patient outcomes and associations between SUVMax and cancer-associated weight loss were assessed by multivariate, categorical, and survival analyses. Results: Patients were found to have an increased likelihood of having WL at diagnosis associated with increasing primary tumor SUVMax after controlling for potentially confounding patient and tumor characteristics on multivariate logistic regression (OR 1.038; 95% CI: 1.012, 1.064; P=0.0037). After stratifying the cohort by WL and dichotomized SUVMax, both factors were found to be relevant in predicting survival outcomes when the alternative variable was constant. Of note, the most striking survival differences contributed by WL status occurred in high SUVMax groups, where the presence of WL predicted a median survival time detriment of up to 10 months, significant regardless of cutpoint determination method applied to categorize high SUVMax patients. SUVMax classification was found to be most consistently relevant in both WL and no WL groups. Conclusions: The significant positive association between significant pretreatment cancer-associated weight loss and primary tumor SUVMax underscores increased glucose uptake as a component of catabolic tumor phenotypes. This substantiates 18F-FDG PET analysis as a prospective tool for assessment of cancer-associated weight loss and corresponding survival outcomes. Furthermore, the survival differences observed between WL groups across multiple SUVMax classifications supports the importance of weight loss monitoring in oncologic workups. Weight loss in the setting of NSCLCs with higher metabolic activity as determined by 18F-FDG PET signal should encourage more aggressive and earlier palliative care interventions.

The use of automated data extraction tools to develop a solid organ transplant registry: Proof of concept study of bloodstream infections.

BACKGROUND: We created an electronic health record-based registry using automated data extraction tools to study the epidemiology of bloodstream infections (BSI) in solid organ transplant recipients. The overarching goal was to determine the usefulness of an electronic health record-based registry using data extraction tools for clinical research in solid organ transplantation. METHODS: We performed a retrospective single-center cohort study of adult solid organ transplant recipients from 2010 to 2015. Extraction tools were used to retrieve data from the electronic health record, which was integrated with national data sources. Electronic health records of subjects with positive blood cultures were manually adjudicated using consensus definitions. One-year cumulative incidence, risk factors for BSI acquisition, and 1-year mortality were analyzed by Kaplan-Meier method and Cox modeling, and 30-day mortality with logistic regression. RESULTS: In 917 solid organ transplant recipients the cumulative incidence of BSI was 8.4% (95% confidence interval 6.8-10.4) with central line-associated BSI as the most common source. The proportion of multidrug-resistant isolates increased from 0% in 2010 to 47% in 2015 (p = 0.03). BSI was the strongest risk factor for 1-year mortality (HR=8.44; 4.99-14.27; p<0.001). In 11 of 14 deaths, BSI was the main cause or contributory in patients with non-rapidly fatal underlying conditions. CONCLUSIONS: Our study illustrates the usefulness of an electronic health record-based registry using automated extraction tools for clinical research in the field of solid organ transplantation. A BSI reduces the 1-year survival of solid organ transplant recipients. The most common sources of BSIs in our studies are preventable.

Development of a Coronavirus Disease 2019 (COVID-19) Application Ontology for the Accrual to Clinical Trials (ACT) network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Development of a COVID-19 Application Ontology for the ACT Network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Contemporary Trends in Reviewing Test Results Through the Electronic Patient Portal Among Patients With Cancer.

This cohort study among patients with cancer examines changes in the time from posting of test results in the electronic health record to patient viewing in the patient portal before and after implementation of the 21st Century Cures Act.

Mobile Device Applications for Electronic Patient Portals in Oncology.

PURPOSE: Mobile devices provide individuals with rapid and frequent access to electronic patient portals. We investigated how oncology patients use this technology to review test results and communicate with providers. PATIENTS AND METHODS: We performed a retrospective study of patients enrolled in the MyChart electronic health portal associated with the Epic electronic medical record at the Harold C. Simmons Comprehensive Cancer Center from 2012 to 2017. We recorded type of portal access according to year and patient characteristics. Associations among patient characteristics and types of portal access were tested using Mann-Whitney U test, χ2 test, and linear Gaussian regression models. RESULTS: Since the availability of a mobile device application in 2012, 2,524 patients with cancer accessed MyChart from a mobile device at least once, which accounted for 291,526 mobile log-ins. The number of patients with MyChart mobile application log-ins increased from 4% in 2012 to 13% in 2017 ( P = .004). Among these patients, the median proportion of log-ins that occurred through mobile device use increased from 22% to 72% during this time period ( P < .001). Mobile access occurred more frequently among younger ( P < .001), black ( P = .002), and Hispanic ( P = .004) patients. Since 2012, total portal log-in frequency increased approximately 110% among patients who used the mobile application compared with 25% among those who did not use the mobile application ( P < .001). CONCLUSION: Mobile access to electronic health portals has increased patient portal use, particularly among traditionally underserved populations. How this widely and immediately available technology affects patient expectations and experiences warrants additional study.

Hierarchical model of gene regulation by transforming growth factor beta.

Transforming growth factor betas (TGF-betas) regulate key aspects of embryonic development and major human diseases. Although Smad2, Smad3, and extracellular signal-regulated kinase (ERK) mitogen-activated protein kinases (MAPKs) have been proposed as key mediators in TGF-beta signaling, their functional specificities and interactivity in controlling transcriptional programs in different cell types and (patho)physiological contexts are not known. We investigated expression profiles of genes controlled by TGF-beta in fibroblasts with ablations of Smad2, Smad3, and ERK MAPK. Our results suggest that Smad3 is the essential mediator of TGF-beta signaling and directly activates genes encoding regulators of transcription and signal transducers through Smad3/Smad4 DNA-binding motif repeats that are characteristic for immediate-early target genes of TGF-beta but absent in intermediate target genes. In contrast, Smad2 and ERK predominantly transmodulated regulation of both immediate-early and intermediate genes by TGF-beta/Smad3. These results suggest a previously uncharacterized hierarchical model of gene regulation by TGF-beta in which TGF-beta causes direct activation by Smad3 of cascades of regulators of transcription and signaling that are transmodulated by Smad2 and/or ERK.