Using passive extraction of real-world data from eConsent, electronic patient reported outcomes (ePRO) and electronic health record (EHR) data loaded to an electronic data capture (EDC) system for a multi-center, prospective, observational study in diabetic patients.

As clinical trial complexity has increased over the past decade, using electronic methods to simplify recruitment and data management have been investigated. In this study, the Optum Digital Research Network (DRN) has demonstrated the use of electronic source (eSource) data to ease subject identification, recruitment burden, and used data extracted from electronic health records (EHR) to load to an electronic data capture (EDC) system. This study utilized electronic Informed Consent, electronic patient reported outcomes (SF-12) and included three sites using 3 different EHR systems. Patients with type 2 diabetes with an HbA1c ≥ 7.0% treated with metformin monotherapy were recruited. Endpoints consisted of changes in HbA1c, medications, and quality of life measures over 12-weeks of study participation using data from the subjects' eSources listed above. The study began in June of 2020 and the last patient last visit occurred in January of 2021. Forty-eight participants were consented and enrolled. HbA1c was repeated for 33 and ePRO was obtained from all subjects at baseline and 28 at 12-week follow-up. Using eSource data eliminated transcription errors. Medication changes, healthcare encounters and lab results were identified when they occurred in standard clinical practice from the EHR systems. Minimal data transformation and normalization was required. Data for this observational trial where clinical outcomes are available using lab results, diagnoses, and encounters may be achieved via direct access to eSources. This methodology was successful and could be expanded for larger trials and will significantly reduce staff effort and exemplified clinical research as a care option.

An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825.

Background: Glioblastoma (GBM) is the most common primary malignant brain tumor. Nomograms are often used for individualized estimation of prognosis. This study aimed to build and independently validate a nomogram to estimate individualized survival probabilities for patients with newly diagnosed GBM, using data from 2 independent NRG Oncology Radiation Therapy Oncology Group (RTOG) clinical trials. Methods: This analysis included information on 799 (RTOG 0525) and 555 (RTOG 0825) eligible and randomized patients with newly diagnosed GBM and contained the following variables: age at diagnosis, race, gender, Karnofsky performance status (KPS), extent of resection, O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and survival (in months). Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the 0525 data and were independently validated using the 0825 data. Models were internally validated using 10-fold cross-validation, and individually predicted 6-, 12-, and 24-month survival probabilities were generated to measure the predictive accuracy and calibration against the actual survival status. Results: A final nomogram was built using the Cox proportional hazards model. Factors that increased the probability of shorter survival included greater age at diagnosis, male gender, lower KPS, not having total resection, and unmethylated MGMT status. Conclusions: A nomogram that assesses individualized survival probabilities (6-, 12-, and 24-mo) for patients with newly diagnosed GBM could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: http://cancer4.case.edu/rCalculator/rCalculator.html.

The impact of surveillance and rapid reduction in immunosuppression to control BK virus-related graft injury in kidney transplantation.

We prospectively screened 609 consecutive kidney (538) and kidney-pancreas (71) transplant recipients for BK viremia over a 4-year interval using polymerase chain reaction viral load detection and protocol kidney biopsies. We found that BK viremia is common at our center: total cases 26.7%, cases during first year 21.3% (mean 4 months), and recipients with ≥ 10 000 copies/ml 12.3%. We found few predictive clinical or demographic risk factors for any BK viremia or viral loads ≥ 10,000 copies/ml, other than prior treatment of biopsy confirmed acute rejection and/or higher immunosuppressive blood levels of tacrolimus (P = 0.001) or mycophenolate mofetil (P = 0.007). Viral loads at diagnosis (<10 000 copies/ml) demonstrated little impact on graft function or survival. However, rising copy numbers demand early reductions in immunosuppressive drug doses of at least 30-50%. Viral loads >185 000 copies/ml at diagnosis were predictive of BK virus-associated nephropathy (BKVAN; OR: 113.25, 95% CI: 17.22-744.6, P < 0.001). Surveillance for BK viremia and rapid reduction of immunosuppression limited the incidence of BKVAN to 1.3%. The addition of leflunomide or ciprofloxacin to immunosuppressive dose reduction did not result in greater rates of viral clearance. These data support the role of early surveillance for BK viremia to limit the impact on transplant outcome, although the most effective schedule for screening awaits further investigation.

Gout, stone composition and urinary stone risk: a matched case comparative study.

PURPOSE: We established the most common stone composition, and serum and urinary biochemical features in patients with gout and urolithiasis. MATERIALS AND METHODS: We retrospectively searched for the records of patients diagnosed with gout among all those in our stone registry. A matched case cohort of stone formers was generated from our registry who had the same age, gender and body mass index. Primary end points were baseline 24-hour urinary metabolic panels and stone composition. Medications were considered. Groups were compared using the Student t and chi-square/Fisher exact tests with significance considered at p <0.05. RESULTS: For stone panel evaluation 181 patients met our inclusion criteria. There was no significant difference in 24-hour uric acid between the nongout and gout cohorts but hyperoxaluria was more common in patients with gout (74% vs 61%, p = 0.009). For stone composition analysis 393 patients were included. The gout cohort had lower calcium oxalate monohydrate (39.4% vs 54.7%), calcium oxalate dihydrate (6.0% vs 11.2%) and calcium phosphate (9.6% vs 14.1%) but higher uric acid (42.7% vs 18.2%, each p <0.001). Pure uric acid stones were more common in patients with gout (52.2% vs 22.3%, p <0.001), while calcium oxalate monohydrate (45.2% vs 68.6%, p <0.001), calcium oxalate dihydrate (0.6% vs 3.5%, p = 0.017) and calcium phosphate (1.6% vs 4.9%, p = 0.033) were more common in nongout cases. Patients with gout who were on allopurinol had fewer pure uric acid stones (30.4% vs 56.4%) and more calcium oxalate monohydrate stones (69.6% vs 40.7%, each p <0.001) than those without medication. CONCLUSIONS: Uric acid stones are the most common pure stone composition in patients with gout but 48% have nonuric acid stones. Allopurinol changes the stone composition distribution in patients with gout to a pattern similar to that in stone formers without gout.

Rapid patient cohort selection utilizing a bit array database field.

Most clinical information systems have leveraged relational databases to identify patient cohorts. However, as the number of criteria increase for cohort selection, there is an exponential rise in the query complexity. Nevertheless, by identifying criteria a priori, assigning a Boolean value and aggregating the results into a bit array database field, we can reduce the complexity of such cohort patient selection to a linear model dependent only the number of patients and criteria.