Browsing by Author "Wilson, F. Perry"

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    Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data
    (Oxford University Press, 2022) Moledina, Dennis G.; Eadon, Michael T.; Calderon, Frida; Yamamoto, Yu; Shaw, Melissa; Perazella, Mark A.; Simonov, Michael; Luciano, Randy; Schwantes-An, Tae-Hwi; Moeckel, Gilbert; Kashgarian, Michael; Kuperman, Michael; Obeid, Wassim; Cantley, Lloyd G.; Parikh, Chirag R.; Wilson, F. Perry; Medicine, School of Medicine
    Background: Patients with acute interstitial nephritis (AIN) can present without typical clinical features, leading to a delay in diagnosis and treatment. We therefore developed and validated a diagnostic model to identify patients at risk of AIN using variables from the electronic health record. Methods: In patients who underwent a kidney biopsy at Yale University between 2013 and 2018, we tested the association of >150 variables with AIN, including demographics, comorbidities, vital signs and laboratory tests (training set 70%). We used least absolute shrinkage and selection operator methodology to select prebiopsy features associated with AIN. We performed area under the receiver operating characteristics curve (AUC) analysis with internal (held-out test set 30%) and external validation (Biopsy Biobank Cohort of Indiana). We tested the change in model performance after the addition of urine biomarkers in the Yale AIN study. Results: We included 393 patients (AIN 22%) in the training set, 158 patients (AIN 27%) in the test set, 1118 patients (AIN 11%) in the validation set and 265 patients (AIN 11%) in the Yale AIN study. Variables in the selected model included serum creatinine {adjusted odds ratio [aOR] 2.31 [95% confidence interval (CI) 1.42-3.76]}, blood urea nitrogen:creatinine ratio [aOR 0.40 (95% CI 0.20-0.78)] and urine dipstick specific gravity [aOR 0.95 (95% CI 0.91-0.99)] and protein [aOR 0.39 (95% CI 0.23-0.68)]. This model showed an AUC of 0.73 (95% CI 0.64-0.81) in the test set, which was similar to the AUC in the external validation cohort [0.74 (95% CI 0.69-0.79)]. The AUC improved to 0.84 (95% CI 0.76-0.91) upon the addition of urine interleukin-9 and tumor necrosis factor-α. Conclusions: We developed and validated a statistical model that showed a modest AUC for AIN diagnosis, which improved upon the addition of urine biomarkers. Future studies could evaluate this model and biomarkers to identify unrecognized cases of AIN.
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    Geographical affiliation with top 10 NIH-funded academic medical centers and differences between mortality from cardiovascular disease and cancer
    (Elsevier, 2020-12) Angraal, Suveen; Caraballo, César; Kahn, Peter; Bhatnagar, Ambika; Singh, Bikramjot; Wilson, F. Perry; Fiuzat, Mona; O’Connor, Christopher M.; Allen, Larry A.; Desai, Nihar R.; Mamtani, Ronac; Ahmad, Tariq; Pediatrics, School of Medicine
    Community engagement and rapid translation of findings for the benefit of patients has been noted as a major criterion for NIH decisions regarding allocation of funds for research priorities. We aimed to examine whether the presence of top NIH-funded institutions resulted in a benefit on the cardiovascular and cancer mortality of their local population. METHODS AND RESULTS: Based on the annual NIH funding of every academic medical from 1995 through 2014, the top 10 funded institutes were identified and the counties where they were located constituted the index group. The comparison group was created by matching each index county to another county which lacks an NIH-funded institute based on sociodemographic characteristics. We compared temporal trends of age-standardized cardiovascular mortality between the index counties and matched counties and states. This analysis was repeated for cancer mortality as a sensitivity analysis. From 1980 through 2014, the annual cardiovascular mortality rates declined in all counties. In the index group, the average decline in cardiovascular mortality rate was 51.5 per 100,000 population (95% CI, 46.8-56.2), compared to 49.7 per 100,000 population (95% CI, 45.9-53.5) in the matched group (P = .27). Trends in cardiovascular mortality of the index counties were similar to the cardiovascular mortality trends of their respective states. Cancer mortality rates declined at higher rates in counties with top NIH-funded medical centers (P < .001). CONCLUSIONS: Cardiovascular mortality rates have decreased with no apparent incremental benefit for communities with top NIH-funded institutions, underscoring the need for an increased focus on implementation science in cardiovascular diseases.