Risk factors for dysplasia in recurrent respiratory papillomatosis in an adult and pediatric population

Aim: Recurrent respiratory papillomatosis (RRP) is classically described as a benign neoplasm of the larynx caused by the low-risk human papillomavirus (HPV) viral subtypes. Nevertheless, transformation to dysplasia and invasive carcinoma can occur. We aimed to assess the prevalence of dysplasia and carcinoma-ex-papilloma in both adult-onset and juvenile-onset RRP and identify patient risk factors for this dysplastic transformation. Material and Methods: Ten-year retrospective chart review of a tertiary otolaryngology referral center. Patients with papilloma were identified from a review of a pathology database and clinical records. Patient demographics, pathologic data, and treatment history, including use of cidofovir as an adjunctive therapy for papilloma, were extracted from electronic medical records. Results: One hundred fifty-nine RRP patients were identified, 96 adult-onset (AORRP) and 63 juvenile-onset (JORRP) cases. Of this cohort, 139 (87%) had only benign papilloma as a pathologic diagnosis. In the AORRP cohort, 10 patients (10%) were diagnosed with dysplasia or carcinoma in situ in addition to papilloma, and 5 patients (5%) had malignant transformation to invasive carcinoma-ex-papilloma. There was a significantly higher age of disease onset for those with dysplasia or carcinoma versus those without dysplasia or carcinoma (56 vs 45 years old; P = .0005). Of the 63 JORRP patients, there were no cases of dysplasia but 3 (5%) cases of invasive carcinoma-ex-papilloma, all involving pulmonary disease. The JORRP patients with carcinoma-ex-papilloma had a younger average disease onset (2 vs 6 years old; P = .009) and a higher rate of tracheal involvement than those without carcinoma. Gender, smoking history, number of operations, or use of cidofovir showed no association with the development of dysplasia or carcinoma-ex-papillomatosis in either the AORRP or JORRP population. Conclusion: In a large series of RRP, age of disease onset is the strongest predictor of dysplastic transformation in the adult and pediatric population. Carcinoma-ex-papillomatosis was uniformly associated with pulmonary disease in the JORRP population in this series. No other demographic or behavioral factors, including adjunctive therapy with cidofovir, were statistically associated with dysplasia or carcinoma-ex-papilloma

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (Cindex) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction