Comparison Between REVEAL Lite 2 and COMPERA 2.0 for Risk Stratification in Pulmonary Arterial Hypertension

BACKGROUND

Risk stratification is the cornerstone of the management of pulmonary arterial hypertension (PAH). Current European Society of Cardiology/European Respiratory Society guidelines recommend using the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) three-strata risk model at baseline and the COMPERA 2.0 four-strata model at follow-up. However, the guidelines did not take into consideration other available risk scores such as the Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) Lite 2.

RESEARCH QUESTION

Is REVEAL Lite 2 better at discriminating risk than the COMPERA risk assessment models at baseline or follow-up evaluations?

STUDY DESIGN AND METHODS

This study analyzed data from patients with PAH consecutively enrolled between June 2011 and February 2022 in the PAH registry at our expert Pulmonary Hypertension Center. Patients were stratified according to REVEAL Lite 2 and COMPERA three- and four-strata risk scores at baseline and follow-up to predict the composite outcome for lung transplantation or death. Receiver-operating characteristic curves in predicting the binary outcome at 3, 5, and 7 years were plotted. Areas under the curve of the scores were compared by using the χ2 test. The performance of the scores was determined according to Harrel's C statistic.

RESULTS

A total of 296 patients were included for baseline and 196 for follow-up evaluation. The overall transplant-free survival in the patient population at 1, 3, 5, and 7 years was 93.6%, 81.3%, 75.1%, and 68.8%, respectively. At baseline, the C statistic of REVEAL Lite 2 was 0.74 (95% CI, 0.69-0.80), compared with 0.68 (95% CI, 0.63-0.74) for the COMPERA four-strata model and 0.63 (95% CI, 0.58-0.69) for the COMPERA three-strata model. All C statistic differences between REVEAL Lite 2 and the other models were statistically significant at baseline.

INTERPRETATION

Our analysis showed that REVEAL Lite 2 was better at baseline at discriminating risk in this patient population. Future guidelines should consider including REVEAL Lite 2 in the management algorithm to help clinicians make informed decisions. Further analysis in larger cohorts could help validate these findings.

Age-Related Differences in the Contribution of Systolic Blood Pressure and Biomarkers to Cardiovascular Disease Risk Prediction: The Atherosclerosis Risk in Communities (ARIC) Study

We sought to determine how biomarkers known to be associated with hypertension-induced end-organ injury complement the use of systolic blood pressure (SBP) for cardiovascular disease (CVD) risk prediction at different ages. Using data from visits 2 (1990 to 1992) and 5 (2011 to 2013) of the Atherosclerosis Risk in Communities (ARIC) study, 3 models were used to predict CVD (composite of coronary heart disease, stroke, and heart failure). Model A included traditional risk factors (TRFs) except SBP, model B-TRF plus SBP, and model C-TRF plus biomarkers (high-sensitivity troponin T [hsTnT] and N-terminal pro-B-type natriuretic peptide [NT-proBNP]). Harrel's C-statistics were used to assess risk discrimination for CVD comparing models B and A and C and B. At visit 2, the addition of SBP to TRF (model B vs model A) significantly improved the C-statistic (∆C-statistic, 95% confidence interval 0.010, 0.007 to 0.013) whereas the addition of hsTnT to TRF (model C vs model B) decreased the C-statistic (∆C-statistic -0.0038, -0.0075 to -0.0001) compared with SBP. At visit 5, the addition of SBP to TRF did not significantly improve the C-statistic (∆C-statistic 0.001, -0.002 to 0.005) whereas the addition of both hsTnT and NT-proBNP to TRF significantly improved the C-statistic compared with SBP (∆C-statistic 0.028, 0.015 to 0.041 and 0.055, 0.036 to 0.074, respectively). In summary, the incremental value of SBP for CVD risk prediction diminishes with age whereas the incremental value of hsTnT and NT-proBNP increases with age.

Pitfalls of the concordance index for survival outcomes

Prognostic models are useful tools for assessing a patient's risk of experiencing adverse health events. In practice, these models must be validated before implementation to ensure that they are clinically useful. The concordance index (C-Index) is a popular statistic that is used for model validation, and it is often applied to models with binary or survival outcome variables. In this paper, we summarize existing criticism of the C-Index and show that many limitations are accentuated when applied to survival outcomes, and to continuous outcomes more generally. We present several examples that show the challenges in achieving high concordance with survival outcomes, and we argue that the C-Index is often not clinically meaningful in this setting. We derive a relationship between the concordance probability and the coefficient of determination under an ordinary least squares model with normally distributed predictors, which highlights the limitations of the C-Index for continuous outcomes. Finally, we recommend existing alternatives that more closely align with common uses of survival models.

Which is the best management for women with normal cervical cytologic findings despite positivity for non-16/18 high risk human papillomaviruses?

Women who test positive for the human papillomavirus (HPV) but have normal cytology constitute the predominant subgroup of patients in the screening population in the post-vaccination era. The distribution of HPV genotypes changed dramatically, which was attributable to an increase in HPV vaccination coverage. These changes have created uncertainty about how to properly manage women with normal cytology, non-HPV16/18 infections, or persistent infections. Current recommendations include retesting and continued surveillance in the absence of HPV16/18 infection. However, these are not always applicable. The ability to implement genotyping or incorporate HPV16/18 with some additional high-risk HPV (HR-HPV) types for triage and management with the aim of identifying type-specific risks in this population could be acceptable. When the next set of guidelines is updated, generating potential triage strategies for detecting high-grade cervical lesions, such as the p16/Ki67 cytology assay and other alternatives that incorporate genotyping with newer tests, should be considered. Current clinical management is shifting to risk-based strategies; however, no specific risk threshold has been established in this population. Importantly, innovative triage testing should be evaluated in combination with primary screening and management. Furthermore, there is an untapped opportunity to coordinate HPV genotyping in combination with colposcopic characteristics to modify risk in this group. Hence, providing a more personalized schedule through the efficient application of risk stratification and improving the detection of pre-cancer and cancer is an option worth exploring.

Use of urine biomarker-derived clusters to predict the risk of chronic kidney disease and all-cause mortality in HIV-infected women

BACKGROUND

Although individual urine biomarkers are associated with chronic kidney disease (CKD) incidence and all-cause mortality in the setting of HIV infection, their combined utility for prediction remains unknown.

METHODS

We measured eight urine biomarkers shown previously to be associated with incident CKD and mortality risk among 902 HIV-infected women in the Women's Interagency HIV Study: N-acetyl-β-d-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), alpha-1 microglobulin (α1m), interleukin 18, neutrophil gelatinase-associated lipocalin, albumin-to-creatinine ratio, liver fatty acid-binding protein and α-1-acid-glycoprotein. A group-based cluster method classified participants into three distinct clusters using the three most distinguishing biomarkers (NAG, KIM-1 and α1m), independent of the study outcomes. We then evaluated associations of each cluster with incident CKD (estimated glomerular filtration rate <60 mL/min/1.73 m(2) by cystatin C) and all-cause mortality, adjusting for traditional and HIV-related risk factors.

RESULTS

Over 8 years of follow-up, 177 CKD events and 128 deaths occurred. The first set of clusters partitioned women into three groups, containing 301 (Cluster 1), 470 (Cluster 2) and 131 (Cluster 3) participants. The rate of CKD incidence was 13, 21 and 50% across the three clusters; mortality rates were 7.3, 13 and 34%. After multivariable adjustment, Cluster 3 remained associated with a nearly 3-fold increased risk of both CKD and mortality, relative to Cluster 1 (both P < 0.001). The addition of the multi-biomarker cluster to the multivariable model improved discrimination for CKD (c-statistic = 0.72-0.76, P = 0.0029), but only modestly for mortality (c = 0.79-0.80, P = 0.099). Clusters derived with all eight markers were no better for discrimination than the three-biomarker clusters.

CONCLUSIONS

For predicting incident CKD in HIV-infected women, clusters developed from three urine-based kidney disease biomarkers were as effective as an eight-marker panel in improving risk discrimination.