Back

Limited Utility of Cardiovascular Risk Scores for People Living with HIV in Malawi

Goh, C.; Mwandumba, H.; Rapala, A.; Tingao, W.; Sheha, I.; Chammudzi, M.; Mallon, P. W.; Klein, N.; Khoo, S.; Kelly, C.

2020-08-04 hiv aids
10.1101/2020.08.01.20166462 medRxiv
Show abstract

HIV is associated with increased cardiovascular disease (CVD) risk. Despite the high prevalence of HIV in low income sub-Saharan Africa, there are few data on the assessment of CVD risk in the region. In this study, we aimed to compare the utility of existing CVD risk scores in a cohort of Malawian adults, and assess to what extent they correlate with established markers of endothelial damage: carotid intima-media thickness (IMT) and pulse wave velocity (PWV). WHO/ISH, SCORE, FRS, ASCVD, QRISK2 and D:A:D scores were calculated for 279 Malawian adults presenting with HIV and low CD4. Correlation of the calculated 10-year CVD risk score with IMT and PWV was assessed using Spearmans rho. The median (IQR) age of patients was 37 (31 - 43) years and 122 (44%) were female. Median (IQR) blood pressure was 120/73mmHg (108/68 - 128/80) and 88 (32%) study participants had a new diagnosis of hypertension. The FRS and QRISK2 scores included the largest number of participants in this cohort (96% and 100% respectively). D:A:D, a risk score specific for people living with HIV, identified more patients in moderate and high-risk groups. Although all scores correlated well with physiological markers of endothelial damage, FRS and QRISK2 correlated most closely with both IMT [r2 0.51, p<0.0001 and r2 0.47, p<0.0001 respectively] and PWV [r2 0.47, p<0.0001 and r2 0.5, p<0.0001 respectively]. Larger cohort studies are required to adapt and validate risk prediction scores in this region, so that limited healthcare resources can be effectively targeted.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.