Use of sexually transmitted disease risk assessment algorithms for selection of intrauterine device candidates

Sexually transmitted diseases (STD) are an important contraindication for i ntrauterine device (IUD) insertion. Nevertheless, laboratory testing for ST D is not possible in many settings. The objective of this study is to evalu ate the use of risk assessment algorithms to predict STD and subsequent IUD -related complications among IUD candidates. Among 615 IUD users in Kenya, the following algorithms were evaluated: 1) an STD algorithm based on US Ag ency for International Development (USAID) Technical Working Group guidelin es; 2) a Centers for Disease Control and Prevention (CDC) algorithm for man agement of chlamydia; and 3) a data-derived algorithm modeled from study da ta. Algorithms were evaluated for prediction of chlamydial and gonococcal i nfection at 1 month and complications (pelvic inflammatory disease [PID], I UD removals, and IUD expulsions) over 4 months. Women with STD were more li kely to develop complications than women without STD (19% vs 6%; risk ratio = 2.9; 95% CI 1.3-6.5). For STD prediction, the USAID algorithm was 75% se nsitive and 48% specific, with a positive likelihood ratio (LR+) of 1.4. Th e CDC algorithm was 44% sensitive and 72% specific, LR+ = 1.6. The data-der ived algorithm was 91% sensitive and 56% specific, with LR+ = 2.0 and LR- = 0.2. Category-specific LR for this algorithm identified women with very lo w (<1%) and very high (29%) infection probabilities. The data-derived algor ithm was also the best predictor of IUD-related complications. These result s suggest that use of STD algorithms may improve selection of IUD users. Wo men at high risk for STD could be counseled to avoid IUD, whereas women at moderate risk should be monitored closely and counseled to use condoms. (C) 1999 Elsevier Science Inc. All rights reserved.