Mathematical modeling suggests that rapid diagnostics that report antibiotic susceptibility have the potential to extend the usefulness of existing antibiotics for treatment of gonorrhea compared with the current guidelines for empiric 2-drug treatment.

Increasing antibiotic resistance limits treatment options for gonorrhea. We examined the impact of a hypothetical point-of-care (POC) test reporting antibiotic susceptibility profiles on slowing resistance spread.

A mathematical model describing gonorrhea transmission incorporated resistance emergence probabilities and fitness costs associated with resistance based on characteristics of ciprofloxacin (A), azithromycin (B), and ceftriaxone (C). We evaluated time to 1% and 5% prevalence of resistant strains among all isolates with the following: (1) empiric treatment (B and C), and treatment guided by POC tests determining susceptibility to (2) A only and (3) all 3 antibiotics.

Continued empiric treatment without POC testing was projected to result in >5% of isolates being resistant to both B and C within 15 years. Use of either POC test in 10% of identified cases delayed this by 5 years. The 3 antibiotic POC test delayed the time to reach 1% prevalence of triply-resistant strains by 6 years, whereas the A-only test resulted in no delay. Results were less sensitive to assumptions about fitness costs and test characteristics with increasing test uptake.

Rapid diagnostics reporting antibiotic susceptibility may extend the usefulness of existing antibiotics for gonorrhea treatment, but ongoing monitoring of resistance patterns will be critical.

Increasing antibiotic resistance poses an immense challenge to the clinical and public health community [

Gonorrhea treatment guidelines are based on population resistance surveys, with antibiotics no longer recommended once resistance prevalence exceeds 5%. Only ceftriaxone and azithromycin remain as first-line therapy [

Given the prevalence of susceptible isolates, one proposed strategy to control resistance is the use of rapid diagnostics that allow clinicians to both diagnose gonorrhea infections and tailor treatment to the antibiotic susceptibilities of individual infections [

Underlying the promise of rapidly determining antibiotic susceptibility is the hypothesis that tailored therapy will prolong the utility of antigonococcal agents and better control resistance than empiric treatment. Previous mathematical modeling studies have considered the impact of treatment on resistance, including the role that rapid diagnostics may have for curbing resistance spread and improving timeliness of resistance detection [

We developed a dynamic compartmental model that describes gonorrhea transmission in a single sex population stratified by sexual risk. This model represented a population of men who have sex with men (MSM), who experience a significant burden of gonorrhea in the United States and in whom emergence of resistance is of concern [

Overview of gonorrhea transmission model. (A) The model includes 3 states: susceptible, symptomatic infectious, and asymptomatic infectious. Infected individuals can return to the susceptible state via treatment or natural clearance of infection. (B) Expanded view of the different possible infected states, where subscripts indicate resistance to antibiotics A, B, and/or C. I_{0} indicates infection with a completely drug susceptible strain. Note that the same series of infectious states and transitions exist for symptomatic and asymptomatic infections. The model is further stratified by 3 sexual activity classes.

Model Population, Gonorrhea Natural History, and Treatment Parameters

Parameter | Details | Symbol | Value | Source |
---|---|---|---|---|

Population size | - | N | 10^{6} | Assumption |

Gonorrhea prevalence at start (%) | Calibration target | 2.3 (1.2–2.8) | [ | |

Proportion of cases resistant to drug X at start of evaluation period | - | θ | - | [ |

A (ciprofloxacin) | θ_{A} | 0.189 | ||

B (azithromycin) | θ_{B} | 0.023 | ||

C (ceftriaxone) | θ_{C} | 0.0001 | ||

A and B | θ_{AB} | 0.0022 | ||

A and C | θ_{AC} | 0.0009 | ||

B and C | θ_{BC} | 1/10^{5} | Assumption | |

A, B, and C | θ_{ABC} | 1/10^{6} | Assumption | |

Sexual risk group distribution | - | n | - | Assumption |

High | 0.1 | |||

Intermediate | 0.6 | |||

Low | 0.3 | |||

Relative rate of partner change in risk groups | rp | [ | ||

High | 20 | |||

Intermediate | 5 | |||

Low | 1 | |||

Rate of partner change in low risk group (per year) | - | c_{min} | 1.16 | Model fitting |

Mixing parameter | - | ε | 0.23 | Model fitting |

Rate of model entry/exit (years) | - | ρ | 1/20 | Assumption |

Transmission probability per partnership | - | β | 0.44 | [ |

Probability symptomatic infection | - | σ | 0.6 | Assumption; model fitting |

Average duration of infection without treatment (years) | - | 1/δ | 0.5 | [ |

Average time to treatment (years) | Symptomatic | 1/τ_{s} | 0.026 | [ |

Screening rate (per year) | Asymptomatic | 1/τ_{m} | 0.39 (0.20–1.56) | Assumption; model fitting |

Probability of retreatment with effective drug, if initial treatment failure | - | - | - | Assumption |

Symptomatic infection | π_{s} | 0.9 | ||

Asymptomatic infection | π_{m} | 0 | ||

Treatment rate if initial treatment failure (per year) | - | - | - | Assumption |

Symptomatic infection | τ_{sr} | τ_{s}/3 | ||

Asymptomatic infection | τ_{mr} | τ_{m}/3 |

We modeled treatment with 3 different antibiotics, which could be used individually or in combination. Each antibiotic had a probability of resistance emergence on treatment and a fitness cost associated with resistance, where fitness refers to the capability of the pathogen to survive [

Characteristics Associated With Point-of-Care Test and Drug-Resistant Neisseria

Parameter | Details | Symbol | Value (Base Case and Range) |
---|---|---|---|

Probability of resistance with treatment | |||

Antibiotic A | ω_{A} | 10^{–6} (10^{–9} to 10^{–3}) | |

Antibiotic B | ω_{B} | 5 × 10^{–7} (10^{–9} to 10^{–3}) | |

Antibiotic C | ω_{C} | 10^{–8} (10^{–9} to 10^{–3}) | |

Relative fitness of resistant strain | |||

Susceptible | f_{0} | 1 | |

Antibiotic A resistant | f_{A} | 1 (0.85–1) | |

Antibiotic B resistant | f_{B} | 0.94 (0.85–1) | |

Antibiotic C resistant | f_{C} | 0.98 (0.85–1) | |

Antibiotics AB resistant | f_{AB} | 0.94 (0.72–1) | |

Antibiotics AC resistant | f_{AC} | 0.98 (0.72–1) | |

Antibiotics BC resistant | f_{BC} | 0.92 (0.72–1) | |

Antibiotics ABC resistant | f_{ABC} | 0.92 (0.61–1) | |

Point-of-care test sensitivity | |||

Antibiotic A | κ_{A} | 1 (0.5–1) | |

Antibiotic B | κ_{B} | 1 (0.5–1) | |

Antibiotic C | κ_{C} | 1 (0.5–1) | |

Point-of-care test specificity | |||

Antibiotic A | ψ_{A} | 1 (0.5–1) | |

Antibiotic B | ψ_{B} | 1 (0.5–1) | |

Antibiotic C | ψ_{C} | 1 (0.5–1) |

We compared empirical treatment to treatment guided by a hypothetical POC test that rapidly diagnoses gonorrhea infections and determines susceptibility to the following: (1) a single antibiotic (A) and (2) all 3 antibiotics. Based on a case’s resistance profile, antibiotic treatment was selected (Supplementary Table S1), with the probability that the chosen antibiotic effectively treated the infection dependent on the test characteristics.

Antibiotic A was used to treat A-susceptible infections, while A-resistant infections were treated with combination BC therapy.

If multiple antibiotics would be effective at treating a case (ie, infected with a completely susceptible strain or a strain resistant to only one antibiotic), we treated with the antibiotic with the highest fitness cost associated with resistance. In scenario II, if an individual was identified as having a triply-resistant infection, we assumed that the infection was ultimately successfully treated, with an alternative agent or higher antibiotic doses [

We evaluated different levels of POC test uptake in the population. When susceptibility was not determined before treatment, we assumed treatment according to guidelines, as described above.

We calibrated model parameters describing gonorrhea natural history and sexual behavior using maximum likelihood estimation. Details are provided in the Technical Appendix.

The model was initiated at the equilibrium prevalence determined through model fitting in the absence of resistant strains. The initial distribution of resistant isolates was based on surveillance data [

We conducted sensitivity analyses for parameters describing the properties of the different resistant strains, test characteristics, test coverage, and frequency of asymptomatic screening (ranging from every 3 months to every 2 years). We varied the fitness cost associated with resistance to antibiotic B (the antibiotic associated with the highest fitness cost for resistance) from 0% to 15%. We varied the “relative” fitness cost for antibiotics A and C from 0 to 1, and we calculated the fitness cost for antibiotics A or C as follows: fitness cost for antibiotic B × relative fitness cost for antibiotic A or C. In addition, we allowed the probability of de novo resistance acquisition to range from 10–3 to 10–9 per treatment event with each antibiotic.

For simplicity, we assumed that the POC test detected resistance with perfect sensitivity and specificity in the main analysis. We then varied test sensitivity and specificity to reflect that a test with perfect sensitivity and specificity is unlikely to be achieved in practice and that deoxyribonucleic acid-based tests may miss unrecognized mechanisms of resistance [

For the single resistance test, we assessed the impact of a fitness cost for antibiotic A resistance. For the 3-resistance POC test, we evaluated the alternate antibiotic selection strategy of treating with the antibiotic with the greatest barrier to resistance emergence (lowest probability of resistance emergence) when multiple treatment options were available.

Under our baseline assumptions regarding fitness costs and probabilities of de novo resistance emergence, as well as current patterns of resistance in the US population, the continued use of dual antibiotic treatment in the population was projected to result in >1% of isolates being resistant to both of these antibiotics within 12 years (

Projected impact of point-of-care (POC) tests on gonorrhea prevalence and resistance. Population prevalence and prevalence of different strains are shown in the face of (A) no POC testing, (B and E) 10%, (C and F) 25%, and (D and G) 50% of cases tested. B–D show the results for a POC test for resistance to antibiotic A only. E–G show results for a POC test for resistance to all 3 antibiotics. For the 3-resistance POC test, cases undergoing testing and displaying susceptibility to >1 antibiotic were treated with the antibiotic with the highest fitness cost associated with resistance acquisition. For both scenarios, all untested cases were treated in combination with antibiotics B and C. Results are shown for tests with perfect sensitivity and specificity.

Under the baseline assumption of perfect test sensitivity and specificity, both POC test strategies had the identical probability that a BC-resistant infection would be effectively treated (supplementary figure S1). However, the single resistance POC test prompted a binary treatment decision based only on antibiotic A resistance status. Thus, all infections that tested resistant to A (including ABC-resistant infections) were assigned BC treatment, making the probability of treating a triple-resistant infection with an effective antibiotic zero, regardless of test coverage (

Time to resistance emergence with varying use of point-of-care (POC) tests. Time for BC or ABC resistant strains to comprise 5% of prevalent gonorrhea isolates in the population with different POC test use in the population. Results are shown for POC tests that identify resistance to (A) antibiotic A only, or (B) all 3 antibiotics, with perfect sensitivity and specificity. Note that results are qualitatively similar for the 1% threshold, although times required to reach the threshold are reduced.

Unlike the base case, where BC resistance increased in the population, followed by ABC resistance, use of a single resistance POC delayed the spread of BC-resistant gonococcal isolates but not ABC-resistant isolates (

By contrast, using a test that identifies resistance to all 3 antibiotics reduced overall equilibrium prevalence and delayed the spread of BC- and ABC-resistant strains in the population (

Assuming a minor (1%) fitness cost for resistance to antibiotic A, triple-resistant isolates were not projected to reach the 5% threshold during the 40-year time horizon when combination therapy was used to treat all identified infections. By contrast, use of the single resistance POC test (scenario I) resulted in this threshold being crossed in approximately 20 years once the test was used in greater than 5% of identified cases (supplementary figure S2).

In sensitivity analyses, when fitness costs were relatively high (>15% for strains resistant to antibiotic B), resistant strains were outcompeted by other strains and did not reach the resistance thresholds, even without a POC test to guide antibiotic choice. When fitness costs were minor, the impact of the POC tests was diminished, in terms of the amount of time gained before resistance thresholds were crossed (supplementary figure S3). As in our main analysis, the single resistance POC did not delay emergence of triple-resistant isolates. Our results were less sensitive to fitness assumptions in scenarios with higher test coverage in the population (supplementary figure S4 for triple resistance test, similar results for single resistance POC test).

Our findings were minimally sensitive to assumptions about the probability of resistance acquisition. Model projections changed only when probabilities were 10^{–3} per treatment event, larger values than would be considered biologically plausible [

Test specificity only affected the projected impact of the single resistance test (supplementary figure S6). For the triple resistance test, a false-positive result would limit treatment options, but cases would still receive an effective treatment. For example, a BC-resistant infection that was also falsely identified as A-resistant would receive an alternate treatment. In the case of the single resistance test, a BC-resistant infection falsely identified as A-resistant would be treated with BC, resulting in treatment failure.

Increased test sensitivity modestly increased the time until resistance thresholds were reached in the population (supplementary figure S6). For example, when the test was used for 10% of cases, time to the reach the 5% BC resistance threshold increased by 4 years, as test sensitivity increased from 50% to 100%.

For the triple resistance test, relaxing the assumption that test sensitivity was identical for all 3 antibiotic-resistant strains did not dramatically change our findings. As described above, with reduced test sensitivities for detecting antibiotic A, B, and/or C resistance, the utility of the test for reducing gonorrhea burden and delaying the time until resistance thresholds were crossed was diminished (supplementary figure S7).

Basing antibiotic choice on probability of resistance acquisition on treatment rather than fitness costs associated with resistance did not have an impact on time to resistance emergence, regardless of assumed test sensitivity and test coverage, under the time horizon considered here (supplementary figure S8).

Our base case assumed an annual screening rate of 39%. With more frequent screening (every 3 or 6 months) in the population to identify asymptomatic cases, we projected an initial decrease in population prevalence, followed by a rapid increase in prevalence of resistant isolates (supplementary figure S9). Reducing screening frequency to every 2 years resulted in a gradual increase in prevalence and a longer time for dual and triple-resistant strains to become established in the population, compared with the base-case analysis. Despite the different dynamics observed with different screening intensities, our qualitative findings on the impact of the POC tests remained unchanged: both tests delayed the time until BC-resistant strains crossed different resistance thresholds, whereas only the test for all 3 antibiotics was beneficial for preventing triple resistance. With more frequent screening in the population, the overall benefit of the POC tests, in terms of absolute number of years gained before an antibiotic would no longer be recommended for use, was diminished. For example, when screening occurred every 3 months, use of a POC test delayed the time until the 5% BC resistance threshold was crossed by only 1 year, whereas with biannual screening, there was 12–14 year delay.

Using a mathematical model, we have shown that rapid diagnostics that report antibiotic susceptibility have the potential to extend the usefulness of existing antibiotics for treatment of gonorrhea compared with the current guidelines for empiric 2-drug treatment. Although most impactful when used in a large proportion of cases, even modest levels of use in the population can delay the establishment of resistance and reduce overall infection burden in the population. Using an infection’s susceptibility profile to guide treatment also has the beneficial effect of allowing for the reintroduction of antimicrobials, such as fluoroquinolones, that are no longer recommended for general population use due to widespread resistance.

Although our model projected a net benefit of a POC test, we found that a test for determining resistance to a single antimicrobial is not expected to delay, and may accelerate, emergence of triply-resistant gonococcal isolates. The single antimicrobial test scenario was designed to replicate the potential effect of introduction of a rapid diagnostic for determining fluoroquinolone susceptibility. Although genomic and experimental analyses suggest there may not be a fitness cost associated with ciprofloxacin resistance [

A POC test would enable clinicians to select antibiotic treatment from the set of drugs to which the pathogen is susceptible. Given that resistance to antibiotics can incur fitness costs, and that those costs differ by antibiotic, we evaluated a treatment strategy in which an infection with a strain susceptible to more than 1 antibiotic was treated with the antibiotic associated with the highest fitness cost, as inferred from population genomic and experimental data [

Our study has several limitations. We used a deterministic model to determine how resistance would spread and assumed that the population was seeded with each of the resistant strains. Other modeling approaches that capture the stochastic nature of emergence and transmission are better suited to represent the inherent randomness in the emergence of resistance. However, use of a deterministic system allows us to draw initial inferences about the impact of POC test use, which can then be further explored using alternate modeling approaches. We did not explicitly model mixed infections (ie, infections with multiple gonorrhea strains with different drug-susceptibility profiles). However, imperfect test sensitivity in our model captures the impact of unrecognized resistant infections, whether they occur because a particular resistance marker is not included in the test, or because an individual has a mixed infection, with the resistant strain in low abundance. Actual treatment regimens used in the population are not 100% consistent with guidelines [

Despite these limitations, this mathematical model demonstrates both the promise and potential need for caution associated with future POC tests for determining antibiotic susceptibility of gonococcal infections. The use of such tests cannot be done in isolation; continued real-time surveillance will be critical for guiding decision-making and monitoring resistance emergence.

Supplementary materials are available at

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