Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance

<div><p>High antibiotic resistance frequencies have become a major public health issue. The decrease in new antibiotics' production, combined with increasing frequencies of multi-drug resistant (MDR) bacteria, cause substantial limitations in treatment options for some bacterial infections. To diminish overall resistance, and especially the occurrence of bacteria that are resistant to all antibiotics, certain drugs are deliberately scarcely used—mainly when other options are exhausted. We use a mathematical model to explore the efficiency of such antibiotic restrictions. We assume two commonly used drugs and one restricted drug. The model is examined for the mixing strategy of antibiotic prescription, in which one of the drugs is randomly assigned to each incoming patient. Data obtained from Rabin medical center, Israel, is used to estimate realistic single and double antibiotic resistance frequencies in incoming patients. We find that broad usage of the hitherto restricted drug can reduce the number of incorrectly treated patients, and reduce the spread of bacteria resistant to both common antibiotics. Such double resistant infections are often eventually treated with the restricted drug, and therefore are prone to become resistant to all three antibiotics. Thus, counterintuitively, a broader usage of a formerly restricted drug can sometimes lead to a decrease in the emergence of bacteria resistant to all drugs. We recommend re-examining restriction of specific drugs, when multiple resistance to the relevant alternative drugs already exists.</p></div

An illustration of the dynamic system presented in Eq 1.

<p>The black frame represents the hospital, with hollowed arrows signifying patients moving in and out between the hospital and the community; circles representing patient frequencies within the hospital, and squares representing the frequencies of patients infected with bacterial strains in the community, with respect to variable names within the shapes (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#sec002" target="_blank">methods</a>). Colored arrows show the direction of resistance acquisition due to treatment; solid black arrows are recovery from infected to cleared states, while dashed lines are infections. Several arrows are marked with the corresponding rates of flow between variables.</p

The mixing strategy for varying double resistance frequencies.

<p>We measure the fraction of incorrect treatment (A), and the rate of triple resistance emergence (B) for varying levels of double resistance to the commonly used antibiotics (</p><p></p><p></p><p></p><p><mi>f</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>2</mn></p><p></p><p></p><p></p><p></p><p></p><p></p>). Red curves represent the results under mixing 2 antibiotics and restricting antibiotic 3 (<i>mix</i>2), while green curves are the results under mixing 3 antibiotics (<i>mix</i>3). Dotted and solid lines are the results of the model with and without community feedback, respectively. Parameters are <p></p><p></p><p></p><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>1</mn></p><p></p><p></p><mo>=</mo><mo> </mo><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>2</mn></p><p></p><p></p><mo>=</mo><mn>0.1</mn><mo>,</mo><mi>β</mi><mo>=</mo><mn>0.3</mn><mo>,</mo><mo> </mo><p><mi>λ</mi><mi>X</mi></p><mo>=</mo><mn>0.07</mn><mo>,</mo><p><mi>p</mi><mn>1</mn></p><mo>=</mo><p><mi>p</mi><mn>2</mn></p><mo>=</mo><p><mi>p</mi><mn>3</mn></p><mo>=</mo><mn>0.07</mn><p></p><p></p><p></p><p></p><p></p><p><mo> </mo></p><p><mi>λ</mi><mi>S</mi></p><mo>=</mo><mi>c</mi><mo>−</mo><p><mi>λ</mi><mi>X</mi></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi><mn>1</mn></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi><mn>2</mn></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi><mn>3</mn></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>2</mn></p><p></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>3</mn></p><p></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi></p><p><mn>2</mn><mo>,</mo><mn>3</mn></p><p></p><p></p><p></p><p></p><p></p><p></p>, and the rest are given at <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.t001" target="_blank">Table 1</a>. The system is simulated for 20 years and other parameter values are given in the text.<p></p

Time series of <i>mix</i>2 (A) and <i>mix</i>3 (B).

<p>We plot the frequencies of double resistant infections resistant to the third antibiotic (<i>R</i>_1,3 + <i>R</i>_2,3), the double resistant infections resistant to the two commonly used antibiotics (<i>R</i>_1,2), the measured incorrectly treated patients, and the emergence of triple resistance. The model is simulated for an extended period of time (100 years) to capture long term effects and the rest of the parameters are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.g002" target="_blank">Fig 2</a>.</p

The mixing strategy for estimated resistance frequencies.

<p>For each data point, its location on the X axis represents the predicted ratio of incorrect treatment under <i>mix</i>3 relative to <i>mix</i>2 according to our model, and its location on the Y axis represents the predicted ratio of triple resistance emergence. A red line is drawn where the strategies inhibit triple resistance equally well, so below the line <i>mix</i>3 reduces both incorrect treatment and triple resistance emergence more efficiently than <i>mix</i>2. Panels A and B present the results of the models without community feedback and with it, respectively. Antibiotic resistance frequencies among incoming patients are estimated from data (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#sec002" target="_blank">Methods</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.s006" target="_blank">S1 Table</a>). The color indicates the estimated resistance frequencies to the common antibiotics (</p><p></p><p></p><p></p><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>1</mn></p><p></p><p></p><mo>+</mo><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>2</mn></p><p></p><p></p><mo>+</mo><p><mi>f</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>2</mn></p><p></p><p></p><p></p><p></p><p></p><p></p>). The system is simulated for 20 years and the rest of the parameters are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.g002" target="_blank">Fig 2</a>.<p></p

Parameters, their meaning and values.

<p>Parameters, their meaning and values.</p

Predictors and Outcomes of Infection-Related Hospital Admissions of Heart Failure Patients

<div><p>Background</p><p>Infections are one of the most common causes for hospitalization of patients with heart failure (HF). Yet, little is known regarding the prevalence and predictors of different types of acute infections as well as their impact on outcome among this growing population.</p><p>Methods and Results</p><p>We identified all patients aged 50 or older with a major diagnosis of HF and at least one echocardiography examination who had been hospitalized over a 10-year period (January 2000 and December 2009). Infection-associated admissions were identified according to discharge diagnoses. Among 9,335 HF patients, 3530 (38%) were hospitalized at least once due to infections. The most frequent diagnoses were respiratory infection (52.6%) and sepsis/bacteremia (23.6%) followed by urinary (15.7%) and skin and soft tissue infections (7.8%). Hospitalizations due to infections compared to other indications were associated with increased 30-day mortality (13% vs. 8%, p<0.0001). These higher mortality rates were predominately related to respiratory infections (OR 1.28 [95% CI 1.09, 1.5]) and sepsis\bacteremia (OR 3.13 [95% CI 2.6, 3.7]). Important predictors for these serious infections included female gender, chronic obstructive pulmonary disease, past myocardial infarction and echocardiography-defined significant right (RV) but not left ventricular dysfunction.</p><p>Conclusions</p><p>Major infection-related hospitalizations are frequent among patients with HF and are associated with increased mortality rates. Elderly female patients with multiple comorbidities and those with severe RV dysfunction are at higher risk for these infections.</p></div

Predictors for respiratory infection sepsis or bacteremia.

<p>Predictors for respiratory infection sepsis or bacteremia.</p

High sensitivity troponin value distribution.

<p>Distribution of hs-cTnT levels (expressed as ng/L) among 10,021 measurements. Only 38.4% of the measurements were below the 99th percentile.</p

Patient characteristics.

<p>Patient characteristics.</p