Impact assessment in a non-government organisation

<p>Supplemental_figure for A Simple Scoring System Using the Red Blood Cell Distribution Width, Delta Neutrophil Index, and Platelet Count to Predict Mortality in Patients With Severe Sepsis and Septic Shock by Yong Chan Kim, Je Eun Song, Eun Jin Kim, Heun Choi, Woo Yong Jeong, In Young Jung, Su Jin Jeong, Nam Su Ku, Jun Yong Choi, Young Goo Song, and June Myung Kim in Journal of Intensive Care Medicine</p

Supplemental_table_2 - A Simple Scoring System Using the Red Blood Cell Distribution Width, Delta Neutrophil Index, and Platelet Count to Predict Mortality in Patients With Severe Sepsis and Septic Shock

<p>Supplemental_table_2 for A Simple Scoring System Using the Red Blood Cell Distribution Width, Delta Neutrophil Index, and Platelet Count to Predict Mortality in Patients With Severe Sepsis and Septic Shock by Yong Chan Kim, Je Eun Song, Eun Jin Kim, Heun Choi, Woo Yong Jeong, In Young Jung, Su Jin Jeong, Nam Su Ku, Jun Yong Choi, Young Goo Song, and June Myung Kim in Journal of Intensive Care Medicine</p

Mathematical Modeling of HIV Prevention Measures Including Pre-Exposure Prophylaxis on HIV Incidence in South Korea

<div><p>Background</p><p>Multiple prevention measures have the possibility of impacting HIV incidence in South Korea, including early diagnosis, early treatment, and pre-exposure prophylaxis (PrEP). We investigated how each of these interventions could impact the local HIV epidemic, especially among men who have sex with men (MSM), who have become the major risk group in South Korea. A mathematical model was used to estimate the effects of each these interventions on the HIV epidemic in South Korea over the next 40 years, as compared to the current situation.</p><p>Methods</p><p>We constructed a mathematical model of HIV infection among MSM in South Korea, dividing the MSM population into seven groups, and simulated the effects of early antiretroviral therapy (ART), early diagnosis, PrEP, and combination interventions on the incidence and prevalence of HIV infection, as compared to the current situation that would be expected without any new prevention measures.</p><p>Results</p><p>Overall, the model suggested that the most effective prevention measure would be PrEP. Even though PrEP effectiveness could be lessened by increased unsafe sex behavior, PrEP use was still more beneficial than the current situation. In the model, early diagnosis of HIV infection was also effectively decreased HIV incidence. However, early ART did not show considerable effectiveness. As expected, it would be most effective if all interventions (PrEP, early diagnosis and early treatment) were implemented together.</p><p>Conclusions</p><p>This model suggests that PrEP and early diagnosis could be a very effective way to reduce HIV incidence in South Korea among MSM.</p></div

Variables and parameters for model.

<p><b>NOTE.</b> MSM, men who sex with men; UAIC, unprotected anal intercourse.</p><p>Value of variables (X, Y₁, Y₂, Y₃, Y₄) meant to be initial value for mathematical model. Value of parameters (from nx to <i>f_p</i>) meant to be current value for model.</p><p>a, s, v₃, and v₄ are calculated by following equation: If there z% in t years, then the yearly rate r was computed by .</p><p>v₁, v₂, h₁, h₂, h₃, and h₄ are calculated by following equation: If it takes t years on average to move to the next compartment, then the yearly rate r was computed by .</p><p>K,K₁,K₂,K₃,K₄ are calculated by transmission equations supplemented by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone.0090080.s001" target="_blank">Fig. S1</a>.</p><p>Dimensionless: it has no unit, such as a ratio or a percentage.</p

Multistate HIV infection model for a MSM population.

<p>NOTE: See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone-0090080-t001" target="_blank">table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone.0090080.s002" target="_blank">S1</a> for referring the meaning of each abbreviations</p

Comparison of number of incident HIV cases (KX) and prevalent HIV cases (Y_1–4) between current situation and efficacy of PrEP modulated by unsafe sex practices (scenario 3 to 4-1,4-2, and 4-3).

<p>A. Comparison of number of incident HIV cases (KX) between current situation and scenarios 3 to 4-1, 4-2, and 4-3. B. Comparison of number of prevalent HIV cases (Y_1–4) between current situation and efficacy of PrEP modulated by unsafe sex practices (scenario 3 to 4-1, 4-2, and 4-3). NOTE: Stream of current status, scenarios 3. 4-1, 4-2, and 4-3. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone-0090080-g003" target="_blank">Figure 3A:</a> X-axis represented time sequence from now to 40 years and Y-axis represented stream of number of incident HIV cases (KX) compared to current situation with PrEP scenarios 3 to 4-1,4-2,4-3. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone-0090080-g003" target="_blank">Figure 3B:</a> X-axis represented time sequence from now to 40 years, and Y-axis represented the number of prevalent HIV cases (Y₁ to Y₄) comparing current situation with PrEP scenarios 3, 4-1, 4-2, and 4-3.</p

Modeled HIV incidence under different scenarios.

<p>A-1. Ratio of incident HIV cases (KX) compared early ART (scenario 1) with current situation. A-2. Ratio of incident HIV cases (KX) compared early diagnosis (scenario 2) with current situation. A-3. Ratio of incident HIV cases (KX) compared PrEP (scenario 3) with current situation. A-4. Ratio of incident HIV cases (KX) compared all interventions (scenario 5) with current situation. B-1. Ratio of prevalent HIV cases (Y_1–4) compared early ART (scenario 1) with current situation B-2. Ratio of prevalent HIV cases (Y_1–4) compared early diagnosis (scenario 2) with current situation. B-3. Ratio of prevalent HIV cases (Y_1–4) compared PrEP (scenario 3) with current situation. B-4. Ratio of prevalent HIV cases (Y_1–4) compared all interventions (scenario 5) with current situation. NOTE: The boxplot contains the median value of the data (horizontal red line), and extends from the first to the third quartile when simple random sampling with uniform distributions between ±10% of baseline of all parameters are used. Whisker bars are extended to minimum and maximum. Figures are the simulations of scenarios 1, 2, and 3, and 5. The X-axis represented the time sequence from initial to 40 years, and the Y-axis represented the ratio of HIV incidence (KX) and prevalence (Y_1–4) compared with current status. Plots over zero represented a decrease over HIV incidence and prevalence than the current situation, while plots under zero meant an increase in HIV incidence and prevalence over current situation.</p

Sensitivity analysis for the number of incident HIV cases (KX) and the number of prevalent HIV cases (Y_1–4).

<p>A-1. Averaged sensitivities in magnitudes of the number of incident HIV cases (KX) per year to each parameter. A-2. Sensitivities of the number of incident HIV cases (KX) per year to each parameter at 10 year. B-1. Averaged sensitivities in magnitudes of the number of prevalent HIV cases (Y_1–4) per year to each parameter. B-2. Sensitivities of the number of prevalent HIV cases (Y_1–4) per year to each parameter at 10 year. NOTE: The elasticity of KX with respect to a parameter is defined as: . The X-axis of Figures A and B represents the elasticity of incident (KX) and prevalent (Y_1–4) HIV cases, respectively. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone-0090080-t001" target="_blank">table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone.0090080.s002" target="_blank">S1</a> for referring the meaning of each abbreviations within Y-axis. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone-0090080-g004" target="_blank">Figures 4A</a>-1 and 4B-1 show the average elasticities of outcomes over 40 years and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090080#pone-0090080-g004" target="_blank">Figures 4A</a>-2 and 4B-2 show the elasticities of outcomes at the time point of 10 years. The boxplot contains the median value (horizontal red line), and extends from the first to the third quartile when simple random sampling with uniform distributions between ±10% of baseline of all parameters are used. Whisker bars are extended to minimum and maximum.</p

The C-Reactive Protein/Albumin Ratio as an Independent Predictor of Mortality in Patients with Severe Sepsis or Septic Shock Treated with Early Goal-Directed Therapy

<div><p>Background</p><p>Sepsis, including severe sepsis and septic shock, is a major cause of morbidity and mortality. Albumin and C-reactive protein (CRP) are considered as good diagnostic markers for sepsis. Thus, initial CRP and albumin levels were combined to ascertain their value as an independent predictor of 180-day mortality in patients with severe sepsis and septic shock.</p><p>Materials and Methods</p><p>We conducted a retrospective cohort study involving 670 patients (>18 years old) who were admitted to the emergency department and who had received a standardized resuscitation algorithm (early goal-directed therapy) for severe sepsis and septic shock, from November 2007 to February 2013, at a tertiary hospital in Seoul, Korea. The outcome measured was 180-day all-cause mortality. A multivariate Cox proportional hazard model was used to identify the independent risk factors for mortality. A receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the CRP/albumin ratio at admission.</p><p>Results</p><p>The 180-day mortality was 28.35% (190/670). Based on the multivariate Cox proportional hazard analysis, age, the CRP/albumin ratio at admission (adjusted HR 1.06, 95% CI 1.03–1.10, p<0.001), lactate level at admission (adjusted HR 1.10, 95% CI 1.05–1.14, p<0.001), and the Sequential Organ Failure Assessment (SOFA) score at admission (adjusted HR 1.12, 95% CI 1.07–1.18, p<0.001) were independent predictors of 180-day mortality. The area under the curve of CRP alone and the CRP/albumin ratio at admission for 180-day mortality were 0.5620 (P<0.001) and 0.6211 (P<0.001), respectively.</p><p>Conclusion</p><p>The CRP/albumin ratio was an independent predictor of mortality in patients with severe sepsis or septic shock.</p></div

Receiver operating characteristic (ROC) curve of CRP and CRP/albumin ratio in predicting 180-day mortality.

<p>The area under curve (AUC) for the CRP/albumin ratio is higher compared to CRP alone (p<0.001).</p