Validity of Charlson Comorbidity Index in patients hospitalised with acute coronary syndrome. Insights from the nationwide AMIS Plus registry 2002-2012

OBJECTIVE This study aimed to assess the impact of individual comorbid conditions as well as the weight assignment, predictive properties and discriminating power of the Charlson Comorbidity Index (CCI) on outcome in patients with acute coronary syndrome (ACS). METHODS A prospective multicentre observational study (AMIS Plus Registry) from 69 Swiss hospitals with 29 620 ACS patients enrolled from 2002 to 2012. The main outcome measures were in-hospital and 1-year follow-up mortality. RESULTS Of the patients, 27% were female (age 72.1±12.6 years) and 73% were male (64.2±12.9 years). 46.8% had comorbidities and they were less likely to receive guideline-recommended drug therapy and reperfusion. Heart failure (adjusted OR 1.88; 95% CI 1.57 to 2.25), metastatic tumours (OR 2.25; 95% CI 1.60 to 3.19), renal diseases (OR 1.84; 95% CI 1.60 to 2.11) and diabetes (OR 1.35; 95% CI 1.19 to 1.54) were strong predictors of in-hospital mortality. In this population, CCI weighted the history of prior myocardial infarction higher (1 instead of -0.4, 95% CI -1.2 to 0.3 points) but heart failure (1 instead of 3.7, 95% CI 2.6 to 4.7) and renal disease (2 instead of 3.5, 95% CI 2.7 to 4.4) lower than the benchmark, where all comorbidities, age and gender were used as predictors. However, the model with CCI and age has an identical discrimination to this benchmark (areas under the receiver operating characteristic curves were both 0.76). CONCLUSIONS Comorbidities greatly influenced clinical presentation, therapies received and the outcome of patients admitted with ACS. Heart failure, diabetes, renal disease or metastatic tumours had a major impact on mortality. CCI seems to be an appropriate prognostic indicator for in-hospital and 1-year outcomes in ACS patients. CLINICALTRIALSGOV IDENTIFIER NCT01305785.

Health care index score and risk of death following tuberculosis diagnosis in HIV-positive patients

OBJECTIVES: To assess health care utilisation for patients co-infected with TB and HIV (TB-HIV), and to develop a weighted health care index (HCI) score based on commonly used interventions and compare it with patient outcome. METHODS: A total of 1061 HIV patients diagnosed with TB in four regions, Central/Northern, Southern and Eastern Europe and Argentina, between January 2004 and December 2006 were enrolled in the TB-HIV study. A weighted HCI score (range 0–5), based on independent prognostic factors identified in multivariable Cox models and the final score, included performance of TB drug susceptibility testing (DST), an initial TB regimen containing a rifamycin, isoniazid and pyrazinamide, and start of combination antiretroviral treatment (cART). RESULTS: The mean HCI score was highest in Central/Northern Europe (3.2, 95%CI 3.1–3.3) and lowest in Eastern Europe (1.6, 95%CI 1.5–1.7). The cumulative probability of death 1 year after TB diagnosis decreased from 39% (95%CI 31–48) among patients with an HCI score of 0, to 9% (95%CI 6–13) among those with a score of ≥4. In an adjusted Cox model, a 1-unit increase in the HCI score was associated with 27% reduced mortality (relative hazard 0.73, 95%CI 0.64–0.84). CONCLUSIONS: Our results suggest that DST, standard anti-tuberculosis treatment and early cART may improve outcome for TB-HIV patients. The proposed HCI score provides a tool for future research and monitoring of the management of TB-HIV patients. The highest HCI score may serve as a benchmark to assess TB-HIV management, encouraging continuous health care improvement.

Carbon stocks, tree diversity, and the role of organic certification in different cocoa production systems in Alto Beni, Bolivia

This study compares aboveground and belowground carbon stocks and tree diversity in different cocoa cultivation systems in Bolivia: monoculture, simple agroforestry, and successional agroforestry, as well as fallow as a control. Since diversified, agroforestry-based cultivation systems are often considered important for sustainable development, we also evaluated the links between carbon stocks and tree diversity, as well as the role of organic certification in transitioning from monoculture to agroforestry. Biomass, tree diversity, and soil physiochemical parameters were sampled in 15 plots measuring 48 × 48 m. Semi-structured interviews with 52 cocoa farmers were used to evaluate the role of organic certification and farmers’ organizations (e.g., cocoa cooperatives) in promoting tree diversity. Total carbon stocks in simple agroforestry systems (128.4 ± 20 Mg ha−1) were similar to those on fallow plots (125.2 ± 10 Mg ha−1). Successional agroforestry systems had the highest carbon stocks (143.7 ± 5.3 Mg ha−1). Monocultures stored significantly less carbon than all other systems (86.3 ± 4.0 Mg ha−1, posterior probability P(Diff > 0) of 0.000–0.006). Among shade tree species, Schizolobium amazonicum, Centrolobium ochroxylum, and Anadenanthera sp. accumulated the most biomass. High-value timber species (S. amazonicum, C. ochroxylum, Amburana cearensis, and Swietenia macrophylla) accounted for 22.0 % of shade tree biomass. The Shannon index and tree species richness were highest in successional agroforestry systems. Cocoa plots on certified organic farms displayed significantly higher tree species richness than plots on non-certified farms. Thus, expanding the coverage of organic farmers’ organizations may be an effective strategy for fostering transitions from monoculture to agroforestry systems.

Simulating Carbon Stocks and Fluxes of an African Tropical Montane Forest with an Individual-Based Forest Model

Tropical forests are carbon-dense and highly productive ecosystems. Consequently, they play an important role in the global carbon cycle. In the present study we used an individual-based forest model (FORMIND) to analyze the carbon balances of a tropical forest. The main processes of this model are tree growth, mortality, regeneration, and competition. Model parameters were calibrated using forest inventory data from a tropical forest at Mt. Kilimanjaro. The simulation results showed that the model successfully reproduces important characteristics of tropical forests (aboveground biomass, stem size distribution and leaf area index). The estimated aboveground biomass (385 t/ha) is comparable to biomass values in the Amazon and other tropical forests in Africa. The simulated forest reveals a gross primary production of 24 tcha-1yr-1. Modeling above- and belowground carbon stocks, we analyzed the carbon balance of the investigated tropical forest. The simulated carbon balance of this old-growth forest is zero on average. This study provides an example of how forest models can be used in combination with forest inventory data to investigate forest structure and local carbon balances.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.