Adverse treatment outcomes in multidrug resistant tuberculosis go beyond the microbe-drug interaction: Results of a multiple correspondence analysis.

Tobón, Ángela; Rueda, Johana; Cáceres, Diego H.; Mejía, Gloria Isabel; Zapata, Elsa; Montes, Fernando; Ospina, Antonio; Fadul, Santiago; Paniagua, Lizeth; Robledo, Jaime

doi:10.7705/biomedica.5072

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…These include cluster analysis and classification techniques [3,4] that can be used to separate groups with similar characteristics. Correspondence Analysis (CA) [5] separates groups without considering affinities but is an excellent support for global visualization in clinical cases. Moreover, Decision Trees (DT) [6,7] are a valuable tool for the detection of an optimal classification of different classes of variables.…”

Section: Introductionmentioning

confidence: 99%

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Boukichou-Abdelkader,

Montero-Alonso,

Muñoz-García

2024

Computation

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Recently, many methods and algorithms have been developed that can be quickly adapted to different situations within a population of interest, especially in the health sector. Success has been achieved by generating better models and higher-quality results to facilitate decision making, as well as to propose new diagnostic procedures and treatments adapted to each patient. These models can also improve people’s quality of life, dissuade bad health habits, reinforce good habits, and modify the pre-existing ones. In this sense, the objective of this study was to apply supervised and unsupervised classification techniques, where the clustering algorithm was the key factor for grouping. This led to the development of three optimal groups of clinical pattern based on their characteristics. The supervised classification methods used in this study were Correspondence (CA) and Decision Trees (DT), which served as visual aids to identify the possible groups. At the same time, they were used as exploratory mechanisms to confirm the results for the existing information, which enhanced the value of the final results. In conclusion, this multi-technique approach was found to be a feasible method that can be used in different situations when there are sufficient data. It was thus necessary to reduce the dimensional space, provide missing values for high-quality information, and apply classification models to search for patterns in the clinical profiles, with a view to grouping the patients efficiently and accurately so that the clinical results can be applied in other research studies.

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Section: Introductionmentioning

confidence: 99%

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Boukichou-Abdelkader,

Montero-Alonso,

Muñoz-García

2024

Computation

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Development and Validation of a Nomogram for the Prediction of Unfavorable Treatment Outcome Among Multi-Drug Resistant Tuberculosis Patients in North West Ethiopia: An Application of Prediction Modelling

Anley¹,

Akalu²,

Merid³

et al. 2022

IDR

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Background Multidrug-resistant tuberculosis (MDR-TB) is a global problem and a health security threat, which makes “Ending the global TB epidemic in 2035” unachievable. Globally, the unfavourable treatment outcome remains unacceptably high. Therefore, this study aimed to develop a risk prediction model for unfavorable treatment outcomes in MDR-TB patients, which can be used by clinicians as a simple clinical tool in their decision-making. Objective The objective of this study was to develop and validate a risk prediction model for the prediction of unfavorable treatment outcomes among MDR-TB patients in North-West Ethiopia. Methods We used MDR-TB data collected from the University of Gondar and Debre Markos referral hospitals. A retrospective follow-up study was conducted and a total of 517 patients were included in the study. STATA version 16 statistical software and R version 4.0.5 were used for the analysis. Descriptive statistics were carried out. A multivariable model was fitted using all potent predictors selected by the lasso regression method. A simplified risk prediction model (nomogram) was developed based on the binomial logit-based model, and its performance was described by assessing its discriminatory power and calibration. Finally, decision curve analysis (DCA) was done to evaluate the clinical and public health impact of the developed model. Results The developed nomogram comprised six predictors: baseline anemia, major adverse event, comorbidity, age, marital status, and treatment supporter. The model has a discriminatory power of 0.753 (95% CI: 0.708, 0.798) and calibration test of (P-value = 0.695). It was internally validated by bootstrapping method, and it has a relatively corrected discrimination performance (AUC = 0.744, 95CI: 0.699, 0.788). The optimism coefficient was found to be 0.009. The decision curve analysis showed the net benefit of the model as threshold probabilities varied. Conclusion The developed nomogram can be used for individualized prediction of unfavorable treatment outcomes in MDR-TB patients for it has a satisfactory level of accuracy and good calibration. The model is clinically interpretable and was found to have added benefits in clinical practice.

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Tratamiento para tuberculosis resistente a rifampicina o multirresistente: análisis comparativo de indicadores programáticos entre Buenaventura y otros municipios del Valle del Cauca, Colombia

Hoyos,

Meza,

Forero

et al. 2024

Biomed.

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Introducción. El manejo adecuado de la tuberculosis multirresistente es una estrategia priorizada para el control de la tuberculosis en el mundo.Objetivo. Evaluar las diferencias entre las características demográficas y clínicas, y los indicadores programáticos de los pacientes con diagnóstico confirmado de tuberculosis pulmonar resistente a rifampicina o multirresistente en Buenaventura, frente a la cohorte de los demás municipios del Valle del Cauca entre 2013 y 2016.Materiales y métodos. Se desarrolló un estudio analítico de cohortes para comparar los registros de pacientes mayores de 15 años con tuberculosis multirresistente, del Programa de Tuberculosis de Buenaventura (con ácido paraaminosalicílico), frente a los demás municipios del Valle del Cauca (sin ácido paraaminosalicílico).Resultados. Se registraron 99 casos con una mediana de edad de 40 años (RIC = 26- 53); en Buenaventura, el 56 % eran mujeres; en los demás municipios, predominaron los hombres (67 %); el 95 % de los evaluados tenía aseguramiento en salud. La comorbilidad más frecuente fue diabetes (14 %). Las reacciones adversas a medicamentos antituberculosos en Buenaventura fueron 1,3 veces más frecuentes que en los demás municipios (OR = 2,3; IC95 %: 0,993 - 5,568; p = 0,04). En Buenaventura falleció el 5 % de los casos frente al 15 % reportado en los demás municipios. No hubo fracasos con el tratamiento en Buenaventura, pero se reportó un 35 % de pérdida del seguimiento. El éxito del tratamiento fue mayor en Buenaventura en el 56 %.Conclusión. El programa fortalecido de Buenaventura presentó mejores resultados programáticos que los demás municipios del Valle del Cauca. El acceso a pruebas moleculares, la disponibilidad de tratamientos acortados y el seguimiento continuo para identificar reacciones adversas a medicamentos antituberculosos son un derrotero para todos los programas de control.

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Adverse treatment outcomes in multidrug resistant tuberculosis go beyond the microbe-drug interaction: Results of a multiple correspondence analysis.

Cited by 3 publications

References 18 publications

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Development and Validation of a Nomogram for the Prediction of Unfavorable Treatment Outcome Among Multi-Drug Resistant Tuberculosis Patients in North West Ethiopia: An Application of Prediction Modelling

Tratamiento para tuberculosis resistente a rifampicina o multirresistente: análisis comparativo de indicadores programáticos entre Buenaventura y otros municipios del Valle del Cauca, Colombia

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