Caracterización de patrones clínicos del paciente fumador antes y después de una intervención terapéutica: un análisis cualitativo y de minería de datos

El tabaquismo es una enfermedad adictiva y crónica Por ello, el fumador necesita la asistencia de un profesional sanitario mediante asesoramiento psicológico y tratamiento farmacológico. No obstante, el abordaje personalizado que nos ofrece la nueva ciencia de los datos y las posibilidades que aportan las técnicas de manejo de datos no estructurados pueden ayudar a aumentar la efectividad de estas intervenciones. Todo ello nos ha llevado a caracterizar patrones clínicos de fumadores que acuden a un servicio de cesación tabáquica antes y después del tratamiento aplicando metodología cualitativa y minería de datos. Como resultado observamos la dependencia a la nicotina como eje central de todo el discurso del fumador y modificaciones en el relato del mismo tras la intervención. Se identifican varios clústers de fumadores subsidiarios de tratamiento dirigido en próximas investigaciones

Development and validation of a clinical score to estimate progression to severe or critical state in Covid-19 pneumonia hospitalized patients

The prognosis of a patient with Covid-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with Covid-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, analytical, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1,152 patients presented with Covid-19 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 hours of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤5%, 6-25%, and >25% exhibited disease progression, respectively. A simple risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.Carlos III Health Institute, Spain, Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER)Instituto de Salud Carlos II