Factores clínicos y electrocardiográficos como predictores de mortalidad en pacientes en fase aguda de un primer evento cerebrovascular Clinical and electrocardiographic factors as mortality predictors in patients in the acute phase of a first cerebrovasc
Bibliographic record
Abstract
RESUMENIntroducción: El accidente cerebrovascular (ACV) es la segunda causa de muerte y tercera causa dediscapacidad en el mundo.Objetivo:Evaluar la asociación entre variables clínicas, electrocardiográficas,escalas neurológicas en pacientes con ACV como predictoras de mortalidad a 3 meses posteriores al egreso hospitalario. Materiales y métodos: Estudio de cohorte prospectivo con muestreo no probabilístico, en pacientes mayores de 18 años con primer ACV. Se evaluaron variables demográficos,clínicas, escalas neurológicas del Instituto Nacional de Salud (NIHSS) y canadiense (CNS), variabilidad de la frecuencia cardiaca (VFC) y del QT (QTV), dispersión del QT. Se determinó la mortalidad a los tres meses de seguimiento. Se realizó análisis bivariado y de regresión logística múltiple cuyo desenlacefue mortalidad a tres meses post egreso hospitalario, incluyendo variables con baja correlación (r< 0.4) y significancia estadística (p<0.05).Resultados: Se incluyeron 92 pacientes, 13 de los cuales fallecieron en la fase de tratamiento intrahospitalario. Se realizó seguimiento durante tres meses después del egreso hospitalario en 81 pacientes. La mortalidad total en tres meses de seguimiento fue del 21.7%(n=20).Se identificaron cinco variables predictoras de mortalidad en el modelo final: puntaje de escala NIHSS,frecuencia cardiaca media, VLF QT ≥36.311, LF/HF ≤1.019, valores extremos r-MSD (≥7.985o≤2.363) de VFC. La capacidad discriminatoria del modelo mediante el análisis del área bajo la curva fue de 0.95, con valores de sensibilidad y especificidad del 60% y 93% respectivamente.Conclusión:Altos puntajes de escala NIHSS, VLF-QT, frecuencia cardiaca media, así como valores bajos LF/HF y valores extremosr-MSD, fueron factores de riesgo independientes para mortalidad a los 90 días después de un primer ACV.Palabras Clave: Accidente cerebrovascular, Electrocardiografia, Frecuencia cardiaca, Mortalidad, Predicción.ABSTRACTIntroduction:Stroke is the second cause of death and third cause of disability worldwide.Objective: To assess association between clinical and electrocardiographic variables, neurological scales in stroke patients like predictorsof mortality at three months after hospital discharge.Subjects and methods:Prospective cohort with nonprobabilistic sampling, in patients over 18 years with first stroke. Demographic and clinical variables, neurological scales (NIHSS, Canadian), heart rate (HRV) and QT variability (QTV), QT dispersion were evaluated. Mortality was determined during the 3 months follow up. Bivariate and multiple logistic regression analysis were performed with mortality at three months after discharge as outcome. Variables were included in the model if they have low correlation (r<0.4) and significant statistically p values (P< 0.05).Results: 92 patients were included in the study,13 patients died during the intra-hospital stay, 81 were followed at 3 months after their hospital discharge. Totalmortality in patients included at three months follow-up was 21.7 % (n=20). We identified five predictors of mortality in the final model: NIHSS score, mean heart rate, VLF QT ≥36,311, LF/HF ≤ 1,019, extreme values of r-MSD (≥ 7,985 or ≤ 2,363) of HRV. The area under the curve (AUC) of the model was 0,95 with sensitivity of 60% and specificity of 93%. Conclusions: High NIHSS scores, VLF-QT, mean heart rate, low values of LF/HF and high extreme values of r-MSD were independent risk factors for mortality at 90 days after a first stroke.Keywords: Stroke, Electrocardiography, Heart Rate, Mortality Prediction. Forma de citar: Rueda Ochoa OL, Torres Mantilla HA, Fernández Dulcey CA, Villa Acuña MM, Velasco Gómez ST, Niño Niño CA, Sierra Bueno DA. Factores clínicos y electrocardiográficos como predictores de mortalidad en pacientes en fase aguda de un primer evento cerebrovascular. rev.univ.ind.santander.salud 2014; 46(2): 147-158.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".