What question did this study set out to answer?

To identify predictors of mortality in sepsis patients and develop a predictive model using LASSO.

A Least Absolute Shrinkage and Selection Operator (LASSO)-derived AAGC Model for In-hospital Mortality Prediction in Sepsis Incorporating Age, APACHE II Score, Glasgow Coma Scale, and Creatinine: A Prospective Study

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Prospective trial develops a predictive model for mortality in sepsis, indicating its clinical significance.

To identify predictors of mortality in sepsis patients and develop a predictive model using LASSO.
Prospective cohort study involving 150 adults diagnosed with sepsis.
Utilized LASSO regression for variable selection followed by multivariable logistic regression.
Model performance assessed through ROC analysis and calibration plots.
Of 150 patients, 86 (57.3%) died during hospitalization.
AAGC model showed excellent discrimination with AUC: 0.95 (95% CI: 0.92-0.99).
Independent mortality predictors included high respiratory rate, culture-negative sepsis, respiratory failure, and septic shock.

Gulia et al. (2026) studied this question.

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View Full Paper

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Authors

Prospective trial develops a predictive model for mortality in sepsis, indicating its clinical significance.

To identify predictors of mortality in sepsis patients and develop a predictive model using LASSO.
Prospective cohort study involving 150 adults diagnosed with sepsis.
Utilized LASSO regression for variable selection followed by multivariable logistic regression.
Model performance assessed through ROC analysis and calibration plots.
Of 150 patients, 86 (57.3%) died during hospitalization.
AAGC model showed excellent discrimination with AUC: 0.95 (95% CI: 0.92-0.99).
Independent mortality predictors included high respiratory rate, culture-negative sepsis, respiratory failure, and septic shock.

Gulia et al. (2026) studied this question.

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