Derivation and Internal Validation of a Clinical Prediction Tool for 30-Day Mortality in Lower Gastrointestinal Bleeding

Topic: Risk, prevention and care in population studies

Background There are limited data to predict which patients with lower gastrointestinal bleeding are at risk for adverse outcomes. We aimed to develop a clinical tool based on admission variables to predict 30-day mortality in lower gastrointestinal bleeding. Methods We used a validated machine learning algorithm to identify adult patients hospitalized with lower gastrointestinal bleeding at an academic medical center between 2008 and 2015. The cohort was split randomly into derivation and validation cohorts. In the derivation cohort, we used multiple logistic regression on all candidate admission variables to create a prediction model for 30-day mortality, using area under the receiving operator characteristic curve and misclassification rate to estimate prediction accuracy. Regression coefficients were used to derive an integer score, and mortality risk associated with point totals was assessed. Results In the derivation cohort (n = 4044), 8 variables were most associated with 30-day mortality: age, dementia, metastatic cancer, chronic kidney disease, chronic pulmonary disease, anticoagulant use, admission hematocrit, and albumin. The model yielded a misclassification rate of 0.06 and area under the curve of 0.81. The integer score ranged from -10 to 26 in the derivation cohort, with a misclassification rate of 0.11 and area under the curve of 0.74. In the validation cohort (n = 2060), the score had an area under the curve of 0.72 with a misclassification rate of 0.12. After dividing the score into 4 quartiles of risk, 30-day mortality in the derivation and validation sets was 3.6% and 4.4% in quartile 1, 4.9% and 7.3% in quartile 2, 9.9% and 9.1% in quartile 3, and 24% and 26% in quartile 4, respectively. Conclusions A clinical tool can be used to predict 30-day mortality in patients hospitalized with lower gastrointestinal bleeding.

Key findings

• Regression coefficients were used to derive an integer score, and mortality risk associated with point totals was assessed.
• Results In the derivation cohort (n = 4044), 8 variables were most associated with 30-day mortality: age, dementia, metastatic cancer, chronic kidney disease, chronic pulmonary disease, anticoagulant use, admission hematocrit, and albumin.

Why it matters

This study developed a clinical prediction tool for 30-day mortality in lower gastrointestinal bleeding that included dementia as one of eight variables. It is about predicting death risk in a different condition, not about treating or preventing Alzheimer's disease.

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Study details

• Participants4,044
• Effect size3.6%; 4.4%; 4.9%
• Evidence strengthModerate

Read the original

• DOI: 10.1016/j.amjmed.2016.12.009
• PubMed: 28065767
• Original source →
• Free full text: The American Journal of Medicine →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{sengupta2017-28065767,
  author  = {Sengupta, N. and Tapper, E. B.},
  title   = {{Derivation and Internal Validation of a Clinical Prediction Tool for 30-Day Mortality in Lower Gastrointestinal Bleeding}},
  journal = {The American journal of medicine},
  year    = {2017},
  doi     = {10.1016/j.amjmed.2016.12.009},
  pmid    = {28065767},
  url     = {https://doi.org/10.1016/j.amjmed.2016.12.009}
}

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