ArticleSurgery2023
Development and validation of a multivariable preoperative prediction model for postoperative length of stay in a broad inpatient surgical population.
Article in Surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Artificial Intelligence Approaches to Predict Postoperative Length of Hospital Stay in Head and Neck Cancer Patients: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPostoperative length of stay is a meaningful patient-centered outcome and an important determinant of healthcare costs. The Surgical Risk Preoperative Assessment System preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict postoperative length of stay has not been assessed. We aimed to determine whether the Surgical Risk Preoperative Assessment System variables could accurately predict postoperative length of stay up to 30 days in a broad inpatient surgical population.
methodsThis was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database from 2012 to 2018. A model using the Surgical Risk Preoperative Assessment System variables and a 28-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables, were fit to the analytical cohort (2012-2018) using multiple linear regression and compared using model performance metrics. Internal chronological validation of the Surgical Risk Preoperative Assessment System model was conducted using training (2012-2017) and test (2018) datasets.
resultsWe analyzed 3,295,028 procedures. The adjusted R
conclusionThe parsimonious Surgical Risk Preoperative Assessment System model can preoperatively predict postoperative length of stay up to 30 days for inpatient surgical procedures almost as accurately as a model using all 28 American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables and has shown acceptable internal chronological validation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.