ArticleInternational journal of environmental research and public health2020
Predicting Length of Stay and Discharge Destination for Surgical Patients: A Cohort Study.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 12 citations in OpenAlex.
- Skeletal muscle index and hand grip strength as predictors of postoperative recovery in patients with gastrointestinal tumors.BMC gastroenterology · 2026Observational
- Predictors of Need for Home Health Services at Discharge for Thoracic Surgery Patients.Annals of thoracic surgery short reports · 2025Article
- Impact of accurate initial discharge planning and inpatient transfers of care on discharge delays: a retrospective cohort study.BMJ open · 2025Article
- Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.BMC medical informatics and decision making · 2024Article
- Does Alcohol Use Influence Hospitalization Outcomes in Adults Suffering Craniomaxillofacial Fractures From Street Fighting?Craniomaxillofacial trauma & reconstruction · 2024Article
- A comparison of statistical methods for modeling count data with an application to hospital length of stay.BMC medical research methodology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discharge planning is important to prevent surgical site infections, reduce costs, and improve the hospitalization experience. The identification of early variables that can predict a longer-than-expected length of stay or the need for a discharge with additional needs can improve this process. A cohort study was conducted in the largest hospital of Northern Italy, collecting discharge records from January 2017 to January 2020 and pre-admission visits in the last three months. Socio-demographic and clinical data were collected. Linear and logistic regression models were fitted. The main outcomes were the length of stay (LOS) and discharge destination. The main predictors of a longer LOS were the need for additional care at discharge (+10.76 days), hospitalization from the emergency department (ED) (+5.21 days), and age (+0.04 days per year), accounting for clinical variables (
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.