ArticlePakistan journal of medical sciences2024
Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients.
Article in Pakistan journal of medical sciences, 2024. 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.
- Lasso-Enhanced Logistic Regression for Early Prediction of Pulmonary Infection in Critically Ill Post-Abdominal Surgery Patients.Medicina (Kaunas, Lithuania) · 2026Article
- A nomogram for predicting early mortality in patients with traumatic brain injury requiring mechanical ventilation based on clinical laboratory data.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model. Methods: Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve. Results: A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value. Conclusions: Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value.
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.