ArticleBMC anesthesiology2026
Predictors and nomogram of midazolam-induced somnolence: a retrospective cohort study.
Article in BMC anesthesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop and validate a nomogram for predicting the risk of prolonged somnolence in hospitalized patients receiving intravenous midazolam.
methodsWe retrospectively collected clinical data from hospitalized patients at Liuzhou People’s Hospital who were treated with intravenous midazolam and and required flumazenil for sedation reversal between July 2020 and December 2021. These patients were randomly divided in a specific ratio (7:3) into a training cohort (n = 710) and an internal validation cohort (n = 303). Univariate analysis was performed in the training cohort to screen for relevant factors associated with prolonged somnolence. Subsequently, least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were employed to identify independent risk factors for prolonged somnolence, based on which a predictive nomogram model was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and Hosmer-Lemeshow (H-L) test were employed to assess the discrimination and calibration of the model. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the model.
resultsA total of 1,013 patients were included. Multivariate logistic regression analysis revealed that serum albumin (ALB) < 35 g/L (OR = 1.73, 95%CI 1.20–2.50, P < 0.01), male sex (OR = 1.41, 95%CI 1.04–1.91, P = 0.026), and alanine aminotransferase (ALT) > 160 U/L (OR = 3.16, 95%CI 1.11–11.37, P = 0.046) were independent risk factors for midazolam-induced somnolence in patients receiving intravenous midazolam. A nomogram was developed and validated using the three independent risk factors identified above. The model demonstrated an acceptable discrimination, with an area under the ROC curve of 0.603 in the training cohort and 0.615 in the internal validation cohort. The calibration curve and H-L test both demonstrated good consistency of the model, with the training cohort showing χ²=0.0028 and P = 1.00 and the validation cohort showing χ²=0.0054 and P = 0.997. DCA indicated that the model provided net clinical benefit when the threshold probabilities ranged from 3% to 67% for the training cohort and 3% to 85% for the validation cohort.
conclusionTo facilitate the rational administration of midazolam, we developed a nomogram that can help assess the likelihood of prolonged somnolence in patients receiving intravenous midazolam.
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.