Observational studyBMJ open2020
Protocol for the derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery (PAUSE score-Probability of mortality Associated with Urgent/emergent general Surgery in oldEr patients score).
Observational study in BMJ open, 2020. 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
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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, 2 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPeople 65 years and older represent the fastest growing segment of the surgical population. Older age is associated with doubling of risk when undergoing emergency general surgery (EGS) procedures and often coexists with medical complexity and considerations of end-of-life care, creating prognostic and decisional uncertainty. Combined with the time-sensitive nature of EGS, it is challenging to gauge perioperative risk and ensure that clinical decisions are aligned with the patient values. Current preoperative risk prediction models for older EGS patients have major limitations regarding derivation and validation, and do not address the specific risk profile of older patients. Accurate and externally validated models specific to older patients are needed to inform care and decision making. METHODS AND ANALYSIS: We will derive, internally and externally validate a multivariable model to predict 30-day mortality in EGS patients >65 years old. Our derivation sample will be individuals enrolled in the National Surgical Quality Improvement Program (NSQIP) database between 2012 and 2016 having 1 of 7 core EGS procedures. Postulated predictor variables have been identified based on previous research, clinical and epidemiological knowledge. Our model will be derived using logistic regression penalised with elastic net regularisation and ensembled using bootstrap aggregation. The resulting model will be internally validated using k-fold cross-validation and bootstrap validation techniques and externally validated using population-based health administrative data. Discrimination and calibration will be reported at each step. ETHICS AND DISSEMINATION: Ethics for NSQIP data use was obtained from the Ottawa Hospital Research Ethics Board; external validation will use routinely collected anonymised data legally exempt from research ethics review. The final risk score will be published in a peer-reviewed journal. We plan to further disseminate the model as an online calculator or application for clinical use. Future research will be required to test the clinical application of the final model.
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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.