• J. Surg. Res. · Jun 2017

    Predictors of readmission in nonagenarians: analysis of the American College of Surgeons National Surgical Quality Improvement Project dataset.

    • Zachary Hothem, Dustin Baker, Christina S Jenkins, Jason Douglas, Rose E Callahan, Catherine C Shuell, Graham W Long, and Robert J Welsh.
    • Department of Surgery, Beaumont Health, Royal Oak, Michigan. Electronic address: zachary.hothem@beaumont.edu.
    • J. Surg. Res. 2017 Jun 1; 213: 32-38.

    BackgroundIncreased longevity has led to more nonagenarians undergoing elective surgery. Development of predictive models for hospital readmission may identify patients who benefit from preoperative optimization and postoperative transition of care intervention. Our goal was to identify significant predictors of 30-d readmission in nonagenarians undergoing elective surgery.MethodsNonagenarians undergoing elective surgery from January 2011 to December 2012 were identified using the American College of Surgeons National Surgical Quality Improvement Project participant use data files. This population was randomly divided into a 70% derivation cohort for model development and 30% validation cohort. Using multivariate step-down regression, predictive models were developed for 30-d readmission.ResultsOf 7092 nonagenarians undergoing elective surgery, 798 (11.3%) were readmitted within 30 d. Factors significant in univariate analysis were used to develop predictive models for 30-d readmissions. Diabetes (odds ratio [OR]: 1.51, 95% confidence interval [CI]: 1.24-1.84), dialysis dependence (OR: 2.97, CI: 1.77-4.99), functional status (OR: 1.52, CI: 1.29-1.79), American Society of Anesthesiologists class II or higher (American Society of Anesthesiologist physical status classification system; OR: 1.80, CI: 1.42-2.28), operative time (OR: 1.05, CI: 1.02-1.08), myocardial infarction (OR: 5.17, CI: 3.38-7.90), organ space surgical site infection (OR: 8.63, CI: 4.04-18.4), wound disruption (OR: 14.3, CI: 4.80-42.9), pneumonia (OR: 8.59, CI: 6.17-12.0), urinary tract infection (OR: 3.88, CI: 3.02-4.99), stroke (OR: 6.37, CI: 3.47-11.7), deep venous thrombosis (OR: 5.96, CI: 3.70-9.60), pulmonary embolism (OR: 20.3, CI: 9.7-42.5), and sepsis (OR: 13.1, CI: 8.57-20.1), septic shock (OR: 43.8, CI: 18.2-105.0), were included in the final model. This model had a c-statistic of 0.73, indicating a fair association of predicted probabilities with observed outcomes. However, when applied to the validation cohort, the c-statistic dropped to 0.69, and six variables lost significance.ConclusionsA reliable predictive model for readmission in nonagenarians undergoing elective surgery remains elusive. Investigation into other determinants of surgical outcomes, including social factors and access to skilled home care, might improve model predictability, identify areas for intervention to prevent readmission, and improve quality of care.Copyright © 2017 Elsevier Inc. All rights reserved.

      Pubmed     Full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…

Want more great medical articles?

Keep up to date with a free trial of metajournal, personalized for your practice.
1,694,794 articles already indexed!

We guarantee your privacy. Your email address will not be shared.