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A prediction model for adverse outcome in hospitalized patients with diabetes.

ABSTRACT: OBJECTIVE: There are no formal prognostic models predicting adverse outcomes (excessive length of stay or mortality) in hospitalized patients with diabetes. In this study, we aimed to develop a prediction model that will help identify patients with diabetes who are most likely to have an adverse event during their hospital stay. RESEARCH DESIGN AND METHODS: Analysis was based on 25,118 admissions with diabetes to University Hospital Birmingham, Birmingham, U.K., over 4 years (2007-2010). Adverse events are defined as either excessive length of stay or inpatient mortality. Key predictors were variables that are often available in the first 72 h of admission and included demographic characteristics, clinical pathological test results, and use of insulin. Models were constructed using logistic regression, discrimination and calibration was assessed, and internal validation was carried out. RESULTS: The model performed well with an area under the curve (AUC) of 0.802 with only a mild reduction being noted in the internal validation (AUC 0.798). At a cutoff value of 25% probability of having an adverse outcome the sensitivity was 76%, specificity was 70%, and the positive predictive value was 49%. If it is used for a case-finding approach limiting to noncritical care settings, then at the same cutoff value, two-thirds (sensitivity 69%) of the admissions with adverse outcomes could potentially be identified. CONCLUSIONS: Once externally validated, we suggest that our model will be a useful tool for identifying diabetic patients who are at risk for poor outcomes when admitted to hospital.

SUBMITTER: Nirantharakumar K

PROVIDER: S-EPMC3816890 | biostudies-other | 2013 Nov

REPOSITORIES: biostudies-other

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A prediction model for adverse outcome in hospitalized patients with diabetes.

Nirantharakumar Krishnarajah K Hemming Karla K Narendran Parth P Marshall Tom T Coleman Jamie J JJ

Diabetes care 20130911 11

<h4>Objective</h4>There are no formal prognostic models predicting adverse outcomes (excessive length of stay or mortality) in hospitalized patients with diabetes. In this study, we aimed to develop a prediction model that will help identify patients with diabetes who are most likely to have an adverse event during their hospital stay.<h4>Research design and methods</h4>Analysis was based on 25,118 admissions with diabetes to University Hospital Birmingham, Birmingham, U.K., over 4 years (2007-2 ...[more]

PMID: 24026555

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Project description:Background:The purpose of this study was to examine the prevalence and effects of current smoking on adverse outcomes among hospitalized COVID-19 patients.Methods:A systematic review of the literature (PubMed) identified 18 (from a total of 1398) relevant studies. Pooled current smoking prevalence was compared with the gender-adjusted and gender and age-adjusted, population-based expected prevalence by calculating prevalence odds ratio (POR). The association between current, compared with non-current and former, smoking and adverse outcome was examined. A secondary analysis was performed by including 12 pre-publications (30 studies in total). All analyses were performed using random-effects meta-analysis.Results:Among 6515 patients, the pooled prevalence of current smoking was 6.8% [95% confidence interval (CI): 4.8–9.1%]. The gender-adjusted POR was 0.20 (95% CI: 0.16–0.25, p?<?0.001), and the gender and age-adjusted POR was 0.24 (95% CI: 0.19–0.30, p?<?0.001). Current smokers were more likely to have an adverse outcome compared with non-current smokers [odds ratio (OR): 1.53, 95%CI: 1.06–2.20, p?=?0.022] but less likely compared with former smokers (OR: 0.42, 95% CI: 0.27–0.74, p?=?0.003). When pre-publications were added (n?=?10,631), the gender-adjusted POR was 0.27 (95% CI: 0.19–0.38, p?<?0.001) and the gender and age-adjusted POR was 0.34 (95% CI: 0.24–0.48, p?<?0.001).Conclusion:This meta-analysis of retrospective observational case series found an unexpectedly low prevalence of current smoking among hospitalized patients with COVID-19. Hospitalized current smokers had higher odds compared with non-current smokers but lower odds compared with former smokers for an adverse outcome. Smoking cannot be considered a protective measure for COVID-19. However, the hypothesis that nicotine may have a protective effect in COVID-19 that is partially masked by smoking-related toxicity and by the abrupt cessation of nicotine intake when smokers are hospitalized should be explored in laboratory studies and clinical trials using pharmaceutical nicotine products.

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A prediction model for adverse outcome in hospitalized patients with diabetes.

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A prediction model for adverse outcome in hospitalized patients with diabetes.

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