Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2019 Jul 24;14(7):e0219767.
doi: 10.1371/journal.pone.0219767. eCollection 2019.

Prognostic value for mortality of the new FADOI-COMPLIMED score(s) in patients hospitalized in medical wards

Roberto Nardi  1 , Carlo Nozzoli  2 , Franco Berti  3 , Erminio Bonizzoni  4 , Leonardo M Fabbri  5 , Stefania Frasson  6 , Maurizia Gambacorta  7 , Marilisa Martini  8 , Antonino Mazzone  9 , Carlo Lorenzo Muzzulini  10 , Alessandro Nobili  11 , Mauro Campanini  12
Affiliations
Observational Study

Prognostic value for mortality of the new FADOI-COMPLIMED score(s) in patients hospitalized in medical wards

Roberto Nardi et al. PLoS One. .

Abstract

Background: Recently we defined a user-friendly tool (FADOI-COMPLIMED scores-FCS) to assess complexity of patients hospitalized in medical wards. FCS-1 is an average between the Barthel Index and the Exton-Smith score, while FCS-2 is obtained by using the Charlson score. The aim of this paper is to assess the ability of the FCS to predict mortality in-hospital and after 1-3-6-12-months. In this perspective, we performed comparisons with the validated Multidimensional Prognostic Index (MPI).

Methods: It is a multicenter, prospective observational study, enrolling patients aged over 40, suffering from at least two chronic diseases and consecutively admitted to Internal Medicine departments. For each patient, data from 13 questionnaires were collected. Survival follow-up was conducted at 1-3-6-12 months after discharge. The relationships between cumulative incidences of death with FCS were investigated with logistic regression analyses. ROC curve analyses were performed in order to compare the predictiveness of the logistic models based on FCS with respect to those with MPI taken as reference.

Results: A cohort of 541 patients was evaluated. A 10-point higher value for FCS-1 and FCS-2 leads to an increased risk of 1-year death equal to 25.0% and 27.1%, respectively. In case of in-hospital mortality, the relevant percentages were 63.1% and 15.3%. The logistic model based on FCS is significantly more predictive than the model based on MPI (which requires an almost doubled number of items) for all the time-points considered.

Conclusions: Assessment of prognosis of patients has the potential to guide clinical decision-making and lead to better care. We propose a new, efficient and easy-to-use instrument based on FCS, which demonstrated a good predictive power for mortality in patients hospitalized in medical wards. This tool may be of interest for clinical practice, since it well balances feasibility (requiring the compilation of 34 items, taking around 10 minutes) and performance.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Radar plot displaying the coefficients of 1st and 2nd principal components derived by means of the principal component analysis.
The values shown in the Radar Plot correspond to the weight (importance) assigned to each questionnaire in the calculation of the two principal components [15].
Fig 2
Fig 2. Surface plot showing the probability of in-hospital mortality (Fig 2a) and 1-year mortality (Fig 2b) as a function of FADOI-COMPLIMED score(s).
Fig 3
Fig 3. ROC curves for the MPI and FADOI-COMPLIMED score(s) predicting in-hospital mortality (Fig 3a) and 1-year mortality (Fig 3b), respectively.
See this image and copyright information in PMC

References

    1. Yourman LC, Lee SJ, Schonberg MA, Widera EW, Smith AK. Prognostic indices for older adults: a systematic review. JAMA 2012; 307: 182–192 10.1001/jama.2011.1966 - DOI - PMC - PubMed
    1. Cristakis NA, Iwashina TJ. Attitude and self-reported practice regarding prognostication in a national sample of internists. Arch Intern Med 1998; 158: 2389–2395 - PubMed
    1. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty and comorbidity implications for improved targeting and care. J Gerontol A Biol Sci Med Sci 2004; 59: 255–263 10.1093/gerona/59.3.m255 - DOI - PubMed
    1. Boyd CM, McNabney MK, Brandt N, Correa-de-Araujuo R, Daniel KM et al. AGS Expert Panel on the Care of Older Adults with Multimorbidity, Patient-Centered Care for Older Adults with Multiple Chronic Conditions: A Stepwise Approach from the American Geriatrics Society, American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity. JAGS 2012; 60: 1957–1968 - PMC - PubMed
    1. Marengoni A, Bonometti F, Nobili A, Tettamanti M, Salerno F, Corrao S, et al. In-hospital death and adverse clinical events in elderly patients according to disease clustering: the REPOSI study. Rejuvenation Res 2010; 13: 469–477 10.1089/rej.2009.1002 - DOI - PubMed

Publication types