Mar 28, 2020 Last Updated 2:10 PM, Oct 30, 2019

DruckenE-Mail Adresse

prognostic accuracy study of the “Selbstpflegeindex” (SPI) and the “Post-acute care discharge score” (PACD) to predict transfer of medical in-patients to a post-acute care facility

6. Sep 2019
6. Sep 2019
HS 3


prognostic accuracy study of the “Selbstpflegeindex” (SPI) and the “Post-acute care discharge score” (PACD) to predict transfer of medical in-patients to a post-acute care facility

Background: Delays in discharge not only cause economic waste at the hospital level, but can also lead to deterioration especially among geriatric patients. However, many of these delays and their concomitant losses may be preventable via focused assessment and stratification, near admission to identify patients requiring transfer to a post-acute care facility. Interprofessional discharge planning is therefore crucial in order to fit an appropriate discharge destination. At one single teaching hospital the PACD and SPI scores and a combination of both predicted transfer to post-acute care facilities, indicating potential as screening instruments to accelerate discharge planning. Objectives: We aim to replicate the previous findings whether PACD, SPI or the combination of PACD and SPI can reliably identify patients requiring transfer to post-acute care facilities to allow generalization. Design and methods: This study is embedded in a pre-post study “In-HospiTOOL” conducted at 7 university, teaching and regional hospitals in urban and rural areas aiming to safely reduce hospital length of stay by implementing an interprofessional discharge management tool. Consecutive medical patients admitted to the hospitals through the emergency department will be included. Exclusion criteria are: transfer from or to another hospital, admission from facility, or in-hospital death. We aim to include a sample of 9000 patients with PACD and SPI assessment in 4 centres during an 18-month period i.e., 1.7.2017 – 28.02.2019. PACD, SPI within 24-48 hours of admission will be documented in patient records as part of discharge planning by physicians, and nurses. Predictors and outcome will be extracted from the clinical and medical coding data base. For modelling we will use logistic regression. To test prognostic accuracy we will plot ROC curves, calculate AUC, likelihood ratios, sensitivity, specificity, and Brier score. To test AUC differences between the scores we will use a nonparametric approach. Statistical analyses will be conducted using Stata Version 15.0. Results: The study started in July 2017. The main expected result will be evidence on the prognostic performance of the combined PACD/SPI score, the PACD or SPI alone and the direct comparison of the different scores regarding their scoring and their suitability for screening purpose.


Conca, A., et al., [OPTIMA - Optimized patient transfer through innovative multidisciplinary assessment: Project description phase I]. Pflegewissenschaft, 2012. 14(5): p. 291-298. Conca, A., et al., Erfassung eines Nachakutpflegebedarf bei hospitalisierten, medizinischen Patienten durch die „Post-Acute Care Discharge scores“ (PACD). Pflegewissenschaft, 2015. 17(11): p. 582-595. Conca, A., et al. (2018). "Prediction of post-acute care demand in medical and neurological inpatients: diagnostic assessment of the post-acute discharge score - a prospective cohort study." BMC Health Serv Res 18(1): 111.

Im Artikelarchiv von hpsmedia nach Antoinette Conca suchen





epaCC GmbH



Logo LEP rgb cmyk 1400pixel



Für diesen Kongress werden im Rahmen der freiwilligen Registrierung 10 Fortbildungspunkte verliehen.
Kursnummer: 20190314190001


logo akk

Für diesen Kongress werden von der Ärztekammer Schleswig-Holstein 9 Fortbildungspunkte der Kategorie B verliehen.
Anerkennungsnummer 201901450