data: article: 1 row where where ISSN contains "2044-5415" and funder_dois = "["10.13039/501100000272"]" sorted by author

1 row where ISSN contains "2044-5415" and funder_dois = "["10.13039/501100000272"]" sorted by author_number

descending

Link	rowid	title	DOI	URL	created	subject	references-count	is-referenced-by-count	ISSN	container-title	abstract	author_number ▼	orcids	names	award_numbers	funder_names	funder_dois
32	32	["Medication-related harm in older adults following hospital discharge: development and validation of a prediction tool"]	10.1136/bmjqs-2019-009587	http://dx.doi.org/10.1136/bmjqs-2019-009587	2019-09-16T21:15:37Z	[]	94	2	["2044-5415", "2044-5423"]	BMJ Quality & Safety	<jats:sec><jats:title>Objectives</jats:title><jats:p>To develop and validate a tool to predict the risk of an older adult experiencing medication-related harm (MRH) requiring healthcare use following hospital discharge.</jats:p></jats:sec><jats:sec><jats:title>Design, setting, participants</jats:title><jats:p>Multicentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015.</jats:p></jats:sec><jats:sec><jats:title>Primary outcome measure</jats:title><jats:p>Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non-adherence, harm from medication error). Three data sources provided MRH and healthcare use information: hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases (‘possible’ and ‘probable’) were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced ‘definite’ MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool’s discrimination C-statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:ti…	10	["http://orcid.org/0000-0002-9988-3717"]	["Nikesh Parekh", "Khalid Ali", "John Graham Davies", "Jennifer M Stevenson", "Winston Banya", "Stephen Nyangoma", "Rebekah Schiff", "Tischa van der Cammen", "Jatinder Harchowal", "Chakravarthi Rajkumar"]	["PB-PG-0711-25094"]	["National Institute for Health Research"]	["10.13039/501100000272"]

Advanced export

JSON shape: default, array, newline-delimited

CREATE TABLE [article] (
   [title] TEXT,
   [DOI] TEXT,
   [URL] TEXT,
   [created] TEXT,
   [subject] TEXT,
   [references-count] TEXT,
   [is-referenced-by-count] TEXT,
   [ISSN] TEXT,
   [container-title] TEXT,
   [abstract] TEXT,
   [author_number] TEXT,
   [orcids] TEXT,
   [names] TEXT,
   [award_numbers] TEXT,
   [funder_names] TEXT,
   [funder_dois] TEXT
);