Methods
Study design and
population
The present study was a post-hoc analysis of the MedBridge trial
[11,12]. The trial was conducted from February 2017 until October
2018 at eight wards within four hospitals in Sweden: Uppsala University
Hospital and the hospitals in Enköping, Gävle and Västerås. The wards
differed in terms of medical specialty: internal medicine (three wards),
stroke and neurology (two wards), acute internal medicine, diabetes and
nephrology, and geriatrics. The
trial population (n=2,637; median age 81 years; median number of
medications 9) was used to identify risk factors for drug-related
readmissions. To assess
preventability, Microsoft Excel was used to randomly select a sample of
400 patients from among all trial participants, stratified by county
(hospital): 200 from Uppsala County (Uppsala and Enköping), 100 from
Gävleborg County (Gävle) and 100 from Västmanland County (Västerås). We
aimed for a representative sample, but no formal sample size calculation
was performed.
Outcomes, data collection and
assessment
Baseline (index admission) and outcome data were extracted from the
patients’ electronic health records (EHRs) and the counties’ healthcare
registries. The primary outcome for risk factor analysis was
experiencing a possibly drug-related readmission within 12 months after
hospital discharge from the index admission. In the MedBridge trial, all
participants’ unplanned hospital readmissions were assessed by a pair of
final-year pharmacy students with a validated tool to identify
readmissions that were possible drug-related or unlikely to be
drug-related (AT-HARM10, [13]). In case of doubt, an experienced
clinical pharmacist was available to cast a deciding vote. In a
validation study, the tool’s inter-rater reliability was moderate to
substantial (Cohen’s kappa values within pairs were between 0.45 and
0.75 and Fleiss’ kappa values between pairs were between 0.46 and 0.58
[13]). Sensitivity, specificity and positive and negative predictive
values were between 70% and 86%. In the present study, all possibly
drug-related readmissions were used as the primary outcome. Secondary
outcomes were all-cause unplanned hospital readmissions and all-cause ED
visits.
The assessment of preventability
of drug-related revisits followed a stepwise approach:
- Step 1: All ED visits of the 400 participants within 12 months were
assessed with AT-HARM10 by a final-year pharmacy student (CJ) and a
clinical pharmacist (AH), in addition to the previously assessed
hospital admissions (drug-related ED visits were not an outcome in the
MedBridge trial and were therefore not previously assessed). ED visits
that were followed by a hospital admission within four hours were
considered part of the admission and therefore not assessed
separately.
- Step 2: All possibly drug-related revisits of the 400
participants were assessed by an expert panel of either an experienced
clinical pharmacist and senior researcher (UG for all hospitals) and
an experienced geriatrician (KF for Uppsala, Enköping and Västerås) or
a second clinical pharmacist and researcher (TK, Gävle). The expert
panel had full access to the patients’ EHRs, containing information
from both hospital and primary care within each county, and applied
the amended Hallas criteria for causality and the Hepler criteria for
preventability, as proposed by Howard et al. [14]. For a
drug-related revisit to be classified as potentially preventable, its
cause had to be identifiable with reasonable probability (probably or
definitely for causality), reasonably foreseeable and controllable
within the context and objectives of treatment (detailed description
in Supporting Information S1). A one-sentence explanation of the cause
was given by the expert panel.
- Step 3: Further data collection for all potentially preventable
drug-related revisits was performed by a postgraduate clinical
pharmacy student (ME, Uppsala and Enköping) and one of two clinical
pharmacists (AH, Västerås, or JS, Gävle) under the supervision of two
researchers (UG and TK) with full access to the patients’ EHRs. This
data collection included (detailed description in Supporting
Information S1): 1) the main
disease related to the preventable revisit; 2) the cause, with a
classification inspired by the five causes of drug-related morbidity
proposed by Hepler and Strand [15] and reformulated based on the
causes described by the expert panel; 3) the perceived origin of the
cause in healthcare (hospital care or primary care); and 4) whether
the revisit could reasonably have been prevented or was caused by
actions related to the interventions (i.e., medication reviews and
follow-up calls) performed in the MedBridge trial.
Statistical data analysis
Categorical variables were summarised as frequencies and percentages.
Numerical variables were summarised as mean, median, standard deviation
and quartile. To investigate differences in baseline characteristics
(potential risk factors) for each primary (drug-related readmission) and
secondary outcome (all-cause readmission and all-cause ED visit),
categorical baseline variables were compared using the χ² test and
continuous variables using the Wilcoxon non-parametric test. Baseline
characteristics included were sociodemographics, unplanned hospital
visits within 12 months prior to admission, diagnoses in medical
history, medication use, estimated glomerular filtration rate (eGFR)
upon admission, length of hospital stay and discharge diagnosis (full
list of variables in Supporting Information S2). In order to a test for
multicollinearity, the Cramer’s V correlation and Point-Biserial
correlation were calculated. Highly correlated variables were not used
in the same model.
A multivariate Cox proportional
hazards model was developed for each primary and secondary outcome, with
adjustment for MedBridge trial treatment group. All baseline
characteristics that were significant in the univariate test were
initially included. All non-significant variables were then removed from
the multivariate model in a stepwise way, starting with the least
significant, until only significant characteristics remained. Hazard
ratios (HRs) and 95% confidence intervals (CIs) were calculated. The
underlying proportional hazards assumptions of the Cox proportional
hazards models were verified by visual inspection of Schoenfeld
residuals. Significance was specified as p < 0.05. All
statistical analyses were performed with R version 3.5.1 (R Foundation
for Statistical Computing, Vienna, Austria). For preventability of
drug-related visits, descriptive statistics were analysed with Microsoft
Excel.