5,470 research outputs found
Prehospital Management of Traumatic Brain Injury across Europe: A CENTER-TBI Study.
No full textBACKGROUND: Prehospital care for traumatic brain injury (TBI) is important to prevent secondary brain injury. We aim to compare prehospital care systems within Europe and investigate the association of system characteristics with the stability of patients at hospital arrival. METHODS: We studied TBI patients who were transported to CENTER-TBI centers, a pan-European, prospective TBI cohort study, by emergency medical services between 2014 and 2017. The association of demographic factors, injury severity, situational factors, and interventions associated with on-scene time was assessed using linear regression. We used mixed effects models to investigate the case mix adjusted variation between countries in prehospital times and interventions. The case mix adjusted impact of on-scene time and interventions on hypoxia (oxygen saturation <90%) and hypotension (systolic blood pressure <100mmHg) at hospital arrival was analyzed with logistic regression. RESULTS: Among 3878 patients, the greatest driver of longer on-scene time was intubation (+8.3 min, 95% CI: 5.6-11.1). Secondary referral was associated with shorter on-scene time (-5.0 min 95% CI: -6.2- -3.8). Between countries, there was a large variation in response (range: 12-25 min), on-scene (range: 16-36 min) and travel time (range: 15-32 min) and in prehospital interventions. These variations were not explained by patient factors such as conscious level or severity of injury (expected OR between countries: 1.8 for intubation, 1.8 for IV fluids, 2.0 for helicopter). On-scene time was not associated with the regional EMS policy (p= 0.58). Hypotension and/or hypoxia were seen in 180 (6%) and 97 (3%) patients in the overall cohort and in 13% and 7% of patients with severe TBI (GCS <8). The largest association with secondary insults at hospital arrival was with major extracranial injury: the OR was 3.6 (95% CI: 2.6-5.0) for hypotension and 4.4 (95% CI: 2.9-6.7) for hypoxia. DISCUSSION: Hypoxia and hypotension continue to occur in patients who suffer a TBI, and remain relatively common in severe TBI. Substantial variation in prehospital care exists for patients after TBI in Europe, which is only partially explained by patient factors
Between-centre differences and treatment effects in randomized controlled trials: a case study in traumatic brain injury.
No full textBACKGROUND: In Traumatic Brain Injury (TBI), large between-centre differences in outcome exist and many clinicians believe that such differences influence estimation of the treatment effect in randomized controlled trial (RCTs). The aim of this study was to assess the influence of between-centre differences in outcome on the estimated treatment effect in a large RCT in TBI. METHODS: We used data from the MRC CRASH trial on the efficacy of corticosteroid infusion in patients with TBI. We analyzed the effect of the treatment on 14 day mortality with fixed effect logistic regression. Next we used random effects logistic regression with a random intercept to estimate the treatment effect taking into account between-centre differences in outcome. Between-centre differences in outcome were expressed with a 95% range of odds ratios (OR) for centres compared to the average, based on the variance of the random effects (tau2). A random effects logistic regression model with random slopes was used to allow the treatment effect to vary by centre. The variation in treatment effect between the centres was expressed in a 95% range of the estimated treatment ORs. RESULTS: In 9978 patients from 237 centres, 14-day mortality was 19.5%. Mortality was higher in the treatment group (OR = 1.22, p = 0.00010). Using a random effects model showed large between-centre differences in outcome (95% range of centre effects: 0.27- 3.71), but did not substantially change the estimated treatment effect (OR = 1.24, p = 0.00003). There was limited, although statistically significant, between-centre variation in the treatment effect (OR = 1.22, 95% treatment OR range: 1.17-1.26). CONCLUSION: Large between-centre differences in outcome do not necessarily affect the estimated treatment effect in RCTs, in contrast to current beliefs in the clinical area of TBI
Translation and Linguistic Validation of Outcome Instruments for Traumatic Brain Injury Research and Clinical Practice: A Step-by-Step Approach within the Observational CENTER-TBI Study.
No full textAssessing outcomes in multinational studies on traumatic brain injury (TBI) poses major challenges and requires relevant instruments in languages other than English. Of the 19 outcome instruments selected for use in the observational Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, 17 measures lacked translations in at least one target language. To fill this gap, we aimed to develop well-translated linguistically and psychometrically validated instruments. We performed translations and linguistic validations of patient-reported measures (PROMs), clinician-reported (ClinRO), and performance-based (PerfO) outcome instruments, using forward and backward translations, reconciliations, cognitive debriefings with up to 10 participants, iterative revisions, and international harmonization with input from over 150 international collaborators. In total, 237 translations and 211 linguistic validations were carried out in up to 20 languages. Translations were evaluated at the linguistic and cultural level by coding changes when the original versions are compared with subsequent translation steps, using the output of cognitive debriefings, and using comprehension rates. The average comprehension rate per instrument varied from 88% to 98%, indicating a good quality of the translations. These outcome instruments provide a solid basis for future TBI research and clinical practice and allow the aggregation and analysis of data across different countries and languages
Variation in guideline implementation and adherence regarding severe traumatic brain injury treatment: a CENTER-TBI survey study in Europe
No full textGuidelines may reduce practice variation and optimize patient care. We aimed to study differences in guideline use in the management of traumatic brain injury (TBI) patients and analyze reasons for guideline non-adherence. As part of a prospective, observational, multi-center European cohort study, participants from 68 centers in 20 countries were asked to complete 72-item questionnaires regarding their management of severe TBI. Six questions with multiple sub-questions focused on guideline use and implementation. Questionnaires were completed by 65 centers. Of these, 49 (75%) reported use of the Brain Trauma Foundation Guidelines for the medical management of TBI or related institutional protocols, 11 (17%) used no guidelines and 5 used other guidelines (8%). Of 54 centers reporting use of any guidelines, 41 (75%) relied on written guidelines. Four centers of the 54 (7%) reported no formal implementation efforts. Structural attention to the guidelines during daily clinical rounds was reported by 21 centers (38%). The most often reported reasons for non-adherence were ‘every patient is unique’ and the presence of extracranial injuries, both for centers that did and did not report the use of guidelines. There is substantial variability in the use and implementation of guidelines in neurotrauma centers in Europe. Further research is needed to strengthen the evidence underlying guidelines and to overcome implementation barriers
One-year employment outcome prediction after traumatic brain injury:A CENTER-TBI study
No full textBackground: Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work. Objectives: This study aims to make accurate patient-specific predictions on one-year return to work after TBI using machine learning algorithms. Within this process, specific research questions were defined: 1 How can we make accurate predictions on employment outcome, and does this require follow-up data beyond hospitalization? 2 Which predictors are required to make accurate predictions? 3 Are predictions accurate enough for use in clinical practice? Methods: This study used the core CENTER-TBI observational cohort dataset, collected across 18 European countries between 2014 and 2017. Hospitalized patients with sufficient follow-up data were selected for the current analysis (N = 586). Data regarding hospital stay and follow-up until three months post-injury were used to predict return to work after one year. Three distinct algorithms were used to predict employment outcomes: elastic net logistic regression, random forest and gradient boosting. Finally, a reduced model and corresponding ROC-curve was created. Results: Full models without follow-up achieved an area under the curve (AUC) of about 81 %, which increased up to 88 % with follow-up data. A reduced model with five predictors achieved similar results with an AUC of 90 %. Conclusion: The addition of three-month follow-up data causes a notable increase in model performance. The reduced model - containing Glasgow Outcome Scale Extended, pre-injury job class, pre-injury employment status, length of stay and age - matched the predictive performance of the full models. Accurate predictions on post-TBI vocational outcomes contribute to realistic prognosis and goal setting, targeting the right interventions to the right patients
Prehospital Management of Traumatic Brain Injury across Europe: A CENTER-TBI Study
No full textBackground Prehospital care for traumatic brain injury (TBI) is important to prevent secondary brain injury. We aim to compare prehospital care systems within Europe and investigate the association of system characteristics with the stability of patients at hospital arrival. Methods We studied TBI patients who were transported to CENTER-TBI centers, a pan-European, prospective TBI cohort study, by emergency medical services between 2014 and 2017. The association of demographic factors, injury severity, situational factors, and interventions associated with on-scene time was assessed using linear regression. We used mixed effects models to investigate the case mix adjusted variation between countries in prehospital times and interventions. The case mix adjusted impact of on-scene time and interventions on hypoxia (oxygen saturation Results Among 3878 patients, the greatest driver of longer on-scene time was intubation (+8.3 min, 95% CI: 5.6-11.1). Secondary referral was associated with shorter on-scene time (-5.0 min 95% CI: -6.2- -3.8). Between countries, there was a large variation in response (range: 12-25 min), on-scene (range: 16-36 min) and travel time (range: 15-32 min) and in prehospital interventions. These variations were not explained by patient factors such as conscious level or severity of injury (expected OR between countries: 1.8 for intubation, 1.8 for IV fluids, 2.0 for helicopter). On-scene time was not associated with the regional EMS policy (p= 0.58). Hypotension and/or hypoxia were seen in 180 (6%) and 97 (3%) patients in the overall cohort and in 13% and 7% of patients with severe TBI (GCS Discussion Hypoxia and hypotension continue to occur in patients who suffer a TBI, and remain relatively common in severe TBI. Substantial variation in prehospital care exists for patients after TBI in Europe, which is only partially explained by patient factors.</div
The added value of ordinal analysis in clinical trials: an example in traumatic brain injury.
No full textINTRODUCTION: In clinical trials, ordinal outcome measures are often dichotomized into two categories. In traumatic brain injury (TBI) the 5-point Glasgow outcome scale (GOS) is collapsed into unfavourable versus favourable outcome. Simulation studies have shown that exploiting the ordinal nature of the GOS increases chances of detecting treatment effects. The objective of this study is to quantify the benefits of ordinal analysis in the real-life situation of a large TBI trial. METHODS: We used data from the CRASH trial that investigated the efficacy of corticosteroids in TBI patients (n = 9,554). We applied two techniques for ordinal analysis: proportional odds analysis and the sliding dichotomy approach, where the GOS is dichotomized at different cut-offs according to baseline prognostic risk. These approaches were compared to dichotomous analysis. The information density in each analysis was indicated by a Wald statistic. All analyses were adjusted for baseline characteristics. RESULTS: Dichotomous analysis of the six-month GOS showed a non-significant treatment effect (OR = 1.09, 95% CI 0.98 to 1.21, P = 0.096). Ordinal analysis with proportional odds regression or sliding dichotomy showed highly statistically significant treatment effects (OR 1.15, 95% CI 1.06 to 1.25, P = 0.0007 and 1.19, 95% CI 1.08 to 1.30, P = 0.0002), with 2.05-fold and 2.56-fold higher information density compared to the dichotomous approach respectively. CONCLUSIONS: Analysis of the CRASH trial data confirmed that ordinal analysis of outcome substantially increases statistical power. We expect these results to hold for other fields of critical care medicine that use ordinal outcome measures and recommend that future trials adopt ordinal analyses. This will permit detection of smaller treatment effects
Development of prognostic models for Health-Related Quality of Life following traumatic brain injury
No full textBackground Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R-2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R-2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R-2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.Development and application of statistical models for medical scientific researchAnalysis and support of clinical decision makin
Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
No full textObjective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. (C) 2020 The Authors. Published by Elsevier Inc.Peer reviewe
Prognosis in traumatic brain injury
No full textIntroduction:
The
general
purpose
of
this
thesis
was
to
study
prognosis
in
traumatic
brain
injury
(TBI)
patients,
with
the
aim
of
providing
useful
and
practical
information
in
clinical
practice
and
clinical
research.
The
specific
objectives
were:
to
develop
and
validate
practical
prognostic
models
for
TBI
patients
and
to
assess
the
validity
of
the
Modified
Oxford
Handicap
Scale
(mOHS)
for
predicting
disability
at
six
months.
Methods:
A
survey
was
first
conducted
to
understand
the
importance
of
prognostic
information
among
physicians.
A
systematic
review
of
prognostic
models
for
TBI
patients
was
then
carried
out.
Prognostic
models
were
developed
using
data
from
a
cohort
of
10,008
TBI
patients
(CRASH
trial)
and
validated
in
a
cohort
of
8,509
TBI
patients
(IMPACT
study).
Two
focus
groups
and
a
survey
were
conducted
to
develop
a
paper-based
prognostic
score
card.
The
correlation
between
the
mOHS
and
the
Glasgow
Outcome
Scale
(GOS)
was
assessed,
the
validity
of
different
mOHS
dichotomies
was
assessed,
and
the
discriminative
ability
of
the
mOHS
to
predict
GOS
was
evaluated.
Results:
Doctors
considered
prognostic
information
to
be
very important
in
the
clinical
management
of
TBI
patients,
and
believed
that
an
accurate
prognostic
model
would
change
their
current
clinical
practice.
Many
prognostic
models
for
TBI
have
been
published,
but
they
have
many
methodological
flaws
which
limit
their
validity.
Valid
prognostic
models
for
patients
from
high
income
countries
and
low
&
middle
income
.countries
were
developed
and
made
available
as
a
web
calculator,
and
as
a
paper
based
score
card.
The
mOHS
was
strongly
correlated
with
and
was
predictive
of
GOS
at
six
months.
Conclusion:
The
prognostic
models
developed
are
valid
and
practical
to
use
in
the
clinical
setting.
The
association
between
mOHS
and
GOS
suggest
that
the
mOHS
could
be
used
for
interim
analysis
in
randomised
clinical
trials
in
TBI
patients,
for
dealing
with
loss
to
follow-up,
or
could
be
used
as
simple
tool
to
inform
patients
and
relatives
about
their
prognosis
at
hospital
discharg
- …
