Endovascular Treatment of Ischemic Stroke: Treat the right patient, at the right time, in the right place

The effect of endovascular treatment for ischemic stroke varies between individual patients and is highly time-dependent. The overall aim of this thesis was to increase the benefit of endovascular treatment by optimizing prediction of outcome and treatment effect (Part I), reducing treatment delay (Part II), and improving prehospital triage strategies (Part III)

Effect of Workflow Improvements in Endovascular Stroke Treatment A Systematic Review and Meta-Analysis

Background and Purpose—Rapid initiation of endovascular stroke treatment is associated with better clinical outcome. The effect of specific improvements is not well known. We performed a systematic review and meta-analysis on the effectiveness of specific workflow improvements on time to treatment and outcome. Methods—A random-effects meta-analysis was used to evaluate the difference in mean time to treatment between intervention group and control group. Secondary outcomes included good functional outcome at 90 days (modified Rankin Scale score 0–2). Results—Fifty-one studies (3 randomized controlled trials, 13 prepost intervention studies, and 35 observational studies) with in total 8467 patients were included. Most frequently reported workflow intervention types concerned anesthetic management (n=26), in-hospital patient transfer management (n=14), and prehospital management (n=11). Patients in the intervention group had shorter time to treatment intervals (weighted mean difference, 26 minutes; 95% CI, 19–33; P<0.001) compared with controls. Subgroup meta-analysis of intervention types also showed a shorter time to treatment in the intervention group: a mean difference of 12 minutes (95% CI, 6–17; P<0.001) for anesthetic management, 37 minutes (95% CI, 22–52; P<0.001) for prehospital management, 41 minutes (95% CI, 27–54; P<0.001) for in-hospital patient transfer management, 47 minutes (95% CI, 28–67; P<0.001) for teamwork, and 64 minutes (95% CI, 24–104; P=0.002) for feedback. The mean difference in time to treatment of studies with multiple interventions implemented simultaneously was 50 minutes (95% CI, 31–69; P<0.001) in favor of the intervention group. Patients in the intervention group had increased likelihood of favorable outcome (risk ratio [RR], 1.39; 95% CI, 1.15–1.66; P<0.001). Conclusions—Interventions in the workflow of endovascular stroke treatment lead to a significant reduction in time to treatment and results in an increased likelihood of favorable outcome. Acute stroke care should be reorganized by making use of the examples of workflow interventions described in this review to ensure the best medical care for stroke patients

Letter to the Editor regarding the article: "identifying pre-hospital factors associated with outcome for major trauma patients in a regional trauma network: An exploratory study"

The aim of this Letter to the Editor was to report some methodological shortcomings in a recently published article. Issues regarding missing values and overfitting are mentioned. First, Complete Case (CC) analysis was used instead of an imputation method. Second, there was a high chance of overfitting and lack of model validation. In conclusion, the results of this study should be interpret with caution and further research is necessary

Prehospital Triage Strategies for the Transportation of Suspected Stroke Patients in the United States

BACKGROUND AND PURPOSE: Ischemic stroke patients with large vessel occlusion (LVO) could benefit from direct transportation to an intervention center for endovascular treatment, but non-LVO patients need rapid IV thrombolysis in the nearest center. Our aim was to evaluate prehospital triage strategies for suspected stroke patients in the United States. METHODS: We used a decision tree model and geographic information system to estimate outcome of suspected stroke patients transported by ambulance within 4.5 hours after symptom onset. We compared the following strategies: (1) Always to nearest center, (2) American Heart Association algorithm (ie, directly to intervention center if a prehospital stroke scale suggests LVO and total driving time from scene to intervention center is <30 minutes, provided that the delay would not exclude from thrombolysis), (3) modified algorithms with a maximum additional driving time to the intervention center of <30 minutes, <60 minutes, or without time limit, and (4) always to intervention center. Primary outcome was the annual number of good outcomes, defined as modified Rankin Scale score of 0–2. The preferred strategy was the one that resulted in the best outcomes with an incremental number needed to transport to intervention center (NNTI) <100 to prevent one death or severe disability (modified Rankin Scale score of >2). RESULTS: Nationwide implementation of the American Heart Association algorithm increased the number of good outcomes by 594 (+1.0%) compared with transportation to the nearest center. The associated number of non-LVO patients transported to the intervention center was 16714 (NNTI 28). The modified algorithms yielded an increase of 1013 (+1.8

The volume-outcome relationship for hip fractures: a systematic review and meta-analysis of 2,023,469 patients

Background and purpose — It has been hypothesized that hospitals and surgeons with high caseloads of hip fracture patients have better outcomes, but empirical studies have reported contradictory results. This systematic review and meta-analysis evaluates the volume–outcome relationship among patients with hip fracture patients. Methods — A search of different databases was performed up to February 2018. Selection of relevant studies, data extraction, and critical appraisal of the methodological quality was performed by 2 independent reviewers. A random-effects meta-analysis using studies with comparative cut-offs was performed to estimate the effect of hospital and surgeon volume on outcome, defined as in-hospital mortality and postoperative complications. Results — 24 studies comprising 2,023,469 patients were included. Overall, the quality was reasonable. 11 studies reported better health outcomes in high-volume centers and 2 studies reported better health outcomes in low-volume centers. In the meta-analysis of 11 studies there was a statistically non-significant association between higher hospital volume and both lower in-hospital mortality (adjusted odds ratio (aOR) 0.87, 95% confidence interval (CI) 0.73–1.04) and fewer postoperative complications (aOR 0.87, CI 0.75– 1.02). Four studies on surgeon volume were included in the meta-analysis and showed a minor association between higher surgeon volume and in-hospital mortality (aOR 0.92, CI 0.76–1.12). Interpretation — This systematic review and metaanalysis did not find an evident effect of hospital or surgeon volume on health outcomes. Future research without volume cut-offs is needed to examine whether a true volume–outcome relationship exists

The volume-outcome relationship in severely injured patients: A systematic review and meta-analysis

BACKGROUND The volume-outcome relationship in severely injured patients remains under debate and this has consequences for the designation of trauma centers. OBJECTIVES The aim of this study was to evaluate the relationship between hospital or surgeon volume and health outcomes in severely injured patients. METHODS Six electronic databases were searched from 1980 up to January 30, 2018, to identify studies that describe the relationship between hospital or surgeon volume and health outcomes in severely injured patients (preferably Injury Severity Score above 15). Selection of relevant studies, data extraction, and critical appraisal of the methodological quality were performed by two independent reviewers. Pooled adjusted and unadjusted estimates of the effect of volume on in-hospital mortality, only in study populations with Injury Severity Score greater than 15, were calculated with a random-effects meta-analysis. A mixed effects linear regression model was used to assess hospital volume as continuous parameter. RESULTS Eighteen observational cohort studies were included. The majority (13 [72%] of 18) reported an association between higher hospital or surgeon volume and lower mortality rate. Overall, the quality of the included studies was reasonable, with insufficient adjustment as one of the most common limitations. Eight studies were included in the meta-analysis with a total of 222,418 patients. High hospital volume (>240 admitted severely injured patients per year) was associated with a lower risk of mortality (adjusted odds ratio, 0.85; 95% confidence interval, 0.76-0.94). Four studies were included in the regression model, providing a beta of-0.17 per 10 patients (95% CI,-0.27 to-0.07). There was no clear association between surgeon volume and mortality rates based on three available studies. CONCLUSION Our systematic overview of the literature reveals a modest association between high-volume centers and lower mortality in severely injured patients, suggesting that designation of high-volume centers might improve outcomes among severely injured patients

Clinical and Imaging Determinants of Collateral Status in Patients With Acute Ischemic Stroke in MR CLEAN Trial and Registry

Background and Purpose—Collateral circulation status at baseline is associated with functional outcome after ischemic stroke and effect of endovascular treatment. We aimed to identify clinical and imaging determinants that are associated with collateral grade on baseline computed tomography angiography in patients with acute ischemic stroke due to an anterior circulation large vessel occlusion. Methods—Patients included in the MR CLEAN trial (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands; n=500) and MR CLEAN Registry (n=1488) were studied. Collateral status on baseline computed tomography angiography was scored from 0 (absent) to 3 (good). Multivariable ordinal logistic regression analyses were used to test the association of selected determinants with collateral status. Results—In total, 1988 patients were analyzed. Distribution of the collateral status was as follows: absent (7%, n=123), poor (32%, n=596), moderate (39%, n=735), and good (23%, n=422). Associations for a poor collateral status in a multivariable model existed for age (adjusted common odds ratio, 0.92 per 10 years [95% CI, 0.886–0.98]), male (adjusted common odds ratio, 0.64 [95% CI, 0.53–0.76]), blood glucose level (adjusted common odds ratio, 0.97 [95% CI, 0.95–1.00]), and occlusion of the intracranial segment of the internal carotid artery with occlusion of the terminus (adjusted common odds ratio 0.50 [95% CI, 0.41–0.61]). In contrast to previous studies, we did not find an association between cardiovascular risk factors and collateral status. Conclusions—Older age, male sex, high glucose levels, and intracranial internal carotid artery with occlusion of the terminus occlusions are associated with poor computed tomography angiography collateral grades in patients with acute ischemic stroke eligible for endovascular treatment

National Institutes of Health Stroke Scale: An Alternative Primary Outcome Measure for Trials of Acute Treatment for Ischemic Stroke

Background and Purpose- The modified Rankin Scale (mRS) at 3 months is the most commonly used primary outcome measure in stroke treatment trials, but it lacks specificity and requires long-term follow-up interviews, which consume time and resources. An alternative may be the National Institutes of Health Stroke Scale (NIHSS), early after stroke. Our aim was to evaluate whether the NIHSS assessed within 1 week after treatment could serve as a primary outcome measure for trials of acute treatment for ischemic stroke. Methods- We used data from 2 randomized controlled trials of endovascular treatment for ischemic stroke: the positive MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands; N=500) and the neutral IMS (Interventional Management of Stroke) III trial (N=656). We used a causal mediation model, with linear and ordinal logistic regression adjusted for confounders, to evaluate the NIHSS 24 hours and 5 to 7 days after endovascular treatment as primary outcome measures (instead of the mRS at 3 months) in both trials. Patients who had died before the NIHSS was assessed received the maximum score of 42. NIHSS+1 was then log10-transformed. Results- In both trials, there was a significant correlation between the NIHSS at 24 hours and 5 to 7 days and the mRS. In MR CLEAN, we found a significant effect of endovascular treatment on the mRS and on the NIHSS at 24 hours and 5 to 7 days. After adjustment for NIHSS at 24 hours and 5 to 7 days, the effect of endovascular treatment on the mRS decreased from common odds ratio 1.68 (95% CI, 1.22-2.32) to respectively 1.36 (95% CI, 0.97-1.91) and 1.24 (95% CI, 0.87-1.79), indicating that treatment effect on the mRS is in large part mediated by the NIHSS. In the IMS III trial there was no treatment effect on the NIHSS at 24 hours and 5 to 7 days, corresponding with the absence of a treatment effect on the mRS. Conclusions- The NIHSS within 1 week satisfies the requirements for a surrogate end point and may be used as a primary outcome measure in trials of acute treatment for ischemic stroke, particularly in phase II(b) trials. This could reduce stroke-outcome assessment to its essentials (ie, neurological deficit), and reduce trial duration and costs. Whether and under which conditions it could be used in phase III trials requires a debate in the field with all parties. Clinical Trial Registration- URL: http://www.isrctn.com. Unique identifier: ISRCTN10888758; https://www.clinicaltrials.gov. Unique identifier: NCT00359424

National Institutes of Health Stroke Scale An Alternative Primary Outcome Measure for Trials of Acute Treatment for Ischemic Stroke

Background and Purpose- The modified Rankin Scale (mRS) at 3 months is the most commonly used primary outcome measure in stroke treatment trials, but it lacks specificity and requires long-term follow-up interviews, which consume time and resources. An alternative may be the National Institutes of Health Stroke Scale (NIHSS), early after stroke. Our aim was to evaluate whether the NIHSS assessed within 1 week after treatment could serve as a primary outcome measure for trials of acute treatment for ischemic stroke. Methods- We used data from 2 randomized controlled trials of endovascular treatment for ischemic stroke: the positive MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands; N=500) and the neutral IMS (Interventional Management of Stroke) III trial (N=656). We used a causal mediation model, with linear and ordinal logistic regression adjusted for confounders, to evaluate the NIHSS 24 hours and 5 to 7 days after endovascular treatment as primary outcome measures (instead of the mRS at 3 months) in both trials. Patients who had died before the NIHSS was assessed received the maximum score of 42. NIHSS+1 was then log10-transformed. Results- In both trials, there was a significant correlation between the NIHSS at 24 hours and 5 to 7 days and the mRS. In MR CLEAN, we found a significant effect of endovascular treatment on the mRS and on the NIHSS at 24 hours and 5 to 7 days. After adjustment for NIHSS at 24 hours and 5 to 7 days, the effect of endovascular treatment on the mRS decreased from common odds ratio 1.68 (95% CI, 1.22-2.32) to respectively 1.36 (95% CI, 0.97-1.91) and 1.24 (95% CI, 0.87-1.79), indicating that treatment effect on the mRS is in large part mediated by the NIHSS. In the IMS III trial there was no treatment effect on the NIHSS at 24 hours and 5 to 7 days, corresponding with the absence of a treatment effect on the mRS. Conclusions- The NIHSS within 1 week satisfies the requirements for a surrogate end point and may be used as a primary outcome measure in trials of acute treatment for ischemic stroke, particularly in phase II(b) trials. This could reduce stroke-outcome assessment to its essentials (ie, neurological deficit), and reduce trial duration and costs. Whether and under which conditions it could be used in phase III trials requires a debate in the field with all parties. Clinical Trial Registration- URL: http://www.isrctn.com. Unique identifier: ISRCTN10888758; https://www.clinicaltrials.gov. Unique identifier: NCT00359424.</p

Value-based healthcare in ischemic stroke care: Case-mix adjustment models for clinical and patient-reported outcomes

Background: Patient-Reported Outcome Measures (PROMs) have been proposed for benchmarking health care quality across hospitals, which requires extensive case-mix adjustment. The current study's aim was to develop and compare case-mix models for mortality, a functional outcome, and a patient-reported outcome measure (PROM) in ischemic stroke care. Methods: Data from ischemic stroke patients, admitted to four stroke centers in the Netherlands between 2014 and 2016 with available outcome information (N = 1022), was analyzed. Case-mix adjustment models were developed for mortality, modified Rankin Scale (mRS) scores and EQ-5D index scores with respectively binary logistic, proportional odds and linear regression models with stepwise backward selection. Predictive ability of these models was determined with R-squared (R2) and area-under-The-receiver-operating-characteristic-curve (AUC) statistics. Results: Age, NIHSS score on admission, and heart failure were the only common predictors across all three case-mix adjustment models. Specific predictors for the EQ-5D index score were sex (β = 0.041), socio-economic status (β =-0.019) and nationality (β =-0.074). R2-values for the regression models for mortality (5 predictors), mRS score (9 predictors) and EQ-5D utility score (12 predictors), were respectively R2 = 0.44, R2 = 0.42 and R2 = 0.37. Conclusions: The set of case-mix adjustment variables for the EQ-5D at three months differed considerably from the set for clinical outcomes in stroke care. The case-mix adjustment variables that were specific to this PROM were sex, socio-economic status and nationality. These variables should be considered in future attempts to risk-Adjust for PROMs during benchmarking of hospitals