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  1. Article ; Online: Intraoperative dexmedetomidine to prevent postoperative delirium: in search of the magic bullet.

    Donovan, Anne L / Whitlock, Elizabeth L

    Canadian journal of anaesthesia = Journal canadien d'anesthesie

    2019  Volume 66, Issue 4, Page(s) 365–370

    Title translation À la recherche du remède miracle : administration peropératoire de dexmédétomidine pour prévenir le delirium postopératoire.
    MeSH term(s) Delirium ; Dexmedetomidine ; Emergence Delirium ; Humans ; Hypnotics and Sedatives ; Thoracic Surgery
    Chemical Substances Hypnotics and Sedatives ; Dexmedetomidine (67VB76HONO)
    Language English
    Publishing date 2019-01-28
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 91002-8
    ISSN 1496-8975 ; 0832-610X
    ISSN (online) 1496-8975
    ISSN 0832-610X
    DOI 10.1007/s12630-019-01300-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Geriatric Physiology and the Frailty Syndrome.

    Khan, Kashif T / Hemati, Kaveh / Donovan, Anne L

    Anesthesiology clinics

    2019  Volume 37, Issue 3, Page(s) 453–474

    Abstract: Understanding geriatric physiology is critical for successful perioperative management of older surgical patients. The frailty syndrome is evolving as an important, potentially modifiable process capturing a patient's biologic age and is more predictive ... ...

    Abstract Understanding geriatric physiology is critical for successful perioperative management of older surgical patients. The frailty syndrome is evolving as an important, potentially modifiable process capturing a patient's biologic age and is more predictive of adverse perioperative outcomes than chronologic age. Use of frailty in risk stratification and perioperative decision-making allows providers to effectively diagnose, risk stratify, and treat patients in the perioperative setting. Further study is needed to develop a universal definition of frailty, to identify comprehensive yet feasible screening tools that allow for accurate detection of frailty in the perioperative setting, and to refine treatment programs for frail surgical patients.
    MeSH term(s) Advance Directives ; Aged ; Aged, 80 and over ; Aging/pathology ; Clinical Decision-Making ; Frail Elderly ; Frailty/diagnosis ; Frailty/physiopathology ; General Surgery ; Geriatric Assessment ; Humans
    Language English
    Publishing date 2019-06-18
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2228899-5
    ISSN 2210-3538 ; 1932-2275 ; 0889-8537
    ISSN (online) 2210-3538
    ISSN 1932-2275 ; 0889-8537
    DOI 10.1016/j.anclin.2019.04.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Postoperative delirium: why, what, and how to confront it at your institution.

    Curtis, Michael S / Forman, Nell A / Donovan, Anne L / Whitlock, Elizabeth L

    Current opinion in anaesthesiology

    2020  Volume 33, Issue 5, Page(s) 668–673

    Abstract: Purpose of review: The current article reviews the importance of postoperative delirium (POD), focusing on the older surgical population, and summarizes the best-practice guidelines about POD prevention and treatment which have been published within the ...

    Abstract Purpose of review: The current article reviews the importance of postoperative delirium (POD), focusing on the older surgical population, and summarizes the best-practice guidelines about POD prevention and treatment which have been published within the last several years. We also describe our local experience with implementing a perioperative delirium risk stratification and prevention pathway, and review implementation science principles which others may find useful as they move toward risk stratification and prevention in their own institutions.
    Recent findings: There are few areas of consensus, backed by strong experimental data, in POD best-practice guidelines. Most guidelines recommend preoperative cognitive screening, nonpharmacologic delirium prevention measures, and avoidance of deliriogenic medications. The field of implementation science offers strategies for closing the evidence-practice gap, which we supplement with lessons learned from our own experience implementing a perioperative delirium risk stratification and prevention pathway.
    Summary: POD continues to be a serious perioperative complication commonly experienced by older adults. Growing appreciation of its prognostic implications and evidence behind multidisciplinary, collaborative, and focused prevention strategies rooted in implementation science have prompted several major groups to issue consensus guidelines. Adopting best practices POD risk stratification and prevention pathways will improve perioperative care for older adults.
    MeSH term(s) Aged ; Delirium/prevention & control ; Humans ; Perioperative Care ; Postoperative Complications
    Language English
    Publishing date 2020-08-11
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 645203-6
    ISSN 1473-6500 ; 0952-7907
    ISSN (online) 1473-6500
    ISSN 0952-7907
    DOI 10.1097/ACO.0000000000000907
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

    Bishara, Andrew / Chiu, Catherine / Whitlock, Elizabeth L / Douglas, Vanja C / Lee, Sei / Butte, Atul J / Leung, Jacqueline M / Donovan, Anne L

    BMC anesthesiology

    2022  Volume 22, Issue 1, Page(s) 8

    Abstract: Background: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data ...

    Abstract Background: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.
    Methods: This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models ("clinician-guided" and "ML hybrid"), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded.
    Results: POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816-0.863] and for XGBoost was 0.851 [95% CI 0.827-0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734-0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800-0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713-0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk.
    Conclusion: Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.
    MeSH term(s) Aged ; Cohort Studies ; Delirium/diagnosis ; Electronic Health Records/statistics & numerical data ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Postoperative Complications/diagnosis ; Predictive Value of Tests ; Preoperative Period ; Reproducibility of Results ; Retrospective Studies
    Language English
    Publishing date 2022-01-03
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2091252-3
    ISSN 1471-2253 ; 1471-2253
    ISSN (online) 1471-2253
    ISSN 1471-2253
    DOI 10.1186/s12871-021-01543-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data.

    Jing, Bocheng / Boscardin, W John / Deardorff, W James / Jeon, Sun Young / Lee, Alexandra K / Donovan, Anne L / Lee, Sei J

    Medical care

    2022  Volume 60, Issue 6, Page(s) 470–479

    Abstract: Background: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods.: Objective: The objective of this study was to compare machine learning and ... ...

    Abstract Background: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods.
    Objective: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data.
    Design: This was a cohort study.
    Setting: Veterans Affairs (VA) EHR data.
    Participants: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each).
    Measurements and analytic methods: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models.
    Results: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835-0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics.
    Limitation: Our results should be confirmed in non-VA EHRs.
    Conclusion: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.
    MeSH term(s) Cohort Studies ; Electronic Health Records ; Female ; Humans ; Machine Learning ; Male ; Regression Analysis ; Veterans
    Language English
    Publishing date 2022-03-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 411646-x
    ISSN 1537-1948 ; 0025-7079
    ISSN (online) 1537-1948
    ISSN 0025-7079
    DOI 10.1097/MLR.0000000000001720
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: An Implementation-Effectiveness Study of a Perioperative Delirium Prevention Initiative for Older Adults.

    Donovan, Anne L / Braehler, Matthias R / Robinowitz, David L / Lazar, Ann A / Finlayson, Emily / Rogers, Stephanie / Douglas, Vanja C / Whitlock, Elizabeth L

    Anesthesia and analgesia

    2020  Volume 131, Issue 6, Page(s) 1911–1922

    Abstract: Background: Postoperative delirium is a common and serious problem for older adults. To better align local practices with delirium prevention consensus guidelines, we implemented a 5-component intervention followed by a quality improvement (QI) project ... ...

    Abstract Background: Postoperative delirium is a common and serious problem for older adults. To better align local practices with delirium prevention consensus guidelines, we implemented a 5-component intervention followed by a quality improvement (QI) project at our institution.
    Methods: This hybrid implementation-effectiveness study took place at 2 adult hospitals within a tertiary care academic health care system. We implemented a 5-component intervention: preoperative delirium risk stratification, multidisciplinary education, written memory aids, delirium prevention postanesthesia care unit (PACU) orderset, and electronic health record enhancements between December 1, 2017 and June 30, 2018. This was followed by a department-wide QI project to increase uptake of the intervention from July 1, 2018 to June 30, 2019. We tracked process outcomes during the QI period, including frequency of preoperative delirium risk screening, percentage of "high-risk" screens, and frequency of appropriate PACU orderset use. We measured practice change after the interventions using interrupted time series analysis of perioperative medication prescribing practices during baseline (December 1, 2016 to November 30, 2017), intervention (December 1, 2017 to June 30, 2018), and QI (July 1, 2018 to June 30, 2019) periods. Participants were consecutive older patients (≥65 years of age) who underwent surgery during the above timeframes and received care in the PACU, compared to a concurrent control group <65 years of age. The a priori primary outcome was a composite of perioperative American Geriatrics Society Beers Criteria for Potentially Inappropriate Medication Use (Beers PIM) medications. The secondary outcome, delirium incidence, was measured in the subset of older patients who were admitted to the hospital for at least 1 night.
    Results: During the 12-month QI period, preoperative delirium risk stratification improved from 67% (714 of 1068 patients) in month 1 to 83% in month 12 (776 of 931 patients). Forty percent of patients were stratified as "high risk" during the 12-month period (4246 of 10,494 patients). Appropriate PACU orderset use in high-risk patients increased from 19% in month 1 to 85% in month 12. We analyzed medication use in 7212, 4416, and 8311 PACU care episodes during the baseline, intervention, and QI periods, respectively. Beers PIM administration decreased from 33% to 27% to 23% during the 3 time periods, with adjusted odds ratio (aOR) 0.97 (95% confidence interval [CI], 0.95-0.998; P = .03) per month during the QI period in comparison to baseline. Delirium incidence was 7.5%, 9.2%, and 8.5% during the 3 time periods with aOR of delirium of 0.98 (95% CI, 0.91-1.05, P = .52) per month during the QI period in comparison to baseline.
    Conclusions: A perioperative delirium prevention intervention was associated with reduced administration of Beers PIMs to older adults.
    MeSH term(s) Aged ; Electronic Health Records/standards ; Emergence Delirium/etiology ; Emergence Delirium/prevention & control ; Female ; Humans ; Male ; Perioperative Care/methods ; Perioperative Care/standards ; Practice Guidelines as Topic/standards ; Treatment Outcome
    Language English
    Publishing date 2020-11-17
    Publishing country United States
    Document type Journal Article ; Multicenter Study ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000005223
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Derivation, Validation, Sustained Performance, and Clinical Impact of an Electronic Medical Record-Based Perioperative Delirium Risk Stratification Tool.

    Whitlock, Elizabeth L / Braehler, Matthias R / Kaplan, Jennifer A / Finlayson, Emily / Rogers, Stephanie E / Douglas, Vanja / Donovan, Anne L

    Anesthesia and analgesia

    2020  Volume 131, Issue 6, Page(s) 1901–1910

    Abstract: Background: Postoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, ...

    Abstract Background: Postoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.
    Methods: The risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.
    Results: The logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.
    Conclusions: The AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.
    MeSH term(s) Adult ; Aged ; Cohort Studies ; Electronic Health Records/standards ; Electronic Health Records/trends ; Emergence Delirium/diagnosis ; Emergence Delirium/etiology ; Emergence Delirium/prevention & control ; Female ; Humans ; Male ; Middle Aged ; Perioperative Care/standards ; Perioperative Care/trends ; Reproducibility of Results ; Treatment Outcome
    Language English
    Publishing date 2020-11-17
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000005085
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Prediction is very difficult, especially about the future-Niels Bohr.

    Donovan, Anne L / Gropper, Michael A

    Critical care medicine

    2011  Volume 39, Issue 8, Page(s) 2005–2007

    MeSH term(s) Biomarkers/blood ; Critical Care/methods ; Critical Illness/mortality ; Erythrocyte Indices/physiology ; Erythrocyte Volume ; Female ; Hospital Mortality/trends ; Humans ; Intensive Care Units ; Male ; Predictive Value of Tests ; Sensitivity and Specificity ; Survival Rate
    Chemical Substances Biomarkers
    Language English
    Publishing date 2011-08
    Publishing country United States
    Document type Comment ; Comparative Study ; Editorial
    ZDB-ID 197890-1
    ISSN 1530-0293 ; 0090-3493
    ISSN (online) 1530-0293
    ISSN 0090-3493
    DOI 10.1097/CCM.0b013e31821e85dc
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Blood pressure management in stroke.

    Donovan, Anne L / Flexman, Alana M / Gelb, Adrian W

    Current opinion in anaesthesiology

    2012  Volume 25, Issue 5, Page(s) 516–522

    Abstract: Purpose of review: Cerebrovascular disease is a common cause of death and disability worldwide. The current literature supports an association between blood pressure (BP) and patient outcome during acute stroke. This review will provide an overview of ... ...

    Abstract Purpose of review: Cerebrovascular disease is a common cause of death and disability worldwide. The current literature supports an association between blood pressure (BP) and patient outcome during acute stroke. This review will provide an overview of the evidence to guide BP management during acute stroke.
    Recent findings: Hypotension and hypertension are correlated with poor outcome in acute ischemic stroke, but the effect of reducing or augmenting BP is unclear. In most cases, BP should be treated only when SBP is greater than 220 or greater than 180 in candidates for thrombolysis. There is a lack of evidence to support the choice of specific agents. Use of vasopressor drugs to treat hypotension in acute stroke should be limited to selective situations. In acute hemorrhagic stroke, SBP greater than 140 has been correlated with poor outcomes. Two recent studies report the safety and feasibility of early BP reduction in hemorrhagic stroke.
    Summary: Both hypertension and hypotension are associated with worse outcomes during acute stroke; however, the optimal hemodynamic parameters are not clearly defined in this patient population. Despite active research, there is a lack of high-quality data guiding current BP management in stroke. Several trials currently underway may clarify the many existing questions on this topic.
    MeSH term(s) Anesthesia ; Antihypertensive Agents/therapeutic use ; Arterial Pressure ; Blood Pressure/physiology ; Guidelines as Topic ; Humans ; Hypertension/etiology ; Hypertension/physiopathology ; Hypertension/therapy ; Stroke/physiopathology ; Stroke/surgery ; Stroke/therapy ; Treatment Outcome
    Chemical Substances Antihypertensive Agents
    Language English
    Publishing date 2012-10
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 645203-6
    ISSN 1473-6500 ; 0952-7907
    ISSN (online) 1473-6500
    ISSN 0952-7907
    DOI 10.1097/ACO.0b013e32835721a5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Anesthetic management of patients with acute stroke.

    Flexman, Alana M / Donovan, Anne L / Gelb, Adrian W

    Anesthesiology clinics

    2012  Volume 30, Issue 2, Page(s) 175–190

    Abstract: Stroke is a major cause of death and disability. Anesthesiologists are likely to encounter patients with stroke and must be aware of the anesthetic considerations for these patients. Intravenous thrombolysis and intra-arterial thrombolysis are effective ... ...

    Abstract Stroke is a major cause of death and disability. Anesthesiologists are likely to encounter patients with stroke and must be aware of the anesthetic considerations for these patients. Intravenous thrombolysis and intra-arterial thrombolysis are effective treatments for acuteischemic stroke as well as evolving endovascular techniques such as mechanical clot retrieval. Recent retrospective studies have found an association between general anesthesia and poor clinical outcome. The results of these studies have several limitations, and current evidence is inadequate to guide the choice of anesthesia in patients with acute stroke. The choice of anesthesia must be based on individual patient factors until further research is completed.
    MeSH term(s) Anesthesia/adverse effects ; Anesthesia/methods ; Anesthesia, General/methods ; Anesthesia, Local/methods ; Anesthetics/therapeutic use ; Brain Ischemia/complications ; Brain Ischemia/therapy ; Cerebral Hemorrhage/complications ; Cerebral Hemorrhage/therapy ; Decompression, Surgical ; Hemodynamics/physiology ; Humans ; Neuroprotective Agents/therapeutic use ; Physical Examination ; Stroke/diagnosis ; Stroke/etiology ; Stroke/physiopathology ; Stroke/surgery ; Stroke/therapy ; Thrombolytic Therapy
    Chemical Substances Anesthetics ; Neuroprotective Agents
    Language English
    Publishing date 2012-06
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2228899-5
    ISSN 2210-3538 ; 1932-2275 ; 0889-8537
    ISSN (online) 2210-3538
    ISSN 1932-2275 ; 0889-8537
    DOI 10.1016/j.anclin.2012.04.002
    Database MEDical Literature Analysis and Retrieval System OnLINE

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