The Experts below are selected from a list of 9624 Experts worldwide ranked by ideXlab platform
Carrie L Randall - One of the best experts on this subject based on the ideXlab platform.
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a double blind trial of gabapentin versus lorazepam in the treatment of Alcohol Withdrawal
Alcoholism: Clinical and Experimental Research, 2009Co-Authors: Hugh Myrick, Robert Malcolm, Patrick K Randall, Elizabeth Boyle, Raymond F Anton, Howard C Becker, Carrie L RandallAbstract:Introduction Some anticonvulsants ameliorate signs and symptoms of Alcohol Withdrawal, but have an unacceptable side effect burden. Among the advantages of using anticonvulsant agents in this capacity is their purported lack of interaction with Alcohol that could increase psychomotor deficits, increase cognitive impairment, or increase intoxication. The aim of the current study was to evaluate Alcohol use and symptom reduction of gabapentin as compared to lorazepam in the treatment of Alcohol Withdrawal in a double-blinded randomized clinical trial.
Pratima Murthy - One of the best experts on this subject based on the ideXlab platform.
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a randomized double blind comparison of lorazepam and chlordiazepoxide in patients with uncomplicated Alcohol Withdrawal
Journal of Studies on Alcohol and Drugs, 2009Co-Authors: Channaveerachari Naveen Kumar, Chittaranjan Andrade, Pratima MurthyAbstract:Objective For important reasons, lorazepam (Ativan) and chlordiazepoxide (Librium) are both popular treatments for Alcohol-Withdrawal syndrome. Nevertheless, there is little literature directly comparing the two drugs. A formal comparison is desirable because of pharmacokinetic and other differences that could affect safety and efficacy considerations relevant to practice in developing countries. Method One hundred consecutive consenting male inpatients in a state of moderately severe, uncomplicated Alcohol Withdrawal at screening were randomized to receive either lorazepam (8 mg/day) or chlordiazepoxide (80 mg/day) with dosing down-titrated to zero in a fixed-dose schedule across 8 treatment days. Double-blind assessments of Withdrawal-symptom severity and impairing adverse events were obtained during treatment and for 4 days afterward. Results One chlordiazepoxide patient developed Withdrawal delirium. Lorazepam and chlordiazepoxide showed similar efficacy in reducing symptoms of Alcohol Withdrawal as assessed using the revised Clinical Institute Withdrawal Assessment for Alcohol scale. During Withdrawal, irritability and dizziness were more common with lorazepam, and palpitations were more common with chlordiazepoxide. No difficulties in drug discontinuation or differences in impairing adverse events were observed with either drug. Conclusions With the treatment schedule used in this study, lorazepam is as effective as the more traditional drug chlordiazepoxide in attenuating uncomplicated Alcohol Withdrawal. Lorazepam, therefore, could be used with confidence when liver disease or the inability to determine liver function status renders chlordiazepoxide therapy problematic. The absence of clinically significant Withdrawal complications with lorazepam in this large study contrasts with findings from previously published studies and suggests that higher doses of lorazepam than those formerly used may be necessary during Alcohol Withdrawal.
Sandra L Kanegill - One of the best experts on this subject based on the ideXlab platform.
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predictors of resistant Alcohol Withdrawal raw a retrospective case control study
Drug and Alcohol Dependence, 2018Co-Authors: Neal Benedict, Anthony F Pizon, Adrian Wong, Elizabeth Cassidy, Brian Lohr, Pamela L Smithburger, Bonnie A Falcione, Levent Kirisci, Sandra L KanegillAbstract:Abstract Background Benzodiazepine-resistant Alcohol Withdrawal (RAW), defined by a requirement of ≥ 40 mg of diazepam in 1 h, represents a severe form of Withdrawal without predictive parameters. This study was designed to identify risk factors associated with RAW versus Withdrawal without benzodiazepine resistance (nRAW). Methods A retrospective cohort of adults with severe Alcohol Withdrawal were screened. Demographic and clinical variables, collected through chart review, underwent logistic regression to select the subset that predicst RAW. Results 736 patients (515 nRAW, 221 RAW) were analyzed. RAW patients were younger (P Conclusion These data demonstrate the predictive ability of a history of psychiatric illness, thrombocytopenia, gender, race, baseline severity of illness and comorbidity scores for developing RAW. Considering these characteristics in early Withdrawal management may prevent progression to RAW outcomes.
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multicenter evaluation of pharmacologic management and outcomes associated with severe resistant Alcohol Withdrawal
Journal of Critical Care, 2015Co-Authors: Adrian Wong, Neal Benedict, Sandra L KanegillAbstract:Abstract Introduction A subset of patients with Alcohol Withdrawal syndrome does not respond to benzodiazepine treatment despite escalating doses. Resistant Alcohol Withdrawal (RAW) is associated with higher incidences of mechanical ventilation and nosocomial pneumonia and longer intensive care unit (ICU) stay. The objective of this study is to characterize pharmacologic management of RAW and outcomes. Methods Adult patients were identified retrospectively via International Classification of Diseases, Ninth Revision codes for severe Alcohol Withdrawal from 2009 to 2012 at 3 hospitals. Data collected included pharmacologic management and clinical outcomes. Results A total of 184 patients met inclusion criteria. Sixteen medications and 74 combinations of medications were used for management. Propofol was the most common adjunct agent, with dexmedetomidine and antipsychotics also used. One hundred seventy-five patients (96.2%) were admitted to the ICU, with 149 patients (81.9%) requiring ventilator support. Median time to resolution of Alcohol Withdrawal syndrome from RAW designation was 6.0 days. Median ICU and hospital length of stay were 9.0 and 12.7 days, respectively. Conclusion Diverse patterns exist in the management of patients meeting RAW criteria, indicating lack of refined approach to treatment. High doses of sedatives used for these patients may result in a high level of care, illustrating a need for evidence-based clinical guidelines to optimize outcomes.
Jeffrey A. Gold - One of the best experts on this subject based on the ideXlab platform.
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Alcohol Withdrawal syndromes in the intensive care unit.
Critical care medicine, 2010Co-Authors: Maryclare Sarff, Jeffrey A. GoldAbstract:This article reviews the pathophysiology, diagnosis, and treatment of Alcohol Withdrawal syndromes in the intensive care unit as well as the literature on the optimal pharmacologic strategies for treatment of Alcohol Withdrawal syndromes in the critically ill. Treatment of Alcohol Withdrawal in the intensive care unit mirrors that of the general acute care wards and detoxification centers. In addition to adequate supportive care, benzodiazepines administered in a symptom-triggered fashion, guided by the Clinical Institute Withdrawal Assessment of Alcohol scale, revised (CIWA-Ar), still seem to be the optimal strategy in the intensive care unit. In cases of benzodiazepine resistance, numerous options are available, including high individual doses of benzodiazepines, barbiturates, and propofol. Intensivists should be familiar with the diagnosis and treatment strategies for Alcohol Withdrawal syndromes in the intensive care unit.
Dominic B Dwyer - One of the best experts on this subject based on the ideXlab platform.
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a machine learning approach to risk assessment for Alcohol Withdrawal syndrome
European Neuropsychopharmacology, 2020Co-Authors: Gerrit Burkhardt, Kristina Adorjan, Joseph Kambeitz, Lana Kambeitzilankovic, Peter Falkai, Florian Eyer, Gabi Koller, Oliver Pogarell, Nikolaos Koutsouleris, Dominic B DwyerAbstract:Abstract At present, risk assessment for Alcohol Withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict Alcohol Withdrawal outcomes at the individual subject level using information easily attainable at patients’ admission. An observational machine learning analysis using nested cross-validation and out-of-sample validation was applied to Alcohol-dependent patients at two major detoxification wards (LMU, n = 389; TU, n = 805). 121 retrospectively derived clinical, blood-derived, and sociodemographic measures were used to predict 1) moderate to severe Withdrawal defined by the Alcohol Withdrawal scale, 2) delirium tremens, and 3) Withdrawal seizures. Mild and more severe Withdrawal cases could be separated with significant, although highly variable accuracy in both samples (LMU, balanced accuracy [BAC] = 69.4%; TU, BAC = 55.9%). Poor outcome predictions were associated with higher cumulative clomethiazole doses during the Withdrawal course. Delirium tremens was predicted in the TU cohort with BAC of 75%. No significant model predicting Withdrawal seizures could be found. Our models were unique to each treatment site and thus did not generalize. For both treatment sites and Withdrawal outcome different variable sets informed our models’ decisions. Besides previously described variables (most notably, thrombocytopenia), we identified new predictors (history of blood pressure abnormalities, urine screening for benzodiazepines and educational attainment). In conclusion, machine learning approaches may facilitate generalizable, individualized predictions for Alcohol Withdrawal severity. Since predictive patterns highly vary for different outcomes of Withdrawal severity and across treatment sites, prediction tools should not be recommended for clinical practice unless adequately validated in specific cohorts.