Thiamine utilization and the lack of prescribing standardization: A critical examination.

OBJECTIVES: Thiamine is often prescribed for thiamine deficiency during hospitalization despite the lack of US-based clinical guidelines. This study aims to evaluate thiamine prescribing patterns and key characteristics associated with the deficiency to address gaps in care. METHODS: Data were obtained from electronic health records of hospitalized patients between September 1, 2021, and March 30, 2022. Alcohol use disorder (AUD) was defined by a positive Clinical Institute Withdrawal Assessment score or a positive serum alcohol level upon admission. Geriatric patients were defined as age ≥65. Cohort 1 was defined as: AUD, albumin <4 g/L, INR >1.5, and total bilirubin >3 mg/dL. Cohort 2 was defined as: age >65, albumin <4 g/L, hemoglobin <15 g/dL, and folate <4 ng/mL. A multivariable LASSO regression model was used to identify characteristics associated with higher thiamine dosing (>100 mg/day). RESULTS: Among 780 patients, 520 (66.7%) were identified as AUD, of which 265 (50.1%) were between the ages of 45-64 years. The AUD cohort was significantly different (p < 0.05) in the mean serum albumin 4.16 g/L (IQR: 3.8-4.5), AST 73.55 U/L (23.75-82.00), ALT 52.57 U/L (17.00-57.00), total bilirubin 0.98 (0.3-1.0), and INR 1.1 (0.99-1.12), compared to non-AUD patients with a mean serum albumin 3.75 g/L (3.3-4.2), AST 35.07 U/L (11.00-42.00), ALT 32.77 U/L (5.00-34.00), total bilirubin 0.89 (0.2-0.9), and INR 1.21 (1.0-1.22). In the geriatric cohort, 136 patients (17%) had a mean serum albumin 3.77 g/L (3.4-4.2), AST 38.66 U/L (14.0-41.0), ALT 29.36 U/L (9.0-37.0), total bilirubin 0.62 mg/dL (0.30-0.90), and direct bilirubin 0.12 mg/dL (0.00-0.20), compared to the non-geriatric cohort with a mean serum albumin 4.10 g/L (3.8-4.40), AST 66.44 U/L (21.0-75.0), ALT 50.03 U/L (16.00-53.75), total bilirubin 1.02 mg/dL (0.30-1.00), and direct bilirubin 0.31 mg/dL (0.00-0.20). In cohort 1, 40.6% patients were between 51 and 64 years old, (66.5%) male, and had a BMI <25 (36.4%). In cohort 2, 52.6% were between 65 and 70 years old, (57.9%) male, and had a BMI <25 (57.9%). Cohort 1 were prescribed a dose of 100 mg (47.7 %), oral (63.5%), intramuscular (18.2%), daily (58.9%), one-day duration (49.4%) most frequently. Cohort 2 were prescribed a dose of 100 mg (56.0%), oral (77.2%), daily (77.2%), one-day duration (29.8%) most frequently. The AUD was significantly associated with having a higher dosage (e.g., >100 mg) of thiamine prescribed per day OR 1.62 (1.11-2.37) (p < 0.01). CONCLUSIONS: This study confirms that thiamine prescribing patterns vary during hospitalization and suggest specific laboratory findings may aid in identifying cohorts associated with the deficiency.

Comparative Safety Analysis of Opioid Agonist Treatment in Pregnant Women with Opioid Use Disorder: A Population-Based Study.

INTRODUCTION AND OBJECTIVE: Receipt of opioid agonist treatment during early and late pregnancy for opioid use disorder may relate to varying perinatal risks. We aimed to assess the effect of time-varying prenatal exposure to opioid agonist treatment using buprenorphine or methadone on adverse neonatal and pregnancy outcomes. METHODS: We conducted a retrospective cohort study of pregnant women with opioid use disorder using Rhode Island Medicaid claims data and vital statistics during 2008-16. Time-varying exposure was evaluated in early (0-20 weeks) and late (≥ 21 weeks) pregnancy. Marginal structural models with inverse probability of treatment weighting were applied. RESULTS: Of 400 eligible pregnancies, 85 and 137 individuals received buprenorphine and methadone, respectively, during early pregnancy. Compared with 152 untreated pregnancies with opioid use disorders, methadone exposure in both periods was associated with an increased risk of preterm birth (adjusted odds ratio [aOR]: 2.52; 95% confidence interval [CI] 1.07-5.95), low birth weight (aOR: 2.99; 95% CI 1.34-6.66), neonatal intensive care unit admission (aOR, 5.04; 95% CI 2.49-10.21), neonatal abstinence syndrome (aOR: 11.36; 95% CI 5.65-22.82), respiratory symptoms (aOR, 2.71; 95% CI 1.17-6.24), and maternal hospital stay > 7 days (aOR, 14.51; 95% CI 7.23-29.12). Similar patterns emerged for buprenorphine regarding neonatal abstinence syndrome (aOR: 10.27; 95% CI 4.91-21.47) and extended maternal hospital stay (aOR: 3.84; 95% CI 1.83-8.07). However, differences were found favoring the use of buprenorphine for preterm birth versus untreated pregnancies (aOR: 0.17; 95% CI 0.04-0.77), and for several outcomes versus methadone. CONCLUSIONS: Methadone and buprenorphine prescribed for the treatment of opioid use disorder during pregnancy are associated with varying perinatal risks. However, buprenorphine may be preferred in the setting of pregnancy opioid agonist treatment. Further research is necessary to confirm our findings and minimize residual confounding.

Evaluating the Medication Regimen Complexity Score as a Predictor of Clinical Outcomes in the Critically Ill.

Background: Medication Regimen Complexity (MRC) refers to the combination of medication classes, dosages, and frequencies. The objective of this study was to examine the relationship between the scores of different MRC tools and the clinical outcomes. Methods: We conducted a retrospective cohort study at Roger William Medical Center, Providence, Rhode Island, which included 317 adult patients admitted to the intensive care unit (ICU) between 1 February 2020 and 30 August 2020. MRC was assessed using the MRC Index (MRCI) and MRC for the Intensive Care Unit (MRC-ICU). A multivariable logistic regression model was used to identify associations among MRC scores, clinical outcomes, and a logistic classifier to predict clinical outcomes. Results: Higher MRC scores were associated with increased mortality, a longer ICU length of stay (LOS), and the need for mechanical ventilation (MV). MRC-ICU scores at 24 h were significantly (p < 0.001) associated with increased ICU mortality, LOS, and MV, with ORs of 1.12 (95% CI: 1.06−1.19), 1.17 (1.1−1.24), and 1.21 (1.14−1.29), respectively. Mortality prediction was similar using both scoring tools (AUC: 0.88 [0.75−0.97] vs. 0.88 [0.76−0.97]. The model with 15 medication classes outperformed others in predicting the ICU LOS and the need for MV with AUCs of 0.82 (0.71−0.93) and 0.87 (0.77−0.96), respectively. Conclusion: Our results demonstrated that both MRC scores were associated with poorer clinical outcomes. The incorporation of MRC scores in real-time therapeutic decision making can aid clinicians to prescribe safer alternatives.

Survival and recovery modeling of acute kidney injury in critically ill adults.

Objectives: Acute kidney injury is common among the critically ill. However, the incidence, medication use, and outcomes of acute kidney injury have been variably described. We conducted a single-center, retrospective cohort study to examine the risk factors and correlates associated with acute kidney injury in critically ill adults with a particular focus on medication class usage. Methods: We reviewed the electronic medical records of all adult patients admitted to an intensive care unit between 1 February and 30 August 2020. Acute kidney injury was defined by the 2012 Kidney Disease: Improving Global Outcomes guidelines. Data included were demographics, comorbidities, symptoms, laboratory parameters, interventions, and outcomes. The primary outcome was acute kidney injury incidence. A Least Absolute Shrinkage and Selection Operator regression model was used to determine risk factors associated with acute kidney injury. Secondary outcomes including acute kidney injury recovery and intensive care unit mortality were analyzed using a Cox regression model. Results: Among 226 admitted patients, 108 (47.8%) experienced acute kidney injury. 37 (34.3%), 39 (36.1%), and 32 patients (29.6%) were classified as acute kidney injury stages I-III, respectively. Among the recovery and mortality cohorts, analgesics/sedatives, anti-infectives, and intravenous fluids were significant (p-value < 0.05). The medication classes IV-fluid electrolytes nutrition (96.7%), gastrointestinal (90.2%), and anti-infectives (81.5%) were associated with an increased odds of developing acute kidney injury, odd ratios: 1.27, 1.71, and 1.70, respectively. Cox regression analyses revealed a significantly increased time-varying mortality risk for acute kidney injury-stage III, hazard ratio: 4.72 (95% confidence interval: 1-22.33). In the recovery cohort, time to acute kidney injury recovery was significantly faster in stage I, hazard ratio: 9.14 (95% confidence interval: 2.14-39.06) cohort when compared to the stage III cohort. Conclusion: Evaluation of vital signs, laboratory, and medication use data may be useful to determine acute kidney injury risk stratification. The influence of particular medication classes further impacts the risk of developing acute kidney injury, necessitating the importance of examining pharmacotherapeutic regimens for early recognition of renal impairment and prevention.

Drug-Induced QTc Prolongation: What We Know and Where We Are Going.

Drug-induced QTc prolongation is a concerning electrocardiogram (ECG) abnormality. This cardiac disturbance carries a 10% risk of sudden cardiac death due to the malignant arrhythmia, Torsades de Pointes. The Arizona Center for Education and Research on Therapeutics (AzCERT) has classified QTc prolonging therapeutic classes, such as antiarrhythmics, antipsychotics, anti-infectives, and others. AzCERT criteria categorize medications into three risk categories: "known," "possible," and "conditional risk" of QTc prolongation and Torsades de Pointes. The list of QTc prolonging medications continues to expand as new drug classes are approved and studied. Risk factors for QTc prolongation can be delineated into modifiable or non-modifiable. A validated risk scoring tool may be utilized to predict the likelihood of prolongation in patients receiving AzCERT classified medication. The resultant risk score may be applied to a clinical decision support system, which offers mitigation strategies. Mitigation strategies including discontinuation of possible offending agents with a selection of an alternative agent, assessment of potential drug interactions or dose adjustments through pharmacokinetic and pharmacodynamic monitoring, and initiation of both ECG and electrolyte monitoring are essential to prevent a drug-induced arrhythmia. The challenges presented by the COVID-19 pandemic have led to the development of innovative continuous monitoring technology, increasing protection for both patients and healthcare workers. Early intervention strategies may reduce adverse events and improve clinical outcomes in patients identified to be at risk of QTc prolongation.