Pharmacotherapy最新文献

Introduction: Levetiracetam (LEV) is indicated for benzodiazepine (BZD) refractory status epilepticus (SE) and is traditionally administered as an intravenous piggyback (IVPB) infused over 15 min, although rapid intravenous push (IVP) administration over 2 to 5 min has gained popularity. Current literature surrounding IVP LEV administration supports increased efficiency and equal safety of IVP compared with IVPB, though efficacy comparisons, such as seizure duration, are limited. The objective of this study was to assess the impact of IVP LEV on seizure duration and BZD requirements.

Methods: This retrospective cohort study assessed adult patients who received IVP or IVPB LEV following a BZD for an acute or suspected seizure. The primary outcome was the number of patients who required additional BZD doses between LEV order and administration. Secondary outcomes included additional BZD requirement within 6 h after LEV administration, time from LEV order to administration, need for intubation, and intensive care unit (ICU) admission. Safety outcomes assessed included bradycardia, hypotension, and infusion site reactions.

Results: A total of 299 patients were included, 144 in the IVP group and 155 in the IVPB group. Fewer patients required additional BZD doses between LEV order and administration in the IVP group than the IVPB group (8 patients [5.6%] vs. 27 patients [17.4%]; p = 0.002). Additionally, the median time from LEV order to administration was shorter in the IVP group than in the IVPB group (14.5 min vs. 29.0 min; p < 0.001). Bradycardia occurred more frequently in the IVPB group compared with the IVP group (8.8% vs. 2.3%; p = 0.03).

Conclusion: IVP LEV was associated with less frequent requirement of additional BZD doses for treatment of acute or suspected seizures compared with IVPB, as well as a faster time to medication administration and potentially a lower risk of bradycardia.

Introduction: Intravenous regular insulin is often used for the management of hyperkalemia due to its rapid onset of action and predictable potassium-lowering effects. Various studies have been conducted to determine optimal insulin dosing strategies that reduce serum potassium levels without increasing hypoglycemia risk. As data shifts towards lower or fixed insulin doses, validating the appropriateness of these dosing regimens for the management of hyperkalemia in overweight patients is warranted. The purpose of this study was to evaluate the serum potassium-lowering effects of 5 units versus 10 units of intravenous regular insulin in hyperkalemic patients with a body mass index (BMI) ≥ 25 kg/m².

Methods: A multicenter, retrospective study was performed in adult patients with BMI ≥ 25 kg/m² who received 5 or 10 units of intravenous regular insulin for the treatment of hyperkalemia. The primary outcome was the potassium-lowering effects of 5 units versus 10 units of intravenous regular insulin. Secondary outcomes include the incidence of hypoglycemic episodes within 6 h of insulin administration, hospital length of stay (LOS), and treatment failure.

Results: Of 699 patients screened, 81 patients received 5 units and 81 patients received 10 units. There was no difference in the serum potassium-lowering effects of 5 units versus 10 units of intravenous regular insulin (0.5 (0.1-1.1) mmol/L vs. 0.5 (0.2-1) mmol/L; p = 0.65). No significant differences were observed for any secondary outcomes. Subgroup analyses revealed no significant differences for BMI; the number of concomitant acute potassium-lowering therapies received; or the degree of renal impairment, aside from a significantly larger potassium-lowering effect with 10 units of intravenous regular insulin observed in the subgroup receiving no concomitant acute potassium-lowering therapies as well as the subgroup with a creatinine clearance of 30-60 mL/min.

Conclusion: In this small, retrospective cohort study, treatment with 5 units of intravenous regular insulin did not compromise the serum potassium-lowering effect when compared to 10 units in overweight patients with hyperkalemia. Further controlled studies are warranted to confirm these findings.

V. Shah, D. Cordwin, S. L. Hummel, M. P. Dorsch, "Chloride Dipstick to Rapidly Estimate Urine Sodium During Diuresis in Acute Heart Failure." Pharmacotherapy. 45, (2025): 352-355. https://doi.org/10.1002/phar.70026. The author's name, V Shah, was corrected to Vacha Shah in the online version. We apologize for this error.

Introduction: Opioids are commonly used to manage chronic pain in older adults, despite their well-documented adverse events (AEs) and the potential for additional less well-known risks. Post-marketing surveillance methods applied in real-world settings are essential to monitor AEs. Prescription Sequence Symmetry Analysis (PSSA) is a method that compares medication initiation patterns within individuals and can detect signals of prescribing cascades and drug AEs. This study applied PSSA to Medicare claims data to explore potential AEs following the initiation of long-term opioid therapy (LOT).

Methods: We used Texas-Medicare data to identify adults who initiated LOT (≥ 90 consecutive days) in 2016-2019. Prescription sequence symmetry analysis (PSSA) was performed to explore associations between opioids and related adverse events treated by marker drugs. The observation period for sequences of incident marker drug prescriptions was limited to 12 months before and after opioid initiation. Marker drugs were categorized based on the Anatomical Therapeutic Chemical (ATC) Classification System. Adjusted sequence symmetry ratios (aSSR) and 95% confidence intervals were calculated to account for prescribing trend changes.

Results: Among 11,233 incident opioid users, we identified incident marker drugs belonging to 145 distinct ATC classes, 36 of which had statistically significant aSSRs. We found signals of increased post-opioid prescriptions related to known opioid AEs (e.g., propulsives, antiemetics, laxatives, naltrexone) and less well-documented associations (antimicrobials, hormones, antiarrhythmics, and antipsychotics).

Conclusions: PSSA applied to administrative claims data effectively identified both expected and potentially underrecognized adverse effects of long-term opioid use. This approach can enhance post-marketing surveillance by uncovering real-world prescribing cascades in older adults.

Objective: To develop a statistical model to capture medication dosing for proton pump inhibitors (PPIs) using structured data from electronic health records (EHR).

Methods: Medication data for PPIs was extracted from a single health care system EHR to develop a statistical model. Nearly 20 years' worth of PPI prescriptions were extracted and 25% of unique dosing regimens were manually labeled by two clinical pharmacists. Several machine learning models were trained and evaluated to predict dose. Training was applied to 70% of the unique dosing regimens. The remaining unique dosing regimens were tested and validated with standard regression metrics: root mean squared error (RMSE) and R-squared.

Results: A total of 17,271 distinct patients had orders for a PPI comprising 186,801 unique PPI orders. Distinct pairs built on medication descriptions and SIG combinations resulted in 10,739 unique entities. Clinical pharmacists manually labeled 2679 examples for medication entity extraction. Regression metrics (R-squared, RMSE) were chosen as metrics to evaluate model performance. A stacked ensembled model proved to have the best results with a 0.09 RMSE and an R-squared of 0.825.

Conclusion: The development of a statistical model to capture PPI dosing for both maintenance and complex dosing strategies was highly sensitive and accurate. A supervised learning prediction model helps overcome challenges in medication dosing identification by addressing concerns related to variability and complexity. Future strategies should focus on integrating unstructured data within the algorithm to further refine medication dosing capture.

Background: Peroxisome proliferator-activated receptor α (PPARα) has been reported to exert protective roles in immune-mediated intestinal diseases through inhibition of interleukin-6 (IL-6)-induced signal transducer and activator of transcription factor 3 (STAT3) activation.

Aim: To investigate the potential anti-inflammatory effect of fenofibrate, as an add-on therapy to mesalamine, on IL-6/STAT3 and nitric oxide (NO) in patients with ulcerative colitis (UC).

Methods: This pilot, double-blind, randomized, controlled trial included 60 patients diagnosed with mild-to-moderate UC. Patients were randomly allocated into two groups. The placebo group (n = 30) received placebo plus mesalamine 1 g three times daily, and the fenofibrate group (n = 30) received mesalamine 1 g three times daily and fenofibrate 160 mg once daily. The study duration was 6 months. The severity of UC was evaluated using the Disease Activity Index (DAI), and quality of life (QoL) was assessed using the Short Form-36 questionnaire (SF-36). Serum levels of IL-6, NO, C-reactive protein (CRP), and STAT3 were measured for all patients.

Results: After treatment, both groups showed a significant reduction in DAI, IL-6, STAT3, NO, and CRP, along with an increase in SF-36 scores. Furthermore, the fenofibrate group demonstrated a significantly greater decrease in DAI (p = 0.0002), IL-6 (p = 0.04), STAT3 (p = 0.004), NO (p = 0.013), and CRP (p = 0.034), as well as a greater increase in SF-36 (p = 0.04) compared with the placebo group.

Conclusion: Fenofibrate may represent a promising add-on therapy in patients with mild-to-moderate UC by modulating inflammation and improving QoL.

Trial registration: NCT05753267.

Background: Dopaminergic therapies such as levodopa and dopamine receptor agonists (DRA) improve motor function in people with Parkinson's disease. These therapies are also linked to the advent of motor complications such as dyskinesias and wearing-off episodes.

Objectives: We illustrate a method that creates a personalized treatment rule that takes patient-specific information and provides a recommended first-line therapy for Parkinson's disease that will provide the best mean improvement in motor function while constraining the probability of a motor complication within the first 2 years of therapy below a level mutually deemed to be the maximum acceptable risk by the patient and clinician.

Methods: We apply a machine learning technique that simultaneously optimizes for benefit and risk outcomes to a harmonized clinical dataset based on the CALM-PD and STEADY-PD III randomized clinical trials. This generates a decision rule for allocating patients to levodopa or a DRA, based on a specified risk threshold. We evaluate the individualized decision rule by comparing the mean benefit and risk outcomes under the decision rule to the mean outcomes from policies that assign all patients to either levodopa or a DRA.

Results: The optimal decision rule improves the mean change from baseline in MDS-UPDRS (Movement Disorder Society Unified Parkinson's Disease Rating Scale) motor (Part 3) score compared to assigning all patients to a DRA and provides a smaller mean probability of motor complications than assigning all patients to levodopa. More data are required to further develop and validate this decision rule.

Conclusions: An optimal decision rule can provide improved data adaptive treatment decisions that balance benefit and risk outcomes given a maximum acceptable risk.

Background: Falls and related injuries (FRI) pose a large burden among older adults with depression. Proactively identifying individuals at high FRI risk enables timely and tailored interventions, reducing unnecessary health care resource utilization. However, prior prediction models relied on fixed time intervals and failed to capture dynamic changes in health status over time.

Objectives: To develop and validate machine-learning algorithms (i.e., elastic net, random forest, and gradient boosting machine) for predicting 3-month FRI risk among older adults with depression.

Methods: This prognostic modeling study included fee-for-service Medicare beneficiaries aged 65 years or older with a depression diagnosis in 2017. Beneficiaries were followed in 3-month episodes from the first depression diagnosis until the earliest of death, hospice services or nursing facility utilization, switching to Medicare Advantage plans, or the end of the study period (i.e., December 31, 2019). A total of 261 time-varying predictors, spanning patient-, provider-, health system- and region-related factors, were updated every 3 months to predict incident FRI risk in the subsequent 3 months. We assessed prediction performance using c-statistics and stratified patients into different risk subgroups using the best-performing model.

Results: Among 274,268 eligible beneficiaries, the mean age was 74.6 (standard deviation [SD] = 7.2) years, 32.0% were male, 85.2% were White, and 15.1% experienced at least one FRI event throughout the study period. Using the random forest model (c-statistics = 0.68), 68.9% of the actual FRI cases were captured in the top three deciles of predicted risk. Individuals in the bottom seven deciles had a minimal FRI incidence (< 1.7%). Key predictors included frailty, age, prior FRI history, and daily dose of antidepressants.

Conclusion: Using a nationally representative cohort and time-varying predictors, our model offers a practical approach for efficiently identifying older adults at high FRI risk, which can be updated over time. This approach can inform clinical decision-making and optimize the allocation of fall prevention resources.

The treatment of migraine is hampered by inter-individual variability, leading to an inefficient "trial and error" approach. Artificial intelligence (AI) and machine learning (ML) offer a path towards precision medicine by predicting therapeutic outcomes. This scoping review systematically evaluates the evidence for AI and ML models for predicting pharmacologic response in migraine. A systematic search of four databases (PubMed, Web of Knowledge, Cochrane Library, and OpenGrey) identified 12 eligible studies using AI/ML to predict acute or prophylactic response to migraine treatment. These studies, which date back to articles published in 2006 and have been increasingly published recently, used a wide range of methods, from classical algorithms like support vector machines to deep learning and probabilistic models. The models primarily utilized clinical phenotyping and neuroimaging data and reported high predictive accuracy for novel biologics (e.g., anti-calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs)) and acute treatments (e.g., nonsteroidal anti-inflammatory drugs (NSAIDs)). However, our systematic review finds that this apparent success is undermined by critical and pervasive methodological weaknesses. The central finding is that most studies relied solely on internal validation, carrying a high risk of overfitting, with external validation being exceptionally rare. Furthermore, several publications were based on overlapping patient cohorts, and a complete lack of biomarker or genetic data was noted. Consequently, the clinical application of AI and ML is currently stalled. Future progress depends on overcoming the "crisis of generalizability" by mandating external validation, addressing the "data bottleneck" with large, diverse datasets, and expanding data modalities to include "omic" data. These measures are critical to begin to realize the potential of AI and ML to personalize migraine treatment and significantly improve patient outcomes.