Pharmacotherapy [JOURNAL]

INTRODUCTION: Prediction algorithms for prolonged mechanical ventilation (PMV) in the intensive care unit (ICU) have rarely incorporated detailed medication data, despite medications being important causal contributors t... INTRODUCTION: Prediction algorithms for prolonged mechanical ventilation (PMV) in the intensive care unit (ICU) have rarely incorporated detailed medication data, despite medications being important causal contributors to patient outcomes. The purpose of this study was to develop and validate PMV prediction models to assess the contribution of medication-related variables alongside established physiologic predictors. METHODS: In this retrospective cohort study, models were developed using data from a random sample of 318 adults admitted to ICUs within the University of North Carolina (UNC) health system who received mechanical ventilation for ≥ 24 h from October 2015 to October 2020. Validation was performed in two datasets: a temporally distinct cohort from UNC from June 2021 to June 2023, and a cohort from Oregon Health Sciences University from June 2020 to June 2023. Logistic regression and supervised, classification-based machine learning (ML) models [XGBoost, Random Forest, Support Vector Machine (SVM)] were trained on 30 demographic, clinical, laboratory, and medication-related variables. The primary outcome was area under the receiver operating characteristic (AUROC) of developed prediction models for the occurrence of PMV. RESULTS: The base logistic regression model with medication regimen complexity and severity of illness data added was the best-performing regression model, achieving an AUROC of 0.75. Random Forest and SVM ML models achieved AUROCs of 0.78. Model discrimination decreased modestly in external validation. Explainability analyses of ML models expectedly included severity of illness scores and respiratory indices among the most important features, but also consistently included the medication regimen complexity-intensive care unit (MRC-ICU) score and other medication metrics. Incorporation of medication data yielded modest improvements in overall discrimination and negative predictive value. CONCLUSIONS: Medication-related variables contributed incremental value to PMV prediction. ML methods provided marginal improvements over regression models. These findings highlight the potential value of medication data in prediction modeling for patient outcomes but emphasize the need to contextualize the value of complex models over simpler alternatives.

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Effect of Testosterone Therapy on Cytochrome P450 3A and P-Glycoprotein Activities Using Midazolam and Digoxin as Probe Substrates Among Transgender Adults.

Hunter M, Coig R, Risler L … +7 more , Patton KK, Narla RR, Greene DN, Burke AK, Micks E, Hebert MF, Cirrincione LR

Pharmacotherapy · 2026 Feb · PMID 41452771 · Full text

BACKGROUND: Gender-affirming testosterone therapy is one part of the standard of care for more than 1 million transgender adults in the United States. Testosterone therapy may influence the activities of drug-metabolizin... BACKGROUND: Gender-affirming testosterone therapy is one part of the standard of care for more than 1 million transgender adults in the United States. Testosterone therapy may influence the activities of drug-metabolizing enzymes and transporters, but knowledge about its effect on the pharmacokinetics of other medications is limited. We determined the effects of gender-affirming testosterone therapy on apparent cytochrome P450 (CYP) 3A and P-glycoprotein activities using midazolam and digoxin as model probe substrates among transgender adults. METHODS: This was a longitudinal (pre-treatment and with concomitant testosterone therapy), prospective, non-randomized, open-label, three-phase probe substrate study. Eligible participants started testosterone therapy based on clinical need. Participants received one oral dose of midazolam 2 mg and digoxin 0.25 mg (simultaneous dosing) under fasted conditions before starting gender-affirming testosterone therapy (baseline), and at 1-month and 3-months on gender-affirming testosterone therapy. Midazolam, 1'-hydroxymidazolam, 4-hydroxymidazolam, digoxin, and total testosterone concentrations were determined by liquid chromatography-tandem mass spectrometry assays. We estimated single-dose pharmacokinetic parameters of midazolam, its metabolites, and digoxin using standard noncompartmental methods. Pharmacokinetic parameters were compared with testosterone therapy at 1-month and 3-months to baseline as geometric mean ratios (90% confidence intervals) and paired t-tests after log transformation. A p < 0.025 was considered significant. RESULTS: Among 14 participants (mean age: 24 ± 3 years; weight: 82.9 ± 20.9 kg; race/ethnicity: 71% White, non-Hispanic, 14% Hispanic, 7% Asian, 7% mixed race), nine participants started weekly testosterone injections (20 mg to 80 mg once weekly) and five started daily transdermal testosterone applications (12.5 mg to 50 mg once daily gel or cream, 2 mg daily patch). Mean total testosterone concentrations at 3 months increased more than 20-fold from baseline concentrations (25 ± 7 ng/dL to 507 ± 263 ng/dL). Geometric mean midazolam and metabolite pharmacokinetic parameters and digoxin parameters were not significantly different at baseline and with testosterone therapy. CONCLUSION: Gender-affirming testosterone therapy did not significantly affect CYP3A or P-glycoprotein activities. Gender-affirming testosterone therapy may have minimal effects on the pharmacokinetics of other medications that are substrates of CYP3A and P-glycoprotein. Caution may be warranted for medications with a narrow therapeutic index.

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C-Reactive Protein and Neutrophil-To-Lymphocyte Ratio: Can They Be Used Interchangeably in Tracking Clozapine-Related Inflammation?

Bihelek N, Bousman CA, Honer WG … +1 more , Rafizadeh R

Pharmacotherapy · 2026 Feb · PMID 41449624 · Full text

BACKGROUND: Clozapine initiation often triggers inflammatory responses that can alter metabolism via Cytochrome P450 1A2 (CYP1A2) suppression. Although C-reactive protein (CRP) is the recommended marker, it may be unavai... BACKGROUND: Clozapine initiation often triggers inflammatory responses that can alter metabolism via Cytochrome P450 1A2 (CYP1A2) suppression. Although C-reactive protein (CRP) is the recommended marker, it may be unavailable in community settings. Neutrophil-to-lymphocyte ratio (NLR), routinely measured, could serve as a surrogate, though its value in detecting clozapine-related inflammation and metabolic changes remains unclear. AIMS: This study aimed to assess the relationship between CRP and NLR in individuals treated with clozapine, evaluate whether NLR can act as a proxy for elevated CRP (> 5 mg/L), and determine whether NLR, like CRP, explains variability in clozapine metabolism (concentration to dose (C/D) ratios) after adjusting for covariates. METHODS: We performed a retrospective cohort study of clozapine-treated inpatients at the British Columbia Psychosis Program (2012-2021). Patients with clozapine levels and matched complete blood counts (CBCs) (±7 days) were included, with CRP added when available. Multivariate mixed models assessed associations between CRP, NLR, and clozapine C/D ratios, while receiver operating characteristic (ROC) analyses evaluated NLR as a proxy for elevated CRP. RESULTS: Among 150 patients, 760 clozapine serum/CBC pairs and 212 CRP measurements met eligibility criteria. NLR was modestly associated with CRP (estimate = 0.027, p < 0.001). ROC analysis indicated that NLR had limited predictive utility, with an area under the curve (AUC) of 0.640 for detecting CRP > 5 mg/L. Subsequent analyses for higher CRP thresholds (> 10 and > 20 mg/L) produced comparable NLR AUC values of 0.621 and 0.669, respectively. Neutrophil count alone demonstrated marginally better performance but remained similarly limited in predictive value. In multivariate models, CRP but not NLR, was independently associated with clozapine C/D ratios. CONCLUSION: Our findings indicate that although NLR and other hematological indices are easily accessible and may provide some indication of inflammation, they cannot substitute for CRP in guiding clozapine titration decisions. Where CRP is unavailable, NLR > 3 may be cautiously informative, though CRP remains the preferred marker for early detection and dose adjustment to optimize tolerability, adherence, and safety during clozapine initiation.

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Real-World Use of Letermovir and Maribavir for CMV Prophylaxis and Treatment in Solid Organ Transplant Recipients: Clinical Outcomes and Resistance Patterns From a Dual-Center Cohort.

Xu MT, Wright A, Mah A … +5 more , Matic N, Lowe C, Hong C, Belga S, Tsai WP

Pharmacotherapy · 2025 Dec · PMID 41439370 · Full text

BACKGROUND: Cytomegalovirus (CMV) is a major opportunistic infection in solid organ transplant (SOT) recipients. Although valganciclovir is first-line for CMV treatment, its use may be limited by resistance and myelotoxi... BACKGROUND: Cytomegalovirus (CMV) is a major opportunistic infection in solid organ transplant (SOT) recipients. Although valganciclovir is first-line for CMV treatment, its use may be limited by resistance and myelotoxicity. Letermovir (LET) and maribavir (MBV) are increasingly used as alternative therapies, though real-world outcomes remain incompletely defined. This study evaluated real-world outcomes of LET and MBV in SOT recipients for whom standard CMV therapy was inadequate or not feasible. Viral clearance, breakthrough infection, refractory infection, and resistance emergence were assessed. METHODS: We conducted a retrospective cohort analysis of adult SOT patients receiving LET (2018-2025) and/or MBV (2019-2025) at two transplant centers. Primary outcomes included CMV breakthrough (≥ 500 IU/mL) during LET prophylaxis and CMV clearance (< 200 IU/mL) at the end of MBV treatment. Secondary outcomes included resistant, refractory, and recurrent CMV infection within 3 months post-therapy. RESULTS: Among 47 LET courses (41 patients), breakthrough CMV occurred in 14/32 (44%) secondary prophylaxis (SP) courses (median viral load [VL]: 1037 IU/mL, interquartile range [IQR] 673-3427) but in none of the 15 primary prophylaxis (PP) courses. No resistance to LET was detected during or after prophylaxis. MBV was administered in 21 courses (17 patients); MBV-refractory/resistant (R/R) infection occurred in 8 courses (38%), and 11 (52%) courses achieved CMV clearance. Within 3 months post-MBV, 14 (67%) courses had CMV VL ≥ 1000 IU/mL, and two (10%) courses experienced recurrence. MBV resistance emerged in 6/17 (35%) patients during or after treatment. CONCLUSIONS: LET was effective for PP but associated with frequent breakthroughs in SP patients. MBV was limited by incomplete viral suppression and emergent resistance. These findings highlight the variable effectiveness of LET and MBV in clinical practice and support the need for further research to define optimal antiviral use in SP and refractory CMV treatment.

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Correction to "Pharmacokinetics of Ceftolozane/Tazobactam in Patients With Partial-and Full-Thickness Skin Burns".

Pharmacotherapy · 2026 Feb · PMID 41424296 · Publisher ↗

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Circulating Profiles of the Bile Acid Metabolomics in Patients With Polycystic Ovary Syndrome Treated With Metformin or Canagliflozin.

Yan Q, Cai M, Wang N … +6 more , Wu W, Zhang H, Zhu Y, Luo J, Zhang M, Li J

Pharmacotherapy · 2026 Feb · PMID 41401816 · Full text

OBJECTIVE: Bile acids are indispensable modulators in the development of polycystic ovary syndrome (PCOS). Our previous study identified that metformin and canagliflozin have similar efficacy in patients with PCOS combin... OBJECTIVE: Bile acids are indispensable modulators in the development of polycystic ovary syndrome (PCOS). Our previous study identified that metformin and canagliflozin have similar efficacy in patients with PCOS combined with insulin resistance (IR). However, the effect of metformin or canagliflozin on bile acid metabolism in patients with PCOS has not been elucidated. The objective of this study was to use targeted metabolomics technology to compare alterations of circulating bile acid metabolites in patients with PCOS before and after treatment with metformin or canagliflozin. DESIGN AND PATIENTS: This study was a subanalysis of a previous randomized open-label study, in which patients with PCOS combined with IR were enrolled and treated with either metformin (n = 35) or canagliflozin (n = 33) for 12 weeks. MEASUREMENTS: The serum bile acid profile was measured using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The differences in serum bile acid metabolites in patients with PCOS before and after treatment were analyzed. In addition, the correlation between bile acid metabolites and PCOS-related clinical characteristics was evaluated. RESULTS: There were no significant differences in serum bile acid metabolites in patients with PCOS before and after canagliflozin treatment. Metformin treatment substantially decreased serum total bile acid levels in patients with PCOS, especially primary conjugated bile acids. The levels of taurochenodeoxycholic acid (TCDCA), glycocholic acid (GCA), and glycochenodeoxycholic acid (GCDCA) showed significant differences from baseline in the serum of patients with PCOS after treatment with metformin. Correlation analysis showed that alterations of GCA, TCDCA, and GCDCA were associated with changes in multiple clinical parameters of patients with PCOS treated with metformin. CONCLUSION: The effects of metformin and canagliflozin on bile acids metabolism in patients with PCOS are different. The beneficial effects of metformin on PCOS may be related to the changes in bile acid metabolites. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04700839.

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Assessment of Artificial Intelligence (AI)-Powered Self-Care Recommendations for Management of Minor Ailments: A Comparative Analysis.

ZainAlAbdin S, Kendakji S, Alrahbi F … +6 more , Alazeezi K, Jawas M, Alshamsi S, Almessabi T, Zaki N, Aburuz S

Pharmacotherapy · 2026 Feb · PMID 41339129 · Publisher ↗

INTRODUCTION: Self-care and self-medication are increasingly viewed as helpful approaches to managing minor ailments; however, patients are often not confident in making informed choices. Pharmacists have traditionally a... INTRODUCTION: Self-care and self-medication are increasingly viewed as helpful approaches to managing minor ailments; however, patients are often not confident in making informed choices. Pharmacists have traditionally assisted patients in this domain, but the emergence of digital health technologies has transformed the way individuals seek information towards the use of artificial intelligence (AI) tools. ChatGPT-4o mini, Gemini, and Copilot are recently growing popular for health-related guidance. Despite the accessibility and ease of use that these AI tools offer, their accuracy, patient-centeredness, and reliability in supporting self-care remain insufficiently evaluated. AIMS AND OBJECTIVES: The primary objective of this study is to evaluate and compare the performance of ChatGPT-4o mini, Gemini, and Copilot in the context of patient self-care by assessing the accuracy, patient-centeredness, and comprehensiveness of their responses against standard recommendations. MATERIALS AND METHODS: Ninety-one case scenarios representing the most common minor ailments were introduced to the three AI models to generate responses that were subsequently assessed and compared with established standard recommendations by three of the study investigators. Evaluation of the responses was conducted on their accuracy, patient-centeredness, comprehensiveness, and similarity. An inter-reliability test was also carried out to confirm the consistency between the three evaluators' assessments. RESULTS: The study findings indicate that ChatGPT-4o mini significantly exceeded Gemini and Copilot in terms of accuracy and presented as mean ± SD (ChatGPT-4o mini: 4.4 ± 0.6, Gemini: 4.1 ± 0.8, Copilot: 3.7 ± 0.7, p < 0.001), patient-centeredness (ChatGPT-4o mini: 4.7 ± 0.6, Gemini: 4.3 ± 1.0, Copilot: 4.2 ± 0.8, p < 0.001), and comprehensiveness (ChatGPT-4o mini: 4.6 ± 0.7, Gemini: 4.2 ± 0.8, Copilot: 3.4 ± 0.7; p < 0.001) among 91 minor ailment case scenarios. Gemini and Copilot showed moderate and low performance, respectively, particularly in complex cases, in contrast to ChatGPT-4o mini. Inter-rater reliability was excellent (Cronbach's alpha ≥ 0.9), confirming assessment consistency. Cosine similarity analysis indicated high overlap between AI and standard recommendations. CONCLUSION: This study shows that AI tools are reliable and precise instruments for self-care of mild diseases. These findings highlight ChatGPT-4o mini's superior reliability and patient-centeredness for self-medication guidance, while underscoring the need for human oversight. However, there is a small chance of variation and errors in the AI-generated responses, which may prohibit complete dependence on AI for self-care recommendations.

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Therapeutic Modulation of IL-6/STAT-3 and Nitric Oxide by Fenofibrate in Patients With Ulcerative Colitis: A Randomized Controlled Pilot Study.

Aldossary KM, Abdallah MS, Kamal N … +6 more , Bahgat MM, Alrubia S, Alsegiani AS, Bahaa MM, Hassan AS, El-Khateeb E

Pharmacotherapy · 2025 Dec · PMID 41330887 · Publisher ↗

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... 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.

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Developing a Statistical Modeling-Based Machine Learning Approach for Capturing Drug Dosing Using a Proton Pump Inhibitor Case.

Massmann A, Baye JF, Weaver M

Pharmacotherapy · 2026 Feb · PMID 41326004 · Publisher ↗

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... 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.

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Terlipressin Administration Strategies in Hepatorenal Syndrome-Acute Kidney Injury: A Narrative Review of Continuous Infusion and Intermittent Bolus Approaches.

Pham TQ, Bass SN, Yerke JR

Pharmacotherapy · 2025 Dec · PMID 41314419 · Publisher ↗

Hepatorenal syndrome-acute kidney injury (HRS-AKI) is a life-threatening complication of cirrhosis, marked by profound splanchnic vasodilation leading to renal hypoperfusion. Guidelines throughout the years have refined... Hepatorenal syndrome-acute kidney injury (HRS-AKI) is a life-threatening complication of cirrhosis, marked by profound splanchnic vasodilation leading to renal hypoperfusion. Guidelines throughout the years have refined the definitions, classifications, and diagnostic criteria of HRS-AKI to promote earlier recognition and intervention. Vasoconstrictor therapy remains the cornerstone of the management of HRS-AKI. Among the available agents, terlipressin is the most well-studied and has been endorsed by multiple international guidelines as first-line therapy for HRS-AKI. Although approved by the United States Food and Drug Administration for use in HRS-AKI with an intermittent intravenous bolus dosing strategy, recent consensus guidance recommends continuous infusion as the preferred route of administration. This narrative review summarizes current evidence from clinical trials and comparative studies evaluating terlipressin continuous infusion versus intermittent bolus dosing strategies, with a focus on efficacy, safety, and operational considerations. Clinical trials comparing the two strategies have shown that the continuous infusion strategy provides comparable, and possibly superior, efficacy in HRS reversal compared to intermittent bolus dosing, while also demonstrating a lower incidence of adverse effects. Operational feasibility is supported by physical and chemical stability in various diluents over 24-h infusion periods; however, implementation challenges remain, including limited line compatibility data and line access issues in patients with cirrhosis. In conclusion, the terlipressin continuous infusion strategy appears to be an efficacious, safe, and operationally plausible alternative to intermittent bolus dosing and may be considered for routine use in clinical practice.

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Constructing a Personalized Treatment Rule for Initial Therapy in Early Parkinson's Disease.

Brehm ZP, Schneider RB, Venuto CS … +4 more , Smith G, Pham CT, McDermott MP, Ertefaie A

Pharmacotherapy · 2026 Jan · PMID 41312798 · Full text

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 suc... 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.

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Machine Learning Algorithms for Predicting Injurious Fall Risk Among Older Adults With Depression: A Prognostic Modeling Study.

Wang GH, Lee YA, Goodin AJ … +3 more , Reise RC, Shorr RI, Lo-Ciganic WH

Pharmacotherapy · 2026 Feb · PMID 41310296 · Publisher ↗

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 he... 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.

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Intravenous Push Levetiracetam Administration Compared With Intravenous Piggyback on Benzodiazepine Use for Acute Seizure Treatment.

Markovich SC, Miller J, Lucarelli R … +2 more , Wilkinson BA, Kavelak HL

Pharmacotherapy · 2025 Dec · PMID 41288267 · Publisher ↗

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 intraven... 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.

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Correction to "Relationship of the Revised Anticholinergic Drug Scale With Cultured Cell-Based Serum Anticholinergic Activity and Cognitive Measures in Older Adults With Mild Cognitive Impairment or Remitted Depression".

Pharmacotherapy · 2025 Dec · PMID 41287893 · Full text

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Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions.

Giacon M, Terrazzino S

Pharmacotherapy · 2026 Feb · PMID 41276479 · Full text

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... 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.

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Intravenous Regular Insulin for Treatment of Hyperkalemia in Overweight Patients.

Kelley D, Piccuirro S, Gandhi P … +1 more , Kim H

Pharmacotherapy · 2025 Dec · PMID 41252223 · Full text

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 opt... 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/m. METHODS: A multicenter, retrospective study was performed in adult patients with BMI ≥ 25 kg/m 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.

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Correlation Between Analgesic and Sedative Drug Withdrawal and Salivary Cortisol Levels in Children in PICUs: A Prospective Cohort Study.

Zhan Y, Lin S, Lin L … +3 more , Lin M, Wang Y, Bao S

Pharmacotherapy · 2025 Dec · PMID 41251283 · Publisher ↗

BACKGROUND AND OBJECTIVE: Opioids and benzodiazepines are the most widely utilized sedative-analgesic agents in pediatric intensive care units (PICUs). However, prolonged exposure to these drugs may lead to drug toleranc... BACKGROUND AND OBJECTIVE: Opioids and benzodiazepines are the most widely utilized sedative-analgesic agents in pediatric intensive care units (PICUs). However, prolonged exposure to these drugs may lead to drug tolerance and dependence, whereas abrupt discontinuation or rapid dose reduction can precipitate withdrawal symptoms, which increase the risk of complications such as medical device dislodgement, exacerbated patient discomfort, and prolonged mechanical ventilation dependency and hospital stays. Studies have revealed that up to 94% of children in PICUs develop iatrogenic withdrawal syndrome (IWS), but there is currently no gold standard for a definitive diagnosis of IWS. This study aims to explore the correlation between salivary cortisol levels, sedation-analgesia status, and the occurrence of IWS in critically ill children. METHODS: A total of 118 critically ill children who were exposed to sedative and/or analgesic drugs for a continuous period of ≥ 5 days at Yuying Children's Hospital, Affiliated Second Hospital of Wenzhou Medical University, from October 2022 to September 2024, were screened, and 106 patients were included in the study. Based on the Sofia observation withdrawal symptoms scale (SOS) score, 42 patients were divided into the IWS group (SOS ≥ 4) and 64 patients into the non-IWS group (SOS < 4). The usage of sedative and analgesic drugs was compared between the two groups, as well as the salivary cortisol concentrations at 24, 48, and 72 h after drug withdrawal. The influencing factors of IWS occurrence and the predictive value of related indicators were analyzed. RESULTS: The cumulative doses of sedative and analgesic drugs administered in the IWS group were statistically significantly higher than those in the non-IWS group (p < 0.05). The median salivary cortisol concentration in the IWS group was significantly higher at 24 h after drug discontinuation compared with the non-IWS group. Logistic results of binary regression analysis showed that a higher level of salivary cortisol at 24 h after discontinuation was an independent predictor for the occurrence of IWS (p < 0.05). Receiver operating characteristic (ROC) curve analysis indicated an area under the curve of 0.857 (95% confidence interval (CI): 0.782-0.932) with sensitivity and specificity of 88.1% and 84.4%, respectively. CONCLUSIONS: A statistically significant correlation was observed between salivary cortisol levels, the cumulative dosage of sedative-analgesic drugs, and the occurrence of IWS. Higher levels of salivary cortisol are a predictor of IWS occurrence. Detecting salivary cortisol levels has clinical application for early diagnosis of IWS and judgment of withdrawal severity in critically ill children.

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