Pharmacotherapy最新文献

Background: Vancomycin (VCM), a first-line treatment option for infections caused by methicillin-resistant Staphylococcus aureus, has been reported to cause nephrotoxicity even within therapeutic concentration ranges. Traditional therapeutic drug monitoring strategies rely primarily on the area under the concentration-time curve (AUC), without adequately accounting for multiple clinical risk factors associated with nephrotoxicity.

Objective: The present study aimed to develop a novel scoring model that integrates clinical risk factors and pharmacokinetic parameters to predict VCM-induced nephrotoxicity and validate its predictive performance.

Methods: We conducted a single-center retrospective cohort study on patients who received VCM therapy between April 2021 and March 2023. A multivariable logistic regression analysis was performed to identify independent risk factors for VCM-induced nephrotoxicity, and regression coefficients were used to construct the scoring model. The predictive performance of the proposed model was compared with a conventional AUC-based model using the area under the receiver operating characteristic curve (ROC AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: The scoring model consisted of the following components: the steady-state area under the concentration-time curve (0-8 points), the concomitant use of tazobactam/piperacillin (2 points), the use of loop diuretics (1 point), and the presence of chronic liver disease (2 points). The proposed model demonstrated high predictive performance, with ROC AUC values of 0.79 (95% confidence interval [CI]: 0.71-0.87) in the derivation cohort and 0.84 (95% CI: 0.72-0.96) in the validation cohort. Furthermore, the proposed model showed significantly better performance than the conventional model in terms of NRI (derivation cohort: 0.78, 95% CI: 0.47-1.08; validation cohort: 1.23, 95% CI: 0.79-1.67) and IDI (derivation cohort: 0.07, 95% CI: 0.04-0.11; validation cohort: 0.27, 95% CI: 0.15-0.39) (p < 0.001).

Conclusion: The scoring model developed in the present study may enhance risk stratification for VCM-induced nephrotoxicity and contribute to advances in individualized dosing strategies in clinical practice.

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.

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.

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.

Backgrounds: Hydroxyurea (HU) remains underutilized in adults with sickle cell disease (SCD) despite proven benefits. Current HU guidelines primarily target sickle cell anemia (SCA), overlooking other genotypes.

Objectives: This study examined HU utilization patterns across genotypes among adults considered to have moderate to severe SCD manifestations by the 2014 National Heart, Lung, and Blood Institute (NHLBI) guideline criteria and identified factors associated with early HU use.

Methods: This retrospective cohort study analyzed electronic health records from the University of Pittsburgh Medical Center (2014-2024) of adults with SCD experiencing three or more vaso-occlusive crises (VOCs) within 12 months. HU utilization rates, stratified by genotype, were assessed at 30-, 90-, 180-, and 365-day intervals after the third VOC episode (index date). Multivariable logistic regression was used to identify factors associated with HU use within 90 days post-index.

Results: Among 411 adults with moderate to severe SCD (≥ 3 VOCs within a year), with a mean age of 42.4 ± 17.9 years and 61.3% female, only 19.5% received HU within 90 days post-index. Although 42.8% of SCA patients received HU within 1 year, only 8.0% of non-SCA patients received the treatment. The SCA genotype was the strongest predictor of HU use (odds ratio [OR] = 4.5, 95% confidence interval [CI]: 2.4-8.7), followed by pulmonary complications. Additional barriers included older age.

Conclusion: Despite guideline recommendations since 2014, HU remains underutilized. Non-SCA patients meeting the severity threshold for HU use are consistently undertreated, highlighting an urgent need for studies establishing HU safety and efficacy in non-SCA genotypes. Future studies should also address age barriers to optimize HU use.

Aims: Guideline-directed medical therapy (GDMT) implementation in heart failure with reduced ejection fraction (HFrEF) remains suboptimal. A computable algorithm was developed to generate a medication optimization score (MOS) and provide guideline-based recommendations. This computable algorithm was previously validated using clinical trial data, and an updated version was developed in 2021 to include sodium-glucose co-transporter 2 inhibitors. This study evaluated the association between the medication optimization information generated by this version of the algorithm and clinical outcomes using real-world data.

Methods: We conducted a retrospective cohort study of 1352 ambulatory adult patients with chronic HFrEF who received care from the advanced heart failure service at the University of Michigan between July 1, 2021, and October 14, 2024. The algorithm-generated MOS was calculated using electronic health record data. The primary outcome was a composite of all-cause mortality or hospitalization. Cox proportional hazards models were used to evaluate the association between baseline MOS and the primary outcome. A time-varying Cox model using the running cumulative MOS and a marginal structural model (MSM) was also conducted. A linear mixed-effects model was used to assess improvement in MOS over time as the secondary outcome.

Results: In the analysis adjusted for HF severity and comorbidities, baseline MOS was associated with a lower hazard of the composite outcome (hazard ratio (HR) 0.96, 95% confidence interval (95% CI): 0.92, 0.99, p = 0.040). In the cumulative time-varying Cox model and the marginal structural model, the association with time-varying MOS became stronger, with HRs of 0.88 (95% CI 0.81-0.95; p = 0.0015) and 0.88 (95% CI 0.83-0.93; p < 0.001), respectively. The event rates per 100 person-years were 44.1 in MOS 0%-33%, 39.5 in MOS 34%-66%, and 31.8 in MOS 67%-100%. Longitudinally, MOS improved over time.

Conclusion: Higher algorithm-generated MOS values were significantly associated with lower all-cause mortality or hospitalization, and the MOS values increased over the follow-up period. This suggested that this algorithm effectively identifies opportunities for GDMT optimization in real-world clinical settings.

Background: Although donor-derived cell-free DNA (dd-cfDNA) serves as a monitoring tool for rejection, few studies have examined its utility in guiding immunosuppression management. Here, we present the largest kidney transplant population in which immunosuppression minimization and subsequent surveillance were guided by dd-cfDNA.

Methods: This retrospective case series evaluated our immunosuppression minimization practice to tacrolimus and prednisone in kidney transplant recipients (KTR) from November 20, 2020, to September 26, 2024. Baseline dd-cfDNA ≤ 0.5% was required before minimization. Immune tolerance was defined by the absence of any immune event after minimization: absolute dd-cfDNA > 0.5%, relative change value (RCV) > 60% from baseline, biopsy-proven acute rejection (BPAR), or de novo donor-specific antibody (DSA). All other KTR were labeled intolerant. The primary endpoint was the rate of immune tolerance.

Results: Immunosuppression was modified to tacrolimus and prednisone in 38 KTR at a median of 223 days post-transplant. Most KTR were older adults at low immunological risk: mean of 69 years and all had a calculated panel reactive antibody of 0%. 21 (55.3%) KTR met the primary end point of tolerance. The remaining 17 KTR were labeled intolerant secondary to dd-cfDNA elevations including absolute > 0.5% or RCV > 60% (n = 16 of 17, 94%), de novo DSA (n = 2 of 17, 11.8%), and/or BPAR (n = 4 of 17, 23.5%). Although not statistically significant, intolerant KTR were numerically more likely to have 5-6 HLA mismatches (82.5% vs. 52.4%, p = 0.31), less likely to have thymoglobulin induction (29.4% vs. 42.9%, p = 0.39), and were minimized earlier after transplant (196 vs. 256 days, p = 0.08) compared with tolerant KTR, respectively. Intervention after dd-cfDNA elevations included immunosuppression increase (50%), additional dd-cfDNA monitoring (81.3%), DSA testing (50%), and allograft biopsy (18.7%).

Conclusion: Approximately 50% of low immunological risk KTR with a baseline dd-cfDNA < 0.5% tolerated immunosuppression minimization to tacrolimus and prednisone without concerning dd-cfDNA elevations, BPAR, or DSA. Our study highlights the role of dd-cfDNA as part of the armamentarium for identifying minimization candidates and performing subsequent surveillance.