Validity Of Approach Research Articles

Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management

A key best practice to prevent and treat pressure injuries (PIs) is to ensure at-risk individuals are repositioned regularly. Our team designed a non-contact position detection system that predicts an individual’s position in bed using data from load cells under the bed legs. The system was originally designed to predict the individual’s position as left-side lying, right-side lying, or supine. Our previous work suggested that a higher precision for detecting position (classifying more than three positions) may be needed to determine whether key bony prominences on the pelvis at high risk of PIs have been off-loaded. The objective of this study was to determine the impact of categorizing participant position with higher precision using the system prediction F1 score. Data from 18 participants was collected from four load cells placed under the bed legs and a pelvis-mounted inertial measurement unit while the participants assumed 21 positions. The data was used to train classifiers to predict the participants’ transverse pelvic angle using three different position bin sizes (45°, ~30°, and 15°). A leave-one-participant-out cross validation approach was used to evaluate classifier performance for each bin size. Results indicated that our prediction F1 score dropped as the position category precision was increased.

A hybrid approach for Bengali sentence validation

Bengali is the official language of Bangladesh and is widely used in Bangladesh and West Bengal in India. Due to the growing accessibility of the internet and smart devices, the use of digital text material and documents in Bengali is growing with time. An automated Bengali Sentence Validation System is proposed in this study to effectively determine the correctness of sentences in such extensively available Bengali content. As far as we know, no substantial work has been done in the field of Bengali Sentence Validation utilizing deep learning approaches. Due to the lack of linguistic resources, sophisticated Natural Language Processing tools, and benchmark datasets, developing an automated Sentence Validation System for a limited-resource language like Bengali is challenging. Additionally, Bengali Sentences come in two morphological varieties (Sadhu-bhasha and Cholito-bhasha), making the validation process more challenging. The proposed automated Bengali Sentence Validation system contains the CNN-BiLSTM hybrid classifier model. As of now, there is no standard dataset for Bengali sentence validation. Due to the lack of a standard dataset, we collected Bengali sentences from different sources in Bangladesh and developed a Bengali Sentence Validation (BSV) Dataset with around 5000 labelled sentences arranged into two categories such as correct and incorrect. Experimental results demonstrate that the proposed system outperformed other classifier models and existing approaches for Bengali Sentence Validation and is able to categorize a wide range of Bengali sentences based on their correctness. The system’s F1 score for the Bengali Sentence Validation is 98%.

Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current < 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50-56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation. Microarray and RNA-sequencing datasets (GSE47184, GSE96804, GSE104948, GSE104954, GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database. Differential expression analysis identified the differentially expressed genes (DEGs) between patients with DN and controls. Diverse machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, were used to enhance gene selection accuracy and predictive power. We integrated summary-level data from genome-wide association studies on DN with expression quantitative trait loci data to identify genes with potential causal relationships to DN. The predictive performance of the biomarker gene was validated using receiver operating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted to investigate potential mechanisms. Finally, the biomarker gene was validated using quantitative real-time polymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314 DEGs, seven characteristic genes with high predictive performance were identified using three integrated machine learning algorithms. MR analysis revealed 219 genes with significant causal effects on DN, ultimately identifying one co-expressed gene, carbonic anhydrase II (CA2), as a key biomarker for DN. The ROC curves demonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analysis indicated a significant association between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights. This biomarker was validated using clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we successfully identified and validated CA2 as a promising biomarker for DN with excellent predictive performance. The biomarker may play a role in the pathogenesis and progression of DN via immune-related pathways. These findings provide important insights into the molecular mechanisms underlying DN and may inform the development of personalized treatment strategies for this disease.

Resolving Phenotypic Variability in Mitochondrial Diseases: Preliminary Findings of a Proteomic Approach

The introduction of new sequencing approaches into clinical practice has radically changed the diagnostic approach to mitochondrial diseases, significantly improving the molecular definition rate in this group of neurogenetic disorders. At the same time, there have been no equal successes in the area of in-depth understanding of disease mechanisms and few innovative therapeutic approaches have been proposed recently. In this regard, the identification of the molecular basis of phenotypic variability in primary mitochondrial disorders represents a key aspect for deciphering disease mechanisms with important therapeutic implications. In this study, we present data from proteomic investigations in two subjects affected by mitochondrial disease characterized by a different clinical severity and associated with the same variant in the TWNK gene, encoding the mitochondrial DNA and RNA helicase with a specific role in the mtDNA replisome. Heterozygous pathogenic variants in this gene are associated with progressive external ophthalmoplegia and ptosis, usually with adult onset. The overall results suggest an imbalance in glucose metabolism and ROS production/regulation, with possible consequences on the phenotypic manifestations of the enrolled subjects. Although the data will need to be validated in a large cohort, proteomic investigations have proven to be a valid approach for a deep understanding of these neurometabolic disorders.

Regression Approaches to Assess Effect of Treatments That Arrest Progression of Symptoms.

Motivated by a small sample example in neonatal onset multisystem inflammatory disease (NOMID), we propose a method that can be used when the interest is testing for an association between a changes in disease progression with start of treatment compared to historical disease progression prior to treatment. Our method estimates the longitudinal trajectory of the outcome variable and adds an interaction term between an intervention indicator variable and the time since initiation of the intervention. This method is appropriate for a situation in which the intervention slows or arrests the effect of the disease on the outcome, as is the case in our motivating example. By simulation in small samples and restricted sets of treatment initiation times, we show that the generalized estimating equations (GEE) formulation with small sample adjustments can bound the Type I error rate better than GEE and linear mixed models without small sample adjustments. Permutation tests (permuting the time of treatment initiation) is another valid approach that can also be useful. We illustrate the methodology through an application to a prospective cohort of NOMID patients enrolled at the NIH clinical center.

Virtual validation of a Metro rolling-stock for exterior and interior noise in standard controlled conditions

Validation of rolling-stock is a time and cost expensive task during the development of train. Replacing type tests by simulation (virtual validation) that fits the need of the complete railway industry in terms of accuracy and uncertainty, is possible in some cases. Simulation is widely used by train manufacturers during development of new products with good accuracy of prediction in a controlled environment as free field and ballasted track. Methodologies available are able to predict correctly in controlled conditions (ISO 3095/3381) interior and exterior noise for different operating conditions. In this paper, simulation cases will be presented for a standard metro product operating in controlled conditions. Simulations will be compared with experimental data for interior and exterior noise. Exterior noise modelling and propagation is done with analytical tools while interior noise is modeled using an experimental-numerical (beam-tracing) approach. Input data for simulations corresponds to numerical models of rolling noise and measured sound power level of equipment. Validation of the models are discussed and analyzed to confirm the reliability of virtual validation for rolling stock operating at controlled conditions. A proposal of a virtual validation approach based on uncertainties of the simulation is also discussed.

Efficient tuning of active sound design in electric vehicles - An interactive validation approach

The implementation of active sound design models in a target vehicle requires careful tuning of all synthetic sound elements with respect to existent vehicle interior noise, current driving conditions and general driving styles. Besides the creative aspects in sound design, this tuning process is highly time-consuming. It must be ensured that load feedback, speed feedback and general interactivity with the synthetic sound meet the driver's expectations and match the intended emotional expressions of the sound concept. Testing the tuning parameters in real vehicles requires access to the vehicle prototype and all involved system components, which is often not granted in early development stages. A key approach to overcome this difficulty is the application of acoustic driving simulator technology, directly linked to the main tuning parameters. Combining this methodology with an efficient procedure for perceptive assessment of the sound design model, an interactive approach to active sound design is established. This paper discusses current progress in the field of efficient tuning of active sound design models, presenting a case study for a high-performance electric vehicle.

PopBERT. Detecting Populism and Its Host Ideologies in the German Bundestag

Abstract The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist rhetoric. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013–2021). Following the ideational definition of populism, we label moralizing references to “the virtuous people” or “the corrupt elite” as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is “thickened,” we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.

Validation of Student Project Based Learning Worksheets with a Non-Parametric Statistical Approach on Greenhouse Effect Material

Abstract So that can to produce a PjBL-based Student Worksheet with a valid and practical statistical approach to the Greenhouse Effect material in the Environmental Physics course, this research was conducted. Through the stages of planning, development procedures and Tessmer’s formative evaluation model with expert review stages in order to validate the language, content and design which is the definition of the Rowntree research model used in conducting this research. The use of the Likert scale by questionnaire techniques and walkthroughs is used to design validity data analysis for student worksheet products. As for the data analysis carried out, very valid results were obtained from a maximum score of 5 with a value of 4.3 for validation of the greenhouse effect context using a statistical approach, 4.9 for validation of working width design and 5 for linguistic validation. Tests for practical use are carried out on students. At the one-to-one evaluation stage, test results were obtained which showed the average score and for testing in small groups, it reached 93.7% and 97.5% in the very practical category.

Validation of an algorithm to prioritize patients for comprehensive medication management in primary care settings.

Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings.

Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population. Methods: Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70/30 train/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Naïve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Results: A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (±standard deviation [SD]) of 48.12 (±9.61) and 49.47 (±9.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure. Conclusion: Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies.

Network pharmacology-based investigation and experimental validation of the mechanism of metformin in the treatment of acute myeloid leukemia

Metformin, a widely used anti-diabetic agent, has shown significant anti-cancer properties as reported in in various cancers, including acute myeloid leukemia. However, the detailed mechanisms by which metformin influences acute myeloid leukemia remain unrevealed. Employing a synergistic approach of network pharmacology and experimental validation, this study systematically identifies and analyzes potential metformin targets and AML-related genes. These findings are then cross-referenced with biomedical databases to construct a target-gene network, providing insights into metformin's pharmacodynamics in AML treatment. Protein–Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses are utilized. Results show metformin's effectiveness in inhibiting AML cell proliferation and inducing apoptosis through the AKT/HIF1A/PDK1 signaling pathway. This research provides insights into metformin's clinical application in AML treatment.

Classification of windswept posture in daily life using tri-axial accelerometers.

An asymmetric windswept posture is often seen in children with severe cerebral palsy (CP). However, it is still unclear how long children with CP remain in the windswept posture in daily life. Thus, we developed a triple-accelerometer system for detecting windswept posture. The aim of this study was to assess the validity of a system for classifying various body postures and movements. We assessed the accuracy of our system in nine healthy young adults (age range, 21-23 years). The participants wore acceleration monitors on the sternum and both thighs, then spent 3 min each in eight different positions and three physical activities. Once accuracy was confirmed, we assessed the posture and movements for 24 h in six healthy young adults (age range, 21-23 years) in their home environments. The body postures and activities were correctly detected: the agreement across the subjects were 100% compatible with the subjects' activity logs at least 68% of the time, and at least 96% of the time for recumbent positions. We concluded that the proposed monitoring system is a reliable and valid approach for assessing windswept hip posture in a free-living setting.

Identification of ferroptosis biomarkers and immune infiltration landscapes in atrial fibrillation: A bioinformatics analysis.

Ferroptosis has been recognized as a critical factor in the development of atrial fibrillation (AF), but its precise mechanisms remain unclear. We downloaded the GSE115574 dataset from the gene expression omnibus database to analyze the expression levels of ferroptosis-related genes (FRGs) and identify differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) machine learning techniques were employed to identify key genes associated with AF. The diagnostic performance of these genes was evaluated using Receiver operating characteristic curves (ROC) and validated in an independent AF dataset. miRNA and lncRNA predictions for potential binding to these key genes were conducted using miRBase, miRDB, and TargetScan. Furthermore, gene set enrichment analysis (GSEA) enrichment analysis, immune cell infiltration analysis, and targeted drug prediction were performed. The intersection of LASSO regression and SVM-RFE analyses identified 7 DEGs significantly associated with AF. Validation through ROC and an additional dataset confirmed the importance of MAPK14, CAV1, and ADAM23. Significant infiltration of memory B cells, regulatory T cells, and monocytes was observed in atrial tissues. Seventy-two miRNAs were predicted to potentially target MAPK14, and 2 drugs were identified as targeting CAV1. This study underscores the involvement of FRGs in AF through machine learning and validation approaches. The observed immune cell infiltration suggests a potential link between immune response and AF. The predicted ceRNA network offers new insights into gene regulation, presenting potential biomarkers and therapeutic targets for AF.

Lifecycle Approach to Process Validation and Its Implementation in Pharmaceutical Industry

Process Validation is a GMP concept and is required by the agencies to assure quality of the manufactured drug products. The need, motivation, understanding and the practices of Process validation have evolved enormously from the time of its inception. The lifecycle (modern) approach of process validation and its elements are discussed in detail here. The review article aims to provide a general understanding of the concepts of lifecycle approach of process validation. The review further provides an illustration for practical implementation of the concepts outlined for the pharmaceutical manufacturing industry through an example of a hypothetical sterile injectable product. A properly developed process/product according to principles of lifecycle approach discussed in this article should meet the expectations of majority of regulatory agencies.

Quantification of Bictegravir and its impurities: A novel RP-UPLC Approach for Development and Validation.

A new ultra-pressure liquid chromatography (UPLC) method was developed for accurately and reliably quantifying impurities in Bictegravir. The method utilized Waters Acquity HSSC18 column (50mm x 2.1mm, 1.8 µ) at room temperature. The mobile phase consists of Acetonitrile and Buffer (0.1 percent formic acid) in 80:20 and pumped at a flow rate of 0.2 mL/min. This optimized method achieves exceptional separation of Bictegravir and its impurities with excellent resolution at 281 nm. The method’s accuracy is demonstrated by a % recovery range for Bictegravir and its impurities between 99.1% and 100.3%. The correlation coefficient (R²) for BIC and its impurities is consistently above 0.999, indicating high linearity. The method’s robustness was confirmed by its stability against variations in flow rate and mobile phase ratio. Additionally, forced degradation studies confirmed the stability of Bictegravir, highlighting the method’s reliability. Overall, developed UPLC method is precise, accurate, robust, and linear, making it suitable for routine analysis of BIC-related substances in quality control laboratories at manufacturing sites during commercial production.

A Review on QbD Approach in Analytical Method Development and Validation .

Quality by Design (QbD) is a cutting-edge pharmaceutical development that enhances analytical method robustness by emphasizing a thorough understanding of processes, risk management, and continuous improvement. Unlike traditional methods that often focus on end-product testing, QbD integrates quality into the development stages through a structured process. This involves defining the analytical target profile, identifying critical quality attributes, and employing the design of experiments to optimize method parameters. The implementation of QbD begins with a comprehensive understanding of the analytical objectives, leading to the identification of method variables that impact quality. Using risk assessment tools such as failure mode and effects analysis, developers can pinpoint potential risks and focus on areas of highest concern. Design of Experiments (DoE) plays a crucial role in this approach by exploring the interaction between variables and establishing a robust design space. Organizations can decrease variability, comply with regulations, and perform better methods when they adopt QbD concepts. This systematic approach not only minimizes the risk of method failures but also ensures consistent product quality, leading to increased patient safety and satisfaction. Furthermore, QbD facilitates faster development cycles and cost efficiencies by reducing the need for post-launch troubleshooting and modifications. This abstract provides a comprehensive overview of the role and significance of QbD in analytical method development, highlighting its components, benefits, and impact on the pharmaceutical industry.

Case Studies on Changes and Proposed Process Development Approaches reflecting applicability of PDA Technical Report No. 89 Strategies for Vaccine Development and Lifecycle Management.

Vaccines are complex and a very diverse group of products with relatively long product lifecycles. The manufacturing programs for these vaccines need to be continually updated to comply with evolving regulatory expectations. Members of the Parenteral Drug Association (PDA) Vaccines Interest Group (VIG) authored and published PDA Technical Report No. 89 Strategies for Vaccine Development and Lifecycle Management (TR 89) which seeks to provide context to vaccine developers and manufacturers regarding key aspects of new or legacy vaccines such as control strategy from process development to vaccine lifecycle management, comparability and lifecycle management including technical, validation, quality and regulatory perspectives. To further explain and illustrate the concepts and topics discussed, seven relevant situations were selected as either case studies associated with changes implemented or proposed process development strategies, which are discussed in this article. The situations described are: working cell bank, modification or update of externally supplied product contact components for vaccine manufacturing, raw material change, new product at an existing site, vaccine development acceleration by leveraging existing platforms, selection and implementation of potency method, and modeling for stability forecast prediction. For each situation, the applicable key concepts from TR#89 are discussed as follows: Control Strategy, Prior Knowledge, Relying on PQS, Classification of Parameters, Validation Approach, Use of a Risk-based Approach, Comparability, Use of ICHQ12 and Additional Regulatory Considerations.

Validation of Administrative Data and Timing of Point Prevalence Surveys for Antibiotic Monitoring

Point prevalence surveys (PPSs) are used globally to collect data on antibiotic prescriptions. However, the optimal frequency for data collection to ensure comprehensive understanding of antibiotic use and to target and monitor stewardship interventions remains unknown. To identify the optimal frequency for collecting data on antibiotic use among the pediatric population through PPSs leveraging administrative data. This prognostic study used a cross-sectional validation approach and was conducted in pediatric outpatient and inpatient settings in the Veneto region of Italy. Antibiotics were classified according to the World Health Organization Access, Watch and Reserve criteria. Prescribing rates of access antibiotics were analyzed for pediatric inpatients with records dated between October 1, 2014, and December 31, 2022, and outpatients with records dated between January 1, 2010, and December 31, 2022. The study included children younger than 15 years with an antibiotic prescription who were admitted to the pediatric acute care unit or evaluated by a primary care pediatrician. Data analysis was performed from October 2023 to January 2024. An algorithm was developed to identify optimal time frames for conducting PPSs. This approach sought to minimize the discrepancy between quarterly and yearly PPS results, aiming to accurately estimate annual antibiotic prescribing rates in both inpatient and outpatient settings (primary outcome). External validity of the optimal PPS time frames derived from outpatient data when applied to the inpatient setting was also investigated. Validation involved assessing the effectiveness of administrative data in identifying strategic PPS periods for capturing inpatient antibiotic use patterns (secondary outcome). This analysis included 106 309 children: 3124 were inpatients (1773 males [56.8%]) and 103 185 were outpatients (53 651 males [52.0%]). A total of 5099 and 474 867 antibiotic prescriptions from inpatients and outpatients were analyzed, respectively. Outpatients tended to be older than inpatients, with a median age of 3.2 (IQR, 1.3-6.3) years vs 2.6 (IQR, 0.6-6.6) years, respectively, and with a lower burden of clinical comorbidities (≥1 comorbidity: 6618 [6.4%] vs 1141 [36.5%], respectively). The algorithm successfully identified distinct time frames within the calendar year from inpatient and outpatient records optimized for PPS data collection. Rates obtained from the quarterly PPS during these identified periods exhibited greater agreement with annual antibiotic prescribing rates (inpatient: r = 0.17, P < .001; and outpatient: r = 0.42, P < .001) than those derived from the yearly PPS (inpatient: r = 0.04, P = .58; and outpatient: r = 0.05, P = .34), with a Δ reduction of up to 89.8% (where Δ represents the percentage point change in antibiotic prescribing rates). Furthermore, the optimal PPS time frames gleaned from the outpatient data demonstrated robust applicability to the inpatient setting, yielding comparable results in both scenarios. This study evaluated the potential of administrative data in determining the optimal timing of PPS implementation. The quarterly PPS balanced precision and sustainability, especially when implemented during strategically selected periods across different seasons. Further studies are needed to validate the algorithm used in this study, especially in post-COVID-19 pandemic years and different settings.