The phenotype-genotype reference map: Improving biobank data science through replication.

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA Analyses of Heterogeneous Samples.

Data-independent acquisition has seen breakthroughs that enable comprehensive proteome profiling using short gradients. As the proteome coverage continues to increase, the quality of the data generated becomes much more relevant. Using Spectronaut, we show that the default search parameters can be easily optimized to minimize the occurrence of false positives across different samples. Using an immunological infection model system to demonstrate the impact of adjusting search settings, we analyzed Mus musculus macrophages and compared their proteome to macrophages spiked withCandida albicans. This experimental system enabled the identification of "false positives" as Candida albicans peptides and proteins should not be present in the Mus musculus-only samples. We show that adjusting the search parameters reduced "false positive" identifications by 89% at the peptide and protein level, thereby considerably increasing the quality of the data. We also show that these optimized parameters incurred a moderate cost, only reducing the overall number of "true positive" identifications across each biological replicate by <6.7% at both the peptide and protein level. We believe the value of our updated search parameters extends beyond a two-organism analysis and would be of great value to any DIA experiment analyzing heterogeneous populations of cell types or tissues.

More Than a Decade Since the Latest CONSORT Non-inferiority Trials Extension: Do Infectious Diseases Trials Do Enough?

More than a decade after the Consolidated Standards of Reporting Trials group released a reporting items checklist for non-inferiority randomized controlled trials, the infectious diseases literature continues to underreport these items. Trialists, journals, and peer reviewers should redouble their efforts to ensure infectious diseases studies meet these minimum reporting standards.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

A Bayesian nonparametric approach to correct for underreporting in count data.

We propose a nonparametric compound Poisson model for underreported count data that introduces a latent clustering structure for the reporting probabilities. The latter are estimated with the model's parameters based on experts' opinion and exploiting a proxy for the reporting process. The proposed model is used to estimate the prevalence of chronic kidney disease in Apulia, Italy, based on a unique statistical database covering information on m = 258 municipalities obtained by integrating multisource register information. Accurate prevalence estimates are needed for monitoring, surveillance, and management purposes; yet, counts are deemed to be considerably underreported, especially in some areas of Apulia, one of the most deprived and heterogeneous regions in Italy. Our results agree with previous findings and highlight interesting geographical patterns of the disease. We compare our model to existing approaches in the literature using simulated as well as real data on early neonatal mortality risk in Brazil, described in previous research: the proposed approach proves to be accurate and particularly suitable when partial information about data quality is available.

Metabolomics 2023 workshop report: moving toward consensus on best QA/QC practices in LC-MS-based untargeted metabolomics.

INTRODUCTION: During the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics. OBJECTIVES: The Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document. METHODS: Presentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance. RESULTS: Live polling results and follow-up discussions offered a broad international perspective on QA/QC practices. CONCLUSIONS: Community input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers.

Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps.

BACKGROUND: Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality. PURPOSE: To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps. STUDY TYPE: Retrospective. POPULATION: Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury. FIELD STRENGTH/SEQUENCE: Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts. ASSESSMENT: The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric. STATISTICAL TESTS: Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant. RESULTS: The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72). DATA CONCLUSION: Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

A registered report of a two-site study of variations of the flanker task: ERN experimental effects and data quality.

Error-related negativity is a widely used measure of error monitoring, and many projects are independently moving ERN recorded during a flanker task toward standardization, optimization, and eventual clinical application. However, each project uses a different version of the flanker task and tacitly assumes ERN is functionally equivalent across each version. The routine neglect of a rigorous test of this assumption undermines efforts to integrate ERN findings across tasks, optimize and standardize ERN assessment, and widely apply ERN in clinical trials. The purpose of this registered report was to determine whether ERN shows similar experimental effects (correct vs. error trials) and data quality (intraindividual variability) during three commonly used versions of a flanker task. ERN was recorded from 172 participants during three versions of a flanker task across two study sites. ERN scores showed numerical differences between tasks, raising questions about the comparability of ERN findings across studies and tasks. Although ERN scores from all three versions of the flanker task yielded high data quality and internal consistency, one version did outperform the other two in terms of the size of experimental effects and the data quality. Exploratory analyses of the error positivity (Pe) provided tentative support for the other two versions of the task over the paradigm that appeared optimal for ERN. The present study provides a roadmap for how to statistically compare psychometric characteristics of ERP scores across paradigms and gives preliminary recommendations for flanker tasks to use for ERN- and Pe-focused studies.

Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force.

This ISPOR Good Practices report provides a framework for assessing the suitability of electronic health records data for use in health technology assessments (HTAs). Although electronic health record (EHR) data can fill evidence gaps and improve decisions, several important limitations can affect its validity and relevance. The ISPOR framework includes 2 components: data delineation and data fitness for purpose. Data delineation provides a complete understanding of the data and an assessment of its trustworthiness by describing (1) data characteristics; (2) data provenance; and (3) data governance. Fitness for purpose comprises (1) data reliability items, ie, how accurate and complete the estimates are for answering the question at hand and (2) data relevance items, which assess how well the data are suited to answer the particular question from a decision-making perspective. The report includes a checklist specific to EHR data reporting: the ISPOR SUITABILITY Checklist. It also provides recommendations for HTA agencies and policy makers to improve the use of EHR-derived data over time. The report concludes with a discussion of limitations and future directions in the field, including the potential impact from the substantial and rapid advances in the diffusion and capabilities of large language models and generative artificial intelligence. The report's immediate audiences are HTA evidence developers and users. We anticipate that it will also be useful to other stakeholders, particularly regulators and manufacturers, in the future.

Looking at the future of the medical certification of cause of death (MCCD) in England and Wales.

Internationally, the quality of death certification is poor although there are multiple efforts underway to improve the process. In England, a new medical certification system has been proposed to improve the quality of data. We surveyed general practitioners (n = 95) across the West Yorkshire area of England to appraise their views regarding whether further possible changes to the death certification system could promote their quality.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study.

BACKGROUND AND PURPOSE: The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. METHODS: A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values. RESULTS: Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance. CONCLUSION: This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

A standard to report biological variation data studies - based on an expert opinion.

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.com/bvmindmap) in the form of an interactive mind map that serves as a guideline for researchers planning, performing and reporting BV studies. The mind map addresses study design, data analysis, and reporting criteria, providing embedded links to relevant references and resources. It also incorporates a checklist approach, identifying a Minimum Data Set (MDS) to enable the transportability of BV data and incorporates the Biological Variation Data Critical Appraisal Checklist (BIVAC) to assess study quality. The mind map is open to access and is disseminated through the EFLM BV Database website, promoting accessibility and compliance to a reporting standard, thereby providing a tool to be used to ensure data quality, consistency, and comparability of BV data. Thus, comparable to the STARD initiative for diagnostic accuracy studies, the mind map introduces a Standard for Reporting Biological Variation Data Studies (STARBIV), which can enhance the reporting quality of BV studies, foster user confidence, provide better decision support, and be used as a tool for critical appraisal. Ongoing refinement is expected to adapt to emerging methodologies, ensuring a positive trajectory toward improving the validity and applicability of BV data in clinical practice.

Identifying erroneous height and weight values from adult electronic health records in the All of Us research program.

INTRODUCTION: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us). METHODS: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from âˆ¼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us. RESULTS: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors. DISCUSSION: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values.

Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.

BACKGROUND/AIMS: Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery. METHODS: This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes. RESULTS: In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes. CONCLUSIONS: Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.

FDA experiences with a centralized statistical monitoring tool.

The U.S. Food and Drug Administration (FDA) has broadly supported quality by design initiatives for clinical trials - including monitoring and data validation - by releasing two related guidance documents (FDA 2013 and 2019). Centralized statistical monitoring (CSM) can be a component of a quality by design process. In this article, we describe our experience with a CSM platform as part of a Cooperative Research and Development Agreement between CluePoints and FDA. This agreement's approach to CSM is based on many statistical tests performed on all relevant subject-level data submitted to identify outlying sites. An overall data inconsistency score is calculated to assess the inconsistency of data from one site compared to data from all sites. Sites are ranked by the data inconsistency score (-log10p,where p is an aggregated p-value). Results from a deidentified trial demonstrate the typical data anomaly findings through Statistical Monitoring Applied to Research Trials analyses. Sensitivity analyses were performed after excluding laboratory data and questionnaire data. Graphics from deidentified subject-level trial data illustrate abnormal data patterns. The analyses were performed by site, country/region, and patient separately. Key risk indicator analyses were conducted for the selected endpoints. Potential data anomalies and their possible causes are discussed. This data-driven approach can be effective and efficient in selecting sites that exhibit data anomalies and provides insights to statistical reviewers for conducting sensitivity analyses, subgroup analyses, and site by treatment effect explorations. Messy data, data failing to conform to standards, and other disruptions (e.g. the COVID-19 pandemic) can pose challenges.

Factors impacting clinical data and documentation quality in Australian aged care and disability services: a user-centred perspective.

BACKGROUND: Research has highlighted a need to improve the quality of clinical documentation and data within aged care and disability services in Australia to support improved regulatory reporting and ensure quality and safety of services. However, the specific causes of data quality issues within aged care and disability services and solutions for optimisation are not well understood. OBJECTIVES: This study explored aged care and disability workforce (referred to as 'data-users') experiences and perceived root causes of clinical data quality issues at a large aged care and disability services provider in Western Australia, to inform optimisation solutions. METHODS: A purposive sample of n = 135 aged care and disability staff (including community-based and residential-based) in clinical, care, administrative and/or management roles participated in semi-structured interviews and web-based surveys. Data were analysed using an inductive thematic analysis method, where themes and subthemes were derived. RESULTS: Eight overarching causes of data and documentation quality issues were identified: (1) staff-related challenges, (2) education and training, (3) external barriers, (4) operational guidelines and procedures, (5) organisational practices and culture, (6) technological infrastructure, (7) systems design limitations, and (8) systems configuration-related challenges. CONCLUSION: The quality of clinical data and documentation within aged care and disability services is influenced by a complex interplay of internal and external factors. Coordinated and collaborative effort is required between service providers and the wider sector to identify behavioural and technical optimisation solutions to support safe and high-quality care and improved regulatory reporting.

Smoking data quality of primary care practices in comparison with smoking data from the New Zealand Maori and Pacific abdominal aortic aneurysm screening programme: an observational study.

BACKGROUND: Quality smoking data is crucial for assessing smoking-related health risk and eligibility for interventions related to that risk. Smoking information collected in primary care practices (PCPs) is a major data source; however, little is known about the PCP smoking data quality. This project compared PCP smoking data to that collected in the Maori and Pacific Abdominal Aortic Aneurysm (AAA) screening programme. METHODS: A two stage review was conducted. In Stage 1, data quality was assessed by comparing the PCP smoking data recorded close to AAA screening episodes with the data collected from participants at the AAA screening session. Inter-rater reliability was analysed using Cohen's kappa scores. In Stage 2, an audit of longitudinal smoking status was conducted, of a subset of participants potentially misclassified in Stage 1. Data were compared in three groups: current smoker (smoke at least monthly), ex-smoker (stopped > 1 month ago) and never smoker (smoked < 100 cigarettes in lifetime). RESULTS: Of the 1841 people who underwent AAA screening, 1716 (93%) had PCP smoking information. Stage 1 PCP smoking data showed 82% concordance with the AAA data (adjusted kappa 0.76). Fewer current or ex-smokers were recorded in PCP data. In the Stage 2 analysis of discordant and missing data (N = 313), 212 were enrolled in the 29 participating PCPs, and of these 13% were deceased and 41% had changed PCP. Of the 93 participants still enrolled in the participating PCPs, smoking status had been updated for 43%. Data on quantity, duration, or quit date of smoking were largely missing in PCP records. The AAA data of ex-smokers who were classified as never smokers in the Stage 2 PCP data (N = 27) showed a median smoking cessation duration of 32 years (range 0-50 years), with 85% (N = 23) having quit more than 15 years ago. CONCLUSIONS: PCP smoking data quality compared with the AAA data is consistent with international findings. PCP data captured fewer current and ex-smokers, suggesting ongoing improvement is important. Intervention programmes based on smoking status should consider complementary mechanisms to ensure eligible individuals are not missed from programme invitation.

Assessing the quality and value of metabolic chart data for capturing core outcomes for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency.

BACKGROUND: Generating rigorous evidence to inform care for rare diseases requires reliable, sustainable, and longitudinal measurement of priority outcomes. Having developed a core outcome set for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, we aimed to assess the feasibility of prospective measurement of these core outcomes during routine metabolic clinic visits. METHODS: We used existing cohort data abstracted from charts of 124 children diagnosed with MCAD deficiency who participated in a Canadian study which collected data from birth to a maximum of 11 years of age to investigate the frequency of clinic visits and quality of metabolic chart data for selected outcomes. We recorded all opportunities to collect outcomes from the medical chart as a function of visit rate to the metabolic clinic, by treatment centre and by child age. We applied a data quality framework to evaluate data based on completeness, conformance, and plausibility for four core MCAD outcomes: emergency department use, fasting time, metabolic decompensation, and death. RESULTS: The frequency of metabolic clinic visits decreased with increasing age, from a rate of 2.8 visits per child per year (95% confidence interval, 2.3-3.3) among infants 2 to 6 months, to 1.0 visit per child per year (95% confidence interval, 0.9-1.2) among those ≥ 5 years of age. Rates of emergency department visits followed anticipated trends by child age. Supplemental findings suggested that some emergency visits occur outside of the metabolic care treatment centre but are not captured. Recommended fasting times were updated relatively infrequently in patients' metabolic charts. Episodes of metabolic decompensation were identifiable but required an operational definition based on acute manifestations most commonly recorded in the metabolic chart. Deaths occurred rarely in these patients and quality of mortality data was not evaluated. CONCLUSIONS: Opportunities to record core outcomes at the metabolic clinic occur at least annually for children with MCAD deficiency. Methods to comprehensively capture emergency care received at outside institutions are needed. To reduce substantial heterogeneous recording of core outcome across treatment centres, improved documentation standards are required for recording of recommended fasting times and a consensus definition for metabolic decompensations needs to be developed and implemented.

Wearable device adherence among insufficiently-active young adults is independent of identity and motivation for physical activity.

Wearable devices are increasingly being integrated to improve prevention, chronic disease management and rehabilitation. Inferences about individual differences in device-measured physical activity depends on devices being worn long enough to obtain representative samples of behavior. Little is known about how psychological factors are associated with device wear time adherence. This study evaluated associations between identity, behavioral regulations, and device wear adherence during an ambulatory monitoring period. Young adults who reported insufficient physical activity (N = 271) were recruited for two studies before and after the SARS-COVID-19 pandemic declaration. Participants completed a baseline assessment and wore an Actigraph GT3X + accelerometer on their waist for seven consecutive days. Multiple linear regression indicated that wear time was positively associated with age, negatively associated with integrated regulation for physical activity, and greater after (versus before) the pandemic declaration. Overall, the model accounted for limited variance in device wear time. Exercise identity and exercise motivation were not associated with young adults' adherence to wearing the physical activity monitors. Researchers and clinicians can use wearable devices with young adults with minimal concern about systematic motivational biases impacting adherence to device wear.

An Analysis of the Mode of Delivery, Risk Factors, and Subgroups with High Caesarean Birth Rates Using Robson Classification System.

OBJECTIVE: We aimed to understand the utilization of the mode of delivery and related risk factors. Further aimed to apply the Robson classification system to evaluate the data quality and analyze the CS rates in subgroups. METHODS: We conducted a retrospective descriptive study by reviewing the medical records of all women who delivered at the State Hospital in 2019. A proforma was developed for extracting data from patient records. All women with six obstetric parameters were categorized into Robson groups to determine the absolute and relative contributions of each group to the overall CS rate. RESULTS: Of 797 deliveries, 401 (50.2%) were CSs. Being older, being Turkish Cypriot, having preterm births, previous CS, multiple fetuses, and having breech or transverse fetal presentations were related to having higher risks of CS. The most common medical indication for CSs (52.3%) was a history of previous CSs. Robson Group 5 contributed the most (50.7%) to the overall CS rate, with the highest absolute contribution of 21.8%. Group 10 and Group 8 were the second and third highest contributors to the overall CS rate, with relative contributions of 25.3% and 9.0%, respectively. CONCLUSIONS: Findings revealed the substandard quality of obstetric data and a noticeably high overall CS rate. The top priority should be given to improving the quality of medical records. It underscored the necessity of implementing the Robson classification system as a standard clinical practice to enhance data quality, which helps to effectively evaluate and monitor the CS rates in obstetric populations.

Caesarean section rates are increasing worldwide, and the Robson Classification System is recommended by the WHO to evaluate and monitor the CS rates. This study is the first to use Robson classifications and revealed high CS rates in specific subgroups of the obstetric population. The inadequate, substandard data quality highlighted the areas that urgently needed improvement in clinical practices at the largest state hospital. The study lays the foundation for further nationwide studies and demonstrates the importance of the Robson classification system. Specific recommendations were provided to the hospital management for improving the quality of the obstetric data and monitoring CS rates.