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      string(275) "NCT01340664 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin"
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  ["project_title"]=>
  string(146) "Development and External Validation of a Clinical Prediction Model for Genital Mycotic Infection After Canagliflozin Initiation in Type 2 Diabetes"
  ["project_narrative_summary"]=>
  string(729) "Genital mycotic infection (GMI) is a common adverse effect of sodium-glucose cotransporter-2 (SGLT2) inhibitors, but individual risk varies markedly. Existing studies identify female and previous GMI as major risk factors, yet evidence for rigorously developed and externally validated prediction models remains limited. Using participant-level data from randomized canagliflozin trials, we aim to develop a 26-week GMI risk model in general type 2 diabetes populations and validate it in independent trials representing high cardiovascular risk, older age, and kidney disease. We will compare the full model with a simple model based on sex and prior GMI and evaluate whether additional clinical information improves prediction."
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    string(6) "Qichao"
    ["last_name"]=>
    string(3) "Sun"
    ["degree"]=>
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    ["primary_affiliation"]=>
    string(45) "Suzhou Hospital of Nanjing Medical University"
    ["email"]=>
    string(18) "sunqichaos@163.com"
    ["state_or_province"]=>
    string(7) "Jiangsu"
    ["country"]=>
    string(5) "China"
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  ["property_scientific_abstract"]=>
  string(1502) "Background: Genital mycotic infection (GMI) is an established adverse effect of SGLT2 inhibitors. Although several risk factors are known, independently validated absolute-risk prediction models remain limited.

Objective: To develop and validate a model predicting 26-week GMI risk after canagliflozin initiation in adults with type 2 diabetes (T2DM), and to assess whether additional clinical information improves prediction beyond sex and prior GMI.

Study Design: Clinical prediction model development and trial-level external validation using individual participant data from multiple randomized controlled trials.

Participants: Adults with T2DM receiving canagliflozin in eligible trials. Glycemic-efficacy trials will be used for model development, CANVAS for primary external validation, and trials involving older adults and chronic kidney disease for transportability assessment.

Primary and Secondary Outcome Measure(s): Primary: time to first treatment-emergent GMI within 26 weeks. Secondary: first GMI through 52 weeks, recurrent GMI, and clinically significant GMI where compatible data are available.

Statistical Analysis: A parsimonious sex-plus-prior-GMI model will be compared with a multivariable penalized Cox model and prespecified machine-learning models. Internal-external validation will be performed across development trials, followed by independent trial-level validation. Discrimination, calibration, and clinical utility will be evaluated."
  ["project_brief_bg"]=>
  string(2927) "Sodium-glucose cotransporter-2 (SGLT2) inhibitors provide established glycemic and cardiorenal benefits but increase the risk of genital mycotic infection (GMI), largely in the setting of pharmacologically induced glucosuria. In pooled Phase III canagliflozin trials (N=9,439), GMI occurred more frequently with canagliflozin than with control in both women (14.7% and 13.9% with 100 and 300 mg vs 3.1%) and men (7.3% and 9.3% vs 1.6%). Most events were mild to moderate; in women, most occurred within the first 4 months of treatment, whereas in men events occurred predominantly within the first year [1]. The observed event frequency and early occurrence support the feasibility and clinical relevance of risk prediction.

Substantial heterogeneity in GMI risk has been consistently reported. In a nationwide dapagliflozin audit, female and prior GMI were the principal predictors of infection within 26 weeks [2]. Similarly, among 21,004 individuals initiating SGLT2 inhibitors in routine clinical practice, female sex and prior GMI were the strongest risk factors; 1-year absolute risk reached 23.7% in women and 12.1% in men with a history of GMI. In contrast, baseline HbA1c was not independently associated with GMI risk among SGLT2 inhibitor users [3]. A recent study further identified sex-specific risk factors and demonstrated good discrimination when these factors were combined, but only 36 genital infection events were observed and independent external validation was not performed [4]. Thus, although important risk factors are well recognized, evidence for rigorously developed and externally validated absolute-risk prediction models remains limited.

The proposed study will extend previous risk-factor analyses by developing and validating an absolute-risk prediction model across multiple randomized canagliflozin trials. Glycemic-efficacy trials representing different background glucose-lowering therapies will provide a broad development population, while clinically distinct trials will be reserved for validation, including CANVAS for patients at high cardiovascular risk, a dedicated older-adult trial, and CREDENCE for patients with albuminuric chronic kidney disease. This structure will allow direct assessment of model transportability rather than relying on a random split of a single pooled dataset.

A central clinical question is whether additional model complexity provides meaningful benefit beyond established risk factors. We will therefore compare the full multivariable model with a prespecified parsimonious model based on sex and prior GMI. If additional clinical variables provide little incremental predictive value, the findings would support a simple and readily implementable risk-stratification approach. Conversely, reproducible improvement across independent validation populations would support development of a more comprehensive clinical prediction tool."
  ["project_specific_aims"]=>
  string(1204) "Aim 1: To develop a clinical prediction model for estimating the 26-week absolute risk of genital mycotic infection (GMI) after initiation of SGLT2 inhibitor therapy, using routinely available baseline clinical characteristics from canagliflozin randomized trials.

Aim 2: To externally validate the model across clinically distinct trial populations, including patients at high cardiovascular risk, older adults, and patients with kidney disease, and to assess model transportability across these populations.

Aim 3: To determine whether additional clinical predictors and machine-learning approaches provide meaningful improvement over a parsimonious model based on sex and prior GMI, and to explore heterogeneity in GMI risk using prespecified subgroup and treatment-by-predictor analyses.

Hypotheses: We hypothesize that sex and prior GMI will account for a substantial proportion of predictable risk; that additional baseline clinical variables will improve absolute-risk estimation in at least some populations; and that a model developed in conventional T2DM trial populations will retain acceptable discrimination and calibration across clinically distinct validation cohorts."
  ["project_study_design"]=>
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    ["label"]=>
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  ["project_purposes"]=>
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      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
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  ["project_research_methods"]=>
  string(1483) "Individual participant-level data will be requested from 12 randomized canagliflozin trials in adults with T2DM: NCT01081834, NCT01106625, NCT01106677, NCT01106690, NCT00968812, NCT01137812, NCT01809327, NCT02025907, NCT01340664, NCT01032629 (CANVAS), NCT01106651, and NCT02065791 (CREDENCE).

Glycemic-efficacy trials with compatible baseline predictors, GMI ascertainment, and follow-up will form the development cohort. CANVAS will be reserved for primary trial-level external validation. NCT01106651 will assess transportability in older adults, and CREDENCE will assess transportability in patients with albuminuric chronic kidney disease. Trials with incomplete compatibility for the 26-week endpoint or required predictors may contribute to prespecified supportive or sensitivity analyses rather than the primary model.

For the primary analysis, participants must have T2DM, be randomized to canagliflozin, receive at least one dose, and have post-dose adverse-event follow-up. Participants with active or ongoing GMI at treatment initiation, no usable post-dose follow-up, or indeterminate outcome timing will be excluded. Resolved prior GMI will be retained as a candidate predictor. Sporadically missing predictor data will be handled statistically rather than by complete-case exclusion. Comparator arms will be retained only for exploratory treatment-by-predictor analyses. No non-YODA participant-level data will be combined with the requested data.

"
  ["project_main_outcome_measure"]=>
  string(986) "Primary outcome: Time from first canagliflozin dose to the first treatment-emergent genital mycotic infection (GMI) within 26 weeks (182 days). GMI will be defined using sponsor-specified sex-specific MedDRA adverse-event terms/queries and supplemental GMI case-report-form information where available. Multiple qualifying terms with the same onset date that represent one clinical episode will be treated as one event. Follow-up will end at the earliest of first GMI, day 182, death, loss to follow-up, or the end of compatible treatment-emergent adverse-event surveillance.

Secondary outcomes: (1) time to first GMI through 52 weeks in trials with compatible follow-up; (2) recurrent GMI episodes when onset and resolution dates permit reliable separation; and (3) clinically significant GMI, defined by moderate or severe intensity, serious adverse-event classification, antifungal treatment, or study-drug interruption or discontinuation, where consistently captured.

"
  ["project_main_predictor_indep"]=>
  string(1510) "Because this is a prediction study, variables will be treated as predictors rather than causal exposures. The parsimonious benchmark model will include sex and prior GMI. Prior GMI will be derived from documented pre-randomization genital mycotic/candidal infection history.

The full clinical model will consider a prespecified pool of routinely available pretreatment candidate predictors based on prior evidence, clinical relevance, and cross-trial availability. These may include age, BMI, diabetes duration, HbA1c, fasting glucose, eGFR, UACR, blood pressure, smoking status, cardiovascular and renal disease, diabetic complications, prior urinary tract infection, and baseline glucose-lowering therapies including insulin, sulfonylureas, metformin, DPP-4 inhibitors, and other relevant agents. Where consistently available, menopausal status/estrogen use and circumcision status may be explored. Geographic region and race/ethnicity will primarily be used to characterize heterogeneity and transportability.

Continuous predictors will generally remain continuous, with nonlinear effects considered where appropriate. Final model complexity will be constrained by event numbers and cross-trial availability. Machine-learning models will use the same core predictor set to permit fair comparison with the clinical model. Broad algorithm screening, univariable significance screening, and stepwise selection will not be used. Canagliflozin dose will be examined in sensitivity analyses.

"
  ["project_other_variables_interest"]=>
  string(1643) "Trial identifier, randomized treatment assignment, canagliflozin dose, treatment start and end dates, adverse-event surveillance periods, study discontinuation, death, and censoring dates will be used to define trial structure, treatment exposure, and follow-up. Variables used to characterize the study populations will include age, sex, race/ethnicity, geographic region, height, weight, body mass index, diabetes duration, HbA1c, fasting glucose, serum creatinine/eGFR, urinary albumin-to-creatinine ratio where available, blood pressure, smoking status, diabetes-related microvascular and macrovascular complications, cardiovascular and renal comorbidities, and baseline glucose-lowering medications. For characterization of GMI events, relevant adverse-event variables will include verbatim term where available, MedDRA Preferred Term and code, onset and resolution dates, severity, seriousness, investigator-assessed relationship to study treatment, action taken with study drug, treatment interruption or discontinuation, recurrence, and anti-infective treatment where captured. Additional variables relevant to subgroup and transportability analyses will include sex, prior GMI, age, BMI, kidney function, cardiovascular-risk status, and geographic region. Trial-specific availability, definitions, coding, and measurement units will be documented in a prespecified harmonization table before analysis. Structurally unavailable variables will not be imputed. Variables used only for cohort characterization, subgroup analyses, or definition of validation populations will not automatically be included as predictors in the final model."
  ["project_stat_analysis_plan"]=>
  string(4275) "1. Data harmonization and descriptive analysis

Protocols, clinical study reports, data dictionaries, MedDRA coding conventions, and relevant adverse-event documentation will be reviewed before model development. A prespecified harmonization framework will define predictor coding, outcome definitions, surveillance windows, and the analytical role of each trial. Baseline characteristics, missingness, follow-up, and GMI incidence will be summarized overall and by trial, sex, prior-GMI history, and canagliflozin dose. Time to first GMI will be described using Kaplan-Meier methods. Univariable associations may be summarized descriptively but will not guide predictor selection.

2. Model complexity and missing data

The available sample size, number of GMI events, and candidate predictor degrees of freedom will be assessed using contemporary sample-size principles for time-to-event prediction models [5]. Model complexity will be constrained to reduce overfitting. Sporadically missing predictors will be handled using multiple imputation [6], with development and external-validation datasets handled separately to prevent information leakage. Structurally unavailable variables will not be imputed.

3. Model development

A parsimonious benchmark including sex, prior GMI, and their prespecified interaction will first be developed. The primary model will use penalized Cox regression to estimate 26-week GMI risk using prespecified clinically relevant predictors. Continuous variables will remain continuous, with nonlinear effects considered where appropriate. Proportional-hazards assumptions will be assessed and alternative survival modeling approaches used if necessary.

4. Machine-learning models

Gradient-boosted survival trees and random survival forests will be evaluated as complementary prediction models using the same core candidate predictor set. Model hyperparameter tuning will occur exclusively within development data using nested resampling. These analyses will assess whether nonlinear relationships and higher-order interactions provide meaningful improvement beyond the parsimonious benchmark and penalized regression model.

5. Model validation and transportability

Internal and external validation will be performed across eligible development trials[7]. CANVAS (NCT01032629) will serve as the primary trial-level external-validation cohort because it provides a large and clinically distinct population at elevated cardiovascular risk. Additional transportability assessments will be conducted in NCT01106651 for older adults and CREDENCE (NCT02065791) for patients with albuminuric chronic kidney disease, provided that required predictors, GMI ascertainment, and follow-up can be adequately harmonized. Performance will be reported separately within each validation population before any recalibration or model updating.

6. Model performance

Discrimination will be assessed using Harrell's C-index and 26-week time-dependent AUC. Calibration will be evaluated using calibration-in-the-large, calibration slope, and calibration plots. Overall prediction error will be assessed using the Brier score and, where appropriate, integrated Brier score. Decision-curve analysis will evaluate potential clinical utility [8]. The multivariable clinical and machine-learning models will be compared directly with the parsimonious sex-plus-prior-GMI benchmark, emphasizing clinically meaningful and reproducible improvements rather than statistical significance alone.

7. Secondary and exploratory analyses

Model performance will be examined across clinically relevant subgroups, including sex, prior GMI, age, kidney function, BMI, and geographic region, where event numbers permit. Prediction through 52 weeks, recurrent GMI, and clinically significant GMI will be explored in trials with compatible data. Randomized comparator arms will be used in exploratory treatment-by-predictor analyses to distinguish general GMI susceptibility from differential risk associated with canagliflozin treatment.

All analyses will be conducted using R/RStudio within the secure YODA data-sharing environment. Reporting will follow TRIPOD+AI recommendations [9].

"
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  ["project_timeline"]=>
  string(804) "Project start will be defined as the date of secure data access.

•	Months 0–3: Review trial documentation, harmonize variables and outcome definitions, clean data, assess missingness, and complete descriptive analyses. 

•	Months 3–6: Develop the parsimonious benchmark, penalized regression, and prespecified machine-learning models, and complete internal-external validation. 

•	Months 6–9: Lock the final models, perform external validation and transportability analyses in CANVAS and selected older-adult and kidney-disease populations, and complete sensitivity analyses and result preparation. 

•	Months 9–12: Draft and finalize the manuscript, submit to a peer-reviewed journal, and report study results and publication status to the YODA Project.

"
  ["project_dissemination_plan"]=>
  string(840) "The primary product will be a peer-reviewed manuscript describing the development and validation of a clinical prediction model for GMI after canagliflozin initiation, including comparison with a parsimonious sex-plus-prior-GMI model and selected machine-learning approaches. The target audience will include clinicians involved in diabetes care and researchers in medication safety and clinical prediction.

Potential journals include Diabetes, Obesity and Metabolism, Diabetes & Metabolism, BMJ Open Diabetes Research & Care, and Therapeutic Advances in Drug Safety, depending on the final scope and findings. Results will be reported regardless of whether more complex models materially improve upon the parsimonious benchmark. Findings may also be presented at a relevant diabetes or clinical epidemiology meeting.

"
  ["project_bibliography"]=>
  string(2247) "

Nyirjesy P, Sobel JD, Fung A, et al. Genital mycotic infections with canagliflozin, a sodium glucose co-transporter 2 inhibitor, in patients with type 2 diabetes mellitus: a pooled analysis of clinical studies. Curr Med Res Opin. 2014;30(6):1109-1119. doi:10.1185/03007995.2014.890925
Thong KY, Yadagiri M, Barnes DJ, et al. Clinical risk factors predicting genital fungal infections with sodium-glucose cotransporter 2 inhibitor treatment: The ABCD nationwide dapagliflozin audit. Prim Care Diabetes. 2018;12(1):45-50. doi:10.1016/j.pcd.2017.06.004
McGovern AP, Hogg M, Shields BM, et al. Risk factors for genital infections in people initiating SGLT2 inhibitors and their impact on discontinuation. BMJ Open Diabetes Res Care. 2020;8(1):e001238. doi:10.1136/bmjdrc-2020-001238
Kajiwara-Morita A, Kugimoto S, Oniki K, et al. Sex-specific risk factors for genital infections associated with SGLT2 inhibitors in type 2 diabetes: a retrospective cohort study and disproportionality analysis using JADER. Ther Adv Drug Saf. 2026;17:20420986261462669. Published 2026 Jul 20. doi:10.1177/20420986261462669
Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: PART II – binary and time-to-event outcomes. Stat Med. 2019;38(7):1276-1296. doi:10.1002/sim.7992
White IR, Royston P. Imputing missing covariate values for the Cox model. Stat Med. 2009;28(15):1982-1998. doi:10.1002/sim.3618
Takada T, Nijman S, Denaxas S, et al. Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets. J Clin Epidemiol. 2021;137:83-91. doi:10.1016/j.jclinepi.2021.03.025
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565-574. doi:10.1177/0272989X06295361
Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. Published 2024 Apr 16. doi:10.1136/bmj-2023-078378

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