Search Results (1,996)

Objectives: This study aimed to develop and validate a biparametric MRI (bpMRI)-based radiomics model for the noninvasive prediction of tumor–stroma ratio (TSR) in prostate cancer (PCa). Additionally, we sought to explore lesion distribution patterns in the peripheral zone (PZ) and transition zone (TZ) to provide deeper insights into the biological behavior of PCa. Methods: This multicenter retrospective study included 223 pathologically confirmed PCa patients, with 146 for training and 39 for internal validation at Center 1, and 38 for external testing at Center 2. All patients underwent preoperative bpMRI (T2WI, DWI acquired with a b-value of 1400 s/mm², and ADC maps）, with TSR histopathologically quantified. Regions of interest (ROIs) were manually segmented on bpMRI images using ITK-SNAP software (version 4.0.1), followed by high-throughput radiomic feature extraction. Redundant features were eliminated via Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression. Five machine learning (ML) classifiers—Logistic Regression (LR), Support Vector Machine (SVM), BernoulliNBBayes, Ridge, and Stochastic Gradient Descent (SGD)—were trained and optimized. Model performance was rigorously evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The Ridge demonstrated superior diagnostic performance, achieving AUCs of 0.846, 0.789, and 0.745 in the training, validation, and test cohorts, respectively. Lesion distribution analysis revealed no significant differences between High-TSR and Low-TSR groups (p = 0.867), suggesting that TSR may not be strongly associated with zonal localization. Conclusions: This exploratory study suggests that a bpMRI-based radiomic model holds promise for noninvasive TSR estimation in prostate cancer and may provide complementary insights into tumor aggressiveness beyond conventional pathology. Full article

Background/Objectives: Limited accessibility to hypercholesterolemia diagnosis hinders the primary prevention of cardiovascular disease. Therefore, we conducted a prospective, temporal validation study of two diagnostic prediction models, targeting endpoints of elevated low-density lipoprotein cholesterol (LDL-C, ≥160 mg/dL) and non-high-density lipoprotein cholesterol (non-HDL-C, ≥160 mg/dL). Methods: We prospectively recruited workers aged 20–40 years from a single-center, university hospital from March to June 2024 (n = 1099). We determined two diagnostic endpoints: elevated LDL-C and non-HDL-C. The predicted probabilities were derived from the binary logistic regression based on gender, metabolic age, and diastolic blood pressure. We assessed three prediction performances: discrimination from area under the receiver-operating characteristic curve (AuROC); calibration slope (C-slope) and calibration-in-the-large (CITL) from the calibration plot; clinical net benefit from decision curve analysis. Recalibration was based on C-slope and CITL, with a socioeconomic subgroup fairness assessment of AuROC, C-slope, and CITL. Results: From 1099 eligible participants, we identified 135 (12.3%) elevated LDL-C and 251 (22.8%) elevated non-HDL-C cases. The LDL-C model had poor discrimination (AuROC 0.59; 95%-CI, 0.56–0.62), miscalibration (C-slope 0.64; 95%-CI, 0.39–0.88 and CITL −0.14; 95%-CI, −0.27–−0.02), and negligible investigation reduction. The non-HDL-C model had fair discrimination (AuROC 0.67; 95%-CI, 0.64–0.69), miscalibration (C-slope 0.71; 95%-CI, 0.59–0.83 and CITL −0.07; 95%-CI, −0.17–0.03), and 20% investigation reduction at prevalence threshold probability. Updated model fairness improved compared to the original models. Conclusions: Temporal validation demonstrated modest replicability for the elevated non-HDL-C model, with a potential limitation in participants with normal BMI but low muscle and high fat mass. Health practitioners may use updated elevated non-HDL-C models as a non-invasive triage strategy in young adults, with threshold probabilities within the positive clinical net benefit ranges. Further external validation studies in a larger and more diverse population are necessary. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Radiomics and artificial intelligence (AI) have emerged as promising tools for quantitative imaging analysis and precision staging. This study aimed to evaluate the ability of an AI-based radiomics model to preoperatively predict tumor (T) and nodal (N) stage, lymphovascular invasion (LVI), and postoperative complications in patients with early-stage NSCLC. Material and Methods: This retrospective study included 51 consecutive patients who underwent anatomical lobectomy with systematic lymph node dissection between 2019 and 2024, at the Clinic for Thoracic Surgery of the University Clinical Center of Serbia. Quantitative imaging features were extracted from preoperative CT scans using the Lesion Scout with Auto ID module (syngo.via VB50 MM, Siemens Healthineers). Radiomics and clinical predictors were analyzed using regularized logistic regression (LASSO) with five-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, precision, and F1 score, and calibration was evaluated using the Hosmer–Lemeshow test. Groups were compared using parametric and non-parametric tests. Correlation between the variables was assessed using Spearman’s rank correlation coefficient. All p-values less than 0.05 were considered significant. Results: The AI-based model showed excellent performance for predicting the T component (training AUC = 0.89; test AUC = 0.86; F1 = 0.81) and acceptable calibration (p = 0.41). Nodal metastasis (OR = 0.108; 95% CI: 0.011–1.069; p = 0.057) and LVI (OR = 0.519; 95% CI: 0.139–1.937; p = 0.329) were not significantly predicted. Emphysema was identified as a significant independent predictor of postoperative complications (χ² = 5.13; p = 0.024). Conclusions: The AI-driven radiomics model demonstrated strong predictive ability for the T component and identified emphysema as a clinically relevant predictor of postoperative complications. Full article

Objectives: Hip fractures associated with osteoporosis are indicative of high rates of both disability and mortality. The objective of this study was to analyze the risk factors for recurrent hip fractures following primary osteoporotic hip fracture surgery in older adult patients. Methods: A single-center, retrospective cohort study was conducted on 376 patients suffering from primary osteoporotic hip fractures from 1 January 2020 to 31 December 2021. Multivariate logistic regression was used to identify risk factors for recurrent hip fractures. Results: The study observed 376 patients over a period of three years. The incidence of recurrent hip fractures was 20.5% (77/376). Multiple logistic regression analysis revealed that age ≥ 85 years (odd ratios [OR] = 3.127, 95% confidence interval [CI] = 1.672–5.849, p < 0.001), chronic obstructive pulmonary disease (COPD) (OR = 3.794, 95%CI = 1.747–8.236, p < 0.001), and Parkinson’s disease (PD) (OR = 2.744, 95%CI = 1.249–6.028, p = 0.012) were independent risk factors for recurrent hip fractures; antiosteoporosis drugs (OR = 0.243, 95%CI = 0.131–0.451, p < 0.001), duration of antiosteoporosis drug therapy (OR = 0.564, 95%CI = 0.283–0.830, p = 0.003) and serum albumin ≥ 35 g·L⁻¹ (OR = 0.413, 95%CI = 0.194–0.881, p = 0.022) were independent protective factors for recurrent hip fractures. The receiver operating characteristic (ROC) curve demonstrated that the AUC was 0.802, the sensitivity was 77.8%, and the specificity was 75.5%. A significantly higher three-year mortality rate was observed among patients with recurrent hip fractures (26.0% vs. 15.4%, p = 0.029). Conclusions: Older patients with advanced age, COPD and PD were at greater risk of recurrent hip fractures. Early nutrition intervention and antiosteoporosis drug therapy may decrease the incidence of recurrent hip fractures in older patients, thereby reducing mortality. Full article

Background: Retained products of conception (RPOC) can be managed via hysteroscopic removal using mechanical or electrosurgical techniques. Electrosurgery introduces greater technical complexity and may reflect more adherent or vascular tissue, yet preoperative predictors for its necessity remain poorly defined. Objective: The objective of this study was to evaluate clinical outcomes and identify preoperative predictors associated with the use of electrosurgery during hysteroscopic removal of RPOC. Methods: In this retrospective cohort study conducted at a single tertiary center, we reviewed 551 cases of hysteroscopic RPOC removal performed between January 2008 and December 2022. Patients were categorized based on intraoperative use of electrosurgical instruments. Clinical, sonographic, and operative data were compared between groups. Multivariate logistic regression was used to identify independent predictors of electrosurgical use. Results: Electrosurgical intervention was required in 84 patients (15.2%). Compared with those treated without electricity, these patients were older (33.2 ± 6.4 vs. 31.2 ± 5.8 years, p = 0.004), more likely to be smokers (15.4% vs. 8.1%, p = 0.033), and had higher rates of prior hysteroscopy (5.9% vs. 1.0%, p = 0.002). Electrosurgical use was more common following vaginal delivery than abortion (57.1% vs. 24.8%, p < 0.001), particularly when manual placental removal was performed (23.8% vs. 5.7%, p < 0.001). Larger RPOC size and positive Doppler flow were also associated with the use of electrosurgery. On multivariate analysis, maternal age, postpartum RPOC, manual placental removal, and Doppler vascularity remained independent predictors. No significant differences were observed in short-term postoperative complications. Conclusions: Older age, postpartum RPOC, manualysis, and vascularity on ultrasound are preoperative predictors for the need of electrosurgical intervention during hysteroscopic removal of RPOC. Identifying these factors may improve surgical planning and patient counseling. Future prospective studies incorporating advanced hysteroscopic technologies are warranted. Full article

Background: Metabolic syndrome (MetS) is a growing global health concern. Although sex, smoking, and physical activity are recognized risk factors, their combined effects remain insufficiently studied, particularly among Asian populations. This study aimed to examine the associations of sex, cumulative smoking exposure, and physical activity with MetS among current smokers in Taiwan. Methods: Data were drawn from 15,385 participants recruited between 2013 and 2017 from a health screening center. Demographic characteristics, smoking status, physical activity levels, and biochemical data were analyzed. Mann–Whitney U tests, chi-square tests, and multiple logistic regression were used to identify variables associated with MetS. Results: MetS prevalence differed significantly by sex, with rates of 13.6% in men and 5.1% in women. Women had a lower chance of developing MetS compared to men (OR = 0.607, 95% CI 0.488–0.754). Older age, higher body mass index, and greater cumulative smoking exposure (quantified using a composite ‘smoke area’ index derived from questionnaire data on smoking duration, frequency, and daily amount) were positively associated with MetS risk. Among smokers younger than 45 years, women also had significantly lower odds of MetS than men (OR = 0.590, 95% CI 0.451–0.771). Higher levels of physical activity were linked to reduced MetS risk in both sexes. Conclusions: Among current smokers, being female was inversely associated with the risk of MetS. Greater physical activity and lower smoking exposure were also associated with reduced risk. Future research should use longitudinal designs and comorbidities to clarify mechanisms and inform tailored prevention strategies. Full article

Background/Objectives: Exercise capacity and patient prognosis are heavily influenced by comorbidities. However, the specific impact of individual comorbid conditions on objective measures of exercise performance remains insufficiently characterized. The study aimed to identify predictors of reduced physical capacity in patients qualified for cardiac rehabilitation. Methods: A single-center retrospective analysis was conducted on 518 patients qualified for cardiac rehabilitation. After excluding 51 post-cardiac surgery patients, cardiopulmonary exercise testing data from 425 patients (316 men, median age 63 years) were analyzed. Comorbidities data, peak oxygen uptake (peak VO₂), and the ventilation-to-carbon dioxide output slope (VE/VCO₂ slope) were evaluated. Results: A significantly reduced exercise capacity (peak VO₂ < 70% of the predicted value) was observed in 29.4% of patients, while an increased VE/VCO₂ slope (≥36) was noted in 20.8% of patients. Univariate logistic regression identified sex, heart failure, valvular disease, peripheral artery disease, diabetes mellitus (T2DM), chronic kidney disease (CKD), Charlson Comorbidity Index (CCI), left ventricular ejection fraction <50%, diastolic dysfunction, and anemia as predictors of both reduced peak VO₂ and a steeper VE/VCO₂ slope. Multivariate regression analysis further identified T2DM and CKD as independent predictors of reduced peak VO₂, while sex, CKD, and CCI were independent predictors of a steeper VE/VCO₂ slope. Conclusions: Among patients qualified for cardiac rehabilitation, patient’s sex, T2DM, CKD, and the CCI emerged as key predictors of reduced exercise capacity. Reduced peak VO₂ was more commonly observed in men, while women more frequently exhibited a steeper VE/VCO₂ slope, indicating potential sex-related physiological mechanisms influencing exercise performance. Full article

Background: Hidradenitis suppurativa (HS) is a chronic, relapsing inflammatory dermatosis with substantial quality-of-life impact. HS frequently co-exists with obesity and metabolic comorbidities. Cigarette smoking is highly prevalent and has been linked to heightened inflammatory activity and impaired wound healing. The role of cumulative tobacco exposure (packyears) in relation to metabolic comorbidities in HS is less well defined. We therefore investigated whether lifetime pack-years relate to laboratory parameters and the presence of comorbidities in HS. Methods: We conducted a retrospective, single-center study involving 131 patients with HS. We collected clinical data, including disease severity scores and quality of life indices, along with laboratory markers such as complete blood count and C-reactive protein. Smoking status and cumulative exposure (pack-years) were assessed based on patient history. To compare laboratory parameters between smoking subgroups, we used Mann–Whitney U tests. Additionally, we performed logistic regression analyses to evaluate the association between cumulative cigarette exposure and the presence of comorbidities. Results: Among the cohort, 63.4% were active smokers with a median of 15 pack-years. Smokers had significantly higher leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts, indicating elevated systemic inflammation. Hematocrit, hemoglobin, mean corpuscular volume, and mean corpuscular hemoglobin were also significantly higher in smokers, while C-reactive protein levels did not differ notably between groups. Subgroup analysis revealed that patients with arterial hypertension, diabetes mellitus, and hypercholesterolemia had significantly more pack-years than those without these conditions. These comorbidities, components of metabolic syndrome, were strongly associated with higher lifetime tobacco exposure in HS patients. Conclusions: Smoking contributes not only to heightened inflammatory activity in HS but is also significantly associated with the presence of metabolic comorbidities. These findings underscore the importance of early interdisciplinary intervention and structured smoking cessation programs to improve outcomes in HS patients. Full article

Background/Objectives: Oral candidiasis is frequently encountered in pediatric populations, particularly in infants and toddlers, where the development of immunity and inconsistent oral hygiene contribute to disease susceptibility. While topical antifungal agents remain the standard of care, treatment challenges persist, especially regarding adherence and recurrence. Aloe vera, recognized for its antimicrobial, anti-inflammatory, and mucosal healing properties, may offer therapeutic benefits when used in conjunction with standard regimens. This study aimed to evaluate the adjunctive effect of topical Aloe vera gel, when added to standard antifungal therapy, on reducing fungal load and improving treatment adherence in children with moderate oral candidiasis. Methods: A prospective controlled study was conducted among 54 children diagnosed with moderate oral candidiasis. Participants were randomly assigned to receive either standard topical nystatin or nystatin in conjunction with Aloe vera gel over a 7 day treatment duration. Fungal load was assessed using colony-forming units (CFU) counts from oral swabs collected at baseline and day 7, analyzed via ANCOVA. Additional parameters included treatment adherence, compared using an independent t-test, and clinical recurrence at a 14-day post-treatment follow-up, assessed through logistic regression. Results: Baseline characteristics were similar across groups. By day 7, children in the Aloe vera group exhibited a greater reduction in fungal load compared to those receiving standard therapy alone. Adherence was significantly higher in the aloe group (92.73% vs. 89.21%; p < 0.0001). Regression analysis identified both baseline fungal burden and adherence as factors associated with an increased risk of recurrence. Conclusions: The addition of Aloe vera gel to standard therapy may support a more effective fungal clearance and improved treatment adherence in children with moderate oral candidiasis, suggesting its potential as a complementary treatment option. Given the single-center design, short follow-up, and underpowered recurrence analysis, these findings should be considered preliminary, pending confirmation in larger studies with symptom-anchored endpoints. Full article

Background/Objectives: Non-variceal upper gastrointestinal bleeding (NVUGIB) is associated with considerable morbidity and mortality, particularly in emergency department (ED) settings. While traditional clinical scores such as the Glasgow-Blatchford Score (GBS), AIMS65, and Pre-Endoscopic Rockall are widely used for risk stratification, their accuracy in mortality prediction is limited. This study aimed to evaluate the performance of multiple supervised machine learning (ML) models in predicting 30-day all-cause mortality in NVUGIB and to compare these models with established risk scores. Methods: A retrospective cohort study was conducted on 1233 adult patients with NVUGIB who presented to the ED of a tertiary center between January 2022 and January 2025. Clinical and laboratory data were extracted from electronic records. Seven supervised ML algorithms—logistic regression, ridge regression, support vector machine, random forest, extreme gradient boosting (XGBoost), naïve Bayes, and artificial neural networks—were trained using six feature selection techniques generating 42 distinct models. Performance was assessed using AUROC, F1-score, sensitivity, specificity, and calibration metrics. Traditional scores (GBS, AIMS65, Rockall) were evaluated in parallel. Results: Among the cohort, 96 patients (7.8%) died within 30 days. The best-performing ML model (XGBoost with univariate feature selection) achieved an AUROC > 0.80 and F1-score of 0.909, significantly outperforming all traditional scores (highest AUROC: Rockall, 0.743; p < 0.001). ML models demonstrated higher sensitivity and specificity, with improved calibration. Key predictors consistently included age, comorbidities, hemodynamic parameters, and laboratory markers. The best-performing ML models demonstrated very high apparent AUROC values (up to 0.999 in internal analysis), substantially exceeding conventional scores. These results should be interpreted as apparent performance estimates, likely optimistic in the absence of external validation. Conclusions: While machine-learning models showed markedly higher apparent discrimination than conventional scores, these findings are based on a single-center retrospective dataset and require external multicenter validation before clinical implementation. Full article

Background/Objectives: Left atrial appendage (LAA) morphology has been implicated in atrial fibrillation (AF) recurrence following catheter ablation. However, the predictive value of specific anatomical shapes remains unclear. We aimed to evaluate the association between distinct LAA morphologies and AF recurrence post-ablation. Methods: In this retrospective, single-center study, 463 patients with AF undergoing first-time catheter ablation were included. Pre-ablation contrast-enhanced cardiac computed tomography was performed to classify LAA morphology into chicken-wing, windsock, cauliflower, and cactus types. Patients were followed for one year, with AF recurrence defined as documented atrial tachyarrhythmia episodes lasting more than 30 s occurring between 3 and 12 months post-procedure. Clinical, anatomical, and procedural factors were analyzed using multivariable logistic regression to identify independent predictors of recurrence. Results: Among the four morphologies, the windsock-type LAA had the highest recurrence rate at 48.3%, significantly greater than chicken-wing (25.2%), cauliflower (20.8%), and cactus (18.2%) types (p = 0.017). Multivariable analysis confirmed windsock morphology as an independent predictor for AF recurrence (adjusted OR = 2.720, 95% CI: 1.209–6.118; p = 0.016). Additionally, persistent AF (adjusted OR = 1.748, 95% CI: 1.075–2.842; p = 0.024) and antiarrhythmic drug use in the blanking period (adjusted OR = 2.862, 95% CI: 1.689–4.849; p < 0.001) independently increased the risk of recurrence. Conclusions: Windsock-type LAA morphology significantly predicts increased AF recurrence following catheter ablation, underscoring the importance of morphological assessment in ablation planning. Individualized strategies targeting high-risk LAA morphologies may enhance procedural success and reduce AF recurrence. Future prospective studies are warranted to validate these findings. Full article

Background: In the Republic of Kazakhstan, informal caregivers remain the main source of patient support. Caregivers of individuals with dementia experience an increased caregiver burden and significantly higher levels of stress. We investigated the levels of caregiver burden, depression, anxiety, and stress among informal dementia caregivers and assessed the relationships between these indicators and the caregivers’ socio-demographic characteristics. Methods: With the cooperation of two regional mental health centers and three polyclinics in Kazakhstan, we conducted a cross-sectional study of 174 informal caregivers of dementia patients. The caregiver burden was assessed using the Zarit Burden Interview, and psychological distress was measured with the Depression Anxiety Stress Scale-21. The caregivers’ sociodemographic data were collected and analyzed. The adjusted odds ratios and 95%CIs were calculated using logistic and ordinal regressions. Results: Overall, 75.9% of the caregivers reported experiencing at least a mild burden, and the levels of depression, anxiety, and stress among the caregivers were higher than expected. Spousal caregivers had higher odds of abnormal depression (p = 0.022) and anxiety (p = 0.020); in severity models, spouses more often had a severe burden (p = 0.042) and anxiety (p = 0.006). Conclusions: Caregiver burden and psychological distress are highly prevalent among informal dementia caregivers in Kazakhstan, particularly among spouses. It is especially important to provide support for spouse caregivers. Culturally tailored psychoeducation, support groups, and respite services are urgently needed to mitigate the mental health risks faced by dementia caregivers in Kazakhstan. Full article

Objectives: Urinary tract infections (UTIs) are the most common healthcare-associated infections in Skilled Nursing Facilities (SNFs); they are associated with longer lengths of stay, higher levels of care, increased treatment costs, and higher mortality rates. This study aimed to develop a machine learning classification model to predict the risk of high catheter-associated urinary tract infection rates based on SNF characteristics. Methods: We analyzed 94,877 total SNF-year observations from 2019 to 2024, not unique facilities; thus, individual SNFs may appear in multiple years. The factor variables were average length of stay in days, number of staffed beds, total nurse and total physical therapy staffing hours per resident per day, facility ownership, geographic classification, facility accreditation, Accountable Care Organization affiliations, Centers for Medicare and Medicaid Services SNF Overall Star Rating, and the SNF-year of the observations. We utilized three machine learning models for this analysis: Random Forest, XGBoost, and LightGBM. We used Shapley Additive exPlanations to interpret the best-performing machine learning model by visualizing feature importance and examining the relationship between key predictors and the outcome. Results: We found that machine learning models outperformed traditional logistic regression in predicting UTIs in skilled nursing facilities. Using the best-performing model, Random Forest, we identified rural SNFs, and the number of staffed beds as the most influential predictors of high UTI rates, followed by average length of stay, and geographic location. Conclusions: This study demonstrates the value of using facility-level characteristics to predict the risk of UTIs in SNFs with machine learning models. Results from this study can inform infection prevention efforts in post-acute care settings. Full article

Background: Antibiotic overuse in early-onset sepsis (EOS) remains a significant clinical challenge. Panyananthaphikkhu Chonprathan Medical Center adapted the 2019 American Academy of Pediatrics (AAP) guidelines by integrating the EOS calculator with enhanced observation. This study aimed to evaluate clinical outcomes before and after implementation in Thailand, focusing on timely initiation of empirical antibiotics in neonates with EOS and the reduction in unnecessary investigations and antibiotic exposure. Methods: This retrospective cohort observational study included neonates ≥ 35 weeks’ gestation. Participants were divided into two groups: “before” (1 February 2017–31 January 2018) and “after” (1 February 2023–31 January 2024) guideline implementation. Data were analyzed using Pearson chi-square, Mann–Whitney U-test, and binary logistic regression, with statistical significance defined as p < 0.05. Results: Among 3040 neonates (1639 before and 1401 after guideline implementation), antibiotic use declined from 11% to 7.9% (p < 0.001), with an Odds Ratio of 1.46 (95% Confidence Interval 1.14–1.87). Following the implementation of the Adapted AAP 2019 guidelines, a neonate with GBS septicemia was identified at birth with respiratory distress and was promptly started on antibiotics per the guideline. Conclusions: The Adapted AAP 2019 guidelines improved EOS management by reducing unnecessary investigations and antibiotic use while ensuring timely empirical antibiotic administration, as shown by the prompt management of a GBS septicemia case. Key to this reduction lies in the Adapted AAP 2019 guidelines, which provide clear definitions of EOS and recommend serial clinical observation for asymptomatic neonates born to mothers with risk factors for EOS. Full article

Introduction: Chlamydia trachomatis is an important indicator of overall health and plays a vital role in various health conditions. In 2018, C. trachomatis reached the highest level ever recorded, resulting in USD 691 million in expenditures, with 1.8 million reported cases. This amount reflects a 19% increase since 2014, according to the Centers for Disease Control and Prevention. Depression has also been on the rise between 2013 (8.2%) to 2023 (13.1%). C. trachomatis and depression may have inflammation as a final common pathway. The purpose of this study was to explore a potential connection between Chlamydia and depression, and whether C-reactive protein (CRP) modifies this effect. Methods: For this study, we utilized the 2005–2016 National Health and Nutrition Examination Survey (NHANES) and US adults aged between 20 and 59 years. Depression was determined from the Patient Health Questionnaire (PHQ-9). Logistic regression models were used to determine if C. trachomatis in the previous year was predictive of depression. Models were adjusted for known confounders, including age, gender, race/ethnicity, poverty, and education. Results: For C. trachomatisra, the unadjusted Odds Ratio (OR) for C. trachomatis to no C. trachomatis was OR = 2.9. The adjusted OR was elevated, at OR = 6.3 among individuals who had both CT and elevated CRP, but close to 1.0 among individuals who had C. trachomatis but reported low CRP after adjusting for demographic and health variables. Conclusions: Using a nationally representative sample, our study was the first to demonstrate, across unadjusted and adjusted models, that there is a strong connection between a history of C. trachomatis and high CRP leading to worse outcomes in individuals with depression than C. trachomatis infection alone. This finding indicates the need to conduct mental health screening among individuals with Sexually Transmitted Infections (STIs) or other infectious diseases. Full article