Nevin Manimala

Arch Gynecol Obstet. 2024 Dec 25. doi: 10.1007/s00404-024-07890-8. Online ahead of print.

ABSTRACT

PURPOSE: We aimed to evaluate if ovarian stimulation with individualized dosing of recombinant follicle-stimulating hormone (rec-FSH) with follitropin delta compared with standard gonadotropin dosing reduce occurrence of follicular asynchrony in women with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization (IVF).

METHODS: Matched case-control study analyzed occurrence of follicular growth asynchrony during ovarian stimulation and IVF outcomes in women with PCOS. Follicular growth was considered to be asynchronous when one or two leading follicles were at least 4 mm larger in diameter than the rest of the cohort on day 5 and 9 of stimulation. Analysis encompassed 44 women stimulated with individualized rec-FSH dosing, and 88 women treated with standard dosing. The patients were matched in terms of age, Anti-Müllerian hormone levels and body weight.

RESULTS: Early and late follicular asynchrony were present less frequently in individualized dosing compared to standard dosing group (4.5% vs 17%, p = 0.04 and 2.3% vs 37.5%, p < 0.001, on stimulation day 5 and 9, respectively). Multivariate logistic regression on follicular asynchrony revealed that individualized dosing significantly decreases the occurrence and chances for late follicular asynchrony (Odds Ratio 0.28, p < 0.001). Shorter duration of stimulation (9.6 vs 10.4 days, p = 0.001), lower total gonadotropin dose (1118 vs 1940 IU, p < 0.001), higher number of metaphase II oocytes (7.1 + 4.3 vs 5.4 ± 3.0, p = 0.001), good quality embryos (3.8 vs 2.0, p < 0.001), and implantation rates (31.0 vs 23.4, p = 0.04) were observed in the individualized dosing group.

CONCLUSION: Individualized rec-FSH dosing reduces asynchronous follicular growth and improves ovarian stimulation efficiency in women with PCOS undergoing IVF.

PMID:39720974 | DOI:10.1007/s00404-024-07890-8

Breast Cancer Res Treat. 2024 Dec 25. doi: 10.1007/s10549-024-07578-2. Online ahead of print.

ABSTRACT

PURPOSE: In early-stage breast cancer, steatotic liver disease (SLD) is associated with increased recurrence, cardiovascular events, and non-cancer death. Endocrine therapy (ET) increases the risk of SLD. The impact of cyclin-dependent kinases 4/6 inhibitors (CDK4/6i) on SLD and prognostic association in metastatic breast cancer is unknown. We characterized the presence of SLD, risk factors, and treatment outcomes of SLD in metastatic HR+/HER2- breast cancer receiving CDK4/6i.

METHODS: This single institution, retrospective, cohort study included patients with metastatic HR+/HER2- breast cancer receiving first-line ET and CDK4/6i from January 2018 to June 2022. SLD was defined as a Liver Attenuation Index (LAI) > 25 HU on contrast-enhanced CT scans and/or > 10 HU on plain CT scans. Univariable binary-logistic regression was used to assess associations with SLD. Time to treatment failure (TTF) and overall survival (OS) were analyzed using Cox proportional hazards modeling.

RESULTS: Among 87 patients with a median age of 58 years and 65.5% postmenopausal, 50 (57.5%) had SLD at anytime (24 at baseline, 26 acquired). SLD at baseline was statistically associated with post-menopausal status. It was quantitatively but not statistically associated with age > 65, diabetes, smoking, and HER2-low. SLD at anytime was statistically significantly associated with longer TTF (median 470 vs 830.5 days, HR = 0.38, p < 0.001). No significant differences in OS or grade 3/4 adverse events were observed between groups.

CONCLUSION: This study demonstrated a high prevalence of SLD in this population, with SLD presence correlated with longer TTF. SLD may be an indicator of better outcomes in metastatic HR+/HER2- breast cancer patients treated with CDK4/6i.

PMID:39720971 | DOI:10.1007/s10549-024-07578-2

J Echocardiogr. 2024 Dec 25. doi: 10.1007/s12574-024-00674-8. Online ahead of print.

ABSTRACT

BACKGROUND: The current guidelines recommend patient stratification based on transthoracic echocardiography (TTE) to identify individuals with potential pulmonary hypertension (PH). We validated the relationship between PH and the pulmonary artery diameter (PAD) on computed tomography (CT) with peak tricuspid regurgitant velocity (TRV) measured by TTE for referral of patients with suspected PH for TTE screening.

METHODS: We performed a retrospective analysis of CT-based PAD of 2356 patients who underwent TTE from February 2, 2013 to December 25, 2019 at our institution. The thresholds for suspected PH based on TRV were determined using receiver operating characteristic curves based on PAD. Pearson’s rank correlation coefficient was used to assess the relationship between PAD and TRV.

RESULTS: The area under the curve (AUC) of the PAD for suspected PH was statistically greater or comparable to others. The sex-specific PAD threshold for high PH probability were 29.4 mm (male: AUC, 0.86; sensitivity, 84.9%; specificity, 72.3%) and 27.8 mm (female: AUC, 0.83; sensitivity, 78%; specificity, 75.6%). Pearson’s rank correlation coefficient showed a correlation between the PAD and TRV (male: ρ = 0.40, P < 0.001, female: ρ = 0.43, P < 0.001).

CONCLUSIONS: The main PAD on CT findings served as a suitable marker for referral of patients with suspected PH for TTE screening. Patients exceeding the CT-derived PAD threshold, even incidentally, should undergo additional TTE for a comprehensive PH assessment.

PMID:39720970 | DOI:10.1007/s12574-024-00674-8

Cell Physiol Biochem. 2024 Dec 19;58(6):739-755. doi: 10.33594/000000746.

ABSTRACT

BACKGROUND/AIMS: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early detection is crucial for timely intervention and improved patient outcomes. However, traditional diagnostic tools, such as MRI and PET scans, are costly and less accessible. This study aims to develop an automated, cost-effective digital diagnostic approach using deep learning (DL) and computer-aided detection (CAD) methods for early AD identification and classification.

METHODS: The proposed framework utilizes pretrained convolutional neural networks (CNNs) for feature extraction, integrated with two classifiers: multi-class support vector machine (MSVM) and artificial neural network (ANN). A dataset categorized into four groups-non-demented, very mild demented, mild demented, and moderate demented-was employed for evaluation. To optimize the classification process, a texture-based algorithm was applied for feature reduction, enhancing computational efficiency and reducing processing time.

RESULTS: The system demonstrated high statistical performance, achieving an accuracy of 91%, precision of 95%, and recall of 90%. Among the initial set of twenty-two texture features, seven were identified as particularly effective in differentiating normal cases from mild AD stages, significantly streamlining the classification process. These results validate the robustness and efficacy of the proposed DL-based CAD system.

CONCLUSION: This study presents a reliable and affordable solution for early AD detection and diagnosis. The proposed system outperforms existing state-of-the-art models and offers a valuable tool for timely treatment planning. Future research should explore its application to larger, more diverse datasets and investigate integration with other imaging modalities, such as MRI, to further enhance diagnostic precision.

PMID:39720940 | DOI:10.33594/000000746

Eur J Endocrinol. 2024 Dec 25:lvae166. doi: 10.1093/ejendo/lvae166. Online ahead of print.

ABSTRACT

OBJECTIVE: We have shown that men aged 50 years+ at high risk of type 2 diabetes treated with testosterone together with a lifestyle program reduced the risk of type 2 diabetes at two years by 40% compared to a lifestyle program alone. To develop a personalized approach to treatment, we aimed to explore a prognostic model for incident type 2 diabetes at two years and investigate biomarkers predictive of the testosterone effect.

DESIGN: Model development in 783 men with impaired glucose tolerance but not type 2 diabetes from T4DM; a multicenter, 2-year trial of Testosterone vs placebo. External validation performed in 236 men from the EXamining OuTcomEs in chroNic Disease in the 45 and Up Study (EXTEND-45, n=267,357).

METHODS: Type 2 diabetes at two years defined as 2-hour fasting glucose by oral glucose tolerance test (OGTT) ≥ 11.1mmol/L. Risk factors, including predictive biomarkers of testosterone treatment, were assessed using penalized logistic regression.

RESULTS: Baseline HbA1c and 2-hour OGTT glucose were dominant predictors, together with Testosterone, age, and an interaction between Testosterone and HbA1c (p=0.035, greater benefit with HbA1c≥5.6%, 38mmol/mol). The final model identified men who developed type 2 diabetes, with C-statistics 0.827 in development and 0.798 in validation. After recalibration, the model accurately predicted a participant’s absolute risk of type 2 diabetes.

CONCLUSIONS: Baseline HbA1c and 2-hour OGTT glucose predict incident type 2 diabetes at 2 years in high-risk men, with risk modified independently by Testosterone treatment. Men with HbA1c≥5.6% (38mmol/mol) benefit most from Testosterone treatment, beyond a lifestyle program.

PMID:39720906 | DOI:10.1093/ejendo/lvae166

J Cell Mol Med. 2024 Dec;28(24):e70322. doi: 10.1111/jcmm.70322.

ABSTRACT

Increasingly, emerging research evidence has demonstrated that nonalcoholic fatty liver disease (NAFLD) is a disease closely associated with systemic inflammation. However, the specific upstream inflammatory factors engaged in the pathogenesis of NAFLD remain unclear. Our study aimed to identify the inflammatory regulators causally associated with NAFLD pathogenesis through Mendelian randomisation. A two-sample Mendelian randomisation method was applied to analyse the causal association between 91 circulating inflammatory proteins and NAFLD. Data on circulating inflammatory proteins were derived from samples of European ancestry (14,824 samples) and NAFLD data were obtained from the FinnGen consortium (2025 cases and 284,826 controls). Instrumental variables were selected from the genetic variance and F-statistics were calculated to avoid bias. We adopted the random-effects inverse variance weighting (IVW) method as our primary analytical approach. Supplementary analyses were also implemented, including weighted median, MR-Egger and weighted mode. Moreover, we conducted pleiotropy and heterogeneity analyses to validate the accuracy of the findings. The application of Mendelian randomisation analysis identified four inflammatory factors that might be causally associated with NAFLD at the genetic level. Elevated levels of eotaxin (or = 1.27, 95% CI: 1.05-1.53, p = 0.014), osteoprotegerin (OPG) (or = 1.29, 1.03-1.60, p = 0.023) and TNFRSF9 (or = 1.32, 95% CI: 1.06-1.64, p = 0.014) may be causally related to an increasing risk of NAFLD. Conversely, heightened leukaemia inhibitory factor (LIF) levels (or = 0.63, 0.44-0.92, p = 0.016) were linked to a lower risk of NAFLD onset. There was no causal relationship between levels of other circulating inflammatory proteins and NAFLD. Our analysis uncovered four upstream inflammatory factors genetically associated with the pathogenesis of NAFLD. These results highlight the potential involvement of inflammation in NAFLD, which provides partial insights for further research in this field in the future.

PMID:39720899 | DOI:10.1111/jcmm.70322

Inquiry. 2024 Jan-Dec;61:469580241309903. doi: 10.1177/00469580241309903.

ABSTRACT

This study investigates the relationship between out-of-pocket (OOP) healthcare spending, economic growth, population growth, and government health expenditure as a proportion of general government expenditure using National Health Accounts (NHA) estimates. Out-of-Pocket (OOP) healthcare spending imposes a substantial financial burden on households, especially in developing economies such as India. Understanding the factors that influence OOP payments is crucial for policymakers seeking to enhance healthcare systems and achieve Universal Health Coverage (UHC). High OOP expenditures often lead to impoverishment and inequitable access to healthcare, making it a critical area for reform. Despite the well-known negative economic and social consequences of high OOP spending, there is limited research that thoroughly examines the interplay between key economic variables such as economic growth, population growth, and government healthcare expenditure (GHE) as a proportion of general government expenditure (GGE) in shaping OOP healthcare spending. Furthermore, although the National Health Accounts (NHA) offers comprehensive data across Indian states, few studies have leveraged this data to explore the dynamics of these factors. This study aims to fill this gap by providing empirical insights into how these economic and demographic elements influence OOP healthcare spending in India. The analysis employed fixed and random effects models on data from 19 Indian states spanning the years 2013-14 and 2019-20. Fixed effects models were selected based on the results of the Hausman test, which indicated that these models were more effective for controlling unobserved heterogeneity across states.The results indicate that a 1% increase in Gross State Domestic Product is associated with a 0.5% reduction in OOP payments. No significant relationship was identified between population growth or GHE/GGE ratio and OOP healthcare spending. These results imply that while economic growth can contribute to lowering healthcare costs, other factors, such as public health spending, may not be as effective unless they are more strategically targeted. The study underscores the vital role of economic growth in reducing OOP healthcare spending, especially in states facing significant financial burdens. Policymakers should consider aligning economic growth strategies with healthcare reforms to ensure that the benefits of development lead to reduced OOP expenditures. As the findings also suggest that GHE/GGE does not significantly affect OOP costs, policymakers should enhance the targeting and efficiency of public health expenditures while expanding health insurance coverage, and strengthening primary healthcare systems to mitigate OOP costs.

PMID:39720888 | DOI:10.1177/00469580241309903

Ann Otol Rhinol Laryngol. 2024 Dec 25:34894241308411. doi: 10.1177/00034894241308411. Online ahead of print.

ABSTRACT

BACKGROUND: An endoscopic screening program following successful weaning from prolonged mechanical ventilation maintained through endotracheal tube (ET; prolonged intubation) may be justified to assess the upper (laryngotracheal) airway in children who may not always be symptomatic for intubation-related complications.

OBJECTIVES: To evaluate effects of prolonged intubation in children through endoscopic screening of the laryngotracheal airway.

METHODS: In this cross-sectional pilot project, children (2 months-12 years) successfully extubated following prolonged intubation were selected, irrespective of having symptoms, for a 1-time flexible nasolaryngoscopy at third to sixth month post-extubation (follow-up window). Laryngotracheal airway changes, if present, were noted.

RESULTS: Out of 122 children, 42 developed symptoms of complications. Five of them attended within 3 months post-extubation, the rest were evaluated in the follow-up window. Eighty children aged ≤6 years and 4 children >6 years were intubated with uncuffed ET. Symptoms, when present, included respiratory distress (100%), noisy breathing (~36%), cough (~29%), and dysphagia (~12%). Screening revealed positive findings in 40 out of 42 symptomatic children, and in 8 out of 80 asymptomatic children (χ² = 80.314; after Yate’s correction; significant at P < .0001). The commonest lesion was subglottic stenosis (~54%) and intubation granuloma (~48%). Relationship between the nature of ET (cuffed/uncuffed) and complications of prolonged intubation was statistically significant (χ²46.553; significant at P < .0001).

CONCLUSION: The present study proposes the potential utility of follow-up endoscopic screening of upper (laryngotracheal) airway in children successfully weaned from prolonged intubation. A statistically significant relationship existed between prolonged intubation and upper airway complications that were not always symptomatic.

PMID:39720852 | DOI:10.1177/00034894241308411

Med Decis Making. 2024 Dec 25:272989X241305414. doi: 10.1177/0272989X241305414. Online ahead of print.

ABSTRACT

PURPOSE: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.

METHODS: We compare iSTM-produced estimates of decision uncertainty and VOI to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. We use numerical simulations to illustrate our findings and explore the impact of relaxing normality assumptions or having >2 decision alternatives.

RESULTS: iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias approaches 0 as number of microsimulated individuals approaches infinity). Decision uncertainty depends on 1 tail of the INMB distribution (e.g., P[INMB <0]), which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated since this increases INMB variance.

CONCLUSIONS: iSTMs are useful for probabilistic economic evaluations. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results.

HIGHLIGHTS: Individual-level state-transition microsimulation models (iSTMs) produce biased but consistent estimates of the probability that interventions are cost-effective.iSTMs also produce biased but consistent estimates of the expected value of perfect information.The biases in these decision uncertainty and value-of-information measures are not reduced by more parameter sets being sampled from their population-level uncertainty distribution but rather by more individuals being microsimulated for each parameter set sampled.Analysts using iSTMs to quantify decision uncertainty and value of information should account for these biases when allocating their computational budgets and, at minimum, characterize such bias in their reported results.

PMID:39720850 | DOI:10.1177/0272989X241305414

Clin Trials. 2024 Dec 25:17407745241304331. doi: 10.1177/17407745241304331. Online ahead of print.

ABSTRACT

Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient’s unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK’s commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit’s CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical ‘middle ground’ between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.

PMID:39720844 | DOI:10.1177/17407745241304331