Risk Stratification Algorithms Research Articles

Interpreting positive celiac serology in children with new-onset type 1 diabetes.

The association of celiac disease (CD) in type 1 diabetes mellitus (T1DM) is well-established, yet variation exists in screening practices. This study measures the accuracy of early screening with tissue transglutaminase Immunoglobulin A (TTG-IgA) and endomysial antibody (EMA) in newly diagnosed T1DM. This is a retrospective study of children with T1DM between 2013 and 2019 with early CD screening and follow-up. Data elements included anthropometrics, serologies, blood pH, bicarbonate, and Hemoglobin A1c. Celiac serologies were analyzed using chi-square and receiver operating characteristic curves to calculate optimal levels for predicting CD. A total of 1,292 children met inclusion criteria with 142 having positive celiac serologies; 47 (33.1 %) of whom were subsequently diagnosed with CD- an incidence of 3.6 %. All subjects with positive EMA and TTG-IgA≥8 times upper limit of normal were diagnosed with CD. Gastrointestinal symptoms, BMI, and thyroid disease were not statistically significant variables in this cohort, although there was a trend toward CD in lower BMI patients and higher TTG IgA in those with markedly elevated HgbA1c. Early celiac screening in T1DM is reliable and promotes timely CD diagnosis and treatment. Although transient positive celiac serologies were noted, the degree of TTG-IgA elevation and EMA positivity are strong predictors of coexisting CD. Larger prospective studies using these assays will further define the risk stratification algorithm that is needed for our T1DM community.

Proposal for Clinical Management of Nodules Diagnosed as Atypia of Undetermined Significance via Thyroid Fine-Needle Aspiration Cytology in the Absence of Molecular Testing.

Molecular testing is recommended for risk stratification of atypia of undetermined significance (AUS) nodules in the USA; however, it is not routinely performed in some countries owing to limited availability and affordability. Here, we propose a risk stratification algorithm for AUS nodules when molecular testing is unavailable. We examined 304 (4.3%) AUS nodules among 7073 thyroid fine-needle aspiration cytology specimens examined at Kuma Hospital from January 2020 to December 2020. Clinical data were obtained from the medical records of Kuma Hospital. AUS with nuclear atypia and AUS-other each accounted for half of the total AUS nodules. The repeat aspiration rate was 19.7%; 61.7% of the nodules were reclassified as benign or malignant upon repeat aspiration. Resection rate and overall risk of malignancy (ROM) were 32.6% and 12.8%, respectively. Architectural atypia showed the lowest (1.1%) overall ROM in the AUS nodules. For AUS with nuclear atypia, nodules ≤ 10 mm in size showed significantly lower overall ROM than those of > 10 mm, and nodules with ultrasonographically low suspicion showed significantly lower overall ROM than those with intermediate to high suspicion. AUS nodules with atypical lymphoid cells, possible medullary thyroid carcinoma, or possible parathyroid lesion were confirmed using flow cytometry, biochemical testing using needle washout fluid or immunocytochemistry, respectively. Our proposed clinical management algorithm for each subdivision according to cytological findings, based on repeat aspiration rates, ROM, ultrasound findings and results of ancillary tests except for molecular testing, should be useful for the clinical management of AUS nodules.

Therapeutic Strategies for Tuberculosis: Progress and Lessons Learned.

Tuberculosis (TB) remains a significant global health challenge, ranking second only to COVID-19 as the leading cause of death from a single infectious agent, with 1.3 million TB-related deaths reported in 2022. Treatment efficacy has been compromised by the emergence of drug-resistant strains, including rifampin-resistant TB (RR-TB), multidrug-resistant TB (MDR-TB), and extensively drug-resistant TB (XDR-TB). Although first-line drugs like isoniazid, rifampicin, pyrazinamide, and ethambutol form the cornerstone of TB therapy, the rise of resistant strains necessitates the use of second-line drugs, which often come with increased toxicity and limited accessibility. Recent advances have focused on repurposing existing compounds and developing new drugs with novel mechanisms of action. Promising agents such as second-generation bedaquiline analogs (TBAJ-587, TBAJ-876), sudapyridine (WX-081), delamanid, pretomanid, and TBI-166 (pyrifazimine) have shown efficacy against resistant Mtb strains. Innovative treatment regimens like the BPaLM protocol-combining bedaquiline, pretomanid, linezolid, and moxifloxacin-offer shorter, all-oral therapies with higher cure rates. Personalized treatment durations and dose optimizations are becoming feasible through risk stratification algorithms and pharmacokinetic/pharmacodynamic studies. Immunotherapy is emerging as a complementary strategy to enhance the host's immune response against Mtb. Agents such as vitamin D, corticosteroids, non-steroidal anti-inflammatory drugs (NSAIDs), statins, metformin, and biological agents like interleukins and granulocyte-macrophage colony-stimulating factor are under exploration. Additionally, cell therapies involving mesenchymal stem cells and immune effector cells present new therapeutic avenues. Despite these advancements, significant challenges remain in achieving the World Health Organization's "End TB Strategy" goals, particularly as the COVID-19 pandemic has diverted resources and attention. Ongoing research and global collaboration are crucial to develop novel therapeutic strategies, optimize treatment regimens, and ultimately reduce the global burden of TB.

What is proven in the treatment of lipid disorders?

Cardiovascular diseases are still the leading global cause of mortality. Modifiable cardiovascular risk factors have been well-defined for many years and are amenable to multimodal treatment. The scope of pharmaceutical interventions targeting atherogenic lipoproteins in general and low-density lipoprotein (LDL)-cholesterol concentrations in particular, has significantly broadened over the last years, leading to an intensification of appropriate treatment targets, which are not sufficiently achieved in the clinical routine. Current prevention guidelines issued by the European Society for Cardiology (ESC) define patient cohorts according to their individual cardiovascular risk, which results in arisk-adapted lipid-lowering treatment. The aim is to avoid overtreatment of patients with a low or moderate risk and undertreatment of patients with a high or very high risk. In addition to secondary prevention in patients with an already established cardiovascular disease, primary prevention uses age-adapted risk stratification algorithms to assess the 10-year risk, e.g., systematic coronary risk evaluation 2 (SCORE2), SCORE2-older persons (SCORE2-OP) and the lifetime risk, e.g., lifetime-perspective cardiovascular disease model (LIFE-CVD) as the foundation for therapeutic decision making. Special patient groups with known elevated cardiovascular risk (familial hypercholesterolemia, diabetes mellitus, chronic kidney disease) are stratified by incorporating disease-specific parameters and are treated according to defined lipid-lowering targets.

Abstract 4117438: Machine Learning Predicts Successful Transcatheter Mitral Valve Edge to Edge Repair: A Systematic Review and Meta-Analysis

Introduction: Transcatheter Mitral Valve Edge to Edge Repair (TEER) is an established percutaneous treatment for patients with severe symptomatic Mitral Regurgitation (MR). The current AHA/ACC guidelines recommend TEER for inoperable patients with severe primary MR or patients with symptomatic severe secondary MR despite medical therapy. Machine learning (ML) has emerged as a tool for TEER risk stratification due to the paucity of established risk scores. To address the lack of consensus on its efficacy, we conducted a systematic review and meta-analysis of studies that utilized ML to predict the success of TEER. Methods: Electronic databases, including Embase, MEDLINE, and the Cochrane Library, were searched from inception through April 2024. We included studies that used TEER and employed at least one ML model to predict the success of TEER. The Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the accuracy of ML risk stratification algorithms. Results: 102 publications were screened, with seven eventually included in this analysis. Two studies employed clustering techniques, two utilized extreme gradient boosting, and three used multiple ML algorithms to predict outcomes. Of the four studies that compared the accuracy of ML with traditional Cox regression, all four demonstrated higher accuracy with ML, and this difference was statistically significant in three of the four studies. The mean AUC of the aggregated ML data was 0.737 [95% CI: 0.717, 0.758], compared to 0.627 [95% CI: 0.600, 0.653] for the pooled traditional methods. Conclusions: To our knowledge, we conducted the first systematic review and meta-analysis of ML methods for prediction of TEER success. ML outperformed established risk scores, demonstrating promising potential. Future ML models, trained on larger patient datasets, may further improve predictive accuracy in this patient population.

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan-Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.

Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: aretrospective study.

The aim of this study was to develop and assess aradiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). An analysis was performed retrospectively on agroup of 121 patients who received ahistological diagnosis of GIST. They were then divided into two sets, with85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing arandom forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with apathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct aprognostic prediction model. The clinical model (ModelC) displayed limited predictive efficacy in the context of GIST. Conversely, aradiomics model (ModelR) incorporating five parameters exhibited robust discriminative capabilities across both the training and validation sets, yielding area under the ROC curve (AUC) values of 0.893 (95% confidence interval [CI]: 0.807-0.949) and 0.855 (95% CI: 0.732-0.978), respectively. The F1max scores derived from the P‑R curves were 0.741 and 0.842 for the training and validation sets, respectively. Noteworthy was the exclusion of the two-dimensional tumor diameter and tumor location when constructing ahybrid model (ModelCR) that amalgamated radiomics and clinical features. ModelR demonstrated asubstantially enhanced discriminative capacity in the training set compared with ModelC (p < 0.005). The net reclassification improvement (NRI) corroborated the superior performance of ModelR over ModelC, thereby enhancing diagnostic accuracy and clinical applicability. Patients in the high-risk group had significantly worse recurrence-free survival (RFS, p < 0.001) and overall survival (OS, p = 0.004), and the radiomics signature is an independent risk factor for RFS. The extended model incorporating the radiomics signature outperformed the baseline model in terms of risk assessment accuracy (p < 0.001). Our investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.

Acute kidney injury in relation to high-sensitivity cardiac troponin T levels in the emergency department

Abstract Background The clinical implications of high-sensitivity cardiac troponin T (hs-cTnT) measurements in all-comers with AKI in the emergency department (ED) is largely unknown. Purpose To investigate the prevalence of myocardial injury, associations between serum-creatinine concentrations (SCC) and hs-cTnT kinetics, and the accuracy of established hs-cTnT-based myocardial infarction (MI) risk stratification algorithm criteria in patients with AKI. Methods This observational cohort study was based on patient visits to 7 EDs in Sweden from December 9, 2010 to August 31, 2017. All visits by patients ≥18 years fulfilling criteria for AKI with ≥1 hs-cTnT measurement and the exposure was dynamic change in SCC (ΔSCC) during the visits. Linear mixed models were used to estimate linear predictors of kinetic change in hs-cTnT (Δhs-cTnT). Logistic regression models were applied to calculate odds ratios (ORs) for Δhs-cTnT indicative of acute myocardial injury (Δhs-cTnT &amp;gt;20% and elevated hs-cTnT) in relation to ΔSCC among patients without MI, and to assess the diagnostic performance of hs-cTnT for MI in a subgroup of patients presenting with chest pain. Results A total of 15,211 visits by patients with AKI were included, of whom 1174 (8%) had an MI. Four of five (81%) patients without MI had myocardial injury (hs-cTnT &amp;gt;14 ng/l), and almost one of three (31%) had acute myocardial injury. These entities were common in both cardiac and non-cardiac conditions. The Δhs-cTnT in non-MI patients was 1.8-fold (β: 1.78, 95% CI: 1.62-1.96) in the highest quartile of ΔSCC and was paralleled by a 2-fold risk of acute myocardial injury (OR: 2.32, 95% CI: 2.08-2.59), compared with the lowest ΔSCC quartile. Using a 0 h hs-cTnT cut-off ≥52 ng/l assigned 627 (26%) patients to a high-risk group in whom the specificity and positive predictive value (PPV) for MI was low (78.5% (95% CI: 76.7-80.2) and 27.6% (95% CI: 24.1-31.3), respectively). Conclusions Hs-cTnT concentrations indicative of acute myocardial injury are frequently observed among all-comers with AKI in the ED and are associated with dynamic changes in SCC. These observations are accompanied by poor performance of recommended hs-cTnT-based algorithms for MI risk stratification in patients with chest pain.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

Application of the world guidelines for falls prevention and management’s risk stratification algorithm to patients on a frailty intervention pathway and the potential utility of sensory impairment information

BackgroundThe 2022 world guidelines for falls prevention and management suggest measuring sensory function including dizziness, vision, and hearing. These variables are not included in the falls risk stratification algorithm. This study sought to investigate the utility of the guidelines and potential avenues for improvement. This study applied the falls risk stratification recommendations and reviewed the individual sensory impairment risk factor variables predictive of falls and falls risk grouping in those assessed by a frailty intervention team (FIT) based in an emergency department (ED).MethodsPatients over 65 years old who attended the ED and had a comprehensive geriatric assessment carried out by FIT over a period of four months were included in this retrospective cross-sectional study. Patient characteristics, medication, physical and sensory function status data was retrieved and analysed with respect to falls and falls risk grouping.ResultsData was gathered retrospectively from 392 patients. Excluding those with missing data, almost all attendees were in the high-risk of falls category (n = 170, 43.4%), or the low-risk category (n = 149, 38.0%). Few people were in the intermediate-risk category (n = 19, 4.8%). Hearing loss and dizziness were significantly associated with falls incidence, whereas vision and balance were not. Hearing loss, balance and dizziness were significantly associated with risk grouping, whereas vision was not.ConclusionsMost older adults included in the analysis fell into the low- or high-risk categories, with a minority in the intermediate-risk category. This suggests that the inclusion criteria for the intermediate category could be altered for greater sensitivity. While impaired balance and vision were the most common impairments, hearing status, balance and dizziness were associated with risk group. These results, through a practical application of the world guidelines for falls to an acute clinical sample, raise the possibility of refining the falls risk stratification criteria, and highlight the capacity for additional sensory intervention to mitigate falls risk.

High Risk PFO-Associated Stroke: Proposed Algorithm for Better Risk Stratification.

High Risk PFO-Associated Stroke: Proposed Algorithm for Better Risk Stratification.

AI-PEDURO – Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository

BackgroundArtificial intelligence (AI) and machine learning (ML) methods are increasingly being applied in pediatric urology across a growing number of settings, with more extensive databases and wider interest for use in clinical practice. More than 30 ML models have been published in the pediatric urology literature, but many lack items required by contemporary reporting frameworks to be high quality. For example, most studies lack multi-institution validation, validation over time, and validation within the clinical environment, resulting in a large discrepancy between the number of models developed versus the number of models deployed in a clinical setting, a phenomenon known as the AI chasm. Furthermore, pediatric urology is a unique subspecialty of urology with low frequency conditions and complex phenotypes where clinical management can rely on a lower quality of evidence. ObjectiveTo establish the AI in PEDiatric UROlogy (AI-PEDURO) collaborative, which will carry out a living scoping review and create an online repository (www.aipeduro.com) for models in the field and facilitate an evidence synthesis of AI models in pediatric urology. Methods and AnalysisThe scoping review will follow PRISMA-ScR guidelines. We will include ML models identified through standardized search methods of four databases, hand-search papers, and user-submitted models. Retrieved records will be included if they involve ML algorithms for prediction, classification, or risk stratification for pediatric urology conditions. The results will be tabulated and assessed for trends within the literature. Based on data availability, models will be divided into clinical disease sections (e.g. hydronephrosis, hypospadias, vesicoureteral reflux). A risk assessment will be performed using the APPRAISE-AI tool. The retrieved model cards (brief summary model characteristics in table form) will be uploaded to the online repository for open access to clinicians, patients, and data scientists, and will be linked to the Digital Object Identifier (DOI) for each article. Discussion and ConclusionWe hope this living scoping review and online repository will offer a valuable reference for pediatric urologists to assess disease-specific ML models' scope, validity, and credibility to encourage opportunities for collaboration, external validation, clinical testing, and responsible deployment. In addition, the repository may aid in identifying areas in need of further research.

Correlation Between Complexity of Coronary Lesions and Delta High-sensitivity Troponin (hs-cTn) I Levels in Patients of Non-ST Elevation Myocardial Infarction

Non-ST Elevation Myocardial Infarction is a critical condition where early identification of myocardial injury is essential for risk stratification and treatment. High-sensitivity cardiac troponin I (hs-cTnI) is a well-established biomarker for detecting myocardial damage. Objectives: To assess the association between Syntax scores and initial significant delta hs-cTnI in patients who had been hospitalized with Non-ST Elevation Myocardial Infarction. Methods: Observational cohort study from January 2022 to December 2022 involving a total of one hundred and fifty patients admitted at Hayatabad Medical Complex Peshawar. hs-cTnI on admission and at 1, 2 hours and between (6h-12 h) post-admission daily was measured. Coronary lesion complexity was assessed with Syntax scores according to the results of coronary angiography. Statistical Analysis of data was performed using Pearson correlation to analyze the association between syntax scores delta hs-cTnI levels. Results: SYNTAX scores were correlated with Δhs-cTnI levels at all-time points, and the strongest correlation was found 6-12 hours post-admission (r=0.78). The syntax score had a mean value of 24.11 ± 14.74, and hs-cTnI levels increased over time to reflect the extent of myocardial injury. Conclusions: It was concluded that in Non-ST Elevation Myocardial Infarction patients, Syntax scores demonstrating more complex coronary lesions are related to higher delta hs-cTnI levels. This value highlights the use of hs-cTnI as a biomarker to evaluate the severity of myocardial injury and direct clinical decision-making in Non-ST Elevation Myocardial Infarction. Delta hs-cTnI measurements in diagnostic and risk stratification algorithms may lead to enhanced early identification of disease with improved outcomes.

Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening Among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.

We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Between 2010 and 2021, we monitored 1,237 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types a) 16 and 18; b) 31, 33, and 45; and c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p<0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p<0.001), followed by Hr1-b (9.26, p<0.001) and Hr1-c (7.21, p<0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.

Prognostic algorithm for early diagnosis of subcritical conditions as predictors of sudden cardiac death

Aim. To develop a method for early diagnosis of subcritical homeostasis disorders leading to sudden cardiac death (SCD). The basis is to improve the efficiency of predictive algorithms.Material and methods. This pilot, controlled, open-label, randomized, prospective clinical trial included 220 patients at risk of SCD and 150 patients without risk of SCD. Main and control groups was formed according to the global cardiovascular risk score. Based on the informative features proposed by specialized experts using multivariate statistics methods (discriminant analysis), two condition classes were formed. The conducted exploratory analysis confirmed the significance of diagnostic criteria in relation to SCD manifestation (manifestation of cardiac arrest — MCA), which is an integral assessment of a fatal complication. The development of decision rules was carried out on the basis of soft computing technology.Results. Taking into account the priority of clinical research, namely, the identification of subcritical stages of MCA, a classifier is proposed according to basic severity of patients — the severity of critical condition risk (SCCR). The discriminant function and intersection areas between MCA subclasses in the conditions of early SCD diagnosis determine the transition to soft computing technology. Membership functions for severe MCA are formed, followed by their iteration according to E. Shortliffe. The final decision rule, using a fuzzy classifier, differentiates the MCA into stages with different SCCR. In parallel with standard protocols for the management of severe somatic patients (chronic obstructive pulmonary disease, chronic kidney disease, hepatocellular failure), based on the proposed algorithm with an integral assessment of critical conditions, using the MCA decision rule in the main group in 30,5% of cases, subcritical stage was revealed, followed by targeted treatment and preventive support. In the first group, a subcritical condition was detected in 67 patients (30,5%); a critical condition without SCD — in 3 patients (1,4%). In all noted cases, early prevention of SCD was successfully carried out (these patients were transferred to a class with a lower SCD degree). Using conventional prognostic scores in this group, 46 patients (20,9%) were identified with a subcritical condition and 1 (0,4%) with a critical condition. In the control group, subcritical condition was determined in 35 patients (23,3%), of which 17 patients (11,3%) had a moderate risk of SCD. Using conventional prognostic scores, 23 patients (15,3%) with subcritical condition were identified.Conclusion. In the conditions of intensive care unit, general medicine departments, hemodialysis department, cardiac surgery, and organ transplantation department, an algorithm for early diagnosis and risk stratification of SCD with an integral assessment (MCA) should be used. The fuzzy classifier MCA according to SCCR makes it possible to carry out timely correction of treatment measures in addition to standard protocols.

Efficacy and Diagnostic Reliability of Intraoperative Frozen Section in the Surgical Management of Early-Stage Cervical Cancer

Objectives: The aim of this study of this study was to evaluate preoperative radiology and histopathology findings in cervical cancer lymphadenopathy detection, allowing targeted frozen section examination (FSE). Design: A retrospective analysis was conducted of 203 early-stage cervical cancer patients between 2010 and 2019 in a tertiary centre. Participants/Materials, Setting, and Methods: All patients had histologically confirmed cervical cancer and underwent magnetic resonance imaging (MRI) prior to intraoperative FSE. The primary objectives of the study were to determine the diagnostic accuracy of intraoperative FSE in the identification of lymph node metastases (LNM) in early-stage cervical cancer by correlation with final results obtained using standard histopathology techniques and to examine different preoperative, intraoperative, demographic, radiological, and histopathological factors that could identify those at greatest risk of nodal disease and hence predict those most likely to benefit from FSE, enabling more selective and targeted use. Results: Nineteen patients were found to have LNM (9.36%) at FSE. Patients were at increased risk of LNM by 6-fold with positive LVSI, 3-fold with MRI lymphadenopathy, and 3.5-fold with MRI-visible disease. The presence of lymphadenopathy on MRI and positive LVSI in combination increased the risk of LNM by 19-fold. Limitations: We acknowledge that FSE is expensive and time intensive, exposing patients to increased surgery duration and associated risk. We also recognize that it may not be feasible for all patients. Finally, the analysis is limited by retrospective nature of the study. Conclusions: By application of the preoperative risk stratification algorithm, we may suggest that FSE can be a useful tool in high-risk patients.

U-shaped correlation of lymphocyte count with all-cause hospital mortality in sepsis and septic shock patients: a MIMIC-IV and eICU-CRD database study

BackgroundIn sepsis, the relationship between lymphocyte counts and patient outcomes is complex. Lymphocytopenia and lymphocytosis significantly influence survival, illustrating the dual functionality of lymphocytes in responding to infections. This study investigates this complex interaction, focusing on how variations in lymphocyte counts correlate with all-cause hospital mortality among sepsis patients.MethodsThis retrospective cohort study analyzed data from two extensive critical care databases: the Medical Information Mart for Intensive Care IV 2.0 (MIMIC-IV 2.0) from Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the eICU Collaborative Research Database (eICU-CRD), which was Multi-center database from over 200 hospitals across the United States conducted by Philips eICU Research Institute. We included adult patients aged 18 years and older who met the Sepsis-3 criteria, characterized by documented or suspected infection and a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. Sepsis patients were categorized into quartiles based on lymphocyte counts. The primary outcome was all-cause mortality in the hospital, with 90 and 60-day all-cause mortality as the secondary outcomes. Univariable and multivariable Cox proportional hazard regressions were utilized to assess lymphocyte counts' impact on hospital mortality. An adjusted restricted cubic spline (RCS) analysis was performed to elucidate this relationship further. Subgroup analyses were also conducted to explore the association across various comorbidity groups among sepsis and septic shock patients.ResultsOur study included 37,054 patients, with an observed in-hospital mortality rate of 16.6%. Univariable and multivariable Cox proportional hazard regression models showed that lymphocyte counts were independently associated with in-hospital mortality (HR = 1.04, P < 0.01; HR = 1.06, P < 0.01). RCS regression analysis revealed a U-shaped relationship between lymphocyte levels and hospital mortality risk in sepsis and septic shock patients (P for overall < 0.001, P for nonliner < 0.01; P for overall = 0.002, P for nonliner = 0.014). Subgroup analyses revealed that elevated lymphocyte counts correlated with increased hospital mortality among sepsis patients with liver disease and requiring renal replacement therapy (P for overall = 0.021, P for nonliner = 0.158; P for overall = 0.025, P for nonliner = 0.759). These findings suggest that lymphocytes may have enhanced prognostic value in specific subsets of critically ill sepsis patients.ConclusionOur findings demonstrate that lymphocyte counts are a significant independent predictor of hospital mortality in sepsis and septic shock patients. We observed a U-shaped association between lymphocyte levels and mortality risk, indicating that high and low counts are linked to increased mortality. This result highlights the complex role of lymphocytes in sepsis outcomes and suggests the need for further investigation into the underlying mechanisms and potential therapeutic approaches. Integrating lymphocyte count assessment into risk stratification algorithms and clinical decision support tools could enhance the early identification of high-risk sepsis patients.

External validation of and improvement upon a model for the prediction of placenta accreta spectrum severity using prospectively collected multicenter ultrasound data.

This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model. We conducted a multicenter analysis using data collected for the IS-PAS database. The Sargent model's effectiveness in distinguishing between abnormally adherent placenta (FIGO grade 1) and abnormally invasive placenta (FIGO grades 2 and 3) was evaluated. A new model was developed using multicenter data from the IS-PAS database. The database included 315 cases of suspected PAS, of which 226 had fully documented standardized ultrasound signs. The final diagnosis was normal placentation in 5, abnormally adherent placenta/FIGO grade 1 in 43, and abnormally invasive placenta/FIGO grades 2 and 3 in 178. The external validation of the Sargent model revealed moderate predictive accuracy in a multicenter setting (C-index 0.68), compared to its higher accuracy in a single-center context (C-index 0.90). The newly developed model achieved a C-index of 0.74. The study underscores the difficulty in developing universally applicable PAS prediction models. While models like that of Sargent etal. show promise, their reproducibility varies across settings, likely due to the interpretation of the ultrasound signs. The findings support the need for updating the current ultrasound descriptors and for the development of any new predictive models to use data collected by different operators in multiple clinical settings.

Nationwide registry-based trial of risk-stratified cervical screening.

In well-screened populations, most cervical cancers arise from small groups of women with inadequate screening. The present study aims to assess whether registry-based cancer risk assessment could be used to increase screening intensity among high-risk women. The National Cervical Screening Registry identified the 28,689 women residents in Sweden who had either no previous cervical screening or a screening history indicating high risk. We invited these women by SMS and/or physical letter to order a free human papillomavirus (HPV) self-sampling kit. The Swedish national HPV reference laboratory performed extended HPV genotyping and referred high-risk HPV-positive women to their regional gynecologist. A total of 3691/28,689 (12.9%) women ordered a self-sampling kit and 10.0% (2853/28,689) returned a sample for testing. Participation among women who had never attended screening was low, albeit improved. Up to 22.5% of women in other high-risk groups attended. High-risk HPV types were detected in 8.3% of samples. High-risk HPV-positive women (238/2853) were referred without further triaging and severe cervical precancer or cancer (HSIL+) in histopathology were detected in 36/158 (23%) of biopsied women. Repeat invitations gave modest additional participation. Nationwide contacting of women with high risk for cervical cancer with personal invitations to order HPV self-sampling kits resulted in high yield of detected CIN2+. Further efforts to improve risk-stratified screening strategies should be directed to improving (i) the precision of the risk-stratification algorithm, (ii) the convenience for the women to participate and, (iii) ensuring that screen-positive women are followed-up.

Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.

Tumorigenesis and treatment are closely associated with various programmed cell death (PCD) patterns. However, the coregulatory role of multiple PCD patterns in colorectal cancer (CRC) remains unknown. In this study, we developed a multiple PCD index (MPCDI) based on 19 PCD patterns using two machine learning algorithms for risk stratification, prognostic prediction, construction of nomograms, immune cell infiltration analysis, and chemotherapeutic drug sensitivity analysis. As a result, in the TCGA-COAD, GSE17536, and GSE29621 cohorts, the MPCDI can effectively distinguished survival outcomes in CRC patients and served as an independent factor for CRC patients. We then explored the immune infiltration landscape in two groups using the nine algorithms and found more overall immune infiltration in the high-MPCDI group. TIDE scores suggested that the increased immune evasion potential and immune checkpoint inhibition therapy may be less effective in the high-MPCDI group. Immunophenoscores indicated that anti-PD1, anti-cytotoxic T-lymphocyte associated antigen 4 (anti-CTLA4), and anti-PD1-CTLA4 combination therapies are less effective in the high-MPCDI group. In addition, the high-MPCDI group was more sensitive to AZD1332, Foretinib, and IGF1R_3801, and insensitive to AZD3759, AZD5438, AZD6482, Erlotinib, GSK591, IAP_5620, and Picolinici-acid, which suggests that the MPCDI can guide drug selection for CRC patients. As a new clinical classifier, the MPCDI can more accurately distinguish CRC patients who benefit from immunotherapy and develop personalized treatment strategies for CRC patients.