Area Under The Receiver Operating Characteristic Research Articles

Development and validation of a predictive scoring model for complications following endoscopic endonasal skull base surgery.

The endoscopic endonasal approach (EEA) has evolved into an established technique in skull base surgery. The authors previously examined 1002 EEA procedures and reported factors associated with postoperative complications. Here they report the development and validation of a scoring model based on risk factors to better predict complications following EEA. The authors developed an optimized EEA scoring model for predicting postoperative complications as evidenced by the area under the receiver operating characteristic (AUROC) curve using their previously published data in addition to data collected from the subsequent 292 EEA procedures from years 2010-2020. The model was built systematically by evaluating the contributions that different variables had on the overall predictive ability of the model. The aim was to design a model containing as few variables as possible for practicality and to facilitate calculation and use at the bedside. The Clavien-Dindo grading system was used to classify complications into grades I-V based on the level of intervention that was required to manage the complication, with grades III-V considered to be higher-grade (i.e., those requiring reoperation or ICU-level care or death). The authors identified 1294 EEA operations performed between July 2010 and July 2020 that met their inclusion criteria. Higher-grade complications were identified following 135 EEA operations. The variables that were ultimately included in the model were age, BMI, operative time, meningioma, chordoma, expanded intradural approach, and nasoseptal flap use. The final model yielded an acceptable AUROC curve of 0.72 and predicted a stepwise increase in the rate of higher-grade complications as the score increased. A score of 0-2 (low) on the grading system was associated with an average complication rate of 5.1%. A score of 3-5 (medium) was associated with an average complication rate of 12.6%. A score of 6 or above (high) was associated with an average complication rate of 26%. This EEA complications scoring model accurately categorizes patients into low-, medium-, and high-risk groups with readily obtained variables. A high score in this complications model does not suggest that a patient is ineligible for surgery, but rather highlights the importance of thorough case selection, operating with caution, and appropriate preoperative counseling.

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications. We developed and validated a predictive model for all CV events and for heart failure, renal failure, and cerebrovascular events specifically after HDP. Models were created using the Premier Healthcare Database. The inclusion criteria for the model dataset were delivery with an HDP with discharge from October 1, 2015 to December 31, 2020. Machine learning methods were used to derive predictive models of CV events occurring during delivery hospitalization (Index Model) or during readmission (Readmission Model) using a training set (60%) to estimate model parameters, a validation set (20%) to tune model hyperparameters and select a final model, and a test set (20%) to evaluate final model performance. The total model cohort consisted of 553,658 deliveries with an HDP. A CV event occurred in 6501 (1.2%) of the delivery hospitalizations. Multilabel neural networks were selected for the Index Model and Readmission Model due to favorable performance compared to alternatives. This approach is designed for prediction of multiple events that share risk factors and may cooccur. The Index Model predicted all CV events with area under the receiver operating curve (AUROC) 0.878 and average precision (AP) 0.239 (cerebrovascular events: AUROC 0.941, heart failure: AUROC 0.898, and renal failure: AUROC 0.885). With a positivity threshold set to achieve ≥90% sensitivity, model specificity was 65.0%, 83.5%, 68.6%, and 65.6% for predicting all CV events, cerebrovascular events, heart failure, and renal failure, respectively. CV events within 1 year of delivery occurred in 3018 (0.6%) individuals. The Readmission Model predicted all CV events with AUROC 0.717 and AP 0.022 (renal failure: AUROC 0.748, heart failure: AUROC 0.734, and cerebrovascular events AUROC 0.698). Feature importance analysis indicated that the presence of chronic renal disease, cardiac disease, pulmonary hypertension, and preeclampsia with severe features had the greatest effect on the prediction of CV events. Among individuals with HDP, our multilabel neural network model predicted CV events at delivery admission with good classification and events within 1 year of delivery with fair classification.

Characterisation of Paediatric Neuroblastic Tumours by Quantitative Structural and Diffusion-Weighted MRI

Purpose: To determine the diagnostic accuracy of quantitative diffusion-weighted (DW) MRI apparent diffusion coefficient (ADC) and tumour volumes to differentiate between malignant (neuroblastoma (NB)) and benign types of neuroblastic tumours (ganglioneuroma (GN) and ganglioneuroblastoma (GNB)) using different region-of-interest (ROI) sizes. Materials and Methods: This single-centre retrospective study included malignant and benign paediatric neuroblastic tumours that had undergone DW MRI at diagnosis. The outcome was diagnostic accuracy of the tumour volume from structural and ADC DW MRI, in comparison to histopathology (reference standard). Results: Data from 40 patients (NB, n = 24; GNB, n = 6; GN, n = 10), 18 (45%) females and 22 (55%) males, with a median age at diagnosis of 21 months (NB), 64 months (GNB), and 133 months (GN), respectively, ranging from 0 to 193 months, were evaluated. The area under the receiver operating characteristic (AUROC) curve for ADC for discriminating between neuroblastic tumours’ histopathology for a small ROI was 0.86 (95% CI: 0.75–0.98), and for a large ROI, 0.83 (95% CI: 0.71–0.96). An ADC cut-off value of 1.06 × 10−3 mm2/s was able to distinguish malignant from benign tumours with 83% (68–98%) sensitivity and 75% (95% CI: 54–98%) specificity. Tumour volume was not indicative of malignant vs. benign tumour diagnosis. Conclusions: In this study, both small and large ROIs used to derive ADC DW MRI metrics demonstrated high accuracy to differentiate malignant from benign neuroblastic tumours, with the ADC AUROC for the averaged multiple small ROIs being slightly greater than that of large ROIs, but with overlapping 95% CIs. This should be taken into consideration for standardisation of ROI-related data analysis by international initiatives.

A generalised computer vision model for improved glaucoma screening using fundus images.

Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images could enhance early and accurate disease detection. To develop and validate a generalised deep-learning-based algorithm for screening glaucoma using fundus image. The glaucomatous fundus data were collected from 20 publicly accessible databases worldwide, resulting in 18,468 images from multiple clinical settings, of which 10,900 were classified as healthy and 7568 as glaucoma. All the data were evaluated and downsized to fit the model's input requirements. The potential model was selected from 20 pre-trained models and trained on the whole dataset except Drishti-GS. The best-performing model was further trained to classify healthy and glaucomatous fundus images using Fastai and PyTorch libraries. The model's performance was compared against the actual class using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, precision and the F1-score. The high discriminative ability of the best-performing model was evaluated on a dataset comprising 1364 glaucomatous discs and 2047 healthy discs. The model reflected robust performance metrics, with an AUROC of 0.9920 (95% CI: 0.9920-0.9921) for both the glaucoma and healthy classes. The sensitivity, specificity, accuracy, precision, recall and F1-scores were consistently higher than 0.9530 for both classes. The model performed well on an external validation set of the Drishti-GS dataset, with an AUROC of 0.8751 and an accuracy of 0.8713. This study demonstrated the high efficacy of our classification model in distinguishing between glaucomatous and healthy discs. However, the model's accuracy slightly dropped when evaluated on unseen data, indicating potential inconsistencies among the datasets-the model needs to be refined and validated on larger, more diverse datasets to ensure reliability and generalisability. Despite this, our model can be utilised for screening glaucoma at the population level.

A multiple biomolecules-based rapid life detection protocol embedded in a rover scientific subsystem for soil sample analysis

The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous. This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. While the majority of existing frameworks in this field are designed to identify biomolecules, our goal is to accomplish the same with minimal operational expense and mission complexity. We argue that the thoughtful integration of multiple biomolecular detections with lesser complexity and a robust framework can improve overall mission performance by satisfying the necessary time and resource constraints. In this study, a rapid multiple biomolecules-based life detection protocol (MBLDP-R) from soil samples is developed from scratch and embedded in an operational scientific rover subsystem targeted for planetary analysis missions. The study uses artificial biomolecule samples and simulated extraterrestrial environments to illustrate the suggested protocol’s end-to-end process. First, we list a few significant biomolecules, including lipids, proteins, carbohydrates, nucleic acids, ammonia, and pigments. Then, a weighted qualitative test scoring is applied to sort out the best test method for the finally selected biomolecules which are used as operational analogue to showcase the protocol’s in-situ analysis and decision-making capabilities. Based on the suitable biomolecules, a scientific exploration subsystem is developed, and the implemented protocol is built to perform onboard sample analysis. Evaluation results show that: (1) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro), (2) the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% for Extant, 88% for Extinct, and 92% in the case of NPL), and (3) the protocol is time-efficient with an average completion time of 17.60 min, demonstrating the protocol’s rapid nature in detecting biosignatures in soil samples. The research outcome yields useful additional data for related future studies, particularly in the design of scientific frameworks for mission-specific requirements with limited resources while also serving as a reference point for constraint evaluation methods for similar systems.

A blood-based biomarker panel for non-invasive diagnosis of metabolic dysfunction-associated steatohepatitis.

The current diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) and its severe form, metabolic dysfunction-associated steatohepatitis (MASH), is suboptimal. Here, we recruited 700 individuals, including 184 from Hong Kong as a discovery cohort and 516 from San Diego, Wenzhou, and Hong Kong as three validation cohorts. A panel of 3 parameters (C-X-C motif chemokine ligand 10 [CXCL10], cytokeratin 18 fragments M30 [CK-18], and adjusted body mass index [BMI]) was formulated (termed N3-MASH), which discriminated patients with MASLD from healthy controls with an area under the receiver operating characteristic (AUROC) of 0.954. Among patients with MASLD, N3-MASH could identify patients with MASH with an AUROC of 0.823, achieving 90.0% specificity, 62.9% sensitivity, and 88.6% positive predictive value. The diagnostic performance of N3-MASH was confirmed in three validation cohorts with AUROC of 0.802, 0.805, and 0.823, respectively. Additionally, N3-MASH identifies patients with MASH improvement with an AUROC of 0.857. In summary, we developed a robust blood-based panel for the non-invasive diagnosis of MASH, which might help clinicians reduce unnecessary liver biopsies.

A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning.

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

Exercise-changed gut mycobiome as a potential contributor to metabolic benefits in diabetes prevention: an integrative multi-omics study

ABSTRACT Background The importance of gut microbes in mediating the benefits of lifestyle intervention is increasingly recognized. However, compared to the bacterial microbiome, the role of intestinal fungi in exercise remains elusive. With our established randomized controlled trial of exercise intervention in Chinese males with prediabetes (n = 39, ClinicalTrials.gov:NCT03240978), we investigated the dynamics of human gut mycobiome and further interrogated their associations with exercise-elicited outcomes using multi-omics approaches. Methods Clinical variations and biological samples were collected before and after training. Fecal fungal composition was analyzed using the internal transcribed spacer 2 (ITS2) sequencing and integrated with paired shotgun metagenomics, untargeted metabolomics, and Olink proteomics. Results Twelve weeks of exercise training profoundly promoted fungal ecological diversity and intrakingdom connection. We further identified exercise-responsive genera with potential metabolic benefits, including Verticillium, Sarocladium, and Ceratocystis. Using multi-omics approaches, we elucidated comprehensive associations between changes in gut mycobiome and exercise-shaped metabolic phenotypes, bacterial microbiome, and circulating metabolomics and proteomics profiles. Furthermore, a machine-learning algorithm built using baseline microbial signatures and clinical characteristics predicted exercise responsiveness in improvements of insulin sensitivity, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% CI: 0.85–0.97) in the discovery cohort and of 0.79 (95% CI: 0.74–0.86) in the independent validation cohort (n = 30). Conclusions Our findings suggest that intense exercise training significantly remodels the human fungal microbiome composition. Changes in gut fungal composition are associated with the metabolic benefits of exercise, indicating gut mycobiome is a possible molecular transducer of exercise. Moreover, baseline gut fungal signatures predict exercise responsiveness for diabetes prevention, highlighting that targeting the gut mycobiome emerges as a prospective strategy in tailoring personalized training for diabetes prevention.

Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?

BackgroundBreast cancer (BC) is the most common malignancy in women and the second cause of cancer death. In recent years, there has been a strong development in artificial intelligence (AI) applications in medical imaging for several tasks. Our aim was to evaluate the potential of transfer learning with convolutional neural networks (CNNs) in discriminating suspicious breast lesions on ultrasound images.MethodsTransfer learning performances of five different CNNs (Inception V3, Xception, Densenet121, VGG 16, and ResNet50) were evaluated on a public and on an institutional dataset (526 and 392 images, respectively), customizing the top layers for the specific task. Institutional images were contoured by an expert radiologist and processed to feed the CNNs for training and testing. Postimaging biopsies were used as a reference standard for classification. The area under the receiver operating curve (AUROC) was used to assess diagnostic performance.ResultsNetworks performed very well on the public dataset (AUROC 0.938–0.996). The direct generalization to the institutional dataset resulted in lower performances (max AUROC 0.676); however, when tested on BI-RADS 3 and BI-RADS 5 only, results were improved (max AUROC 0.792). Good results were achieved on the institutional dataset (AUROC 0.759–0.818) and, when selecting a threshold of 2% for classification, a sensitivity of 0.983 was obtained for three of five CNNs, with the potential to spare biopsy in 15.3%–18.6% of patients.ConclusionIn conclusion, transfer learning with CNNs may achieve high sensitivity and might be used as a support tool in managing suspicious breast lesions on ultrasound images.Relevance statementTransfer learning is a powerful technique to exploit the performances of well-trained CNNs for image classification. In a clinical scenario, it might be useful for the management of suspicious breast lesions on breast ultrasound, potentially sparing biopsy in a non-negligible number of patients.Key PointsProperly trained CNNs with transfer learning are highly effective in differentiating benign and malignant lesions on breast ultrasound.Setting clinical thresholds increased sensitivity.CNNs might be useful as support tools in managing suspicious lesions on breast ultrasound.Graphical

SMART-2D: a novel scoring system to predict non diagnostic exercise stress echocardiography in renal transplant candidates

Abstract Background Exercise stress echocardiography (ESE) is often used for pre-renal transplant risk stratification. Nearly 20% of ESE may be non-diagnostic, resulting in delays to transplant and increased healthcare cost. A simple, reliable tool for predicting non-diagnostic ESE may streamline assessment. Methods Retrospective analysis of 898 kidney transplant candidates between 2013-2020 who underwent exercise stress echocardiography for pre-transplant cardiovascular assessment was performed. The cohort was separated into a derivation and validation set. Multivariable logistic regression identified predictors of non-diagnostic ESE. Covariates associated with non-diagnostic ESE (p<0.10) on univariate analysis were included in the multivariable model. From this model, only variables with p<0.05 were included for score development. Model discrimination was assessed by area under the receiver operating curve (AUROC). Results Non-diagnostic ESE occurred in 17% (151/898). Predictors of non-diagnostic study formed the scoring system: 1 point for female sex, 2 points for BMI >40, 1 point for age >55, Heart Rate<55 4 points, <65 3 points and <75 2 points. LV Hypertrophy was assigned 2 points, Type 1 Diabetes 3 points and Type 2 diabetes 1 point, LV Dysfunction was assigned 2 points. This scoring system had excellent discriminative ability (derivation AUROC 0.80, validation AUROC 0.81). A score of 8 or more had a specificity of 97% and a positive likelihood ratio of 9.4 for non-diagnostic ESE. A score of <8 had a negative predictive value of 86%. Patients with Non-Diagnostic ESE had longer time to transplantation (695 vs 636 days), and more investigations performed (43.05% vs 17.94%, p<0.001) and less likely to receive a renal transplant (OR 0.47, p<0.001). Conclusion Non diagnostic ESE in pre-transplant assessment causes significant delays to patient care. A simple clinical scoring system may identify patients likely to have non-diagnostic ESE and can be utilised to identify patients who may benefit from alternative risk-stratification investigations.

White Smoke in the Intermediate risk PE debate, could POPE be the solution?

Abstract Introduction In spite of a multitude of innovative therapeutic strategies emerging in the past decade, acute pulmonary embolism (PE) remains a potentially fatal disease. However, mortality varies significantly according to risk stratification, substantiating current recommendations to use reperfusion therapy only in hemodynamically unstable patients. There is an ongoing debate around whether hemodynamically stable patients showing signs of right ventricle dysfunction and myocardial injury (intermediate-high risk acute PE) may also benefit from reperfusion therapy. While the majority of patients in this category typically improve with anticoagulation alone, up to 10% may experience clinical deterioration. Aim We aimed to characterize the clinical in-hospital progression of patients with intermediate-risk PE and to evaluate the discriminative efficacy of PE scores in anticipating unfavorable clinical outcomes. Methods We conducted a retrospective observational study in patients admitted with the diagnosis of PE classified as intermediate risk in a tertiary center. Clinical, laboratory and ECG data were obtained. The BOVA score, the News score and the Pope score were evaluated. A composite endpoint of in-hospital mortality and use of reperfusion therapy due to hemodynamic deterioration was defined. Predictive abilities of these three scores were compared using area under the receiver operating characteristics (AUC-ROC) curve. Results From January 2019 to December 2020, a total of 166 patients were admitted with acute PE, classified as intermediate-risk, in our center. The mean age 71.9 ± 16.2 years and 57.2% were female. 71.1% of the pts showed bilateral PE in chest CT. The composite endpoint of in-hospital mortality and reperfusion therapy due to hemodynamic deterioration occurred in 9.8% of the pts. The median BOVA score was 3 (2-4), the median POPE score was 1 (0-2) and the median News score was 4 (2-6). The ROC curve analysis showed a significant higher discriminative power of POPE score compared to the other scores (AUC 0.856 95%CI 0.79–.92, p<0.001) (Fig1). Compared to patients with a POPE score 1, the composite endpoint was 4,6 times higher in patients with higher scores (OR 4.6 95%CI 1.63- 6914, p=0.029) (Fig2). Conclusion Despite classification as intermediate risk, these patients displayed a non-negligible in-hospital mortality rate. Close clinical monitoring is warranted in this group of pts and there is pressing need for an early identification of those who might benefit from more than just anticoagulation. In our population POPE score showed to be a simple and accurate tool in predicting those with an unfavorable evolution that could have benefitted from more advanced intervention.

Machine learning to predict incident atrial fibrillation in patients with acute stroke: FIND-AF STROKE

Abstract Background Atrial fibrillation (AF)-related strokes have a high rate of recurrence, and are associated with substantial morbidity, disability, healthcare expenditure, and mortality.(1-3) Detection of AF promises opportunity to prevent recurrent stroke through conversion of antiplatelet to anticoagulant therapy,(4,5) but use of extended monitoring has cost implications.(6) Thus accurately identifying patients with stroke at high risk for AF is important but previous previous models have been developed in small, unrepresentative cohorts.(7) Purpose To develop and validate a simple novel decision support tool for prediction of incident AF after hospitalisation with stroke through machine learning. Methods We developed the model (FIND-AF STROKE) in UK secondary care and death certificate-linked primary care health record data from individuals aged ≥30 years without known AF hospitalized with a stroke or transient ischaemic attack (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and random forest models for prediction of 2-year AF risk in the testing dataset with internal bootstrap validation, and compared against the CHA2DS2VASC score. Youden index was used to set a threshold for higher and lower risk. Results Of 35 530 UK individuals (mean age 72.1 (SD 12.7) years, 50.9% women), 1742 (4.8%) had developed incident AF at 2 years. Using feature importance a parsimonious version of the model was created requiring only age, sex, ethnicity (white versus other) and five comorbidities. In the testing dataset prediction performance for the random forest algorithm (area under the receiver operating characteristic (AUROC) 0.710, 95% CI 0.685 – 0.736) was superior to the CHA2DS2VASC score (0.645, 95% CI 0.617-0.672) (Figure 1). The threshold for higher risk using the Youden index was 4.81% which resulted in a negative predictive value of 97.6%, sensitivity 0.712, specificity 0.583. Of the post-stroke patients 44% were labelled at higher risk of AF, who were older, had a higher prevalence of comorbidities, and a 3.3-fold higher incidence of AF post-stroke, than those identified as lower risk (Table 1). Conclusions The machine learning FIND-AF STROKE decision support tool can accurately identify individuals at risk of incident AF after stroke to enable a targeted and cost-effective approach to extended monitoring. Validation within cohorts that have undergone extended monitoring post-stroke will give a more accurate estimation of yield at different risk thresholds.Figure 1

Comparative evaluation of OCT with OCTA changes at the optic disc and macula in glaucoma suspect and early glaucoma.

To compare the diagnostic ability of macular ganglion cell inner plexiform layer (mGCIPL) and peripapillary retinal nerve fiber layer (pRNFL) thickness on optical coherence tomography (OCT) and macular and peripapillary perfusion changes using OCT angiography (OCTA) in glaucoma suspect and early primary open angle glaucoma (POAG). Ninety patients (30 normal, 30 glaucoma suspects, and 30 early POAG) were recruited in this cross-sectional, prospective study. The average thickness of mGCIPL and pRNFL on spectral domain-OCT and macular vessel density (VD), optic nerve head (ONH) perfusion, and ONH flux index (FI) on OCTA were evaluated for early diagnosis of glaucoma. Macular VD, ONH perfusion, and ONH FI were significantly reduced in early POAG eyes compared to normal. The best correlation was observed between ONH FI and the average RNFL in both glaucoma suspects (r = 0.47, P < 0.01) and early POAG patients (r = 0.53, P < 0.01). Out of all the measured OCTA parameters, only ONH perfusion was significantly lower in glaucomatous eyes compared to glaucoma suspects (P < 0.001). Average GCIPL (0.82) and macular VD (0.76) had the highest area under the receiver operating characteristic (AUROC) curve value among all the OCT and OCTA parameters, respectively, for differentiating glaucoma suspects from controls. Rim area (0.92) and ONH FI (0.81) had the highest AUROC value among all the OCT and OCTA parameters for differentiating early POAG patients from controls. OCTA vascular parameters had a good correlation with structural damage both at the disc and the macula. OCT parameters were superior to OCTA parameters for diagnosis of glaucoma, although OCTA parameters are deranged very early in the disease.

Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study

This study aimed to develop a machine learning (ML) model for predicting pulmonary embolism (PE) in patients with gastrointestinal cancers, a group at increased risk for PE. We conducted a retrospective, multicenter study analyzing patients who underwent computed tomographic pulmonary angiography (CTPA) between 2010 and 2020. The study utilized demographic and clinical data, including the Wells score and D-dimer levels, to train a random forest ML model. The model’s effectiveness was assessed using the area under the receiver operating curve (AUROC). In total, 446 patients from hospital A and 139 from hospital B were included. The training set consisted of 356 patients from hospital A, with internal validation on 90 and external validation on 139 patients from hospital B. The model achieved an AUROC of 0.736 in hospital A and 0.669 in hospital B. The ML model significantly reduced the number of patients recommended for CTPA compared to the conventional diagnostic strategy (hospital A; 100.0% vs. 91.1%, P < 0.001, hospital B; 100.0% vs. 93.5%, P = 0.003). The results indicate that an ML-based prediction model can reduce unnecessary CTPA procedures in gastrointestinal cancer patients, highlighting its potential to enhance diagnostic efficiency and reduce patient burden.

Defining Postinduction Hemodynamic Instability With an Automated Classification Model.

Postinduction hypotension (PIH) may be associated with increased morbidity and mortality. In earlier studies, the definition of PIH is solely based on different absolute or relative thresholds. However, the time-course (eg, how fast blood pressure drops during induction) is rarely incorporated, whereas it might represent the hemodynamic instability of a patient. We propose a comprehensive model to distinguish hemodynamically unstable from stable patients by combining blood pressure thresholds with the magnitude and speed of decline. This prospective study included 375 adult elective noncardiac surgery patients. Noninvasive blood pressure was continuously measured between 5 minutes before up to 15 minutes after the first induction agent had been administered. An expert panel rated whether the patient experienced clinically relevant hemodynamic instability or not. Interrater correlation coefficient and intraclass correlation were computed to check for consistency between experts. Next, an automated classification model for clinically relevant hemodynamic instability was developed using mean, maximum, minimum systolic, mean, diastolic arterial blood pressure (SAP, MAP, and DAP, respectively) and their corresponding time course of decline. The model was trained and tested based on the hemodynamic instability labels provided by the experts. In total 78 patients were classified as having experienced hemodynamic instability and 279 as not. The hemodynamically unstable patients were significantly older (7 years, 95% confidence interval (CI), 4-11, P < .001), with a higher prevalence of chronic obstructive pulmonary disease (COPD) (3% higher, 95% CI, 1-8, P = .036). Before induction, hemodynamically unstable patients had a higher SAP (median (first-third quartile): 161 (145-175) mm Hg vs 150 (134-166) mm Hg, P < .001) compared to hemodynamic stable patients. Interrater agreement between experts was 0.92 (95% CI, 0.89-0.94). The random forest classifier model showed excellent performance with an area under the receiver operating curve (AUROC) of 0.96, a sensitivity of 0.84, and specificity of 0.94. Based on the high sensitivity and specificity, the developed model is able to differentiate between clinically relevant hemodynamic instability and hemodynamic stable patients. This classification model will pave the way for future research concerning hemodynamic instability and its prevention.

Elevated 1-Hour Post Load Glucose as a Predictor for Telomere Attrition: a study based on a Chinese Community population.

1-hour post-load glucose (1h-PG) detects dysglycemia-related disorders more effectively than traditional glycemic parameters. Hyperglycemia accelerates aging, whether 1h-PG outperforms in predicting aging remains unclear. To Compare the effectiveness of 1h-PG with other glycemic parameters in identifying and predicting telomere attrition. We conducted a cross-sectional and longitudinal study based on a Chinese community cohort. Multivariate linear regression and logistic regression were used to analyze the associations between glycemic parameters and telomere length. The area under the receiver operating characteristic (AUROC) curve were used to compare the differentiating and predictive ability. Populations were regrouped by glucose tolerance status and 1h-PG to compare telomere length. Analyses were separately conducted in non-diabetic and diabetic populations. The cross-sectional study included 715 participants. Only 1h-PG was significantly negatively associated with RTL in both non-diabetic (β = -0.106, 95%CI -0.068 to -0.007, P = 0.017) (odds ratio [OR] = 1.151, 95% CI 1.069 to 1.239, P = 0.005) and diabetic (β = -0.222, 95%CI -0.032 to -0.007, P = 0.002) (OR = 1.144, 95% CI 1.041 to 1.258, P = 0.035) populations. The longitudinal study recruited 437 populations and 112 remained in 7-years follow-up. 1h-PG was associated with telomere shortening in the non-diabetic group (β = -0.314, 95%CI -0.276 to -0.032, P = 0.016) (OR = 2.659, 95% CI 1.158 to 6.274, P = 0.021). AUROC analysis showed that 1h-PG outperformed other glycemic parameters in identifying and predicting telomere attrition. Reclassification revealed that normal glucose tolerance and prediabetic individuals with elevated 1h-PG had telomere lengths comparable to prediabetic and diabetic populations, respectively. 1h-PG outperforms other glycemic parameters in predicting telomere attrition and can be a valuable marker for early aging detection.

Development of the Pulmonary Embolism Progression (PEP) score for predicting short-term clinical deterioration in intermediate-risk pulmonary embolism: a single-center retrospective study.

Accurate risk stratification in acute intermediate-risk pulmonary embolism (PE) is essential. Current prediction scores lack the ability to forecast impending clinical decline. The Pulmonary Embolism Progression (PEP) score aims to predict short-term clinical deterioration (respiratory failure or hemodynamic instability within 72h) in patients with intermediate-risk PE. This single-center retrospective cohort study analyzed patients with intermediate PE. The outcome of interest was respiratory failure or hemodynamic instability within 72h. A multivariate logistic regression identified five predictive variables for the final PEP score: use of > 4L/min of supplemental oxygen above baseline, lactate > 2.0 mmol/L, high-sensitivity cardiac troponin T (hs-cTnT) > 40 ng/L, tricuspid annular plane systolic excursion (TAPSE) < 13mm, and the combination of central and subsegmental clot. The derivation cohort included 117 patients, and the validation cohort included 70 patients. The area under the receiver operating characteristic (AUROC) curve for the derivation cohort was 0.8671 (95% CI: 0.7946, 0.9292), and for the validation cohort, it was 0.9264 (95% CI: 0.8680, 0.9847). A PEP score of 4 points yielded the highest combination of sensitivity (93%) and specificity (65%). Each incremental point increase in the PEP score raised the probability of clinical deterioration by a factor of 1.933. The PEP score is a reliable tool for predicting the likelihood of clinical deterioration in intermediate-risk PE patients within 72h, potentially aiding in timely clinical decision-making and improving patient outcomes.

A classification system for identifying persons with an unknown cardiovascular disease (CVD) status for a multiracial/ ethnic Caribbean population.

The need for classification systems for cardiovascular disease (CVD) that is population-specific is important towards understanding the clinical disease and diagnostics associated with the disease. This paper presents the form and validation results of this classification system. The survey data used was captured from 778 participants, 526 persons with no prior CVD, and 252 who reported prior CVD. Binomial logistic regression and Discriminant analysis were utilised to develop classification models. This classification system provided a general measure of severity of disease by utilising scores estimated from two algorithms developed from 13 routine physiologic measurements, along with demographic information of age and ethnicity, inter alia, and previous health status. For each model, specific score ranges were identified, which gave the best classification for those with a prior CVD incident (higher scores) and for others labelled as non-CVD (lower scores). The two classification models (Logistic Regression Model and Discriminant Analysis Model) developed had high area under the receiver-operating characteristic (AUROC) values (98% & 99%) and sensitivity (86 and 90%), which improved discrimination between Non-CVD and CVD participants and, more importantly, correctly classified a greater proportion of CVD participants. New to this type of research was the estimation and detailed evaluation of a range of scores, labelled non-differentiating, which fell in the middle of the spectrum and which contained the higher-end scores for the non-CVD individuals and the lower-end scores for CVD patients, all of whom were incorrectly classified, based on their prior history. The classification system of scores is able to differentiate the CVD status of individuals, with good predictability, and could assist physicians with recommending different treatment plans. The two models in this classification system each individually outperformed the three established models in terms of the strength of their correct classifications of individuals with or without prior reported CVD incidents. More importantly, they have smaller non-differentiating ranges than the three known models and, in that range, the two new models have lower CVD/non-CVD ratios suggesting they are more likely to misclassify non-CVD individuals compared to CVD patients, which is a more benign misclassification. Further, when used in combination, the two models increased the sensitivity, in classifying individuals of different ethnicities, beyond that of either one used independently or of any of the three standard European/North American models. These efforts will be instrumental in advancing personalised CVD management strategies and improving health outcomes across diverse populations.

Artificial Intelligence and Colposcopy: Automatic Identification of Vaginal Squamous Cell Carcinoma Precursors

Background/Objectives: While human papillomavirus (HPV) is well known for its role in cervical cancer, it also affects vaginal cancers. Although colposcopy offers a comprehensive examination of the female genital tract, its diagnostic accuracy remains suboptimal. Integrating artificial intelligence (AI) could enhance the cost-effectiveness of colposcopy, but no AI models specifically differentiate low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. This study aims to develop and validate an AI model for the differentiation of HPV-associated dysplastic lesions in this region. Methods: A convolutional neural network (CNN) model was developed to differentiate HSILs from LSILs in vaginoscopy (during colposcopy) still images. The AI model was developed on a dataset of 57,250 frames (90% training/validation [including a 5-fold cross-validation] and 10% testing) obtained from 71 procedures. The model was evaluated based on its sensitivity, specificity, accuracy and area under the receiver operating curve (AUROC). Results: For HSIL/LSIL differentiation in the vagina, during the training/validation phase, the CNN demonstrated a mean sensitivity, specificity and accuracy of 98.7% (IC95% 96.7–100.0%), 99.1% (IC95% 98.1–100.0%), and 98.9% (IC95% 97.9–99.8%), respectively. The mean AUROC was 0.990 ± 0.004. During testing phase, the sensitivity was 99.6% and 99.7% for both specificity and accuracy. Conclusions: This is the first globally developed AI model capable of HSIL/LSIL differentiation in the vaginal region, demonstrating high and robust performance metrics. Its effective application paves the way for AI-powered colposcopic assessment across the entire female genital tract, offering a significant advancement in women’s healthcare worldwide.

Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning

BackgroundGastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient’s 180-day GIB hospitalization risk.MethodsAn eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017–2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data.ResultsIncidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853). XGBoost showed area under the receiver operating curve (AUROC) = 0.74 (95% confidence interval (CI) 0.72, 0.76) versus logistic regression showed AUROC = 0.68 (95% CI 0.66, 0.71). Sensitivity and specificity were 65.3% (60.9, 69.7) and 68.0% (67.6, 68.5) for XGBoost versus 68.9% (64.7, 73.0) and 57.0% (56.5, 57.5) for logistic regression, respectively. Associations in exposures were consistent for many factors. Both models showed GIB hospitalization risk was associated with older age, disturbances in anemia/iron indices, recent all-cause hospitalizations, and bone mineral metabolism markers. XGBoost showed high importance on outcome prediction for serum 25 hydroxy (25OH) vitamin D levels, while logistic regression showed high importance for parathyroid hormone (PTH) levels.ConclusionsMachine learning can be considered for early detection of GIB event risk in HD. XGBoost outperforms logistic regression, yet both appear suitable. External and prospective validation of these models is needed. Association between bone mineral metabolism markers and GIB events was unexpected and warrants investigation.Trial registrationThis retrospective analysis of real-world data was not a prospective clinical trial and registration is not applicable.Graphical