Predictive Index Research Articles

Coronary artery calcium scans powered by artificial intelligence (AI-CAC) predicts atrial fibrillation and stroke comparably to cardiac magnetic resonance imaging: MESA

Abstract Background AI-CAC provides more actionable information than the Agatston coronary artery calcium (CAC) score. We have recently shown in the Multi-Ethnic Study of Atherosclerosis (MESA) that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) as early as one year. In this study we evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF and stroke, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP. Methods We used 15-year outcomes data from 3552 asymptomatic individuals (52.2% women, age 61.7±10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. CMRI LA volume was previously measured by human experts. Data on BNP, CHARGE-AF risk score and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve (AUC). Results Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The AUC for AI-CAC versus CMRI for AF and stroke were not significantly different (0.802 vs. 0.798 and 0.762 vs. 0.751 respectively, p=0.60). AI-CAC significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF and stroke when added to CHARGE-AF risk score (0.28, 0.21), NT-proBNP (0.43, 0.37), and Agatston score (0.69, 0.41) respectively (p for all<0.0001). Conclusion AI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years. Further studies to investigate the clinical utility of AI-CAC for AF and stroke prediction are warranted.

The Predictive Value of Perioperative Inflammatory Indexes in Major Arterial Surgical Revascularization from Leriche Syndrome

Objectives: The role of inflammation in the pathophysiology of atherosclerosis is extensive. Our study aims to assess the predictive role of inflammatory indexes regarding in-hospital mortality in major vascular surgery of Leriche syndrome as a convenient, low-cost, and noninvasive prognostic marker to optimize the patient’s perioperative course. Methods: Our retrospective single-center study enrolled consecutive patients diagnosed with aortoiliac occlusive disease, Leriche syndrome, who underwent elective major vascular surgery between 2017 and 2023 in a tertiary cardiovascular center. Preoperative, postoperative, and day-one after-surgery data, including systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio, and monocyte–lymphocyte ratio, were studied to the endpoint, in-hospital death. We also tested the delta values of the indexes to the endpoint. The indexes were compared to the Revised Cardiac Risk Index (RCRI) and Vascular Surgery Group Cardiac Risk Index (VSG-CRI) for outcome prediction. Results: The tested inflammatory indexes significantly increased from the preoperative to postoperative and, further, to the day-one settings. Preoperative AISI (p = 0.040) emerged as the only independent risk factor regarding in-hospital death occurrence in Leriche patients who underwent major revascularization surgery. While RCRI did not significantly predict the endpoint (AUC = 0.698, p = 0.057), VSG-CRI (AUC = 0.864, p = 0.001) presented the best result in ROC analysis. Postoperative NLR (AUC = 0.758, p = 0.006) was next, followed by NLR postoperative–preoperative (_Preop-_Postop) delta value (AUC = 0.725, p = 0.004), postoperative SIRI (AUC = 0.716, p = 0.016), SIRI_Preop-_Postop delta value (AUC = 0.712, p = 0.016), postoperative SII (AUC = 0.692, p = 0.032), and SII_Preop-_Postop delta value (AUC = 0.631, p = 0.030). Conclusions: Inflammatory indexes are valuable tools for assessing perioperative risk in major vascular surgery, enhancing the value of the already validated risk scores.

Aroma compounds and physicochemical and functional properties of traditional Tokat bread

AbstractThis study aimed to determine the physical (height, weight, volume, color, etc.), chemical (protein, fat, ash, moisture, etc.), functional (total phenolic, flavonoid, and antioxidant capacity), nutritional (total, soluble, insoluble dietary fiber, and important starch fractions), and texture properties and volatile organic compounds of traditional Tokat bread. In addition, yeast and bacterial species were isolated and identified from the sourdoughs used in its production. Having a 6‐day shelf life, traditional Tokat bread has an average total dietary fiber content of 14.64%. The predictable glycemic index was 83.09, and the slowly digestible starch content was 6.15%. In traditional Tokat bread, 45 different volatile aroma components were identified, and S. cerevisiae, D. hansenii, K. lactis, L. plantarum, and L. mesentorides species were mostly isolated in sourdough. The characteristics of traditional Tokat breads produced by traditional methods in different regions were determined. It is predicted that the taste and aroma of traditional Tokat bread originate from the hornbeam and pelite woods used in the baking stage and the sourdough used. The yeast composition of the breads collected from different regions also varies.

Contribution of body adiposity index and conicity index in prediction of metabolic syndrome risk and components

Background and aimsBody adiposity index (BAI) and conicity index have been known as useful measures in predicting cardio-metabolic diseases. This study aimed to evaluate the predictive potential of BAI and conicity index for the risk of metabolic syndrome (MetS) in comparison with body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). MethodsIn this cross-sectional study, 174 adults (87 with MetS and 87 healthy individuals) were recruited from a medical weight loss center. Anthropometric parameters, systolic and diastolic blood pressures (SDP and DBP), lipid profile, and fasting blood sugar (FBS) were measured. ResultsAll anthropometric parameters were significantly higher in subjects with MetS than in healthy subjects. Both in MetS and healthy subjects, females had significantly higher BAI and BMI than males. In the fully adjusted model, the odds of MetS increased for each unit increase in BAI by 27 % (p = 0.001), in BMI by 33 % (p = 0.001), in WC by 13 % (p < 0.001), and in HC by 9 % (p = 0.005). ROC curve analysis showed that all the anthropometric parameters displayed clinical importance in predicting MetS, but WHR had the largest area under the curve (AUC) in total, male, and female patients. In participants with MetS, the conicity index was negatively correlated with FBS; BAI was positively associated with HDL level. ConclusionAll studied anthropometric parameters had acceptable accuracy for predicting MetS. Traditional parameters, particularly the WHR, exhibited a higher predictive power concerning MetS. The results underscore the reliability of conventional anthropometric measures in clinical and epidemiological settings.

A Prospective Study of CSF Flow Dynamics Across Foramen Magnum in Adult Chiari Malformation/Syringomyelia Complex and its Clinical Correlation with Outcomes after Surgery

Abstract Introduction Chiari I malformation refers to cerebellar tonsillar descent below the foramen magnum and is frequently associated with syringomyelia. Prior cerebrospinal fluid (CSF) flow studies correlated the clinical severity of these lesions with general flow velocity or bulk flow at the foramen magnum; however, these techniques have not assessed the effect on surgical outcomes. The study aims to present clinical and radiological factors and CSF flow parameters (pre- and postoperative) that affect the surgical outcome. Materials and Methods The institutional ethics committee approved the study. We collected the prospective clinical data, including pre- and postoperative symptoms. Functional grades were determined along with the change in clinical improvement based on clinical examination notes, the change in functional grade was calculated, and the radiologic data were analyzed according to the degree of clinical improvement. The surgical procedure included suboccipital bony decompression with duroplasty. Patients were followed up at 1 month, 3 months, and 1 year. Results There were a total of 25 patients. The mean age of the patients was 45.52 ± 13.37 years, with 40% being males and 60% being females. After the surgery, there was a significant increase in the anterior and posterior CSF flows at the foramen magnum. Most had a resolution in the headache and sensory symptoms, while the lower cranial nerve and motor symptoms had a minor resolution at follow-up. Age, sex, and headache do not correlate with the outcome or syrinx improvement. Motor power in the lower limb (preintervention) and syrinx shape (preintervention) were significantly associated with the variable “anterior flow at the foramen magnum” (preintervention; p &lt; 0.05). Average flow, cervicomedullary angle (postintervention), sensory symptom progression, and CSF flow change anterior to the foramen magnum were significantly associated with the variable “anterior flow at the foramen magnum” (postintervention; p &lt; 0.05). Conclusion Even after surgery, persistent foramen magnum CSF outflow obstruction has a robust negative correlation with the outcome. The Chiari outcome predictability index has shown a significant correlation with patient outcomes and can be used to inform patients about the expected outcome. The results of the present study will be helpful in stratifying patients according to their desired outcomes.

Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River-a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

A comparison between invasive and noninvasive measurement of the Hypotension Prediction Index: A post hoc analysis of a prospective cohort study.

Clinical trials and validation studies demonstrate promising hypotension prediction capability by the Hypotension Prediction Index (HPI). Most studies that evaluate HPI derive it from invasive blood pressure readings, but a direct comparison with the noninvasive alternative remains undetermined. Such a comparison could provide valuable insights for clinicians in deciding between invasive and noninvasive monitoring strategies. Evaluating predictive differences between HPI when obtained through noninvasive versus invasive blood pressure monitoring. Post hoc analysis of a prospective observational study conducted between 2018 and 2020. Single-centre study conducted in an academic hospital in the Netherlands. Adult noncardiac surgery patients scheduled for over 2 h long elective procedures. After obtaining informed consent, 91 out of the 105 patients had sufficient data for analysis. The primary outcome was the difference in area under the receiver-operating characteristics (ROC) curve (AUC) obtained for HPI predictions between the two datasets. Additionally, difference in time-to-event estimations were calculated. AUC (95% confidence interval (CI)) results revealed a nonsignificant difference between invasive and noninvasive HPI, with areas of 94.2% (90.5 to 96.8) and 95.3% (90.4 to 98.2), respectively with an estimated difference of 1.1 (-3.9 to 6.1)%; P = 0.673. However, noninvasive HPI demonstrated significantly longer time-to-event estimations for higher HPI values. Noninvasive HPI is reliably accessible to clinicians during noncardiac surgery, showing comparable accuracy in HPI probabilities and the potential for additional response time. Clinicaltrials.gov (NCT03795831) on 10 January 2019. https://clinicaltrials.gov/study/NCT03795831.

COMPARING STAGES OF DIABETIC RETINOPATHY WITH SYSTEMIC VASCULAR STATUS USING FINGER PHOTOPLETHYSMOGRAPHY.

To evaluate systemic vascular fitness measured by finger photoplethysmography (PPG) in diabetic retinopathy (DR). This was a cross sectional observational study on type-II Diabetes Mellitus patients from October 2020 to May 2021. Data collected include systolic/diastolic blood pressure (BP), visual acuity, HbA1c, and central macular thickness (CMT). DR severity was categorised using the ETDRS classification. PPG signals were acquired using pulse-oximeter modules (OEM-60, Dolphin Medical Inc) measured for 90 seconds at 275Hz sampling rate and 16-bits resolution which records photoplethysmography fitness index (PPGF), vascular-risk prediction index (VRPI) and vascular age (VascA). One-hundred-and-forty-one patients were equally distributed into six DR categories. Mean age was 58.8±9.9 years, with female-to-male ratio of 1.27. There were significant differences in mean systolic (125.5±10.0mmHg, p=0.007) and diastolic-BP (80.0±8.8mmHg), mean HbA1c (7.6±1.9%, p=0.005), median LogMAR (0.3, IQR:0.2-0.5, p<0.001) and CMT (p=0.003) across DR severity. Significant differences were also seen in PPGF (p=0.001), VRPI (p<0.001) and VascA (p=0.001), with poorer values in severe compared to mild/moderate DR. After adjusting for age, BP and HbA1c, PPGF reduces by 3.3% (regression coefficient, b= -3.27, p<0.001), VascA increases by 2.5 years (b=2.54, p=0.002) and VRPI increases by 3.1 (b= 3.08, p<0.001) with every DR worsening. More severe DR were associated with poorer PPG vascular markers.

Embracing market dynamics in the post-COVID era: A data-driven analysis of investor sentiment and behavioral characteristics in stock index futures returns

This paper aims to enhance the understanding and prediction of stock market behavior during unexpected events like the COVID-19 pandemic, with a specific focus on the role of market attention, social media sentiment indicators, and the development and evolution of unexpected events. We highlight that the common trading and technical indicators used in forecasting the stock index futures prices often overlook investor sentiment and pandemic-related data, which can be instrumental in predicting stock market behavior during significant emergencies. In response, we propose a multi-faceted approach that incorporates these overlooked factors. First, we enhance the predictive index system by integrating investor sentiment, derived from stock message board commentary, and investor behavior influenced by the development and evolution of the pandemic. This innovative approach refines our model's predictive capabilities and is validated through comparative analysis. Second, we introduce a hybrid framework for predicting stock index futures closing prices. By decomposing the closing price series into long-term trends, cyclical variations, and random fluctuations, we create a more nuanced forecast. Each component is predicted separately using appropriate time-series algorithms, improving the overall predictive accuracy and offering generalizability and scalability. Third, we devise a dynamic trading strategy that recognizes pandemic-related data, evolving over time, as a pivotal factor. This strategy is adaptable to evolving market conditions, and our experimental evidence demonstrates its effectiveness in yielding higher returns and reducing associated risks. Our findings underline the importance of incorporating investor sentiment and pandemic-related data into stock market predictions, thus offering a more comprehensive and accurate approach to market forecasting and risk management.

Effects of Specially Designed Energy-Restricted Diet on Anthropometric Parameters and Cardiometabolic Risk in Overweight and Obese Adults: Pilot Study.

This study examined the effects of a specially designed energy-restricted diet with alternate carbohydrate intake on body composition and cardiometabolic risk factors in overweight and obese adults. The aim was to assess whether the intervention could lead to significant weight loss, improve body composition, and reduce cardiometabolic risks. Sixty-five participants (34 women, 31 men) with an average BMI of 31.8 ± 9.1 kg/m2 (women) and 34.1 ± 6.4 kg/m2 (men) participated in a 14-week intervention. The diet included different days of carbohydrate intake and a 20% reduction in total daily energy consumption. Anthropometric measurements and biochemical parameters, including predictive indices of cardiometabolic risk, were determined at baseline and after the intervention. The intervention resulted in a significant reduction in body weight (mean weight loss of 17%, p < 0.001), with 64.6% of participants achieving a weight loss of at least 10%. Muscle mass as a percentage of total body weight increased. Cardiometabolic improvements were observed in fasting blood glucose (from 5.4 to 4.9 mmol/L, p < 0.001) and LDL cholesterol (from 3.38 to 2.81 mmol/L, p < 0.001). Gender-specific differences were found, particularly in HDL-C, which decreased significantly in women (p = 0.013), while there was a non-significant increase in men. Cardiometabolic indices, including the Visceral Adiposity Index (VAI) and the Cardiometabolic Index (CMI), also improved significantly. The alternate carbohydrate diet improved body composition, cardiometabolic health, and treatment adherence through metabolic flexibility. However, the short duration of this study and the lack of a control group suggest that further research is needed to assess long-term sustainability.

Comparing the Hypotension Prediction Index to Mean Arterial Pressure and Linear Extrapolated Mean Arterial Pressure for the Prediction of Intraoperative Hypotension: A Secondary Analysis.

Comparing the Hypotension Prediction Index to Mean Arterial Pressure and Linear Extrapolated Mean Arterial Pressure for the Prediction of Intraoperative Hypotension: A Secondary Analysis.

Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning

BackgroundPrecise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT) and can offer clues for the selection of subsequent treatment strategies. Most current predictive models for pCR focus primarily on pre-treatment factors, neglecting the dynamic systemic changes that occur during neoadjuvant chemoradiotherapy, and are constrained by low accuracy and lack of integrity. PurposeThis study devised a novel predictor of pCR using dynamic alterations in systemic inflammation-nutritional marker indexes (SINI) during neoadjuvant therapy and developed a machine-learning model to predict pCR. MethodsTwo cohorts of patients with LARC from center one from 2012 to 2017 and from center two from 2020 to 2023 were integrated for analysis. This study compared dynamic changes in blood indexes before and after neoadjuvant therapy and surgical operation. A least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to mitigate collinearity and identify key indexes, constructing the SINI. Univariate and multiple logistic regression analyses were used to identify the independent risk factors associated with pCR. Additionally, 10 machine learning algorithms were employed to develop predictive models to assess risk. The hyperparameters of the machine learning models were optimized using a random search and 10-fold cross-validation. The models were assessed by examining various metrics, including the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal and external validation cohorts. Additionally, Shapley's additive explanations (SHAP) were employed to interpret the machine learning models. ResultsThe study cohort comprised 677 patients from the center one and 224 patients from the center two. Six key indexes were identified, and a predictive index, SINI, was constructed. Univariate and multiple logistic regression analyses revealed that SINI, clinical T-stage, clinical N-stage, tumor size, and the distance from the anal verge were independent risk factors for pCR in patients with LARC following NCRT. The mean AUC value of the extreme gradient boosting (XGB) model in the 10-fold cross-validation of the training set was 0.877. The XGB model demonstrated superior performance in the internal and external validation sets. Specifically, in the internal test set, the XGB model achieved an AUC of 0.86, AUPRC of 0.707, accuracy of 0.82, and precision of 0.80. In the external validation set, the XGB model exhibited an AUC of 0.83, AUPRC of 0.702, accuracy of 0.81, and precision of 0.81. Additionally, the predictions generated by the XGB model were analyzed using SHAP. ConclusionThis study involved developing and validating an XGB model using SINI to predict pCR in patients with LARC. Besides, a SINI-based machine learning model shows promise in accurately predicting pCR following NCRT in patients with resectable LARC, offering valuable insights for personalized treatment approaches.

Clinical and immune responses to neoadjuvant fulvestrant with or without enzalutamide in ER+/Her2- breast cancer.

Most ER+ breast cancers (BC) express androgen receptors (AR). This randomized phase II trial of 4 months of neoadjuvant fulvestrant (Fulv) alone or with enzalutamide (Combo) assessed whether adding AR blockade to Fulv would limit residual tumor at the time of surgery, as measured by modified preoperative endocrine predictive index (PEPI) score. Eligible patients were women with ER+/HER2- primary BC cT2 or greater. Stratification factors were clinical node and T-stage. Fresh tumor biopsies were required at study entry, after 4 weeks on therapy (W5), and at surgery. Laboratory analyses on tumors included immunochemistry (IHC) for ER/PR/AR/GR and Ki67 protein, evaluation of gene expression, multiplex for myeloid lineage immune cells, reverse-phase protein array, and plasma metabolomic analyses. Of 69 consented patients, 59 were evaluable. Toxicity was as expected with endocrine therapy. Combo achieved PEPI = 0 more frequently (24%: 8/33) than Fulv (8%: 2/26). Ki67 was ≤10% across arms by W5 in 76% of tumors. Activation of mTOR pathway proteins was elevated in tumors with poor Ki67 response. Tumors in both arms showed decreased estrogen-regulated and cell division gene sets, while Combo arm tumors uniquely exhibited enrichment of immune activation gene sets, including interferon gamma, complement, inflammation, antigen processing, and B and T cell activation. Multiplex IHC showed significantly reduced tumor-associated macrophages and CD14+/HLADR-/CD68- MDSCs in Combo tumors at W5. In summary, Combo tumors showed a higher PEPI = 0 response, Ki67 response, and more activated tumor immune microenvironment than Fulv. The odds of response were 4.6-fold higher for patients with ILC versus IDC. (Trial registration: This trial is registered at Clinicaltrials.gov ( https://www.clinicaltrials.gov/study/NCT02955394?id=16-1042&rank=1 ). The trial registration number is NCT02955394. The full trial protocol is available under Study Details at the Clinicaltrials.gov link provided).

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model.

The monitoring of arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Several efforts have been devoted to develop artificial intelligence-based hypotension prediction indices. However, the use of such indices is limited because they may not provide a compelling interpretation of the association between the predictors and hypotension. Herein, an interpretable deep learning model is developed that forecasts hypotension occurrence 10 min before a given 90-s ABP record. Internal and external validations of the model performance show the area under the receiver operating characteristic curves of 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted using the predictors automatically generated from the proposed model for representing ABP trends. Finally, the applicability of a deep learning model with high accuracy is demonstrated, thus providing an interpretation of the association between ABP trends and hypotension in clinical practice.

Practical Resources for Effective Postpartum Parenting (PREPP): a randomized controlled trial of a novel parent-infant dyadic intervention to reduce symptoms of postpartum depression: RCT of the PREPP intervention for postpartum depression

BackgroundThere is little known about the effectiveness of psychological interventions to prevent postpartum depression that focus on the birthing parent and infant dyad. Risk factors for PPD include depression during pregnancy, low parenting self-efficacy, and disrupted parental nocturnal sleep. The brief Practical Resources for Effective Postpartum Parenting (PREPP) intervention is designed for pregnant individuals at-risk for postpartum depression, providing them with 1) caregiving strategies aimed at reducing infant fuss and cry behavior and increasing infant sleep, 2) self-reflection and mindfulness skills and 3) psychoeducation about the postpartum period and infant behavior. The aims are to set more realistic postpartum expectations, enhance parenting confidence and sleep via supporting infant regulatory behavior, improve parental affect regulation and ultimately reduce the risk of postpartum depression. ObjectivesAssess the effectiveness of PREPP compared to enhanced treatment as usual in reducing symptoms of depression and anxiety and episodes of infant fuss and cry behavior and improving parental and infant sleep in the early postpartum. Study designA randomized controlled trial was conducted of 175 pregnant individuals. Inclusion criteria were standard prenatal care for a singleton pregnancy, aged 18 to 45 and at increased risk of PPD as determined by a score of ≥19 on the Predictive Index of Postnatal Depression or ≥7 on the Edinburgh Postnatal Depression Scale. Participants assigned to the PREPP group (n=88) received three coaching sessions and two phone check-ins. Participants assigned to the enhanced treatment as usual group (n=87) received three sessions which included psychoeducation, mood assessments, and clinical referral and follow-up as indicated. Assessments were conducted at 28 to 32 weeks and 34 to 39 weeks gestation, and at six, 12 and 16 weeks postpartum and included measures of parental depression, anxiety and sleep (Edinburgh Postnatal Depression Scale, Patient Health Questionnaire (PHQ-9), Hamilton Rating Scales of Depression and Anxiety and Pittsburgh Sleep Quality Index) and infant sleep and fuss and cry behavior (Baby Day Diary). ResultsCompared to enhanced treatment as usual, PREPP significantly reduced depressive symptoms at six, 12 and 16 weeks postpartum with medium to large effect sizes (d=0.7-0.98). Compared to enhanced treatment as usual, participants receiving PREPP reported less of a decline in sleep quality at six weeks postpartum (p=0.035) and the effect size was large (d=0.83). Moreover, parents receiving the PREPP intervention reported less infant fussing and crying, with the largest effect for morning crying (p<0.001; d=-1.48). ConclusionsThe results provide evidence that PREPP, initiated during pregnancy, reduced symptoms of parental depression and infant fuss and cry behavior in the early postpartum, compared to enhanced treatment as usual, in a sample at-risk for postpartum depression.

Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI's efficacy as a clinical decision support system. The findings demonstrated AHI-PI's potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes.

Pulmonary function is reduced in children with recurrent wheezing irrespective of Asthma Predictive Index results

Pulmonary function is reduced in children with recurrent wheezing irrespective of Asthma Predictive Index results

The morphospace of the brain-cognition organisation.

Over the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space-morphospacecapturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others-the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.

Identifying reliable obesity indices for hyperuricemia among middle-aged and elderly populations: a longitudinal study

BackgroundGiven the established link between obesity and hyperuricemia (HUA), the research want to investigate the relationship between different obesity indices and HUA, and further analyze which obesity index can better predict HUA.MethodsThe data were obtained from a longitudinal study involving middle-aged and elderly populations in Dalian, China. The research encompassed individuals who exhibited typical uric acid levels initially and tracked their progress over a three-year period. 8 obesity indices were evaluated retrospectively. Subgroup analyses were conducted to identify susceptible populations. Restricted cubic splines (RCS) were utilized to model the dose-response relationships between obesity indices and HUA. Receiver operating characteristic (ROC) curves were applied to visualize and compare the predictive value of both traditional and new obesity indices for HUA.ResultsAmong 4,112 individuals with normal baseline uric acid levels, 950 developed HUA. Significant associations with HUA were observed for body mass index (BMI), waist circumference (WC), body roundness index (BRI), cardiometabolic index (CMI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), and abdominal volume index (AVI). Subgroup analysis indicated that all obesity indices proved more effective in assessing the onset of HUA in women without Metabolic Syndrome (MetS). Further analysis using RCS revealed non-linear dose-response relationships between LAP, CMI, VAI, and HUA in males, with similar non-linear relationships observed for all indices in females. The results from the ROC curves indicate that LAP may serve as a better predictor of HUA in males, and CVAI may serve as a better predictor in females.ConclusionHUA is closely associated with obesity indices. Among females, CVAI emerges as the preferred predictive index for HUA. In males, LAP emerges as the preferred predictive index for HUA.

Diagnostic performances of the Ovarian Adnexal Reporting and Data System, the Risk of Ovarian Malignancy Algorithm, and the Copenhagen Index in the preoperative prediction of ovarian cancer: a prospective cohort study.

This study aimed to assess the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH-I), and Ovarian Adnexal Reporting and Data System (O-RADS) for the preoperative prediction of ovarian cancer (OC). A prospective cohort study was conducted on 462 patients diagnosed with ovarian tumors admitted to the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital, and Hue Central Hospital from May 2020 to December 2022. ROMA and CPH-I were calculated using cancer antigen 125 (CA125), human epididymal protein 4 (HE4) levels, and patient characteristics (age and menopausal status). O-RADS criteria were applied to describe ovarian tumor characteristics from ultrasound findings. Compared with histopathological results, the predictive values of ROMA, CPH-I, and O-RADS alone or in combination with CA125/HE4 for OC were calculated. Among 462 patients, 381 had benign tumors, 11 had borderline tumors, and 50 had OC. At optimal cut-off points, ROMA's and CPH-I's areas under the curves (AUCs) were 0.880 (95% confidence interval [CI]=0.846-0.909) and 0.890 (95% CI=0.857-0.918), respectively, and ROMA and CPH-I sensitivities/specificities (Se/Sp) were 68.85%/95.01% and 77.05%/91.08%, respectively. O-RADS ≥3 yielded an AUCs of 0.949 (95% CI=0.924-0.968), with Se/Sp of 88.52%/88.98% (p<0.001). Combining O-RADS with CA125 demonstrated the highest predictive value, with AUCs of 0.969 (95% CI=0.949-0.983) and Se/Sp of 98.36%/86.09% (p<0.001). The ROMA, CPH-I, O-RADS, O-RADS + CA125, and O-RADS + HE4 models demonstrated good predictive values for OC; the combination of O-RADS and CA125 yielded the highest values.