Simple Prediction Research Articles

P-1940. Low Visceral Fat Volume and Hypoalbuminemia as Prognostic Markers in Hospitalized Patients with COVID-19 During the Omicron Variant Epidemic

Abstract Background The rate of severe cases of coronavirus disease 2019 (COVID-19) has decreased since the Omicron variant became epidemic. Visceral fat volume was a risk factor for COVID-19 severity with prior prevalent variants, but whether visceral fat volume remains a risk factor for the Omicron variant is unclear. We investigated the associations of clinical factors including visceral fat volume with severity and mortality among hospitalized patients with COVID-19 during the Omicron variant epidemic.Table 1.Multivariable analysis of clinical factors associated with COVID-19 severity. Methods We included hospitalized patients with COVID-19 during the Omicron variant epidemic who underwent computed tomography of the abdomen. Clinical data were obtained from the medical records and visceral fat area (VFA) was measured using a 3-dimensional image analysis system volume analyzer. Severity was determined by the presence or absence of oxygen supplementation.Table 2.Multivariable analysis of clinical factors associated with mortality in hospitalized 206 patients with COVID-19. Results Among the 226 patients, 66 patients showed moderate severity and 29 patients were non-survivors. Hypoalbuminemia was associated with severity (odds ratio [OR] 3.93, 95% confidence interval [CI] 1.91–8.07; p=0.0002)(Table 1), and hypoalbuminemia (OR 8.38, 95%CI 2.37–29.58; p=0.0010) and low VFA (OR 3.40, 95%CI 1.15–10.06; p=0.027) were associated with mortality (Table 2). Decision tree analysis showed that mortality rate in the hypoalbuminemia and low-VFA group (37.3%) was significantly higher than in other groups (p< 0.01)(Fig.1).Fig 1.Decision tree analysis of patient mortality rates classified by serum albumin level and visceral fat area.Patients were initially classified with a cutoff value of 3.5 g/dL for serum albumin, then classified into four groups using a cutoff value of 100 cm² for visceral fat area. Conclusion Low visceral fat volume and hypoalbuminemia were associated with mortality in hospitalized patients with COVID-19 during the Omicron variant epidemic. Classification by VFA and serum albumin may allow simple prediction of mortality risk among hospitalized patients with COVID-19. Disclosures All Authors: No reported disclosures

163. Novel Factors Predicting Mortality in Allogeneic Hematopoietic Cell Transplantation Recipients with Viral Upper Respiratory Tract Infection

Abstract Background We previously identified novel risk factors for progression to lower respiratory tract disease (LRTD) among allogeneic hematopoietic cell transplant (HCT) recipients presenting with upper respiratory tract infection (URTI) with 12 viruses in the PCR era and proposed a simple prediction model by a number of risk factors (BMT Ogimi C, et al. Bone Marrow Transplant 2022; 57(4):649-657). We aimed to investigate whether the presence of these risk factors at the time of URTI diagnosis also predicts overall mortality and pulmonary death.Figure 1.Kaplan-Meier plot of overall death and cumulative incidence plot of pulmonary death by day 90 among patients with any viral URTI (N=947) Methods We retrospectively analyzed patients with the first respiratory virus detected by multiplex PCR after allogeneic HCT (4/2008-9/2018). We created Kaplan-Meier plots for overall mortality and cumulative incidence plots for pulmonary death within 90 days among patients presenting with URTI by a single virus. Candidate risk factors were age >=40 years, a history of multiple HCT, early timing post-HCT (< =30 days), albumin < =3 g/dL, monocytopenia (< =100 cells/µL), highest glucose value >200 mg/dl, and systemic steroid use within 14 days.Figure 2.Kaplan-Meier plot of overall death by day 90 among patients with viral URTI Results A total of 947 patients (199 children and 748 adults) presented with URTI only as follows: 407 human rhinoviruses (HRV), 174 parainfluenza viruses (PIV) 1–4, 140 common cold coronaviruses, 84 respiratory syncytial virus (RSV), 58 influenza A/B, 43 human metapneumovirus (MPV), and 41 adenoviruses. Among these, 81 (18%) died with 44 pulmonary deaths within 90 days from URTI. No patients with any viral URTI without risk factors died (Figure 1). Patients with 3 or more risk factors appear to have high overall mortality, and pulmonary death accounted for more than half of cases as a cause of death (Figure 1). These trends were seen across viruses evaluated (Figure 2). Conclusion Established risk factors for progression from viral URTI to LRTD also predict overall mortality and pulmonary death. Patients without any risk factors appeared to be completely protected from death, while patients with >3 risk factors were at the highest risk for death, suggesting an intervention opportunity and close monitoring. These trends were observed across several viruses and future studies are needed to validate our findings in different cohorts. Disclosures Chikara Ogimi, MD, PhD, AstraZeneca: Honoraria|bioMerieux Japan Ltd.: Honoraria|ELSEVIER: Honoraria|KYORIN: Honoraria|Miyarisan: Honoraria|MSD: Honoraria|NOVARTIS: Honoraria|Pfizer: Honoraria Alpana waghmare, MD, Allovir: Grant/Research Support|Ansun Biopharma: Grant/Research Support|GlaxoKlineSmith: Advisor/Consultant|GlaxoKlineSmith: Grant/Research Support|Pfizer: Grant/Research Support|Vir: Advisor/Consultant Janet A. Englund, MD, Abbvie: Advisor/Consultant|AstraZeneca: Advisor/Consultant|AstraZeneca: Grant/Research Support|GlaxoSmithKline: Advisor/Consultant|GlaxoSmithKline: Grant/Research Support|Meissa Vaccines: Advisor/Consultant|Merck: Advisor/Consultant|Pfizer: Board Member|Pfizer: Grant/Research Support|Pfizer: Speaker at meeting|SanofiPasteur: Advisor/Consultant|Shinogi: Advisor/Consultant Michael J. Boeckh, MD PhD, Allovir: Advisor/Consultant|Allovir: Grant/Research Support|AstraZeneca: Advisor/Consultant|AstraZeneca: Grant/Research Support|Merck: Advisor/Consultant|Merck: Grant/Research Support|Moderna: Advisor/Consultant|Moderna: Grant/Research Support|Symbio: Advisor/Consultant

Extensive genome evolution distinguishes maize within a stable tribe of grasses.

Over the last 20 million years, the Andropogoneae tribe of grasses has evolved to dominate 17% of global land area. Domestication of these grasses in the last 10,000 years has yielded our most productive crops, including maize, sugarcane, and sorghum. The majority of Andropogoneae species, including maize, show a history of polyploidy - a condition that, while offering the evolutionary advantage of multiple gene copies, poses challenges to basic cellular processes, gene expression, and epigenetic regulation. Genomic studies of polyploidy have been limited by sparse sampling of taxa in groups with multiple polyploidy events. Here, we present 33 genome assemblies from 27 species, including chromosome-scale assemblies of maize relatives Zea and Tripsacum . In maize, the after-effects of polyploidy have been widely studied, showing reduced chromosome number, biased fractionation of duplicate genes, and transposable element (TE) expansions. While we observe these patterns within the genus Zea , 12 other polyploidy events deviate significantly. Those tetraploids and hexaploids retain elevated chromosome number, maintain nearly complete complements of duplicate genes, and have only stochastic TE amplifications. These genomes reveal variable outcomes of polyploidy, challenging simple predictions and providing a foundation for understanding its evolutionary implications in an ecologically and economically important clade.

Adult patients with Philadelphia chromosome positive acute lymphoblastic leukemia undergoing allogeneic hematopoietic stem cell transplantation and tyrosine kinase inhibitors: development and validation of a clinical prediction model based on cytogenetics, IKZF1 deletions and minimal residual disease.

The aim of this study was to develop and validate a nomogram predicting progression-free survival (PFS) for adult patients with positive acute lymphoblastic leukemia(Ph + ALL) who have undergone allogeneic hematopoietic stem cell transplantation(allo-HSCT) and tyrosine kinase inhibitor(TKI) treatment. Data were retrospectively collected from 176 adult patients diagnosed with Ph + ALL and treated with allo-HSCT and TKIs at The First Affiliated Hospital, Zhejiang University School of Medicine, between January 2015 and May 2023. 70% of the patients were randomly assigned to the training group(n = 124) and 30% of the patients were assigned to the validation group(n = 52). Univariate Cox regression analysis and Akaike Information Criterion(AIC) were utilized to identify significant predictive factors, leading to the development of a nomogram designed to forecast the probability of PFS at 6, 9, and 12 months post-transplantation. The final nomogram incorporated three key variables: presence of complex additional cytogenetic abnormalities (ACAs), minimal residual disease (MRD) status prior to allo-HSCT, and IKZF1 gene deletions. The calibration curves showed excellent consistency between the nomogram prediction and actual observation for 6-, 9- and 12-month PFS in the training set and validation set. The C-index of the training set was 0.726(95%CI: 0.635-0.816), which was no significantly different from the validation set(C-index = 0.774, 95%CI: 0.674-0.875, P > 0.05). This study may provide a simple and efficient prediction model for patients with Ph + ALL undergoing allo-HSCT and TKIs, which can accurately predict PFS subsequent to transplantation. This tool could potentially aid clinicians in decision-making processes and improve patient outcomes.

Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling.

Patients with severe tricuspid regurgitation (TR) typically present with heterogeneity in the extent of cardiac dysfunction and extra-cardiac comorbidities, which play a decisive role for survival after transcatheter tricuspid valve intervention (TTVI). This aim of this study was to create a survival tree-based model to determine the cardiac and extra-cardiac features associated with 2-year survival after TTVI. The study included 918 patients (derivation set, n =631; validation set, n=287) undergoing TTVI for severe TR. Supervised machine learning-derived survival tree-based modelling was applied to preprocedural clinical, laboratory, echocardiographic, and hemodynamic data. Following univariate regression analysis to pre-select candidate variables for 2-year mortality prediction, a survival tree-based model was constructed using 4 key parameters. Three distinct cluster-related risk categories were identified, which differed significantly in survival after TTVI. Patients from the low-risk category (n=261) were defined by mean pulmonary artery pressure≤28mmHg and N-terminal pro-B-type natriuretic peptide≤2,728 pg/mL, and they exhibited a 2-year survival rate of 85.5%. Patients from the high-risk category (n=190) were defined by mean pulmonary artery pressure >28mmHg, right atrial area >32.5cm2, and estimated glomerular filtration rate≤51mL/min, and they showed a significantly worse 2-year survival of only 52.6% (HR for 2-year mortality: 4.3, P< 0.001). Net re-classification improvement analysis demonstrated that this model was comparable to the TRI-Score and outperformed the EuroScore II in identifying high-risk patients. The prognostic value of risk phenotypes was confirmed by external validation. This simple survival tree-based model effectively stratifies patients with severe TR into distinct risk categories, demonstrating significant differences in 2-year survival after TTVI.

A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm.

The soil ecosystem has been severely damaged because of the increasingly severe environmental problems caused by excessive application of phosphorus (P) fertilizer, which seriously hinders soil fertility restoration and sustainable farmland development. Shoot P uptake (SPU) is an important parameter for monitoring crop growth and health and for improving field nutrition management and fertilization strategies. Achieving on-site measurement of large-scale data is difficult, and effective nondestructive prediction methods are lacking. Improving spatiotemporal SPU estimation at the regional scale still poses challenges. In this study, we proposed a combination prediction model based on some representative samples. Furthermore, using the experimental area of Henan Province, as an example, we explored the potential of the hyperspectral prediction of maize SPU at the canopy scale. The combination model comprises predicted P uptake by maize leaves, stems, and grains. Results show that (1) the prediction accuracy of the combined prediction model has been greatly improved compared with simple empirical prediction models, with accuracy test results of R 2 = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer's active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model's prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development.

Precision psychiatry: thinking beyond simple prediction models - enhancing causal predictions.

Making informed clinical decisions based on individualised outcome predictions is the cornerstone of precision psychiatry. Prediction models currently employed in psychiatry rely on algorithms that map a statistical relationship between clinical features (predictors/risk factors) and subsequent clinical outcomes. They rely on associations that overlook the underlying causal structures within the data, including the presence of latent variables, and the evolution of predictors and outcomes over time. As a result, predictions from sparse associative models from routinely collected data are rarely actionable at an individual level. To be actionable, prediction models should address these shortcomings. We provide a brief overview of a general framework for the rationale for implementing causal and actionable predictions using counterfactual explanations to advance predictive modelling studies, which has translational implications. We have included an extensive glossary of terminology used in this paper and the literature (Supplementary Box 1) and provide a concrete example to demonstrate this conceptually, and a reading list for those interested in this field (Supplementary Box 2).

Machine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.

Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort. Data of aneurysmal SAH patients treated by surgical clipping or endovascular coiling between 2003 and 2019 were obtained from the Japanese Stroke Databank (derivation cohort, n = 9,657) and "Predict for Outcome Study of Aneurysmal Subarachnoid Hemorrhage" (validation cohort, n = 5,085). Generalized additive models (GAMs) for poor outcome (modified Rankin Scale score ≥ 3 at discharge) were fitted with age transformation using spline curves for each World Federation of Neurological Societies grade. The discrimination property and calibration plot of unadjusted and adjusted models were assessed using the validation cohort. The derivation and validation cohorts included 3,610 and 3,251 patients, respectively. Regarding discrimination, areas under the receiver operating characteristic curve for the derivation and validation cohorts were 0.835 and 0.827, respectively, in the unadjusted model and 0.844 and 0.836, respectively, in the adjusted model. An unbiased correlation was confirmed between predicted and observed probabilities of poor outcomes. GAM could help visualize the correlation between age and clinical outcomes. Our prediction model can quantitatively aid in treatment decision-making and can be effective for most diseases and treatment settings. Trial Registration: UMIN Clinical Trials Registry (Date 2/22/2022/ ID, UMIN000046282 number, R000052809 URL, https//www.umin.ac.jp/ctr/index.htm) and the Japan Registry of Clinical Trials (Date 3/28/2022 /No. jRCT1060210092 URL, https//jrct.niph.go.jp/).

Validity and Utility of a Risk Prediction Model for Wound Infection After Lower Third Molar Surgery.

To externally validate a clinical prediction model for surgical site infection (SSI) after lower third molar (L3M) surgery and evaluate its clinical usefulness. We conducted a retrospective cohort study of patients who underwent L3M surgery at Hokkaido University Hospital. The study was designed to evaluate the historical and methodological transportability. Clinical usefulness was evaluated using decision curve analysis on the data of the non-antibiotic-treated patients. We obtained 2543 validation cohorts from April 2020 to March 2023, and 640 non-antibiotic cohorts from July 2010 to September 2023. The incidences of SSI after L3M surgery were 5.3% (135/2543) and 7.7% (49/640) in the validation and non-antibiotic cohorts, respectively. The discrimination ability of the prediction model was acceptable for the external validation cohort (c-statistic: 0.67; 95% CI: 0.62-0.71) and adequate for the non-antibiotic cohort (c-statistic: 0.72; 95% CI: 0.63-0.79). In both cohorts, the model showed excellent calibration between the observed and predicted probabilities. Decision curve analysis showed increased net benefit across a range of meaningful risk thresholds. A simple risk prediction model for SSI after L3M surgery demonstrated clinical transportability and usefulness. This model may help surgeons/clinicians determine the appropriateness of prophylactic antibiotics administration for patients in L3M surgery.

Development of a simple prediction model for tracheostomy requirement after surgical resection of medulloblastoma in children

ObjectivePostoperative tracheostomy is a significant complication following medulloblastoma (MB) resection. This study aimed to develop a predictive model for postoperative tracheostomy requirement in children undergoing MB surgical resection. This model was derived as a side product of a larger research project analyzing surgical outcomes in pediatric MB patients.ResultsForty-five patients (26%) required tracheostomy postoperatively. Using multivariable logistic regression, five models were developed, and the final model was selected based on performance and simplicity. The simplified version included two predictors: preoperative brainstem invasion and postoperative brainstem contusion, each contributing equally to the score. The model demonstrated an AUC of 0.845. Predicted risks of requiring a tracheostomy were 5.8%, 57.7%, and 75% for scores of 0, 1, and 2, respectively. This tool provides clinicians with a quantitative approach to assess tracheostomy risk, improving decision-making and patient management.

Millennial axially symmetric Magneto-Coriolis modes in Earth’s fluid core

SUMMARY Magneto-Coriolis (MC) modes in Earth’s fluid core involve oscillations sustained by the combined effect of the Lorentz and Coriolis forces. Here, we investigate the properties of MC modes that involve purely axisymmetric flow, which we term axiMC modes. We provide a basic description of the wave dynamics of these modes, and simple predictions for the expected scalings of their frequency $\omega$, decay rate $\lambda$ and quality factor Q based on a uniform ambient magnetic field. In particular, Q scales with the Elsasser number $\Lambda$, which depends on the square of the r.m.s. strength of the azimuthally averaged meridional field. When $\Lambda \gt 1$, $Q\gt 1$ and axiMC modes may be excited; when $\Lambda \ll 1$, $Q\ll 1$ and axiMC modes revert to quasi-free magnetic decay modes. We present computations of axiMC modes in an inviscid, electrically conducting sphere for two idealized ambient magnetic field configurations, a uniform axial field and an axial poloidal field. We show that a flow gradient in the axial direction is a key property of axiMC modes. For the uniform axial field, $\omega$, $\lambda$ and Q follow the scalings expected for a uniform field. For the axial poloidal field, the structure of the modes changes substantially when $\Lambda \gtrsim 1$, becoming more concentrated in regions of lower field strength. The combination of this structural change and advection of field lines by flow significantly increases $\lambda$, resulting in a Q that remains close to 1 even at high $\Lambda$. For a magnetic field strength inside the Earth’s core of a few mT, the gravest axiMC modes are expected to have periods in the range of one thousand to a few thousand years and a Q not substantially above 1. AxiMC modes may be connected to a part of the observed millennial changes in Earth’s magnetic field, may exchange axial angular momentum with the mantle, and hence may also explain a part of the observed millennial changes in length of day.

Validation of the 5-SNP score for the prediction of venous thromboembolism in a Danish fast-track cohort of 6,789 total hip and total knee arthroplasty patients

BackgroundVenous thromboembolism (VTE) is a serious complication following total hip arthroplasty (THA) and total knee arthroplasty (TKA). Despite improvements with fast-track treatment protocols, still 0.5% of patients develop a VTE within 90-days postoperatively. Previously, the 5-SNP genetic risk scores (weighted and simplified), were developed to identify people at a high risk of VTE within the general population. ObjectivesWe aimed to assess whether the 5-SNP scores could be used to identify high-risk patients in a cohort of fast-track THA/TKA patients. Patients/MethodsA subset of patients from the Lundbeck Centre for Fast-track Hip and Knee Replacement Database, based on the availability of genetic information, was included. Within these patients, the 5-SNP scores were calculated and their discriminatory performance was determined by c-statistic. Furthermore, the 5-SNP scores were added to a simple logistic prediction model containing clinical predictors to assess the added predictive value. Results7,753 THA and TKA procedures (6,798 patients) were included in this study. The c-statistics for the weighted and simple 5-SNP score were 0.50 (95% confidence interval [CI] 0.39 – 0.61) and 0.48 (95%CI 0.38 – 0.58), respectively. For the model with clinical predictors, the c-statistic was 0.67 (95%CI 0.56 – 0.77). Addition of either of the 5-SNP scores did not improve discrimination in this model. ConclusionsThese findings do not support genetic risk profiling in fast-track THA/TKA patients to predict VTE. Hence, efforts should be directed at optimizing prediction models with clinical predictors.

Hemodialysis Outcomes Score In Trauma (HOST): A novel and easy model for predicting death in patients receiving pre-injury hemodialysis.

We sought to construct a simple clinical mortality prediction model in trauma patients that required pre-injury hemodialysis: Hemodialysis Outcomes Score in Trauma (HOST). Trauma patients on pre-injury hemodialysis admitted between July 2013 to December 2021 were reviewed. Univariate and multivariable analysis was used to determine independent predictors of mortality and construct the HOST score. There were 663 patients identified as receiving pre-injury hemodialysis. Most patients were male (54.6​%), suffered a blunt mechanism (97.4​%), and were severely injured (median ISS 21). Mortality at 28-days for patients receiving pre-injury hemodialysis was 6.8​% compared to 4.8​% in injured patients that did not require pre-injury hemodialysis (p​=​0.03). Multivariate logistic regression identified GCS, HR, and hematocrit to be associated with 28-day mortality. HOST may serve as a tool with readily accessible input variables that is able to predict 28-day mortality.

The PANDA score: External validation and modification of a simple upfront prediction tool for poor outcomes despite successful stroke thrombectomy.

While endovascular thrombectomy (EVT) has become standard of care for patients' acute ischemic stroke (AIS) due to large vessel occlusion (LVO), many patients still suffer profound neurological disability, also termed futile recanalization (FR). The BAND score, which incorporates baseline disability, age, stroke severity, and treatment time window, is derived as a simple tool for upfront prediction of FR prior to EVT. This study aims to externally validate the BAND score and to incorporate upfront imaging biomarkers into the prediction tool. Consecutive stroke thrombectomy patients with anterior circulation LVO who achieved successful recanalization (mTICI 2b or greater) were retrospectively identified at a single institution from 2019 to 2023. Clinical information, procedural details, and 90-day outcomes were recorded. The performance of the BAND score in predicting FR (90-day modified Rankin scale [mRS] >3) and loss of complete independence (LCID, 90-day mRS>2) was assessed. Then, Alberta stroke programme early CT score (ASPECTS) was added to create the PANDA score (pre-stroke disability, age, NIH stroke scale, delay from last known normal, and ASPECTS). The performance of PANDA to predict FR was assessed and compared with the original BAND score and also the widely validated THRIVE score. 296 patients were included; 36.5 % experienced FR. BAND had areas under the receiver-operating curve (AUCs) of 0.72 and 0.74 for predicting FR and LCID, respectively (both p < 0.001). The new PANDA score had AUCs of 0.76 and 0.78 for predicting FR and LCID, respectively (both p < 0.001), and it outperformed both BAND and THRIVE (all p < 0.05). Of the 30 patients (11.2 %) with high PANDA scores (≥7), 24 patients (80.0 %) suffered FR and 26 (86.7 %) suffered LCID. This external validation study confirmed the adequate performance of BAND in predicting FR. The improved PANDA score performed better than the original BAND score and the widely validated THRIVE score.

Respiratory Severity Score (RSS): A Predictive Tool for Successful Extubation in Preterm Neonates

Introduction: The coexistence of immature lungs, poor respiratory drive, overly compliant chest wall, and surfactant deficiency in preterm neonates, leads to dependency on mechanical ventilation during the first days of life. Respiratory Severity Score (RSS) is a readily available, simple and non-invasive extubation failure prediction tool. Objective This study aims to evaluate the utility of pre-extubation RSS as a tool for predicting successful extubation among preterms. Methods and Analysis: This was a retrospective study with a duration of two years started after obtaining clearance from the Institutional Ethics Committee. All preterm neonates ventilated in NICU were included in the study. The hospital unit number was noted and the respective patient records were procured from the hospital records department. Demographic data were recorded from the files. Pre-extubation RSS was calculated. Other findings related to clinical review such as duration of invasive and non-invasive ventilation, surfactant requirement, intraventricular hemorrhage (IVH), early and late-onset sepsis, length of hospital stay (LOS), retinopathy of prematurity (ROP), bronchopulmonary dysplasia (BPD) mortality data etc. were also recorded. The age, time of extubation and weight of those babies who underwent planned extubation were also recorded. Results: The RSS and RSS/kg were significantly higher among the neonates who failed extubation ( P &lt; .001). The Area under curve (AUC) for RSS and RSS/kg was 0.921 and 0.85, respectively. There was a significantly higher proportion of morbidities such as late-onset sepsis ( P = .046), grade ≥3 IVH ( P = .004) and ROP ( P = .043) observed among the neonates with extubation failure. The prevalence of in-hospital mortality was observed only among neonates with extubation failure which was statistically significant ( P &lt; .001). Conclusion: This study shows that pre-extubation RSS is an excellent tool for predicting successful extubation and is more accurate than RSS/kg. RSS could be a useful tool for clinicians to assess readiness of extubation. However, more prospective, multicentric studies are necessary to validate the generalizability of RSS.

Fatigue damage accumulation model and residual life prediction method of composite laminates based on strength degradation

This paper proposes a residual fatigue life prediction method that considers the loading interaction effect, to predict the fatigue life of composite laminates under variable amplitude loading. Based on the study of static mechanical and fatigue properties of composite laminates, the influence mechanism of the loading interaction effect on material fatigue life is analyzed, and an exponential function including adjacent load amplitudes and fatigue life is proposed as a parameter to describe the loading interaction effect. Then, an equation describing the damage evolution of composite laminates during fatigue is proposed. A simple prediction method for residual fatigue life under variable loading considering the loading interaction effect is proposed based on the damage equivalent theory. Finally, through experimental validation, it is found that the prediction error of the proposed model does not exceed 50%, which is lower than the other models mentioned in the paper.

Accurate and Simple Prediction of Ice-Melting Capacity in Chloride Deicer Brine Blends

ABSTRACT The use of brines is among the most effective ways to reduce application rates and the corresponding environmental impact of chloride deicing salts. Understanding the ice-melting performance of brines is critical for optimizing their application. The ice-melting capacities of 23.3 percent NaCl, 30.0 percent MgCl2, and 30.0 percent CaCl2, as well as binary 70/30 and ternary 70/15/15 (by weight) blends of the NaCl brine with MgCl2 and/or CaCl2 brines, were measured by a “tracer dilution” approach with coefficients of variation between 0.03 and 0.44 percent. The ice-melting capacities of the individual chloride salts agreed reasonably well with the values predicted from standard reference freezing point depression curves for the individual salts but consistently overestimated them by ∼3 to 5 percent. The ice-melting capacities of the binary brine blends agreed even more closely with theoretical values calculated from the aggregate freezing point depression data for the chloride salts and blends in the literature as determined by an empirical virial equation, giving high confidence that the ice-melting capacity of chloride salt blends can be easily calculated with good accuracy for field application purposes from existing freezing point depression data without the need to test every different brine blend at every temperature. In both binary and ternary blends of NaCl, MgCl2, and CaCl2 brines, the ice-melting capacity is essentially the sum of the ice-melting capacities of the individual brine components with no significant synergism apparent.

Impact assessment of wastewater on water quality using new quality indices and multiple linear regression

The Wastewater Quality Index (WWQI) is a unique measure that synthesizes the overall quality of wastewater. The main objective of the study was to evaluate whether the quality of wastewater reaching the treatment plant is suitable for optimal disposal using the WWQI approach. To understand the correlation between the determined indicators, Pearson correlation coefficients were calculated, so that the results of the analyses reflect the overall quality of the evaluated wastewater. Additionally, statistical analysis was employed to develop a simple prediction model through multiple linear regression (MLR) to predict WWQI based on various wastewater quality parameters. The results indicate good and regular quality of treated wastewater for safe disposal, according to the classification of WWQI levels, and the prediction model yielded values similar to those estimated.

Impact assessment of wastewater on water quality using new quality indices and multiple linear regression

The Wastewater Quality Index (WWQI) is a unique measure that synthesizes the overall quality of wastewater. The main objective of the study was to evaluate whether the quality of wastewater reaching the treatment plant is suitable for optimal disposal using the WWQI approach. To understand the correlation between the determined indicators, Pearson correlation coefficients were calculated, so that the results of the analyses reflect the overall quality of the evaluated wastewater. Additionally, statistical analysis was employed to develop a simple prediction model through multiple linear regression (MLR) to predict WWQI based on various wastewater quality parameters. The results indicate good and regular quality of treated wastewater for safe disposal, according to the classification of WWQI levels, and the prediction model yielded values similar to those estimated.

A Simple Prediction Model for Clostridioides difficile Infection: A Hospital-Based Administrative Database Study.

Few prediction scores for Clostridioides difficile infection (CDI), a potentially life-threatening nosocomial diarrhea, combine high accuracy with simplicity. A simple prediction score for routine clinical practice is needed. We conducted a retrospective cohort study of all inpatients aged ≥ 18 at a secondary care hospital in Japan. The derivation and validation cohorts consisted of patients from January 2016 to December 2020 and January 2021 to September 2022, respectively. Demographic and clinical data were retrieved using electronic medical records and an administrative database. The primary outcome was to derive and validate an accurate, simple prediction score for primary hospital-onset CDI. A derived prediction score by logistic regression analysis was calibrated and validated. CDI developed in 102 of 25 517 and 25 of 6259 patients in the derived and validation cohorts (2.7 cases/10 000 patient-days). The derived model for predicting CDI, including antibiotic use, acid suppressant (proton pump inhibitors or vonoprazan) use, Charlson comorbidity index, and Barthel index, yielded c-statistics of 0.89 and 0.82 in the derivation and validation cohort. The model was well calibrated. This simple prediction score enables early medical intervention and modification of treatment plans to reduce the risk of developing primary hospital-onset CDI.