Individual Probability Research Articles

Urban segregation on multilayered transport networks: a random walk approach

We present a novel method for analysing socio-spatial segregation in cities by considering constraints imposed by transportation networks. Using a multilayered network approach, we model the interaction probabilities of socio-economic groups with random walks and Lévy flights. This method allows for evaluation of new transport infrastructure’s impact on segregation while quantifying each network’s contribution to interaction opportunities. The proposed random walk segregation index measures the probability of individuals encountering diverse social groups based on their available means of transit via random walks. The index incorporates temporal constraints in urban mobility with a parameter, α∈[0,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha \\in [0,1)$$\\end{document}, of the probability of the random walk continuing at each time step. By applying this to a toy model and conducting a sensitivity analysis, we explore how the index changes dependent on this temporal constraint. When the parameter equals zero, the measure simplifies to an isolation index. When the parameter approaches one it represents the city’s overall socio-economic distribution by mirroring the steady-state of the random walk process. Using Cuenca, Ecuador as a case study, we illustrate the method’s applicability in transportation planning as a valuable tool for policymakers, addressing spatial distribution of socio-economic groups and the connectivity of existing transport networks, thus promoting equitable interactions throughout the city.

Size estimation of key populations and ‘bridge populations’ based on the network scale-up method in Ukraine

IntroductionCorrect estimation of the size of key and bridge populations is crucial for an efficient HIV/AIDS response in resource-limited settings, enabling efficient program planning and resource allocation. The hidden nature of these groups poses challenges to traditional methods, leading to the adoption of innovative approaches like the network scale-up method (NSUM). In this article we present the results of a NSUM study conducted in 2020 in Ukraine, focusing on four key populations and three bridge populations, highlighting challenges and contributions to development of the method.MethodsFrom July to September 2020, we conducted a nationally representative survey in Ukraine via computer-assisted telephone interviews, and applied the known population method and summation method to estimate social networks sizes. Results were weighted based on individual sampling probability and adjusted for social respect and visibility factors to address potential limitations.ResultsOur study achieved a 20% response rate with 10,000 completed interviews. The social network size, using the known population method, was 213 people, and 125 using the summation method. Adjusting for the social respect and visibility, estimated key populations sizes were 295,857 [248,714–343,001] people who inject drugs, 152,267 [109,960–194,573] men who have sex with men, 78,385 [57,146–99,619] sex workers, and 9,963 [7,352–12,571] transgender people, detailed by age and gender. Bridge populations were estimated at 62,162 [50,445–73,879] sexual partners of people who inject drugs, 284,348 [233,113–335,583] clients of sex workers, and 13,697 [7,370–20,026] female partners of men who have sex with men.ConclusionsNSUM proves reliable for estimating key populations size with appropriate corrections. It shows promise for further use in Ukraine, considering limited geographic coverage of the integrated bio-behavioral studies to use multiplier-based methods. However, the validity concerns persist for estimating bridge populations size, emphasizing the need for further method refinement and addressing implementation issues, particularly those related to data collection.

Agnostic Administration of Targeted Anticancer Drugs: Looking for a Balance between Hype and Caution.

Many tumors have well-defined vulnerabilities, thus potentially allowing highly specific and effective treatment. There is a spectrum of actionable genetic alterations which are shared across various tumor types and, therefore, can be targeted by a given drug irrespective of tumor histology. Several agnostic drug-target matches have already been approved for clinical use, e.g., immune therapy for tumors with microsatellite instability (MSI) and/or high tumor mutation burden (TMB), NTRK1-3 and RET inhibitors for cancers carrying rearrangements in these kinases, and dabrafenib plus trametinib for BRAF V600E mutated malignancies. Multiple lines of evidence suggest that this histology-independent approach is also reasonable for tumors carrying ALK and ROS1 translocations, biallelic BRCA1/2 inactivation and/or homologous recombination deficiency (HRD), strong HER2 amplification/overexpression coupled with the absence of other MAPK pathway-activating mutations, etc. On the other hand, some well-known targets are not agnostic: for example, PD-L1 expression is predictive for the efficacy of PD-L1/PD1 inhibitors only in some but not all cancer types. Unfortunately, the individual probability of finding a druggable target in a given tumor is relatively low, even with the use of comprehensive next-generation sequencing (NGS) assays. Nevertheless, the rapidly growing utilization of NGS will significantly increase the number of patients with highly unusual or exceptionally rare tumor-target combinations. Clinical trials may provide only a framework for treatment attitudes, while the decisions for individual patients usually require case-by-case consideration of the probability of deriving benefit from agnostic versus standard therapy, drug availability, associated costs, and other circumstances. The existing format of data dissemination may not be optimal for agnostic cancer medicine, as conventional scientific journals are understandably biased towards the publication of positive findings and usually discourage the submission of case reports. Despite all the limitations and concerns, histology-independent drug-target matching is certainly feasible and, therefore, will be increasingly utilized in the future.

Development and Validation of the Scoring System of Appendicitis Severity 2.0

When considering nonoperative treatment in a patient with acute appendicitis, it is crucial to accurately rule out complicated appendicitis. The Atema score, also referred to as the Scoring System of Appendicitis Severity (SAS), has been designed to differentiate between uncomplicated and complicated appendicitis but has not been prospectively externally validated. To externally validate the SAS and, in case of failure, to develop an improved SAS (2.0) for estimating the probability of complicated appendicitis. This prospective study included adult patients who underwent operations for suspected acute appendicitis at 11 hospitals in the Netherlands between January 2020 and August 2021. Appendicitis severity was predicted according to the SAS in 795 patients and its sensitivity and negative predictive value (NPV) for complicated appendicitis were calculated. Since the predefined targets of 95% for both were not met, the SAS 2.0 was developed using the same cohort. This clinical prediction model was developed with multivariable regression using clinical, biochemical, and imaging findings. The SAS 2.0 was externally validated in a temporal validation cohort consisting of 565 patients. In total, 1360 patients were included, 463 of whom (34.5%) had complicated appendicitis. Validation of the SAS resulted in a sensitivity of 83.6% (95% CI, 78.8-87.6) and an NPV of 85.0% (95% CI, 80.6-88.8), meaning that the predefined targets were not achieved. Therefore, the SAS 2.0 was developed, internally validated (C statistic, 0.87; 95% CI, 0.84-0.89), and subsequently externally validated (C statistic, 0.86; 95% CI, 0.82-0.89). The SAS 2.0 was designed to calculate a patient's individual probability of having complicated appendicitis along with a 95% CI. In this study, external validation of the SAS fell short in accurately distinguishing complicated from uncomplicated appendicitis. The newly developed and externally validated SAS 2.0 was able to assess an individual patient's probability of having complicated appendicitis with high accuracy in patients with acute appendicitis. Use of this patient-specific risk assessment tool can be helpful when considering and discussing nonoperative treatment of acute appendicitis with patients.

Abstract 7641: Prognostic impact of functional germline mutations in muscle-invasive bladder cancer identified by neural network analysis

Abstract Background: An accurate prognostic algorithm based on germline whole exome sequencing (WES) would complement tumor omics as a non-invasive source of biomarkers for guiding cancer patient therapy. In a previous study, we used WES and RNA-seq data from muscle-invasive bladder cancer (MIBC) and normal host cells to train a survival model, wherein we discovered that mutated germline genes accounted for 90% of feature importance. We have since defined a larger cohort to identify key germline biomarkers of overall survival (OS) that may be scalable for application in the clinic. Methods: 169 patients from The Cancer Genome Atlas (TCGA) with MIBC stages T2-T4 that underwent radical cystectomy (RC) w/o neoadjuvant treatment were eligible. OS at 4 years was the primary clinical endpoint as it is associated with death due to metastatic tumor recurrence. Patients were dichotomized to control survivors who were alive at 4+ years, and cases who died within 4 years but no less than 4 months after RC. 49 qualifying survivors were propensity matched against 49 dead patients by stage, adjuvant treatment, gender, race, and age. The remaining 71 dead patients served as a holdout. Only high (e.g. frameshift) and moderate (e.g. indel) impact germline variants were included. Mutations were weighted for allele frequency (penalize common) and genotype (reward homozygous) before grouping at the gene level. A panel of the 21 most differential genes in stratified patients were selected for neural network training using AIQC. The model forced protective/pathogenic genes to interact and produced individual survival probabilities. Gene importance was determined via permutation. Results: OS was accurately predicted for 98.2% of patients: 1 FN, 2 FP, 37 TN, 119 TP. The top 3 permuted genes (and related pathways) were: SORL1 (Aβ), KIF27 (hedgehog), CUL7 (p53). The most differential gene was CUL7: 31% of survivors mutated vs 2% of dead. Conclusion: Neural network analysis identified the promise of germline genetics as an accurate predictor of survival and tumor recurrence in MIBC patients. Upon validation in an external cohort, these biomarkers could be used to inform adjuvant therapy and complement molecular residual disease alongside circulating tumor (ct)-DNA. Citation Format: Guru P. Sonpavde, Amin H. Nassar, Arvind Ravi, Layne Sadler. Prognostic impact of functional germline mutations in muscle-invasive bladder cancer identified by neural network analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7641.

Brushing aside doubts: an evaluation of the beat-sheet brushing technique for detecting the Nearctic tree trunk sheetweaver (Araneae: Linyphiidae).

It is important to have reliable information on the presence/absence, population structure, and density of animals across their natural range. Detecting small organisms, however, such as the Nearctic tree trunk sheetweaver spider Drapetisca alteranda Chamberlin 1909 (Araneae: Linyphiidae), presents challenges due to its diminutive size and cryptic nature. We used a capture/recapture study to determine the detection and recapture probabilities of this spider using a standard beat sheet technique adopted for surveying tree trunks. Spiders were released on 3 different tree species that provided a range of microhabitats, including variable bark surface area and furrow depth/width. Microhabitat features played a small role in the timing of spider recapture (i.e., slower rate of recapture as furrowing increased). However, our results demonstrated 100% detection across replicate experiments and individual recapture probabilities exceeding 90% in most situations, with no significant differences in recapture observed among tree species and with respect to tree circumference. Furthermore, we show that most spiders could be recaptured within 2 sampling revolutions around the tree trunk, and there was no difference in the probability of collecting male and female spiders (although they differ markedly in size). Finally, we found no difference among brushers, supporting the idea that this method is replicable across collectors and studies. Collectively, we establish confidence in the ecological knowledge obtained with this technique and encourage its application with similar species and systems.

The popular gene of hot event: A hot event propagation mechanism based on information coupling and information energy interaction

Recently, the research on the propagation of hot events has received widespread attention. By analyzing the data of hot events and the data of the common events in the same period on the network, we found that hot events usually break out quickly and opinion leaders and cluster behaviors exist in their propagation process. At the same time, the media public opinion fields of reporting hot events overlap and promote each other. Based on the common factors that drive an event to become a hot event, we used the heat calculation formula and entropy method to put forward the propagation model of hot events based on information coupling and information energy interaction. In the model, the coupling values of different event information are quantified based on the information fragment coupling effect. The heat calculation formula is used to dynamically adjust the propagation probability of different individuals in the propagation process of hot event, and the sensational effect threshold is introduced based on the overall energy value of the event. Finally, we used the real social relationship networks to simulate the evolution propagation process of the hot events, and compared it with the crawling real propagation curve of the events. The proposed model provides a good supplement for the study of the hot events.

Effects of agriculture and nature reserves on avian behavior in northwestern Costa Rica.

Behavioral changes are often animals' first responses to environmental change and may act as a bellwether for population viability. Nonetheless, most studies of habitat conversion focus on changes in species occurrences or abundances. We analyzed >14,000 behavioral observations across 55 bird species in communities in northwestern Costa Rica to determine how land use affects reproductive, foraging, and other passive kinds of behaviors not associated with either foraging or reproduction. Specifically, we quantified differences in behaviors between farms, privately owned forests, and protected areas and implemented a novel modeling framework to account for variation in detection among behaviors. This framework entailed estimating abundances of birds performing different behaviors while allowing detection probabilities of individuals to vary by behavior. Birds were 1.2 times more likely to exhibit reproductive behaviors in forest than in agriculture and 1.5 times more likely to exhibit reproductive behaviors in protected areas than in private forests. Species were not always most abundant in the habitats where they were most likely to exhibit foraging or reproductive behaviors. Finally, species of higher conservation concern were less abundant in agriculture than in forest. Together, our results highlight the importance of behavioral analyses for elucidating the conservation value of different land uses.

Development and validation of a prediction model for unexpected poor ovarian response during IVF/ICSI.

Identifying poor ovarian response (POR) among patients with good ovarian reserve poses a significant challenge within reproductive medicine. Currently, there is a lack of published data on the potential risk factors that could predict the occurrence of unexpected POR. The objective of this study was to develop a predictive model to assess the individual probability of unexpected POR during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatments. The development of the nomogram involved a cohort of 10,404 patients with normal ovarian reserve [age, ≤40 years; antral follicle count (AFC), ≥5; and anti-Müllerian hormone (AMH), ≥1.2 ng/ml] from January 2019 to December 2022. Univariate regression analyses and least absolute shrinkage and selection operator regression analysis were employed to ascertain the characteristics associated with POR. Subsequently, the selected variables were utilized to construct the nomogram. The predictors included in our model were body mass index, basal follicle-stimulating hormone, AMH, AFC, homeostasis model assessment of insulin resistance (HOMA-IR), protocol, and initial dose of gonadotropin. The area under the receiver operating characteristic curve (AUC) was 0.753 [95% confidence interval (CI) = 0.7257-0.7735]. The AUC, along with the Hosmer-Lemeshow test (p = 0.167), demonstrated a satisfactory level of congruence and discrimination ability of the developed model. The nomogram can anticipate the probability of unexpected POR in IVF/ICSI treatment, thereby assisting professionals in making appropriate clinical judgments and in helping patients to effectively manage expectations.

A Metropolized Adaptive Subspace Algorithm for High-Dimensional Bayesian Variable Selection

A simple and efficient adaptive Markov Chain Monte Carlo (MCMC) method, called the Metropolized Adaptive Subspace (MAdaSub) algorithm, is proposed for sampling from high-dimensional posterior model distributions in Bayesian variable selection. The MAdaSub algorithm is based on an independent Metropolis-Hastings sampler, where the individual proposal probabilities of the explanatory variables are updated after each iteration using a form of Bayesian adaptive learning, in a way that they finally converge to the respective covariates’ posterior inclusion probabilities. We prove the ergodicity of the algorithm and present a parallel version of MAdaSub with an adaptation scheme for the proposal probabilities based on the combination of information from multiple chains. The effectiveness of the algorithm is demonstrated via various simulated and real data examples, including a high-dimensional problem with more than 20,000 covariates.

PO‐AKID‐teller: An interpretable machine learning tool for predicting acute kidney injury requiring dialysis after acute type A aortic dissection surgery

AbstractPostoperative acute kidney injury requiring dialysis (PO‐AKID) is a serious adverse event that not only affects acute morbidity and mortality, but also long‐term prognosis. Here, we developed a practical and explainable web‐based calculator (PO‐AKID‐teller) to detect patients who might experience PO‐AKID after acute type A aortic dissection (ATAAD) surgery. This retrospective study reviewed 549 patients undergoing ATAAD surgery from October 2016 to June 2021. PO‐AKID frequency was 19.7% (108 of 549 patients). The initial dataset was split into an 80% training cohort (n = 439) and a 20% test cohort (n = 110). There were seven predictors that could indicate PO‐AKID, including prior cardiovascular surgery, platelet, serum creatinine, the terminal site of dissection involvement, right coronary artery involvement, estimated blood loss, and urine output. Among six machine learning classifiers, the random forest model exhibited the best predictive performance, with an area under the curve of 0.863 in the training cohort and 0.763 in the test cohort. This model was translated into a web‐based risk calculator PO‐AKID‐teller to estimate an individual's probability of PO‐AKID. The PO‐AKID‐teller can accurately estimate an individual's risk for PO‐AKID in an interpretable manner, which may aid in informed decision‐making, patient counseling, perioperative optimization, and longer‐term care provision.

Co‐contribution Opportunities for Superannuation Funds and Policy‐Makers

AbstractThis article explores enhancement opportunities for Australian Government co‐contributions to superannuation accounts. The article's empirical foundation includes the distributions for variables related to saving, investment and risk perceptions in the 2022 wave of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. As income or wealth increase, there are increases in the probabilities of individuals making additional superannuation contributions, having a saving horizon of at least five years, and being prepared to take more than average investment risks. Four policy changes are suggested in the article, with the potential for superannuation funds to independently implement some of these changes.

Dispersal synchronizes giant kelp forests.

Spatial synchrony is the tendency for population fluctuations to be correlated among different locations. This phenomenon is a ubiquitous feature of population dynamics and is important for ecosystem stability, but several aspects of synchrony remain unresolved. In particular, the extent to which any particular mechanism, such as dispersal, contributes to observed synchrony in natural populations has been difficult to determine. To address this gap, we leveraged recent methodological improvements to determine how dispersal structures synchrony in giant kelp (Macrocystis pyrifera), a global marine foundation species that has served as a useful system for understanding synchrony. We quantified population synchrony and fecundity with satellite imagery across 11 years and 880 km of coastline in southern California, USA, and estimated propagule dispersal probabilities using a high-resolution ocean circulation model. Using matrix regression models that control for the influence of geographic distance, resources (seawater nitrate), and disturbance (destructive waves), we discovered that dispersal was an important driver of synchrony. Our findings were robust to assumptions about propagule mortality during dispersal and consistent between two metrics of dispersal: (1) the individual probability of dispersal and (2) estimates of demographic connectivity that incorporate fecundity (the number of propagules dispersing). We also found that dispersal and environmental conditions resulted in geographic clusters with distinct patterns of synchrony. This study is among the few to statistically associate synchrony with dispersal in a natural population and the first to do so in a marine organism. The synchronizing effects of dispersal and environmental conditions on foundation species, such as giant kelp, likely have cascading effects on the spatial stability of biodiversity and ecosystem function.

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context

We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users’ data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.

The mismatch between swimming speeds and flow regimes when optimising exercise regimes to improve Chinook salmon, Oncorhynchus tshawytscha, performance

Exercise regimes provide a promising opportunity to enhance production performance during hatchery rearing, but exercise-enhanced traits are presently poorly understood in Chinook salmon. In addition, spinal health can be a concern in some farms in New Zealand and unfavourable correlations between higher condition factor and spinal curvature have been detected. Exercise regimes, in other salmonids, have improved bone mineralisation and lowered condition factor. Therefore, we hypothesise that sustained swimming could be used as a tool to improve spinal health. Here we tested the influence of low (0.3 bl s−1) and moderate (0.8 bl s−1) flow regimes on post-smolt (initial size: wet weight = 82.9 ± 0.3 g, fork length = 174.6 ± 0.2 mm) Chinook salmon feed efficiency and growth performance, as well as spinal health. We first measured the actual swimming speeds of Chinook salmon under low and moderate flow regimes to determine differences in exercise levels. Swimming speeds were not different between the flow regimes and were much higher than the set flow, because the Chinook salmon were advancing around the tank, swimming faster than the set flow at their own chosen swimming speed. Moderate flow regimes improved feed efficiency but did not influence other feed and growth metrics or spinal curvature and vertebral anomalies prevalence or severity. Production performance significantly differed between individuals with normal spinal health and those that developed spinal anomalies (vertebral compression, −fusion, and/or -vertical shift) or spinal curvature (lordosis, kyphosis, and/or scoliosis). Chinook salmon that developed spinal anomalies were smaller, grew slower, and exhibited poorer feed efficiency, whereas individuals that developed spinal curvature were significantly heavier, longer, and had higher condition factor. The probability of individuals developing spinal curvature increased with higher condition factor. These results provide critical information for industry to consider in their selective breeding objectives to curb the prevalence and severity of spinal curvature incidences.

Integration of basement membrane-related genes in a risk signature for prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by high heterogeneity and recurrence rates, posing significant challenges for stratification and treatment. Basement membrane-related genes (BMGs) play a crucial role in tumor initiation and progression. Clinical and transcriptomic data of ccRCC patients were extracted from TCGA and GEO databases. We employed univariate regression and LASSO-Cox stepwise regression analysis to construct a BMscore model based on BMGs expression level. A nomogram combining clinical features and BMscore was constructed to predict individual survival probabilities. Further enrichment analysis and immune-related analysis were conducted to explore the enriched pathways and immune features associated with BMGs. High-risk individuals predicted by BMscore exhibited poorer overall survival, which was consistent with the validation dataset. BMscore was identified as an independent risk factor for ccRCC. Functional analysis revealed that BMGs were related to cell–matrix and tumor-associated signaling pathways. Immune profiling suggests that BMGs play a key role in immune interactions and the tumor microenvironment. BMGs serve as a novel prognostic predictor for ccRCC and play a role in the immune microenvironment and treatment response. Targeting the BM may represent an alternative therapeutic approach for ccRCC.

Reducing Social Stratification Bias in Referendum Participation: Evidence from the German Local Level

Political participation is socially distorted: Socio-economic resources affecting the probability of individuals becoming politically active question the principle of political equality. Such inequality in participation based on social stratification is well documented for elections, while research on inequality in referendum participation is still scarce. Based on the observation that such inequality varies between referendums, this paper explores referendum-specific contextual factors that may affect socially distorted referendum participation. We leverage information from an original dataset covering 1788 districts in 35 German municipalities for 68 local referendums held between 2000 and 2019. The results of our multilevel models indicate that concurrent first-order elections boost referendum turnout and decrease social stratification, while the closeness of the decision increases turnout but does not affect stratification bias. Moreover, we find a curvilinear relationship between turnout and stratification, with stratification only diminishing from participation levels above 40–50%.

Predictors of effectiveness in the medical treatment of early abortion with misoprostol: development of a nomogram to estimate the individual probability of therapy failure

Predictors of effectiveness in the medical treatment of early abortion with misoprostol: development of a nomogram to estimate the individual probability of therapy failure

Development and validation a nomogram for predicting new-onset postoperative atrial fibrillation following pulmonary resection

BackgroundThe new-onset postoperative atrial fibrillation (NOPAF) following pulmonary resection is a common clinical concern. The aim of this study was to construct a nomogram to intuitively predict the risk of NOPAF and offered protective treatments.MethodsPatients who underwent pulmonary resection between January 2018 and December 2020 were consecutively enrolled. Forward stepwise multivariable logistic regression analyses were used to screen independent predictors, and a derived nomogram model was built. The model performance was evaluated in terms of calibration, discrimination and clinical utility and validated with bootstrap resampling.ResultsA total of 3583 patients who met the research criteria were recruited for this study. The incidence of NOPAF was 1.507% (54/3583). A nomogram, composed of five independent predictors, namely age, admission heart rate, extent of resection, laterality, percent maximum ventilation volume per minute (%MVV), was constructed. The concordance index (C-index) was 0.811. The nomogram showed substantial discriminative ability, with an area under the receiver operating characteristic curve of 0.811 (95% CI 0.758-0.864). Moreover, the model shows prominent calibration performance and higher net clinical benefits.ConclusionWe developed a novel nomogram that can predict the risk of NOPAF following pulmonary resection, which may assist clinicians predict the individual probability of NOPAF and perform available prophylaxis. By using bootstrap resampling for validation, the optimal discrimination and calibration were demonstrated, indicating that the nomogram may have clinical practicality.

Prevalence, early predictors, and outcomes of sepsis in neurocritical illnesses: A prospective cohort study

Patients with neurocritically illness are an under-recognized population at high risk of sepsis. We aimed to investigate the prevalence, early predictors, and outcomes of sepsis in neuro-ICU. Daily and accumulative incidences of sepsis in neuro-ICU were explored. Demographics, medical history, baseline disease severity scores, and baseline biomarkers regarding inflammation, immunology, organ function, and nutritional status were collected and analyzed as potential predictors of sepsis. Logistic regression analyses were used to determine the independent predictors, and a nomogram was used to estimate the individual probability of sepsis in neuro-ICU. 153 patients were included in this study. Fifty-nine (38.6%) patients developed sepsis, and 21 (14%) patients developed septic shock. More than 86% of the septic cases occurred within the first week. Sequential organ failure assessment score ((relative risk) RR 1.334, P=.026), history of diabetes (RR 2.346, P=.049), and transferrin (RR 0.128, P=.042) on admission are independent predictors of sepsis. Septic patients had significantly higher mortality (P=.011), higher medical cost (P=.028), and a lower rate of functional independence (P=.010), compared to patients without sepsis. Sepsis afflicted more than one-third of neurocritically-ill patients and occurred mostly in the first week of admission. History of diabetes, serum transferrin, and sequential organ failure assessment score on admission were early predictors. Sepsis led to significantly worse outcomes and higher medical costs.