What are the risk factors of hospital length of stay in the novel coronavirus pneumonia (COVID-19) patients? A survival analysis in southwest China

Background The global epidemic of novel coronavirus pneumonia (COVID-19) has resulted in substantial healthcare resource consumption. Since patients’ hospital length of stay (LoS) is at stake in the process, an investigation of COVID-19 patients’ LoS and its risk factors becomes urgent for a better understanding of regional capabilities to cope with COVID-19 outbreaks. Methods First, we obtained retrospective data of confirmed COVID-19 patients in Sichuan province via National Notifiable Diseases Reporting System (NNDRS) and field surveys, including their demographic, epidemiological, clinical characteristics and LoS. Then we estimated the relationship between LoS and the possibly determinant factors, including demographic characteristics of confirmed patients, individual treatment behavior, local medical resources and hospital grade. The Kaplan-Meier method and the Cox Proportional Hazards Model were applied for single factor and multi-factor survival analysis. Results From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment, including 271 (50%) patients aged ≥ 45, 285 (53%) males, and 450 patients (84%) with mild symptoms. The median LoS was 19 (interquartile range (IQR): 14–23, range: 3–41) days. Univariate analysis showed that age and clinical grade were strongly related to LoS (P<0.01). Adjusted multivariate analysis showed that the longer LoS was associated with those aged ≥ 45 (Hazard ratio (HR): 0.74, 95% confidence interval (CI): 0.60–0.91), admission to provincial hospital (HR: 0.73, 95% CI: 0.54–0.99), and severe illness (HR: 0.66, 95% CI: 0.48–0.90). By contrast, the shorter LoS was linked with residential areas with more than 5.5 healthcare workers per 1,000 population (HR: 1.32, 95% CI: 1.05–1.65). Neither gender factor nor time interval from illness onset to diagnosis showed significant impact on LoS. Conclusions Understanding COVID-19 patients’ hospital LoS and its risk factors is critical for governments’ efficient allocation of resources in respective regions. In areas with older and more vulnerable population and in want of primary medical resources, early reserving and strengthening of the construction of multi-level medical institutions are strongly suggested to cope with COVID-19 outbreaks.

Melena Revealing an Attenuated Familial Adenomatous Polyposis in an Elderly Patient: A Case Report

We describe a patient who was diagnosed with multiple tubulleuvillous adenomas with focus of high-grade tubular dysplasia all over the colonic mucosa, discovered during a colonoscopy performed during an episode of melena. Genetic testing has identified a germline truncating mutation at the codon (5q22.2) of the adenomatous polyposis (APC) gene. This mutation is localized in the alternately spliced region of exon 12, a region which is associated with an attenuated familial adenomatous polyposis (PAFA) phenotype. Our patient had no extracolic manifestations of PAFA and none of her relatives had a history of rectocolic polyposis. Treatment consisted of colectomy with ileorectal anastomosis. PAFA is an ill-defined condition of unknown prevalence and penetrance, requiring individual treatment and lifelong monitoring. It is essential to identify these patients with a view to setting up appropriate endoscopic surveillance at an early age in family members carrying this mutation, due to the marked intra-family phenotypic variance.

Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity

Background Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.

A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644–0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586–0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571–0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.

Development and Validation of a Three-Gene-Based Prognostic Model for Predicting the Overall Survival of Head and Neck Squamous Cell Carcinoma Through Bioinformatics Analysis

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer worldwide and seriously threats public health safety. Despite the improvement of diagnostic and treatment methods, the overall survival for advanced patients has not improved yet. This study aimed to sort out prognosis-related molecular biomarkers for HNSCC and establish a prognostic model to stratify the risk hazards and predicate the prognosis for these patients, providing a theoretical basis for the formulation of individual treatment plans. We firstly identified differentially expressed genes (DEGs) between HNSCC tissues and normal tissues via joint analysis based on GEO databases. Then a total of 11 hub genes were selected for single-gene prognostic analysis to identify the prognostic genes. Later, the clinical information and transcription information of HNSCC were downloaded from the TCGA database. With the application of least absolute shrinkage and selection operator (LASSO) algorithm analyses for the prognostic genes on the TCGA cohort, a prognostic model consisting of three genes (COL4A1, PLAU and ITGA5) was successfully established and the survival analyses showed that the prognostic model possessed a robust performance in the overall survival prediction. Afterward, the univariate and multivariate regression analysis indicated that the prognostic model could be an independent prognostic factor. Finally, the predicative efficiency of this model was well confirmed in an independent external HNSCC cohort.

Functional implications of aging-related lncRNAs for predicting prognosis and immune status in glioma patients

Glioma, is the most prevalent intracranial tumor with high recurrence and mortality rate. Long noncoding RNAs (lncRNAs) play a critical role in the occurrence and progression of tumors as well as in aging regulation. Our study aimed to establish a new glioma prognosis model by integrating aging-related lncRNAs expression profiles and clinical parameters in glioma patients from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) datasets. The Pearson correlation analysis ( |R|> 0.6, P＜0.001) was performed to explore the aging-related lncRNAs, and univariate cox tregresion and least absolute shrinkage and selection operator (LASSO) regression were used to screening prognostic signature in glioma patients. Based on the fifteen lncRNAs, we can divide glioma patients into three subtypes, and developed a prognostic model. Kaplan-Meier survival curve analysis showed that low-risk patients had longer survival time than high-risk group. Principal component analysis indicated that aging-related lncRNAs signature had a clear distinction between high- and low-risk groups. We also found that fifteen target lncRNAs were closely correlated with 119 genes by establishing a co-expression network. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis displayed different function and pathways enrichment in high-and low-risk groups. The different missense mutations were observed in two groups, and the most frequent variant types were single nucleotide polymorphism (SNP). This study demonstrated that the novel aging-related lncRNAs signature had an important prognosis prediction and may contribute to individual treatment for glioma.

Development and Validation of a Robust Apoptosis-related Prognostic Classifier in Patients With Osteosarcoma

Background: Apoptosis plays an important role in the tumorigenesis and the development of osteosarcoma, but the reliable biomarkers for individual treatment and prognosis of osteosarcoma based on apoptosis is lacking.Methods: A total of 1476 apoptosis-related genes were extracted from pathways and biological processes associated with apoptosis downloaded from MSigDB. All of those genes were used to identified the prognosis-related genes by univariate cox regression in the TARGET dataset and the ARS was constructed using the LASSO regression. The performance of the classifier was verified in the training and validation groups. The infiltration of immune cells and the expression levels of the immune checkpoint in different groups were also analyzed. Finally, a nomogram based on ARS and other Clinicopathological factors was constructed to facilitate clinical application.Results: ARS containing 22 apoptosis-related genes were identified, and its predictive ability performed well in both the training and validation groups. Macrophages M1 were highly expressed in the low-score group, and NK cells resting was highly expressed in the high-score group. The samples with low-score had higher expression of CTLA4 and PDL1. A nomogram with excellent predictive effectiveness (AUC= 0.932, 0.984, 0.939, 0.939, 0.948) was constructed to facilitate clinical decision-making.Conclusion: A prognostic classifier based on 22 apoptosis-related genes and a nomogram were constructed to predict the overall survival of patients with osteosarcoma. The classifier also provides a reference for selecting suitable patients for immunotherapy and targeted therapy.

Clinical and diagnostic value of the method of chromato-mass spectrometry of microbial markers in case of damage to the oral mucosa in children by rheumatic diseases

Due to the presence of a large percentage of 42.6% secondary oral infection in children with rheumatic diseases [1, 2], which arose during long-term treatment of shock and maintenance doses of anti-inflammatory therapy, it was important to study the microbiota [16, 17]. This paper for the first time applied a modern method for assessing the microbiota of various biotopes of the affected oral mucosa in children with rheumatic diseases – chromatosis-mass-spectrometry (CMSM), based on the quantitative determination of the level of markers of microorganisms: fatty acids, aldehydes, alcohols [5, 7, 10, 11]. СMSM is a highly sensitive method with a wide diagnostic spectrum. The study of a wide range of microorganisms provides new opportunities in the diagnosis of oral dysbacteriosis and increasing the effectiveness of individual treatment. The aim of the study is to improve the level of diagnosis and treatment of oral mucosal diseases in children with rheumatic diseases, through the use of chromato-mass-spectrometry of the oral microbiota.

Computational approaches to predicting treatment response to obesity using neuroimaging

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include “incentive salience” and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

Background: Decision making for the “best” treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC). Methods: In German claims data for the calendar years 2014–2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation. Results: A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: −0.78 % [−1.40; −0.03]). Conclusions: If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients’ complexity deviates from “typical” study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making. Highlights It was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly. ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding. When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option. Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.