Physiological microbial exposure transiently inhibits mouse lung ILC2 responses to allergens.

Lung group 2 innate lymphoid cells (ILC2s) control the nature of immune responses to airway allergens. Some microbial products, including those that stimulate interferons, block ILC2 activation, but whether this occurs after natural infections or causes durable ILC2 inhibition is unclear. In the present study, we cohoused laboratory and pet store mice as a model of physiological microbial exposure. Laboratory mice cohoused for 2 weeks had impaired ILC2 responses and reduced lung eosinophilia to intranasal allergens, whereas these responses were restored in mice cohoused for ≥2 months. ILC2 inhibition at 2 weeks correlated with increased interferon receptor signaling, which waned by 2 months of cohousing. Reinduction of interferons in 2-month cohoused mice blocked ILC2 activation. These findings suggest that ILC2s respond dynamically to environmental cues and that microbial exposures do not control long-term desensitization of innate type 2 responses to allergens.

Reduced T Cell Priming in Microbially Experienced "Dirty" Mice Results from Limited IL-27 Production by XCR1+ Dendritic Cells.

Successful vaccination strategies offer the potential for lifelong immunity against infectious diseases and cancer. There has been increased attention regarding the limited translation of some preclinical findings generated using specific pathogen-free (SPF) laboratory mice to humans. One potential reason for the difference between preclinical and clinical findings lies in maturation status of the immune system at the time of challenge. In this study, we used a "dirty" mouse model, where SPF laboratory mice were cohoused (CoH) with pet store mice to permit microbe transfer and immune system maturation, to investigate the priming of a naive T cell response after vaccination with a peptide subunit mixed with polyinosinic-polycytidylic acid and agonistic anti-CD40 mAb. Although this vaccination platform induced robust antitumor immunity in SPF mice, it failed to do so in microbially experienced CoH mice. Subsequent investigation revealed that despite similar numbers of Ag-specific naive CD4 and CD8 T cell precursors, the expansion, differentiation, and recall responses of these CD4 and CD8 T cell populations in CoH mice were significantly reduced compared with SPF mice after vaccination. Evaluation of the dendritic cell compartment revealed reduced IL-27p28 expression by XCR1+ dendritic cells from CoH mice after vaccination, correlating with reduced T cell expansion. Importantly, administration of recombinant IL-27:EBI3 complex to CoH mice shortly after vaccination significantly boosted Ag-specific CD8 and CD4 T cell expansion, further implicating the defect to be T cell extrinsic. Collectively, our data show the potential limitation of exclusive use of SPF mice when testing vaccine efficacy.

Age-related trends in the incidence of metastatic colorectal cancer from 2010 to 2019 in the USA.

Aim: To evaluate temporal changes in metastatic colorectal cancer (mCRC), incidence, and use of chemotherapy treatment by age group using real-world data (RWD) from the USA. Methods: A retrospective, observational study describing temporal trends in mCRC incidence and FOLFOXIRI treatment by age group using a nationwide database of commercially and Medicare Advantage-insured patients from 2010 to 2019. Results: Incidence of mCRC increased by 22.1 and 14.9% in the 18-49 and 50-64 years cohorts, respectively, and decreased by 21.6% in the ≥65 years cohort. Overall, younger patients were more likely to receive FOLFOXIRI treatment versus older patients. Conclusion: The shifting age distribution of mCRC should be considered when recommending screening and treatment. Further research is needed to inform age-specific treatment guidelines.

What is this article about? This article reports the results of a study that used a US database of commercially and Medicare Advantage-insured adults to evaluate how the number of adults with metastatic colorectal cancer (mCRC) in three age groups (18­49 years, 50­64 years and 65 years and over) changed from 2010 to 2019. The study also looked at the use of an aggressive chemotherapy treatment, known as 5-fluorouracil, oxaliplatin, leucovorin calcium and irinotecan (FOLFOXIRI), by age group. What were the results? Overall, 23,970 adults with mCRC were included in the study. From 2010 to 2019, the number of adults with mCRC increased by 22.1% among those aged 18­49 years, increased by 14.9% among those aged 50­64 years, and decreased by 21.6% among those aged 65 years and over. There were some differences between age groups; a higher percentage of younger patients (18­49 years) were Hispanic or Asian, and from the South compared with the older age groups. In comparison, those aged 65 years and over were more likely to be from the West and Northeast of the USA. The study also found that a higher proportion of those aged 18­49 years received FOLFOXIRI (8.4%) compared with adults aged 50­64 years (4.4%) and 65 years and over (1.9%). What do the results of the study mean? Healthcare providers should be aware that early-onset mCRC is becoming more common and consider this when recommending screening and treatment.

Causation without correlation: parasite-mediated frequency-dependent selection and infection prevalence.

Parasite-mediated selection is thought to maintain host genetic diversity for resistance. We might thus expect to find a strong positive correlation between host genetic diversity and infection prevalence across natural populations. Here, we used computer simulations to examine host-parasite coevolution in 20 simi-isolated clonal populations across a broad range of values for both parasite virulence and parasite fecundity. We found that the correlation between host genetic diversity and infection prevalence can be significantly positive for intermediate values of parasite virulence and fecundity. But the correlation can also be weak and statistically non-significant, even when parasite-mediated frequency-dependent selection is the sole force maintaining host diversity. Hence correlational analyses of field populations, while useful, might underestimate the role of parasites in maintaining host diversity.

Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.

BACKGROUND: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. OBJECTIVE: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. METHODS: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19-related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. RESULTS: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19-related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. CONCLUSIONS: This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public's response to COVID-19 and help officials navigate the pandemic.

Application of machine learning for diagnostic prediction of root caries.

OBJECTIVE: This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. BACKGROUND: Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. METHODS: Data were obtained from the 2015-2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. RESULTS: Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. CONCLUSION: The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.

Use of FOLFOXIRI Plus Bevacizumab and Subsequent Therapies in Metastatic Colorectal Cancer: An Age-Stratified Analysis.

BACKGROUND: Treatment recommendations for metastatic colorectal cancer (mCRC) do not differ by age group; nevertheless, aggressive multiagent chemotherapy comprising FOLFOXIRI+bevacizumab (triplet+bev) is routinely administered in younger patients. This study analyzed real-world data on index triplet+bev use and subsequent systemic therapies. MATERIALS AND METHODS: This retrospective, observational cohort study was conducted in patients aged ≥ 18 years with mCRC, who were initiated on triplet+bev. Data were derived from the Optum de-identified electronic health record dataset. RESULTS: Of 36,056 patients, 14%, 36%, and 50% were aged 18-49, 50-64, and ≥ 65 years, respectively. During the study period (2010-2021), triplet+bev use increased in patients aged 18-49 years (1%-4%) but remained at approximately 3% and 1% in patients aged 50-64 and ≥ 65 years, respectively. Patient demographics and clinical characteristics varied slightly; of patients receiving triplet+bev (n = 921) versus nontriplet+bev (n = 35,132) most were male (57% vs. 52%), resided in the Midwest (54% vs. 49%) and Northeast (18% vs. 14%) US regions, and had secondary malignancies (86% vs. 73%). Following triplet+bev, most patients received subsequent therapies (including continued triplet component therapies; 97%) or subsequent "new" therapies (therapies that did not include any agents comprising triplet+bev; 57%), most frequently EGFR inhibitors (28%) and regorafenib (21%), with a similar trend among all age groups. CONCLUSIONS: Overall, this study shows that younger patients with mCRC are more likely to receive first-line triplet+bev. These results also reveal that nonchemotherapy options are often used beyond first-line triplet chemotherapy for patients with mCRC.

Development and optimization of a diluted whole blood ELISpot assay to test immune function.

Sepsis remains a leading cause of death worldwide with no proven immunomodulatory therapies. Stratifying Patient Immune Endotypes in Sepsis ('SPIES') is a prospective, multicenter observational study testing the utility of ELISpot as a functional bioassay specifically measuring cytokine-producing cells after stimulation to identify the immunosuppressed endotype, predict clinical outcomes in septic patients, and test potential immune stimulants for clinical development. Most ELISpot protocols call for the isolation of PBMC prior to their inclusion in the assay. In contrast, we developed a diluted whole blood (DWB) ELISpot protocol that has been validated across multiple laboratories. Heparinized whole blood was collected from healthy donors and septic patients and tested under different stimulation conditions to evaluate the impact of blood dilution, stimulant concentration, blood storage, and length of stimulation on ex vivo IFNγ and TNFα production as measured by ELISpot. We demonstrate a dynamic range of whole blood dilutions that give a robust ex vivo cytokine response to stimuli. Additionally, a wide range of stimulant concentrations can be utilized to induce cytokine production. Further modifications demonstrate anticoagulated whole blood can be stored up to 24 h at room temperature without losing significant functionality. Finally, we show ex vivo stimulation can be as brief as 4 h allowing for a substantial decrease in processing time. The data demonstrate the feasibility of using ELISpot to measure the functional capacity of cells within DWB under a variety of stimulation conditions to inform clinicians on the extent of immune dysregulation in septic patients.

Adverse outcomes and an immunosuppressed endotype in septic patients with reduced IFN-γ ELISpot.

BACKGROUNDSepsis remains a major clinical challenge for which successful treatment requires greater precision in identifying patients at increased risk of adverse outcomes requiring different therapeutic approaches. Predicting clinical outcomes and immunological endotyping of septic patients generally relies on using blood protein or mRNA biomarkers, or static cell phenotyping. Here, we sought to determine whether functional immune responsiveness would yield improved precision.METHODSAn ex vivo whole-blood enzyme-linked immunosorbent spot (ELISpot) assay for cellular production of interferon γ (IFN-γ) was evaluated in 107 septic and 68 nonseptic patients from 5 academic health centers using blood samples collected on days 1, 4, and 7 following ICU admission.RESULTSCompared with 46 healthy participants, unstimulated and stimulated whole-blood IFN-γ expression was either increased or unchanged, respectively, in septic and nonseptic ICU patients. However, in septic patients who did not survive 180 days, stimulated whole-blood IFN-γ expression was significantly reduced on ICU days 1, 4, and 7 (all P < 0.05), due to both significant reductions in total number of IFN-γ-producing cells and amount of IFN-γ produced per cell (all P < 0.05). Importantly, IFN-γ total expression on days 1 and 4 after admission could discriminate 180-day mortality better than absolute lymphocyte count (ALC), IL-6, and procalcitonin. Septic patients with low IFN-γ expression were older and had lower ALCs and higher soluble PD-L1 and IL-10 concentrations, consistent with an immunosuppressed endotype.CONCLUSIONSA whole-blood IFN-γ ELISpot assay can both identify septic patients at increased risk of late mortality and identify immunosuppressed septic patients.TRIAL REGISTRYN/A.FUNDINGThis prospective, observational, multicenter clinical study was directly supported by National Institute of General Medical Sciences grant R01 GM-139046, including a supplement (R01 GM-139046-03S1) from 2022 to 2024.

Virtual Visits for Developmental Screening in High-Risk Infants.

INTRODUCTION: The High-Risk Infant Follow-Up program screens neonatal intensive care unit graduates for developmental delays and refers patients to physical, occupational, or speech therapy. At Kaiser Permanente Downey, the High-Risk Infant Follow-Up program assessed development via video visits during the COVID-19 pandemic. This study compares rates of enrollment in and referral to therapies and California Regional Center services between infants assessed by video visits and those assessed in person. METHODS: Study participants included infants in the High-Risk Infant Follow-Up program between March 2020 and March 2021 with developmental assessments at adjusted ages 6, 12, and 18 months. Rates of enrollment in services and referral to physical, occupational, and speech therapy and Regional Center services were compared between infants assessed virtually and in person. RESULTS: There were no significant differences in rates of enrollment or referral to physical, occupational, or speech therapy services or Regional Center services. The rate of referral at any of the 3 visits was 8.6% at in-person visits and 10.9% at virtual visits (p = 0.49). Infants whose first visit was virtual were just as likely to complete all 3 developmental assessments as those whose first visit was in person. DISCUSSION: The COVID-19 pandemic stay-at-home orders provided a "natural experiment" demonstrating the effectiveness of virtual visits as compared to in-person visits. Although the developmental assessment tool used was not standardized to be used virtually, virtual visits were just as likely to result in referrals to services and did not lead to loss of follow-up. CONCLUSION: Virtual visits may be useful and convenient for some families, but further study is required.

CD115+ monocytes protect microbially experienced mice against E. coli-induced sepsis.

Uropathogenic E. coli (UPEC) is a primary organism responsible for urinary tract infections and a common cause of sepsis. Microbially experienced laboratory mice, generated by cohousing with pet store mice, exhibit increased morbidity and mortality to polymicrobial sepsis or lipopolysaccharide challenge. By contrast, cohoused mice display significant resistance, compared with specific pathogen-free mice, to a monomicrobial sepsis model using UPEC. CD115+ monocytes mediate protection in the cohoused mice, as depletion of these cells leads to increased mortality and UPEC pathogen burden. Further study of the cohoused mice reveals increased TNF-α production by monocytes, a skewing toward Ly6ChiCD115+ "classical" monocytes, and enhanced egress of Ly6ChiCD115+ monocytes from the bone marrow. Analysis of cohoused bone marrow also finds increased frequency and number of myeloid multipotent progenitor cells. These results show that a history of microbial exposure impacts innate immunity in mice, which can have important implications for the preclinical study of sepsis.

Adverse Long-Term Outcomes and an Immune Suppressed Endotype in Sepsis Patients with Reduced Interferon-γELISpot: A Multicenter, Prospective Observational Study.

BACKGROUND: Sepsis remains a major clinical challenge for which successful treatment requires greater precision in identifying patients at increased risk of adverse outcomes requiring different therapeutic approaches. Predicting clinical outcomes and immunological endotyping of septic patients has generally relied on using blood protein or mRNA biomarkers, or static cell phenotyping. Here, we sought to determine whether functional immune responsiveness would yield improved precision. METHODS: An ex vivo whole blood enzyme-linked immunosorbent (ELISpot) assay for cellular production of interferon-γ (IFN-γ) was evaluated in 107 septic and 68 non-septic patients from five academic health centers using blood samples collected on days 1, 4 and 7 following ICU admission. RESULTS: Compared with 46 healthy subjects, unstimulated and stimulated whole blood IFNγ expression were either increased or unchanged, respectively, in septic and nonseptic ICU patients. However, in septic patients who did not survive 180 days, stimulated whole blood IFNγ expression was significantly reduced on ICU days 1, 4 and 7 (all p<0.05), due to both significant reductions in total number of IFNγ producing cells and amount of IFNγ produced per cell (all p<0.05). Importantly, IFNγ total expression on day 1 and 4 after admission could discriminate 180-day mortality better than absolute lymphocyte count (ALC), IL-6 and procalcitonin. Septic patients with low IFNγ expression were older and had lower ALC and higher sPD-L1 and IL-10 concentrations, consistent with an immune suppressed endotype. CONCLUSIONS: A whole blood IFNγ ELISpot assay can both identify septic patients at increased risk of late mortality, and identify immune-suppressed, sepsis patients.

Plasmid-Mediated Stabilization of Prophages.

Mobile genetic elements (MGEs) drive bacterial evolution, alter gene availability within microbial communities, and facilitate adaptation to ecological niches. In natural systems, bacteria simultaneously possess or encounter multiple MGEs, yet their combined influences on microbial communities are poorly understood. Here, we investigate interactions among MGEs in the marine bacterium Sulfitobacter pontiacus. Two related strains, CB-D and CB-A, each harbor a single prophage. These prophages share high sequence identity with one another and an integration site within the host genome, yet these strains exhibit differences in "spontaneous" prophage induction (SPI) and consequent fitness. To better understand mechanisms underlying variation in SPI between these lysogens, we closed their genomes, which revealed that in addition to harboring different prophage genotypes, CB-A lacks two of the four large, low-copy-number plasmids possessed by CB-D. To assess the relative roles of plasmid content versus prophage genotype on host physiology, a panel of derivative strains varying in MGE content were generated. Characterization of these derivatives revealed a robust link between plasmid content and SPI, regardless of prophage genotype. Strains possessing all four plasmids had undetectable phage in cell-free lysates, while strains lacking either one plasmid (pSpoCB-1) or a combination of two plasmids (pSpoCB-2 and pSpoCB-4) produced high (>105 PFU/mL) phage titers. Homologous plasmid sequences were identified in related bacteria, and plasmid and phage genes were found to be widespread in Tara Oceans metagenomic data sets. This suggests that plasmid-dependent stabilization of prophages may be commonplace throughout the oceans. IMPORTANCE The consequences of prophage induction on the physiology of microbial populations are varied and include enhanced biofilm formation, conferral of virulence, and increased opportunity for horizontal gene transfer. These traits lead to competitive advantages for lysogenized bacteria and influence bacterial lifestyles in a variety of niches. However, biological controls of "spontaneous" prophage induction, the initiation of phage replication and phage-mediated cell lysis without an overt stressor, are not well understood. In this study, we observed a novel interaction between plasmids and prophages in the marine bacterium Sulfitobacter pontiacus. We found that loss of one or more distinct plasmids-which we show carry genes ubiquitous in the world's oceans-resulted in a marked increase in prophage induction within lysogenized strains. These results demonstrate cross talk between different mobile genetic elements and have implications for our understanding of the lysogenic-lytic switches of prophages found not only in marine environments, but throughout all ecosystems.

Complete Genome Sequence of the Anabaena Myophage Elbi.

Here, we report the genome sequence of bacteriophage Elbi, which infects the cyanobacterium Anabaena sp. strain PCC 7120, a model organism for prokaryotic multicellular development. The 68,626 bp encode 108 proteins, of which 31 can be assigned a function. Elbi is similar to two Anabaena myophages, namely, A-1 and N-1, isolated in the 1970s.

Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare.

BACKGROUND: Atrial fibrillation (AF) in the elderly population is projected to increase over the next several decades. Catheter ablation shows promise as a treatment option and is becoming increasingly available. We examined 90-day hospital readmission for AF patients undergoing catheter ablation and utilized machine learning methods to explore the risk factors associated with these readmission trends. METHODS: Data from the 2013 Nationwide Readmissions Database on AF cases were used to predict 90-day readmissions for AF with catheter ablation. Multiple machine learning methods such as k-Nearest Neighbors, Decision Tree, and Support Vector Machine were employed to determine variable importance and build risk prediction models. Accuracy, precision, sensitivity, specificity, and area under the curve were compared for each model. RESULTS: The 90-day hospital readmission rate was 17.6%; the average age of the patients was 64.9 years; 62.9% of patients were male. Important variables in predicting 90-day hospital readmissions in patients with AF undergoing catheter ablation included the age of the patient, number of diagnoses on the patient's record, and the total number of discharges from a hospital. The k-Nearest Neighbor had the best performance with a prediction accuracy of 85%. This was closely followed by Decision Tree, but Support Vector Machine was less ideal. CONCLUSIONS: Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AF. The likelihood of readmission to the hospital increases as the patient age, total number of hospital discharges, and total number of patient diagnoses increase. Findings from this study can inform quality improvement in healthcare and in achieving patient-centered care.

Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation.

Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as k-nearest neighbors, decision tree, and support vector machine were utilized to develop predictive models with their accuracy, precision, sensitivity, specificity, and area under the curve computed and compared. We found that the most important variables in predicting 30-day hospital readmissions in patients with AF undergoing catheter ablation were the age of the patient, the total number of discharges from a hospital, and the number of diagnoses on the patient's record, among others. Out of the methods used, k-nearest neighbor had the highest prediction accuracy of 85%, closely followed by decision tree, while support vector machine was less desirable for these data. Hospital readmissions for AF with catheter ablation can be predicted with relatively high accuracy, utilizing machine learning methods. As patient age, the total number of hospital discharges, and the total number of patient diagnoses increase, the risk of hospital readmissions increases.

Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning.

The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.

Health and dental care expenditures in the United States from 1996 to 2016.

INTRODUCTION: As total health and dental care expenditures in the United States continue to rise, healthcare disparities for low to middle-income Americans creates an imperative to analyze existing expenditures. This study examined health and dental care expenditures in the United States from 1996 to 2016 and explored trends in spending across various population subgroups. METHODS: Using data collected by the Medical Expenditure Panel Survey, this study examined health and dental care expenditures in the United States from 1996 to 2016. Trends in spending were displayed graphically and spending across subgroups examined. All expenditures were adjusted for inflation or deflation to the 2016 dollar. RESULTS: Both total health and dental expenditures increased between 1996 and 2016 with total healthcare expenditures increasing from $838.33 billion in 1996 to $1.62 trillion in 2016, a 1.9-fold increase. Despite an overall increase, total expenditures slowed between 2004 and 2012 with the exception of the older adult population. Over the study period, expenditures increased across all groups with the greatest increases seen in older adult health and dental care. The per capita geriatric dental care expenditure increased 59% while the per capita geriatric healthcare expenditure increased 50% across the two decades. For the overall US population, the per capita dental care expenditure increased 27% while the per capita healthcare expenditure increased 60% over the two decades. All groups except the uninsured experienced increased dental care expenditure over the study period. CONCLUSIONS: Healthcare spending is not inherently bad since it brings benefits while exacting costs. Our findings indicate that while there were increases in both health and dental care expenditures from 1996 to 2016, these increases were non-uniform both across population subgroups and time. Further research to understand these trends in detail will be helpful to develop strategies to address health and dental care disparities and to maximize resource utilization.

Pediatric Patient-Reported Outcomes Measurement Information System is Equivalent to Scoliosis Research Society-22 in Assessing Health Status in Adolescent Idiopathic Scoliosis.

STUDY DESIGN: This was a correlational study. OBJECTIVE: Determine the range of pediatric Patient-Reported Outcomes Measurement Information System (PROMIS) scores for patients treated for adolescent idiopathic scoliosis (AIS) and assess correlation with Scoliosis Research Society-22 (SRS-22) domain scores. SUMMARY OF BACKGROUND DATA: Patient reported outcome (PRO) measures are important metrics for measuring health status in diverse patient populations. PROMIS is increasingly being used in orthopedic practice. Existing literature compares PROMIS measures favorably to legacy measures in numerous adult orthopedic conditions. This study sought to define the range of PROMIS mobility, pain interference, and peer relationships scores for adolescents treated for AIS. Furthermore, correlations between these domains and equivalent domains in the legacy PRO, SRS-22, were determined. METHODS: Pediatric PROMIS and SRS-22 were obtained at routine clinical visits for AIS at a tertiary care children's hospital from January 2017 to October 2017. Spearman correlations were performed to examine the associations between three pediatric PROMIS domains and the SRS-22 domains. Only patients who completed both PRO measures were included in the analyses. Radiographic measurements were performed at each visit assessing sagittal and coronal deformity and overall spinal balance. RESULTS: One hundred thirteen patients with a mean age of 14.4 (standard deviation [SD] = 2.1) years completed the assessments. The mean pediatric PROMIS domain scores included: mobility 50.9 (interquartile range [IQR] 36.2-65.6); pain interference 45.9 (IQR 28.9-62.9); peer relations 52.6 (IQR 38.3-64.9).PROMIS mobility was strongly correlated with SRS-22 function (r = 0.65; P < 0.001). PROMIS pain interference was strongly correlated with SRS-22 pain (r = 0.70; P < 0.001). PROMIS peer relations was moderately correlated with SRS-22 Mental Health (r = 0.41; P < 0.001) and self-image (r = 0.34; P < 0.001). CONCLUSION: In AIS patients pediatric PROMIS pain interference and mobility correlate strongly with SRS-22 pain and function domains, while PROMIS peer relationships demonstrates moderate correlations with SRS-22 mental health and self-image. LEVEL OF EVIDENCE: 2.

Evaluating the Prediction of Breast Cancer Survival Using Lymph Node Ratio.

PURPOSE: Previous oncological studies showed that lymph node ratio (LNR) (ratio of number of lymph nodes that tested positive for metastasis to the total number of lymph nodes examined) is a negative indicator of cancer survival. The American Joint Committee on Cancer (AJCC) staging system incorporates tumor size, lymph node involvement, and metastasis in a comprehensive model of cancer progression, but LNR alone has been shown to outperform the AJCC system in prognostic and survival predictions for various types of cancer. The effectiveness of LNR has not been evaluated in breast cancer staging. Evaluating LNR for predicting cancer staging in breast cancer has the potential to improve treatment recommendations. METHODS: The Surveillance, Epidemiology, and End Results dataset was used to identify 10,655 breast cancer patients who underwent nodal evaluation from 2010 to 2013, and their LNRs were calculated. Descriptive statistics of lymph node evaluation in the patients are provided. Logistic regression with LNR as the continuous independent variable was conducted to determine whether LNR could predict cancer progression, coded as regional or distant. Analysis was conducted using SPSS version 24. RESULTS: Patient's mean age was 59.43±18.62. Logistic regression analysis revealed that for every 1.3% increase in LNR, the odds of falling into the distant stage of the TNM staging system increased by 13.7% (odds ratio, 14.73; 95% confidence interval, 12.00-18.08). CONCLUSION: LNR, while correlated with breast cancer staging, serves as a better predictor of survival. Precision staging can influence treatment modality, and improved treatments can significantly improve quality of life. Additional research and diagnostic examinations using LNR as a potential tool for accurate staging in breast cancer patients are warranted.