Joint Application of the Target Trial Causal Framework and Machine Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial Skin and Skin Structure Infections due to Methicillin-resistant Staphylococcus aureus

Bacterial infections are responsible for high mortality worldwide. Antimicrobial resistance underlying the infection, and multifaceted patient's clinical status can hamper the correct choice of antibiotic treatment. Randomized clinical trials provide average treatment effect estimates but are not ideal for risk stratification and optimization of therapeutic choice, i.e., individualized treatment effects (ITE). Here, we leverage large-scale electronic health record data, collected from Southern US academic clinics, to emulate a clinical trial, i.e., 'target trial', and develop a machine learning model of mortality prediction and ITE estimation for patients diagnosed with acute bacterial skin and skin structure infection (ABSSSI) due to methicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects. First, we use propensity score matching to emulate the trial and create a treatment randomized (vancomycin vs. other antibiotics) dataset. Next, we use this data to train various machine learning methods (including boosted/LASSO logistic regression, support vector machines, and random forest) and choose the best model in terms of area under the receiver characteristic (AUC) through bootstrap validation. Lastly, we use the models to calculate ITE and identify possible averted deaths by therapy change. The out-of-bag tests indicate that SVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but BLR/LASSO is not far behind (76%). By calculating the counterfactuals using the BLR/LASSO, vancomycin increases the risk of death, but it shows a large variation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome probability is modest. Instead, the RF exhibits stronger changes in ITE, suggesting more complex treatment heterogeneity.Comment: This is the Proceedings of the KDD workshop on Applied Data Science for Healthcare (DSHealth 2022), which was held on Washington D.C, August 14 202

Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.Comment: Accepted by IEEE PES 2018 General Meetin

Closest string with outliers

Background: Given n strings s1, …, sn each of length ℓ and a nonnegative integer d, the CLOSEST STRING problem asks to find a center string s such that none of the input strings has Hamming distance greater than d from s. Finding a common pattern in many – but not necessarily all – input strings is an important task that plays a role in many applications in bioinformatics. Results: Although the closest string model is robust to the oversampling of strings in the input, it is severely affected by the existence of outliers. We propose a refined model, the CLOSEST STRING WITH OUTLIERS (CSWO) problem, to overcome this limitation. This new model asks for a center string s that is within Hamming distance d to at least n – k of the n input strings, where k is a parameter describing the maximum number of outliers. A CSWO solution not only provides the center string as a representative for the set of strings but also reveals the outliers of the set. We provide fixed parameter algorithms for CSWO when d and k are parameters, for both bounded and unbounded alphabets. We also show that when the alphabet is unbounded the problem is W[1]-hard with respect to n – k, ℓ, and d. Conclusions: Our refined model abstractly models finding common patterns in several but not all input strings

Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable.

There is obvious interest in gaining insights into the epidemiology and evolution of the virus that has recently emerged in humans as the cause of the coronavirus disease 2019 (COVID-19) pandemic. The recent paper by Forster et al. (1), analyzed 160 SARS-CoV-2 full genomes available (https://www.gisaid.org/) in early March 2020. The central claim is the identification of three main SARS-CoV-2 types, named A, B, and C, circulating in different proportions among Europeans and Americans (types A and C) and East Asian (type B). According to a median-joining network analysis, variant A is proposed to be the ancestral type because it links to the sequence of a coronavirus from bats, used as an outgroup to trace the ancestral origin of the human strains. The authors further suggest that the “ancestral Wuhan B-type virus is immunologically or environmentally adapted to a large section of the East Asian population, and may need to mutate to overcome resistance outside East Asia”. There are several serious flaws with their findings and interpretation. First, and most obviously, the sequence identity between SARS-CoV-2 and the bat virus is only 96.2%, implying that these viral genomes (which are nearly 30,000 nucleotides long) differ by more than 1,000 mutations. Such a distant outgroup is unlikely to provide a reliable root for the network. Yet, strangely, the branch to the bat virus, in Figure 1 of the paper, is only 16 or 17 mutations in length. Indeed, the network seems to be mis-rooted because (see Supplementary Figure 4) a virus from Wuhan from week 0 (24th December 2019) is portrayed as a descendant of a clade of viruses collected in weeks 1-9 (presumably from many places outside China), which makes no evolutionary (2), nor epidemiological sense (3).N

Ruxolitinib versus best available therapy for polycythemia vera intolerant or resistant to hydroxycarbamide in a randomized trial

Purpose Polycythemia vera (PV) is characterized by JAK/STAT activation, thrombotic/hemorrhagic events, systemic symptoms, and disease transformation. In high-risk PV, ruxolitinib controls blood counts and improves symptoms. Patients and Methods MAJIC-PV is a randomized phase II trial of ruxolitinib versus best available therapy (BAT) in patients resistant/intolerant to hydroxycarbamide (HC-INT/RES). Primary outcome was complete response (CR) within 1 year. Secondary outcomes included duration of response, event-free survival (EFS), symptom, and molecular response. Results One hundred eighty patients were randomly assigned. CR was achieved in 40 (43%) patients on ruxolitinib versus 23 (26%) on BAT (odds ratio, 2.12; 90% CI, 1.25 to 3.60; P = .02). Duration of CR was superior for ruxolitinib (hazard ratio [HR], 0.38; 95% CI, 0.24 to 0.61; P < .001). Symptom responses were better with ruxolitinib and durable. EFS (major thrombosis, hemorrhage, transformation, and death) was superior for patients attaining CR within 1 year (HR, 0.41; 95% CI, 0.21 to 0.78; P = .01); and those on ruxolitinib (HR, 0.58; 95% CI, 0.35 to 0.94; P = .03). Serial analysis of JAK2V617F variant allele fraction revealed molecular response was more frequent with ruxolitinib and was associated with improved outcomes (progression-free survival [PFS] P = .001, EFS P = .001, overall survival P = .01) and clearance of JAK2V617F stem/progenitor cells. ASXL1 mutations predicted for adverse EFS (HR, 3.02; 95% CI, 1.47 to 6.17; P = .003). The safety profile of ruxolitinib was as previously reported. Conclusion The MAJIC-PV study demonstrates ruxolitinib treatment benefits HC-INT/RES PV patients with superior CR, and EFS as well as molecular response; importantly also demonstrating for the first time, to our knowledge, that molecular response is linked to EFS, PFS, and OS

Investigating Effects of Tulathromycin Metaphylaxis on the Fecal Resistome and Microbiome of Commercial Feedlot Cattle Early in the Feeding Period

The objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot—and associated changes in diet, geography, conspecific exposure, and environment—may exert a greater influence over the fecal resistome and microbiome of feedlot cattle than common metaphylactic antimicrobial drug treatment

Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits

Peer reviewe

A Cautionary Report for Pathogen Identification Using Shotgun Metagenomics; A Comparison to Aerobic Culture and Polymerase Chain Reaction for Salmonella enterica Identification.

This study was conducted to compare aerobic culture, polymerase chain reaction (PCR), lateral flow immunoassay (LFI), and shotgun metagenomics for identification of Salmonella enterica in feces collected from feedlot cattle. Samples were analyzed in parallel using all four tests. Results from aerobic culture and PCR were 100% concordant and indicated low S. enterica prevalence (3/60 samples positive). Although low S. enterica prevalence restricted formal statistical comparisons, LFI and deep metagenomic sequencing results were discordant with these results. Specifically, metagenomic analysis using k-mer-based classification against the RefSeq database indicated that 11/60 of samples contained sequence reads that matched to the S. enterica genome and uniquely identified this species of bacteria within the sample. However, further examination revealed that plasmid sequences were often included with bacterial genomic sequence data submitted to NCBI, which can lead to incorrect taxonomic classification. To circumvent this classification problem, we separated all plasmid sequences included in bacterial RefSeq genomes and reassigned them to a unique taxon so that they would not be uniquely associated with specific bacterial species such as S. enterica. Using this revised database and taxonomic structure, we found that only 6/60 samples contained sequences specific for S. enterica, suggesting increased relative specificity. Reads identified as S. enterica in these six samples were further evaluated using BLAST and NCBI's nr/nt database, which identified that only 2/60 samples contained reads exclusive to S. enterica chromosomal genomes. These two samples were culture- and PCR-negative, suggesting that even deep metagenomic sequencing suffers from lower sensitivity and specificity in comparison to more traditional pathogen detection methods. Additionally, no sample reads were taxonomically classified as S. enterica with two other metagenomic tools, Metagenomic Intra-species Diversity Analysis System (MIDAS) and Metagenomic Phylogenetic Analysis 2 (MetaPhlAn2). This study re-affirmed that the traditional techniques of aerobic culture and PCR provide similar results for S. enterica identification in cattle feces. On the other hand, metagenomic results are highly influenced by the classification method and reference database employed. These results highlight the nuances of computational detection of species-level sequences within short-read metagenomic sequence data, and emphasize the need for cautious interpretation of such results

Assessing the validity of a self-administered food-frequency questionnaire (FFQ) in the adult population of Newfoundland and Labrador, Canada

Background: The Food- Frequency Questionnaire (FFQ) is a dietary assessment tool frequently used in large-scale nutritional epidemiology studies. The goal of the present study is to validate a self-administered version of the Hawaii FFQ modified for use in the general adult population of Newfoundland and Labrador (NL). Methods: Over a one year period, 195 randomly selected adults completed four 24-hour dietary recalls (24-HDRs) by telephone and one subsequent self-administered FFQ. Estimates of energy and nutrients derived from the 24-HDRs and FFQs were compared (protein, carbohydrate, fibre, fat, vitamin A, carotene, vitamin D, and calcium). Data were analyzed using the Pearson’s correlation coefficients, cross-classification method, and Bland–Altman plots. Results: The mean nutrient intake values of the 24-HDRs were lower than those of the FFQs, except for protein in men. Sex and energy-adjusted de-attenuated Pearson correlation coefficients for each nutrient varied from 0.13 to 0.61. Except for protein in men, all correlations were statistically significant with p < 0.05. Cross-classification analysis revealed that on average, 74% women and 78% men were classified in the same or adjacent quartile of nutrient intake when comparing data from the FFQ and 24-HDRs. Bland–Altman plots showed no serious systematic bias between the administration of the two instruments over the range of mean intakes. Conclusion: This 169-item FFQ developed specifically for the adult NL population had moderate relative validity and therefore can be used in studies to assess food consumption in the general adult population of NL. This tool can be used to classify individual energy and nutrient intakes into quartiles, which is useful in examining relationships between diet and chronic disease