Tackling the Antibiotic Resistant Bacteria Crisis Using Longitudinal Antibiograms

Antibiotic resistant bacteria, a growing health crisis, arise due to antibiotic overuse and misuse. Resistant infections endanger the lives of patients and are financially burdensome. Aggregate antimicrobial susceptibility reports, called antibiograms, are critical for tracking antibiotic susceptibility and evaluating the likelihood of the effectiveness of different antibiotics to treat an infection prior to the availability of patient specific susceptibility data. This research leverages the Massachusetts Statewide Antibiogram database, a rich dataset composed of antibiograms for $754$ antibiotic-bacteria pairs collected by the Massachusetts Department of Public Health from $2002$ to $2016$. However, these antibiograms are at least a year old, meaning antibiotics are prescribed based on outdated data which unnecessarily furthers resistance. Our objective is to employ data science techniques on these antibiograms to assist in developing more responsible antibiotic prescription practices. First, we use model selectors with regression-based techniques to forecast the current antimicrobial resistance. Next, we develop an assistant to immediately identify clinically and statistically significant changes in antimicrobial resistance between years once the most recent year of antibiograms are collected. Lastly, we use k-means clustering on resistance trends to detect antibiotic-bacteria pairs with resistance trends for which forecasting will not be effective. These three strategies can be implemented to guide more responsible antibiotic prescription practices and thus reduce unnecessary increases in antibiotic resistance

Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models

One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for clinicians to determine an effective antibiotic treatment. This usual clinical procedure takes approximately 48 hours and it is named antibiogram. It tests the bacterium resistance to one or more antimicrobial families (six of them considered in this work). This article focuses on cultures of the Pseudomonas Aeruginosa bacterium because is one of the most dangerous in the ICU. Several temporal data-driven models are proposed and analyzed to predict the resistance or susceptibility to a determined antibiotic family previously to know the antibiogram result and only using the available past information from a data set. This data set is formed by anonymized electronic health records data from more than 3300 ICU patients during 15 years. Several data-driven classifier methods are used in combination with several temporal modeling approaches. The results show that our predictions are reasonably accurate for some antimicrobial families, and could be used by clinicians to determine the best antibiotic therapy in advance. This early prediction can save valuable time to start the adequate treatment for an ICU patient. This study corroborates the results of a previous work pointing that the antimicrobial resistance of bacteria in the ICU is related to other recent resistance tests of ICU patients. This information is very valuable for making accurate antimicrobial resistance predictions

Applications of Machine Learning in Medical Prognosis Using Electronic Medical Records

Approximately 84 % of hospitals are adopting electronic medical records (EMR) In the United States. EMR is a vital resource to help clinicians diagnose the onset or predict the future condition of a specific disease. With machine learning advances, many research projects attempt to extract medically relevant and actionable data from massive EMR databases using machine learning algorithms. However, collecting patients\u27 prognosis factors from Electronic EMR is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed medical generative adversarial networks (GANs) to generate synthetic EMR prognosis factors using minimal information collected during routine care in specialized healthcare facilities. The generated prognosis variables used in developing predictive models for (1) chronic wound healing in patients diagnosed with Venous Leg Ulcers (VLUs) and (2) antibiotic resistance in patients diagnosed with Skin and soft tissue infections (SSTIs). Our proposed medical GANs, EMR-TCWGAN and DermaGAN, can produce both continuous and categorical features from EMR. We utilized conditional training strategies to enhance training and generate classified data regarding healing vs. non-healing in EMR-TCWGAN and susceptibility vs. resistance in DermGAN. The ability of the proposed GAN models to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We analyzed the synthetic data augmentation technique\u27s practicality in improving the wound healing prognostic model and antibiotic resistance classifier. We achieved the area under the curve (AUC) of 0.875 in the wound healing prognosis model and an average AUC of 0.830 in the antibiotic resistance classifier by using the synthetic samples generated by GANs in the training process. These results suggest that GANs can be considered a data augmentation method to generate realistic EMR data

Genomic heterogeneity underlies multidrug resistance in Pseudomonas aeruginosa: A population-level analysis beyond susceptibility testing.

BACKGROUND: Pseudomonas aeruginosa is a persistent and difficult-to-treat pathogen in many patients, especially those with Cystic Fibrosis (CF). Herein, we describe a longitudinal analysis of a series of multidrug resistant (MDR) P. aeruginosa isolates recovered in a 17-month period, from a young female CF patient who underwent double lung transplantation. Our goal was to understand the genetic basis of the observed resistance phenotypes, establish the genomic population diversity, and define the nature of sequence evolution over time. METHODS: Twenty-two sequential P. aeruginosa isolates were obtained within a 17-month period, before and after a double-lung transplant. At the end of the study period, antimicrobial susceptibility testing, whole genome sequencing (WGS), phylogenetic analyses and RNAseq were performed in order to understand the genetic basis of the observed resistance phenotypes, establish the genomic population diversity, and define the nature of sequence changes over time. RESULTS: The majority of isolates were resistant to almost all tested antibiotics. A phylogenetic reconstruction revealed 3 major clades representing a genotypically and phenotypically heterogeneous population. The pattern of mutation accumulation and variation of gene expression suggested that a group of closely related strains was present in the patient prior to transplantation and continued to change throughout the course of treatment. A trend toward accumulation of mutations over time was observed. Different mutations in the DNA mismatch repair gene mutL consistent with a hypermutator phenotype were observed in two clades. RNAseq performed on 12 representative isolates revealed substantial differences in the expression of genes associated with antibiotic resistance and virulence traits. CONCLUSIONS: The overwhelming current practice in the clinical laboratories setting relies on obtaining a pure culture and reporting the antibiogram from a few isolated colonies to inform therapy decisions. Our analyses revealed significant underlying genomic heterogeneity and unpredictable evolutionary patterns that were independent of prior antibiotic treatment, highlighting the need for comprehensive sampling and population-level analysis when gathering microbiological data in the context of CF P. aeruginosa chronic infection. Our findings challenge the applicability of antimicrobial stewardship programs based on single-isolate resistance profiles for the selection of antibiotic regimens in chronic infections such as CF

Serum Lipopolysaccharide Binding Protein Levels Predict Severity of Lung Injury and Mortality in Patients with Severe Sepsis

Background: There is a need for biomarkers insuring identification of septic patients at high-risk for death. We performed a prospective, multicenter, observational study to investigate the time-course of lipopolysaccharide binding protein (LBP) serum levels in patients with severe sepsis and examined whether serial serum levels of LBP could be used as a marker of outcome. Methodology/Principal Findings: LBP serum levels at study entry, at 48 hours and at day-7 were measured in 180 patients with severe sepsis. Data regarding the nature of infections, disease severity, development of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), and intensive care unit (ICU) outcome were recorded. LBP serum levels were similar in survivors and non-survivors at study entry (117.4±75.7 µg/mL vs. 129.8±71.3 µg/mL, P = 0.249) but there were significant differences at 48 hours (77.2±57.0 vs. 121.2±73.4 µg/mL, P<0.0001) and at day-7 (64.7±45.8 vs. 89.7±61.1 µg/ml, p = 0.017). At 48 hours, LBP levels were significantly higher in ARDS patients than in ALI patients (112.5±71.8 µg/ml vs. 76.6±55.9 µg/ml, P = 0.0001). An increase of LBP levels at 48 hours was associated with higher mortality (odds ratio 3.97; 95%CI: 1.84–8.56; P<0.001). Conclusions/Significance: Serial LBP serum measurements may offer a clinically useful biomarker for identification of patients with severe sepsis having the worst outcomes and the highest probability of developing sepsis-induced ARDS

Epidemiological studies of Streptococcus pneumoniae carriage in the post-vaccination era among two risk groups: children and the elderly

Dissertation presented to obtain the Ph.D. degree in Biology/ Molecular BiologyStreptococcus pneumoniae is a global cause of disease including pneumonia, otitis media, conjunctivitis, sepsis, and bacterial meningitis. These infections are not essential to the transmission or long-term survival of the bacterium; indeed, S. pneumoniae depends on asymptomatic colonization of the human nasopharynx for its dissemination to additional hosts. Considering this, colonization studies are a good way to monitor changes in the pneumococcal epidemiology that may result from the use of antibiotics and vaccines. The molecular characterization of pneumococci is crucial to assess these changes which highlight the need for the development and validation of easier and faster methods of molecular typing. Since 1996 our group has been monitoring the pneumococcal population colonizing children attending day care centers. However, for several years these studies have been confined to the Lisbon area. In this PhD we have addressed this situation by including other regions of Portugal in our study. In addition, we have started to study pneumococcal colonization in the elderly, the other age group where the incidence of pneumococcal infections is high. This thesis summarizes five studies conducted during this PhD. The first four studies were focused on the pneumococcal epidemiology among the two age groups where the rates of pneumococcal disease are highest: children up to six years old and adults older than 60 years. The fifth and last study describes the evaluation and validation of a new genotyping strategy for pneumococci.(...)Financial support from Fundação para a Ciência e a Tecnologia, Portugal through grant SFRH/BD/40706/2007 awarded to Sónia Nunes

Pharmacokinetics of Extended vs Intermittent Intravenous Infusion of Meropenem in Critically Ill Patients Receiving Continuous Veno-Venous Haemofiltration

peer reviewe

Nield v. Pocatello Health Services Clerk\u27s Record v. 3 Dckt. 38823

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