Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples.

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Short-term Impact of Meaningful Use Stage 1 Implementation: A Comparison of Health Outcomes in 2 Primary Care Clinics.

We studied a primary care clinic transitioning to Meaningful Use stage 1 and a comparison clinic within the same health system. In the 6 months following implementation, after adjusting for confounders, mean systolic blood pressure worsened (+3.3 mm Hg; P = .004) in the intervention clinic compared with the comparison clinic. We did not see a change in the mean or proportion of patients meeting target goals for diabetes (hemoglobin A1c) or obesity (body mass index). Our findings suggest that the worsening of systolic blood pressure associated with Meaningful Use implementation is likely due to distractions of system changes negatively impacting health outcomes.

External Validation of Two Classification and Regression Tree Models to Predict the Outcome of Inpatient Cardiopulmonary Resuscitation.

OBJECTIVE: To prospectively validate a previously developed classification and regression tree (CART) model that predicts the likelihood of a good outcome among patients undergoing inpatient cardiopulmonary resuscitation. DESIGN: Prospective validation of a clinical decision rule. SETTING: Skåne University Hospital in Malmo, Sweden. PATIENTS: All adult patients (N = 287) experiencing in-hospital cardiopulmonary arrest and undergoing cardiopulmonary resuscitation between 2007 and 2010. INTERVENTIONS: Patients from Skåne University Hospital who underwent CPR (N = 287) were classified using the CART models to predict their likelihood of survival neurologically intact or with minimal deficits, based on a cerebral performance category score of 1. Discrimination and classification accuracy of the score in the Swedish population was compared to that in the original (derivation and internal validation) populations. MEASUREMENTS AND MAIN RESULTS: For model 1, the area under the receiver-operating characteristic curve (AUROCC) was 0.77, compared with 0.76 and 0.73 in the original derivation and validation populations, respectively. Model 1 classified 71 (2.8%) of 287 patients as being at a very low risk of a good neurologic outcome compared with 157 (26.1%) of 287 patients predicted to be at an above average risk of a good neurologic outcome. Model 2 had a similar AUROCC as the original validation population of 0.71 but lower than the original derivation population. Model 2 performed similarly to Model 1 with regards to its ability to correctly classify patients as very low or higher than average likelihood of a good neurologic outcome. CONCLUSION: Two CART models validated well in a different population, displaying similar discrimination and classification accuracy compared to the original population. Although additional validation in larger populations is desirable before widespread adoption, these results are very encouraging.

Prediction of survival to discharge following cardiopulmonary resuscitation using classification and regression trees.

OBJECTIVES: To predict the likelihood that an inpatient who experiences cardiopulmonary arrest and undergoes cardiopulmonary resuscitation survives to discharge with good neurologic function or with mild deficits (Cerebral Performance Category score = 1). DESIGN: Classification and Regression Trees were used to develop branching algorithms that optimize the ability of a series of tests to correctly classify patients into two or more groups. Data from 2007 to 2008 (n = 38,092) were used to develop candidate Classification and Regression Trees models to predict the outcome of inpatient cardiopulmonary resuscitation episodes and data from 2009 (n = 14,435) to evaluate the accuracy of the models and judge the degree of over fitting. Both supervised and unsupervised approaches to model development were used. SETTING: 366 hospitals participating in the Get With the Guidelines-Resuscitation registry. SUBJECTS: Adult inpatients experiencing an index episode of cardiopulmonary arrest and undergoing cardiopulmonary resuscitation in the hospital. MEASUREMENTS AND MAIN RESULTS: The five candidate models had between 8 and 21 nodes and an area under the receiver operating characteristic curve from 0.718 to 0.766 in the derivation group and from 0.683 to 0.746 in the validation group. One of the supervised models had 14 nodes and classified 27.9% of patients as very unlikely to survive neurologically intact or with mild deficits (< 3%); the best unsupervised model had 11 nodes and classified 21.7% as very unlikely to survive. CONCLUSIONS: We have developed and validated Classification and Regression Tree models that predict survival to discharge with good neurologic function or with mild deficits following in-hospital cardiopulmonary arrest. Models like this can assist physicians and patients who are considering do-not-resuscitate orders.

A systematic review of high quality diagnostic tests for Chagas disease.

BACKGROUND: There is significant heterogeneity in reported sensitivities and specificities of diagnostic serological assays for Chagas disease, as might be expected from studies that vary widely according to setting, research design, antigens employed, and reference standard. The purpose of this study is to summarize the reported accuracy of serological assays and to identify sources of heterogeneity including quality of research design. To avoid associated spectrum bias, our analysis was limited to cohort studies. METHODS: We completed a search of PubMed, a bibliographic review of potentially relevant articles, and a review of articles identified by a study author involved in this area of research. Studies were limited to prospective cohort studies of adults published since 1985. Measures of diagnostic accuracy were pooled using a Der Simonian Laird Random Effects Model. A subgroup analysis and meta regression were employed to identify sources of heterogeneity. The QUADAS tool was used to assess quality of included studies and Begg's funnel plot was used to assess publication bias. RESULTS: Eighteen studies and 61 assays were included in the final analysis. Significant heterogeneity was found in all pre-determined subgroups. Overall sensitivity was 90% (95% CI: 89%-91%) and overall specificity was 98% (95% CI: 98%-98%). CONCLUSION: Sensitivity and specificity of serological assays for the diagnosis of Chagas disease appear less accurate than previously thought. Suggestions to improve the accuracy of reporting include the enrollment of patients in a prospective manner, double blinding, and providing an explicit method of addressing subjects that have an indeterminate diagnosis by either the reference standard or index test.

The use of classification and regression trees to predict the likelihood of seasonal influenza.

BACKGROUND: Individual signs and symptoms are of limited value for the diagnosis of influenza. OBJECTIVE: To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. METHODS: Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. RESULTS: Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. CONCLUSIONS: A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therapy.

Development and validation of a clinical decision rule for the diagnosis of influenza.

INTRODUCTION: A clinical decision rule to improve the accuracy of a diagnosis of influenza could help clinicians avoid unnecessary use of diagnostic tests and treatments. Our objective was to develop and validate a simple clinical decision rule for diagnosis of influenza. METHODS: We combined data from 2 studies of influenza diagnosis in adult outpatients with suspected influenza: one set in California and one in Switzerland. Patients in both studies underwent a structured history and physical examination and had a reference standard test for influenza (polymerase chain reaction or culture). We randomly divided the dataset into derivation and validation groups and then evaluated simple heuristics and decision rules from previous studies and 3 rules based on our own multivariate analysis. Cutpoints for stratification of risk groups in each model were determined using the derivation group before evaluating them in the validation group. For each decision rule, the positive predictive value and likelihood ratio for influenza in low-, moderate-, and high-risk groups, and the percentage of patients allocated to each risk group, were reported. RESULTS: The simple heuristics (fever and cough; fever, cough, and acute onset) were helpful when positive but not when negative. The most useful and accurate clinical rule assigned 2 points for fever plus cough, 2 points for myalgias, and 1 point each for duration <48 hours and chills or sweats. The risk of influenza was 8% for 0 to 2 points, 30% for 3 points, and 59% for 4 to 6 points; the rule performed similarly in derivation and validation groups. Approximately two-thirds of patients fell into the low- or high-risk group and would not require further diagnostic testing. CONCLUSION: A simple, valid clinical rule can be used to guide point-of-care testing and empiric therapy for patients with suspected influenza.

Pre-arrest predictors of failure to survive after in-hospital cardiopulmonary resuscitation: a meta-analysis.

PURPOSE: Our objective was to perform a systematic review of pre-arrest predictors of the outcome of in-hospital cardiopulmonary resuscitation (CPR) in adults. METHODS: We searched PubMed for studies published since 1985 and bibliographies of previous meta-analyses. We included studies with predominantly adult patients, limited to in-hospital arrest, using an explicit definition of cardiopulmonary arrest and CPR and reporting survival to discharge by at least one pre-arrest variable. A total of 35 studies were included in the final analysis. Inclusion criteria, design elements and results were abstracted in parallel by both investigators. Discrepancies were resolved by consensus. RESULTS: The rate of survival to discharge was 17.5%; we found a trend towards increasing survival in more recent studies. Metastatic malignancy [odds ratio (OR) 3.9] or haematologic malignancy (OR 3.9), age over 70, 75 or 80 years (OR 1.5, 2.8 and 2.7, respectively), black race (OR 2.1), altered mental status (OR 2.2), dependency for activities of daily living (range OR 3.2-7.0 depending on specific activity), impaired renal function (OR 1.9), hypotension on admission (OR 1.8) and admission for pneumonia (OR 1.7), trauma (OR 1.7) or medical non-cardiac diagnosis (OR 2.2) were significantly associated with failure to survive to discharge; cardiovascular diagnoses and co-morbidities were associated with improved survival (range OR 0.23-0.53). Elevated CPR risk scores predicted failure to survive but have not been validated consistently in different populations. CONCLUSIONS: We identified several pre-arrest variables associated with failure to survive to discharge. This information should be shared with patients as part of a shared decision-making process regarding the use of do not resuscitate orders.