IMPORTANCE Acute myocardial infarction (AMI) complicated by cardiogenic shock is associated with substantial morbidity and mortality. Although intravascular microaxial left ventricular assist devices (LVADs) provide greater hemodynamic support as compared with intra-aortic balloon pumps (IABPs), little is known about clinical outcomes associated with intravascular microaxial LVAD use in clinical practice. OBJECTIVE To examine outcomes among patients undergoing percutaneous coronary intervention (PCI) for AMI complicated by cardiogenic shock treated with mechanical circulatory support (MCS) devices. DESIGN, SETTING, AND PARTICIPANTS A propensity-matched registry-based retrospective cohort study of patients with AMI complicated by cardiogenic shock undergoing PCI between October 1, 2015, and December 31, 2017, who were included in data from hospitals participating in the CathPCI and the Chest Pain-MI registries, both part of the American College of Cardiology's National Cardiovascular Data Registry. Patients receiving an intravascular microaxial LVAD were matched with those receiving IABP on demographics, clinical history, presentation, infarct location, coronary anatomy, and clinical laboratory data, with final follow-up through December 31, 2017. EXPOSURES Hemodynamic support, categorized as intravascular microaxial LVAD use only, IABP only, other (such as use of a percutaneous extracorporeal ventricular assist system, extracorporeal membrane oxygenation, or a combination of MCS device use), or medical therapy only. MAIN OUTCOMES AND MEASURES The primary outcomes were in-hospital mortality and in-hospital major bleeding. RESULTS Among 28 304 patients undergoing PCI for AMI complicated by cardiogenic shock, the mean (SD) age was 65.0 (12.6) years, 67.0% were men, 81.3% had an ST-elevation myocardial infarction, and 43.3% had cardiac arrest. Over the study period among patients with AMI, an intravascular microaxial LVAD was used in 6.2% of patients, and IABP was used in 29.9%. Among 1680 propensity-matched pairs, there was a significantly higher risk of in-hospital death associated with use of an intravascular microaxial LVAD (45.0%) vs with an IABP (34.1% [absolute risk difference, 10.9 percentage points {95% CI, 7.6-14.2}; P < .001) and also higher risk of in-hospital major bleeding (intravascular microaxial LVAD [31.3%] vs IABP [16.0%]; absolute risk difference, 15.4 percentage points [95% CI, 12.5-18.2]; P < .001). These associations were consistent regardless of whether patients received a device before or after initiation of PCI. CONCLUSIONS AND RELEVANCE Among patients undergoing PCI for AMI complicated by cardiogenic shock from 2015 to 2017, use of an intravascular microaxial LVAD compared with IABP was associated with higher adjusted risk of in-hospital death and major bleeding complications, although study interpretation is limited by the observational design. Further research may be needed to understand optimal device choice for these patients.

Background The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning (ML) techniques that address higher dimensional, non-linear relationships among variables would enhance prediction. We sought to compare the effectiveness of several ML algorithms for predicting readmissions. Methods and Results Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of Random Forests (RF), Boosting, RF combined hierarchically with Support Vector Machines (SVM) or Logistic Regression (LR) and Poisson Regression against traditional LR to predict 30-day and 180-day all-cause and heart fauilre-only readmissions. We randomly selected 50% of patients for a derivation set and the remaining patients comprised a validation set, repeated 100 times. We compared c-statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing ML model, RF, provided a 17.8% improvement over LR (mean c-statistics 0.628 and 0.533, respectively). For readmissions due to heart failure, Boosting improved the c-statistic by 24.9% over LR (mean c-statistic 0.678 and 0.543, respectively). For 30-day all cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with RF (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Conclusions ML methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.

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Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.

Ticks and other arthropods often are hosts to nutrient providing bacterial endosymbionts, which contribute to their host’s fitness by supplying nutrients such as vitamins and amino acids. It has been detected, in our lab, that Ixodes pacificus is host to Rickettsia species phylotype G021. This endosymbiont is predominantly present, and 100% maternally transmitted in I. pacificus. To study roles of phylotype G021 in I. pacificus, bioinformatic and molecular approaches were carried out. MUMmer genome alignments of whole genome sequence of I. scapularis, a close relative to I. pacificus, against completely sequenced genomes of R. bellii OSU85-389, R. conorii, and R. felis, identified 8,190 unique sequences that are homologous to Rickettsia sequences in the NCBI Trace Archive. MetaCyc metabolic reconstructions revealed that all folate gene orthologues (folA, folC, folE, folKP, ptpS) required for de novo folate biosynthesis are present in the genome of Rickettsia buchneri in I. scapularis. To examine the metabolic capability of phylotype G021 in I. pacificus, genes of the folate biosynthesis pathway of the bacterium were PCR amplified using degenerate primers. BLAST searches identified that nucleotide sequences of the folA, folC, folE, folKP, and ptpS genes possess 98.6%, 98.8%, 98.9%, 98.5% and 99.0% identity respectively to the corresponding genes of Rickettsia buchneri. Phylogenetic tree constructions show that the folate genes of phylotype G021 and homologous genes from various Rickettsia species are monophyletic. This study has shown that all folate genes exist in the genome of Rickettsia species phylotype G021 and that this bacterium has the genetic capability for de novo folate synthesis.