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Pertussis: Microbiology, Disease, Treatment, and Prevention.

ABSTRACT: Pertussis is a severe respiratory infection caused by Bordetella pertussis, and in 2008, pertussis was associated with an estimated 16 million cases and 195,000 deaths globally. Sizeable outbreaks of pertussis have been reported over the past 5 years, and disease reemergence has been the focus of international attention to develop a deeper understanding of pathogen virulence and genetic evolution of B. pertussis strains. During the past 20 years, the scientific community has recognized pertussis among adults as well as infants and children. Increased recognition that older children and adolescents are at risk for disease and may transmit B. pertussis to younger siblings has underscored the need to better understand the role of innate, humoral, and cell-mediated immunity, including the role of waning immunity. Although recognition of adult pertussis has increased in tandem with a better understanding of B. pertussis pathogenesis, pertussis in neonates and adults can manifest with atypical clinical presentations. Such disease patterns make pertussis recognition difficult and lead to delays in treatment. Ongoing research using newer tools for molecular analysis holds promise for improved understanding of pertussis epidemiology, bacterial pathogenesis, bioinformatics, and immunology. Together, these advances provide a foundation for the development of new-generation diagnostics, therapeutics, and vaccines.

SUBMITTER: Kilgore PE

PROVIDER: S-EPMC4861987 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. Optimal statin therapy decisions rely on shared decision making and may be uncertain for a given patient. In areas of clinical uncertainty, personalized approaches based on real-world data may help inform treatment decisions. We sought to develop a personalized statin recommendation approach for primary ASCVD prevention based on historical real-world outcomes in similar patients. Our retrospective cohort included adults from a large Northern California electronic health record (EHR) aged 40-79 years with no prior cardiovascular disease or statin use. The cohort was split into training and test sets. Weighted-K-nearest-neighbor (wKNN) regression models were used to identify historical EHR patients similar to a candidate patient. We modeled four statin decisions for each patient: none, low-intensity, moderate-intensity, and high-intensity. For each candidate patient, the algorithm recommended the statin decision that was associated with the greatest percentage reduction in LDL-C after 1 year in similar patients. The overall cohort consisted of 50,576 patients (age 54.6 ± 9.8 years) with 55% female, 48% non-Hispanic White, 32% Asian, and 7.4% Hispanic patients. Among 8383 test-set patients, 52%, 44%, and 4% were recommended high-, moderate-, and low-intensity statins, respectively, for a maximum predicted average 1-yr LDL-C reduction of 16.9%, 20.4%, and 14.9%, in each group, respectively. Overall, using aggregate EHR data, a personalized statin recommendation approach identified the statin intensity associated with the greatest LDL-C reduction in historical patients similar to a candidate patient. Recommendations included low- or moderate-intensity statins for maximum LDL-C lowering in nearly half the test set, which is discordant with their expected guideline-based efficacy. A data-driven personalized statin recommendation approach may inform shared decision making in areas of uncertainty, and highlight unexpected efficacy-effectiveness gaps.

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Pertussis: Microbiology, Disease, Treatment, and Prevention.

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