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Consensus guidelines for hospitalized, non-severe community-acquired pneumonia (CAP) recommend empiric macrolide + β-lactam or respiratory fluoroquinolone monotherapy in patients with no risk factors for resistant organisms. In patients with allergies or contraindications, doxycycline + β-lactam is a recommended alternative. The purpose of this study was to compare differences in outcomes among guideline-recommended regimens in this population. This retrospective, multicenter cohort study included patients ≥18 years of age with CAP who received respiratory fluoroquinolone monotherapy, empiric macrolide + β-lactam, or doxycycline + β-lactam. Major exclusion criteria included patients with immunocompromising conditions, requiring vasopressors or invasive mechanical ventilation within 48 hours of admission, and receiving less than 2 days of total antibiotic therapy. The primary outcome was in-hospital mortality. Secondary outcomes included clinical failure, 14- and 30-day hospital readmission, and hospital length of stay. Safety outcomes included incidence of new Clostridioides difficile infection and aortic aneurysm ruptures. Of 4685 included patients, 1722 patients received empiric respiratory fluoroquinolone monotherapy, 159 received empiric doxycycline + β-lactam, and 2804 received empiric macrolide + β-lactam. Incidence of in-hospital mortality was not observed to be significantly different among empiric regimens (doxycycline + β-lactam group: 1.9% vs macrolide + β-lactam: 1.9% vs respiratory fluoroquinolone monotherapy: 1.5%, P = 0.588). No secondary outcomes were observed to differ significantly among groups. We observed no differences in clinical or safety outcomes among three guideline-recommended empiric CAP regimens. Empiric doxycycline + β-lactam may be a safe empiric regimen for hospitalized CAP patients with non-severe CAP, although additional research is needed to corroborate these observations with larger samples.

•The role of non-macrolide antibiotics in microbiologically culture negative critically ill patients presenting with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) has not been described•Short courses (≤ 3 days) of non-macrolide antibiotics in critically ill patients with AECOPD did not affect the primary composite endpoint of in-hospital mortality, progression of ventilation, and readmission for AECOPD within 30 days•Short courses of non-macrolide antibiotics in critically ill patients with AECOPD did not affect incidence of adverse drug events•Additional studies exploring the role of shorter courses of non-macrolide antibiotics in critically ill patients with AECOPD are warranted In critically ill patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and without positive microbiological data, the efficacy and tolerability of short-course nonmacrolide antibiotics are ill-described and have pertinent implications in antimicrobial stewardship. This study compared the efficacy and tolerability of nonmacrolide antibiotic strategies in critically ill patients with AECOPD and without pertinent positive microbiological testing. This single-center, retrospective cohort study was conducted in culture-negative critically ill adults admitted to an intensive care unit (ICU) between July 1, 2014, and July 1, 2019, for the treatment of AECOPD. Included patients received treatment with an empiric corticosteroid, azithromycin, and/or a nonmacrolide antibiotic. Patients treated with a nonmacrolide antibiotic for ≤3 and >3 days made up the short- and standard-course groups, respectively. The prevalence of in-hospital mortality, progression to the need for ventilation, and/or readmission for AECOPD within 30 days (primary composite end point) was compared between the two groups. Additional end points included hospital and ICU lengths of stay (LOS), all-cause 30-day readmission, and prevalence of antibiotic-related adverse events. A total of 135 patients were included (short course, 66; standard course, 69). The differences in the primary composite end point (short vs standard, 24.2% vs 39.1%; P = 0.06) and its individual components were not significant. The median ICU LOS (2 vs 3 days) and hospital LOS (4 vs 6 days) were shorter in the short-course group (both, P < 0.01). Multivariate logistic regression confirmed no association between group assignment and the primary end point. Short-course nonmacrolide therapy in patients with AECOPD and no positive microbiological testing was not associated with differences in mortality, progression to ventilation, readmission rate, or prevalence of adverse drug events. Larger-scale prospective studies are needed to validate these findings.

Current data suggest potential benefits with β-lactam plus macrolide combination therapy for empiric treatment of intensive care unit (ICU) patients with severe community-acquired pneumonia (CAP). However, it is unclear whether deescalation to β-lactam monotherapy in the absence of positive results on diagnostic tests, such as the BioFire FilmArray Respiratory Panel 2 (BioFire polymerase chain reaction [PCR]), affects clinical outcomes. The purpose of this study was to compare outcomes between patients with negative BioFire PCR results deescalated to β-lactam monotherapy with those not deescalated. This single-center, retrospective cohort study assessed the in-hospital mortality rates of critically ill adults with CAP treated for ≥48 h with combination β-lactam and azithromycin therapy. Additional end points included hospital length of stay (LOS), ICU LOS, duration of mechanical ventilatory support, 30-day readmission, and incidence of azithromycin-related adverse effects. A total of 94 patients were included: 53 in the deescalation group and 41 in the nondeescalation group. No difference was observed with respect to in-hospital mortality (2.4% vs 11.3%, P = 0.312), although patients in the deescalated group experienced shorter ICU (1.9 vs 3.4 days, P = 0.029) and hospital LOS (6 vs 7 days, P = 0.025). No differences were found between groups with respect to additional secondary end points. Simple logistic regression confirmed that deescalation was not associated with hospital mortality (odds ratio = 0.17, 95% CI, 0.02–1.70). In this study of ICU patients with severe CAP and a negative BioFire PCR result, deescalation from combination β-lactam and macrolide therapy to β-lactam monotherapy was not associated with increased in-hospital mortality but was associated with decreased hospital and ICU LOS. Larger prospective studies are warranted to verify these findings.

Even though the study failed to meet its primary endpoint of a 50% reduction in S aureus pneumonia in the suvratoximab 5000 mg compared with the placebo group, these data support the need for additional studies to evaluate monoclonal antibodies as therapeutic and preventive agents for bacterial infections. In 2018, the Combatting Bacterial Resistance in Europe's STAT-Net group recommended using rank-based composite endpoints to assess new treatments directed against antibiotic-resistant infections to increase the likelihood of identifying true positive results.6 Given the association between escalating antibiotic resistance and increasing antibiotic exposure,7 these would seem to be reasonable endpoints as part of a rank-based composite endpoint for the evaluation of non-antibiotic therapies. Additionally, platform trials evaluating several interventions against a common control group are another example of how monoclonal antibodies could be evaluated with or without other interventions, such as concomitant antibiotic administration.6 The RECOVERY trial platform for SARS-CoV-19 is an example of how successful a platform design can be in testing multiple therapeutic interventions for an infectious disease.8 In summary, it is unlikely that traditional antibiotics will suffice as long-term solutions for the treatment of increasingly resistant bacterial infections.

To develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design. Retrospective analysis of data extracted from electronic health records (EHR). Single, tertiary-care academic medical center in St. Louis, MO, USA. Adult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019. None. Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating Characteristic curve (AUROC or c-statistic) of 0.862 ± 0.011 and Area Under the Precision-Recall Curve (AUPRC) of 0.294 ± 0.021 compared to that of Logistic Regression (0.857 ± 0.008 and 0.256 ± 0.024) and NEWS 2 (0.699 ± 0.012 and 0.092 ± 0.009). In the pseudo-prospective trial, 388 (69.7%) septic patients were alerted on with a specificity of 81.4%. Within 24 h of crossing the alert threshold, 20.9% had a sepsis-related event occur. A machine learning model capable of predicting sepsis in the general ward setting was developed using the EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance and demonstrated a 29.1% Positive Predictive Value (PPV) for sepsis-related intervention or outcome within 48 h.

Abstract We carried out a prospective de-escalation study based on methicillin-resistant Staphylococcus aureus (MRSA) nasal cultures in intensive care unit patients with suspected pneumonia. Days of anti-MRSA therapy was significantly reduced in the intervention group (2 [0–3] days vs 1 [0–2] day; P < .01). Time to MRSA de-escalation was also shortened in the intervention group.

Cognitive decline is often influenced by Alzheimer's disease (AD) pathology (e.g., beta-amyloid burden) and other pathology (e.g., vascular abnormalities). Amyloid onset and chronicity estimation using Sampled Iterative Local Approximation (SILA), enhanced our understanding of AD progression and its preclinical phase. This study has three aims: 1) estimate White Matter Hyperintensities (WMH) onset and chronicity; 2) assess whether baseline WMH chronicity/burden moderates the association between Aβ chronicity/burden and Clinical Dementia Rating-sum of boxes (CDR-SB) trajectories; 3) explore whether tau burden mediates WMH and amyloid associations with CDR-SB. Participants were from the University of Wisconsin WRAP and ADRC cohorts and had completed at least one T1-weighted and T2-weighted FLAIR scans (Aim 1: n = 877, age 43-93y). WMH values were aligned to a duration scale using SILA and WMH positivity threshold of ∼2.06 mL for WMH+ chronicity = 0. We compared mixed effects models with up to cubic time (age vs WMH chronicity) terms for characterizing WMH trajectories. We also examined baseline WMH burden*amyloid burden at each CDR-SB assessment (PiB A+ DVR threshold ∼17CL; linear and quadratic amyloid burden) relative to longitudinal CDR-SB (Aim 2; n = 426), and tau PET SUVR's mediating effects on WMH*amyloid associations with last CDR-SB (Aim 3; n = 385; meta-temporal (MTC) SUVR; florquinitau tracer). Mean(SD) age at baseline and last MRI were 65.36(8.49) and 67.26(8.14) years, respectively (sample details in Table 1). Comparisons of simple slopes (95%CI) show overlapping annualized WMH- slope estimates using age (0.028(0.022,0.034)) or chronicity (0.037(0.033,0.040)); in contrast, the WMH+ slopes are 2.26 times larger when aligned to WMH chronicity vs age (WMH+: 0.042(0.036,0.047), WMH-: (0.095(0.091,0.10); Figure 1A&B). Whether biomarker \"burden\" was modeled as ±, estimated DVR, or chronicity (Figure 1C&D), significant interactions showed a synergistic effect of WMH and amyloid on accelerated CDR-SB trajectory. Moderated mediation models revealed MTC-tau accumulation partially mediated (∼65%) the synergistic effect of WMH and amyloid on last CDR-SB (Figure 2). WMH accumulation follows a predictable trajectory post-onset and appears to exacerbate cognitive decline in those with amyloid pathology. Future analyses will further elucidate the complex relationships between vascular risk, amyloid, tau accumulation and cognitive decline.

Background Cognitive decline is often influenced by Alzheimer’s disease (AD) pathology (e.g., beta‐amyloid burden) and other pathology (e.g., vascular abnormalities). Amyloid onset and chronicity estimation using Sampled Iterative Local Approximation (SILA), enhanced our understanding of AD progression and its preclinical phase. This study has three aims: 1) estimate White Matter Hyperintensities (WMH) onset and chronicity; 2) assess whether baseline WMH chronicity/burden moderates the association between Aß chronicity/burden and Clinical Dementia Rating‐sum of boxes (CDR‐SB) trajectories; 3) explore whether tau burden mediates WMH and amyloid associations with CDR‐SB. Method Participants were from the University of Wisconsin WRAP and ADRC cohorts and had completed at least one T1‐weighted and T2‐weighted FLAIR scans (Aim 1: n = 877, age 43‐93y). WMH values were aligned to a duration scale using SILA and WMH positivity threshold of ∼2.06 mL for WMH+ chronicity = 0. We compared mixed effects models with up to cubic time (age vs WMH chronicity) terms for characterizing WMH trajectories. We also examined baseline WMH burden*amyloid burden at each CDR‐SB assessment (PiB A+ DVR threshold ∼17CL; linear and quadratic amyloid burden) relative to longitudinal CDR‐SB (Aim 2; n = 426), and tau PET SUVR’s mediating effects on WMH*amyloid associations with last CDR‐SB (Aim 3; n = 385; meta‐temporal (MTC) SUVR; florquinitau tracer). Result Mean(SD) age at baseline and last MRI were 65.36(8.49) and 67.26(8.14) years, respectively (sample details in Table 1). Comparisons of simple slopes (95%CI) show overlapping annualized WMH‐ slope estimates using age (0.028(0.022,0.034)) or chronicity (0.037(0.033,0.040)); in contrast, the WMH+ slopes are 2.26 times larger when aligned to WMH chronicity vs age (WMH+: 0.042(0.036,0.047), WMH‐: (0.095(0.091,0.10); Figure 1A&B). Whether biomarker “burden” was modeled as ±, estimated DVR, or chronicity (Figure 1C&D), significant interactions showed a synergistic effect of WMH and amyloid on accelerated CDR‐SB trajectory. Moderated mediation models revealed MTC‐tau accumulation partially mediated (∼65%) the synergistic effect of WMH and amyloid on last CDR‐SB (Figure 2). Conclusion WMH accumulation follows a predictable trajectory post‐onset and appears to exacerbate cognitive decline in those with amyloid pathology. Future analyses will further elucidate the complex relationships between vascular risk, amyloid, tau accumulation and cognitive decline.

Background Cognitive decline is often influenced by Alzheimer’s disease (AD) pathology (e.g., beta‐amyloid burden) and other pathology (e.g., vascular abnormalities). Amyloid onset and chronicity estimation using Sampled Iterative Local Approximation (SILA), enhanced our understanding of AD progression and its preclinical phase. This study has three aims: 1) estimate White Matter Hyperintensities (WMH) onset and chronicity; 2) assess whether baseline WMH chronicity/burden moderates the association between Aβ chronicity/burden and Clinical Dementia Rating‐sum of boxes (CDR‐SB) trajectories; 3) explore whether tau burden mediates WMH and amyloid associations with CDR‐SB. Method Participants were from the University of Wisconsin WRAP and ADRC cohorts and had completed at least one T1‐weighted and T2‐weighted FLAIR scans (Aim 1: n = 877, age 43‐93y). WMH values were aligned to a duration scale using SILA and WMH positivity threshold of ∼2.06 mL for WMH+ chronicity = 0. We compared mixed effects models with up to cubic time (age vs WMH chronicity) terms for characterizing WMH trajectories. We also examined baseline WMH burden*amyloid burden at each CDR‐SB assessment (PiB A+ DVR threshold ∼17CL; linear and quadratic amyloid burden) relative to longitudinal CDR‐SB (Aim 2; n = 426), and tau PET SUVR’s mediating effects on WMH*amyloid associations with last CDR‐SB (Aim 3; n = 385; meta‐temporal (MTC) SUVR; florquinitau tracer). Result Mean(SD) age at baseline and last MRI were 65.36(8.49) and 67.26(8.14) years, respectively (sample details in Table 1). Comparisons of simple slopes (95%CI) show overlapping annualized WMH‐ slope estimates using age (0.028(0.022,0.034)) or chronicity (0.037(0.033,0.040)); in contrast, the WMH+ slopes are 2.26 times larger when aligned to WMH chronicity vs age (WMH+: 0.042(0.036,0.047), WMH‐: (0.095(0.091,0.10); Figure 1A&B). Whether biomarker “burden” was modeled as ±, estimated DVR, or chronicity (Figure 1C&D), significant interactions showed a synergistic effect of WMH and amyloid on accelerated CDR‐SB trajectory. Moderated mediation models revealed MTC‐tau accumulation partially mediated (∼65%) the synergistic effect of WMH and amyloid on last CDR‐SB (Figure 2). Conclusion WMH accumulation follows a predictable trajectory post‐onset and appears to exacerbate cognitive decline in those with amyloid pathology. Future analyses will further elucidate the complex relationships between vascular risk, amyloid, tau accumulation and cognitive decline.