Odeyemi, Yewande E

Abstract Background Doxycycline is a frequently used alternative treatment for community-acquired pneumonia (CAP) in hospitalized patients; however, the evidence base is limited. We aimed to compare outcomes in CAP patients treated with doxycycline or azithromycin in combination with beta-lactams. Methods We evaluated patients with CAP admitted to Mayo Clinic hospitals from May 2018 to September 2022 and treated with either azithromycin or doxycycline in combination with a beta-lactam. Patients who received both antibiotics or were on either antibiotic as chronic therapy prior to admission were excluded. The primary outcome was mortality. Patients were 1-to-1 matched according to the presence of congestive heart failure, coronary artery disease, chronic obstructive pulmonary disease, human immunodeficiency virus infection, recent immunosuppressant use, and pneumonia severity index class. Results Among 8492 patients (5810 for azithromycin, 2682 for doxycycline), 2671 doxycycline-treated patients were matched with azithromycin-treated patients. In-hospital mortality was significantly lower in the azithromycin group than in the doxycycline group (odds ratio, 0.71; 95% confidence interval [CI], .56–.9), as was 90-day mortality (hazard ratio, 0.83; 95% CI, .73–.95). Patients on azithromycin also had significantly more hospital-free days (HFDs) than those on doxycycline (estimate, 1.37; 95% CI, .99–1.74). Conclusions Among hospitalized patients with CAP, azithromycin was associated with lower mortality and more HFDs compared with doxycycline in combination with beta-lactams. Randomized, controlled trials are needed to assess the effectiveness of each regimen and to compare their potential side effects.

We aimed to assess the prognostic role of the neutrophil/lymphocyte ratio (NLR) in community-acquired pneumonia (CAP) via a single-center retrospective cohort of hospitalized adult patients from 1/2009 to 12/2019. Patients were dichotomized into lower NLR (≤12) and higher NLR (>12). The primary outcome was mortality. ICU admission and hospital- and ICU-free days were secondary outcomes. The pneumonia severity index (PSI) and the NLR’s ability to predict outcomes was also tested. An NLR ≤12 was observed in 2513 (62.2%) patients and >12 in 1526 (37.8%). After adjusting for PSI, the NLR was not associated with hospital mortality (odds ratio [OR] 1.115; 95% confidence interval [CI] 0.774, 1.606; p = 0.559), but it was associated with a higher risk of ICU admission (OR 1.405; 95% CI 1.216, 1.624; p < 0.001). The PSI demonstrated acceptable discrimination for mortality (area under the receiver operating characteristic curve [AUC] 0.78; 95% CI 0.75, 0.82) which was not improved by adding the NLR (AUC 0.78; 95% CI 0.75, 0.82, p = 0.4476). The PSI’s performance in predicting ICU admission was also acceptable (AUC 0.75; 95% CI 0.74, 0.77) and improved by including the NLR (AUC 0.76, 95% CI 0.74, 0.77, p = 0.008), although with limited clinical significance. The NLR was not superior to the PSI for predicting mortality in hospitalized CAP patients.

BACKGROUND: Several markers of oxygenation are used as prognostic markers in acute hypoxemic respiratory failure. Real-world use is limited by the need for invasive measurements and unreliable availability in the electronic health record. A pragmatic, reliable, and accurate marker of acute hypoxemic respiratory failure is needed to facilitate epidemiologic studies, clinical trials, and shared decision-making with patients. F IO 2 is easily obtained at the bedside and from the electronic health record. The F IO 2 trajectory may be a valuable marker of recovery in patients with acute hypoxemic respiratory failure. METHODS: This was a historical cohort study of adult subjects admitted to an ICU with acute hypoxemic respiratory failure secondary to community-acquired pneumonia and/or ARDS. RESULTS: Our study included 2,670 subjects. F IO 2 and S pO 2 were consistently more available than was P aO 2 in the electronic health record: (F IO 2 vs S pO 2 vs P aO 2 : 100 vs 100 vs 72.8% on day 1, and 100 vs 99 vs 21% on day 5). A worsening F IO 2 trajectory was associated with reduced ventilator-free days. From days 2 to 5, every increase in F IO 2 by 10% from the previous day was associated with fewer ventilator-free days (on day 2: adjusted mean –1.25 [95% CI –1.45 to –1.05] d, P < .001). The S pO 2 /F IO 2 trajectory also provided prognostic information. On days 3 – 5, an increase in S pO 2 /F IO 2 from the previous day was associated with increased ventilator-free days (on day 3: adjusted mean 2.09 (95% CI 1.44–2.74) d; P < .001). S pO 2 /F IO 2 models did not add predictive information compared with models with F IO 2 alone (on day 2: adjusted F IO 2 vs S pO 2 /F IO 2 R2 0.122 vs 0.119; and on day 3: 0.153 vs 0.163). CONCLUSIONS: F IO 2 and S pO 2 /F IO 2 are pragmatic and readily available intermediate prognostic markers in acute hypoxic respiratory failure. The F IO 2 trajectory in the first 5 d of ICU admission provided important prognostic information (ventilator-free days). Although the S pO 2 /F IO 2 trajectory was also associated with ventilator-free days, it did not provide more information than the F IO 2 trajectory alone.

OBJECTIVES: Accurate identification of acute respiratory distress syndrome is essential for understanding its epidemiology, patterns of care, and outcomes. We aimed to design a computable phenotyping strategy to detect acute respiratory distress syndrome in electronic health records of critically ill patients. DESIGN: This is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition. SETTING: Twenty multidisciplinary ICUs in Mayo Clinic Health System. SUBJECTS: The phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019. INTERVENTIONS: The acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard. MEASUREMENTS AND MAIN RESULTS: Among the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao 2/Fio 2 ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao 2/Fio 2 ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao 2/Fio 2 ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians’ documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001). CONCLUSIONS: Our rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.

AbstractAcute respiratory distress syndrome (ARDS) remains an important clinical entity in the intensive care unit with a significant impact on morbidity and mortality. Effective therapeutic interventions are limited; thus current research focus has shifted from treatment to the prevention of this pulmonary syndrome. In recent decades, a decrease in the incidence of ARDS has been observed and this reduction is largely due to preventive strategies including safe lung ventilation practices, avoidance of iatrogenic exposures, and improvement in care of predisposing conditions such as sepsis and pneumonia. Early identification of at-risk patients, prompt treatment of predisposing conditions, and adoption of evidence-based best practice including restrictive transfusion strategies, conservative fluid management, avoidance of large tidal volume ventilation, and aspiration precaution practices are key preventive strategies with demonstrated benefits. There are currently no effective pharmacological preventive strategies for ARDS.