Microorganisms and clinical outcomes of early- and late-onset ventilator-associated pneumonia at Srinagarind Hospital, a tertiary center in Northeastern Thailand

Arayasukawat, Pavarit; So-ngern, Apichart; Reechaipichitkul, Wipa; Chumpangern, Worawat; Arunsurat, Itthiphat; Ratanawatkul, Pailin; Chuennok, Wanna

doi:10.1186/s12890-021-01415-8

Cited by 16 publications

(11 citation statements)

References 33 publications

(43 reference statements)

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“…Pathogens from this study did not differ between EOVAP and LOVAP. The results of our study were similar to other tertiary centers in India and Thailand ( Arayasukawat et al., 2012 ; Jakribettu et al., 2016 ). In addition, we observed 6 cases of fungal co-infections caused by Candida species.…”

Section: Discussionsupporting

confidence: 90%

Secondary Bacterial Infection and Clinical Characteristics in Patients With COVID-19 Admitted to Two Intensive Care Units of an Academic Hospital in Iran During the First Wave of the Pandemic

Pourajam

Kalantari

Talebzadeh

et al. 2022

Front. Cell. Infect. Microbiol.

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Data on the prevalence of bacterial co-infections and secondary infection among adults with COVID-19 admitted to the intensive care unit (ICU) are rare. We aimed to determine the frequency of secondary bacterial infection, antibiotic use, and clinical characteristics in patients admitted to the ICU with severe SARS-CoV-2 pneumonia. This was a retrospective cohort study of adults with severe COVID-19 admitted to two ICUs from March 6 to September 7, 2020 in an academic medical center in Isfahan, Iran. To detect COVID-19, reverse transcription real-time polymerase chain reaction was performed and also typical pattern of CT scan was used for the diagnosis of COVID-19. Data collection included the age, gender, main symptoms, history of underlying disease, demographics, hospital stay, outcomes, and antibiotic regimen of the patient. Antimicrobial susceptibility testing was carried out according to the CLSI guidelines. During the study period, 553 patients were referred to the both ICUs for COVID-19 with severe pneumonia. Secondary bacterial infection was detected in 65 (11.9%) patients. The median age was 69.4 (range 21–95) years; 42 (63.6%) were men. Notably, 100% (n = 65) of the patients with superinfection were prescribed empirical antibiotics before first positive culture, predominantly meropenem (86.2%) with a median duration of 12 (range 2–32) days and levofloxacin (73.8%) with a median duration of nine (range 2–24) days. Most prevalent causative agents for secondary bacterial infection were Klebsiella pneumoniae (n = 44) and Acinetobacter baumannii (n = 33). Most patients with secondary bacterial infection showed extensive drug-resistance. The mortality among patients who acquired superinfections was 83% against an overall mortality of 38.1% in total admitted COVID-19 patients. We found a high prevalence of carbapenem-resistant Gram-negative bacilli in COVID-19 patients admitted to our ICUs, with a high proportion of K. pneumoniae followed by A. baumannii. These findings emphasize the importance of implementation of strict infection control measures and highlight the role of antimicrobial stewardship during a pandemic.

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Section: Discussionsupporting

confidence: 90%

Secondary Bacterial Infection and Clinical Characteristics in Patients With COVID-19 Admitted to Two Intensive Care Units of an Academic Hospital in Iran During the First Wave of the Pandemic

Pourajam

Kalantari

Talebzadeh

et al. 2022

Front. Cell. Infect. Microbiol.

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“…Previous studies have suggested that both patients with hospital-acquired pneumonia and those with VAP had a large burden of MDR pathogens [30], which were mainly associated with late-onset VAP [31,32]. Our results confirmed these reports.…”

Section: Discussionsupporting

confidence: 91%

Risk Factors and Protective Factors against Ventilator-Associated Pneumonia—A Single-Center Mixed Prospective and Retrospective Cohort Study

Pawlik

Tomaszek

Mazurek

et al. 2022

JPM

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Introduction: Understanding the factors associated with the development of ventilator-associated pneumonia (VAP) in critically ill patients in the intensive care unit (ICU) will allow for better prevention and control of VAP. The aim of the study was to evaluate the incidence of VAP, as well as to determine risk factors and protective factors against VAP. Design: Mixed prospective and retrospective cohort study. Methods: The cohort involved 371 critically ill patients who received standard interventions to prevent VAP. Additionally, patients in the prospective cohort were provided with continuous automatic pressure control in tapered cuffs of endotracheal or tracheostomy tubes and continuous automatic subglottic secretion suction. Logistic regression was used to assess factors affecting VAP. Results: 52 (14%) patients developed VAP, and the incidence density of VAP per 1000 ventilator days was 9.7. The median days to onset of VAP was 7 [4; 13]. Early and late onset VAP was 6.2% and 7.8%, respectively. According to multivariable logistic regression analysis, tracheotomy (OR = 1.6; CI 95%: 1.1 to 2.31), multidrug-resistant bacteria isolated in the culture of lower respiratory secretions (OR = 2.73; Cl 95%: 1.83 to 4.07) and ICU length of stay >5 days (OR = 3.32; Cl 95%: 1.53 to 7.19) were positively correlated with VAP, while continuous control of cuff pressure and subglottic secretion suction used together were negatively correlated with VAP (OR = 0.61; Cl 95%: 0.43 to 0.87). Conclusions: Tracheotomy, multidrug-resistant bacteria, and ICU length of stay >5 days were independent risk factors of VAP, whereas continuous control of cuff pressure and subglottic secretion suction used together were protective factors against VAP.

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“…8 Prevention of VAP prior to its occurrence may also encourage antibiotic stewardship, which is imperative to reduce mortality resulting from VAP caused by multidrug resistant bacteria and inappropriate administration of antibiotics. [25][26][27] Our study advances the field in several key ways. In our prior research, we developed an ML model that utilizes readily available EHR data to make predictions of VAP at the 48 th hour of intubation, the earliest time at which VAP can be diagnosed.…”

Section: Discussionmentioning

confidence: 99%

Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm

Faucher¹,

Chetty²,

Shokouhi³

et al. 2022

Preprint

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(1) Background: Ventilator-associated pneumonia (VAP) causes high mortality among patients with respiratory disease and imposes major burdens on healthcare infrastructure. Models that use electronic health record data to predict the onset of VAP may spur earlier treatment and improve patient outcomes. We developed and studied the performance of interpretable machine learning (ML) models that predict the onset of VAP from electronic health records (EHRs); (2) Methods: We trained Logistic Regression (LR), full feature Explainable Boosting Machine (fEBM), and eXtreme Gradient Boosting (XGBoost) ML models on data from the MIMIC- III (v1.3) database. Model performance was measured by area under the receiver operating characteristic curves (AUCs). We trained a minimal-feature EBM model (mEBM) with features derived from white blood cell (WBC) counts, duration of ventilation, and Glasgow Coma Scale (GCS). Finally, model robustness was evaluated on randomly sparsified EHR datasets; (3) Results: The fEBM model outperformed the XGBoost and LR models at 24 hours post-intubation. The mEBM model maintained an AUC of 0.893. The fEBM model performance remained robust on sparsified datasets; (4) Conclusions: Our novel interpretable ML algorithm reliably predicts the onset of VAP in intubated patients. Integration of this EBM-based model into clinical practice may enable clinicians to better anticipate and prevent VAP.

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Microorganisms and clinical outcomes of early- and late-onset ventilator-associated pneumonia at Srinagarind Hospital, a tertiary center in Northeastern Thailand

Cited by 16 publications

References 33 publications

Secondary Bacterial Infection and Clinical Characteristics in Patients With COVID-19 Admitted to Two Intensive Care Units of an Academic Hospital in Iran During the First Wave of the Pandemic

Secondary Bacterial Infection and Clinical Characteristics in Patients With COVID-19 Admitted to Two Intensive Care Units of an Academic Hospital in Iran During the First Wave of the Pandemic

Risk Factors and Protective Factors against Ventilator-Associated Pneumonia—A Single-Center Mixed Prospective and Retrospective Cohort Study

Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm

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