Non-ventilator health care-associated pneumonia (NV-HAP): NV-HAP Risk Factors

Vignari, Michelle

doi:10.1016/j.ajic.2020.03.010

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…In addition, four medical interventions (enteral tube feeding, use of sedatives, NSAIDs, and inhaled steroids) were significantly associated with a high risk of NV-HAP in older hospitalized patients. In particular, the use of feeding tubes increased oral colonization by pathogenic organisms, causing bacteria to migrate to the lungs via the tube [7,35]. In addition, this procedure can raise gastric volume and pressure, hence increasing the risk of gastric reflux and pulmonary aspiration [7,35].…”

Section: Discussionmentioning

confidence: 99%

“…In particular, the use of feeding tubes increased oral colonization by pathogenic organisms, causing bacteria to migrate to the lungs via the tube [7,35]. In addition, this procedure can raise gastric volume and pressure, hence increasing the risk of gastric reflux and pulmonary aspiration [7,35]. Several previous studies have also reported that the use of sedatives, NSAIDs, and inhaled steroids increases the risk of HAI.…”

Section: Discussionmentioning

confidence: 99%

“…Nonventilator hospitalacquired pneumonia has been linked to longer hospital stays, extensive use of intensive care services, higher medical costs, and a high risk of hospital mortality [5,6]. Several unmodifiable and modifiable risk factors, such as age (> 65 years), surgery, enteral feedings, lack of mobility, malnutrition, high blood glucose, inhibition of gastric acid, and central nervous system depressants, that predict the occurrence of NV-HAP have been previously profiled [7]. Evidence from a previous systematic review indicates that interventions such as improved oral hygiene, dysphagia screening, and early mobilization targeting potential modifiable risk factors might reduce the risk of NV-HAP [8].…”

Section: Introductionmentioning

confidence: 99%

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Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients

Chen

Wang

et al. 2022

BMC Pulm Med

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Background Currently, there is no effective tool for predicting the risk of nonventilator hospital-acquired pneumonia (NV-HAP) in older hospitalized patients. The current study aimed to develop and validate a simple nomogram and a dynamic web-based calculator for predicting the risk of NV-HAP among older hospitalized patients. Methods A retrospective evaluation was conducted on 15,420 consecutive older hospitalized patients admitted to a tertiary hospital in China between September 2017 and June 2020. The patients were randomly divided into training (n = 10,796) and validation (n = 4624) cohorts at a ratio of 7:3. Predictors of NV-HAP were screened using the least absolute shrinkage and selection operator method and multivariate logistic regression. The identified predictors were integrated to construct a nomogram using R software. Furthermore, the optimum cut-off value for the clinical application of the model was calculated using the Youden index. The concordance index (C-index), GiViTI calibration belts, and decision curve were analysed to validate the discrimination, calibration, and clinical utility of the model, respectively. Finally, a dynamic web-based calculator was developed to facilitate utilization of the nomogram. Results Predictors included in the nomogram were the Charlson comorbidity index, NRS-2002, enteral tube feeding, Barthel Index, use of sedatives, use of NSAIDs, use of inhaled steroids, and "time at risk". The C-index of the nomogram for the training and validation cohorts was 0.813 and 0.821, respectively. The 95% CI region of the GiViTI calibration belt in the training (P = 0.694) and validation (P = 0.614) cohorts did not cross the diagonal bisector line, suggesting that the prediction model had good discrimination and calibration. Furthermore, the optimal cut-off values for the training and validation cohorts were 1.58 and 1.74%, respectively. Analysis of the decision curve showed that the nomogram had good clinical value when the threshold likelihood was between 0 and 49%. Conclusion The developed nomogram can be used to predict the risk of NV-HAP among older hospitalized patients. It can, therefore, help healthcare providers initiate targeted medical interventions in a timely manner for high-risk groups.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients

Chen

Wang

et al. 2022

BMC Pulm Med

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“…There are several risk factors for NV-HAP including older age, 31 increased co-morbidities, 25 poor mobility 32 and chronic lung disease. 32 Consistent with previous work highlighting a greater risk of NV-HAP in older adults, 2,31 our cases with NV-HAP had a median age of 78.2 years (IQR 18.8) suggesting an older cohort. Our findings suggest that older adults may also be at a greater risk of NV-HAP in regional Australian hospitals.…”

Section: Cases With Nv-hap Total Admissionsmentioning

confidence: 99%

The incidence, characteristics and in‐hospital mortality of non‐ventilator‐associated hospital‐acquired pneumonia in regional Queensland: A retrospective descriptive study

Gardiner

Brown

Richardson

et al. 2022

Australian J Rural Health

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Objective:The aim of this study was to determine the incidence, characteristics and in-hospital mortality of non-ventilator-associated hospital-acquired pneumonia (NV-HAP) in a regional (Modified Monash Model 2) Australian hospital.Methods: All cases with NV-HAP were obtained from the Business Analysis and Decision Support (BADS) Unit between 1st January 2013 and 31st December 2018. Medical records were reviewed, and data pertaining to incidence, characteristics (age and gender), length of stay, co-morbidities (measured using the Charlson Comorbidity Index) and in-hospital mortality were extracted. Incidence rate was calculated as a proportion of NV-HAP cases per 1000 bed-days.Design: A retrospective study design was used to review all cases of NV-HAP between 1 January 2013 and 31 December 2018 at a single regional Australian hospital. Using the Modified Monash Model (MMM), our regional setting is classified as a regional centre (MMM-2).

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“…Nosocomial pneumonia, or HAP is the most prevalent cause of pneumonia among critical care unit patients and those on mechanical ventilation. As much as 47% of all nosocomial infections in the ICU are caused by HAP, which occurs when bacteria from the upper respiratory tract are aspirated into the lower respiratory tract and then ascend from the stomach (Vignari, 2020). Non-ventilator-associated pneumonia accounts for about 68% of all HAP, appear within 48 h after admission; the remainder is VAP.…”

Section: Introductionmentioning

confidence: 99%

Wireless sensor and wireless body area network assisted biosensor network for effective monitoring and prevention of non-ventilator hospital-acquired pneumonia

Baskar

2022

Front. Sustain. Cities

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Air pollution, climate change, and chemical exposure constitute the world's most significant environmental health concern, resulting in the early deaths of 6. 5 million people annually. Reducing child mortality from preventable causes, primarily pneumonia and other respiratory illnesses, would have contributed to the united nation's sustainable development goals (SDG). Some significant goals are sustainable cities, industry innovation, green and resilient infrastructure, good health, and well-being. Non-ventilator hospital-acquired pneumonia (NV-HAP) is a severe but preventable cause of morbidity and mortality in hospitalized patients. Despite being the most frequent and fatal hospital-acquired infection (HAI), NV-HAP is not tracked, documented, or avoided in most hospitals. The success of NV-HAP prevention and monitoring initiatives relies on reliable, up-to-date surveillance data. Surveillance offers the information needed to target, analyze, and quantify the efficacy of preventative activities by identifying patients at the highest risk for NV-HAP. However, pneumonia monitoring is complex due to the clinical criteria's subjective, imprecise, inconsistently recorded, and labor-intensive nature. Non-ventilator hospital-acquired pneumonia must be monitored and standardized, which demands cutting-edge technologies and the deployment of advanced sensors. In the framework of this research, initially, a wireless body area networks (WBANs) architecture has built by making use of wearable biosensors, and then real-time sensor data were uploaded to a cloud platform. Researchers have devised a wireless sensor network (WSN) to track volatile organic compounds (VOC) and other atmospheric characteristics in real time to curb the spread of NV-HAP. The ESP32 Internet of Things (IoT) and Raspberry Pi 4B graphical processing unit platforms host the finalized WBAN and WSN network. To reduce the mortality rate of NV-HAP, this research aims to investigate clinics' and hospitals' indoor and outdoor air quality. The developed biosensor-assisted IoT enabled framework is used in hospitals to keep tabs on the conditions of individual patient rooms, treatment areas, and critical care units in real time. The research found the suggested technique achieves better results than existing state-of-the-art methods regarding computing cost, communication overhead, storage cost, and energy utilization.

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Non-ventilator health care-associated pneumonia (NV-HAP): NV-HAP Risk Factors

Cited by 10 publications

References 15 publications

Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients

Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients

The incidence, characteristics and in‐hospital mortality of non‐ventilator‐associated hospital‐acquired pneumonia in regional Queensland: A retrospective descriptive study

Wireless sensor and wireless body area network assisted biosensor network for effective monitoring and prevention of non-ventilator hospital-acquired pneumonia

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