scholarly journals Beta-Lactams Toxicity in the Intensive Care Unit: An Underestimated Collateral Damage?

2021 ◽  
Vol 9 (7) ◽  
pp. 1505
Author(s):  
Claire Roger ◽  
Benjamin Louart
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Manifestations ◽  
Acute Interstitial Nephritis ◽  
Convulsive Status Epilepticus ◽  
Beta Lactam ◽  
Beta Lactams ◽  
Drug Induced ◽  
Icu Patients

Beta-lactams are the most commonly prescribed antimicrobials in intensive care unit (ICU) settings and remain one of the safest antimicrobials prescribed. However, the misdiagnosis of beta-lactam-related adverse events may alter ICU patient management and impact clinical outcomes. To describe the clinical manifestations, risk factors and beta-lactam-induced neurological and renal adverse effects in the ICU setting, we performed a comprehensive literature review via an electronic search on PubMed up to April 2021 to provide updated clinical data. Beta-lactam neurotoxicity occurs in 10–15% of ICU patients and may be responsible for a large panel of clinical manifestations, ranging from confusion, encephalopathy and hallucinations to myoclonus, convulsions and non-convulsive status epilepticus. Renal impairment, underlying brain abnormalities and advanced age have been recognized as the main risk factors for neurotoxicity. In ICU patients, trough concentrations above 22 mg/L for cefepime, 64 mg/L for meropenem, 125 mg/L for flucloxacillin and 360 mg/L for piperacillin (used without tazobactam) are associated with neurotoxicity in 50% of patients. Even though renal complications (especially severe complications, such as acute interstitial nephritis, renal damage associated with drug induced hemolytic anemia and renal obstruction by crystallization) remain rare, there is compelling evidence of increased nephrotoxicity using well-known nephrotoxic drugs such as vancomycin combined with beta-lactams. Treatment mainly relies on the discontinuation of the offending drug but in the near future, antimicrobial optimal dosing regimens should be defined, not only based on pharmacokinetics/pharmacodynamic (PK/PD) targets associated with clinical and microbiological efficacy, but also on PK/toxicodynamic targets. The use of dosing software may help to achieve these goals.

Risk Factors Associated With Resistance to Ciprofloxacin in Clinical Bacterial Isolates From Intensive Care Unit Patients

2007 ◽  
Vol 28 (3) ◽  
pp. 331-336 ◽  
Author(s):  
Phillip D. Levin ◽  
Robert A. Fowler ◽  
Cameron Guest ◽  
William J. Sibbald ◽  
Alex Kiss ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prior Treatment ◽  
Bacterial Isolates ◽  
Icu Patients ◽  
Gram Negative ◽  
Factors Associated ◽  
Icu Admission ◽  
Ciprofloxacin Resistance

Objective.To determine risk factors and outcomes associated with ciprofloxacin resistance in clinical bacterial isolates from intensive care unit (ICU) patients.Design.Prospective cohort study.Setting.Twenty-bed medical-surgical ICU in a Canadian tertiary care teaching hospital.Patients.All patients admitted to the ICU with a stay of at least 72 hours between January 1 and December 31, 2003.Methods.Prospective surveillance to determine patient comorbidities, use of medical devices, nosocomial infections, use of antimicrobials, and outcomes. Characteristics of patients with a ciprofloxacin-resistant gram-negative bacterial organism were compared with characteristics of patients without these pathogens.Results.Ciprofloxacin-resistant organisms were recovered from 20 (6%) of 338 ICU patients, representing 38 (21%) of 178 nonduplicate isolates of gram-negative bacilli. Forty-nine percent ofPseudomonas aeruginosaisolates and 29% ofEscherichia coliisolates were resistant to ciprofloxacin. In a multivariate analysis, independent risk factors associated with the recovery of a ciprofloxacin-resistant organism included duration of prior treatment with ciprofloxacin (relative risk [RR], 1.15 per day [95% confidence interval {CI}, 1.08-1.23];P< .001), duration of prior treatment with levofloxacin (RR, 1.39 per day [95% CI, 1.01-1.91];P= .04), and length of hospital stay prior to ICU admission (RR, 1.02 per day [95% CI, 1.01-1.03];P= .005). Neither ICU mortality (15% of patients with a ciprofloxacin-resistant isolate vs 23% of patients with a ciprofloxacin-susceptible isolate;P= .58 ) nor in-hospital mortality (30% vs 34%;P= .81 ) were statistically significantly associated with ciprofloxacin resistance.Conclusions.ICU patients are at risk of developing infections due to ciprofloxacin-resistant organisms. Variables associated with ciprofloxacin resistance include prior use of fluoroquinolones and duration of hospitalization prior to ICU admission. Recognition of these risk factors may influence antibiotic treatment decisions.

scholarly journals Staphylococcus aureusNasal Colonization and Subsequent Infection in Intensive Care Unit Patients: Does Methicillin Resistance Matter?

2010 ◽  
Vol 31 (6) ◽  
pp. 584-591 ◽  
Author(s):  
Hitoshi Honda ◽  
Melissa J. Krauss ◽  
Craig M. Coopersmith ◽  
Marin H. Kollef ◽  
Amy M. Richmond ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Confidence Interval ◽  
Methicillin Resistance ◽  
Hazard Ratio ◽  
Adjusted Hazard Ratio ◽  
Patient Specific ◽  
Icu Patients ◽  
Icu Admission

Background.Staphylococcus aureusis an important cause of infection in intensive care unit (ICU) patients. Colonization with methicillin-resistantS. aureus(MRSA) is a risk factor for subsequentS. aureusinfection. However, MRSA-colonized patients may have more comorbidities than methicillin-susceptibleS. aureus(MSSA)-colonized or noncolonized patients and therefore may be more susceptible to infection on that basis.Objective.To determine whether MRSA-colonized patients who are admitted to medical and surgical ICUs are more likely to develop anyS. aureusinfection in the ICU, compared with patients colonized with MSSA or not colonized withS. aureus,independent of predisposing patient risk factors.Design.Prospective cohort study.Setting.A 24-bed surgical ICU and a 19-bed medical ICU of a 1,252-bed, academic hospital.Patients.A total of 9,523 patients for whom nasal swab samples were cultured forS. aureusat ICU admission during the period from December 2002 through August 2007.Methods.Patients in the ICU for more than 48 hours were examined for an ICU-acquired S.aureusinfection, defined as development ofS. aureusinfection more than 48 hours after ICU admission.Results.S. aureuscolonization was present at admission for 1,433 (27.8%) of 5,161 patients (674 [47.0%] with MRSA and 759 [53.0%] with MSSA). An ICU-acquiredS. aureusinfection developed in 113 (2.19%) patients, of whom 75 (66.4%) had an infection due to MRSA. Risk factors associated with an ICU-acquiredS. aureusinfection included MRSA colonization at admission (adjusted hazard ratio, 4.70 [95% confidence interval, 3.07-7.21]) and MSSA colonization at admission (adjusted hazard ratio, 2.47 [95% confidence interval, 1.52-4.01]).Conclusion.ICU patients colonized with S.aureuswere at greater risk of developing aS. aureusinfection in the ICU. Even after adjusting for patient-specific risk factors, MRSA-colonized patients were more likely to developS. aureusinfection, compared with MSSA-colonized or noncolonized patients.

Abstract 15875: Prediction of Intensive Care Unit Admission Among Patients Hospitalized for COVID-19: The Intermountain Coronavirus ICU Risk Model (CORONA-ICU)

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Heidi T May ◽  
Joseph B Muhlestein ◽  
Benjamin D Horne ◽  
Kirk U Knowlton ◽  
Tami L Bair ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Beta Blockers ◽  
Risk Scores ◽  
Channel Blockers ◽  
Icu Patients

Background: Treatment for COVID-19 has created surges in hospitalizations, intensive care unit (ICU) admissions, and the need for advanced medical therapy and equipment, including ventilators. Identifying patients early on who are at risk for more intensive hospital resource use and poor outcomes could result in shorter hospital stays, lower costs, and improved outcomes. Therefore, we created clinical risk scores (CORONA-ICU and -ICU+) to predict ICU admission among patients hospitalized for COVID-19. Methods: Intermountain Healthcare patients who tested positive for SARS-CoV-2 and were hospitalized between March 4, 2020 and June 8, 2020 were studied. Derivation of CORONA-ICU risk score models used weightings of commonly collected risk factors and medicines. The primary outcome was admission to the ICU during hospitalization, and secondary outcomes included death and ventilator use. Results: A total of 451 patients were hospitalized for a SARS-CoV-2 positive infection, and 191 (42.4%) required admission to the ICU. Patients admitted to the ICU were older (58.2 vs. 53.6 years), more often male (61.3% vs. 48.5%), and had higher rates of hyperlipidemia, hypertension, diabetes, and peripheral arterial disease. ICU patients more often took ACE inhibitors, beta-blockers, calcium channel blockers, diuretics, and statins. Table 1 shows variables that were evaluated and included in the CORONA-ICU risk prediction models. Models adding medications (CORONA-ICU+) improved risk-prediction. Though not created to predict death and ventilator use, these models did so with high accuracy (Table 2). Conclusion: The CORONA-ICU and -ICU+ models, composed of commonly collected risk factors without or with medications, were shown to be highly predictive of ICU admissions, death, and ventilator use. These models can be efficiently derived and effectively identify high-risk patients who require more careful observation and increased use of advanced medical therapies.

Drug-Induced QTc-Interval Prolongation in the Intensive Care Unit: Incidence and Predictors

2010 ◽  
Vol 23 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Tien M. H. Ng ◽  
Keith M. Olsen ◽  
Megan A. McCartan ◽  
Susan E. Puumala ◽  
Katie M. Speidel ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Beta Blockers ◽  
Qtc Interval ◽  
Qtc Prolongation ◽  
Drug Induced ◽  
Icu Patients ◽  
End Point ◽  
Increased Risk ◽  
Qtc Interval Prolongation

There is a paucity of information regarding QTc prolongation in critically ill patients. A prospective observational study was conducted to assess the incidence and predictors of QTc prolongation associated with medications in intensive care unit (ICU) patients. Consecutive adult patients prescribed prespecified QTc-prolonging medications were assessed for development of the combined incidence of QTc >500 ms at anytime and QTc increase >60 ms above baseline. Over 3 months, 200 consecutive patients (63 ± 18 years; 52% female; 73% Caucasian; baseline QTc 447.3 ± 51.5 ms) were evaluated. The primary end point occurred in 48% of the patients (QTc >500 ms 40%, QTc increase >60 ms 29%). The majority of patients experienced a QTc >470 or 450 ms (60.5%). Mean increase in QTc at 48 hours was 20 ± 35 ms. Upon multivariate analysis, length of stay [odds ratio 1.30, 95% confidence interval (1.15, 1.47)] and baseline QTc [1.01 (1.01, 1.02)] were associated with an increased risk for the primary end point, while beta-blockers [0.41 (0.20, 0.81)] were associated with a risk reduction. In conclusion, increased risk of proarrhythmia, as assessed by QTc prolongation, occurs in the majority of ICU patients when prescribed medications with electrophysiologic properties. Increased vigilance is warranted. The possible protective effect of beta-blockers requires confirmation.

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Factors of cardiovascular risk in a cardiology intensive care unit

2009 ◽  
Vol 4 (1) ◽  
pp. 191 ◽  
Author(s):  
Etiane De Oliveira Freitas ◽  
Luiza De Oliveira Pitthan ◽  
Laura De Azevedo Guido ◽  
Graciele Fernanda da Costa Linch ◽  
Juliane Umann
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cardiovascular Risk ◽  
Coronary Angiography ◽  
Clinical Manifestations ◽  
Coronary Event ◽  
Study Data ◽  
Coronary Syndrome ◽  
Santa Maria

ABSTRACTObjectives: to identify the epidemiological profile, factors of cardiovascular risk, clinical manifestations, and coronary angiography findings in patients hospitalized in a Cardiology Intensive Care Unit, after a coronary event. Methods: this is a transversal study. Data were collected through a questionnaire. The criteria of inclusion were: diagnosis of the acute coronary syndrome, conduction of a coronary angiography, age >21 years old, both gender, conscious and able to interact, and with a minimum time hospitalized of 24 hours. In the analysis, the category variables were expressed with percentages or an absolute value, and the data on the average and standard deviation. The Ethics in Research of the Federal University of Santa Maria approved this study (0010.0.243.000-09). Results: the population was constituted by 30 patients, 63.33% male, the age average was 62.3 years. The most prevalent risk factors were SAH (83.3%) and obesity (63.3%). 40% of the patients were diagnosed with angina and coronary lesion of a vessel. They were submitted to PTCA 46.6% of the patients. Conclusions: knowing the characteristics of the patients in a Cardiology Intensive Care Unit enables the nursing team to plan and/or intensify the actions of education in health in order to change life habits of this population. Descriptors: risk factors; cardiovascular diseases; health education.  RESUMOObjetivos: identificar o perfil epidemiológico, fatores de risco cardiovascular, manifestações clínicas e achados cinecoronariográficos em pacientes internados em uma Unidade de Cardiologia Intensiva, após evento coronariano. Métodos: trata-se de estudo transversal cujos dados foram coletados por meio de questionário. Os critérios de inclusão foram: diagnóstico de síndrome coronariana aguda, realização de cineangiocoronariografia, idade >21 anos, ambos os sexos, com capacidade de interação e com tempo mínimo de 24 horas de internação. Para análise, as variáveis categóricas foram expressas com percentual ou valor absoluto, as contínuas como média e desvio padrão. O Comitê de Ética em Pesquisa da Universidade Federal de Santa Maria aprovou este estudo (0010.0.243.000-09). Resultados: a população constituiu-se de 30 pacientes, 63,3% do sexo masculino, média de idade de 62,3 anos. Os fatores de risco prevalentes foram a HAS (83,3%) e a obesidade (63,3%).40,0% dos pacientes tiveram diagnóstico de angina e lesão coronariana de um vaso. Foram submetidos à ACTP 46,6% dos pacientes. Conclusões: conhecer as características dos pacientes em uma Unidade de Cardiologia Intensiva, possibilita à equipe de enfermagem planejar e/ou intensificar ações de educação em saúde, voltada à mudança de hábitos de vida dessa população. Descritores: fatores de risco; doenças cardiovasculares; educação em saúde. RESUMENObjetivos: identificar el perfil epidemiológico, fatores de riesgo cardiovascular, manifestaciones clínicas y hallazgos cinecoronariográficos en pacientes internados en una Unidad de Cardiologia Intensiva, luego de evento coronariano.  Métodos: este es un estudio transversal. Los datos fueron recogidos a través de un cuestionario. Los critérios de inclusión fueron: diagnóstico de síndrome coronariana aguda, realización de cineangiocoronariografia, edad: mayores de 21 años, ambos sexos, que estuvieran concientes,  con capacidad de interacción y con un tiempo mínimo de 24 horas de internación. El análisis, las variables categóricas fueron expresadas con percentual o valor absoluto, las contínuas como media y desvio patrón. El Comité de Ética en Investigación de la Universidad Fedral de Santa Maria aprobó este estúdio (0010.0.243.000-09). Resultados: la población constituye por 30 pacientes, 63,33% sexo masculino. La media de edad fue de 62,3 años. Los fatores de riesgo que prevalecieron fueron las HAS(833%) y la obesidad (63,3%). 40% de los pacientes tuvo diagnóstico de angina y  lesión coronariana de un vaso. Fueron sometidos a ACTP 46,6% de los pacientes. Conclusiones: conocer las características de los pacientes en una Unidad de Cardiologia Intensiva, posibilita al equipo de enfermería planear y/o intensificar acciones  de educación en salud, con foco al cambio de hábitos de vida de dicha población.  Descriptores: factores de riesgo; enfermedades cardiovasculares; educación en salud.  

Impact of Early Reinitiation of Neuropsychiatric Medications on Agitation and Delirium in the Intensive Care Unit: A Retrospective Study

2020 ◽  
Vol 55 (1) ◽  
pp. 15-24
Author(s):  
Michaelia D. Cucci ◽  
Brittany S. Cunningham ◽  
Jaimini S. Patel ◽  
Alan T. Shimer ◽  
Dania I. Mofleh ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Primary Outcome ◽  
Tertiary Care ◽  
Group Versus ◽  
Care Hospital ◽  
Icu Patients ◽  
Independent Risk Factors ◽  
The Impact

Background: Approximately 17% of intensive care unit (ICU) patients are prescribed at least 1 home neuropsychiatric medication (NPM). When abruptly discontinued, withdrawal symptoms may occur manifesting as agitation or delirium in the ICU setting. Objective: To evaluate the impact of early reinitiation of NPMs. Methods: This was a retrospective, observational cohort of adult ICU patients in a tertiary care hospital. Patients were included if admitted to the ICU and prescribed a NPM prior to arrival. Study groups were based on the timing of reinitiation of at least 50% of NPMs: ≤72 hours (early group) versus >72 hours (late group). Results: The primary outcome was the proportion of patients with at least 1 agitation or delirium episode in the first 72 hours. Agitation and delirium were defined as at least 1 RASS assessment between +2 to +4 and a positive CAM-ICU assessment, respectively. A total of 300 patients were included, with 187 (62%) and 113 (38%) in the early and late groups, respectively. There was no difference in agitation or delirium (late 54 [48%] vs early 62 [33%]; adjusted odds ratio [aOR] = 1.5; 95% CI = 0.8-2.8; P = 0.193). Independent risk factors found to be associated with the primary outcome were restraints (aOR = 12.9; 95% CI = 6.9-24.0; P < 0.001) and benzodiazepines (BZDs; aOR = 2.0; 95% CI = 1.0-3.7; P = 0.038). Conclusions: After adjustment for baseline differences, there was no difference in agitation or delirium. Independent risk factors were restraint use and newly initiated BZDs.

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

BACKGROUND Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. OBJECTIVE The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. METHODS In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. RESULTS Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. CONCLUSIONS The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Analysis of Risk Factors for Early-onset Ventilator-associated Pneumonia in a Neurosurgical Intensive Care Unit: A Single-center Retrospective Study

2021 ◽  
Author(s):  
guojie teng ◽  
Ning Wang ◽  
Xiuhong Nie ◽  
Lin Zhang ◽  
Hongjun Liu
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Therapeutic Hypothermia ◽  
Early Onset ◽  
Clinical Manifestations ◽  
Older Age ◽  
Resistant Bacteria ◽  
Ventilator Associated Pneumonia ◽  
Hypothermia Treatment

Abstract Background:Ventilator-associated pneumonia (VAP) is a severe infection among patients in the neurosurgery intensive care unit (NICU).Methods:We retrospectively evaluated risk factors for early-onset ventilator-associated pneumonia (EOVAP) from January 2019 to December 2019 at a NICU. A total of 89 NICU patients who were intubated within 48 hours of onset and whose mechanical ventilation time was longer than 7 days were enrolled. The enrolled patients had no history of chronic lung disease and no clinical manifestations of infection before intubation. Clinical data of patients were recorded, and the incidence of and risk factors for EOVAP were analyzed. Patients were also grouped by age (≥65 vs. <65 years) and whether they had received hypothermia treatment or not.Results:Among 89 mechanically ventilated patients (49 men and 40 women; median age 60.1±14.3 years), 40 patients (44.9%) developed EOVAP in 7 days and 14 patients (15.7%) had multidrug resistant bacteria. Binary logistic regression analysis indicated that older age (≥65years) (odds ratio [OR]: 0.267, 95% confidence interval [CI]: 0.101-0.709, P=0.008) and therapeutic hypothermia (OR: 0.235, CI: 0.075-0.738, p=0.013) were independent predictors of EOVAP. Levels of peripheral blood leukocytes, neutrophils and platelets were lower in the therapeutic hypothermia group than those that did not receive hypothermia treatment.Conclusions:This study found that older age (≥65years) and therapeutic hypothermia were independently associated with the risk of EOVAP in NICU patients.

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