Establishment and validation of a nomogram to predict the in-hospital death risk of nosocomial infections in cancer patients

Impact of high-speed nanodroplets on various pathogenic bacterial cell walls

Although the development of disinfection technologies with novel mechanisms has stagnated, we demonstrate the bactericidal effects and mechanisms of high-speed nanodroplet generation technology. The first development of this technology in 2017 gushes out a water droplet of 10 nm in size at 50 m/s; however, the target surface does not become completely wet. Nanodroplets were exposed to biofilm models of Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Serratia marcescens. This phenomenon was verified when the nanodroplets collide with the surface of the bacteria at an impact pressure of ~75 MPa. S. aureus was exposed to nanodroplets for 30 seconds at 75 MPa, which exploded the bacterial body and completely sterilized. Eighteen MPa damaged the bacterial surface, causing peptidoglycan leakage. S. aureus was repaired and survives in this state. In contrast, in Gram-negative bacteria, nanodroplets with 18 MPa penetrated some biofilm-forming bacteria but did not hit all of them, and the viable count was not significantly reduced. Although all three bacterial species were completely sterilized at 75 MPa, the disinfectant effect was affected by the biomass of the biofilm formed. In summary, our findings prove that nanodroplets at 18 MPa on the bacterial surface were ineffective in killing bacteria, whereas at 75 MPa, all four bacterial species were completely sterilized. The disinfection mechanism involved a high-velocity collision of nanodroplets with the bacteria, physically destroying them. Our results showed that disinfection using this technology could be an innovative method that is completely different from existing disinfection techniques.

IMPORTANCE

Although existing disinfection techniques demonstrate bactericidal effects through chemical reactions, concerns regarding human toxicity and environmental contamination have been raised. To the best of our knowledge, this study is the first in the world to reveal that the use of this technology, with nanodroplets of less than 100 nm, can destroy and sterilize bacterial cells by colliding with biofilm-forming bacteria at 75 MPa. Furthermore, because this technology uses only water, it can solve the problems of human toxicity and environmental contamination caused by existing disinfection techniques. Because of its minimal water usage, it can be employed for sanitation worldwide without being limited to specific regions. Our report proposes an unprecedented physical disinfection approach that utilizes a high-speed nanodroplet generation technology.

A Nomogram of Predicting Healthcare-Associated Infections in Burned Children

BACKGROUND

Healthcare-associated infections (HAIs) are a common clinical concern associated with adverse prognosis and mortality in burned children. This study aimed to construct a predictive nomogram of the risk of HAIs in burned children.

METHODS

Children admitted to the burn unit of Wuhan Third Hospital between 2020 and 2022 were included. The univariate and multivariate logistic regression analyses were adopted to ascertain predictors of HAIs. A nomogram was developed to predict the HAI risk of each patient, with receiver operating characteristic curves and calibration curves being generated to assess its predictive ability. Furthermore, decision and impact curves were used to assess the clinical utility.

RESULTS

Of 1122 burned children, 61 (5.5%) patients experienced HAIs. The multivariate analysis indicated that total burn surface area, length of stay, surgery, central venous catheter use and urinary catheter use were the independent risk factors of HAIs. Using these variables, we developed a predictive nomogram of the occurrence of HAIs in burned children, and the internal validation results demonstrated good discrimination and calibration of the nomogram. The area under the curve values of the nomogram was 0.926 (95% CI, 0.896-0.957). The calibration curve showed high consistency between the actual and predicted HAIs. The decision and impact curve indicated that the nomogram was of good clinical utility and more credible net clinical benefits in predicting HAIs.

CONCLUSIONS

The present study constructed a nomogram for predicting the risk of HAIs in burned children. This nomogram may strengthen the effective screening of patients at high risk of HAIs.

Efficacy of Random Forest Models in Predicting Multidrug-Resistant Gram-Negative Bacterial Nosocomial Infections Compared to Traditional Logistic Regression Models

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application.

Development and validation of a clinical prediction model for early ventilator weaning in post-cardiac surgery

Background

Weaning patients from mechanical ventilation is a critical clinical challenge post cardiac surgery. The effective liberation of patients from the ventilator significantly improves their recovery and survival rates. This study aimed to develop and validate a clinical prediction model to evaluate the likelihood of successful extubation in post-cardiac surgery patients.

Method

A predictive nomogram was constructed for extubation success in individual patients, and receiver operating characteristic (ROC) and calibration curves were generated to assess its predictive capability. The superior performance of the model was confirmed using Delong's test in the ROC analysis. A decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram.

Results

Among 270 adults included in our study, 107 (28.84%) experienced delayed extubation. A predictive nomogram system was derived based on five identified risk factors, including the proportion of male patients, EuroSCORE II, operation time, pump time, bleeding during operation, and brain natriuretic peptide (BNP) level. Based on the predictive system, five independent predictors were used to construct a full nomogram. The area under the curve values of the nomogram were 0.880 and 0.753 for the training and validation cohorts, respectively. The DCA and clinical impact curves showed good clinical utility of this model.

Conclusion

Delayed extubation and weaning failure, common and potentially hazardous complications following cardiac surgery, vary in timing based on factors such as sex, EuroSCORE II, pump duration, bleeding, and postoperative BNP reduction. The nomogram developed and validated in this study can accurately predict when extubation should occur in these patients. This tool is vital for assessing risks on an individual basis and making well-informed clinical decisions.

Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database

PURPOSE

The present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning.

METHODS

We used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve.

RESULTS

A total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance.

CONCLUSION

We developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

Background

Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China.

Methods

Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate.

Results

A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model.

Conclusions

The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults.

A nomogram to predict nosocomial infection in patients on venoarterial extracorporeal membrane oxygenation after cardiac surgery

INTRODUCTION

After cardiac surgery, patients on venoarterial extracorporeal membrane oxygenation (VA-ECMO) have a higher risk of nosocomial infection in the intensive care unit (ICU). We aimed to establish an intuitive nomogram to predict the probability of nosocomial infection in patients on VA-ECMO after cardiac surgery.

METHODS

We included patients on VA-ECMO after cardiac surgery between January 2011 and December 2020 at a single center. We developed a nomogram based on independent predictors identified using univariate and multivariate logistic regression analyses. We selected the optimal model and assessed its performance through internal validation and decision-curve analyses.

RESULTS

Overall, 503 patients were included; 363 and 140 patients were randomly divided into development and validation sets, respectively. Independent predictors derived from the development set to predict nosocomial infection included older age, white blood cell (WBC) count abnormality, ECMO environment in the ICU, and mechanical ventilation (MV) duration, which were entered into the model to create the nomogram. The model showed good discrimination, with areas under the curve (95% confidence interval) of 0.743 (0.692-0.794) in the development set and 0.732 (0.643-0.820) in the validation set. The optimal cutoff probability of the model was 0.457 in the development set (sensitivity, 0.683; specificity, 0.719). The model showed qualified calibration in both the development and validation sets (Hosmer-Lemeshow test, p > .05). The threshold probabilities ranged from 0.20 to 0.70.

CONCLUSIONS

For adult patients receiving VA-ECMO treatment after cardiac surgery, a nomogram-monitoring tool could be used in clinical practice to identify patients with high-risk nosocomial infections and provide an early warning.

Evaluation of an established oncology triage hotline in a model of emergency department avoidance: assessing the UKONS triage tool and call outcomes

PURPOSE

In response to a demonstrable need for 24/7, specialist oncology advice for patients undergoing systemic anti-cancer therapy, many healthcare institutions have adopted a telephone triage (TT) service. This is true of the Clatterbridge Cancer Centre which uses the UKONS framework to guide its decisions. This study aims to investigate the utilisation and outcomes of this TT service, with a focus on the most unwell call outcomes and factors leading to referrals to accident and emergency departments that could be mitigated with service development and modifications.

METHODS

A retrospective evaluation study was conducted of calls occurring between 1st September 2021 and 31st August 2022. A descriptive analysis of call UKONS grading, triage outcome and primary complaint was performed.

RESULTS

The TT hotline received 23,766 calls of which only 9066 were for clinical advice. Of the clinical calls, 45.2% were UKONS red. The majority of red calls 53.3% were directed to AED. The proportion of red calls going to AED changed drastically depending on the timing of call and the corresponding services available at those times, with 38.3% of reds being sent to AED in hours but 72.3% out of hours. The profile of complaints also showed significant differences in hours versus out of hours.

CONCLUSION

Significant use of the hotline supports a genuine demand for oncology TT services. In order to reduce referrals to AED, this study supports the creation of alternative destinations of emergency care, especially out of hours.

Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province

OBJECTIVE

To establish the exponential smoothing prediction model and SARIMA model to predict the number of inpatients in a third-class hospital in Zhejiang Province, and evaluate the prediction effect of the two models, and select the best number prediction model.

METHODS

The data of hospital admissions from January 2019 to September 2022 were selected to establish the exponential smoothing prediction model and the SARIMA model respectively. Then compare the fitting parameters of different models: R2_adjusted, R2, Root Mean Square Error (RMSE)、Mean Absolute Percentage Error (MAPE)、Mean Absolute Error(MAE) and standardized BIC to select the best model. Finally, the established model was used to predict the number of hospital admissions from October to December 2022, and the prediction effect of the average relative error judgment model was compared.

RESULTS

The best fitting exponential smoothing prediction model was Winters Addition model, whose R2_adjusted was 0.533, R2 was 0.817, MAPE was 6.133, MAE was 447.341. The best SARIMA model is SARIMA(2,2,2)(0,1,1)12 model, whose R2_adjusted is 0.449, R2 is 0.199, MAPE is 8.240, MAE is 718.965. The Winters addition model and SARIMA(2,2,2)(0,1,1)12 model were used to predict the number of hospital admissions in October-December 2022, respectively. The results showed that the average relative error was 0.038 and 0.015, respectively. The SARIMA(2,2,2)(0,1,1)12 model had a good prediction effect.

CONCLUSION

Both models can better fit the number of admissions, and SARIMA model has better prediction effect.

A nomogram for predicting hospital mortality of critical ill patients with sepsis and cancer: a retrospective cohort study based on MIMIC-IV and eICU-CRD

OBJECTIVE

Sepsis remains a high cause of death, particularly in immunocompromised patients with cancer. The study was to develop a model to predict hospital mortality of septic patients with cancer in intensive care unit (ICU).

DESIGN

Retrospective observational study.

SETTING

Medical Information Mart for Intensive Care IV (MIMIC IV) and eICU Collaborative Research Database (eICU-CRD).

PARTICIPANTS

A total of 3796 patients in MIMIC IV and 549 patients in eICU-CRD were included.

PRIMARY OUTCOME MEASURES

The model was developed based on MIMIC IV. The internal validation and external validation were based on MIMIC IV and eICU-CRD, respectively. Candidate factors were processed with the least absolute shrinkage and selection operator regression and cross-validation. Hospital mortality was predicted by the multivariable logistical regression and visualised by the nomogram. The model was assessed by the area under the curve (AUC), calibration curve and decision curve analysis curve.

RESULTS

The model exhibited favourable discrimination (AUC: 0.726 (95% CI: 0.709 to 0.744) and 0.756 (95% CI: 0.712 to 0.801)) in the internal and external validation sets, respectively, and better calibration capacity than Acute Physiology and Chronic Health Evaluation IV in external validation.

CONCLUSIONS

Despite that the predicted model was based on a retrospective study, it may also be helpful to predict the hospital morality of patients with solid cancer and sepsis.

Establishment of a risk classifier to predict the in-hospital death risk of nosocomial fungal infections in cancer patients

BACKGROUND

Patients with malignancy are at a higher risk of developing nosocomial infections. However, limited studies investigated the clinical features and prognostic factors of nosocomial infections due to fungi in cancer patients. Herein, this study aims to investigate the clinical characteristics of in-hospital fungal infections and develop a nomogram to predict the risk of in-hospital death during fungal infection of hospitalized cancer patients.

METHODS

This retrospective observational study enrolled cancer patients who experienced in-hospital fungal infections between September 2013 and September 2021. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of in-hospital mortality. Variables demonstrating significant statistical differences in the multivariate analysis were utilized to construct a nomogram for personalized prediction of in-hospital death risk associated with nosocomial fungal infections. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.

RESULTS

A total of 216 participants were included in the study, of which 57 experienced in-hospital death. C.albicans was identified as the most prevalent fungal species (68.0%). Respiratory infection accounted for the highest proportion of fungal infections (59.0%), followed by intra-abdominal infection (8.8%). The multivariate regression analysis revealed that Eastern Cooperative Oncology Group Performance Status (ECOG-PS) 3-4 (odds ratio [OR] = 6.08, 95% confidence interval [CI]: 2.04-18.12), pulmonary metastases (OR = 2.76, 95%CI: 1.11-6.85), thrombocytopenia (OR = 2.58, 95%CI: 1.21-5.47), hypoalbuminemia (OR = 2.44, 95%CI: 1.22-4.90), and mechanical ventilation (OR = 2.64, 95%CI: 1.03-6.73) were independent risk factors of in-hospital death. A nomogram based on the identified risk factors was developed to predict the individual probability of in-hospital mortality. The nomogram demonstrated satisfactory performance in terms of classification ability (area under the curve [AUC]: 0.759), calibration ability, and net clinical benefit.

CONCLUSIONS

Fungi-related nosocomial infections are prevalent among cancer patients and are associated with poor prognosis. The constructed nomogram provides an invaluable tool for oncologists, enabling them to make timely and informed clinical decisions that offer substantial net clinical benefit to patients.

Establishment of a risk prediction model for multidrug-resistant bacteria in deceased organ donors: a retrospective cohort study in China

Background

Multidrug resistance in bacteria is a serious problem in organ transplantations. This study aimed to identify risk factors and establish a predictive model for screening deceased organ donors for multidrug-resistant (MDR) bacteria.

Methods

A retrospective cohort study was conducted at the First Affiliated Hospital of Zhejiang University School of Medicine from July 1, 2019 to December 31, 2022. The univariate and multivariate logistic regression analysis was used to determine independent risk factors associated with MDR bacteria in organ donors. A nomogram was established based on these risk factors. A calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to estimated the model.

Results

In 164 organ donors, the incidence of MDR bacteria in culture was 29.9%. The duration of antibiotic use ≥3 days (odds ratio [OR] 3.78, 95% confidence interval [CI] 1.62-8.81, p=0.002), length of intensive care unit (ICU) stay per day(OR 1.06, 95% CI 1.02-1.11, p=0.005) and neurosurgery (OR 3.31, 95% CI 1.44-7.58, p=0.005) were significant independent predictive factors for MDR bacteria. The nomogram constructed using these three predictors displayed good predictive ability, with an area under the ROC curve value of 0.79. The calibration curve showed a high consistency between the probabilities and observed values. DCA also revealed the potential clinical usefulness of this nomogram.

Conclusions

The duration of antibiotic use ≥3 days, length of ICU stay and neurosurgery are independent risk factors for MDR bacteria in organ donors. The nomogram can be used to monitor MDR bacteria acquisition risk in organ donors.

A novel risk classifier to predict the in-hospital death risk of nosocomial infections in elderly cancer patients

Background

Elderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population.

Methods

Retrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. The performance of the nomogram was evaluated using receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

A total of 569 elderly cancer patients were included in this study, and the estimated in-hospital mortality rate was 13.9%. The results of multivariate logistic regression analysis showed that ECOG-PS (odds ratio [OR]: 4.41, 95% confidence interval [CI]: 1.95-9.99), surgery type (OR: 0.18, 95%CI: 0.04-0.85), septic shock (OR: 5.92, 95%CI: 2.43-14.44), length of antibiotics treatment (OR: 0.21, 95%CI: 0.09-0.50), and prognostic nutritional index (PNI) (OR: 0.14, 95%CI: 0.06-0.33) were independent predictors of the in-hospital death risk of nosocomial infections in elderly cancer patients. A nomogram was then constructed to achieve personalized in-hospital death risk prediction. ROC curves yield excellent discrimination ability in the training (area under the curve [AUC]=0.882) and validation (AUC=0.825) cohorts. Additionally, the nomogram showed good calibration ability and net clinical benefit in both cohorts.

Conclusion

Nosocomial infections are a common and potentially fatal complication in elderly cancer patients. Clinical characteristics and infection types can vary among different age groups. The risk classifier developed in this study could accurately predict the in-hospital death risk for these patients, providing an important tool for personalized risk assessment and clinical decision-making.

Development and validation of nomograms for predicting the risk probability of carbapenem resistance and 28-day all-cause mortality in gram-negative bacteremia among patients with hematological diseases

Objectives

Gram-negative bacteria (GNB) bloodstream infections (BSIs) are the most widespread and serious complications in hospitalized patients with hematological diseases. The emergence and prevalence of carbapenem-resistant (CR) pathogens has developed into a considerable challenge in clinical practice. Currently, nomograms have been extensively applied in the field of medicine to facilitate clinical diagnosis and treatment. The purpose of this study was to explore risk indicators predicting mortality and carbapenem resistance in hematological (HM) patients with GNB BSI and to construct two nomograms to achieve personalized prediction.

Methods

A single-center retrospective case-control study enrolled 244 hospitalized HM patients with GNB-BSI from January 2015 to December 2019. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were conducted to select potential characteristic predictors of plotting nomograms. Subsequently, to evaluate the prediction performance of the models, the prediction models were internally validated using the bootstrap approach (resampling = 1000) and 10-fold cross validation.

Results

Of all 244 eligible patients with BSI attributed to GNB in this study, 77 (31.6%) were resistant to carbapenems. The rate of carbapenem resistance exhibited a growing tendency year by year, from 20.4% in 2015 to 42.6% in 2019 (p = 0.004). The carbapenem resistance nomogram constructed with the parameters of hypoproteinemia, duration of neutropenia ≥ 6 days, previous exposure to carbapenems, and previous exposure to cephalosporin/β-lactamase inhibitors indicated a favorable discrimination ability with a modified concordance index (C-index) of 0.788 and 0.781 in both the bootstrapping and 10-fold cross validation procedures. The 28-day all-cause mortality was 28.3% (68/240). The prognosis nomogram plotted with the variables of hypoproteinemia, septic shock, isolation of CR-GNB, and the incomplete remission status of underlying diseases showed a superior discriminative ability of poorer clinical prognosis. The modified C-index of the prognosis nomogram was 0.873 with bootstrapping and 0.887 with 10-fold cross validation. The decision curve analysis (DCA) for two nomogram models both demonstrated better clinical practicality.

Conclusions

For clinicians, nomogram models were effective individualized risk prediction tools to facilitate the early identification of HM patients with GNB BSI at high risk of mortality and carbapenem resistance.

Relation of Malnutrition and Nosocomical Infections in Cancer Patients in Hospital: An Observational Study

Aim

To investigate the relation between malnutrition and nosocomial infections (NI) in hospitalized cancer patients.

Methods

This observational, cross-sectional, noninterventional, descriptive study was conducted in a 500-bed university hospital in Valencia (Spain). Adult cancer patients admitted to the oncology ward were consecutively enrolled regardless of their nutritional status between November 2019 and March 2020. Patients were nutritionally assessed 24 to 48 hours after admission. Body weight, height and BMI, body composition through measurement of bioelectrical impedance analysis (BIA), and muscle strength and functionality using hand grip strength (HGS) were prospectively collected. The diagnosis of malnutrition and sarcopenia was assessed using the Global Leadership Initiative on Malnutrition (GLIM) criteria and the European Working Group on Sarcopenia in Older People (EWGSOP) criteria, respectively. Patients were followed up during their hospital stay or outpatient oncology visits to identify possible NI.

Results

A total of 107 patients were included in this study (mean age 66 years; 66.4% were men). The most frequent reason for admission was cancer treatment (19.6%), followed by infections (18.7%) and digestive tract symptoms (18.7%). Overall, 77.5% (83/107) of the patients were malnourished at admission according to the GLIM criteria, while 52.3% (56/107) were sarcopenic. Nosocomial infections (NI) were significantly more frequent in malnourished (52.1%; 25/48) and severely malnourished (42.1%; 8/19) patients, compared with well-nourished patients without malnutrition (25%; 10/40; p=0.035). The mean length of hospital stay was 13.9 days, significantly longer in patients with an NI compared to those without infections (18.6 vs. 10.8 days, p < 0.024).

Conclusion

This study evidenced the need to implement a routine protocol for the nutritional assessment and support of cancer patients at risk of malnutrition and sarcopenia to reduce the risk of NI during their hospital stay.