Establishment and Validation of a Nomogram to Predict Hospital-Acquired Infection in Elderly Patients After Cardiac Surgery

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.

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.

Development of a nomogram for predicting nosocomial infections among patients after cardiac valve replacement surgery

AIMS AND OBJECTIVES

To construct a predictive nomogram of the risk of nosocomial infections among patients after cardiac valve replacement surgery.

BACKGROUND

Nosocomial infections are a standout challenge that worsens the prognosis of patients after valve replacement surgery. However, studies on the nomogram of nosocomial infections in these patients have remained scarce.

DESIGN

A retrospective cohort study.

METHODS

Patients (n = 720) following valve replacement surgery from 2018 to 2019 were selected. LASSO regression and multivariate logistic regression were utilised to ascertain predictors of nosocomial infections. The predictive performance of the nomogram was appraised by calibration and discrimination. Decision and impact curves were used to assess the clinical utility. Internal validation was implemented via 1000 bootstrap samples to mitigate overfitting. TRIPOD guidelines were used in this study.

RESULTS

One hundred and fifty one patients (20.97%) experienced nosocomial infections following valve replacement surgery. Heart failure, preoperative anaemia, valve material, American Society of Anesthesiologists score ≥ IV, prolonged duration of surgery, duration of mechanical ventilation ≥ 24 h and indwelling nasogastric tube were predictors of nosocomial infections. Using these variables, we developed a predictive nomogram of the occurrence of nosocomial infections and the internal validation results demonstrated good discrimination and calibration of the nomogram. The clinical decision and impact curve revealed significant clinical utility.

CONCLUSIONS

The present study constructed a nomogram for predicting the risk of nosocomial infections in patients following cardiac valve replacement surgery. This nomogram may strengthen the effective screening of patients at high risk of nosocomial infections.

RELEVANCE TO CLINICAL PRACTICE

This risk warning tool can assist clinical staff in making decisions and providing individualised infection control measures for patients, which has a significant reference value for clinical practice.

NO PATIENT OR PUBLIC CONTRIBUTION

The data for this study were obtained from the hospital database, and the entire process of the study did not involve patient participation.

A web-based calculator combining Geriatric Nutritional Risk Index (GNRI) and Tilburg Frailty Indicator (TFI) predicts postoperative complications among young elderly patients with gastric cancer

AIM

Nutritional status and frailty are significant indicators reflecting physiological reserve. We sought to establish and validate a web-based calculator containing the Geriatric Nutritional Risk Index (GNRI) and the Tilburg Frailty Indicator (TFI) together with general clinical information to predict total complications among elderly patients with gastric cancer.

METHODS

This was a prospective cohort study of 582 elderly patients with gastric cancer in a tertiary hospital in China. Nutritional status and frailty were assessed by the GNRI and the TFI, respectively. The nomogram was built and further converted into a web-based calculator. The receiver operating characteristic analysis was performed to evaluate the discrimination of the nomogram. Calibration was assessed using the calibration curve and Hosmer-Lemeshow test via the bootstrap resampling procedure. The decision curve analyses (DCAs) were employed to quantify the net benefits of a certain threshold probability for assessing the clinical values.

RESULTS

The GNRI (odds ratio [OR], 0.921; 95% confidence interval [CI], 0.895-0.949; P < 0.001), the TFI (OR, 1.243; 95% CI, 1.113-1.386; P < 0.001), surgical approach (OR, 1.913; 95% CI, 1.073-3.408; P = 0.028) and comorbidity (OR = 1.599, 95%CI = 1.028-2.486, P = 0.037) were independently associated with total complications. The nomogram demonstrated good discrimination (area under the receiver operating characteristic curve: training cohort, 0.735; validation cohort, 0.777) and calibration (P = 0.135). The DCA curves of the nomogram also showed good positive net benefits.

CONCLUSIONS

The web-based calculator incorporating the GNRI, the TFI, surgical approach, and comorbidity could successfully predict total complications among elderly patients with gastric cancer with good accuracy in a convenient manner. Geriatr Gerontol Int 2023; 23: 205-212.

Rates and Determinants of Hospital-Acquired Infection among ICU Patients Undergoing Cardiac Surgery in Developing Countries: Results from EMERGENCY'NGO's Hospital in Sudan

Introduction. Knowledge of local and regional antimicrobial resistance (AMR) is crucial in clinical decision-making, especially with critically ill patients. The aim of this study was to investigate the rate and pattern of infections in valvular heart disease patients admitted to the intensive care unit (ICU) at the Salam Centre for Cardiac Surgery in Khartoum, Sudan (run by EMERGENCY NGO). Methods. This is a retrospective, observational study from a single, large international referral centre (part of a Regional Programme), which enrolled patients admitted to the ICU between 1 January and 31 December 2019. Data collected for each patient included demographic data, operating theatre/ICU data and microbiological cultures. Results. Over the study period, 611 patients were enrolled (elective surgery n = 491, urgent surgery n = 34 and urgent medical care n = 86). The infection rate was 14.2% and turned out to be higher in medical than in surgical patients (25.6% vs. 12.4%; p = 0.002; OR = 2.43) and higher in those undergoing urgent surgery than those undergoing elective (29.4% vs. 11.2%; p = 0.004; OR = 3.3). Infection was related to (a) SOFA score (p < 0.001), (b) ICU length of stay (p < 0.001) and (c) days from ICU admission to OT (p = 0.003). A significant relationship between the type of admission (elective, urgent surgery or medical) and the presence of infections was found (p < 0.001). The mortality rate was higher among infected patients (infected vs. infection-free: 10.3% vs. 2.1%; p < 0.001; OR = 5.38; 95% CI: 2.16−13.4; p < 0.001). Conclusions. Hospital-acquired infections remain a relevant preventable cause of mortality in our particular population.