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

Abstract

Background Attributed to the immunosuppression caused by malignancy itself and its treatments, cancer patients are vulnerable to developing nosocomial infections. This study aimed to develop a nomogram to predict the in-hospital death risk of these patients. Methods This retrospective study was conducted at a medical center in Northwestern China. The univariate and multivariate logistic regression analyses were adopted to identify predictive factors for in-hospital mortality of nosocomial infections in cancer patients. A nomogram was developed to predict the in-hospital mortality of each patient, with receiver operating characteristic curves and calibration curves being generated to assess its predictive ability. Furthermore, decision curve analysis (DCA) was also performed to estimate the clinical utility of the nomogram. Results A total of 1,008 nosocomial infection episodes were recognized from 14,695 cancer patients. Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (15.5%) was the most predominant causative pathogen. Besides, multidrug-resistant strains were discovered in 25.5% of cases. The multivariate analysis indicated that Eastern Cooperative Oncology Group Performance Status 3–4, mechanical ventilation, septic shock, hypoproteinemia, and length of antimicrobial treatment < 7 days were correlated with higher in-hospital mortality. Patients who received curative surgery were correlated with favorable survival outcomes. Ultimately, a nomogram was constructed to predict the in-hospital mortality of nosocomial infections in cancer patients. The area under the curve values of the nomogram were 0.811 and 0.795 in the training and validation cohorts. The calibration curve showed high consistency between the actual and predicted in-hospital mortality. DCA indicated that the nomogram was of good clinical utility and more credible net clinical benefits in predicting in-hospital mortality. Conclusions Nosocomial infections stay conjoint in cancer patients, with gram-negative bacteria being the most frequent causative pathogens. We developed and verified a nomogram that could effectively predict the in-hospital death risk of nosocomial infections among these patients. Precise management of high-risk patients, early recognition of septic shock, rapid and adequate antimicrobial treatment, and dynamic monitoring of serum albumin levels may improve the prognosis of these individuals.

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

“…The percentage of malnourished patients in our study concurs with previously reported data in cancer hospitalized patients and according to GLIM criteria [ 29 ]. Compared to other studies reporting NI in cancer patients, the frequency of NI in our study was higher [ 20 ]. These differences may be partly attributed to the demographic and clinical characteristics of the study population.…”

“…Nosocomial infections (NI) are one of the many consequences of malnutrition in a hospital setting, affecting care and outcomes in cancer and increasing healthcare costs [19]. Cancer patients are more vulnerable to developing severe infections (especially NI) owing to the malignancy itself and its treatments [20,21]. Earlier studies have shown that 2.1% to 48% of cancer patients develop NI, varying widely across regions [22][23][24][25][26].…”

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

“…Our results cannot be generalized to other centers because we did not have external validation. Third, similar to other models for predicting nosocomial infection, 27,44,46 our model predicts the probability of nosocomial infection, but does not describe the exact infection site.…”

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

“…Meanwhile, hypoproteinemia was a statistically screened independent risk factor associated with carbapenem resistance in our study, and yet has rarely been observed to be related to carbapenem resistance in previous studies. Given that hypoproteinemia has been linked to disease progression and clinical prognosis in different clinical settings ( Murthy et al., 2018 ; Aziz et al., 2020 ; Omiya et al., 2021 ; Jiang et al., 2022 ), it was also incorporated into the plotting of the carbapenem resistance nomogram.…”

“…Currently, nomograms have been diffusely applied in medical research as an effective complementary tool to implement clinical decisions for clinicians ( Balachandran et al., 2015 ; Wu et al., 2020 ; Lin et al., 2020 ; Song et al., 2021 ; Dong et al., 2021 ; He et al., 2022 ). As a graphical tool, compared with traditional predictive scoring systems based on regression analysis, the nomogram can provide a visual representation of the complex statistical model and accurately estimate the individual probability of a clinical event by integrating multiple predictive variables that can diagnose diseases or predict clinical outcomes ( Park, 2018 ; Jiang et al., 2022 ). However, nomograms predicting the risk factors of mortality and carbapenem resistance in HM patients with GNB BSI have rarely received attention.…”