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

Aimin Jiang,Xuan Liang,Haoran Zheng,Xiaoqiang Zheng,Xin Shi,Zhiping Ruan,Tao Tian,Na Liu,Yu Yao,Huan Gao,Shu Chen,Mengdi Ren,Xiao Fu

doi:10.1186/s13756-022-01073-3

Abstract

BackgroundAttributed 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.MethodsThis 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.ResultsA 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.ConclusionsNosocomial 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.

Highlights

Malignant tumors have become one of the major public health problems that threaten human health worldwide
After excluding 13,612 cases of non-nosocomial infection, 10 cases without complete electronic medical records, and 65 cases diagnosed with benign disease, a total of 1,008 participants developed nosocomial infections during the period (Fig. 1)
We found that gram-negative bacteria were the main causative pathogens of nosocomial infections, accounting for 63.3% of all pathogenic bacteria, followed by gram-positive cocci (16.8%) and fungi (11.4%)

Summary

Introduction

Malignant tumors have become one of the major public health problems that threaten human health worldwide. Cancer patients are more vulnerable to developing severe infection owing to the malignancy itself and its treatments, especially surgery and cytotoxic therapies [2]. As a result, it delays the initiation of chemotherapy and reduces its relative dosage intensity (RDI). It is pivotal for clinicians to fully understand the local epidemiological and microbiological characteristics of nosocomial infections. It can play an essential role in reducing the mortality of these individuals [2]. This study aimed to develop a nomogram to predict the in-hospital death risk of these patients

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