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Prognostic value of NPR and CLR-based nomogram modeling in elderly patients with Acinetobacter baumannii bloodstream infection

BMC Geriatrics volume 25, Article number: 234 (2025) Cite this article

Abstract

Background

Acinetobacter baumannii (A. baumannii) is one of the main pathogens that causesbloodstream infection (BSI) in elderly patients, with high morbidity and mortality rates once infected; new inflammatory indicators, such as the neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio(PLR), have been proposed in recent years, and the prognostic effects of these new inflammatory indicators have not yet been adequately investigated in A.baumannii BSI in elderly patients. Therefore, we verified the effects of these inflammatory indicators on A.baumannii BSIprognosis in elderly patients by constructing a nomogram model.

Methods

The clinical data of 126 elderly patients with A. baumannii BSIwere retrospectively analyzed, and they were divided into a survival group (87 patients) and a death group (39 patients) according to survival status 28 days after infection. Variables were screened by univariate Cox regression analysis and LASSO regression, respectively, and different prognostic models were constructed, and the final models were screened by cross-validation and other means, and the performance of the final models, such as differentiation, was evaluated. Finally, 47 exceptions of data were used to validate the prognostic model of A.baumanniiBSI in elderly patients.

Results

Out of 126 patients, 39 died, for a mortality rate of 31.0%. A high neutrophil–plateletratio(NPR)(hazard ratio [HR] of 35.948,95% confidence interval [CI], 6.890–187.548) and a high C-reactive protein (CRP)-to-lymphocyte ratio (CLR) (HR,1.004;95% CI, 1.002–1.006) are independent risk factors for death in elderly patients with A. baumannii BSI. The model constructed by LASSO regression screening variables avoided the overfitting situation of the model and performed better overall and was considered as the final model.In the final model, the nomogram model predicted the highest discriminatory 7-day prognosis of A. baumanniiBSI in elderly patients, with an area under the working curve (AUC) of 0.821 for subjects, 0.777 and 0.783 for 14 and 28 days, respectively, and a standardized model with good agreement; The clinical decision curve revealed that the model provided good net benefit, ranging from 20 to 100%.

Conclusion

The NPR and CLR are closely associated with the prognosis of A. baumanniiBSI in elderly patients, and in clinical practice, a focus should be placed on these new indicators of inflammation, especially the NPR and CLR, to help physicians better assess the prognosis of A. baumanniiBSI in elderly patients and to develop a more effective therapeutic regimen to improve the survival rate of patients.

Peer Review reports

Background

The aim of this study was to investigate the predictors of overall survival in elderly patients with A. baumanniiBSI. BSI is an important infectious disease that affects the prognosis of infection in hospitalized patients, and elderly patients are at high risk of bacterial BSI due to their own characteristics, such as frequent comorbidities of multiple underlying diseases, mobility problems, and low immunity [1–2]. A. baumannii is the main pathogen that causes BSI in elderly patients, according to the China Antimicrobial Resistance Surveillance System [3,4,5]. In clinical practice, the widespread presence of A. baumannii and its multiresistant bacteria poses a great challenge to antimicrobial therapy, and the morbidity and mortality rates of elderly patients can reach 56.3% once A. baumanniiBSI occurs [6]. Timely and accurate etiologic diagnosis as well as early and appropriate antimicrobial therapy are important factors affecting the prognosis of patients with A. baumanniiBSI.

New inflammatory indices, such as the systemic immune inflammatory index (SII), NLR, and PLR, have been developed. are new indices proposed in recent years, and the prognostic role of these new inflammatory indices in A. baumanniiBSI in elderly patients has not yet been fully investigated. Understanding the relationships among the NPR, CLR, and overall survival in this population could help clinicians identify high-risk patients and adjust treatment strategies accordingly.

To address this gap in knowledge, this study used Cox regression analysis and construction of a nomogram to investigate the predictive value of the NPR and CLR in elderly patients with A. baumannii bacteremia. By examining the relationships among the NPR, CLR, and overall survival in elderly patients with A. baumanniiBSIs, this study aims to provide valuable insights into the prognostic factors of this challenging clinical condition. Ultimately, the results of this study will contribute to the development of personalized treatment strategies to improve the prognosis of elderly patients with A. baumanniiBSI.

Methods

Study design and patient data

The demographic data and clinical data related to patients diagnosed with A.baumanniiBSI admitted to the ward of Guangdong Provincial Second Hospital of Traditional Chinese Medicine for 13 consecutive years from January 2011 to December 2023 were collected from 126 patients. The case inclusion criteria were as follows: patients diagnosed with A. baumanniiBSI, complete overall survival data and inflammation data, and patients aged 65 years or older at the time of diagnosis of A. baumanniiBSI. If patients presented with multiple blood culture isolates of A. baumannii, the data were taken at the time of the first positivity.

The inclusion criteria were as follows:

  1. 1.

    Patients diagnosed with A. baumannii BSI: (1)Meet the clinical diagnostic requirements for bloodstream infection: fever > 38 °C or hypothermia < 36 °C, which may be accompanied by chills, and combined with one of the following conditions: Invasive portal vein lesions or migrating lesions; Systemic signs of toxicity without obvious foci of infection; Skin rash or haemorrhagic spots, hepatosplenomegaly, blood neutrophilia with left shift of nuclei that cannot be explained by any other cause; Systolic blood pressure less than 12 kPa (90mmHg) or a decrease of more than 5.3 kPa (40mmHg) from the original systolic blood pressure; (2) Positive blood culture with isolation of A.baumannii.

  2. 2.

    Be 65 years of age or older at the time of diagnosis of A. baumannii BSI.

  3. 3.

    Have complete overall survival data and inflammation data, or if the patient has multiple A. baumannii isolated from blood culture, the data at the time of the first positivity.

A total of 126 patients, 82 males (65.1%), with a median age of 80 years, were categorized into 87 patients in the survival group and 39 patients in the death group on the basis of 28-day survival. (Fig. 1)

Fig. 1
figure 1

Study design: patients ≥ 65 years of age with A.baumanniiBSI

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Additionally, clinical data of 27 patients with A.baumannii BSI admitted to Guangzhou Xinhai Hospital and 20 patients admitted to the Guangzhou Twelfth People’s Hospital were collected as an external validation dataset.

Definitions of feature variables

Leukocyte counts, neutrophil values, lymphocyte values, monocyte values, platelet counts, C-reactive protein levels, and serum albumin levels were collected from the patient’s hospitalization system and from the laboratory information system for blood analyses on the day of the infection or during a 24-hour period via a self-designed form. The systemic immune inflammation index (SII) was calculated from the above information: platelet count × neutrophil count/lymphocyte count, neutrophil‒lymphocyte ratio (NLR), platelet‒lymphocyte ratio (PLR), lymphomonocyte ratio (LMR), neutrophil–platelet ratio(NPR), platelet‒albumin ratio (PAR), neutrophil‒albumin ratio (NPAR), C-reactive protein‒albumin ratio (CAR), CRP‒lymphocyte ratio (CLR), C-reactive protein-albumin-lymphocyte index (CALLY index): albumin × lymphocyte ÷ (CRP × 10).

Data analysis

The data were analyzed, and models were constructed via R software (version 4.3.1). The dataset collected from the Guangzhou Xinhai Hospital and Guangzhou Twelfth People’s Hospital served as the testcohort.The training cohort dataset, collected from the Guangdong Provincial Second Hospital of Traditional Chinese Medicine, was used to develop the model. The 10 calculated inflammation indices conformed to a normal distribution and were expressed as x ± s, and an independent samples t test was used to compare the two groups. The nonnormal distribution was expressed as the median (quartile) [M (P25, P75)], and a nonparametric rank-sum test was used to compare the groups. P < 0.05 was considered statistically significant, and the meaningful independent variables in the one-way analysis were filtered into the multifactorial regression analysis by Cox, and the related factors affecting the prognosis were screened.

Univariate Cox regression was performed on each of the 10 variables, screening for variables with P < 0.05. A Cox model fit to the variables that passed this screening criterion was referred to as model 1.Separately, LASSO regression was performed on the 10 variables, using a penalty term selected via cross-validation. The LASSO approach to variable selection was referred to as model 2. Models constructed by the two methods were evaluated by comparing predictive performance for the primary 28-day mortality endpoint in a 5-fold cross-validation procedure with 3 repeats in the training set. Performance metrics estimated via cross-validation included area under the receiver operating curve (AUC), root mean squared error (RMSE), and mean absolute error (MAE). The model with the best performance was selected as the final modeling strategy, and was fit to the entire training dataset. This model was evaluated using AUC, calibration curves and clinical decision curve analysis (DCA) in the validation set.

Finally, Nomogram were constructed for the final model, and the model was evaluated with area under ROC curve (AUC), calibration curves, and clinical decision curve analysis (DCA).

Results

Results of Cox regression analysis of the two groups

One-way Cox regression analysis of the variables in the two groups revealed that statistically significant risk factors for A. baumanniiBSI in elderly patients were elevated SII, NLR, NPR, NPAR, CAR, and CLR at the time of infection as well as decreased CALLY and LMR. However, the PLR and PAR were not significantly different. Inflammation indicators such as the SII, NLR, and NPR, with P < 0.05 in the univariate analysis, were included in the multivariate Cox regression analysis, and the results revealed that the NPR and CLR were independent risk factors for death in elderly patients with A. baumanniiBSI (Table 1). A multivariable, unpenalized model was fitted to the univariate Cox regression with P < 0.05 and regarded as model 1, containing the variables SII, NLR, LMR, NPR, NPAR, CAR, CLR and CALLY.

Table 1 Results of the Cox regression
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LASSO regression screening variables

Because of the small amount of data and to avoid covariance and overfitting, we used LASSO regression to validate the model variable screening for the multifactor cox regression analysis. The folds of 10 cross-validations were selected for the 10 new inflammatory conditions in the data, such as the SII, NLR, PLR, LMR, NPR, PAR, NPAR, CAR, CLR and CALLY, for LASSO regression analysis. The paths of the LASSO regression coefficients and cross-validations are shown in Fig.  2. The cross-validated errors are within one standard error of the minimum value of the most regularized and most plausible model, which contains 2 variables: NPR and CLR.

Fig. 2
figure 2

LASSO regression. A LASSO regression cross-validation plot; B LASSO regression coefficient path plot

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Constructing the prediction model2

According to the results of the LASSO regression analysis, model2 was constructed using the variables NPR and CLR. The coefficients are shown in the following Table 2.

Table 2 The coefficients of Lasso regression analysis
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Comparison of the performance of the two models

Using Repeated k-fold Cross Validation (K = 5, replicates = 3) for the two models, Model 1 has an R-squared of 0.122, an RMSE of 0.527, and an MAE of 0.396; Model 2 has an R-squared of 0.180, an RMSE of 0.432, and an MAE of 0.371. MAE was 0.371. Comparing the two models, in the original training set, model 1 has an AUC of 0.779, an R-squaredof 0.212, an RMSE of 0.410, and an MAE of 0.345. Model 2 has an AUC of 0.783, an R-squaredof 0.186, an RMSE of 0.417, and an MAE of 0.357.

In the original training set, the two models are close to each other in terms of their AUCs but model 1 outperforms model 2 in terms of goodness-of-fit and mean error. However, the cross-validation results showed that the goodness of fit of model 2 by LASSO regression was higher than that of model 1, and the RMSE and MAE showed that the average error between the predicted results and the actual observations of model 2 was smaller than that of model 1, which avoided the overfitting situation of the model, and model 2 wasregarded as the final model.

Construction of the model nomogram

The final model was used to construct a predictive model of A. baumanniiBSI in elderly patients via R software to construct a nomogram; Meanwhile, we estimated the Spearman correlation coefficient between NPR and CLR, and the result shows 0.291 (P < 0.001) which indicates that there is a significant positive correlation between them.the model revealed that the model scores NPR and CLR were positively correlated and that the survival rate of the patients decreased as the NPR and CLR increased (Fig. 3).

Fig. 3
figure 3

Nomogram prediction model. NPR, neutrophil–platelet ratio; CLR, C-reactive protein–lymphocyte ratio

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Model evaluation

The Bootstrap, ROC curves, calibration curves, and clinical decision curves of the models were plotted separately via R.

Bootstrap resampling internal validation

We performed bootstrap 1000 resampling of the model for internal validation. The resampling results show that the Bootstrap statistic is stable, the bootstrap bias is low (bias = 0.002), the c_index is 0.743, and the model has good results in internal validation (Fig. 4).

Fig. 4
figure 4

Bootstrap results

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The AUC and calibration curves are compared at different time points to measure the accuracy and stability of the model

The ROC curves revealed that the model had the highest discriminatory ability for survival at 7 days, with an AUC of 0.821, and the AUCs were 0.777 and 0.783 at 14 and 28 days, respectively.We performed bootstrap 1000 resampling of the training data and the results showed CI for AUC statistic:0.763, 95% 0.674–0.853.

In the test cohort, the AUC values of the model at 7, 14, and 28 days were 0.886, 0.874, and 0.886, respectively.The AUCs suggesting that the model had good discriminatory ability for survival at all 28 days (Fig. 5).

Fig. 5
figure 5

A. ROC curves for different survival times in training cohort; B. ROC curves at 28 days for different variables in training cohort; C. ROC curves for different survival times in test colony; D. ROC curves at 28 days for different variables in test cohort.AUC: areaunder the curve; NPR: neutrophil–platelet ratio; CLR: C-reactive protein–lymphocyte ratio

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The calibration curves show that the 7-day, 14-day, and 28-day calibration curves are all at a 45-degree angle in both the training and test cohort, indicating that the prediction model has good calibration capability (Fig. 6).

Fig. 6
figure 6

A. Calibration curves for different survival time models in training colony. B. Calibration curves for different survival time models in test colony. OS: overall survival

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DCA curves to assess the value of predictive modelling in clinical decision making.

The clinical decision curves revealed that the decision curves at all three time points of the model, 7, 14, and 28 days, provided good net benefits at 20-100%, with the 7-day range being larger, providing good net benefits at 10-100% (Fig. 7A). Similar to the results in the training cohort, the clinical decision curves in the test cohort provided good net benefits in the 20-100% range for the 7-, 14-, and 28-day decision curves (Fig. 7B).

Fig. 7
figure 7

A. DCA curves for different survival time models in training cohort. B. DCA curves for different survival time models in test cohort

Full size image

For elderly patients with A.baumannii BSI, which are insidious and difficult to detect after onset, the benefit of early intervention is higher, and the clinical benefit of a false-positive outcome is better than that of a false-negative. Our model showed positive net benefits for both the training and test cohorts at various risk thresholds of patient preference, with our model generating risks between 20% and 100%, enabling more effective identification of high-risk patients with always higher net gains compared to strategies that treat all patients. In the 10–25% risk region, our model is able to identify more high-risk patients than a strategy of treating all patients; in the greater than 25% risk region, the net benefit of our model is in the range of 5–10% overall, which corresponds to the identification of 5–10 true high-risk patients from 100 patients with no false positives, which are true high-risk patients for whom timely clinical intervention has a high clinical benefit.

Among the models at different time points, comparing the prognosis of death at 14 and 28 days post-infection, the model with a prognosis of death at 7 days post-infection had a higher clinical benefit for all interventions, which means that the model is more effective in identifying patients with a poor prognosis in the early stages of infection. In contrast, there was little difference in the identification and clinical benefit of the model for high-risk patients at different time points in the > 25% risk region. The results of the test cohort were similar to the training cohort.

Discussion

In this study, on the basis of a group of elderly patients with A. baumanniiBSI and different 28-day survival outcomes, we showed via univariate analysis that elevated indicators such as the SII, NLR, NPR, NPAR, CAR, and CLR, as well as reduced indicators such as CALLY and the LMR, were risk factors for death in elderly patients with A.baumannii sepsis or BSI. These new inflammatory indicators have become popular in recent years. Liu et al. [7] reported that the SII can be used to predict the prognosis of BSI effectively in combination with indicators such as rapid sequential organ failure assessment (qSOFA). Wu et al. used MATA analysis [8] and reported that the NLR is a reliable and valuable biomarker for predicting the prognosis and risk of death of adult patients with BSI. In addition, studies have reported that the CALLY index, NLR, LMR and other indicators can be used to identify BSI-critical patients with poor outcomes or predict the prognosis of BSI patients, which are closely related to the inflammation and immune response situation of the body and can predict the prognosis of BSIpatients [9,10,11]. However, few reports have been published on the prognostic value of these indicators in elderly patients with A. baumanniiBSI.

Multivariate Cox regression analysis revealed that an elevated NPR and CLR were independent risk factors for death in elderly patients with A. baumanniiBSI, and LASSO regression screening revealed that the model variables were the NPR and CLR, suggesting that an elevated NPR and CLR at the time of BSI were key predictors of the prognosis of elderly patients with A.baumanniiBSI in this study. The model established by NPR and CLR variables was well validated in the validation set, with an AUC value of not less than 0.870 in the post-infection prognosis, which enables timely and effective identification of high-risk patients, and timely intervention is expected to achieve a high clinical benefit.

The NPR is the ratio of peripheral blood neutrophils to platelets, and neutrophils are the “sentinel” of infection, which increases the levels of cytokines, such as granulocyte colony-stimulating factor (GCSF), and promotes the production and release of neutrophils [12]. Platelets are important mediators of hemostasis and thrombosis, and recent studies have shown [13–14] that platelets contain a variety of immune receptors, such as Toll-like receptors, C-type lectin receptors, and nucleotide-binding oligomerization domain-like receptors, which activate immune cells to clear pathogens during infection, thus exerting anti-inflammatory effects. On the other hand, neutrophils can affect the function of platelets during inflammation through mechanisms such as the formation of neutrophil extracellular traps [15–16], thereby affecting the course of inflammation in patients with BSI and thereby affecting the prognosis of patients, and a reduction in platelets during BSI increases the mortality rate of patients [17,18,19]. The NPR, as a marker for the dynamic interactions between neutrophils and platelets, is useful for understanding the inflammatory and immune dysregulation that occurs in elderly patients with A. baumanniiBSI, suggesting a good prognosis.

This study also revealed that an elevated CLR was strongly associated with death due to A. baumanniiBSI in elderly patients. Like the NPR, the CLR consists of the C-reactive protein/lymphocyte ratio, which is also used to assess inflammation and immune function. CRP is currently a widely used marker of inflammation in clinical practice, and studies have shown [20–21] that patient mortality in patients with BSI is strongly correlated with CRP levels. Lymphocyte apoptosis plays a key role in the immunosuppressive phase of BSI. Jiang et al. reported that lymphopenia is an independent risk indicator for 28-day mortality in BSI patients, whereas long-term lymphopenia is a risk factor for mortality in elderly patients. On the other hand, compared with inflammatory markers such as PCT and IL6, CRP is a better marker for respiratory infection-induced BSI, whereas in elderly patients, the prevalence of COPD and pneumonia diseases is high [22–23], and respiratory infection is one of the most important pathogens of BSI secondary to A. baumannii [24–25]. Thus, combining these two aspects, an elevated CLR is a high-risk factor for poor prognosis of A. baumanniiBSI in elderly patients in this study.

In summary, two indicators, the NPR and CLR, respond to the body’s inflammation level and provide comprehensive information on immune function, and the NPR and CLR are considered indicators of inflammatory prognostic factors affecting COVID-19, nephritis, endocarditis and other inflammatory diseases [26,27,28]. The NPR also has a certain value in the diagnosis of septic infections [29], but current research on the associations between the NPR and the CLR and the prognosis of BSI is limited. In our study, elderly patients had unique clinical characteristics after A. baumanniiBSI infection due to their unique immune status as well as comorbid underlying diseases, and the NPR and CLR became noteworthy independent risk predictors of 28-day mortality in BSI patients. the results also show that the combined markers NPR and CLR are important markers of prognosis in elderly A. baumanniiBSI patients.

However, at the same time, we must recognize some limitations of this study. First, the study population is a single-region multicentre patient, which may not be representative of a wider and more regional population due to the number of cases as well as patient admissions, which may be affected by patient admissions, information bias, etc. Secondly, we are a regression study, and it needs to be further observed that improvement of some of the model variables can have the effect of improving the prognosis. In the next step, we should strive to externally validate our model in different populations and environments, integrating new predictors or biomarkers on the basis of the existing results can improve the predictive accuracy of the model, and combining with the NLR, CLR data for follow-up observation. On the other hand, we will select appropriate patients for timely correction based on the model indicators to further validate the reliability and accuracy of the model.

Conclusion

In conclusion, A.baumannii bacteremia remains an important clinical problem due to its high morbidity and mortality, especially in some elderly individuals with multiple comorbid underlying conditions, and understanding the prognostic risk factors for infection in these individuals is essential for improving patient care and prognosis. In our study, the NPR and CLR were closely associated with the prognosis of A. baumanniiBSI in elderly patients. Enhanced monitoring of the NPR and CLR can help physicians better assess the prognosis of A. baumanniiBSI in elderly patients and develop more effective treatment regimens to improve patient survival.

Data availability

The data used in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Not applicable.

Funding

This study was supported by the Health Commission of Guangdong Province (B2023181) and the Traditional Chinese Medicine Bureau of Guangdong Province (20231045).

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Authors and Affiliations

  1. Clinical Laboratory, Guangzhou Twelfth People’s Hospital, Guangzhou, China

    Shaoqin Lai

  2. Administration Department of Nosocomial Infection, Guangdong Provincial Second Hospital of Traditional Chinese Medicine (Guangdong Provincial Engineering Technology Research Institute of Traditional Chinese Medicine), Guangzhou, China

    Xiaojun Li

  3. Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine (Guangdong Province Engineering Technology Research Institute of Traditional Chinese Medicine), Guangzhou, China

    Donghao Cai & Chuangchuang Mei

  4. Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine, Guangzhou, China

    Xiaojun Li, Donghao Cai & Chuangchuang Mei

  5. Clinical Laboratory, Guangzhou Xinhai Hospital, Guangzhou, China

    Zhihui Liang

Authors
  1. Shaoqin Lai
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  2. Xiaojun Li
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  3. Donghao Cai
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  4. Chuangchuang Mei
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  5. Zhihui Liang
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Contributions

Shaoqin Lai and Xiaojun Li designed the study and drafted the manuscript. Xiaojun Li, Donghao Cai, Chuangchuang Mei and Zhihui Liang collected and analyzed the data. All the authors reviewed the manuscript. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Xiaojun Li.

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Ethical approval and consent to participate

The study was approved by the Hospital Ethics Committee and conducted according to the Declaration of Helsinki (Approved No. of ethics committee: Z202404-002-01). Theethics committee waived the requirement for informed consent because the study was retrospective in design.

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Not applicable.

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The authors declare no competing interests.

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Lai, S., Li, X., Cai, D. et al. Prognostic value of NPR and CLR-based nomogram modeling in elderly patients with Acinetobacter baumannii bloodstream infection. BMC Geriatr 25, 234 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05884-y

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05884-y

Keywords

BMC Geriatrics

ISSN: 1471-2318

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