Abstract
Introduction
Tracheal intubation in intensive care unit (ICU) patients is associated with significant risks, necessitating simple mortality predictors. We investigated the relationship between neutrophil percentage-to-albumin ratio (NPAR) and in-hospital and ICU mortality in intubated patients.
Methods
This retrospective cohort study analyzed 1,401 intubated ICU patients from MIMIC-IV. Multivariable Cox regression and smooth curve fitting were used to assess the association between NPAR and mortality. A two-stage linear regression model and threshold analysis evaluated nonlinear relationships.
Results
Among patients (mean age 62.7 ± 17.1 years; 43.6% female), NPAR exhibited a U-shaped relationship with mortality, with an inflection point at 2.2 observed in this cohort. Below this inflection point, hazard ratios (HRs) were 0.656 (95% CI: 0.492–0.875) for in-hospital mortality and 0.586 (95% CI: 0.413–0.831) for ICU mortality. Above 2.2, HRs increased to 1.187 (95% CI: 1.097–1.286) and 1.15 (95% CI: 1.047–1.264), respectively.
Conclusion
NPAR demonstrates a U-shaped association with mortality in intubated ICU patients, with elevated risks above 2.2. Further large-scale studies are needed to validate these findings.
Keywords: intensive care unit, mechanical ventilation, mortality, neutrophil-to-albumin ratio, retrospective cohort study
1. Introduction
Tracheal intubation and mechanical ventilation are vital interventions for airway management across clinical settings (1). These procedures provide crucial respiratory support during surgery, intensive care, and perioperative stabilization of critically ill patients (1). Endotracheal intubation is the gold standard for airway management (2, 3), with 10%–15% of intensive care unit (ICU) patients worldwide requiring this procedure annually (4, 5). In the U.S., over 1.5 million patients require endotracheal intubation annually, with increasing prevalence (6). This procedure primarily maintains airway patency and safety (7), while also facilitating secretion clearance and supporting diagnostic/therapeutic interventions. Although endotracheal intubation plays a critical role in clinical treatment, related procedures (such as repeated intubation attempts in difficult airways, intubation duration, and changes in posture during intubation) may increase the risk of airway injury (8, 9), while subsequent mechanical ventilation management may lead to complications such as oxygen toxicity, acquired muscle weakness, and ventilator-induced lung injury (10). Therefore, identifying easily accessible and low-cost prognostic indicators is crucial for improving outcomes in intubated ICU patients. This helps to reduce the burden on patients and lower the mortality rate.
Scoring systems such as SOFA and APACHE II are widely used in intensive care, but their reliance on complex parameters limits their application value in scenarios requiring rapid decision-making for endotracheal intubation. The neutrophil percentage-to-albumin ratio (NPAR) is an easily obtainable and cost-effective biomarker (11), which may provide a more efficient auxiliary tool for intubation decision-making. As an inflammation-based prognostic predictor (12), NPAR has been associated with mortality risk in various diseases, including chronic obstructive pulmonary disease (COPD), stroke, coronary artery disease, sepsis, acute kidney injury, and peritoneal dialysis patients (13–18). In addition, previous studies have found that NPAR has significant clinical value in assessing the prognosis of elderly patients with hip fractures 1 year after surgery (19). NPAR is not only capable of predicting the occurrence of stroke-associated infection (20), but it is also an independent prognostic factor for ischemic stroke patients admitted to the ICU (21). The prognostic value of NPAR stems from the pathophysiological interplay between neutrophils and albumin. Lower neutrophil percentages reflect a moderated inflammatory response, reducing the risk of excessive inflammation-induced tissue damage (22). Conversely, higher albumin levels help maintain endothelial integrity, scavenge oxygen free radicals, mitigate oxidative stress, and regulate the balance between pro- and anti-inflammatory factors (23). This dual regulatory mechanism involving both inflammatory response and nutritional status may constitute the potential biological basis for the association between NPAR and clinical outcomes. Despite NPAR’s established prognostic value in various diseases, its association with outcomes in intubated ICU patients remains unexplored. Using the Medical Information Marketplace in Intensive Care IV (MIMIC-IV) database, we investigated NPAR’s relationship with in-hospital and ICU mortality in this population, findings that could inform clinical decision-making.
2. Materials and methods
2.1. Database introduction
Our study data was extracted from the open-source MIMIC-IV clinical database. The MIMIC-IV database contains a comprehensive dataset of 50,920 patients admitted to the ICU at Beth Israel Deaconess Medical Center from 2008 to 2019 (24), including demographic baseline characteristics, vital signs, laboratory tests, medication records, and diagnoses coded according to the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). After successfully completing the online check and executing the data usage agreement, the principal investigator Weide Lin obtained access to the database (certification number: 62407435). The study adheres to the ethical guidelines of the Declaration of Helsinki. All datasets used in this study can be freely accessed in the MIMIC-IV v2.2 database1. The establishment of the MIMIC-IV database was approved by the Institutional Review Board of Beth Israel Deaconess Medical Center and its affiliated institution, the Massachusetts Institute of Technology. Due to the anonymization of the data, informed consent was not sought.
2.2. Patients
The MIMIC-IV database includes a total of 73,181 patients, of which there are 4,012 intubated patients admitted to the ICU who are over 18 years old. We excluded 1,991 patients with missing albumin data and 459 patients with missing neutrophil percentage data, resulting in a final study population of 1,562 intubated patients. Subsequently, we excluded 149 patients with ICU stay less than 24 h and 12 patients with hospital stay less than 24 h, ultimately including 1,401 intubated patients. We only include the data for the patient’s first ICU admission and the corresponding hospital stay (25).
2.3. Variable extraction and outcome
We extracted data including demographics, vital signs, and laboratory tests using PostgreSQL version 13.9. The selection of covariates was based on variables previously established in the literature as being associated with mortality and the biological plausibility of confounding effects. All laboratory indicators and clinical variables were collected after admission to the ICU, and tracheal intubation procedures were performed during the ICU stay. Covariates included in our analysis were age, gender, race, heart rate, systolic blood pressure (SBP), respiratory rate (Resp), oxygen saturation (SpO2), glucose, prothrombin time (PT), international normalized ratio (INR), sodium (Na), potassium (K), calcium (Ca), chlorine (Cl), acute kidney injury stage (AKI stage), myocardial infarction (MI), chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CVD), peripheral vascular disease (PVD), sepsis, mechanical ventilation (MV), elective surgery, hospital mortality, and intensive care unit mortality (ICU mortality). NPAR was calculated using the following formula: NPAR = neutrophil percentage (%) × 100/albumin concentration (g/dL) (26). The neutrophil percentage and albumin concentration were both determined using the first measured values after ICU admission. If multiple records existed at the same time point, the earliest one was selected for analysis. Additionally, patients were grouped according to NPAR, with the primary outcome being in-hospital mortality and the secondary outcome being ICU mortality.
2.4. Statistical analysis
Patients were divided into three groups based on trichotomous NPAR levels (NPAR <2.41, 2.41 ≤ NPAR <3.38, NPAR ≥3.38).
Categorical variables are expressed as proportions (%) and were tested with a chi-square test or Fisher’s exact test. Continuous variables with a normal distribution are depicted as the mean ± standard deviation (SD) and were compared using Student’s t test or one-way ANOVA. For continuous variables not conforming to a normal distribution, the data are shown as the median and interquartile range (IQR), and the Kruskal–Wallis H test was used for statistical analysis. Since all covariates had low missing rates (range: 0.57%–2.28%), this study employed complete-case analysis for the statistical analysis.
Multivariable Cox regression analyses were adopted to assess the independent association between NPAR levels and the risk of in-hospital mortality and ICU mortality. The model adjusted for different covariates uses the extended Cox proportional hazards model. Prior to modeling, variance inflation factors (VIF) were calculated for all covariates, with all values <5, indicating no substantial multicollinearity. The models are as follows: The crude model was unadjusted. Model 1 was adjusted for sex and age. Model 2, in addition to the variables in Model 1, further adjusted for covariates with an effect size change greater than 10%, including MI, COPD, CVD, PVD, and sepsis. Model 3, on top of Model 2, adjusted for covariates with a univariate analysis p-value < 0.1, including race, heart rate, systolic blood pressure, Resp., SpO2, glucose, PT, INR, Na, K, Cl, and AKI stage. Model 4, beyond Model 3, adjusted for covariates reported as clinically significant in previous studies, additionally incorporating Ca, MV, and elective surgery.
We employed a two-piecewise regression model and smooth curve analysis to explore the nonlinear relationship between NPAR levels and in-hospital as well as ICU mortality. The inflection point was determined using the maximum likelihood method. To compare the fit of the models, we employed the likelihood ratio test to evaluate the differences between the single-variable regression model and the two-piecewise regression model. Furthermore, we conducted threshold effect analysis to assess the potential of NPAR levels in predicting in-hospital and ICU mortality. The identified threshold of 2.2 is data-driven and exploratory, and therefore requires external validation before clinical application.
Furthermore, to determine whether the relationship between NPAR levels and patients with tracheal intubation in the ICU was stable across populations, interaction and subgroup analyses were performed according to age (<65, or ≥65 years), gender (female or male), MI (yes or no), COPD (yes or no), sepsis (yes or no) and MV (yes or no). In the multivariable Cox regression model 4, adjustments are made for all stratified factors, and interactions between subgroups are analyzed using the likelihood ratio test. To evaluate the incremental predictive value of NPAR over the SOFA score, we calculated the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The baseline model included the SOFA score alone, and the new model added NPAR.
All analyses were performed using the statistical software package R. version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria) and Free Statistics software version 1.9.2 (27). p values < 0.05 (two-sided) were considered statistically significant.
2.5. Sensitivity analysis
In the sensitivity analysis, we categorized the primary diagnoses of intubated patients into six groups (neurological diseases, respiratory diseases, cardiovascular diseases, digestive diseases, infectious diseases, and others) and included the primary diagnosis as a covariate in the Cox regression model, while conducting subgroup analyses based on primary diagnoses to assess the stability of the results. Additionally, we adjusted for clinically important covariates including mechanical ventilation duration (missing rate 1.3%), acute respiratory distress syndrome (missing rate 30.2%), creatinine, glucocorticoid use, and albumin dosage. For variables with missing data such as mechanical ventilation duration and ARDS, we employed multiple imputation to generate five imputed datasets and selected one of the imputed datasets for analysis to ensure the reliability of the findings.
3. Results
3.1. Population of the study
Based on the above inclusion and exclusion criteria, 1,401 patients were identified in the final cohort. The flow chart of the study patients is presented in Figure 1.
Figure 1.
Schematic representation of the participant selection process and distribution of participant groups.
3.2. Participants and demographic characteristics
The baseline characteristics of patients are presented in Table 1. This cohort’s in-hospital mortality rate is 28.41%; the ICU mortality rate is 20.84%. The patient age was 62.7 ± 17.1 years, and approximately 56.4% patients were men, most of whom were white. Patients enrolled were categorized into three groups based on the tertiles of NPAR: NPAR <2.41 was considered as the lower group, 2.41 ≤ NPAR < 3.38 was considered as the middle group, and ≥3.38 was considered as the higher group. Compared to the lower NPAR group, patients in the higher NPAR group had faster heart and respiratory rates, higher PT and INR levels, higher in-hospital and ICU mortality, were more prone to sepsis, less likely to develop CVD, and had lower SBP.
Table 1.
Baseline characteristics of participants.
| Variables | Total (n = 1,401) | NPAR <2.41 (n = 465) | 2.41 ≤ NPAR <3.38 (n = 471) | NPAR ≥3.38 (n = 465) | p-value |
|---|---|---|---|---|---|
| Age (years) | 62.7 ± 17.1 | 61.3 ± 17.6 | 64.5 ± 17.0 | 62.1 ± 16.6 | 0.01 |
| Gender, n (%) | 0.033 | ||||
| Female | 611 (43.6) | 182 (39.1) | 224 (47.6) | 205 (44.1) | |
| Male | 790 (56.4) | 283 (60.9) | 247 (52.4) | 260 (55.9) | |
| Race/ethnicity, n (%) | 0.177 | ||||
| White | 874 (62.4) | 280 (60.2) | 293 (62.2) | 301 (64.7) | |
| African Americans | 148 (10.6) | 57 (12.3) | 55 (11.7) | 36 (7.7) | |
| Other | 379 (27.1) | 128 (27.5) | 123 (26.1) | 128 (27.5) | |
| Heart rate (bpm) | 112.6 ± 22.9 | 110.2 ± 21.1 | 111.5 ± 23.9 | 116.2 ± 23.2 | <0.001 |
| SBP (mmHg) | 151.6 ± 25.2 | 154.4 ± 25.8 | 151.4 ± 25.0 | 149.0 ± 24.3 | 0.004 |
| Resp (bpm) | 29.2 ± 6.9 | 28.5 ± 6.6 | 29.1 ± 6.9 | 30.1 ± 7.0 | 0.002 |
| SpO2 (%) | 99.7 ± 0.9 | 99.7 ± 0.8 | 99.6 ± 0.9 | 99.7 ± 1.1 | 0.297 |
| Glucose (mmol/L) | 8.8 (6.9, 12.4) | 8.4 (6.8, 12.5) | 8.7 (6.9, 11.7) | 9.1 (7.1, 13.1) | 0.076 |
| PT (s) | 15.1 (13.2, 20.1) | 13.8 (12.4, 17.8) | 14.7 (13.3, 18.6) | 16.3 (13.9, 23.8) | <0.001 |
| INR (s) | 1.4 (1.2, 1.8) | 1.2 (1.1, 1.6) | 1.3 (1.2, 1.7) | 1.5 (1.2, 2.2) | <0.001 |
| Na (mmol/L) | 140.3 ± 6.1 | 140.5 ± 5.9 | 140.3 ± 6.1 | 140.0 ± 6.3 | 0.486 |
| K (mmol/L) | 4.8 ± 1.0 | 4.7 ± 1.0 | 4.8 ± 1.0 | 4.8 ± 0.9 | 0.389 |
| Ca (mmol/L) | 2.1 ± 0.3 | 2.2 ± 0.3 | 2.1 ± 0.3 | 2.1 ± 0.4 | <0.001 |
| Cl (mmol/L) | 107.3 ± 7.5 | 106.6 ± 7.3 | 106.9 ± 7.5 | 108.3 ± 7.6 | <0.001 |
| AKI stage | <0.001 | ||||
| 0 | 701 (50.0) | 270 (58.1) | 223 (47.3) | 208 (44.7) | |
| 1 | 378 (27.0) | 122 (26.2) | 139 (29.5) | 117 (25.2) | |
| 2 | 145 (10.3) | 35 (7.5) | 54 (11.5) | 56 (12) | |
| 3 | 177 (12.6) | 38 (8.2) | 55 (11.7) | 84 (18.1) | |
| MI, n (%) | 0.144 | ||||
| No | 1,214 (86.7) | 414 (89) | 399 (84.7) | 401 (86.2) | |
| Yes | 187 (13.3) | 51 (11) | 72 (15.3) | 64 (13.8) | |
| COPD, n (%) | 0.008 | ||||
| No | 1,019 (72.7) | 351 (75.5) | 318 (67.5) | 350 (75.3) | |
| Yes | 382 (27.3) | 114 (24.5) | 153 (32.5) | 115 (24.7) | |
| CVD, n (%) | 0.004 | ||||
| No | 1,167 (83.3) | 366 (78.7) | 400 (84.9) | 401 (86.2) | |
| Yes | 234 (16.7) | 99 (21.3) | 71 (15.1) | 64 (13.8) | |
| PVD, n (%) | 0.259 | ||||
| No | 1,257 (89.7) | 426 (91.6) | 418 (88.7) | 413 (88.8) | |
| Yes | 144 (10.3) | 39 (8.4) | 53 (11.3) | 52 (11.2) | |
| Sepsis, n (%) | <0.001 | ||||
| No | 237 (16.9) | 127 (27.3) | 58 (12.3) | 52 (11.2) | |
| Yes | 1,164 (83.1) | 338 (72.7) | 413 (87.7) | 413 (88.8) | |
| MV, n (%) | 0.616 | ||||
| No | 357 (25.5) | 114 (24.5) | 117 (24.8) | 126 (27.1) | |
| Yes | 1,044 (74.5) | 351 (75.5) | 354 (75.2) | 339 (72.9) | |
| Elective surgery, n (%) | 0.716 | ||||
| No | 1,394 (99.5) | 464 (99.8) | 468 (99.4) | 462 (99.4) | |
| Yes | 7 (0.5) | 1 (0.2) | 3 (0.6) | 3 (0.6) |
Continuous variables are presented as mean ± SD or median (quartile), while categorical variables are presented as absolute numbers (percentages).
SBP, systolic blood pressure; Resp., respiratory; SpO2, pulse oximetry derived oxygen saturation; PT, prothrombin time; INR, international normalized ratio; Na, sodium; K, potassium; Ca, calcium; Cl, chlorine; AKI stage, acute kidney injury stage; MI, myocardial infarction; COPD, chronic obstructive pulmonary disease; CVD, cerebrovascular disease; PVD, peripheral vascular disease; MV, mechanical ventilation; ICU mortality, intensive care unit mortality.
3.3. Results of cox regression analysis
The univariate analysis of in-hospital mortality and ICU mortality for patients with tracheal intubation in the ICU is shown in Supplementary Table S1. It indicated that age, Heart rate, SBP, Resp., SpO2, Glucose, PT, INR, Na, K, Cl, AKI stage, MI and NPAR are risk factors for in-hospital mortality (p < 0.05); age, Heart rate, SBP, Resp., SpO2, Glucose, PT, INR, K, Cl, AKI stage, MI and NPAR are risk factors for ICU mortality (p < 0.05). The K–M curves show a significant correlation between NPAR and the risk of in-hospital mortality as well as ICU mortality (Supplementary Figure S1).
Table 2 presents the results of multivariable Cox regression analysis for the association between NPAR and mortality. When analyzed as a continuous variable, NPAR showed a significant positive association with in-hospital and ICU mortality in the crude model (HR range 1.04–1.05, all p < 0.05). However, this association was no longer significant in Models 3 and 4 after full adjustment. Nevertheless, when considered as a categorical variable, with the second group of NPAR (2.41 ≤ NPAR < 3.38) as the baseline reference, higher NPAR levels (NPAR ≥ 3.38) were associated with a higher risk of in-hospital mortality and ICU mortality (Crude model, HR range 1.58–1.59, p < 0.05 for all). The hazard ratios for NPAR levels were significant in all four models. According to the fully adjusted model 4, patients with higher NPAR levels (NPAR ≥ 3.38) had a 61% increased risk of in-hospital mortality and a 62% increased risk of ICU mortality compared to the reference group of NPAR (2.41 ≤ NPAR < 3.38) (Model IV, HR range 1.61–1.62, p < 0.05 for all).
Table 2.
Relationships between NPAR, in-hospital mortality, and ICU mortality in different models.
| Variable | Crude model | Model I | Model II | Model III | Model IV | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p-value | HR (95% CI) | p-value | HR (95% CI) | P-value | HR (95% CI) | p-value | HR (95% CI) | p-value | |
| Hospital mortality | ||||||||||
| NPAR as continuous | 1.05 (1.02 ~ 1.07) | 0.001 | 1.05 (1.02 ~ 1.08) | <0.001 | 1.05 (1.02 ~ 1.08) | <0.001 | 1.01 (0.99 ~ 1.04) | 0.324 | 1.02 (0.99 ~ 1.04) | 0.31 |
| 2.41 ≤ NPAR <3.38 | Ref | Ref | Ref | Ref | Ref | |||||
| NPAR <2.41 | 0.92 (0.71 ~ 1.19) | 0.524 | 1.02 (0.78 ~ 1.32) | 0.897 | 1.03 (0.79 ~ 1.35) | 0.804 | 1.14 (0.87 ~ 1.49) | 0.331 | 1.11 (0.85 ~ 1.45) | 0.457 |
| NPAR ≥3.38 | 1.58 (1.25 ~ 1.99) | <0.001 | 1.7 (1.34 ~ 2.15) | <0.001 | 1.69 (1.33 ~ 2.14) | <0.001 | 1.6 (1.25 ~ 2.05) | <0.001 | 1.61 (1.26 ~ 2.07) | <0.001 |
| P for trend | <0.001 | <0.001 | <0.001 | 0.006 | 0.003 | |||||
| ICU mortality | ||||||||||
| NPAR as continuous | 1.04 (1 ~ 1.07) | 0.025 | 1.04 (1.01 ~ 1.08) | 0.012 | 1.04 (1.01 ~ 1.08) | 0.021 | 1 (0.96 ~ 1.04) | 0.933 | 1 (0.96 ~ 1.04) | 0.915 |
| 2.41 ≤ NPAR <3.38 | Ref | Ref | Ref | Ref | Ref | |||||
| NPAR <2.41 | 0.95 (0.7 ~ 1.29) | 0.738 | 1.04 (0.76 ~ 1.41) | 0.81 | 1.08 (0.79 ~ 1.47) | 0.642 | 1.21 (0.89 ~ 1.66) | 0.226 | 1.18 (0.86 ~ 1.61) | 0.306 |
| NPAR ≥3.38 | 1.59 (1.21 ~ 2.09) | 0.001 | 1.7 (1.29 ~ 2.24) | <0.001 | 1.7 (1.29 ~ 2.24) | <0.001 | 1.6 (1.2 ~ 2.14) | 0.002 | 1.62 (1.21 ~ 2.17) | 0.001 |
| P for trend | <0.001 | <0.001 | 0.001 | 0.047 | 0.025 | |||||
Crude model was not adjusted.
Model 1 was adjusted for gender + age.
Model 2 was adjusted for model 1 + MI + COPD + CVD + PVD + sepsis.
Model 3 was adjusted for model 2 + race + heart rate + SBP + Resp + SpO2 + glucose + PT + INR + Na + K + Cl + AKI stage.
Model 4 was adjusted for model 3 + Ca + MV + Elective surgery.
MI, myocardial infarction; COPD, chronic obstructive pulmonary disease; CVD, cerebrovascular disease; PVD, peripheral vascular disease; SBP, systolic blood pressure; Resp., respiratory; SpO2, pulse oximetry derived oxygen saturation; PT, prothrombin time; INR, international normalized ratio; Na, sodium; K, potassium; Cl, chlorine; AKI stage, acute kidney injury stage; Ca, calcium; MV, mechanical ventilation.
3.4. Nonlinear relationship between NPAR levels and in-hospital mortality and ICU mortality
Multivariable Cox regression and smooth curve analysis reveal a nonlinear relationship between NPAR levels and the risks of in-hospital and ICU mortality (Figure 2). The piecewise linear regression model indicates that the NPAR threshold is 2.2 (Table 3), beyond which there is a significant increase in the risk of in-hospital and ICU mortality (HR from 1.15 to 1.187, p < 0.05, Table 3 and Figure 2). For each one-unit increase in NPAR, the risk of in-hospital mortality increases by 18.7%, and the risk of ICU mortality increases by 15%. Below the threshold, the risk of in-hospital mortality and ICU mortality decreased rapidly (HR range 0.586–0.656, p < 0.05 for all, Table 3 and Figure 2), for every one-unit decrease in NPAR level, the risk of in-hospital and ICU mortality decreases by 34.4% and 41.4%, respectively.
Figure 2.
(A) Nonlinear dose–response relationship between NPAR and in-hospital mortality. (B) Nonlinear dose–response relationship between NPAR and ICU mortality. The analysis was adjusted for age, gender, race, heart rate, SBP, Resp., SpO2, glucose, PT, INR, Na, K, Ca, Cl, AKI stage, MI, COPD, CVD, PVD, sepsis, MV, and elective surgery. The blue line represents the estimated values, and the green area indicates the corresponding 95% confidence intervals. Only 99% of the data is displayed.
Table 3.
Threshold effect analysis of NPAR levels on in-hospital mortality and ICU mortality.
| Hospital mortality | ICU mortality | ||||||
|---|---|---|---|---|---|---|---|
| Threshold of driving pressure | HR | 95% CI | p-value | Threshold of driving pressure | HR | 95% CI | p-value |
| NPAR <2.2 | 0.656 | 0.492, 0.875 | 0.0042 | NPAR <2.2 | 0.586 | 0.413, 0.831 | 0.0027 |
| NPAR ≥2.2 | 1.187 | 1.097, 1.286 | <0.001 | NPAR ≥2.2 | 1.15 | 1.047, 1.264 | 0.0036 |
| Likelihood ratio test | <0.001 | Likelihood ratio test | <0.001 | ||||
HR, hazard ratio, CI, confidence interval. Both adjustment factors included age, gender, race, heart rate, SBP, Resp., SpO2, glucose, PT, INR, Na, K, Ca, Cl, AKI stage, MI, COPD, CVD, PVD, sepsis, MV, Elective surgery. Only 99% of the data is displayed.
3.5. Subgroup analyses and interaction effects
We conducted stratified analysis and interaction analysis to assess whether the association between NPAR levels and in-hospital mortality and ICU mortality exists in different subgroups. After stratification by age, gender, MI, COPD, sepsis and MV, we found significant interactions between NPAR levels and both in-hospital mortality and ICU mortality in the presence or absence of MV groups. No significant interactions were found in the other subgroups (Supplementary Figure S2).
3.6. Sensitivity analysis
In the sensitivity analysis, we included the primary diagnosis of patients with tracheal intubation as a covariate in the Cox regression model for analysis. It was found that when NPAR was used as a categorical variable and the second group of NPAR (2.41 ≤ NPAR < 3.38) was used as the reference, in the fully adjusted model, patients with higher levels of NPAR (NPAR≥3.38) had a 60% higher risk of in-hospital mortality compared with the NPAR reference group (2.41 ≤ NPAR < 3.38), and a 61% higher risk of ICU mortality compared with the NPAR reference group (2.41 ≤ NPAR < 3.38) (fully adjusted model, HR range 1.6–1.61, p < 0.05) (Supplementary Table S2). This indicates that the relationship between NPAR and in-hospital and ICU mortality remains reliable. We further conducted subgroup analysis based on the primary diagnosis and found no significant interaction (Supplementary Table S3). When we incorporated mechanical ventilation time, ARDS, glucocorticoid use, albumin dosage, and creatinine as covariates into the Cox regression model, the results showed: with NPAR as a categorical variable and using the median group as reference, patients in the high NPAR group still exhibited a 69% increased risk of in-hospital mortality and a 67% increased risk of ICU mortality in the fully adjusted model (fully adjusted model, HR range 1.67–1.69, p < 0.05 for all) (Supplementary Table S4).
3.7. Incremental predictive value of NPAR over SOFA score
To further evaluate whether NPAR provides incremental predictive value beyond the established ICU severity score, we compared the performance of models with and without NPAR using the NRI and IDI. As shown in Supplementary Table S5, adding NPAR to the SOFA score resulted in a continuous NRI of 0.125 (95% CI: 0.034–0.177, p = 0.036). The IDI was 0.002 (p = 0.18), and the median improvement in risk score was 0.003 (p = 0.024).
4. Discussion
Our retrospective study reveals that elevated NPAR (>2.2) in intubated ICU patients associates with increased in-hospital and ICU mortality, demonstrating a U-shaped relationship. The inflection point for mortality risk in this cohort was approximately 2.2, identified through two-piecewise regression analysis. However, this threshold is derived from exploratory data-driven analyses and requires external validation in independent cohorts before its potential clinical significance can be considered.
The biological plausibility of NPAR as a prognostic marker in intubated ICU patients is supported by its integration of two interconnected pathophysiological processes: excessive inflammatory activation and depletion of nutritional metabolic reserves. Neutrophils, as key effectors of the innate immune system, are rapidly recruited to sites of infection during inflammation, where their activation releases reactive oxygen species and chemokines that can lead to oxidative stress and endothelial dysfunction (28–32). Albumin levels, by contrast, primarily reflect nutritional status and colloid osmotic pressure, with serum concentrations inversely correlated with systemic inflammation (33). Low albumin levels, often resulting from malnutrition and inflammation, are well-established prognostic indicators across various diseases (34). In the specific context of intubated ICU patients, tracheal intubation and mechanical ventilation may further amplify this inflammatory-nutritional imbalance. Experimental studies have shown that these procedures alter gene expression related to anaerobic bacteria and biofilms, potentially causing lower respiratory tract dysbiosis and predisposing patients to infections (1, 35). Endotracheal intubation and mechanical ventilation can also directly damage lung tissue through cellular injury and mediator release from activated cells (10).
The prognostic value of NPAR observed in our study is consistent with a growing body of evidence supporting the utility of inflammation-based biomarkers in critically ill populations. Nonlinear relationships between NPAR and clinical outcomes have been reported across diverse diseases, including Kawasaki disease (36) and hypertension (37). Unlike traditional inflammatory markers such as C-reactive protein and procalcitonin, NPAR integrates both inflammatory and nutritional status, potentially offering an advantage in predicting long-term prognosis. As a novel biomarker combining neutrophil percentage and albumin levels, NPAR reflects systemic infection and inflammation (26, 38). A retrospective study from China found that NPAR may become a more accurate and reliable biomarker for predicting spontaneous bacterial peritonitis (39). Another study indicates that elevated NPAR values are associated with an increased 28-day mortality rate in Chinese patients with severe sepsis (40). In critical care settings, elevated NPAR has been independently associated with all-cause mortality in patients with acute kidney injury, myocardial infarction, and cardiogenic shock (17, 41, 42). In the field of oncology, NPAR has also demonstrated its predictive potential to a certain extent. The study by Ko et al. (43), indicates that preoperative NPAR is a strong indicator of the invasiveness of oral squamous cell carcinoma. Another multicenter study points out that NPAR shows to be a clinically significant indicator for predicting worse oncological outcomes in patients with muscle-invasive bladder cancer treated with neoadjuvant chemotherapy and radical cystectomy (44). Other composite inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR), have also shown prognostic value in conditions like acute myocardial infarction (45). However, the predictive performance of these markers may vary by clinical context; for instance, NPAR was not the optimal predictor for all-cause mortality in community-based heart failure patients compared to NLR and platelet-to-lymphocyte ratio (46). This discrepancy underscores the importance of selecting biomarkers based on specific patient characteristics and clinical scenarios.
An interesting observation in our study was the U-shaped relationship, with increased mortality risk at both low and high NPAR values. Below the inflection point of 2.2, lower NPAR values were associated with reduced mortality risk, likely reflecting a well-preserved nutritional status and controlled inflammatory response. However, extremely low NPAR values, though uncommon in this critically ill cohort, may theoretically indicate an immunocompromised state with impaired host defense mechanisms. The clinical implications of low NPAR in intubated patients warrant further investigation in larger studies with greater statistical power to explore this subpopulation.
It should be noted that when NPAR was analyzed as a continuous variable, it was significantly associated with mortality in the crude model; however, this association was no longer statistically significant after full adjustment for confounders, suggesting that it may be influenced by other clinical factors. Nevertheless, when NPAR was analyzed as a categorical variable, higher levels (≥3.38) remained significantly associated with increased mortality risk across all adjusted models, indicating that its prognostic value may be more pronounced at extreme values. This suggests that NPAR should be considered in conjunction with other clinical parameters rather than used in isolation.
In summary, NPAR has demonstrated strong prognostic value across multiple diseases. However, it should be interpreted as a prognostic marker reflecting illness severity and inflammatory burden, rather than a modifiable causal factor, as its components are influenced by multiple interrelated physiological processes. This study is the first to extend this association to the ICU tracheal intubation population and to demonstrate its incremental predictive value over the SOFA score. It should be emphasized that NPAR is not intended to replace established ICU severity scores such as SOFA or APACHE, which integrate multiple organ function parameters. Given its significant improvement in risk reclassification and clinical accessibility, NPAR may serve as a simple and rapid adjunctive tool for initial risk stratification. It is particularly valuable in settings where timely calculation of comprehensive scores is not feasible, serving as a complement to existing scoring systems.
While this study provides valuable insights, we also recognize several limitations. First, this study was conducted using data from a single-center U.S. database (MIMIC-IV). Clinical practices, ICU admission criteria, and patient populations may vary across different regions and healthcare systems, which could limit the generalizability of our findings to other settings. As a single-center retrospective study, it may be subject to selection bias and information bias. It is particularly noteworthy that missing data on endotracheal intubation indications, duration of endotracheal intubation, nutritional indicators, and certain ventilator parameters (such as tidal volume, PEEP, etc.) may introduce residual confounding. Although NPAR was calculated using laboratory values from the first day of ICU admission, whether the measurements were obtained before or after intubation could not be determined. If blood samples were collected after intubation, NPAR values may reflect post-intubation inflammatory effects rather than the baseline status at ICU admission. Additionally, the timing of covariate collection relative to intubation was also unclear, potentially introducing time-dependent confounding. Furthermore, due to missing components of established ICU severity scores in the database, we were unable to calculate these scores and include them in the comparative analysis, which represents another data-related limitation of this study. Although we have strictly controlled for known confounding factors through multivariate regression analysis, the potential influence of unmeasured variables on the study results cannot be completely ruled out. Second, our study was limited to ICU patients with tracheal intubation; whether these findings apply to non-ICU intubated patients requires further investigation. Despite sensitivity analyses adjusting for primary diagnosis, we could not perform subgroup analyses by intubation indication (e.g., surgical vs. medical, elective vs. emergent) or primary etiology (e.g., ARDS vs. neurological) due to database limitations, restricting the applicability of our findings across diverse ICU intubation scenarios. Finally, the observational study design cannot establish a causal relationship between NPAR and outcomes, and all these limitations should be further explored through prospective, multicenter studies.
5. Conclusion
Our study suggests that higher NPAR levels may be associated with increased risks of in-hospital and ICU mortality in intubated patients. A non-linear relationship was observed, with an inflection point at approximately 2.2 in this cohort. However, given the single-center retrospective design, these findings require validation in multicenter prospective studies before clinical application.
Acknowledgments
I am grateful to Dr. Liu Jie of the Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, for his statistical support and insightful guidance on the manuscript.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Edited by: Emanuele Nicastri, National Institute for Infectious Diseases Lazzaro Spallanzani (IRCCS), Italy
Reviewed by: Prafulla Chandra Tiwari, King George’s Medical University, India
Farid Rajaee Rizi, Isfahan University of Medical Sciences, Iran
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://mimic.physionet.org/ (Reference number: 10.13026/C2X).
Ethics statement
The studies involving humans were approved by The Institutional Review Boards (IRB) of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because written informed consent was waived for this study because it involved the retrospective analysis of publicly available, fully de-identified patient data from the MIMIC-IV database. The requirement for individual patient consent was explicitly waived by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology, which approved the original data collection and public release of this de-identified dataset.
Author contributions
XH: Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft. JC: Conceptualization, Data curation, Formal analysis, Validation, Writing – original draft. WL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Resources, Supervision, Writing – review & editing. BL: Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2026.1804074/full#supplementary-material
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found at: https://mimic.physionet.org/ (Reference number: 10.13026/C2X).