Abstract
The prognostic value of the systemic immune–inflammation index (SII) in critically ill patients with acute asthma exacerbations remains unclear. This study examined the association between SII and in-hospital mortality. We performed a retrospective cohort study using the MIMIC-IV (v3.1) database, including adult critically ill patients with asthma exacerbations. SII was calculated from laboratory values obtained during the peri–intensive care unit admission period. The primary outcome was in-hospital mortality. Associations were assessed using restricted cubic spline and Cox proportional hazards models, adjusted for confounders. A total of 575 patients were included, with an overall in-hospital mortality of 6.6%. Mortality rates differed significantly across SII tertiles and were highest in tertile 3 (11.5%), compared with tertile 2 (3.6%) and tertile 1 (4.7%) (P = .003). In Cox proportional hazards models, compared with tertile 2, patients in tertile 3 had a significantly increased risk of in-hospital mortality (unadjusted hazard ratio [HR] 2.88, 95% confidence interval [CI] 1.21–6.83, P = .016; adjusted HR 2.82, 95% CI 1.12–7.10, P = .027), whereas no significant difference was observed for tertile 1. Restricted cubic spline analysis based on natural logarithm-transformed (ln-SII) demonstrated a significant nonlinear association with in-hospital mortality (P for nonlinearity < .001), with risk increasing markedly at higher ln-SII levels. Elevated SII was associated with increased in-hospital mortality and may serve as a simple prognostic marker in critically ill asthma exacerbations.
Keywords: asthma exacerbation, in-hospital mortality, inflammation biomarkers, prognosis, systemic immune–inflammation index
1. Introduction
Severe exacerbations continue to account for a substantial share of intensive care unit (ICU) admissions among patients with asthma, and neither improvements in supportive care nor advances in pharmacotherapy have eliminated the associated morbidity and mortality.[1,2] Bronchodilators and systemic corticosteroids suffice for the majority of patients; a minority nonetheless progress to life-threatening respiratory failure and come to require mechanical ventilation and hemodynamic support.[3,4] Once that threshold is crossed, clinical trajectories diverge widely, and conventional severity scores explain only part of that divergence.[5] Biomarkers that are obtainable at the bedside and that reflect the pathophysiological heterogeneity of severe exacerbations could therefore sharpen risk stratification and inform clinical decision-making.[6]
Asthma has traditionally been conceptualized as a type 2 eosinophilic inflammatory disease;[7,8] however, emerging evidence highlights the importance of non-eosinophilic phenotypes, particularly neutrophil-predominant inflammation, in severe and acute exacerbations.[9,10] A neutrophilic profile carries practical consequences: corticosteroid responsiveness is diminished, airway injury is more extensive, and clinical outcomes are worse.[11] Critical illness perturbs this picture further, since systemic stress responses, infection, and sepsis each drive immune dysregulation and organ dysfunction.[12,13] For patients managed in the ICU, then, indices that describe systemic immune and inflammatory status may prove more informative than measures confined to the airway.[14,15]
One such index is the systemic immune-inflammation index (SII), the product of the platelet and neutrophil counts divided by the lymphocyte count, which condenses three arms of the host response into a single value.[16] Neutrophils reflect innate immune activation and are central to acute inflammatory injury.[17] Lymphopenia, commonly observed in critical illness, is a marker of immune suppression and has been linked to adverse outcomes.[18] Platelets, beyond their role in hemostasis, actively participate in inflammation, endothelial dysfunction, and microvascular thrombosis.[19] Combined, the three describe the balance between pro-inflammatory drive and immunoregulatory capacity. Although the index was first proposed in oncology, its prognostic performance has since been documented across a range of conditions, among them cardiovascular disease and sepsis in critically ill cohorts.[20–22]
Evidence bearing on SII in asthma is accumulating but remains sparse and is drawn largely from non-acute settings. Population-based and observational analyses of stable disease report that higher values accompany a greater likelihood of prevalent and persistent asthma and predict long-term mortality,[23,24] findings that position the index as a measure of systemic inflammatory burden rather than of airway-limited inflammation.[25] SII has additionally been related to neutrophil-predominant phenotypes, which are encountered more often in severe or treatment-refractory disease.[26]
Despite these observations, the role of SII in acute and severe asthma exacerbations remains poorly defined. In critically ill patients, systemic inflammation, immune dysregulation, and organ dysfunction interact in ways that are difficult to disentangle, and the prognostic significance of SII in this context has not been established. In particular, there is a lack of evidence evaluating whether SII is associated with clinically meaningful outcomes such as mortality or the need for advanced organ support in the intensive care unit. Therefore, the present study aimed to investigate the association between SII and in-hospital mortality in critically ill patients with acute exacerbations of asthma and to evaluate its potential as a readily available prognostic marker in this population.
2. Materials and methods
2.1. Materials
2.1.1. Study design and data source
This was a retrospective observational cohort study carried out within the Medical Information Mart for Intensive Care IV (MIMIC-IV), version 3.1.[27] Reporting follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. MIMIC-IV is an openly accessible repository of de-identified critical care records assembled at Beth Israel Deaconess Medical Center in Boston, United States, spanning ICU admissions between 2008 and 2022. Institutional review board approval for the database was granted by the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Access followed completion of the mandated training and execution of the data use agreement (certification record ID: 73990653). Because every record is de-identified, the requirement for individual informed consent was waived.
2.1.2. Study population
Eligible patients were adults (aged ≥ 18 years) admitted to an ICU with acute exacerbation of asthma, ascertained from diagnostic codes of the International Classification of Diseases, ninth and tenth revisions (ICD-9, ICD-10). For ICD-9 we required a code beginning with 493 in which the fifth digit was 1 (status asthmaticus) or 2 (acute exacerbation); for ICD-10 we accepted the exacerbation phenotypes J4521, J4531, J4541, J4551, J45901, J45902, and J45991.
Inclusion required at least one ICU admission satisfying these coded criteria. Where a patient had been admitted to the ICU on more than one occasion, only the earliest stay entered the analysis, so that observations remained independent. Two further exclusions were applied: absence of the laboratory values needed to compute the SII within the peri-admission window, defined as the 24 hours preceding and the 24 hours following arrival in the ICU, and a lymphocyte count of zero, which leaves SII undefined.
2.2. Methods
2.2.1. Data extraction
Records were retrieved with structured query language through Google BigQuery. Cohort assembly, data processing, figure preparation, and all statistical procedures were carried out in R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria).
2.2.1.1. Definition of systemic immune–inflammation index
SII was computed as platelet count × neutrophil count ÷ lymphocyte count, every component being expressed in the same unit (109/L). Eligible measurements fell inside the peri-admission window defined above (24 hours either side of ICU admission); for each component, the value recorded closest to the admission timestamp was retained. Where absolute neutrophil or lymphocyte counts had not been reported, they were derived from the corresponding differential percentage and the total white blood cell count.
2.2.1.2. Covariates
Demographic variables comprised age, sex, and race. Comorbid burden was summarized by the Charlson Comorbidity Index, with congestive heart failure, diabetes, renal disease, malignancy, and liver disease additionally retained as individual indicators. Severity of illness on the day of ICU admission was captured by 2 composite instruments, the Sequential Organ Failure Assessment (SOFA) score and the Simplified Acute Physiology Score II (SAPS II), each computed from data recorded on the first ICU day; neurological status was described by the Glasgow Coma Scale. Physiological variables were taken as the most deranged first-day values (maximum heart rate, minimum systolic blood pressure, minimum mean blood pressure, and maximum respiratory rate). Laboratory variables were handled on the same principle, each summarized by the value denoting greatest clinical severity, whether minimum or maximum (minimum hemoglobin, minimum platelet count, maximum white blood cell count, minimum bicarbonate, maximum blood urea nitrogen, maximum serum creatinine, maximum glucose, maximum sodium, minimum potassium, and maximum chloride concentrations). Three time-sensitive variables were defined over that same peri-admission window (24 hours either side of ICU admission), including maximum serum lactate concentration, vasopressor exposure, taken as the use of any vasoactive agent (norepinephrine, epinephrine, dopamine, or vasopressin), and respiratory support, classified by the highest level received (invasive mechanical ventilation, noninvasive ventilation, high-flow nasal cannula, supplemental oxygen, or no respiratory support).
2.2.1.3. Outcome
In-hospital mortality served as the primary outcome and was defined as death occurring during the index hospitalization, as documented in hospital discharge records.
2.2.2. Handling of outliers and missing data
Overall, the proportion of missing data was low across most baseline variables. Serum lactate was the exception at 15.5%; every remaining variable fell below 2%. Before any imputation, extreme values judged physiologically implausible, and hence attributable to measurement or transcription error, were reset to missing. Given the low overall burden of missingness, continuous variables were completed by median imputation.
2.2.3. Statistical analysis
Continuous variables are presented as median (interquartile range) and were compared across SII tertiles by the Kruskal–Wallis test; categorical variables are presented as counts (percentages) and were compared by the chi-square test. Significance was set at a two-sided P-value < .05.
Because its distribution was right-skewed, SII was transformed to its natural logarithm (ln-SII) for continuous modeling. The shape of the association with in-hospital mortality was then described by restricted cubic spline functions with four knots, placed at the 5th, 35th, 65th, and 95th percentiles, within a Cox proportional hazards framework. Hazard ratios and 95% confidence intervals were referenced to the median ln-SII value. Wald tests evaluated the overall and the nonlinear components of the association, and spline curves were plotted to display the fitted relationship.
For the categorical analyses, SII was additionally divided into tertiles. Cox proportional hazards models estimated HRs and 95% confidence intervals (CIs) against the middle tertile as reference. Model 1 was unadjusted; Model 2 incorporated age, Charlson Comorbidity Index, and SAPS II; Model 3 incorporated a prespecified set of clinically relevant covariates, including age, sex, Charlson Comorbidity Index, malignancy, sepsis status, SAPS II score, vasopressor use, mechanical ventilation, and lactate level. Collinearity was screened with variance inflation factors, and any variable reaching a value ≥ 5 was dropped before the final model was fitted.
Time-to-event analysis used hospital length of stay as the time scale and in-hospital death as the event. Survival across tertiles was displayed with Kaplan–Meier curves and compared by the log-rank test.
3. Results
3.1. Study population and baseline characteristics
The final cohort comprised 575 patients admitted with acute exacerbation of asthma (Fig. 1). Tertiles of SII defined three groups of near-equal size: tertile 1 (≤1045, n = 192), tertile 2 (1045–2802, n = 192), and tertile 3 (>2802, n = 191) (Table 1). Median age was 59.0 years (interquartile range [IQR] 44.5–72.0), and 34.4% of patients were male, neither differing appreciably between tertiles. Comorbid burden was likewise evenly distributed, the median Charlson Comorbidity Index being 4.0 (IQR 2.0–6.0) and the frequencies of congestive heart failure (32.3%), diabetes (31.1%), renal disease (16.7%), and malignancy (7.3%) comparable across groups.
Figure 1.
Flow diagram of study population selection. ICU = intensive care unit, MIMIC-IV = Medical Information Mart for Intensive Care IV.
Table 1.
Baseline characteristics and clinical outcomes of patients with acute exacerbation of asthma stratified by systemic immune-inflammation index tertiles.
| Variable | Tertile 1 (n = 192) SII ≤ 1045 |
Tertile 2 (n = 192) 1045 < SII ≤ 2802 |
Tertile 3 (n = 191) SII > 2802 |
P-value |
|---|---|---|---|---|
| Age (years) | 62.00 (45.75; 75.00) | 58.00 (45.75; 69.25) | 58.00 (43.00; 71.50) | .237 |
| Male (%) | 68 (35.4%) | 69 (35.9%) | 61 (31.9%) | .670 |
| Race (%) | .057 | |||
| White | 82 (42.7%) | 104 (54.2%) | 96 (50.3%) | |
| Black | 58 (30.2%) | 48 (25%) | 39 (20.4%) | |
| Other | 52 (27.1%) | 40 (20.8%) | 56 (29.3%) | |
| Charlson Comorbidity Index | 4.00 (2.00; 7.00) | 4.00 (2.00; 6.00) | 4.00 (2.00; 6.00) | .895 |
| Diabetes (%) | 59 (30.7%) | 67 (34.9%) | 53 (27.7%) | .316 |
| Congestive heart failure (%) | 62 (32.3%) | 62 (32.3%) | 62 (32.5%) | .999 |
| Renal disease (%) | 30 (15.6%) | 34 (17.7%) | 32 (16.8%) | .861 |
| Liver disease (%) | 13 (6.8%) | 8 (4.2%) | 6 (3.1%) | .223 |
| Malignancy (%) | 13 (6.8%) | 13 (6.8%) | 16 (8.4%) | .784 |
| Heart rate (beats per minute) | 108.00 (96.00; 123.00) | 110.00 (98.00; 122.00) | 114.00 (101.00; 122.00) | .074 |
| Systolic blood pressure (mm Hg) | 98.00 (88.00; 109.00) | 97.00 (87.75; 109.00) | 97.00 (88.00; 108.00) | .741 |
| Mean blood pressure (mm Hg) | 62.00 (56.75; 69.00) | 62.00 (54.00; 71.00) | 61.00 (54.50; 71.00) | .809 |
| Respiratory rate (breaths per minute) | 28.00 (24.00; 33.00) | 29.00 (25.00; 35.00) | 29.00 (26.00; 34.00) | .064 |
| Glasgow coma scale | 15.00 (14.00; 15.00) | 15.00 (14.00; 15.00) | 15.00 (13.50; 15.00) | .186 |
| Lactate (mmol/L) | 2.00 (1.50; 2.90) | 2.00 (1.40; 2.80) | 2.00 (1.60; 3.50) | .230 |
| SII value (×109/L) | 596.64 (414.36; 790.20) | 1694.60 (1298.43; 2209.65) | 4731.67 (3555.80; 6279.99) | <.001 |
| ln-SII | 6.39 (6.03; 6.67) | 7.44 (7.17; 7.70) | 8.46 (8.18; 8.75) | <.001 |
| Platelet (×109/L) | 218.50 (161.75; 282.00) | 237.50 (192.00; 278.75) | 280.00 (218.50; 352.50) | <.001 |
| Lymphocyte (×109/L) | 2.01 (1.39; 2.96) | 1.28 (0.80; 1.66) | 0.69 (0.47; 0.95) | <.001 |
| Neutrophil (×109/L) | 5.34 (4.00; 7.11) | 8.83 (7.01; 11.34) | 12.29 (9.34; 16.07) | <.001 |
| White blood cell count (×109/L) | 9.60 (7.40; 13.05) | 12.00 (9.60; 15.12) | 15.40 (11.45; 19.80) | <.001 |
| Hemoglobin (g/dL) | 11.40 (10.17; 12.90) | 11.45 (10.20; 12.93) | 11.20 (10.00; 12.70) | .318 |
| Bicarbonate (mmol/L) | 23.00 (20.00; 26.00) | 23.00 (19.00; 27.00) | 23.00 (20.00; 26.00) | .831 |
| Blood urea nitrogen (mg/dL) | 19.00 (13.00; 27.00) | 18.50 (13.00; 28.25) | 20.00 (14.00; 28.00) | .611 |
| Creatinine (mg/dL) | 1.00 (0.80; 1.30) | 0.90 (0.80; 1.30) | 1.00 (0.80; 1.40) | .651 |
| Glucose (mg/dL) | 165.50 (140.00; 221.00) | 163.00 (131.00; 218.75) | 171.00 (140.00; 245.00) | .077 |
| Sodium (mmol/L) | 140.00 (138.00; 142.00) | 140.00 (138.00; 142.00) | 140.00 (137.00; 142.00) | .219 |
| Potassium (mmol/L) | 4.00 (3.70; 4.30) | 3.90 (3.60; 4.30) | 4.00 (3.60; 4.40) | .745 |
| Chloride (mmol/L) | 104.00 (100.00; 106.00) | 104.00 (101.00; 107.00) | 104.00 (100.00; 107.00) | .581 |
| SAPS II score | 29.50 (20.00; 39.00) | 28.50 (21.00; 36.00) | 30.00 (22.00; 40.00) | .647 |
| SOFA score | 1.00 (0.00; 2.00) | 1.00 (0.00; 2.00) | 1.00 (0.00; 2.00) | .227 |
| Sepsis (%) | 63 (32.8%) | 81 (42.2%) | 95 (49.7%) | .003 |
| Vasopressor use (%) | 17 (8.9%) | 14 (7.3%) | 23 (12%) | .268 |
| Mechanical ventilation (%) | 69 (35.9%) | 77 (40.1%) | 78 (40.8%) | .569 |
| In-hospital mortality (%) | 9 (4.7%) | 7 (3.6%) | 22 (11.5%) | .003 |
| ICU length of stay (days) | 1.63 (0.87; 3.04) | 1.96 (1.10; 4.14) | 2.05 (1.01; 4.20) | .006 |
| Hospital length of stay (days) | 4.74 (2.73; 8.26) | 6.12 (3.51; 10.73) | 5.73 (3.21; 10.47) | .011 |
Data are presented as median (interquartile range) for continuous variables and number (percentage) for categorical variables. Comparisons across tertiles were performed using the Kruskal–Wallis test for continuous variables and the chi-square test for categorical variables.
ICU = intensive care unit, ln-SII = natural logarithm-transformed systemic immune–inflammation index, SAPS II = Simplified Acute Physiology Score II, SII = systemic immune-inflammation index, SOFA = Sequential Organ Failure Assessment.
Severity of illness at ICU admission was low to moderate overall, with a median SAPS II score of 29.0 (IQR 21.0–39.0) and a median SOFA score of 1.0 (IQR 0.0–2.0), without meaningful differences between tertiles. The median lactate level was 2.0 mmol/L (IQR 1.5–3.0), and mechanical ventilation (41.6%) and vasopressor use (9.4%) were also similar across groups (Table 1). Hematologic and inflammatory measures, by contrast, separated the groups sharply: neutrophil, platelet, and white blood cell counts rose and lymphocyte counts fell stepwise from the lowest to the highest tertile (all P < .001). Sepsis became more frequent along the same gradient (32.8%, 42.2%, and 49.7%, P = .003).
3.2. In-hospital mortality
Thirty-eight patients (6.6%) died before discharge. The distribution across tertiles was uneven – 4.7% in tertile 1, 3.6% in tertile 2, and 11.5% in tertile 3 (P = .003) (Table 1). Length of stay followed the same pattern: those in the highest tertile stayed longer both in the ICU (median 2.05 vs 1.63 days, P = .006) and in hospital (median 5.73 vs 4.74 days, P = .011).
3.3. Nonlinear relationship between SII and mortality
Restricted cubic spline analysis showed a significant nonlinear association between ln-SII and in-hospital mortality (P for nonlinearity < .001) (Fig. 2). The predicted hazard ratio reached its lowest point at an ln-SII value of approximately 6.80, before increasing progressively at higher ln-SII levels. The reference value for hazard ratio estimation corresponded to the median ln-SII value of 7.43.
Figure 2.
Nonlinear association between natural logarithm-transformed systemic immune-inflammation index (ln-SII) and in-hospital mortality. The histogram along the x-axis represents the distribution of ln-SII, scaled as relative frequencies and rescaled to align with the y-axis for visualization purposes. CI = confidence interval, HR = hazard ratio, ln-SII = natural logarithm-transformed systemic immune-inflammation index.
3.4. Association between SII and in-hospital mortality
The categorical analyses reproduced the pattern seen on the spline curve. In Cox proportional hazards models (Table 2), patients in the highest SII tertile faced a significantly greater hazard of in-hospital death than the reference group (tertile 2).
Table 2.
Association between systemic immune-inflammation index tertiles and in-hospital mortality.
| Variable | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| HR (95% CI) | P-value | aHR (95% CI) | P-value | aHR (95% CI) | P-value | |
| Tertile 1 vs Tertile 2 | 1.71 (0.63–4.60) | .291 | 1.67 (0.61–4.61) | .320 | 1.81 (0.61–5.37) | .284 |
| Tertile 3 vs Tertile 2 | 2.88 (1.21–6.83) | .016 | 2.67 (1.12–6.38) | .027 | 2.82 (1.12–7.10) | .027 |
| Age | 1.01 (0.99–1.03) | .159 | 0.98 (0.95–1.00) | .102 | 0.99 (0.96–1.02) | .504 |
| Male vs Female | 1.34 (0.67–2.65) | .407 | – | – | 0.88 (0.42–1.84) | .740 |
| Charlson Comorbidity Index | 1.20 (1.08–1.34) | <.001 | 1.15 (0.99–1.33) | .060 | 1.16 (0.96–1.39) | .126 |
| Sepsis | 1.58 (0.79–3.14) | .193 | – | – | 1.33 (0.60–2.95) | .482 |
| Malignancy | 4.22 (2.01–8.85) | <.001 | – | – | 2.17 (0.88–5.37) | .093 |
| SAPS II score | 1.07 (1.05–1.10) | <.001 | 1.07 (1.04–1.10) | <.001 | 1.05 (1.01–1.08) | .005 |
| Mechanical ventilation | 1.73 (0.88–3.43) | .114 | – | – | 0.79 (0.33–1.87) | .589 |
| Vasopressor use | 2.14 (0.99–4.62) | .054 | – | – | 1.22 (0.49–3.01) | .670 |
| Lactate | 1.25 (1.15–1.35) | <.001 | – | – | 1.25 (1.13–1.38) | <.001 |
Hazard ratios (HRs) and adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were estimated using Cox proportional hazards models. Tertile 2 was used as the reference group for SII. Model 1 was unadjusted. Model 2 was adjusted for age, Charlson Comorbidity Index, and SAPS II score. Model 3 was adjusted for age, sex, Charlson Comorbidity Index, sepsis status, malignancy, SAPS II score, lactate level, vasopressor use, and mechanical ventilation.
aHR = adjusted hazard ratio, CI = confidence interval, SAPS II = Simplified Acute Physiology Score II.
In the unadjusted model, tertile 3 was associated with a hazard ratio (HR) of 2.88 (95% CI 1.21–6.83, P = .016). This association remained significant after multivariable adjustment (Model 2: adjusted hazard ratio [aHR] 2.67, 95% CI 1.12–6.38, P = .027; Model 3: aHR 2.82, 95% CI 1.12–7.10, P = .027). In contrast, no significant difference in mortality risk was observed between tertile 1 and tertile 2 across all models. Kaplan–Meier survival analysis further presented a significant difference in survival probability across SII tertiles (log-rank P = .037), with the highest tertile exhibiting the lowest survival over time (Fig. 3).
Figure 3.
Kaplan–Meier survival curves for in-hospital mortality stratified by systemic immune-inflammation index tertiles. ICU = intensive care unit, SII = systemic immune-inflammation index.
Two covariates carried independent associations with death: SAPS II score (aHR 1.05, 95% CI 1.01–1.08, P = .005) and lactate level (aHR 1.25, 95% CI 1.13–1.38, P < .001).
4. Discussion
In this large retrospective cohort study of critically ill patients with acute exacerbation of asthma, we found that an elevated SII was associated with increased in-hospital mortality. The association withstood adjustment for demographic factors, comorbidities, and illness severity, including SAPS II score and lactate level. We identified a significant nonlinear relationship between SII and mortality, with a marked increase in risk at higher SII values. These observations suggest that SII conveys information about systemic inflammation and immune dysregulation that conventional severity scores do not fully capture.
Our results extend prior observations on the prognostic role of SII in critical illness to a population of patients with acute exacerbation of asthma, a group in whom data on systemic inflammatory biomarkers remain limited. Although the classical characterization of asthma emphasizes type 2 eosinophilic disease,[7,8] a neutrophil-dominant phenotype is now well documented in severe and refractory cases.[9,26] Our cohort is consistent with that phenotype: ascending SII tertiles tracked with higher neutrophil and lower lymphocyte counts, the signature of innate immune activation accompanied by relative suppression of adaptive immunity. Such a configuration typifies systemic inflammatory states and has repeatedly been linked to worse outcomes in sepsis and other forms of critical illness.[28–30]
Mechanistic considerations render the association biologically plausible. Neutrophils drive acute lung injury through the release of proteases, reactive oxygen species, and extracellular traps, each of which damages the airway and degrades gas exchange.[31,32] Coexisting lymphopenia may reflect immune exhaustion or redistribution, limiting the host’s ability to regulate inflammation and respond to secondary insults.[18] Platelets act as mediators of inflammation and thrombosis and contribute to endothelial dysfunction and microvascular injury.[19] A composite of the three may therefore describe the host response more completely than any single cell count considered alone.
The nonlinearity of the relationship deserves emphasis. On the spline curve, predicted mortality reached its minimum at an intermediate ln-SII and climbed progressively thereafter; in the tertile analysis, only the uppermost group diverged from the reference, while the two lower groups were indistinguishable. Risk, in other words, is not proportional to SII across its whole range but concentrates at high values of the index. Threshold-like behavior of this kind is described with increasing frequency in critical care, where dysregulated rather than graded immune responses appear to drive harm.[33–35]
It is notable that baseline severity, as measured by SAPS II and SOFA scores, was statistically indistinguishable across SII tertiles. SII therefore encodes something these instruments omit – an interpretation reinforced by the persistence of its association with mortality once SAPS II had been entered into the model. Established predictors retained their own strong associations with death, as befits markers of tissue hypoperfusion and global physiological derangement. The 2 classes of variable thus appear complementary, which argues for testing whether inflammatory biomarkers add discrimination when incorporated into existing risk stratification frameworks.
The stepwise increase in the prevalence of sepsis across tertiles merits comment. Sepsis was not an independent predictor of mortality in the adjusted models, but its graded distribution suggests that part of the elevation in SII reflects infection, or a systemic inflammatory response to it. The index may consequently mark two overlapping processes at once: inflammation intrinsic to the exacerbation, and inflammation attributable to concurrent infection. Separating the two at the bedside is difficult but consequential, since the distinction bears on decisions about antibiotics and immunomodulatory therapy. That the association with mortality persisted after adjustment for sepsis implies that SII reports on the host response more broadly than infection alone.
From a clinical perspective, SII offers several advantages. It is derived from routinely available laboratory parameters, requires no additional cost, and can be calculated early in the course of ICU admission. In acute exacerbation of asthma, flagging patients at elevated risk early could inform the intensity of monitoring, the threshold for escalating therapy, and the allocation of scarce resources. The nonlinear shape of the association implies that any such application would rest on identifying a high-value threshold rather than on treating SII as a uniformly graded risk score, and defining that threshold will require dedicated study.
5. Limitations and future directions
Several constraints qualify these findings. The retrospective design permits no causal inference, and multivariable adjustment cannot exclude residual confounding. Case ascertainment relied on administrative coding, which admits the possibility of misclassification. Although sepsis and other clinical variables were accounted for, the respective contributions of infection and asthma-related inflammation to elevated SII could not be fully separated. Airway-specific measures, eosinophil counts and fractional exhaled nitric oxide among them, were unavailable, so asthma phenotype could not be characterized directly. Finally, the cohort was drawn from a single academic center, and external validation is required before the findings can be generalized to other ICU populations.
Future studies should explore the integration of SII with other biomarkers and clinical variables to develop more refined risk prediction models. Prospective investigations are also needed to evaluate whether SII-guided strategies can improve clinical outcomes. The interaction between SII and treatment response, corticosteroid therapy and adjunctive interventions in particular, remains unexplored and is clinically pertinent. Mechanistic studies of neutrophil-driven inflammation and immune dysregulation in severe asthma would help establish whether the association reported here reflects a causal pathway or a marker of overall inflammatory burden.
6. Conclusions
In a cohort of critically ill patients with acute exacerbation of asthma, higher SII levels were associated with greater in-hospital mortality, with a nonlinear association when SII was analyzed on a natural logarithmic scale. These findings suggest that SII may have prognostic value in this population. Further prospective studies are warranted to validate and clarify the prognostic utility of SII in critically ill patients with acute exacerbations of asthma.
Author contributions
Conceptualization: Phan Ngoc An Huynh, Thanh Luan Nguyen.
Investigation: Phan Ngoc An Huynh, Vu Thanh Le, Ngoc Thu Le, Van Hoang Nam Ho.
Methodology: Phan Ngoc An Huynh, Thanh Luan Nguyen.
Supervision: Phan Ngoc An Huynh, Thanh Luan Nguyen.
Writing – review & editing: Phan Ngoc An Huynh, Vu Thanh Le, Ngoc Thu Le, Ngoc Nhu Y Nguyen, Van Hoang Nam Ho, Thi Thu Phuong Lam, Khoi Minh Tran, Cong Dang Tran, Thanh Luan Nguyen.
Formal analysis: Vu Thanh Le, Khoi Minh Tran, Thanh Luan Nguyen.
Writing – original draft: Vu Thanh Le, Ngoc Thu Le, Ngoc Nhu Y Nguyen, Thanh Luan Nguyen.
Data curation: Ngoc Thu Le, Ngoc Nhu Y Nguyen, Thanh Luan Nguyen.
Validation: Ngoc Nhu Y Nguyen, Van Hoang Nam Ho, Thi Thu Phuong Lam, Cong Dang Tran, Thanh Luan Nguyen.
Visualization: Thi Thu Phuong Lam, Cong Dang Tran, Thanh Luan Nguyen.
Software: Khoi Minh Tran, Cong Dang Tran, Thanh Luan Nguyen.
Project administration: Thanh Luan Nguyen.
Abbreviations:
- aHR
- adjusted hazard ratio
- CI
- confidence interval
- HR
- hazard ratio
- ICD
- International Classification of Diseases
- ICU
- intensive care unit
- IQR
- interquartile range
- ln-SII
- natural logarithm-transformed systemic immune–inflammation index
- MIMIC-IV
- Medical Information Mart for Intensive Care IV
- SAPS II
- simplified acute physiology score II
- SII
- systemic immune-inflammation index
- SOFA
- Sequential Organ Failure Assessment
Medical Information Mart for Intensive Care IV (MIMIC-IV) database was approved by the institutional review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Access to the database was granted to Thanh Luan Nguyen after completion of the Collaborative Institutional Training Initiative (CITI) Program (certification record ID: 73990653). The study was conducted in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was waived for the present study because it used de-identified data.
The authors have no funding and conflicts of interest to declare.
The data that support the findings of this study are available from a third party, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of the third party.
How to cite this article: Huynh PNA, Le VT, Le NT, Nguyen NNY, Ho VHN, Lam TTP, Tran KM, Tran CD, Nguyen TL. Systemic immune–inflammation index and in-hospital mortality in critically ill patients with acute asthma exacerbation: A retrospective cohort study. Medicine 2026;105:42(e51080).
Contributor Information
Phan Ngoc An Huynh, Email: hpnan@nctu.edu.vn.
Vu Thanh Le, Email: lnthu@nctu.edu.vn.
Ngoc Thu Le, Email: lnthu@nctu.edu.vn.
Ngoc Nhu Y Nguyen, Email: ntluan@nctu.edu.vn.
Van Hoang Nam Ho, Email: hvhnam@nctu.edu.vn.
Thi Thu Phuong Lam, Email: lttphuong@nctu.edu.vn.
Khoi Minh Tran, Email: tcdang@nctu.edu.vn.
Cong Dang Tran, Email: tcdang@nctu.edu.vn.
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