Continuous tissue CO₂ monitoring for early microcirculatory assessment across a broad ICU population : a descriptive and prognostic study

Continuous tissue CO₂ monitoring for early microcirculatory assessment across a broad ICU population : a descriptive and prognostic study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Continuous tissue CO₂ monitoring for early microcirculatory assessment across a broad ICU population : a descriptive and prognostic study Baptiste Duchamp, Fabrice Vallée, Jérôme Cartailler, Romain Barthélémy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9495408/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract BACKGROUND : Microcirculatory dysfunction is common in critical care, but has been primarily described in severely ill patients. While its prognostic significance is well established in septic shock, it might be associated with poor outcome in other critical conditions. The tissue-to-arterial CO₂ gradient (PatCO₂) provides a straightforward, non-invasive and dynamic approach to microcirculatory assessment, but its relevance beyond acute circulatory failure remains unclear. We aimed to determine whether the evolution of PatCO₂ over the first 48 hours predicts 28-day mortality in a broad intensive care unit (ICU) population. METHODS : This prospective, descriptive study included 94 adult patients between November 2022 and September 2023, within 24 hours of admission to a mixed surgical ICU, regardless of admission diagnosis. PatCO₂ was measured using a transcutaneous CO₂ sensor and arterial blood gas analysis at regular intervals during the first 48 hours. The primary endpoint was 28-day mortality. RESULTS : Mean age was 61±16 years, mean SOFA score at 24 hours was 5.7±4. Admission diagnoses included acute brain injury (42%), sepsis/septic shock (33%), non-septic shock (16%), and acute respiratory failure (9%). Admission PatCO₂ did not differ between survivors and non survivors (17.0±9 mmHg vs. 20.4±13 mmHg, p = 0.31). In contrast, its evolution between day 1 and day 2 (∆ (d2-d1) PatCO₂) was associated with mortality, independently from macrohemodynamics. Microcirculatory dysfunction improved in survivors (∆ (d2-d1) PatCO₂: – 4.6±8.0 mmHg), whereas it worsened in nonsurvivors (+ 3.6±14.2 mmHg, p < 0.05), with higher PatCO₂ on day 2 in nonsurvivors (23.1±18.3 mmHg vs. 12.7 ±5.4 mmHg, p < 0.05). Based on these findings, we designed a simple prognostic score—SkinScore—based on the average 48-hour and day 2 PatCO₂. SkinScore effectively stratified mortality risk and independently predicted death (AUC 0.90 [0.82–0.96]). Integrating Skinscore into SOFA significantly improved prognostic performance (AUC 0.90 [0.80–0.97] vs. 0.81 [0.68–0.91], p < 0.05). CONCLUSION : Early and dynamic microcirculatory monitoring via tissue capnometry predicts outcome in a diverse and moderately ill ICU population. Our findings indicate that many ICU patients may exhibit microperfusion abnormalities associated with prognosis, supporting broader use of this non-invasive, continuous method as a microcirculatory marker. Microcirculatory dysfunction Microcirculation Capnometry Transcutaneous Non-invasive Hemodynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND Microcirculatory dysfunction has been documented in a wide range of critical care conditions. While it has been extensively characterized in septic shock (1–3), microvascular alterations have also been associated with increased mortality and organ failure in other settings, including polytrauma and hemorrhagic shock (4–6), cardiogenic shock (7,8), acute respiratory distress syndrome (9,10), or under veno-arterial extracorporeal membrane oxygenation (11). Based on preclinical models and indirect clinical evidence, systemic microcirculatory impairments may even play a role in acute brain injury (12,13). Notably, this microcirculatory failure may persist despite adequate resuscitation and normalization of macrocirculatory parameters (14,15), a phenomenon now recognized as “loss of hemodynamic coherence” (16). Despite the recognized relevance of microcirculatory alterations in critical illness, their integration into diagnostic and therapeutic strategies remains limited by the lack of a practical, noninvasive bedside monitoring tool (17,18). While side-stream dark field (SDF) videomicroscopy, allowing direct visualization of the microcirculation, is considered the gold standard, its use in routine clinical practice is hindered by technical constraints and the complexity and delay associated with image acquisition and interpretation (19). Lactate levels, routinely monitored in intensive care, hold well-established prognostic value (20,21), but likely reflect global cellular stress rather than serving as a specific surrogate for microcirculatory perfusion (22). Since microcirculatory dysfunction is characterized by reduced functional capillary density and impaired microvascular flow (1), tissue CO₂ content—which is highly dependent on both parameters (23–26) — has long been considered a relevant functional marker of microvascular impairment. Tissue CO₂ partial pressure (PtiCO₂), historically measured by gastric tonometry, has been identified as a prognostic marker in various settings (27,28). However, despite its physiological rationale, this technique is invasive, costly, and technically demanding (29), making it unsuitable for widespread clinical application. In this context, transcutaneous CO₂ pressure (PtCO₂), measured noninvasively using a simple earlobe sensor, has emerged as a practical, continuous, and easily implementable tool for assessing microcirculatory status (2,26,30,31). In a cohort of 46 patients with adequately resuscitated septic shock, it has been demonstrated that an elevated tissue to arterial PCO₂ gradient (PatCO₂) at 24 hours, or a rising gradient within the first 36 hours, effectively discriminated 28-day nonsurvivors from survivors (2). Similarly, in a separate cohort of 59 patients with various types of shock, the same group reported significantly higher 24-hour PatCO₂ values in nonsurvivors (30). Critical care is not limited to the management of the most severely ill patients requiring agressive organ support; the admission and surveillance of mildly ill but clinically unstable patients "at risk" of deterioration is common practice. In this context, we hypothesized that prompt microcirculatory monitoring using PatCO₂ could provide valuable prognostic information across a broad population of critically ill patients by identifying early microcirculatory dysfunction. The objective of this prospective study was to evaluate the prognostic value of changes in PatCO₂ during the first 48 hours following admission to a mixed intensive care unit. MATERIAL AND METHODS Study design and population This was a prospective, monocentric cohort study conducted between December 2022 and September 2023 in the surgical intensive care unit (ICU) of Lariboisière University Hospital, a 12-bed ICU in a tertiary academic center in Paris, France. The study was approved by the institutional ethics committee (reference No 21.05515.210551-MS01). As the study was considered part of routine care evaluation, patients were enrolled following informed notification and the absence of objection from either themselves or, when required, their next of kin. Any post hoc refusal to participate resulted in immediate withdrawal from the study. Patients were enrolled within the first 24 hours of their ICU admission, regardless of the underlying cause. Inclusion criteria were: Age ≥ 18 years Admission to the ICU for < 24 hours Presence of an investigator within the first 24 hours after admission to initiate patient recruitment Exclusion criteria were: Clinical diagnosis of brain death upon ICU admission Skin or tissue damage of the earlobe Patient or next-of-kin refusal to participate Patient management The study did not interfere with patient management, which was carried out in accordance with current national and international guidelines. Hemodynamic monitoring (arterial catheter, central venous catheter, oesophagal doppler, transthoracic echocardiography and Swan-Ganz catheter if deemed necessary) was introduced when indicated for patients presenting shock, acute respiratory failure, or severe brain injury. Standard hemodynamic targets were a mean arterial pressure ≥65 mmHg, cardiac index ≥2.5 L/min/m², central venous oxygen saturation >70% and an arterio-venous CO₂ gap < 6 mmHg. The treating physicians were blinded to PtCO₂ values, which were not used for therapeutic decision-making and were not considered a treatment target during the study. Transcutaneous CO₂ monitoring PtCO₂ was continuously measured at the earlobe using a Radiometer TCM5 Flex monitor, which was present in each room. After in vitro calibration, the sensor was applied to the earlobe using the dedicated clip and conductive gel, as recommended by the manufacturer. The sensor was initially heated to 42°C during the initial phase, then set to 37°C for continuous monitoring (Smart Heat procedure of the device). PtCO₂ values were only recorded after they remained stable for at least 10 minutes following temperature setting to 37°C. The device and sensors were maintained in accordance with manufacturer guidelines. Study protocol The following variables were recorded after enrollment: demographics, medical and history (including hypertension, peripheral arterial disease, diabetes mellitus, chronic respiratory disease, chronic cardiac disease, active malignancy), surgical history, chronic alcohol or tobacco use, reason for ICU admission, use and dosage of vasopressors, need for mechanical ventilation, admission lactatemia, SOFA score, Charlson comorbidity index and SAPS II score. Admission diagnoses were subsequently categorized into four diagnostic groups: (1) Acute brain injury, (2) Acute respiratory failure, (3) Non-septic shock and (4) Sepsis or septic shock. For each patient, after checking for adequate signal quality, PtCO₂ measurements were recorded at baseline (H0), then at H6, H12, H18, H24, H36, and H48. Simultaneously, the following variables were documented: cardiac frequency, arterial mean, systolic and diastolic pressure, core temperature, cardiac output (if a continuous monitoring device had been placed, such as Edwards Lifescience Ò pulmonary artery catheter or Deltex Ò esophageal Doppler), vasopressor dose, mechanical ventilation status, sedation status, fraction of inspired oxygen, and end-tidal CO₂ for intubated patients. When the investigators were unavailable, data collection was delegated to medical or nursing staff on duty. Arterial and/or central venous blood gas analyses were collected simultaneously when clinically indicated by the physician in charge, after verification of correct catheter positioning by chest radiography. SOFA and SAPS II scores over the first 24 hours were computed for each patient. PatCO₂ values were calculated at each timepoint according to this formula : PatCO₂ = PtCO₂ – PaCO₂. Mean day 1 PatCO₂ and day 2 PatCO₂ are average values for H0, H6, H12, H18 and for H24, H36, H48, respectively. ∆ (d2-d1) PatCO₂ was calculated for each patient, when feasible, according to this formula: ∆ (d2-d1) PatCO₂ = mean day 2 PatCO2 – mean day 1 PatCO2. Based on previous studies, microcirculatory dysfunction was defined as a PatCO₂ above 10mmHg (30). SkinScore calculation Considering the evolution of average PatCO₂ values over the first two days of resuscitation, a classification was proposed that could reflect the severity of the patients' condition: A 48-hour mean PatCO₂ > 30 mmHg defined the high-risk group (SkinScore = 4). Among patients with a 48-hour mean PatCO₂ ≤ 30 mmHg, two subgroups were identified: a low-risk group (SkinScore = 0) with a day-2 mean PatCO₂ < 10 mmHg, and a moderate-risk group (SkinScore = 2) with a day-2 mean PatCO₂ ≥ 10 mmHg. We then tested the predictive value of this score alone and in combination with other severity criteria (see below). Outcomes The principal outcome was mortality at 28 days following ICU admission. The secondary outcome was mortality at 7 days following admission. Nonsurvivors were further stratified into early (≤ day 7) and late (day 7-day 28) nonsurvivors. Statistical analysis All statistical analyses were performed using R software (http://www.r-project.org/). Continuous variables are presented as mean (SD) or median [IQR], as appropriate. Normality test was done using Shapiro-Wilk tests. Groups were compared using the Student’s t -test for simple comparison or one way ANOVA with Tukey’s post-hoc test for multiple comparisons. Adjusted mean values of PatCO₂ were estimated using marginal means derived from a linear model with the SOFA score included as a covariate. We used a linear mixed-effects model to assess potential differences in the evolution of PatCO₂ over time between groups. Time, group, and their interaction were included as fixed effects and random intercept for each group was included to account for within-group correlation. Survival analysis was conducted using the Kaplan–Meier method, and survival curves were compared with the log-rank statistical test. A multivariable Cox proportional hazards model was used to estimate adjusted hazard ratios between different SkinScore categories. The model was adjusted for potential confounders after backward stepwise selection. The predictive value of the SkinScore and the modified SOFA for 28-day mortality were assessed using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) computed from logistic regression adjusted for relevant covariates. Variable selection for these models was also based on backward stepwise procedures to reduce overfitting. ROC curves were compared using DeLong statistical test. A two-sided p -value < 0.05 was considered statistically significant, except for AUC comparisons where a one-sided p -value was used to test the superiority of the modified SOFA score. For sensitivity analyses, missing PaCO₂ and PtCO₂ values were imputed using a two-step method combining cubic spline interpolation and probabilistic extrapolation. Non-terminal gaps (i.e., missing values surrounded by observed values) were imputed using cubic spline interpolation with natural boundary conditions. Terminal missing values (before the first or after the last observed time point) were extrapolated using samples drawn from a normal distribution whose parameters (mean μ, standard deviation σ) were estimated from the empirical distribution of median differences between successive measurements across all patients. The imputed values were centered on a convex combination (30% of the individual series median and 70% of the last known value), thus preserving both central tendency and local temporal dynamics. A minimum of three observed values per patient was required for imputation. RESULTS Data completion and patient characteristics On a total of 390 patients admitted to ICU during the study period, 94 patients were included in the final analysis, after initial screening and exclusion (96 screened; 2 excluded) (Fig. 1). At least one PtCO₂ or PatCO₂ value was collected during day 1 for 94 (100%) or 80 (85%) patients, respectively. At day 2, 81 (86%) and 62 (66%) patients had at least one PtCO₂ or PatCO₂ measurement, respectively. Survival status at day 28 was available for all patients. The mean age of the cohort was 61±16 years, and 52% were male (Table 1). The primary causes for ICU admission were acute brain injury (42%), sepsis or septic shock (33%), non-septic shock (16%), and acute respiratory failure (9%). The median SAPS II score was 44±18 and the mean SOFA score at 24 hours was 5.7±4. At day 28, 22 patients had died, corresponding to an overall mortality rate of 23%. When comparing survivors and nonsurvivors, the latter were significantly older (69±11 vs. 58±16 years, p < 0.05), more frequently male (77 vs. 44%, p < 0.05), and more often admitted for sepsis (50 vs. 28%), whereas survivors were more commonly admitted for acute brain injury (47 vs. 27%) (Table 1). Pre-existing comorbidities were more prevalent among nonsurvivors, including chronic cardiac disease (41 vs. 11%, p < 0.05), chronic alcoholism (32 vs. 7%, p < 0.05), and cancer (32 vs. 11%, p < 0.05). Nonsurvivors also presented with more severe organ dysfunction, as reflected by a higher SOFA score at 24 hours (8.6±3.5 vs. 4.8±3.6, p < 0.05) and a higher SAPS II (54.7±17.3 vs. 40.4±16.7, p < 0.05). Similarly, nonsurvivors exhibited higher lactatemia values over the entire 48h-period (2.9±3.0 vs. 1.2±1.8 mM, p < 0.05). However, macrocirculatory parameters did not differ between survivors and nonsurvivors on day 1 or day 2, except for mean heart rate on day 1 (94±23 vs. 82±19 bpm, p < 0.05; Supplementary Table 2). PtCO₂ and PatCO₂ values during the first 48h following admission. A total of 484 PtCO₂ values and 420 PaCO₂ values were collected throughout the study. The average PtCO₂ value at baseline was 54 ±12 mmHg and remained stable over the first 48 hours, with mean values of 55 ±12 mmHg at day 1, 54 ±12 mmHg at day 2, and 55 ±11mmHg over the entire 48-hour period (Table 2). The mean PatCO₂ at baseline for the entire cohort was 18 ±10 mmHg. At baseline, microcirculatory dysfunction (defined as PatCO₂ > 10 mmHg) was observed in 77% (n = 53) of survivors and 90% (n = 20) of nonsurvivors (Supplementary Fig. 11). The observed values ranged from 2 mmHg to 71 mmHg. Over the first 48 hours following admission, the mean PatCO₂ was 17.6 ±10.4 mmHg. Reported p-values refer to the comparison between Survivors and Nonsurvivors. PatCO₂ trend over 48h is associated with day 28 mortality Although PatCO₂ at baseline did not significantly differ between survivors and nonsurvivors (17±9 vs. 20±13 mmHg, p = 0.3, see supplementary figure 1), its evolution over time was significatively associated with prognosis (p < 0.05, Fig. 2A). The change in PatCO₂ between day 1 and day 2 (∆ (d2-d1) PatCO₂) was significantly different between groups : nonsurvivors exhibited a mean increase of +3.6 (±14.2) mmHg, whereas survivors showed a decrease of −4.6 (±8.0)mmHg (p = 0.03) (Fig. 2B). No significant difference was observed in PatCO₂ during the first 24 hours (22±17 vs. 17±7 mmHg, p = 0.194), but the divergence became significant at day 2 (Fig. 2C), with lower values observed in survivors (13±5 vs. 23±18 mmHg, p 10mmHg over the same period (p < 0.05). We obtained similar results when adjusting PatCO₂ values for disease severity evaluated by SOFA score at day 1 (Supplementary fig. 2). Indeed, adjusted mean ∆ (d2-d1) PatCO₂ was significantly higher in nonsurvivors than survivors (+4.2 vs. -4.8 mmHg, p < 0.01), and lower adjusted mean day 2 PatCO₂ values were observed in survivors (13.0 vs. 22.4 mmHg, p < 0.01). Early and late nonsurvivors exhibit different PatCO₂ profiles When further stratifying nonsurvivors into early (≤ day 7, n = 7) and late (day 7–28, n= 15) deaths, three distinct PatCO₂ trajectories emerged (Fig. 2A). Early nonsurvivors demonstrated significantly greater PatCO₂ levels over the 48-hour period compared to survivors and late nonsurvivors (37±25 vs. 16±5 mmHg and 17±8 mmHg respectively, Fig. 3A). Admission PatCO₂ were significantly higher in early nonsurvivors (28±21 mmHg) compared to the other two groups (16±3 mmHg and 17±9 mmHg) (Supplementary fig. 3). However, between day 1 and day 2, PatCO₂ decreased among survivors (−4.6±8.0 mmHg), while it increased among late nonsurvivors (+3.2±10.1 mmHg), displaying a near significative difference (Fig. 3B, p = 0.05). In alignment with this, late mortality (between day 7 and day 28) was significantly higher in patients having a rising rather than a decreasing PatCO₂ (44% vs. 14%, p < 0.05, Supplementary fig. 5). Day 2 PatCO₂ tended to be higher in late nonsurvivors compared to survivors (18±13 vs. 13±5 mmHg), although this difference was not significant (p = 0.13). Again, similar results were obtained after adjustment for disease severity evaluated by SOFA score at day 1 (Supplementary fig. 4). Adjusted mean PatCO₂ during 48h were 15.8, 16.7 and 35.4 mmHg for survivors, late nonsurvivors and early nonsurvivors respectively (p < 0.05 between early nonsurvivors and survivors or late nonsurvivors), while it reached 12.7 and 18.3mmHg for survivors and late nonsurvivors at day 2. Adjusted ∆ (d2-d1) PatCO₂ was inferior for survivors in comparison to late nonsurvivors (-4.9 and +3.3mmHg, p = 0.05). SkinScore, a post-hoc derived score for microcirculatory assessment Based on these findings, we developed a pragmatic, bedside scoring system—termed « SkinScore »—to stratify patients according to their microcirculatory dysfunction risk using PatCO₂ trajectories. Patients with consistently elevated PatCO₂ levels over the first 48 hours (defined as a 48-hour mean PatCO₂ > 30 mmHg) were classified as “High” risk and assigned a SkinScore of 4. Among those with a mean 48-hour PatCO₂ < 30 mmHg, we identified two subgroups: the “Low” risk group (SkinScore = 0) with a day 2 mean PatCO₂ 10 mmHg. The SkinScore could be calculated in 63 patients, with 19 classified as Low, 39 as Moderate, and 5 as High. 28-day survival was significantly associated with the SkinScore (p < 0.01), displaying a dose-response effect (Fig. 4). Indeed, in multivariate analysis, the hazard ratio for Moderate and High Skinscore was 10.8 [1.38-84.4] and 437.7 [29.7-6449.6], respectively (Supplementary figure 9). When pooling Moderate and High Skinscore together, their hazard ratio was 13.0 [1.7-99.5] in multivariate analysis (Supplementary fig. 10). Low, Moderate and High Skinscore group respectively displayed 5%, 31% and 100% mortality at day 28 (Supplementary fig. 6). Interestingly, a Moderate Skinscore was associated with a later death, as 85% of the nonsurvivors in this group were late nonsurvivors. Mean SkinScore was significantly different between survivors and early or late nonsurvivors (1.18 vs. 3.33 or 2.0, p < 0.05, Supplementary fig. 7). Integrating SkinScore with SOFA To assess the prognostic performance of the SkinScore, we compared it to the traditional 24-hour SOFA score and evaluated a combined “Modified SOFA” score that simply added the SkinScore values to the original SOFA parameters. The modified SOFA demonstrated an AUC of 0.90 [0.80–0.97] in multivariate analysis, which was significantly greater than the AUC of 24-hour SOFA alone (0.81 [0.68-0.91], p < 0.05; Fig. 5). The SkinScore alone had an AUC of 0.90 [0.82-0.96] in multivariate analysis, which was not significantly greater than the AUC of 24-hour SOFA alone (p = 0.06). Univariate analysis yielded similar results (Supplementary fig. 8). Altogether, incorporation of microcirculatory assessment via the SkinScore was associated with improved prognostic accuracy compared with traditional organ dysfunction scores alone. DISCUSSION In this prospective study including 94 critically ill patients, we observed that early microcirculatory monitoring by tissue capnometry over 48 hours was a strong predictor of 28-day mortality. While early microcirculatory impairment - assessed by PatCO₂ measurement - was not discriminative at baseline, its subsequent course was closely associated with outcome: microcirculatory perfusion improved in survivors and worsened in nonsurvivors, remaining significantly impaired in the latter at day 2. PatCO₂ trajectories were also able to discriminate distinct prognostic profiles. Early nonsurvivors (< day 7) displayed persistently elevated levels throughout the first 48 hours. Conversely, both survivors and late nonsurvivors (≥ day 7) presented with moderately elevated baseline values but microcirculatory dysfunction deteriorated in late nonsurvivors, whereas it resolved in survivors. Based on these findings, we developed the SkinScore, integrating mean 48-hour and day-2 PatCO₂ values. It independently stratified 28-day mortality risk, performed comparably to the SOFA score when used alone, and further improved prognostic accuracy when integrated into a modified version of SOFA. This study confirms the prognostic value of early and repeated microcirculatory monitoring using tissue capnometry in a diverse ICU population (2,27,30), independently of macrocirculatory status. While previous work focused primarily on patients in shock (2,27,30), we observed similar patterns in a broader and less severely ill cohort, suggesting wider applicability of this marker. Although ICU patients exhibited a strikingly high prevalence of microcirculatory dysfunction across all admission diagnoses (77% of survivors and 90% of nonsurvivors), baseline PatCO₂ was not prognostic in our study. This finding is consistent with some (2,27) but not all previous reports (30,32). Beyond a potential lack of power, this may reflect the broader case mix and lower initial severity of our cohort, where only early nonsurvivors showed significantly higher admission values than survivors. Importantly, temporal changes—rather than isolated values—were most strongly associated with outcome, underscoring the value of serial assessment and confirming previous work (2,27). While prior studies have reported persistent microvascular abnormalities beyond 24 hours in nonsurvivors, most analyses have relied on group-level comparisons and failed to assess individual dynamics (2–4,30,33). Our study is among the few to evaluate microcirculatory evolution on a per-patient basis, using ∆ (d2–d1) PatCO₂. This individualized approach demonstrated prognostic relevance, contrasting with heterogeneous results from prior work. To date, only one other study has reported consistent —and strikingly similar— findings, associating worsening or sustained microvascular dysfunction with delayed or early mortality, respectively (8). In contrast, other studies have reported conflicting results, likely due to methodological limitations such as infrequent sampling —related to the technical complexity of videomicroscopy—, grouping biases (34), or limited statistical power (35). These discrepancies may also reflect differences in measurement technique. Tissue capnometry more consistently identifies persistent microcirculatory impairment in nonsurvivors (2,27,30), whereas videomicroscopy studies show greater variability (3,4,6,8,33), possibly due to differing sensitivities, temporal resolutions, and underlying conceptual frameworks — either functional, or anatomical (36). Altogether, these results support the feasibility and prognostic value of tissue capnometry beyond high-severity settings, suggesting its potential role in broader ICU triage and monitoring strategies. They underscore the relevance of a dynamic, individualized approach to better capture the prognostic implications of a potentially rapidly evolving microcirculatory dysfunction. In our secondary analysis, we identified three distinct trajectories of microcirculatory dysfunction, each associated with a specific outcome. Early nonsurvivors (death before day 7) showed markedly elevated PatCO₂ values at admission that remained high over 48 hours, suggesting severe and persistent microcirculatory failure. This profile aligns with previous studies linking sustained perfusion abnormalities to early death and worsening organ failure (3). Conversely, in patients surviving beyond day 7, overall PatCO₂—although abnormally elevated in comparison to ICU controls (30)—did not discriminate between late nonsurvivors and survivors. Instead, prognosis appeared linked to the early evolution of microcirculatory status: increasing PatCO₂ values were associated with late mortality, while early improvement predicted survival. This supports previous work showing that the prognostic impact of microcirculatory dysfunction also depends on its early evolution (2,3,8). Notably, the identification of three microcirculatory profiles aligns with three typical ICU mortality patterns: early death, late death, and survival. Early deaths are often driven by the severity of the initial insult and refractory organ failure (37), underpinned by pathophysiological mechanisms such as systemic inflammation, coagulation activation, and endothelial dysfunction (38–41) — all key contributors to microcirculatory impairment (42–44). In contrast, late deaths are frequently related to secondary complications (37), and our findings suggest that persistent or worsening microcirculatory dysfunction, leading to sustained tissue hypoxia, may contribute to delayed organ failure and poor outcomes (3). These findings support the use of early and repeated PatCO₂ monitoring not only to identify patients at high risk of early death, but also to guide clinical vigilance and resource allocation by detecting those likely to deteriorate or, conversely, to recover favorably over the longer term. Based on the microcirculatory profiles we identified, we developed post-hoc the SkinScore , a simple two-step prognostic score. Mean 48-hour PatCO₂ and mean PatCO₂ at day 2 stratify mortality risk into three groups: low (early normalization or absence of dysfunction), intermediate (prolonged but moderate dysfunction), and high (persistent severe impairment) SkinScore. This score was independently associated with 28-day mortality with an AUROC of 0.90 . When integrated into a modified SOFA score (mSOFA), prognostic performance improved compared to SOFA alone, underlining the added value of microcirculatory assessment to organ dysfunction scoring in critically ill patients— a notable feature of our study. In line with the idea of an unstable hemodynamic coherence (16), these results suggest that microcirculatory dysfunction may constitute a distinct organ failure, influencing patient outcome. Earlier studies, in highly selected cohorts, showed strong prognostic value of PatCO₂ measured at 24 or 36 hours and during thermal reactivity tests population (2,30). In our broader and less severe population, these findings were not replicated, likely due to microcirculatory heterogeneity and reduced statistical power. However, our approach’s strength lies in capturing the prognostic significance of early microcirculatory dynamics, which static measurements fail to reflect. Unlike lactate, widely monitored in ICU, or sublingual videomicroscopy measurements, considered as the gold standard for microcirculation assessment, the SkinScore offers a specific, practical, and reproducible tool reflecting the dynamic evolution of microcirculatory failure. Indeed, videomicroscopy is a cumbersome technique (19), still limited to clinical research to this point, and lactataemia, despite being strongly associated with prognosis (20), is probably more a global stress marker than a proxy for microcirculatory dysfunction (22,45). Nevertheless, our study has several limitations. First, although this is the largest cohort to date assessing early PatCO₂ in critically ill patients, its overall size—and particularly the number of early nonsurvivors—remains limited, which may have reduced the statistical power of some analyses. Second, the heterogeneity of the study population, while increasing external validity, likely introduced variability in PatCO₂ trajectories and limited the interpretability, thereby potentially limiting statistical power. Third, missing PatCO₂ values at day 2, mostly among less severely ill patients not requiring arterial blood gas analysis, may have introduced a non-random missing data bias. However, results remained consistent after SOFA adjustment and multiple imputation, suggesting that missing data did not substantially compromise the reliability of our results. Fourth, selection bias cannot be ruled out, as inclusion depended on investigator availability, potentially excluding the most severely ill patients who died shortly after admission, often before PtCO₂ monitoring. However, prognostic assessment in such moribund cases is likely of limited clinical relevance given their predictable outcomes. Finally, the SkinScore was developed post hoc as an exploratory tool; its prognostic value should therefore be confirmed in a dedicated prospective cohort. CONCLUSION To conclude, in this broad prospective cohort of critically ill patients with an overall mild severity, early monitoring using tissue capnometry for 48 hours following ICU admission identified worsening and persistent microcirculatory dysfunction as strong prognostic markers associated with 28-day mortality. In this context we developed the SkinScore, a microcirculatory prognostic index independently predicting mortality and providing complementary information to routine organ failure assessment by SOFA score. These findings support the use of non-invasive tissue capnometry as a practical tool for early detection and follow up of microcirculatory alterations across a broad spectrum of critical illnesses. Abbreviations ABI : Acute brain injury ANOVA : Analysis of variance AUC : Area under the receiver operating characteristics curve CO₂ : Carbon dioxide ECMO : Extra-corporeal membrane oxygenation GCS : Glasgow coma scale HR : Hazard ratio ICU : Intensive care unit IQR : Interquartile range KDIGO : Kidney disease: improving global outcomes m-SOFA : modified SOFA ns : nonsignificant PaO₂ : Arterial O₂ partial pressure PaCO₂ : Arterial CO₂ partial pressure PatCO₂ : tissue-to-arterial CO₂ gradient PtCO₂ : Transcutaneous CO₂ partial pressure ROC : Receiver operating characteristics SOFA : Sequential organ failure assessment SAPS-II: Simplified acute physiology score II SD : Standard deviation SDF : Sidestream darkfield imaging SEM : Standard error of the mean Δ(d2–d1)PatCO₂: PatCO₂ variation between day 1 and day 2 Declarations Ethics approval and consent to participate This observational study was approved by our institutionnal Ethics Committee (reference No 21.05515.210551-MS01). Patients were informed of the study and included in the absence of objection in accordance with French law. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors do not report any competing interests. As part of this study, the research team received monitors and consumables from Radiometer (Denmark). Acknowledgements The authors thank Safa Manaa for technical help and Radiometer for providing Radiometer TCM5 Flex monitor for the study. The sponsor was Assistance Publique – Hôpitaux de Paris (Direction de la Recherche Clinique et de l'Innovation). Authors contributions BD, MK and FV designed the study and analysed the data. BD and MK included the patients and were responsible for clinical data collection. BD and JC performed statistical analysis and missing data imputation. BD mainly wrote the first draft of the article. All authors revised and approved the article. BD is the corresponding author. Funding The study was funded by a grant from Programme Recherche Hospitalo-Universitaire en Santé RHU 2021 (The French National Research Agency – ANR) References De Backer D, Creteur J, Preiser J-C, Dubois M-J, Vincent J-L. Microvascular blood flow is altered in patients with sepsis. Am J Respir Crit Care Med. 2002;166(1):98–104. DOI: 10.1164/rccm.200109-016oc Vallée F, Mateo J, Dubreuil G, Poussant T, Tachon G, Ouanounou I, et al. Cutaneous ear lobe Pco₂ at 37°C to evaluate microperfusion in patients with septic shock. Chest. 2010;138(5):1062–70. DOI: 10.1378/chest.09-2690 Sakr Y, Dubois M-J, De Backer D, Creteur J, Vincent J-L. Persistent microcirculatory alterations are associated with organ failure and death in patients with septic shock. Crit Care Med. 2004;32(9):1825–31. 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DOI: 10.1016/j.bpa.2016.11.001 Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMicrocirculatorymonitoringICUPatCO2.docx Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 03 May, 2026 Editor assigned by journal 29 Apr, 2026 Submission checks completed at journal 29 Apr, 2026 First submitted to journal 22 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9495408","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634044723,"identity":"09f31140-db12-4eb0-9097-02ba755751a9","order_by":0,"name":"Baptiste Duchamp","email":"data:image/png;base64,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","orcid":"","institution":"AP-HP, Hopital Lariboisière","correspondingAuthor":true,"prefix":"","firstName":"Baptiste","middleName":"","lastName":"Duchamp","suffix":""},{"id":634044724,"identity":"173252f6-5bc3-4a59-a182-6c0d74a211e1","order_by":1,"name":"Fabrice Vallée","email":"","orcid":"","institution":"AP-HP, Hopital Lariboisière","correspondingAuthor":false,"prefix":"","firstName":"Fabrice","middleName":"","lastName":"Vallée","suffix":""},{"id":634044725,"identity":"85f5b7ac-b4b0-467c-b7a4-c7f48924ff43","order_by":2,"name":"Jérôme Cartailler","email":"","orcid":"","institution":"Université Paris Cité, INSERM U942 MASCOTT","correspondingAuthor":false,"prefix":"","firstName":"Jérôme","middleName":"","lastName":"Cartailler","suffix":""},{"id":634044726,"identity":"cf6a4406-58b3-4a6c-a99a-0a1ee5b7b346","order_by":3,"name":"Romain Barthélémy","email":"","orcid":"","institution":"Université Paris Cité, INSERM U942 MASCOTT","correspondingAuthor":false,"prefix":"","firstName":"Romain","middleName":"","lastName":"Barthélémy","suffix":""},{"id":634044727,"identity":"41441053-83f5-4af4-bc90-81a47f67ba8c","order_by":4,"name":"Benjamin G. Chousterman","email":"","orcid":"","institution":"Université Paris Cité, INSERM U942 MASCOTT","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"G.","lastName":"Chousterman","suffix":""},{"id":634044728,"identity":"8a168798-bab5-4d45-b891-b3f2467f5c6b","order_by":5,"name":"Manuel Kindermans","email":"","orcid":"","institution":"Université Paris Cité, INSERM U942 MASCOTT","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Kindermans","suffix":""}],"badges":[],"createdAt":"2026-04-22 11:10:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9495408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9495408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109077721,"identity":"9ba6923f-449c-4cbe-8fa2-fb87c72a1e91","added_by":"auto","created_at":"2026-05-12 11:10:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26949,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart.\u003c/p\u003e\n\u003cp\u003eAmong the 390 patients admitted during the study period, 96 (25% of admitted patients) were screened, and 94 included in the study. Survival status at day 28 was available for all 94 patients: 72 were survivors, and 22 were nonsurvivors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/e7c3c30847ee285794c79a9b.png"},{"id":109078074,"identity":"6bf9ac27-c834-4ef4-8a39-b0b39dd3d9e0","added_by":"auto","created_at":"2026-05-12 11:12:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40761,"visible":true,"origin":"","legend":"\u003cp\u003eSurvivors and nonsurvivors exhibit different PatCO₂ evolutions during 48h\u003c/p\u003e\n\u003cp\u003eA. Mean PatCO₂ (mmHg) evolution during the first 48h following admission in ICU for Survivors (blue), Nonsurvivors (red), Early nonsurvivors (dark red) and Late nonsurvivors (orange). Evolution of PatCO₂ during 48h was significatively different between Survivors and Nonsurvivors.\u003c/p\u003e\n\u003cp\u003eB. Comparison of mean ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂, mmHg between Survivors and Nonsurvivors. ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ is significantly higher in Nonsurvivors than in Survivors (+3.6 vs. −4.6 mmHg, p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eC. Comparison of mean PatCO₂ during day 2 between Survivors and Nonsurvivors. Day 2 mean PatCO₂ is significantly higher in nonsurvivors than Survivors (12.7 vs. 23.1mmHg, p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eData are expressed as mean ± SEM. PatCO₂ = arterio-tissular gradient in CO₂, ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ : PatCO₂ variation between day 1 and day 2. Hrs = hours.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/28f2f6508f50e0b8330abc53.png"},{"id":109077718,"identity":"5e841df9-7da1-431c-9a05-e00dcbe46e85","added_by":"auto","created_at":"2026-05-12 11:09:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17681,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of PatCO₂ differentiates Survivors from Early and Late Nonsurvivors\u003c/p\u003e\n\u003cp\u003eA. 48-hour mean PatCO₂ comparison. Mean 48h PatCO₂ is significantly higher in early nonsurvivors than in late nonsurvivors and survivors (36.6 vs. 16.7 and 15.6 mmHg, p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eB. ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ comparison between survivors and nonsurvivors. ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ is higher in late nonsurvivors than in survivors (+3.2 vs. −4.6 mmHg, p = 0.05). The difference between early nonsurvivors and survivors is not statistically significant (+4.8 vs. -4.6 mmHg).\u003c/p\u003e\n\u003cp\u003eData are expressed as mean ± SEM. PatCO₂ : arterio-tissular gradient in CO₂, ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ : PatCO₂ difference between day 2 and day 1 ; ns : nonsignificant.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/00f69adec22f0bed56623444.png"},{"id":109077775,"identity":"1c90c5a1-2730-4ff2-9522-ce953422d637","added_by":"auto","created_at":"2026-05-12 11:10:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81987,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier 28-day survival curve according to SkinScore.\u003c/p\u003e\n\u003cp\u003eHigh SkinScore (red) corresponds to mean 48h PatCO₂ \u0026gt; 30 mmHg.\u003c/p\u003e\n\u003cp\u003eModerate SkinScore (orange) corresponds to mean 48h PatCO₂ \u0026lt; 30 mmHg mean day 2 PatCO₂ \u0026gt; 10 mmHg.\u003c/p\u003e\n\u003cp\u003eLow Skinscore (blue) corresponds to mean 48h PatCO₂\u0026lt; 30 mmHg and mean day 2 PatCO₂ \u0026lt; 10 mmHg.\u003c/p\u003e\n\u003cp\u003eDay 28 mortality for Low, Moderate and High SkinScore are 5.3%, 33% and 100% respectively.\u003c/p\u003e\n\u003cp\u003ePatCO₂ : arterio-tissular gradient in CO₂.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/612029735ea526ffdf37228e.png"},{"id":109077774,"identity":"34e4e71e-764e-4a54-9bc4-e7cfcb358a38","added_by":"auto","created_at":"2026-05-12 11:10:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25894,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of multivariate ROC curves for day-28 mortality prediction using 24h SOFA, SkinScore alone, or modified SOFA\u003c/p\u003e\n\u003cp\u003eModified SOFA, consisting of the addition of 24-hour SOFA and SkinScore, displays a significantly greater AUC than 24-hour SOFA alone (0.90 [0.80–0.97] vs. 0.81 [0.68-0.91]).\u003c/p\u003e\n\u003cp\u003eSkinScore alone has a greater AUC than SOFA alone (0.90 [0.82-0.96]), although not statistically significant (p = 0.06).\u003c/p\u003e\n\u003cp\u003eSOFA Score : Sequential Organ Failure Assessment Score; m-SOFA : modified SOFA ; AUC : Area Under the receiver operating characteristic Curve.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/eae16afc42ff4bf49ca8fd28.png"},{"id":109079391,"identity":"ad8b8b72-96d1-4efb-83f3-b3108178aca7","added_by":"auto","created_at":"2026-05-12 11:20:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":396991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/e81e6f6a-8445-494e-b12f-5225567cecf4.pdf"},{"id":109077769,"identity":"19a00a05-795b-4690-9013-9c56b69aef8a","added_by":"auto","created_at":"2026-05-12 11:10:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4540235,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMicrocirculatorymonitoringICUPatCO2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/6784fd3b88f0b50ce1375987.docx"},{"id":109077925,"identity":"c365f8e2-b256-494c-9226-0be03275a8dc","added_by":"auto","created_at":"2026-05-12 11:11:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39409,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9495408/v1/f68737c778252abba5ab1643.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Continuous tissue CO₂ monitoring for early microcirculatory assessment across a broad ICU population : a descriptive and prognostic study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMicrocirculatory dysfunction has been documented in a wide range of critical care conditions. While it has been extensively characterized in septic shock\u0026nbsp;(1–3), microvascular alterations have also been associated with increased mortality and organ failure in other settings, including polytrauma and hemorrhagic shock\u0026nbsp;(4–6), cardiogenic shock (7,8), acute respiratory distress syndrome (9,10), or under veno-arterial extracorporeal membrane oxygenation (11). Based on preclinical models and indirect clinical evidence, systemic microcirculatory impairments may even play a role in acute brain injury (12,13). Notably, this microcirculatory failure may persist despite adequate resuscitation and normalization of macrocirculatory parameters (14,15), a phenomenon now recognized as “loss of hemodynamic coherence” (16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the recognized relevance of microcirculatory alterations in critical illness, their integration into diagnostic and therapeutic strategies remains limited by the lack of a practical, noninvasive bedside monitoring tool (17,18). \u0026nbsp;While side-stream dark field (SDF) videomicroscopy, allowing direct visualization of the microcirculation, is considered the gold standard, its use in routine clinical practice is hindered by technical constraints and the complexity and delay associated with image acquisition and interpretation (19). \u0026nbsp;Lactate levels, routinely monitored in intensive care, hold well-established prognostic value (20,21), but likely reflect global cellular stress rather than serving as a specific surrogate for microcirculatory perfusion (22).\u003c/p\u003e\n\u003cp\u003eSince microcirculatory dysfunction is characterized by reduced functional capillary density and impaired microvascular flow (1), tissue CO₂\u0026nbsp;content—which is highly dependent on both parameters\u0026nbsp;(23–26)\u0026nbsp;— has long been considered a relevant functional marker of microvascular impairment. Tissue CO₂\u0026nbsp;partial pressure (PtiCO₂), historically measured by gastric tonometry, has been identified as a prognostic marker in various settings\u0026nbsp;(27,28). However, despite its physiological rationale, this technique is invasive, costly, and technically demanding\u0026nbsp;(29), making it unsuitable for widespread clinical application. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this context, transcutaneous CO₂\u0026nbsp;pressure (PtCO₂), measured noninvasively using a simple earlobe sensor, has emerged as a practical, continuous, and easily implementable tool for assessing microcirculatory status (2,26,30,31). In a cohort of 46 patients with adequately resuscitated septic shock, it has been demonstrated that an elevated tissue to arterial PCO₂\u0026nbsp;gradient (PatCO₂) at 24 hours, or a rising gradient within the first 36 hours, effectively discriminated 28-day nonsurvivors from survivors\u0026nbsp;(2). Similarly, in a separate cohort of 59 patients with various types of shock, the same group reported significantly higher 24-hour PatCO₂\u0026nbsp;values in nonsurvivors\u0026nbsp;(30).\u003c/p\u003e\n\u003cp\u003eCritical care is not limited to the management of the most severely ill patients requiring agressive organ support; the admission and surveillance of mildly ill but clinically unstable patients \"at risk\" of deterioration is common practice. In this context, we hypothesized that prompt microcirculatory monitoring using PatCO₂\u0026nbsp;could provide valuable prognostic information across a broad population of critically ill patients by identifying early microcirculatory dysfunction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe objective of this prospective study was to evaluate the prognostic value of changes in PatCO₂ during the first 48 hours following admission to a mixed intensive care unit.\u0026nbsp;\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003ch3\u003eStudy design and population\u003c/h3\u003e\n\u003cp\u003eThis was a prospective, monocentric cohort study conducted between December 2022 and September 2023 in the surgical intensive care unit (ICU) of Lariboisi\u0026egrave;re University Hospital, a 12-bed ICU in a tertiary academic center in Paris, France. The study was approved by the institutional ethics committee (reference No 21.05515.210551-MS01). As the study was considered part of routine care evaluation, patients were enrolled following informed notification and the absence of objection from either themselves or, when required, their next of kin. Any post hoc refusal to participate resulted in immediate withdrawal from the study.\u003c/p\u003e\n\u003cp\u003ePatients were enrolled within the first 24 hours of their ICU admission, regardless of the underlying cause. Inclusion criteria were:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAge \u0026ge; 18 years\u003c/li\u003e\n \u003cli\u003eAdmission to the ICU for \u0026lt; 24 hours\u003c/li\u003e\n \u003cli\u003ePresence of an investigator within the first 24 hours after admission to initiate patient recruitment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExclusion criteria were:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eClinical diagnosis of brain death upon ICU admission\u003c/li\u003e\n \u003cli\u003eSkin or tissue damage of the earlobe\u003c/li\u003e\n \u003cli\u003ePatient or next-of-kin refusal to participate\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003ePatient management\u003c/h3\u003e\n\u003cp\u003eThe study did not interfere with patient management, which was carried out in accordance with current national and international guidelines. Hemodynamic monitoring (arterial catheter, central venous catheter, oesophagal doppler, transthoracic echocardiography and Swan-Ganz catheter if deemed necessary) was introduced when indicated for patients presenting shock, acute respiratory failure, or severe brain injury. Standard hemodynamic targets were a mean arterial pressure \u0026ge;65 mmHg, cardiac index \u0026ge;2.5 L/min/m\u0026sup2;, central venous oxygen saturation \u0026gt;70% and an arterio-venous CO₂ gap \u0026lt; 6 mmHg. The treating physicians were blinded to PtCO₂ values, which were not used for therapeutic decision-making and were not considered a treatment target during the study.\u003c/p\u003e\n\u003ch3\u003eTranscutaneous CO₂ monitoring\u003c/h3\u003e\n\u003cp\u003ePtCO₂\u0026nbsp;was continuously measured at the earlobe using a Radiometer TCM5 Flex monitor, which was present in each room. After in vitro calibration, the sensor was applied to the earlobe using the dedicated clip and conductive gel, as recommended by the manufacturer. The sensor was initially heated to 42\u0026deg;C during the initial phase, then set to 37\u0026deg;C for continuous monitoring (Smart Heat procedure of the device). PtCO₂\u0026nbsp;values were only recorded after they remained stable for at least 10 minutes following temperature setting to 37\u0026deg;C. The device and sensors were maintained in accordance with manufacturer guidelines.\u003c/p\u003e\n\u003ch3\u003eStudy protocol\u003c/h3\u003e\n\u003cp\u003eThe following variables were recorded after enrollment: demographics, medical and history (including hypertension, peripheral arterial disease, diabetes mellitus, chronic respiratory disease, chronic cardiac disease, active malignancy), surgical history, chronic alcohol or tobacco use, reason for ICU admission, use and dosage of vasopressors, need for mechanical ventilation, admission lactatemia, SOFA score, Charlson comorbidity index and SAPS II score.\u003c/p\u003e\n\u003cp\u003eAdmission diagnoses were subsequently categorized into four diagnostic groups: (1) Acute brain injury, (2) Acute respiratory failure, (3) Non-septic shock and (4) Sepsis or septic shock.\u003c/p\u003e\n\u003cp\u003eFor each patient, after checking for adequate signal quality, PtCO₂\u0026nbsp;measurements were recorded at baseline (H0), then at H6, H12, H18, H24, H36, and H48. Simultaneously, the following variables were documented: cardiac frequency, arterial mean, systolic and diastolic pressure, core temperature, cardiac output (if a continuous monitoring device had been placed, such as Edwards Lifescience\u003csup\u003e\u0026Ograve;\u003c/sup\u003e pulmonary artery catheter or Deltex\u003csup\u003e\u0026Ograve;\u003c/sup\u003e esophageal Doppler), vasopressor dose, mechanical ventilation status, sedation status, fraction of inspired oxygen, and end-tidal CO₂\u0026nbsp;for intubated patients. When the investigators were unavailable, data collection was delegated to medical or nursing staff on duty.\u003c/p\u003e\n\u003cp\u003eArterial and/or central venous blood gas analyses were collected simultaneously when clinically indicated by the physician in charge, after verification of correct catheter positioning by chest radiography. SOFA and SAPS II scores over the first 24 hours were computed for each patient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatCO₂\u0026nbsp;values were calculated at each timepoint according to this formula : PatCO₂\u0026nbsp;= PtCO₂\u0026nbsp;\u0026ndash; PaCO₂. Mean day 1 PatCO₂\u0026nbsp;and day 2 PatCO₂\u0026nbsp;are average values for H0, H6, H12, H18 and for H24, H36,\u0026nbsp;H48, respectively. ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ was calculated for each patient, when feasible, according to this formula: ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ = mean day 2 PatCO2 \u0026ndash; mean day 1 PatCO2. Based on previous studies, microcirculatory dysfunction was defined as a PatCO₂ above 10mmHg (30).\u003c/p\u003e\n\u003ch3\u003eSkinScore calculation\u003c/h3\u003e\n\u003cp\u003eConsidering the evolution of average PatCO₂\u0026nbsp;values over the first two days of resuscitation, a classification was proposed that could reflect the severity of the patients\u0026apos; condition: A 48-hour mean PatCO₂\u0026nbsp;\u0026gt; 30 mmHg defined the high-risk group (SkinScore = 4). Among patients with a 48-hour mean PatCO₂\u0026nbsp;\u0026le;\u0026nbsp;30 mmHg, two subgroups were identified: a low-risk group (SkinScore = 0) with a day-2 mean PatCO₂\u0026nbsp;\u0026lt; 10 mmHg, and a moderate-risk group (SkinScore = 2) with a day-2 mean PatCO₂\u0026nbsp;\u0026ge;\u0026nbsp;10 mmHg.\u003c/p\u003e\n\u003cp\u003eWe then tested the predictive value of this score alone and in combination with other severity criteria (see below).\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe principal outcome was mortality at 28 days following ICU admission. The secondary outcome was mortality at 7 days following admission. Nonsurvivors were further stratified into early (\u0026le; day 7) and late (day 7-day 28) nonsurvivors.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (http://www.r-project.org/). Continuous variables are presented as mean (SD) or median [IQR], as appropriate. \u0026nbsp; Normality test was done using Shapiro-Wilk tests. Groups were compared using the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for simple comparison or one way ANOVA with Tukey\u0026rsquo;s post-hoc test for multiple comparisons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdjusted mean values of PatCO₂\u0026nbsp;were estimated using marginal means derived from a linear model with the SOFA score included as a covariate. We used a linear mixed-effects model to assess potential differences in the evolution of PatCO₂\u0026nbsp;over time between groups. Time, group, and their interaction were included as fixed effects and random intercept for each group was included to account for within-group correlation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurvival analysis was conducted using the Kaplan\u0026ndash;Meier method, and survival curves were compared with the log-rank statistical test. A multivariable Cox proportional hazards model was used to estimate adjusted hazard ratios between different SkinScore categories. The model was adjusted for potential confounders after backward stepwise selection. The predictive value of the SkinScore and the modified SOFA for 28-day mortality were assessed using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) computed from logistic regression adjusted for relevant covariates. Variable selection for these models was also based on backward stepwise procedures to reduce overfitting. \u0026nbsp;ROC curves were compared using DeLong statistical test. A two-sided \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 was considered statistically significant, except for AUC comparisons where a one-sided \u003cem\u003ep\u003c/em\u003e-value was used to test the superiority of the modified SOFA score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor sensitivity analyses, missing PaCO₂ and PtCO₂ values were imputed using a two-step method combining cubic spline interpolation and probabilistic extrapolation. Non-terminal gaps (i.e., missing values surrounded by observed values) were imputed using cubic spline interpolation with natural boundary conditions. Terminal missing values (before the first or after the last observed time point) were extrapolated using samples drawn from a normal distribution whose parameters (mean \u0026mu;, standard deviation \u0026sigma;) were estimated from the empirical distribution of median differences between successive measurements across all patients. The imputed values were centered on a convex combination (30% of the individual series median and 70% of the last known value), thus preserving both central tendency and local temporal dynamics. A minimum of three observed values per patient was required for imputation.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch3\u003eData completion and patient characteristics\u003c/h3\u003e\n\u003cp\u003eOn a total of 390 patients admitted to ICU during the study period, 94 patients were included in the final analysis, after initial screening and exclusion (96 screened; 2 excluded) (Fig. 1). At least one PtCO₂ or PatCO₂ value was collected during day 1 for 94 (100%) or 80 (85%) patients, respectively. At day 2, 81 (86%) and 62 (66%) patients had at least one PtCO₂ or PatCO₂ measurement, respectively. Survival status at day 28 was available for all \u0026nbsp;patients.\u003c/p\u003e\n\u003cp\u003eThe mean age of the cohort was 61\u0026plusmn;16 years, and 52% were male (Table 1). The primary causes for ICU admission were acute brain injury (42%), sepsis or septic shock (33%), non-septic shock (16%), and acute respiratory failure (9%). The median SAPS II score was 44\u0026plusmn;18 and the mean SOFA score at 24 hours was 5.7\u0026plusmn;4. At day 28, 22 patients had died, corresponding to an overall mortality rate of 23%.\u003c/p\u003e\n\u003cp\u003eWhen comparing survivors and nonsurvivors, the latter were significantly older (69\u0026plusmn;11 vs. 58\u0026plusmn;16 years, p \u0026lt; 0.05), more frequently male (77 vs. 44%, p \u0026lt; 0.05), and more often admitted for sepsis (50 vs. 28%), whereas survivors were more commonly admitted for acute brain injury (47 vs. 27%) (Table 1). Pre-existing comorbidities were more prevalent among nonsurvivors, including chronic cardiac disease (41 vs. 11%, p \u0026lt; 0.05), chronic alcoholism (32 vs. 7%, p \u0026lt; 0.05), and cancer (32 vs. 11%, p \u0026lt; 0.05). Nonsurvivors also presented with more severe organ dysfunction, as reflected by a higher SOFA score at 24 hours (8.6\u0026plusmn;3.5 vs. 4.8\u0026plusmn;3.6, p \u0026lt; 0.05) and a higher SAPS II (54.7\u0026plusmn;17.3 vs. 40.4\u0026plusmn;16.7, p \u0026lt; 0.05). Similarly, nonsurvivors exhibited higher lactatemia values over the entire 48h-period (2.9\u0026plusmn;3.0 vs. 1.2\u0026plusmn;1.8 mM, p \u0026lt; 0.05). However, macrocirculatory parameters did not differ between survivors and nonsurvivors on day 1 or day 2, except for mean heart rate on day 1 (94\u0026plusmn;23 vs. 82\u0026plusmn;19 bpm, p \u0026lt; 0.05; Supplementary Table 2).\u003c/p\u003e\n\u003ch3\u003ePtCO₂ and PatCO₂ values during the first 48h following admission.\u003c/h3\u003e\n\u003cp\u003eA total of 484 PtCO₂ values and 420 PaCO₂ values were collected throughout the study. The average PtCO₂ value at baseline was 54 \u0026plusmn;12 mmHg and remained stable over the first 48 hours, with mean values of 55 \u0026plusmn;12 mmHg at day 1, 54 \u0026plusmn;12 mmHg at day 2, and 55 \u0026plusmn;11mmHg over the entire 48-hour period (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mean PatCO₂\u0026nbsp;at baseline for the entire cohort was 18 \u0026plusmn;10 mmHg. At baseline, microcirculatory dysfunction (defined as\u003cem\u003e\u0026nbsp;\u003c/em\u003e PatCO₂ \u0026gt; 10 mmHg) was observed in 77% (n = 53) of survivors and 90% (n = 20) of nonsurvivors (Supplementary Fig. 11). The observed values ranged from 2 mmHg to 71 mmHg. Over the first 48 hours following admission, the mean PatCO₂ was 17.6 \u0026plusmn;10.4 mmHg.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReported p-values refer to the comparison between Survivors and Nonsurvivors.\u003c/p\u003e\n\u003ch3\u003ePatCO₂ trend over 48h is associated with day 28 mortality\u003c/h3\u003e\n\u003cp\u003eAlthough PatCO₂\u0026nbsp;at baseline did not significantly differ between survivors and nonsurvivors (17\u0026plusmn;9 vs. 20\u0026plusmn;13 mmHg, p = 0.3, see supplementary figure 1), its evolution over time was significatively associated with prognosis (p \u0026lt; 0.05, Fig. 2A). The change in PatCO₂\u0026nbsp;between day 1 and day 2 (∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂) was significantly different between groups : nonsurvivors exhibited a mean increase of +3.6 (\u0026plusmn;14.2) mmHg, whereas survivors showed a decrease of \u0026minus;4.6 (\u0026plusmn;8.0)mmHg (p = 0.03) (Fig. 2B). No significant difference was observed in PatCO₂ during the first 24 hours (22\u0026plusmn;17 vs. 17\u0026plusmn;7 mmHg, p = 0.194), but the divergence became significant at day 2 (Fig. 2C), with lower values observed in survivors (13\u0026plusmn;5 vs. 23\u0026plusmn;18 mmHg, p \u0026lt; 0.05). Furthermore, microvascular dysfunction was observed in 94% of nonsurvivors at day 2, whereas only 59% of survivors had a mean \u0026nbsp;PatCO₂ \u0026gt; 10mmHg over the same period (p \u0026lt; 0.05). We obtained similar results when adjusting PatCO₂ values for disease severity evaluated by SOFA score at day 1 (Supplementary fig. 2). Indeed, adjusted mean ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ was significantly higher in nonsurvivors than survivors (+4.2 vs. -4.8 mmHg, p \u0026lt; 0.01), and lower adjusted mean day 2 PatCO₂ values were observed in survivors (13.0 vs. 22.4 mmHg, p \u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEarly and late nonsurvivors exhibit different PatCO₂ profiles\u003c/h3\u003e\n\u003cp\u003eWhen further stratifying nonsurvivors into early (\u0026le; day 7, n = 7) and late (day 7\u0026ndash;28, n= 15) deaths, three distinct PatCO₂\u0026nbsp;trajectories emerged (Fig. 2A). Early nonsurvivors demonstrated significantly greater PatCO₂\u0026nbsp;levels over the 48-hour period compared to survivors and late nonsurvivors (37\u0026plusmn;25 vs. 16\u0026plusmn;5 mmHg and 17\u0026plusmn;8 mmHg respectively, Fig. 3A). Admission PatCO₂\u0026nbsp;were significantly higher in early nonsurvivors (28\u0026plusmn;21 mmHg) compared to the other two groups (16\u0026plusmn;3 mmHg and 17\u0026plusmn;9 mmHg) (Supplementary fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, between day 1 and day 2, PatCO₂\u0026nbsp;decreased among survivors (\u0026minus;4.6\u0026plusmn;8.0 mmHg), while it increased among late nonsurvivors (+3.2\u0026plusmn;10.1 mmHg), displaying a near significative difference (Fig. 3B, p = 0.05). In alignment with this, late mortality (between day 7 and day 28) was significantly higher in patients having a rising rather than a decreasing PatCO₂\u0026nbsp;(44% vs. 14%, p \u0026lt; 0.05, Supplementary fig. 5).\u003c/p\u003e\n\u003cp\u003eDay 2 PatCO₂\u0026nbsp;tended to be higher in late nonsurvivors compared to survivors (18\u0026plusmn;13 vs. 13\u0026plusmn;5 mmHg), although this difference was not significant (p = 0.13). Again, similar results were obtained after adjustment for disease severity evaluated by SOFA score at day 1 (Supplementary fig. 4). Adjusted mean PatCO₂\u0026nbsp;during 48h were 15.8, 16.7 and 35.4 mmHg for survivors, late nonsurvivors and early nonsurvivors respectively (p \u0026lt; 0.05 between early nonsurvivors and survivors or late nonsurvivors), while it reached 12.7 and 18.3mmHg for survivors and late nonsurvivors at day 2. Adjusted ∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂ was inferior for survivors in comparison to late nonsurvivors \u0026nbsp;(-4.9 and +3.3mmHg, p = 0.05).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSkinScore, a post-hoc derived score for microcirculatory assessment\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eBased on these findings, we developed a pragmatic, bedside scoring system\u0026mdash;termed \u0026laquo;\u0026nbsp;SkinScore\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026raquo;\u0026mdash;to stratify patients according to their microcirculatory dysfunction risk using PatCO₂ trajectories. Patients with consistently elevated PatCO₂ levels over the first 48 hours (defined as a 48-hour mean PatCO₂ \u0026gt; 30 mmHg) were classified as \u0026ldquo;High\u0026rdquo; risk and assigned a SkinScore of 4. Among those with a mean 48-hour PatCO₂ \u0026lt; 30 mmHg, we identified two subgroups: the \u0026ldquo;Low\u0026rdquo; risk group (SkinScore = 0) with a day 2 mean PatCO₂ \u0026lt; 10 mmHg, and a \u0026ldquo;Moderate\u0026rdquo; risk group (SkinScore = 2) with a day 2 mean PatCO₂ \u0026gt; 10 mmHg. The SkinScore could be calculated in 63 patients, with 19 classified as Low, 39 as Moderate, and 5 as High.\u003c/p\u003e\n\u003cp\u003e28-day survival was significantly associated with the SkinScore (p \u0026lt; 0.01), displaying a dose-response effect (Fig. 4). Indeed, in multivariate analysis, the hazard ratio for Moderate and High Skinscore was 10.8 [1.38-84.4] and 437.7 [29.7-6449.6], respectively (Supplementary figure 9). When pooling Moderate and High Skinscore together, their hazard ratio was 13.0 [1.7-99.5] in multivariate analysis (Supplementary fig. 10). Low, Moderate and High Skinscore group respectively displayed 5%, 31% and 100% mortality at day 28 (Supplementary fig. 6). Interestingly, a Moderate Skinscore was associated with a later death, as 85% of the nonsurvivors in this group were late nonsurvivors. Mean SkinScore was significantly different between survivors and early or late nonsurvivors (1.18 vs. 3.33 or 2.0, p \u0026lt; 0.05, Supplementary fig. 7).\u003c/p\u003e\n\u003ch3\u003eIntegrating SkinScore with SOFA\u003c/h3\u003e\n\u003cp\u003eTo assess the prognostic performance of the SkinScore, we compared it to the traditional 24-hour SOFA score and evaluated a combined \u0026ldquo;Modified SOFA\u0026rdquo; score that simply added the SkinScore values to the original SOFA parameters. The modified SOFA demonstrated an AUC of 0.90 [0.80\u0026ndash;0.97] in multivariate analysis, which was significantly greater than the AUC of 24-hour SOFA alone (0.81 [0.68-0.91], p \u0026lt; 0.05; Fig. 5). The SkinScore alone had an AUC of 0.90 [0.82-0.96] in multivariate analysis, which was not significantly greater than the AUC of 24-hour SOFA alone (p = 0.06).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate analysis yielded similar results (Supplementary fig. 8). Altogether, incorporation of microcirculatory assessment via the SkinScore was associated with improved prognostic accuracy compared with traditional organ dysfunction scores alone.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this prospective study including 94 critically ill patients, we observed that early microcirculatory monitoring by tissue capnometry over 48 hours was a strong predictor of 28-day mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile early microcirculatory impairment - assessed by PatCO₂\u0026nbsp;measurement - was not discriminative at baseline, its subsequent course was closely associated with outcome: microcirculatory perfusion improved in survivors and worsened in nonsurvivors, remaining significantly impaired in the latter at day 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatCO₂\u0026nbsp;trajectories were also able to discriminate distinct prognostic profiles. Early nonsurvivors (\u0026lt; day 7) displayed persistently elevated levels throughout the first 48 hours. Conversely, both survivors and late nonsurvivors (≥ day 7) presented with moderately elevated baseline values but microcirculatory dysfunction deteriorated in late nonsurvivors, whereas it resolved in survivors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on these findings, we developed the SkinScore, integrating mean 48-hour and day-2 PatCO₂\u0026nbsp;values. It independently stratified 28-day mortality risk, performed comparably to the SOFA score when used alone, and further improved prognostic accuracy when integrated into a modified version of SOFA.\u003c/p\u003e\n\u003cp\u003eThis study confirms the prognostic value of early and repeated microcirculatory monitoring using tissue capnometry in a diverse ICU population\u0026nbsp;(2,27,30), independently of macrocirculatory status. While previous work focused primarily on patients in shock (2,27,30), we observed similar patterns in a broader and less severely ill cohort, suggesting wider applicability of this marker. Although ICU patients exhibited a strikingly high prevalence of microcirculatory dysfunction across all admission diagnoses (77% of survivors and 90% of nonsurvivors), baseline PatCO₂\u0026nbsp;was not prognostic in our study. This finding is consistent with some\u0026nbsp;(2,27)\u0026nbsp;but not all previous reports\u0026nbsp;(30,32). Beyond a potential lack of power, this may reflect the broader case mix and lower initial severity of our cohort, where only early nonsurvivors showed significantly higher admission values than survivors.\u003c/p\u003e\n\u003cp\u003eImportantly, temporal changes—rather than isolated values—were most strongly associated with outcome, underscoring the value of serial assessment and confirming previous work (2,27). While prior studies have reported persistent microvascular abnormalities beyond 24 hours in nonsurvivors, most analyses have relied on group-level comparisons and failed to assess individual dynamics (2–4,30,33).\u003c/p\u003e\n\u003cp\u003eOur study is among the few to evaluate microcirculatory evolution on a per-patient basis, using ∆\u003csub\u003e(d2–d1)\u003c/sub\u003ePatCO₂. This individualized approach demonstrated prognostic relevance, contrasting with heterogeneous results from prior work. To date, only one other study has reported consistent —and strikingly similar— findings, associating worsening or sustained microvascular dysfunction with delayed or early mortality, respectively (8). In contrast, other studies have reported conflicting results, likely due to methodological limitations such as infrequent sampling —related to the technical complexity of videomicroscopy—, grouping biases (34), or limited statistical power (35).\u003c/p\u003e\n\u003cp\u003eThese discrepancies may also reflect differences in measurement technique. Tissue capnometry more consistently identifies persistent microcirculatory impairment in nonsurvivors (2,27,30), whereas videomicroscopy studies show greater variability (3,4,6,8,33), possibly due to differing sensitivities, temporal resolutions, and underlying conceptual frameworks — either functional, or anatomical (36).\u003c/p\u003e\n\u003cp\u003eAltogether, these results support the feasibility and prognostic value of tissue capnometry beyond high-severity settings, suggesting its potential role in broader ICU triage and monitoring strategies. They underscore the relevance of a dynamic, individualized approach to better capture the prognostic implications of a potentially rapidly evolving microcirculatory dysfunction.\u003c/p\u003e\n\u003cp\u003eIn our secondary analysis, we identified three distinct trajectories of microcirculatory dysfunction, each associated with a specific outcome. Early nonsurvivors (death before day 7) showed markedly elevated PatCO₂\u0026nbsp;values at admission that remained high over 48 hours, suggesting severe and persistent microcirculatory failure. This profile aligns with previous studies linking sustained perfusion abnormalities to early death and worsening organ failure (3). Conversely, in patients surviving beyond day 7, overall PatCO₂—although abnormally elevated in comparison to ICU controls\u0026nbsp;(30)—did not discriminate between late nonsurvivors and survivors. Instead, prognosis appeared linked to the early evolution of microcirculatory status: increasing PatCO₂\u0026nbsp;values were associated with late mortality, while early improvement predicted survival.\u0026nbsp;This supports previous work showing that the prognostic impact of microcirculatory dysfunction also depends on its early evolution\u0026nbsp;(2,3,8).\u003c/p\u003e\n\u003cp\u003eNotably, the identification of three microcirculatory profiles aligns with three typical ICU mortality patterns: early death, late death, and survival. Early deaths are often driven by the severity of the initial insult and refractory organ failure (37), underpinned by pathophysiological mechanisms such as systemic inflammation, coagulation activation, and endothelial dysfunction\u0026nbsp;(38–41)\u0026nbsp;— all key contributors to microcirculatory impairment\u0026nbsp;(42–44). In contrast, late deaths are frequently related to secondary complications\u0026nbsp;(37), and our findings suggest that persistent or worsening microcirculatory dysfunction, leading to sustained tissue hypoxia, may contribute to delayed organ failure and poor outcomes\u0026nbsp;(3).\u003c/p\u003e\n\u003cp\u003eThese findings support the use of early and repeated PatCO₂\u0026nbsp;monitoring not only to identify patients at high risk of early death, but also to guide clinical vigilance and resource allocation by detecting those likely to deteriorate or, conversely, to recover favorably over the longer term.\u003c/p\u003e\n\u003cp\u003eBased on the microcirculatory profiles we identified, we developed \u003cem\u003epost-hoc\u003c/em\u003e the\u0026nbsp;\u003cstrong\u003eSkinScore\u003c/strong\u003e, a simple two-step prognostic score. Mean 48-hour PatCO₂\u0026nbsp;and mean PatCO₂\u0026nbsp;at day 2 stratify mortality risk into three groups: low (early normalization or absence of dysfunction), intermediate (prolonged but moderate dysfunction), and high (persistent severe impairment) SkinScore. This score was independently associated with 28-day mortality with an AUROC of\u0026nbsp;\u003cstrong\u003e0.90\u003c/strong\u003e. When integrated into a modified SOFA score (mSOFA), prognostic performance improved compared to SOFA alone, underlining the added value of microcirculatory assessment to organ dysfunction scoring in critically ill patients— a notable feature of our study. In line with the idea of an unstable hemodynamic coherence (16), these results suggest that microcirculatory dysfunction may constitute a distinct organ failure, influencing patient outcome.\u003c/p\u003e\n\u003cp\u003eEarlier studies, in highly selected cohorts, showed strong prognostic value of PatCO₂\u0026nbsp;measured at 24 or 36 hours and during thermal reactivity tests population (2,30). In our broader and less severe population, these findings were not replicated, likely due to microcirculatory heterogeneity and reduced statistical power. However, our approach’s strength lies in capturing the prognostic significance of early microcirculatory dynamics, which static measurements fail to reflect.\u003c/p\u003e\n\u003cp\u003eUnlike lactate, widely monitored in ICU, or sublingual videomicroscopy measurements, considered as the gold standard for microcirculation assessment, the SkinScore offers a specific, practical, and reproducible tool reflecting the dynamic evolution of microcirculatory failure. Indeed, videomicroscopy is a cumbersome technique (19), still limited to clinical research to this point, and lactataemia, despite being strongly associated with prognosis (20), is probably more a global stress marker than a proxy for microcirculatory dysfunction (22,45).\u003c/p\u003e\n\u003cp\u003eNevertheless, our study has several limitations. First, although this is the largest cohort to date assessing early PatCO₂\u0026nbsp;in critically ill patients, its overall size—and particularly the number of early nonsurvivors—remains limited, which may have reduced the statistical power of some analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, the heterogeneity of the study population, while increasing external validity, likely introduced variability in PatCO₂\u0026nbsp;trajectories and limited the interpretability, thereby potentially limiting statistical power.\u003c/p\u003e\n\u003cp\u003eThird, missing PatCO₂\u0026nbsp;values at day 2, mostly among less severely ill patients not requiring arterial blood gas analysis, may have introduced a non-random missing data bias. However, results remained consistent after SOFA adjustment and multiple imputation, suggesting that missing data did not substantially compromise the reliability of our results.\u003c/p\u003e\n\u003cp\u003eFourth, selection bias cannot be ruled out, as inclusion depended on investigator availability, potentially excluding the most severely ill patients who died shortly after admission, often before PtCO₂\u0026nbsp;monitoring. However, prognostic assessment in such moribund cases is likely of limited clinical relevance given their predictable outcomes.\u003c/p\u003e\n\u003cp\u003eFinally, the SkinScore was developed post hoc as an exploratory tool; its prognostic value should therefore be confirmed in a dedicated prospective cohort.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eTo conclude, in this broad prospective cohort of critically ill patients with an overall mild severity, early monitoring using tissue capnometry for 48 hours following ICU admission identified worsening and persistent microcirculatory dysfunction as strong prognostic markers associated with 28-day mortality. \u0026nbsp;In this context we developed the SkinScore, a microcirculatory prognostic index independently predicting mortality and providing complementary information to routine organ failure assessment by SOFA score. These findings support the use of non-invasive tissue capnometry as a practical tool for early detection and follow up of microcirculatory alterations across a broad spectrum of critical illnesses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABI : Acute brain injury\u003c/p\u003e\n\u003cp\u003eANOVA : Analysis of variance\u003c/p\u003e\n\u003cp\u003eAUC : Area under the receiver operating characteristics curve\u003c/p\u003e\n\u003cp\u003eCO₂\u0026nbsp;: Carbon dioxide\u003c/p\u003e\n\u003cp\u003eECMO : Extra-corporeal membrane oxygenation\u003c/p\u003e\n\u003cp\u003eGCS : Glasgow coma scale\u003c/p\u003e\n\u003cp\u003eHR : Hazard ratio\u003c/p\u003e\n\u003cp\u003eICU : Intensive care unit\u003c/p\u003e\n\u003cp\u003eIQR : Interquartile range\u003c/p\u003e\n\u003cp\u003eKDIGO : Kidney disease: improving global outcomes\u003c/p\u003e\n\u003cp\u003em-SOFA : modified SOFA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ens : nonsignificant\u003c/p\u003e\n\u003cp\u003ePaO₂\u0026nbsp;: Arterial O₂\u0026nbsp;partial pressure\u003c/p\u003e\n\u003cp\u003ePaCO₂\u0026nbsp;: Arterial CO₂\u0026nbsp;partial pressure\u003c/p\u003e\n\u003cp\u003ePatCO₂\u0026nbsp;: tissue-to-arterial CO₂\u0026nbsp;gradient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePtCO₂\u0026nbsp;: Transcutaneous CO₂\u0026nbsp;partial pressure\u003c/p\u003e\n\u003cp\u003eROC : Receiver operating characteristics\u003c/p\u003e\n\u003cp\u003eSOFA : Sequential organ failure assessment\u003c/p\u003e\n\u003cp\u003eSAPS-II: Simplified acute physiology score II\u003c/p\u003e\n\u003cp\u003eSD : Standard deviation\u003c/p\u003e\n\u003cp\u003eSDF : Sidestream darkfield imaging\u003c/p\u003e\n\u003cp\u003eSEM : Standard error of the mean\u003c/p\u003e\n\u003cp\u003e\u0026Delta;(d2\u0026ndash;d1)PatCO₂: PatCO₂ variation between day 1 and day 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch4\u003eEthics approval and consent to participate\u003c/h4\u003e\n\u003cp\u003eThis observational study was approved by our institutionnal Ethics\u0026nbsp;Committee (reference No 21.05515.210551-MS01). Patients were informed of the study and included in the absence of objection in accordance with French law.\u003c/p\u003e\n\u003ch4\u003eConsent for publication\u003c/h4\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch4\u003eAvailability of data and materials\u003c/h4\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch4\u003eCompeting interests\u003c/h4\u003e\n\u003cp\u003eThe authors do not report any competing interests. As part of this study, the research team received monitors and consumables from Radiometer (Denmark).\u003c/p\u003e\n\u003ch4\u003eAcknowledgements\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe authors thank Safa Manaa for technical help and Radiometer for providing Radiometer TCM5 Flex monitor for the study. The sponsor was Assistance Publique \u0026ndash; H\u0026ocirc;pitaux de Paris (Direction de la Recherche Clinique et de l\u0026apos;Innovation).\u003c/p\u003e\n\u003ch4\u003eAuthors contributions\u003c/h4\u003e\n\u003cp\u003eBD, MK and FV designed the study and analysed the data. BD and MK included the patients and were responsible for clinical data collection. BD and JC performed statistical analysis and missing data imputation. BD mainly wrote the first draft of the article. All authors revised and approved the article. BD is the corresponding author.\u003c/p\u003e\n\u003ch4\u003eFunding\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe study was funded by a grant from Programme Recherche Hospitalo-Universitaire en Sant\u0026eacute; RHU 2021 (The French National Research Agency \u0026ndash; ANR)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDe Backer D, Creteur J, Preiser J-C, Dubois M-J, Vincent J-L. Microvascular blood flow is altered in patients with sepsis. Am J Respir Crit Care Med. 2002;166(1):98\u0026ndash;104. DOI: 10.1164/rccm.200109-016oc\u003c/li\u003e\n\u003cli\u003eVall\u0026eacute;e F, Mateo J, Dubreuil G, Poussant T, Tachon G, Ouanounou I, et al. Cutaneous ear lobe Pco₂ at 37\u0026deg;C to evaluate microperfusion in patients with septic shock. Chest. 2010;138(5):1062\u0026ndash;70. DOI: 10.1378/chest.09-2690\u003c/li\u003e\n\u003cli\u003eSakr Y, Dubois M-J, De Backer D, Creteur J, Vincent J-L. Persistent microcirculatory alterations are associated with organ failure and death in patients with septic shock. Crit Care Med. 2004;32(9):1825\u0026ndash;31. DOI: 10.1097/01.ccm.0000138558.16257.3f\u003c/li\u003e\n\u003cli\u003eTachon G, Harrois A, Tanaka S, Kato H, Huet O, Pottecher J, et al. Microcirculatory alterations in traumatic hemorrhagic shock. Crit Care Med. 2014;42(6):1433\u0026ndash;41. DOI: 10.1097/CCM.0000000000000223\u003c/li\u003e\n\u003cli\u003eTanaka S, Escudier E, Hamada S, Harrois A, Leblanc PE, Vicaut E, et al. Effect of RBC Transfusion on Sublingual Microcirculation in Hemorrhagic Shock Patients: A Pilot Study. Crit Care Med. 2017;45(2):e154\u0026ndash;60. DOI: 10.1097/CCM.0000000000002064\u003c/li\u003e\n\u003cli\u003eHutchings SD, Naumann DN, Hopkins P, Mellis C, Riozzi P, Sartini S, et al. Microcirculatory Impairment Is Associated With Multiple Organ Dysfunction Following Traumatic Hemorrhagic Shock: The MICROSHOCK Study. Critical Care Medicine. 2018;46(9):e889. DOI: 10.1097/CCM.0000000000003275\u003c/li\u003e\n\u003cli\u003eDe Backer D, Creteur J, Dubois M-J, Sakr Y, Vincent J-L. Microvascular alterations in patients with acute severe heart failure and cardiogenic shock. Am Heart J. 2004;147(1):91\u0026ndash;9. DOI: 10.1016/j.ahj.2003.07.006\u003c/li\u003e\n\u003cli\u003eden Uil CA, Lagrand WK, van der Ent M, Jewbali LSD, Cheng JM, Spronk PE, et al. 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Shock. 2019;52(1):5\u0026ndash;12. DOI: 10.1097/SHK.0000000000001239\u003c/li\u003e\n\u003cli\u003eDuranteau J, De Backer D, Donadello K, Shapiro NI, Hutchings SD, Rovas A, et al. The future of intensive care: the study of the microcirculation will help to guide our therapies. Crit Care. BioMed Central; 2023;27(1):1\u0026ndash;13. DOI: 10.1186/s13054-023-04474-x\u003c/li\u003e\n\u003cli\u003eInce C, Boerma EC, Cecconi M, De Backer D, Shapiro NI, Duranteau J, et al. Second consensus on the assessment of sublingual microcirculation in critically ill patients: results from a task force of the European Society of Intensive Care Medicine. Intensive Care Med. 2018;44(3):281\u0026ndash;99. DOI: 10.1007/s00134-018-5070-7\u003c/li\u003e\n\u003cli\u003eHusain FA, Martin MJ, Mullenix PS, Steele SR, Elliott DC. Serum lactate and base deficit as predictors of mortality and morbidity. The American Journal of Surgery. 2003;185(5):485\u0026ndash;91. 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DOI: 10.1186/cc1813\u003c/li\u003e\n\u003cli\u003eCreteur J, De Backer D, Sakr Y, Koch M, Vincent J-L. Sublingual capnometry tracks microcirculatory changes in septic patients. Intensive Care Med. 2006;32(4):516\u0026ndash;23. DOI: 10.1007/s00134-006-0070-4\u003c/li\u003e\n\u003cli\u003eFries M, Weil MH, Sun S, Huang L, Fang X, Cammarata G, et al. Increases in tissue Pco2 during circulatory shock reflect selective decreases in capillary blood flow. Crit Care Med. 2006;34(2):446\u0026ndash;52. DOI: 10.1097/01.ccm.0000196205.23674.23\u003c/li\u003e\n\u003cli\u003eLevy B, Gawalkiewicz P, Vallet B, Briancon S, Nace L, Bollaert P-E. Gastric capnometry with air-automated tonometry predicts outcome in critically ill patients. Crit Care Med. 2003;31(2):474\u0026ndash;80. DOI: 10.1097/01.CCM.0000050445.48656.28\u003c/li\u003e\n\u003cli\u003eGutierrez G, Palizas F, Doglio G, Pusajo J, Wainsztein N, Klein F, et al. Gastric intramucosal pH as a therapeutic index of tissue oxygenation in critically ill patients. The Lancet. 1992;339(8787):195\u0026ndash;9. DOI: 10.1016/0140-6736(92)90002-K\u003c/li\u003e\n\u003cli\u003eMari A, Nougue H, Mateo J, Vallet B, Vall\u0026eacute;e F. Transcutaneous PCO2 monitoring in critically ill patients: update and perspectives. Journal of Thoracic Disease. AME Publishing Company; 2019;11(Suppl 11). DOI: 10.21037/jtd.2019.04.64\u003c/li\u003e\n\u003cli\u003eVall\u0026eacute;e F, Nougu\u0026eacute; H, Mari A, Vodovar N, Dubreuil G, Damoisel C, et al. Variations of Cutaneous Capnometry and Perfusion Index During a Heating Challenge is Early Impaired in Septic Shock and Related to Prognostic in Non-Septic Shock. Shock. 2019;51(5):585\u0026ndash;92. DOI: 10.1097/SHK.0000000000001216\u003c/li\u003e\n\u003cli\u003eVall\u0026eacute;e F, Mateo J, Vallet B, Payen D. Gradients de PCO2 : un reflet fiable de la perfusion macro et microcirculatoire. M\u0026eacute;decine Intensive R\u0026eacute;animation. 2011;20(2):87\u0026ndash;94. DOI: 10.1007/s13546-011-0222-6\u003c/li\u003e\n\u003cli\u003eTatevossian RG, Wo CC, Velmahos GC, Demetriades D, Shoemaker WC. Transcutaneous oxygen and CO2 as early warning of tissue hypoxia and hemodynamic shock in critically ill emergency patients. Crit Care Med. 2000;28(7):2248\u0026ndash;53. DOI: 10.1097/00003246-200007000-00011\u003c/li\u003e\n\u003cli\u003eDomizi R, Damiani E, Scorcella C, Carsetti A, Castagnani R, Vannicola S, et al. Association between sublingual microcirculation, tissue perfusion and organ failure in major trauma: A subgroup analysis of a prospective observational study. PLOS ONE. Public Library of Science; 2019;14(3):e0213085. DOI: 10.1371/journal.pone.0213085\u003c/li\u003e\n\u003cli\u003eScorcella C, Damiani E, Domizi R, Pierantozzi S, Tondi S, Carsetti A, et al. MicroDAIMON study: Microcirculatory DAIly MONitoring in critically ill patients: a prospective observational study. Ann Intensive Care. SpringerOpen; 2018;8(1):1\u0026ndash;9. DOI: 10.1186/s13613-018-0411-9\u003c/li\u003e\n\u003cli\u003eHolley AD, Dulhunty J, Udy A, Midwinter M, Lukin B, Stuart J, et al. Early Sequential Microcirculation Assessment In Shocked Patients as a Predictor of Outcome: A Prospective Observational Cohort Study. Shock. 2021;55(5):581. DOI: 10.1097/SHK.0000000000001578\u003c/li\u003e\n\u003cli\u003eDe Backer D, Ospina-Tascon G, Salgado D, Favory R, Creteur J, Vincent J-L. Monitoring the microcirculation in the critically ill patient: current methods and future approaches. Intensive Care Med. 2010;36(11):1813\u0026ndash;25. DOI: 10.1007/s00134-010-2005-3\u003c/li\u003e\n\u003cli\u003eMartin-Loeches I, Wunderink RG, Nanchal R, Lefrant JY, Kapadia F, Sakr Y, et al. Determinants of time to death in hospital in critically ill patients around the world. Intensive Care Med. 2016;42(9):1454\u0026ndash;60. DOI: 10.1007/s00134-016-4479-0\u003c/li\u003e\n\u003cli\u003eCuinet J, Garbagnati A, Rusca M, Yerly P, Schneider AG, Kirsch M, et al. Cardiogenic shock elicits acute inflammation, delayed eosinophilia, and depletion of immune cells in most severe cases. Sci Rep. Nature Publishing Group; 2020;10(1):7639. DOI: 10.1038/s41598-020-64702-0\u003c/li\u003e\n\u003cli\u003eJung C, Fuernau G, Muench P, Desch S, Eitel I, Schuler G, et al. Impairment of the Endothelial Glycocalyx in Cardiogenic Shock and its Prognostic Relevance. 2015 [cited 2025 Apr 27]; Available from: https://journals.lww.com/shockjournal/FullText/2015/05000/Impairment_of_the_Endothelial_Glycocalyx_in.5.aspx\u003c/li\u003e\n\u003cli\u003eChilds EW, Udobi KF, Wood JG, Hunter FA, Smalley DM, Cheung LY. In vivo visualization of reactive oxidants and leukocyte-endothelial adherence following hemorrhagic shock. Shock. 2002;18(5):423\u0026ndash;7. DOI: 10.1097/00024382-200211000-00006\u003c/li\u003e\n\u003cli\u003eGando S, Otomo Y. Local hemostasis, immunothrombosis, and systemic disseminated intravascular coagulation in trauma and traumatic shock. Crit Care. BioMed Central; 2015;19(1):1\u0026ndash;11. DOI: 10.1186/s13054-015-0735-x\u003c/li\u003e\n\u003cli\u003eRaia L, Zafrani L. Endothelial Activation and Microcirculatory Disorders in Sepsis. Front Med. Frontiers; 2022;9. DOI: 10.3389/fmed.2022.907992\u003c/li\u003e\n\u003cli\u003eTyml K, Wang X, Lidington D, Ouellette Y. Lipopolysaccharide reduces intercellular coupling in vitro and arteriolar conducted response in vivo. Am J Physiol Heart Circ Physiol. 2001;281(3):H1397-1406. DOI: 10.1152/ajpheart.2001.281.3.H1397\u003c/li\u003e\n\u003cli\u003eDe Backer D, Orbegozo Cortes D, Donadello K, Vincent J-L. Pathophysiology of microcirculatory dysfunction and the pathogenesis of septic shock. Virulence. 2014;5(1):73\u0026ndash;9. DOI: 10.4161/viru.26482\u003c/li\u003e\n\u003cli\u003eBakker J. Lactate levels and hemodynamic coherence in acute circulatory failure. Best Practice \u0026amp; Research Clinical Anaesthesiology. 2016;30(4):523\u0026ndash;30. DOI: 10.1016/j.bpa.2016.11.001\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microcirculatory dysfunction, Microcirculation, Capnometry, Transcutaneous, Non-invasive, Hemodynamics","lastPublishedDoi":"10.21203/rs.3.rs-9495408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9495408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND :\u003c/strong\u003e Microcirculatory dysfunction is common in critical care, but has been primarily described in severely ill patients. While its prognostic significance is well established in septic shock, it might be associated with poor outcome in other critical conditions. The tissue-to-arterial CO₂ gradient (PatCO₂) provides a straightforward, non-invasive and dynamic approach to microcirculatory assessment, but its relevance beyond acute circulatory failure remains unclear. We aimed to determine whether the evolution of PatCO₂ over the first 48 hours predicts 28-day mortality in a broad intensive care unit (ICU) population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS :\u003c/strong\u003e This prospective, descriptive study included 94 adult patients between November 2022 and September 2023, within 24 hours of admission to a mixed surgical ICU, regardless of admission diagnosis. PatCO₂ was measured using a transcutaneous CO₂ sensor and arterial blood gas analysis at regular intervals during the first 48 hours. The primary endpoint was 28-day mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS :\u003c/strong\u003e Mean age was 61±16 years, mean SOFA score at 24 hours was 5.7±4. Admission diagnoses included acute brain injury (42%), sepsis/septic shock (33%), non-septic shock (16%), and acute respiratory failure (9%). Admission PatCO₂ did not differ between survivors and non survivors (17.0±9 mmHg vs. 20.4±13 mmHg, \u003cem\u003ep\u003c/em\u003e = 0.31). In contrast, its evolution between day 1 and day 2 (∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂) was associated with mortality, independently from macrohemodynamics. Microcirculatory dysfunction improved in survivors (∆\u003csub\u003e(d2-d1)\u003c/sub\u003ePatCO₂: – 4.6±8.0 mmHg), whereas it worsened in nonsurvivors (+ 3.6±14.2 mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), with higher PatCO₂ on day 2 in nonsurvivors (23.1±18.3 mmHg vs. 12.7 ±5.4 mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Based on these findings, we designed a simple prognostic score—SkinScore—based on the average 48-hour and day 2 PatCO₂. SkinScore effectively stratified mortality risk and independently predicted death (AUC 0.90 [0.82–0.96]). Integrating Skinscore into SOFA significantly improved prognostic performance (AUC 0.90 [0.80–0.97] vs. 0.81 [0.68–0.91], \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION :\u003c/strong\u003e Early and dynamic microcirculatory monitoring via tissue capnometry predicts outcome in a diverse and moderately ill ICU population. Our findings indicate that many ICU patients may exhibit microperfusion abnormalities associated with prognosis, supporting broader use of this non-invasive, continuous method as a microcirculatory marker.\u003c/p\u003e","manuscriptTitle":"Continuous tissue CO₂ monitoring for early microcirculatory assessment across a broad ICU population : a descriptive and prognostic study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 10:49:30","doi":"10.21203/rs.3.rs-9495408/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T17:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73553926741715272121529908306409361684","date":"2026-05-04T06:48:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158558737314456517458324296332252561479","date":"2026-05-04T06:37:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280530909106742110973402795956082165236","date":"2026-05-04T06:26:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"614347616296060848043749156999635105","date":"2026-05-04T03:08:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-03T21:42:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T12:33:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T12:33:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Critical Care","date":"2026-04-22T11:05:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f554a2e-c622-4685-b948-fec5158f590f","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T17:58:23+00:00","index":24,"fulltext":""},{"type":"reviewerAgreed","content":"73553926741715272121529908306409361684","date":"2026-05-04T06:48:22+00:00","index":22,"fulltext":""},{"type":"reviewerAgreed","content":"158558737314456517458324296332252561479","date":"2026-05-04T06:37:22+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"280530909106742110973402795956082165236","date":"2026-05-04T06:26:03+00:00","index":20,"fulltext":""},{"type":"reviewerAgreed","content":"614347616296060848043749156999635105","date":"2026-05-04T03:08:50+00:00","index":18,"fulltext":""},{"type":"reviewersInvited","content":"6","date":"2026-05-03T21:42:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T12:33:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T12:33:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:49:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 10:49:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9495408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9495408","identity":"rs-9495408","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}