---
title: "Persistent critical illness in mechanically ventilated acute hypoxic respiratory failure: prevalen"
id: "plos-one-9-persistent-critical-illness-in-acute-hypoxic-respiratory-failure-a"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-9-persistent-critical-illness-in-acute-hypoxic-respiratory-failure-a"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358431"
published_at: "2026-09-16T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Persistent critical illness in mechanically ventilated acute hypoxic respiratory failure: prevalen
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-9-persistent-critical-illness-in-acute-hypoxic-respiratory-failure-a
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358431)
- **Published At:** 2026-09-16T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This retrospective cohort study examined adults with acute hypoxic respiratory failure (**AHRF**) who required invasive **mechanical ventilation** within 72 hours of ICU admission across a 12-hospital system (2011–2022). - Persistent critical illness (**PerCI**) was defined as an ICU length of stay ≥ 10 days and was used to identify patients who survive the acute phase but remain ICU-dependent. - Among 10,626 eligible patients, 3,107 (29.2%) met criteria for **PerCI**, substantially higher than previously reported rates in general ICU cohorts. - One-year survival was 64.2% in the PerCI group versus 73.8% in the non-PerCI group; after PerCI onset, mortality risk increased threefold (hazard ratio 3.01; 95% CI 2.72–3.33). - A dual-model approach accounted for competing early mortality: logistic regressions compared PerCI to early death (≤10 days) and to early ICU discharge; variables associated in both models were considered robust predictors. - Factors consistently associated with PerCI included **ARDS**, pneumonia, aspiration pneumonitis, heart failure, septic shock, weight loss, surgical admission, and postprocedural respiratory and circulatory failure. - The study used electronic health record data including demographics, Elixhauser comorbidities, LAPS2 scores, worst daily P/F ratio, duration of mechanical ventilation, and discharge disposition; deaths were captured using Department of Health records. - Missing data were minimal (<5%) and handled with complete-case analysis; statistical significance used p<0.05 and analyses were performed in SAS 9.4. - Authors conclude that nearly one in three mechanically ventilated AHRF patients develop PerCI, which is associated with worse one-year survival, and that identifiable clinical risk factors may support earlier goals-of-care discussions and post-ICU planning.
## Clinical Analysis & Structured Key Points
Persistent critical illness in acute hypoxic respiratory failure: A retrospective cohort study | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Introduction Persistent critical illness (PerCI) describes patients with prolonged intensive care unit (ICU) dependence after their acute phase of illness and affects 5–20% of the general ICU population. PerCI has been studied in the general ICU cohort, but prevalence and outcomes remain unclear among mechanically ventilated patients with acute hypoxic respiratory failure (AHRF), a high-risk cohort that is more prone to ICU-associated complications. The objective of this study is to quantify PerCI prevalence, identify risk factors for PerCI development, and describe one-year mortality in this population. Methods We conducted a retrospective study of patients with AHRF (PaO2/FiO2 ratio 300 to enrich for intubations due to hypoxemia rather than airway protection. The P/F ratio was calculated by matching P a O 2 values with the closest F i O 2 recorded within a ± 1-hour window. For patients with multiple P/F ratios, we used the lowest daily P/F ratio. This study was approved by the University of Minnesota Institutional Review Board (STUDY00014815). Data collection We abstracted the following from electronic health records: age, sex, self-reported race/ethnicity (Asian, Black or African American, non-Hispanic White, All Others), admission code status, primary service (medicine vs. surgical), ICU source (emergency department vs. general ward), admission diagnoses (identified using ICD-10 codes; S1 Table ), Elixhauser comorbidities [ 16 ], Laboratory Acute Physiology Score version 2 (LAPS2, admission worst) [ 17 ], tobacco use, worst daily P/F ratio, duration of invasive mechanical ventilation, and discharge disposition. Mortality data is provided by the Department of Health on a monthly basis and ensures we capture deaths that occur after hospitalization, in addition to those that occur during. Statistical analysis Descriptive statistics were calculated and presented as the mean and standard deviation (SD) for normally distributed continuous variables, the median and interquartile range (IQR) for non-normally distributed continuous variables, and frequencies and percentages for categorical variables. Groups were compared using analysis of variance (ANOVA) for continuous variables that are normally distributed, Kruskal-Wallis test for continuous variables that are not normally distributed, and Chi-square tests for categorical variables. Kaplan-Meier survival curves were constructed and a log-rank test was used to compare unadjusted survival between patients with PerCI and those without. Missing data were minimal, less than 5% for all covariates, and were handled using complete-case analysis ( S2 Table ). Statistical analyses were performed in SAS 9.4 (SAS Institute Inc., Cary, NC). P values of less than 0.05 were considered statistically significant. PerCI definition PerCI conceptually represents a state where patients have survived the acute phase of their illness but remain ICU-dependent due to complications that develop during their stay, rather than their original presenting illness. We defined PerCI as an ICU length of stay >=10 days, consistent with prior literature. This threshold has been empirically validated across multiple cohorts as the point which the predictive value of acute illness characteristics progressively declines until it becomes lower than that of antecedent patient characteristics [ 1 , 10 , 12 , 18 ]. We acknowledge that some patients with AHRF, particularly those with severe ARDS, may not have resolved their initial acute illness by day 10. However, our prior work validated this definition in our general ICU population from the same health system [ 18 ]. Moreover, this concern motivated the dual-model approach described below, which distinguishes factors associated with prolonged ICU dependence from those reflecting ongoing acute severity. While we chose 10 days for our primary definition, other studies have found that the PerCI definition for the general ICU population, and some subgroups, may be better defined at a shorter timepoint [ 10 , 11 ]. To test if our findings were limited to our 10-day definition, we performed a sensitivity analysis using a 7-day cutoff. The rational for use of a 7-day threshold for our sensitivity analysis was this cutoff was identified as a transition point in the sensitivity analysis of a previous study focusing on the PerCI respiratory failure population [ 10 ]. Model 1: association between PerCI and one-year mortality A Cox proportional hazards model was used to measure factors associated with 1-year mortality. Candidate covariates were prespecified based on prior literature and selected using stepwise methods to limit overfitting. Our initial set of covariates included age, gender, race, tobacco use, ICU source, primary service, index initial code status, hospital length of stay, pre-ICU length of stay, Elixhauser score, LAPS2 Score admission worst, PerCI, ventilator days, discharge disposition, and hospice discharge. The proportional hazards assumption was assessed using Schoenfeld residuals. Our main exposure was PerCI, defined as an ICU length of stay of 10 days or longer. The primary outcome was one-year mortality, ascertained through linkage with the Department of Health vital records for deaths that occur outside of our facility. Mortality was measured from the date of ICU admission to allow standardized comparisons. We included PerCI as a time-varying exposure in the model to account for immortal time bias [ 19 ]. By modeling PerCI as a time-varying exposure, patients contribute person-days to the PerCI group only after meeting the PerCI criteria (i.e., ICU stay ≥10 days), thereby preventing the misattribution of survival time before PerCI onset to the exposed group and reducing immortal time bias. To prevent the introduction of overadjustment or collider bias, post-PerCI variables (ICU LOS, vent days, discharge disposition) were excluded a priori as potential mediators on the causal pathway between PerCI and mortality as demonstrated in S1 Fig . Model 2: risk factors for PerCI development Identifying risk factors for PerCI is complicated by the fact that patients who die before day 10 cannot develop PerCI, creating a competing risk. To address this, we used two complementary logistic regression models. Model 2A (PerCI vs. Early Death): Among patients who either developed PerCI or died within 10 days (excluding those discharged alive before day 10), this model identified factors associated with surviving to day 10 with ongoing ICU dependence rather than early mortality. Model 2B (PerCI vs. Early Discharge): Among patients who either developed PerCI or were discharged from the ICU within 10 days (excluding those who died before day 10), this model identified factors associated with prolonged ICU dependence among survivors. Risk factors significantly associated with PerCI in both models, in the same direction, were considered robust predictors of survival beyond the acute phase and prolonged ICU dependency. This dual-model approach captures the entire cohort while identifying factors specifically associated with the PerCI phenotype rather than merely acute illness severity. We avoid directly comparing patients who couldn’t have developed PerCI (because they died early) to those who survived and did not develop PerCI, while also still accounting for all the patients across both models, thereby mitigating immortal time bias. This two-comparison approach distinguishes factors associated with surviving to day 10 with ongoing ICU dependence from factors associated with prolonged ICU dependence among survivors [ 20 ]. We chose complementary logistic models over competing risk regression (e.g., Fine-Gray) because the resulting odds ratios are more clinically interpretable and the two-model framework maps directly onto clinicians’ sequential reasoning: first, will this patient survive the acute phase? Second, if they survive, will they recover or remain ICU-dependent? Factors that were statistically significant in both models suggest a phenotype that survives but recovers slowly, consistent with the conceptual definition of PerCI. Results Patient characteristics Among 10,626 eligible patients, 3,107 (29.2%) met criteria for PerCI ( Fig 1 ). The mean age was 60.9 years (SD 15.9), 58.0% were male, and 81.5% were non-Hispanic White. The majority were admitted from the emergency department (59.1%) and under surgical services (54.8%). The median duration of mechanical ventilation was 2 days (IQR 1–7), reflecting a large proportion of patients with brief intubations; among PerCI patients, the median was 10 days (IQR 6–18). Patients who developed PerCI had higher illness severity at presentation than those discharged before day 10 (mean LAPS2 160.2 vs 145.0), though patients who died early had the highest severity (LAPS2 203.5; p < .0001 across groups). PerCI patients had greater comorbidity burden (median Elixhauser score 8 vs 6, p < .0001) and sepsis was more prevalent in both the PerCI (59.8%) and early death (64.3%) groups compared to the non-PerCI patients (29.3%; p < .0001). Among PerCI patients, in-hospital mortality was 24.9% and one-year mortality was 35.8%, compared to 2.4% and 11.7% in non-PerCI patients (p < .0001). Full baseline characteristics are presented in Table 1 (complete data in S3 Table ). Download: PNG larger image TIFF original image Table 1. Baseline characteristics of patients with acute hypoxic respiratory failure stratified by PerCI status (early death, PerCI, no PerCI). https://doi.org/10.1371/journal.pone.0358431.t001 Download: PNG larger image TIFF original image Fig 1. Final study population after applying inclusion and exclusion criteria. https://doi.org/10.1371/journal.pone.0358431.g001 One-year mortality (model 1) One-year survival was 64.2% in the PerCI group compared to 73.8% in the non-PerCI group ( Fig 2 ). In the adjusted Cox model with PerCI as a time-varying exposure, PerCI was associated with a threefold increase in mortality risk (HR 3.01; 95% CI 2.72–3.33). Because the time-varying approach attributes risk only after PerCI onset (day 10), the HR reflects the attributable mortality risk solely related to PerCI status. Other factors independently associated with one-year mortality included age (HR 1.03 per year; 95% CI 1.03–1.03), cardiac arrest (HR 2.22; 95% CI 2.01–2.45), medicine service (HR 1.98; 95% CI 1.81–2.16), and COVID-19 (HR 1.40; 95% CI 1.20–1.63). Full model results are in Table 2 (including hospital-level results in S4 Table ). Download: PNG larger image TIFF original image Table 2. Cox proportional hazards model: factors associated with one-year mortality among mechanically ventilated patients with acute hypoxic respiratory failure. https://doi.org/10.1371/journal.pone.0358431.t002 Download: PNG larger image TIFF original image Fig 2. Kaplan-meier survival curves comparing PerCI and non-PerCI cohorts over one year. https://doi.org/10.1371/journal.pone.0358431.g002 Risk factors for PerCI (model 2) Eight clinical factors were independently associated with PerCI in both Model 2A and 2B ( S5 Table ; Fig 3 ). ARDS had the strongest association with PerCI versus no PerCI among survivors (OR 4.24; 95% CI 3.23–5.57) and was also significant versus early death (OR 1.58; 95% CI 1.09–2.28). Postprocedural respiratory/circulatory failure had the strongest association with PerCI versus early death (OR 3.69; 95% CI 2.49–5.46), followed by surgical admission (OR 3.43; 95% CI 2.82–4.15). The remaining five factors (pneumonia, aspiration pneumonitis, heart failure, septic shock, and weight loss) were significant in both comparisons ( Fig 3 ). These eight overlapping risk factors were considered to be robust predictors of PerCI given our approach to mitigate immortal time bias. Download: PNG larger image TIFF original image Fig 3. Dual-model approach for identifying robust PCI risk factors. https://doi.org/10.1371/journal.pone.0358431.g003 The sensitivity analysis using a 7-day cutoff was completed as a robustness check. This demonstrated a similar mortality association (time-varying HR 2.67; 95% CI 2.40–2.96 vs 3.01; 95% CI 2.72–3.33, S6 Table ) despite nearly doubling PerCI prevalence (44.3% vs 29.2%, S7 Table ). Discussion Nearly one in three mechanically ventilated patients with AHRF developed PerCI in our cohort, which is 5–10 times higher than reported in general ICU populations using the same 10-day threshold [ 1 , 10 , 12 ]. Patients with PerCI had lower one-year survival than those without PerCI (64.2% vs. 73.8%), and significant risk factors for PerCI included: ARDS, aspiration pneumonitis, heart failure, pneumonia, septic shock, weight loss, surgical admission, and postprocedural respiratory and circulatory failure. PerCI prevalence PerCI is conceptually distinct from chronic critical illness (CCI), which is traditionally defined by prolonged mechanical ventilation alone [ 4 , 7 ]. Iwashyna et al. proposed that PerCI begins when baseline patient characteristics surpass acute illness severity in predicting outcomes, a transition that regression modeling has consistently identified at approximately 10 days across multiple cohorts [ 1 , 10 , 12 , 21 ]. This distinction matters because it broadens the definition from patients on prolonged mechanical ventilation to a larger ICU population that remains ICU-dependent beyond their acute admitting illness. Importantly, in our sensitivity analysis, we found that decreasing the definition to 7 days almost doubles the prevalence of PerCI. While our results in this study were robust to the definition, future studies will be required to best operationalize a standard definition that appropriately captures the complex PerCI population. The substantially higher PerCI prevalence in AHRF may reflect two mechanisms. First, mechanical ventilation itself often generates complications that delay recovery (ventilator-associated events, sedation-related delirium, and hemodynamic instabil
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