Peñuelas et al. Ann. Intensive Care (2021) 11:143 https://doi.org/10.1186/s13613-021-00929-y RESEARCH Open Access Long‑term survival of mechanically ventilated patients with severe COVID‑19: an observational cohort study Oscar Peñuelas1,2* , Laura del Campo‑Albendea3, Amanda Lesmes González de Aledo4, José Manuel Añón2,5, Carmen Rodríguez‑Solís1, Jordi Mancebo6, Paula Vera6, Daniel Ballesteros7, Jorge Jiménez1, Emilio Maseda8, Juan Carlos Figueira2,5, Nieves Franco9, Ángela Algaba10, Juan Pablo Avilés11, Ricardo Díaz11, Beatriz Abad12, Alfonso Canabal12, Ana Abella13, Federico Gordo13, Javier García14, Jessica García Suarez14, Jamil Cedeño15, Basilia Martínez‑Palacios16, Eva Manteiga16, Óscar Martínez17, Rafael Blancas17, Tommaso Bardi18, David Pestaña18, José Ángel Lorente1,2, Alfonso Muriel3, Andrés Esteban1 and Fernando Frutos‑Vivar1 Abstract Background: Information is lacking regarding long-term survival and predictive factors for mortality in patients with acute hypoxemic respiratory failure due to coronavirus disease 2019 (COVID-19) and undergoing invasive mechanical ventilation. We aimed to estimate 180-day mortality of patients with COVID-19 requiring invasive ventilation, and to develop a predictive model for long-term mortality. Methods: Retrospective, multicentre, national cohort study between March 8 and April 30, 2020 in 16 intensive care units (ICU) in Spain. Participants were consecutive adults who received invasive mechanical ventilation for COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection detected in positive testing of a nasopharyn‑ geal sample and confirmed by real time reverse-transcriptase polymerase chain reaction (rt-PCR). The primary out‑ comes was 180-day survival after hospital admission. Secondary outcomes were length of ICU and hospital stay, and ICU and in-hospital mortality. A predictive model was developed to estimate the probability of 180-day mortality. Results: 868 patients were included (median age, 64 years [interquartile range [IQR], 56–71 years]; 72% male). Severity at ICU admission, estimated by SAPS3, was 56 points [IQR 50–63]. Prior to intubation, 26% received some type of noninvasive respiratory support. The unadjusted overall 180-day survival rates was 59% (95% CI 56–62%). The predictive factors measured during ICU stay, and associated with 180-day mortality were: age [Odds Ratio [OR] per 1-year increase 1.051, 95% CI 1.033–1.068)), SAPS3 (OR per 1-point increase 1.027, 95% CI 1.011–1.044), diabetes (OR 1.546, 95% CI 1.085–2.204), neutrophils to lymphocytes ratio (OR per 1-unit increase 1.008, 95% CI 1.001–1.016), failed attempt of noninvasive positive pressure ventilation prior to orotracheal intubation (OR 1.878 (95% CI 1.124–3.140), use of selective digestive decontamination strategy during ICU stay (OR 0.590 (95% CI 0.358–0.972) and administra‑ tion of low dosage of corticosteroids (methylprednisolone 1 mg/kg) (OR 2.042 (95% CI 1.205–3.460). Conclusion: The long-term survival of mechanically ventilated patients with severe COVID-19 reaches more than 50% and may help to provide individualized risk stratification and potential treatments. *Correspondence: openuelas@gmail.com 1 Intensive Care Unit, Hospital Universitario de Getafe, Carretera de Toledo km 12,500, 28905 Madrid, Spain Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 2 of 11 Trial registration: ClinicalTrials.gov Identifier: NCT04379258. Registered 10 April 2020 (retrospectively registered) Keywords: Coronavirus disease 2019, COVID-19, ARDS, Intensive care unit, Invasive mechanical ventilation, Mortality, Pneumonia, SARS-CoV-2, Noninvasive ventilation Background analysis if any data regarding relevant variables (Addi- The coronavirus disease 2019 (COVID-19) pandemic is tional file 1: Appendix S1) were missing. one of the most serious health crises in recent decades [1] with over 138 million infections being reported world- Data collection wide. As a result, health care resources in many countries Variables recorded were: age, sex, height, weight, sever- are facing unprecedented challenges [2]. This context has ity at admission estimated by the Simplified Acute Physi- been especially dramatic in intensive care units (ICU) ology Score (SAPS3) [which ranges from 16 points (low in view of the high daily incidence of acute hypoxemic severity) to 226 points (high severity)], comorbidities, respiratory failure (AHRF) secondary to pneumonia by (previous medication use), mode of respiratory support SARS-CoV-2. Reports show that between 14 and 17% of (high flow nasal oxygen cannula and/or noninvasive hospital admissions for COVID-19 require transfer to the positive pressure ventilation [NPPV] as CPAP (continu- intensive care unit (ICU) [3, 4]. Currently available information about critically ill adult ous positive airway pressure) or BiPAP (Bi-Level Posi- patients is heterogeneous because of the case-mix of tive Airway Pressure)), use of compassionate medication patients, and in-hospital mortality rates differ between [antivirals (lopinavir–ritonavir, remdesivir), hydroxychlo- countries [5–9]. Specific data on patient characteris- roquine, and immunomodulatory agents (interleukin-6 receptor antagonists, Janus kinase inhibitor, and corti- tics and long-term survival in critically ill COVID-19 costeroids)] before admission to ICU, length of hospital patients are needed to inform decision-making regarding stay prior to ICU admission, date of intubation, ventila- resource allocation, critical care capacity, and therapeutic tory settings within the first week of mechanical ventila- options. Inter-hospital variation and international clinical tion, daily arterial blood gases, concentrations of plasma/ variability in treatments and outcomes also needs to be serum biomarkers drawn within 7 days of ICU admission, assessed. including high-sensitivity C-reactive protein, D-dimer, We conducted a multicenter cohort study to analyze ferritin, high-sensitivity troponin, procalcitonin, and the long-term mortality of patients who required invasive IL-6, use of adjuvant therapies for acute respiratory fail- mechanical ventilation for severe COVID-19 pneumonia, and determine the impact of predictive variables meas- ure (neuromuscular blocking agents, inhaled pulmonary vasodilators, ventilation in prone position, and extra- ured during ICU stay for long-term mortality. corporeal membrane oxygenation), vasopressor agents, renal replacement therapy, antibacterial agents, antiviral agents, and other immunomodulatory agents, complica- Methods tions, and organ failure during the ICU stay. This observational cohort study was conducted in 16 Patients were followed-up until 180 days after hospital surgical and medical ICU at 12 university hospitals in admission. Patients who died were censored at the date Madrid and Barcelona, the Spanish cities most affected of death for the time-to-discharge analysis. by COVID-19 (a complete list of participating sites is Independent on-site monitoring was performed at each provided in the Supplement). We included all patients participating center in coordination with the main inves- who were admitted to the participating ICUs between tigators who verified all data. March 8, 2020 and April 30, 2020, and who required All data were monitored and reviewed by four external invasive mechanical ventilation for acute hypoxemic res- investigators (OP, AE, CRS, and FFV) to detect erroneous piratory failure secondary to SARS-CoV-2 pneumonia. and missing data. The diagnosis was confirmed by a positive result of real- The study was approved by the institutional ethics time reverse-transcriptase polymerase chain reaction board at each participating site. The requirement for (RT-PCR) testing of a nasopharyngeal swab sample or informed consent from individual patients was waived, from a lower respiratory tract sample [11]. SARS-CoV-2 because the study design was considered minimal-risk pneumonia was also diagnosed when there was a com- research using data collected for routine clinical practice patible clinical condition, and when typical abnormalities during an ongoing public health emergency. on chest X-ray were present in absence of an alterna- The study was registered in ClinicalTrials.gov Identi- tive diagnosis [10, 11]. Patients were excluded from the fier: NCT04379258. We followed the Strengthening the Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 3 of 11 Reporting of Observational Studies in Epidemiology the model was evaluated using the c-statistic: for a binary (STROBE) statement guidelines for observational cohort outcome, c is identical to the area under the receiver studies [12]. operating curve (AUROC). Calibration was evaluated studying the calibration slope and calibration-in-the- Statistical analysis large (CITL). This approach indicates the difference Following the approval of the ethics committee at each between the observed prevalence of death and the pre- participating center, patients were collected retrospec- dicted mean probability. In a perfect model, the CITL tively to allow rapid data collection and over 180 days of should be equal to zero, but our CITL value was small prospective follow-up to analyze clinical outcomes (data and the confidence interval contained 0. extraction date: April 16, 2020). The calibration slope tells us if the model coefficients The main outcome was mortality at 180 days. Second- are underfitted (slope > 1) or overfitted (slope < 1). In ary outcomes were duration of mechanical ventilation, our case, the slope was below 1, so it was insufficient duration of ICU and in-hospital stay, reason for death to obtain the data from our model. However, this is not and destiny at hospital discharge. entirely negative, because as a predictive model, we want Variables included in the predictive model were as fol- the model to be able to explain future data from other lows: age, sex, comorbidities (diabetes, hypertension, patients and units, not just our data. Furthermore, 1 is obesity), and the following pre-admission medications: included in the confidence interval, so we can consider it angiotensin-converting enzyme inhibitors, angiotensin to be a good model in terms of calibration. [15]. receptor blockers and chronic steroids. We also included Subsequently, coefficients were used to generate a in the model non-invasive respiratory support before nomogram to predict the adjusted probability of ICU orotracheal intubation, time from hospital admission mortality individually. until intubation, SAPS3, relevant variables determined To account for differences in patient-level characteris- within the first 48 h of admission to the ICU (neutrophil tics and illness severity between ICUs, we calculated the to lymphocyte ratio, ­PaO2 to ­FiO2 ratio, D-dimer, ventila- median odds ratio (MOR) [16]. tory ratio [14], tidal volume and positive end-expiratory Finally, in a sensitivity analysis for the multilevel model, pressure (PEEP)). We also included treatments given further adjustments were made for the number of ICU within in the first 48 h of mechanical ventilation (vaso- beds at each hospital before the COVID-19 pandemic pressor support, antivirals, immunomodulatory therapy, (≤ 50, and > 50 ICU beds). steroids, anticoagulants, neuromuscular blocking agents, The 180-day survival outcomes were calculated using selective digestive decontamination, and prone position). a Kaplan–Meier survival plot in an overall cohort and All of the variables were selected by clinical criteria. by predefined subgroups (age categorized in ≤ 70 or > 70 Missing data were assumed to be MAR. All missing years; SAPS3 categorized in ≤ 50 or > 50 points; ratio data were multiply imputed via the Multivariate Impu- ­PaO2/FiO2 categorized according to the acute respiratory tation by Chained Equations (MICE) procedure [13]. distress syndrome (ARDS) Berlin definition [17]). Incomplete dichotomous variable was imputed using a The differences between survival curves were evaluated logistic regression model, while linear regression was by log-rank test. To identify the optimal cut-point value used to impute incomplete continuous variables. We in ROC analysis we used the Youden index approach. Sta- generated 10 imputed data sets. Since MAR and MNAR tistical significance was considered at p < 0.05. cannot be distinguished from observed data, additional Analyses were performed using Stata, version 16.1 sensitivity analyses [14] is shown in the supplement (StataCorp LLC). (Additional file 1: Table S10). Mortality outcomes were analyzed using mixed-effects Results logistic regression in the imputed data sets. The analy- Description of the population included in the analysis sis was fitted with a random effect in which the patients During the study period, 868 of the 1,170 patients who were nested in ICUs to characterize ICU-level variation were admitted to the participating ICUs were included and to estimate center-specific rates of ICU-mortality. in the analysis. A flow chart of study patients is pro- The fractional polynomial method was used to explore vided in Additional file 1: Figure S1 and clinical char- the behavior of continuous variables. We applied step- acteristics of excluded patients are summarized in wise selection with backward elimination of predictors Additional file 1: Table S1. Patients’ baseline patient from the full model with p < 0.05. characteristics are shown in Table 1. Seventy-two per The final model was then validated by bootstrapping. cent were males and mean (standard deviation [SD]) Using this procedure we sampled 100 samples for each age was 62 years [11 years]. The median number of days imputed data set with replacement. Discrimination of from onset of symptoms to admission to hospital was Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 4 of 11 Table 1 Baseline characteristics of included patients in the analysis Overall Survivors Non-Survivors p-value N = 868 N = 533 N = 335 Age, mean (SD), years 64 (11) 59 (12) 66 (10) < 0.001 Female sex 243 (28) 134 (30.8) 79 (23.6) 0.013 Body mass index, mean (SD), kg/cm2 29 (5) 29 (5) 29 (5) 0.317 SAPS3, mean (SD), points 57.2 (11.0) 55.2 (9.8) 60.2 (12.1) < 0.001 Comorbidities Hypertension 401 (46) 223 (42) 178 (53) 0.001 Obesity 268 (31) 176 (33) 92 (27) 0.049 Diabetes 209 (24) 103 (19) 106 (32) < 0.001 Previous therapy Angiotensin-converting enzyme inhibitors 172 (20) 95 (18) 77 (23) 0.039 Angiotensin II receptor blockers 122 (15) 69 (13) 53 (16) 0.139 Steroids 34 (4) 15 (3) 19 (6) 0.028 Days from initiation symptoms to admission at hospital, median (P25, P75) 7 (4, 9) 7 (5, 9) 6 (4, 8) 0.078 Days from admission at hospital until orotracheal intubation, median (P25, P75) 3 (2, 6) 3 (1, 5) 3 (1, 7) 0.001 Noninvasive Respiratory support at ward 0.066 Oxygen mask alone 644 (74) 421 (79) 223 (67) High flow oxygen nasal cannula alone 105 (12) 66 (12) 39 (12) Non-invasive positive pressure ventilation alone 98 (11) 54 (10.1) 44 (13) Non-invasive positive pressure ventilation and High flow oxygen nasal cannula 21 (2) 10 (2) 11 (3) Ventilatory management at ICU admission Tidal volume, mean (SD), ml/kg PBW 7.2 (1) 7.2 (1) 7.2 (2) 0.476 PEEP, mean (SD), cm of water 13 (3) 13 (3) 13 (3) 0.407 Ratio ­PaO2/FiO2, mean (SD) 105 (48) 108 (47) 100 (48) 0.016 D-dimer, median ­(P25, ­P75), mg/ml, 3.5 (1, 17) 4 (1, 20) 3.5 (1, 12) 0.425 Ventilatory ratio, mean (SD) 2.1 (0.8) 2.0 (0.8) 2.2 (0.9) 0.009 Ratio neutrophil: lymphocyte, median ­(P25, ­P75) 13 (7, 22) 11 (7, 18) 16 (9, 28) 0.428 Selective Digestive Decontamination 450 (52) 301 (56) 149 (45) < 0.001 Immunomodulator therapy 347 (40) 210 (39) 137 (41) 0.357 Steroids 0.104 No 449 (52) 276 (52) 173 (52) Methylprednisolone ≤ 1 mg/kg 206 (24) 137 (26) 69 (21) Methylprednisolone > 1 mg/kg 213 (24) 120 (22) 93 (28) Compassionate therapies received during hospital stay 0.013 None 155 (17.9) 89 (16.7) 66 (19.7) Antiviral therapy only 196 (22.6) 136 (25.5) 60 (17.9) Immunomodulatory agents only 71 (8.2) 35 (6.6) 36 (10.8) Antiviral and inmunomodulatory therapies 446 (51.4) 273 (51.2) 173 (51.6) Antiviral therapy 796 (92) 492 (92) 304 (91) 0.245 Anticoagulation therapy 90 (10) 55 (10) 35 (10) 0.342 Norepinephrinea < 0.001 No 306 (32) 203 (38) 103 (31) Low doses 105 (12) 44 (8) 61 (18) High doses 457 (53) 286 (54) 171 (51) Days until intubation, median ­(P25, ­P75) 3 (1, 6) 3 (1, 5) 3 (1, 7) 0.002 Meet ARDS criteria at ICU admission At ICU admission 0.009 On day 2 from starting ventilatory support < 0.001 Mild ARDS At ICU admission 220 (25.4) 136 (25.5) 84 (25.1) Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 5 of 11 Table 1 (continued) Overall Survivors Non-Survivors p-value N = 868 N = 533 N = 335 On day 2 from starting ventilatory support 331 (38.2) 232 (43.6) 99 (29.6) Moderate ARDS At t ICU admission 409 (47.1) 267 (50.1) 142 (42.4) On day 2 from starting ventilatory support 410 (47.3) 239 (44.9) 171 (51.2) Severe ARDS At ICU admission 239 (27.5) 130 (24.4) 109 (32.5) On day 2 from starting ventilatory support 125 (14.4) 61 (11.5) 64 (19.2) Data are n (%), unless otherwise indicated 7 days (4, 9 days; p25, p75) and the median length of selective digestive decontamination strategy was asso- stay in the hospital before ICU admission was 3 days (1, ciated with a significant decrease in mortality (Addi- 5 days; p25, p75). The most common comorbidity was tional file 1: Table S1). cardiovascular disease (49%). During ward stay before ICU admission, most patients (75%) received some antiviral and/or immune-modulatory therapy, and Analysis of long‑term mortality noninvasive respiratory support was attempted before The overall 180-day survival was 59.5% (95% CI 56.1– orotracheal intubation was initiated in 28% of patients 62.6%) (Fig. 1). Unadjusted Kaplan–Meier survival curves (cannula nasal high-flow oxygen in 14% and NPPV in by subgroups showed that survival rates at 180 days of 14%), while 68% received conventional oxygen by ven- follow-up were lower in patients over 70 years (40% vs. turi or reservoir mask, and 4% received a combination 66.5%, log rank test < 0.001, respectively) (Additional of cannula nasal high-flow oxygen and NPPV. file 1: Figure S4). Severity at admission to the ICU estimated by SAPS3 Twenty-two variables measured at ICU admission was a mean 57 points (SD, 11 points) (predicted mor- (Additional file 1: Table S8) were included in the multi- tality was 29%). At admission to the ICU, serum bio- variable logistic model. In the final multivariable multi- markers and ventilatory settings, arterial blood gases, ple imputation model, after the backwards strategy, the adjuvant therapies within the first week of mechanical adjusted predictive variables associated with 180-day ventilation (Additional file 1: Table S3 and Figure S2). mortality were: age ( Odds ratio [OR] per 1-year increase According to ARDS Berlin criteria, 231 patients (27%) 1.051, 95% confidence interval [CI] 1.033–1.068), SAPS3 had severe ARDS when mechanical ventilation was (OR per 1-point increase 1.027, 95% CI 1.011–1.044], started. diabetes (OR 1.546, 95% CI 1.085–2.204), neutrophils to Severe ARDS criteria and hypercapnia were also higher lymphocytes ratio (OR per 1-unit increase 1.008, 95% in non-survivors: (61% vs. 53%; p = 0.018) and (mean CI 1.001–1.016), failure of noninvasive positive pressure ­PaCO2 56 mmHg vs. 52 mmHg; p < 0.001), respectively. ventilation prior to orotracheal intubation (OR 1.878 Within the first 48 h, ventilatory management (tidal vol- (95% CI 1.124–3.140), use of selective digestive decon- ume, PEEP) was similar in both cohorts (Additional file 1: tamination strategy during ICU stay (OR 0.590 (95% CI Table S1 and Figure S2). Early use of ventilatory settings 0.358–0.972) and administration of low dosage of cor- and adjuvant therapies within the first week of ICU stay ticosteroids (methylprednisolone < 1 mg/kg) (OR 2.042 (prone position, ECMO, nitric oxide inhaled, and neu- (95% CI 1.205–3.460). The model was internally validated romuscular blockers) were not different in survivors and by bootstrapping (Fig. 2). The algorithm had an AUC non-survivors (Additional file 1: Table S4). of 0.716 (95% CI 0.682–0.749), and a calibration, CITL In addition, level of serum biomarkers, and compli- 0.001 (95% CI -0.318–0.307) with slope 0.925 (95% CI cations within the first week of ICU stay are shown in 0.734–1.116). Additional file 1: Tables S5, S6, S7, Figures S2 and S3. The results of the mixed-effect analysis (Additional The neutrophil to lymphocyte ratio was the most file 1: Table S10) showed that center had an effect relevant biomarker and most significant difference in associated with 180-mortality (median odds ratio serum biomarkers (Additional file 1: Tables S1 and S8). [MOR] = 1.829). Sensitivity analysis also showed effects Of the pharmacological treatments initiated in the when comparing centers that included more than 50 ICU within the first 48 h, only the implementation of patients (N = 7) with those with less than 50 patients Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 6 of 11 Fig. 1 Kaplan–Meier survival curves. Overall survival at 180 days. Grey lines represent the 95% confidence interval Odds ratio (95% CI) for Decreased Increased Charateristic 180-day mortality risk of death risk of death Age 1.05 (1.04, 1.07) Diabetes mellitus 1.56 (1.10, 2.23) SAPS3 1.34 (1.32, 1.36) Neutrophil to lymphocyte ratio 1.01 (1.00, 1.02) Noninvasive respiratory support prior to orotracheal intubation Oxygen mask 1 (Reference) High flow nasal cannula 1.11 (0.66, 1.84) Noninvasive ventilation 1.90 (1.14, 3.16) Noninvasive ventilation & High flow nasal cannula 2.01 (0.74, 5.47) Selective Digestive Decontamination strategy 0.60 (0.37, 0.96) Administration of corticosteroids No 1 (Reference) Methylprednisolone < 1mg/kg 2.04 (1.20, 3.46) Methylprednisolone ≥ 1mg/kg 0.96 (0.68, 1.35) 0.5 1 2 4 Odds ratio (95% CI) Fig. 2 Multivariable-adjusted risk model for death at 180 days and forest plot (N = 9) and varied widely across centers (Additional Among patients discharged alive from the ICU admis- file 1: Figure S5). sion, the median ICU length of stay was 17 days (IQR, Concerning survivors at hospital discharge, 426/553 10–34 days) and the median hospital length of stay was patients were transferred home (77%) and 23% (129/553) 38 days (IQR, 26–65 days). were transferred to a nursing facility. Predictive interpretations compared with 180-day mor- tality were largely unchanged when restricted to ICU mortality or hospital mortality (Table 2), although the Other Clinical Outcomes need of vasopressor support was an independent pre- At the end of ICU stay follow-up (median, 27 days; dictor factor for ICU and hospital mortality (OR 1.878, IQR, 13–46 days). ICU mortality was 38.5% (95% CI 95% CI 1.119–3-152; OR 2.042, 95% CI 1.205–3.460, 35%–42%). respectively). Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 7 of 11 Table 2 Multivariable risk-adjusted predictive model for short- and long-term mortality Variables ICU Mortality OR (95% CI) Hospital Mortality OR 28-day Mortality OR (95% CI) (95% CI) Age, (per 1-year increase) 1.049 (1.032–1.066) 1.050 (1.033–1.068) 1.058 (1.035–1.081) Male Diabetes mellitus 1.546 (1.084–2.203) 1.626 (1.093–2.420) Chronic steroids 3.697 (1.633–8.371) SAPS3, (per 1-point increase) 1.025 (1.008–1.041) 1.027 (1.011–1.044) N:L ratio (per 1-unit increase) 1.009 (1.002–1.016) 1.008 (1.000–1.016) Failed attempt of NPPV prior to orotracheal intubation 2.131 (1.279–3.550) 1.878 (1.123–3.39) Selective Digestive Decontamination strategy 0.587 (0.358–0.963) 0.591 (0.358–0.972) Need of vasopressor support 1.878 (1.119–3.152) 2.042 (1.205–3.460) 2.158 (1.197–3.891) Methylprednisolone < 1 mg/kg 0.500 (0.302–0.828) Methylprednisolone > 1 mg/kg 0.479 (0.283–0.811) AUC (95% CI) 0.712 (0.677; 0.746) 0.728 0.746 (0.705; 0.787) (0.694; 0.761) Calibration, CITL (95% CI) 0.001 (-0.306; 0.308) 0.0007 < 0.001 (-0.398; 0.398) (-0.312;0.313) Slope (95% CI) 1.025 (0.822; 1.228) 1.018 (0.827; 1.209) 1.000 (0.788; 1.214) SAPS, Simplified Acute Physiology Score; NPPV: noninvasive positive pressure ventilation; N: L, neutrophil to lymphocyte ratio; AUC; area under the curve. OR, Odds Ratio; CI, confidence interval The results of the mixed-effect analysis (Additional [24]. It is of note that patients in our study were followed file 1: Table S9) showed that center had an effect asso- for up to 180 days and survival rate was 59%. ciated with ICU mortality (MOR = 1.85). A sensitivity Age has been one of the most controversial aspects analysis of multiple imputation for ICU mortality showed during the outbreak of the COVID-19 pandemic. In our that the estimates obtained from the imputed data and study, the unadjusted 180-day survival curves in severely the estimates from the non-imputed data are almost ill patients over 70 years of age was above 50%, repre- identical (Additional file 1: Table S11). senting a better clinical outcome than in previous stud- ies in older patients undergoing mechanical ventilation Discussion [26, 27]. Despite being considered clinically relevant, this This multicenter cohort study in 868 adult patients with difference in survival implies that age cannot be used severe COVID-19 undergoing invasive mechanical as a clinical criterion to determine initiation of invasive ventilation in Spain provides follow-up data concern- mechanical ventilation. The availability of complemen- ing long-term survival after ICU admission. Independ- tary clinical tools could aid decision-making with respect ent predictors associated with 180-day mortality were to initiating mechanical ventilation. In this sense, assess- older age, higher SAPS3, the need for norepinephrine, an ment of frailty has shown to be a robust guide to resource increased neutrophil-to-lymphocyte ratio at ICU admis- allocation in severely ill older patients with COVID-19 sion, a failed attempt of noninvasive positive pressure [28]. ventilation, and the lack of use of a selective digestive The use of non-invasive positive pressure ventilation decontamination strategy during ICU stay. The propor- (NPPV) for respiratory management of patients with tion of patients who died varied widely between centers. severe acute respiratory distress is controversial [29]. To the best of our knowledge, this is the first report Studies related to non-invasive positive pressure venti- dealing with and long-term outcomes in a large cohort of lation failure have shown that the delay in interrupting mechanically ventilated patients with severe COVID-19. this ventilation may be associated with increased mortal- Overall, in-hospital mortality was 40.5%, which is con- ity [30–33]. Specifically, in a retrospective chart review siderably lower than that in other high-income countries study of patients with COVID-19 in Italy, mortality was [18–24]. However, the age distribution and comorbidi- 76% [34] in those who received noninvasive ventilation. ties of patients on mechanical ventilation was similar to In our study, the application of NPPV was not proto- that in these cited national studies. ICU mortality in our colized, and therefore, patients received any modality cohort was also similar (38%) to that in a large cohort of of NPPV (CPAP and BiPAP), and any kind of interfaces critically ill patients with COVID-19 in the Netherlands were used. Unfortunately, monitoring of respiratory Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 8 of 11 parameters, modalities or management during sessions implemented for long enough to have a positive clinical was not addressed due to the overwhelmed healthcare impact on patients with severe COVID-19. Furthermore, resources consumption during the pandemic, and use this observational study collected the local clinical prac- outside the ICU of these devices as well. Helmet nonin- tice from centers during the pandemic outbreak. Most of vasive ventilation has been evaluated as an alternative the patients, therefore, received all these measures, thus for the noninvasive respiratory support of patients with limiting the possibility to extrapolate conclusions in the hypoxemia with promising results in severe patients absence of adjusted and reliable comparisons. with COVID-19, but not found significant difference in According to recent evidence, treatment with systemic the number of days free of respiratory support. Hence, corticosteroids is associated with reduced mortality for monitoring of patients receiving noninvasive respiratory critically ill patients with COVID-19 [43]. The clinical support during AHRF remains of paramount importance management of corticosteroids during the outbreak, nev- not to delay endotracheal intubation. On the other hand, ertheless, was very heterogeneous. Indeed, in this recent probably patients that received NPPV as a prior attempt meta-analysis, the effect of glucocorticoids on mortality of noninvasive respiratory support were the most severe at 28 days showed a marked heterogeneity ­(I2 44%), sug- patients in which failed NPPV and, therefore, a selected gesting that different populations or different associated type of patients. These could also be a reason for the treatment effects may play a role. On the other hand, the worst outcomes. multivariate model for long-term outcomes (180-day The beneficial effect of the SDD strategy in mechani- mortality) showed that the administration of corticoster- cally ventilated critically ill patients has been widely stud- oids had a beneficial effect on mortality. ied, and meta-analysis based on individual patient data Finally, we have found that the need for low dosages of have consistently shown improved patient outcomes with vasopressor support during ICU admission is a predic- this approach [35–37]. tor for short-term mortality, but not for long term. Due Indeed, the similarity in ICU mortality between our to the observational design of the study we cannot verify findings and Dutch study [24] may be explained, because the target for mean arterial pressure (MAP) or the hemo- SDD are currently widely used in the Spanish participat- dynamic management in these patients during ICU stay, ing ICU and Dutch ICU [38]. This is the first study to neither if those patients had a component of cardiac dys- report that SDD has the beneficial effect of decreasing function secondary to myocarditis that may influence in ICU mortality in mechanically ventilated patients with the prediction for unfavorable outcomes [44]. severe COVID-19. Nevertheless, the low difference of the Our data indicate that 60% of all deaths among venti- use of SDD between ICU survivors compared with ICU lated patients occurred in the first 30 days of ICU stay. non survivors may limit to assume a direct impact in the These deaths were due to refractory hypoxemia and mul- outcomes. We did not, however, analyze the relationship tiorgan failure, and were probably related to the develop- between SDD-nosocomial infections in patients with ment of progressive, fibrotic lung disease. severe COVID-19 and mortality, an objective for post- Although the virus is eradicated in the most severe hoc analysis. COVID-19 patients, the cause of lung damage is not. A hematological dysfunction defined as an increased Linked to the inflammatory response, lung fibrosis neutrophil to absolute lymphocyte ratio (NLR) was emerges as a secondary event related to the progression recently described. The NLR has been identified as bio- of the pathology and worse outcomes [45–48]. marker of higher in-hospital mortality [39]. Another We acknowledge a number of limitations. First, due to study showed that NLR was significantly higher in hos- the fact that most participating centers rapidly reached pitalized patients with severe forms of COVID-19 [40] ICU saturation at the critical moment of the COVID-19 and a higher NLR at hospital admission was associated outbreak, and as intensivists were facing difficult deci- with worse outcome [41, 42]. In our predictive model, an sions, not all patients admitted to the participating ICUs NLR higher than 15 on day 1 was significantly associated during the study period were collected. Nevertheless, we with increased mortality. Our data also confirm that NLR calculated a predefined sample size according to the pro- is a useful independent prognostic factor in COVID19 tocol to reach significant power to detect clinical differ- patients under mechanical ventilation. ences in outcomes. Second, it is plausible that differences We did not find that adjunctive therapies such as prone in clinical outcomes may be explained by variability in positioning or the use of neuromuscular blocking agents clinical practice. Such an increased risk for ICU mortality had a beneficial effect on mortality. It is important to can be explained by differences in practice between cent- highlight that the predictive model described here is ers [49]. Finally, the predictive model could be affected by based on the first 48 h of mechanical ventilation. One can, unmeasured confounders in patient populations in ICUs, therefore, argue that these measures would not have been explaining some of the variation observed in treatments Peñuelas et al. Ann. Intensive Care (2021) 11:143 Page 9 of 11 and outcomes. Accordingly, our findings should be inter- technical or material support: OP. All authors read and approved the final manuscript. preted cautiously. Funding This study was funded by a Grant from Foundation for Biomedical Research of the University Hospital of Getafe, Spain (COVID-19 No.ISCIII:COV20/00977, 2020. Conclusions Madrid (0010604). Overall survival support in mecahnically ventilated patients with severe acute respiratory hypoxemic fail- Availability of data and materials The data sets generated and/or analyzed during the current study are publicly ure due to COVID-19 was slightly more than 50% at 180 available due to ethics guidelines, but are available from the corresponding days but this varied considerably between centers. The author on reasonable request. evolution and management of these patients during the ICU stay may influence long-term outcomes. Among the Declarations clinical modifiable factors that were predictors of death, Ethics approval and consent to participate our findings suggest that a failed attempt of NPPV may This study was conducted following approval from the ethics committee of be associated with worse outcomes. However, the clini- each study site. A waiver of consent was obtained from all participants or their cal challenge is still to early identify patients who benefit representative (Hospital Universitario de Getafe CEIm, registry number#20/06, April 15 2020). from NPPV and those who will have worse outcomes. Further studies based on personalized medicine are war- Consent for publication ranted to evaluate functional outcomes of this population Not applicable. and the multiple candidate therapies to detect treatment- Competing interests responsive subgroups. The authors declare that they have no competing interests. Author details 1 Abbreviations Intensive Care Unit, Hospital Universitario de Getafe, Carretera de Toledo Acute hypoxemic respiratory failure: AHRF; Acute respiratory distress syn‑ km 12,500, 28905 Madrid, Spain. 2 Centro de Investigación Biomédica en Red drome: ARDS; Area under the receiver operating curve: AUROC; Calibration-in- de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain. 3 the-large: CITL; Coronavirus disease 2019: COVID-19; Intensive care units: ICU; Department of Clinical Biostatistics, Hospital Ramón Y Cajal, Instituto Ramón Median odds ratio: MOR; Multivariate imputation by chained equations: MICE; y Cajal de Investigación Sanitaria & Centro de Investigación Biomédica en Red Noninvasive positive pressure ventilation: NPPV; Positive end-expiratory pres‑ de Epidemiología Y Salud Pública, Madrid, Spain. 4 Intensive Care Unit, Hospital sure: PEEP; Real-time reverse-transcriptase polymerase chain reaction: RT-PCR; Universitario Doce de Octubre, Madrid, Spain. 5 Intensive Care Unit, Hospital Simplified Acute Physiology Score: SAPS; Standard deviation: SD; Selective Universitario La Paz, IdiPAZ, Madrid, Spain. 6 Intensive Care Unit, Hospital Uni‑ digestive decontamination: SDD. versitari Sant Pau, Barcelona, Spain. 7 Intensive Care Unit, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. 8 Department of Anesthesiol‑ ogy and Critical Care, Hospital Universitario La Paz, Madrid, Spain. 9 Intensive Supplementary Information Care Unit, Hospital Universitario de Móstoles, Madrid, Spain. 10 Intensive Care The online version contains supplementary material available at https://​doi.​ Unit, Hospital Universitario de Torrejón, Madrid, Spain. 11 Intensive Care Unit, org/​10.​1186/​s13613-​021-​00929-y. Hospital Universitario Severo Ochoa, Leganés, Spain. 12 Intensive Care Unit, Hospital Universitario La Princesa, Madrid, Spain. 13 Intensive Care Unit, Hospi‑ tal Universitario del Henares, Coslada, Spain. 14 Department of Anesthesiology Additional file 1. Additional figures and tables. and Critical Care, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. 15 Intensive Care Unit, Hospital Universitario Gregorio Marañón, Madrid, Spain. 16 Intensive Care Unit, Hospital Universitario Infanta Cristina, Parla, Acknowledgements Madrid, Spain. 17 Intensive Care Unit, Hospital Universitario del Tajo, Aranjuez, The authors would like to thank all the ICU physicians, nurses, and health care Madrid, Spain. 18 Department of Anesthesiology and Critical Care, Hospital personnel for their commitment and solidarity at all times during the outbreak Universitario Ramón Y Cajal, Madrid, Spain. of COVID-19 despite the limitations in health resources and the crisis. Received: 23 April 2021 Accepted: 16 September 2021 Additional contributions We would like to thank Miguel Ángel De la Cal for his assistance in the design of the study, Andrés Esteban Fernández for his contribution in the design of the website, online database and technical maintenance, and Carolyn Newey for her work in editing the manuscript. References 1. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized Authors’ contributions patients with 2019 novel Coronavirus-Infected pneumonia in Wuhan OP, LCA, FFV and AM had full access to all of the data of the study and take China. JAMA. 2020;323(11):1061–9. responsibility for the integrity of the data and the accuracy of data analysis. 2. COVID-19 Dashboard by the Center for Systems Science and Engineering OP and FFV contributed equally to this work. Concept and design: OP, FFV, AE. 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