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2.
Journal of Critical Care ; 71:154050, 2022.
Article in English | ScienceDirect | ID: covidwho-1819524

ABSTRACT

Background During the COVID-19 pandemic, intensive care units (ICU) introduced restrictions to in-person family visiting to safeguard patients, healthcare personnel, and visitors. Methods We conducted a web-based survey (March–July 2021) investigating ICU visiting practices before the pandemic, at peak COVID-19 ICU admissions, and at the time of survey response. We sought data on visiting policies and communication modes including use of virtual visiting (videoconferencing). Results We obtained 667 valid responses representing ICUs in all continents. Before the pandemic, 20% (106/525) had unrestricted visiting hours;6% (30/525) did not allow in-person visiting. At peak, 84% (558/667) did not allow in-person visiting for patients with COVID-19;66% for patients without COVID-19. This proportion had decreased to 55% (369/667) at time of survey reporting. A government mandate to restrict hospital visiting was reported by 53% (354/646). Most ICUs (55%, 353/615) used regular telephone updates;50% (306/667) used telephone for formal meetings and discussions regarding prognosis or end-of-life. Virtual visiting was available in 63% (418/667) at time of survey. Conclusions Highly restrictive visiting policies were introduced at the initial pandemic peaks, were subsequently liberalized, but without returning to pre-pandemic practices. Telephone became the primary communication mode in most ICUs, supplemented with virtual visits.

4.
JMIR Med Inform ; 10(3): e32949, 2022 Mar 31.
Article in English | MEDLINE | ID: covidwho-1770908

ABSTRACT

BACKGROUND: The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. OBJECTIVE: The aim of this study was to evaluate machine learning-based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporated multifaceted clinical information on evolution of the disease. METHODS: This multicenter cohort study (COVIP study) obtained patient data from 151 intensive care units (ICUs) from 26 countries. Different models based on the Sequential Organ Failure Assessment (SOFA) score, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were derived as baseline models that included admission variables only. We subsequently included clinical events and time-to-event as additional variables to derive the final models using the same algorithms and compared their performance with that of the baseline group. Furthermore, we derived baseline and final models on a European patient cohort, which were externally validated on a non-European cohort that included Asian, African, and US patients. RESULTS: In total, 1432 elderly (≥70 years old) COVID-19-positive patients admitted to an ICU were included for analysis. Of these, 809 (56.49%) patients survived up to 30 days after admission. The average length of stay was 21.6 (SD 18.2) days. Final models that incorporated clinical events and time-to-event information provided superior performance (area under the receiver operating characteristic curve of 0.81; 95% CI 0.804-0.811), with respect to both the baseline models that used admission variables only and conventional ICU prediction models (SOFA score, P<.001). The average precision increased from 0.65 (95% CI 0.650-0.655) to 0.77 (95% CI 0.759-0.770). CONCLUSIONS: Integrating important clinical events and time-to-event information led to a superior accuracy of 30-day mortality prediction compared with models based on the admission information and conventional ICU prediction models. This study shows that machine-learning models provide additional information and may support complex decision-making in critically ill elderly COVID-19 patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04321265; https://clinicaltrials.gov/ct2/show/NCT04321265.

5.
Int J Med Inform ; 162: 104755, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1768182

ABSTRACT

INTRODUCTION: SARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results. MATERIAL AND METHODS: Our primary goal was to predict Rt of the current day in the twelve provinces of Lombardy with the highest possible accuracy, and with no influence of the local testing strategies. We gathered data about mobility, weather, and pollution from different public sources as a proxy of human behavior and public health measures. We built four supervised machine learning algorithms with different strategies: the outcome variable was the daily median Rt values per province obtained from officially adopted algorithms. RESULTS: Data from 243 days for every province were presented to our four models (from February 15th, 2020, to October 14th, 2020). Two models using differential calculation of Rt instead of the raw values showed the highest mean coefficient of determination (0.93 for both) and residuals reported the lowest mean error (-0.03 and 0.01) and standard deviation (0.13 for both) as well. The one with access to the value of Rt of the day before heavily relied on that feature for prediction, while the other one had more distributed weights. DISCUSSION: The model that had not access to the Rt value of the previous day and used Rt differential value as outcome (FDRt) was considered the most robust according to the metrics. Its forecasts were able to predict the trend that Rt values would have developed over different weeks, but it was not particularly accurate in predicting the precise value of Rt. A correlation among mobility, atmospheric, features, pollution and Rt values is plausible, but further testing should be performed.

6.
Gastro Hep Adv ; 1(2): 194-209, 2022.
Article in English | MEDLINE | ID: covidwho-1747991

ABSTRACT

Background and Aims: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. Methods: Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. Results: We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. Conclusion: This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

8.
Intensive Care Med ; 48(4): 435-447, 2022 04.
Article in English | MEDLINE | ID: covidwho-1712215

ABSTRACT

PURPOSE: The number of patients ≥ 80 years admitted into critical care is increasing. Coronavirus disease 2019 (COVID-19) added another challenge for clinical decisions for both admission and limitation of life-sustaining treatments (LLST). We aimed to compare the characteristics and mortality of very old critically ill patients with or without COVID-19 with a focus on LLST. METHODS: Patients 80 years or older with acute respiratory failure were recruited from the VIP2 and COVIP studies. Baseline patient characteristics, interventions in intensive care unit (ICU) and outcomes (30-day survival) were recorded. COVID patients were matched to non-COVID patients based on the following factors: age (± 2 years), Sequential Organ Failure Assessment (SOFA) score (± 2 points), clinical frailty scale (± 1 point), gender and region on a 1:2 ratio. Specific ICU procedures and LLST were compared between the cohorts by means of cumulative incidence curves taking into account the competing risk of discharge and death. RESULTS: 693 COVID patients were compared to 1393 non-COVID patients. COVID patients were younger, less frail, less severely ill with lower SOFA score, but were treated more often with invasive mechanical ventilation (MV) and had a lower 30-day survival. 404 COVID patients could be matched to 666 non-COVID patients. For COVID patients, withholding and withdrawing of LST were more frequent than for non-COVID and the 30-day survival was almost half compared to non-COVID patients. CONCLUSION: Very old COVID patients have a different trajectory than non-COVID patients. Whether this finding is due to a decision policy with more active treatment limitation or to an inherent higher risk of death due to COVID-19 is unclear.


Subject(s)
COVID-19 , Respiratory Insufficiency , COVID-19/therapy , Critical Care , Critical Illness , Humans , Intensive Care Units , Respiratory Insufficiency/therapy , SARS-CoV-2
9.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-328716

ABSTRACT

Background: Prone positioning improves survival in moderate-to-severe acute respiratory distress syndrome (ARDS) unrelated to the novel coronavirus disease (COVID-2019). This benefit is probably mediated by a decrease in alveolar collapse and hyperinflation and a more homogeneous distribution of lung aeration, with fewer harms of mechanical ventilation. Herein we aimed to verify whether prone positioning causes analogue changes in lung aeration in COVID-2019. A positive result would support prone positioning even in this other population. MethodsFifteen mechanically-ventilated patients with COVID-19 underwent a lung computed tomography in the supine and prone position within three days of endotracheal intubation and with a constant positive end-expiratory pressure (PEEP). Using quantitative analysis, we measured the volume of the non-aerated, poorly-aerated, well-aerated, and over-aerated compartments and the gas-to-tissue ratio of the ten vertical levels of the lung. In addition, we expressed the heterogeneity of lung aeration with the standardized median absolute deviation of the ten vertical gas-to-tissue ratios, with lower values indicating less heterogeneity. ResultsBy the time of the study, PEEP was 12 (10-14) cmH 2 O and the PaO 2 :FiO 2 107 (84-173) mmHg in the supine position. With prone positioning, the volume of the non-aerated compartment decreased by 82 (26-147) ml, of the poorly-aerated compartment increased by 82 (53-174) ml, of the normally-aerated compartment did not significantly change, and of the over-aerated compartment decreased by 28 (11-186) ml. In eight (53%) patients, the volume of the over-aerated compartment decreased more than the volume of the non-aerated compartment. The gas-to-tissue ratio of the ten vertical levels of the lung decreased by 0.34 (0.25-0.49) ml/g per level in the supine position and by 0.03 (-0.11-0.14) ml/g in the prone position (p<0.001). The standardized median absolute deviation of the gas-to-tissue ratios of those ten levels decreased in all patients, from 0.55 (0.50-0.71) to 0.20 (0.14-0.27) (p<0.001).ConclusionsIn fifteen patients with COVID-19, prone positioning decreased alveolar collapse and hyperinflation, and homogenized lung aeration. A similar response has been observed in other ARDS, where prone positioning improves outcome. Therefore, our data provide a pathophysiological rationale to support prone positioning even in COVID-19.

10.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-313582

ABSTRACT

Background: The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19 ICU patients. Methods: A prospective multi-centre study of COVID-19 patients ≥ 70 years admitted to intensive care in 138 ICUs from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the Clinical Frailty Scale (CFS). Additionally, comorbidities, management strategies and treatment limitations were recorded. Results: The study included 1346 patients (28% female) with a median age of 75 years (IQR 72-78, range 70-96), 16.3% were older than 80 years and 21% of the patients were frail. The overall survival at 30 days was 59% (95%CI 56-62), with 66% (63-69) in fit, 53% (47-61) in vulnerable and 41% (35-47) in frail patients (p<0.001). In frail patients, there was no difference in 30 day survival between different age categories. Frailty was linked to an increased use of treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treatment limitations and comorbidities, frailty was independently associated with lower survival. Conclusion: Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and comorbidities.

11.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-311492

ABSTRACT

Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network. Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models;model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training. Results: 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B. Conclusions: Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

12.
Intensive Care Med ; 48(2): 227-230, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1616113

Subject(s)
COVID-19 , Humans
13.
Br J Anaesth ; 128(3): 482-490, 2022 03.
Article in English | MEDLINE | ID: covidwho-1536454

ABSTRACT

BACKGROUND: Tracheostomy is performed in patients expected to require prolonged mechanical ventilation, but to date optimal timing of tracheostomy has not been established. The evidence concerning tracheostomy in COVID-19 patients is particularly scarce. We aimed to describe the relationship between early tracheostomy (≤10 days since intubation) and outcomes for patients with COVID-19. METHODS: This was a prospective cohort study performed in 152 centres across 16 European countries from February to December 2020. We included patients aged ≥70 yr with confirmed COVID-19 infection admitted to an intensive care unit, requiring invasive mechanical ventilation. Multivariable analyses were performed to evaluate the association between early tracheostomy and clinical outcomes including 3-month mortality, intensive care length of stay, and duration of mechanical ventilation. RESULTS: The final analysis included 1740 patients with a mean age of 74 yr. Tracheostomy was performed in 461 (26.5%) patients. The tracheostomy rate varied across countries, from 8.3% to 52.9%. Early tracheostomy was performed in 135 (29.3%) patients. There was no difference in 3-month mortality between early and late tracheostomy in either our primary analysis (hazard ratio [HR]=0.96; 95% confidence interval [CI], 0.70-1.33) or a secondary landmark analysis (HR=0.78; 95% CI, 0.57-1.06). CONCLUSIONS: There is a wide variation across Europe in the timing of tracheostomy for critically ill patients with COVID-19. However, we found no evidence that early tracheostomy is associated with any effect on survival amongst older critically ill patients with COVID-19. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT04321265.


Subject(s)
COVID-19/mortality , COVID-19/therapy , Critical Care/methods , Critical Care/statistics & numerical data , Critical Illness/mortality , Tracheostomy/mortality , Tracheostomy/statistics & numerical data , Aged , Correlation of Data , Europe , Female , Humans , Intensive Care Units/statistics & numerical data , Length of Stay , Male , Prospective Studies , Respiration, Artificial , Survival Rate/trends , Time Factors , Treatment Outcome
14.
Crit Care Med ; 49(11): 1974-1982, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1475880
16.
Chest ; 161(4): 979-988, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1466219

ABSTRACT

BACKGROUND: International guidelines suggest using a higher (> 10 cm H2O) positive end-expiratory pressure (PEEP) in patients with moderate-to-severe ARDS due to COVID-19. However, even if oxygenation generally improves with a higher PEEP, compliance, and Paco2 frequently do not, as if recruitment was small. RESEARCH QUESTION: Is the potential for lung recruitment small in patients with early ARDS due to COVID-19? STUDY DESIGN AND METHODS: Forty patients with ARDS due to COVID-19 were studied in the supine position within 3 days of endotracheal intubation. They all underwent a PEEP trial, in which oxygenation, compliance, and Paco2 were measured with 5, 10, and 15 cm H2O of PEEP, and all other ventilatory settings unchanged. Twenty underwent a whole-lung static CT scan at 5 and 45 cm H2O, and the other 20 at 5 and 15 cm H2O of airway pressure. Recruitment and hyperinflation were defined as a decrease in the volume of the non-aerated (density above -100 HU) and an increase in the volume of the over-aerated (density below -900 HU) lung compartments, respectively. RESULTS: From 5 to 15 cm H2O, oxygenation improved in 36 (90%) patients but compliance only in 11 (28%) and Paco2 only in 14 (35%). From 5 to 45 cm H2O, recruitment was 351 (161-462) mL and hyperinflation 465 (220-681) mL. From 5 to 15 cm H2O, recruitment was 168 (110-202) mL and hyperinflation 121 (63-270) mL. Hyperinflation variably developed in all patients and exceeded recruitment in more than half of them. INTERPRETATION: Patients with early ARDS due to COVID-19, ventilated in the supine position, present with a large potential for lung recruitment. Even so, their compliance and Paco2 do not generally improve with a higher PEEP, possibly because of hyperinflation.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , COVID-19/complications , COVID-19/therapy , Humans , Lung/diagnostic imaging , Positive-Pressure Respiration , Respiration, Artificial , Respiratory Distress Syndrome/etiology , Respiratory Distress Syndrome/therapy
17.
Crit Care Med ; 49(11): e1157-e1162, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1467424

ABSTRACT

OBJECTIVES: Joblessness is common in survivors from critical care. Our aim was to describe rates of return to work versus unemployment following coronavirus disease 2019 acute respiratory distress syndrome requiring intensive care admission. DESIGN: Single-center, prospective case series. SETTING: Critical Care Follow-Up Clinic, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy. PATIENTS: One hundred and one consecutive laboratory-confirmed coronavirus disease 2019 patients were discharged from our hospital following an ICU stay between March 1, 2020, and June 30, 2020. Twenty-five died in the ICU. Seventy-six were discharged alive from hospital. Two patients refused participation, while three were unreachable. The remaining 71 were alive at 6 months and interviewed. INTERVENTIONS: Baseline and outcome healthcare data were extracted from the electronic patient records. Employment data were collected using a previously published structured interview instrument that included current and previous employment status, hours worked per week, and timing of return to work. Health-related quality of life status was assessed using the Italian EQ-5D-5L questionnaire. MEASUREMENTS AND MAIN RESULTS: Of the 71 interviewed patients, 45 (63%) were employed prior to coronavirus disease 2019, of which 40 (89%) of them worked full-time. Thirty-three (73%) of the previously employed survivors had returned to work by 6 months, 10 (22%) were unemployed, and 2 (5%) were newly retired. Among those who returned to work, 20 (85%) of them reported reduced effectiveness at work. Those who did not return to work were either still on sick leave or lost their job as a consequence of coronavirus disease 2019. Reported quality of life of survivors not returning to work was worse than of those returning to work. CONCLUSIONS: The majority of coronavirus disease 2019 survivors following ICU in our cohort had returned to work by 6 months of follow-up. However, most of them reported reduced work effectiveness. Prolonged sick leave and unemployment were common findings in those not returning.


Subject(s)
COVID-19/epidemiology , Critical Care/statistics & numerical data , Respiratory Distress Syndrome/epidemiology , Return to Work/statistics & numerical data , Unemployment/statistics & numerical data , Age Factors , Aged , Comorbidity , Female , Frailty/epidemiology , Humans , Length of Stay , Male , Middle Aged , Patient Discharge/statistics & numerical data , Quality of Life , Retirement/statistics & numerical data , SARS-CoV-2 , Severity of Illness Index , Sex Factors , Socioeconomic Factors
18.
Applied Sciences ; 11(19):9342, 2021.
Article in English | MDPI | ID: covidwho-1463542

ABSTRACT

The region of Lombardy was the epicenter of the COVID-19 outbreak in Italy. Emergency Hospital 19 (EH19) was built in the Milan metropolitan area during the pandemic’s second wave as a facility of Humanitas Clinical and Research Center (HCRC). The present study aimed to assess whether the implementation of EH19 was effective in improving the quality of care of COVID-19 patients during the second wave compared with the first one. The demographics, mortality rate, and in-hospital length of stay (LOS) of two groups of patients were compared: the study group involved patients admitted at HCRC and managed in EH19 during the second pandemic wave, while the control group included patients managed exclusively at HCRC throughout the first wave. The study and control group included 903 (56.7%) and 690 (43.3%) patients, respectively. The study group was six years older on average and had more pre-existing comorbidities. EH19 was associated with a decrease in the intensive care unit admission rate (16.9% vs. 8.75%, p <0.001), and an equal decrease in invasive oxygen therapy (3.8% vs. 0.23%, p <0.001). Crude mortality was similar but overlap propensity score weighting revealed a trend toward a potential small decrease. The adjusted difference in LOS was not significant. The implementation of an additional COVID-19 hospital facility was effective in improving the overall quality of care of COVID-19 patients during the first wave of the pandemic when compared with the second. Further studies are necessary to validate the suggested approach.

20.
JAMA ; 323(16): 1574-1581, 2020 04 28.
Article in English | MEDLINE | ID: covidwho-1453471

ABSTRACT

Importance: In December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) emerged in China and has spread globally, creating a pandemic. Information about the clinical characteristics of infected patients who require intensive care is limited. Objective: To characterize patients with coronavirus disease 2019 (COVID-19) requiring treatment in an intensive care unit (ICU) in the Lombardy region of Italy. Design, Setting, and Participants: Retrospective case series of 1591 consecutive patients with laboratory-confirmed COVID-19 referred for ICU admission to the coordinator center (Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy) of the COVID-19 Lombardy ICU Network and treated at one of the ICUs of the 72 hospitals in this network between February 20 and March 18, 2020. Date of final follow-up was March 25, 2020. Exposures: SARS-CoV-2 infection confirmed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasal and pharyngeal swabs. Main Outcomes and Measures: Demographic and clinical data were collected, including data on clinical management, respiratory failure, and patient mortality. Data were recorded by the coordinator center on an electronic worksheet during telephone calls by the staff of the COVID-19 Lombardy ICU Network. Results: Of the 1591 patients included in the study, the median (IQR) age was 63 (56-70) years and 1304 (82%) were male. Of the 1043 patients with available data, 709 (68%) had at least 1 comorbidity and 509 (49%) had hypertension. Among 1300 patients with available respiratory support data, 1287 (99% [95% CI, 98%-99%]) needed respiratory support, including 1150 (88% [95% CI, 87%-90%]) who received mechanical ventilation and 137 (11% [95% CI, 9%-12%]) who received noninvasive ventilation. The median positive end-expiratory pressure (PEEP) was 14 (IQR, 12-16) cm H2O, and Fio2 was greater than 50% in 89% of patients. The median Pao2/Fio2 was 160 (IQR, 114-220). The median PEEP level was not different between younger patients (n = 503 aged ≤63 years) and older patients (n = 514 aged ≥64 years) (14 [IQR, 12-15] vs 14 [IQR, 12-16] cm H2O, respectively; median difference, 0 [95% CI, 0-0]; P = .94). Median Fio2 was lower in younger patients: 60% (IQR, 50%-80%) vs 70% (IQR, 50%-80%) (median difference, -10% [95% CI, -14% to 6%]; P = .006), and median Pao2/Fio2 was higher in younger patients: 163.5 (IQR, 120-230) vs 156 (IQR, 110-205) (median difference, 7 [95% CI, -8 to 22]; P = .02). Patients with hypertension (n = 509) were older than those without hypertension (n = 526) (median [IQR] age, 66 years [60-72] vs 62 years [54-68]; P < .001) and had lower Pao2/Fio2 (median [IQR], 146 [105-214] vs 173 [120-222]; median difference, -27 [95% CI, -42 to -12]; P = .005). Among the 1581 patients with ICU disposition data available as of March 25, 2020, 920 patients (58% [95% CI, 56%-61%]) were still in the ICU, 256 (16% [95% CI, 14%-18%]) were discharged from the ICU, and 405 (26% [95% CI, 23%-28%]) had died in the ICU. Older patients (n = 786; age ≥64 years) had higher mortality than younger patients (n = 795; age ≤63 years) (36% vs 15%; difference, 21% [95% CI, 17%-26%]; P < .001). Conclusions and Relevance: In this case series of critically ill patients with laboratory-confirmed COVID-19 admitted to ICUs in Lombardy, Italy, the majority were older men, a large proportion required mechanical ventilation and high levels of PEEP, and ICU mortality was 26%.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Critical Care/statistics & numerical data , Hospital Mortality , Intensive Care Units/statistics & numerical data , Pneumonia, Viral/epidemiology , Positive-Pressure Respiration/statistics & numerical data , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , COVID-19 , Comorbidity , Coronavirus Infections/mortality , Coronavirus Infections/physiopathology , Coronavirus Infections/therapy , Critical Illness/therapy , Female , Hospitalization , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/physiopathology , Pneumonia, Viral/therapy , Respiration, Artificial , Retrospective Studies , SARS-CoV-2 , Sex Distribution , Young Adult
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