Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 176
Filter
Add filters

Document Type
Year range
1.
Lancet Infect Dis ; 21(11): 1518-1528, 2021 11.
Article in English | MEDLINE | ID: covidwho-1636381

ABSTRACT

BACKGROUND: A more transmissible variant of SARS-CoV-2, the variant of concern 202012/01 or lineage B.1.1.7, has emerged in the UK. We aimed to estimate the risk of critical care admission, mortality in patients who are critically ill, and overall mortality associated with lineage B.1.1.7 compared with non-B.1.1.7. We also compared clinical outcomes between these two groups. METHODS: For this observational cohort study, we linked large primary care (QResearch), national critical care (Intensive Care National Audit & Research Centre Case Mix Programme), and national COVID-19 testing (Public Health England) databases. We used SARS-CoV-2 positive samples with S-gene molecular diagnostic assay failure (SGTF) as a proxy for the presence of lineage B.1.1.7. We extracted two cohorts from the data: the primary care cohort, comprising patients in primary care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 26, 2021, and known SGTF status; and the critical care cohort, comprising patients admitted for critical care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 27, 2021, and known SGTF status. We explored the associations between SARS-CoV-2 infection with and without lineage B.1.1.7 and admission to a critical care unit (CCU), 28-day mortality, and 28-day mortality following CCU admission. We used Royston-Parmar models adjusted for age, sex, geographical region, other sociodemographic factors (deprivation index, ethnicity, household housing category, and smoking status for the primary care cohort; and ethnicity, body-mass index, deprivation index, and dependency before admission to acute hospital for the CCU cohort), and comorbidities (asthma, chronic obstructive pulmonary disease, type 1 and 2 diabetes, and hypertension for the primary care cohort; and cardiovascular disease, respiratory disease, metastatic disease, and immunocompromised conditions for the CCU cohort). We reported information on types and duration of organ support for the B.1.1.7 and non-B.1.1.7 groups. FINDINGS: The primary care cohort included 198 420 patients with SARS-CoV-2 infection. Of these, 117 926 (59·4%) had lineage B.1.1.7, 836 (0·4%) were admitted to CCU, and 899 (0·4%) died within 28 days. The critical care cohort included 4272 patients admitted to CCU. Of these, 2685 (62·8%) had lineage B.1.1.7 and 662 (15·5%) died at the end of critical care. In the primary care cohort, we estimated adjusted hazard ratios (HRs) of 2·15 (95% CI 1·75-2·65) for CCU admission and 1·65 (1·36-2·01) for 28-day mortality for patients with lineage B.1.1.7 compared with the non-B.1.1.7 group. The adjusted HR for mortality in critical care, estimated with the critical care cohort, was 0·91 (0·76-1·09) for patients with lineage B.1.1.7 compared with those with non-B.1.1.7 infection. INTERPRETATION: Patients with lineage B.1.1.7 were at increased risk of CCU admission and 28-day mortality compared with patients with non-B.1.1.7 SARS-CoV-2. For patients receiving critical care, mortality appeared to be independent of virus strain. Our findings emphasise the importance of measures to control exposure to and infection with COVID-19. FUNDING: Wellcome Trust, National Institute for Health Research Oxford Biomedical Research Centre, and the Medical Sciences Division of the University of Oxford.


Subject(s)
COVID-19/mortality , Critical Care/statistics & numerical data , Intensive Care Units/statistics & numerical data , SARS-CoV-2/isolation & purification , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/therapy , COVID-19/virology , COVID-19 Nucleic Acid Testing/statistics & numerical data , England/epidemiology , Female , Hospital Mortality , Humans , Male , Middle Aged , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Severity of Illness Index , Young Adult
2.
BMJ ; 376: e068407, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1612964

ABSTRACT

OBJECTIVE: To assess the effect of statin treatment versus placebo on clinical outcomes in patients with covid-19 admitted to the intensive care unit (ICU). DESIGN: INSPIRATION/INSPIRATION-S was a multicenter, randomized controlled trial with a 2×2 factorial design. Results for the anticoagulation randomization have been reported previously. Results for the double blind randomization to atorvastatin versus placebo are reported here. SETTING: 11 hospitals in Iran. PARTICIPANTS: Adults aged ≥18 years with covid-19 admitted to the ICU. INTERVENTION: Atorvastatin 20 mg orally once daily versus placebo, to be continued for 30 days from randomization irrespective of hospital discharge status. MAIN OUTCOME MEASURES: The primary efficacy outcome was a composite of venous or arterial thrombosis, treatment with extracorporeal membrane oxygenation, or all cause mortality within 30 days from randomization. Prespecified safety outcomes included increase in liver enzyme levels more than three times the upper limit of normal and clinically diagnosed myopathy. A clinical events committee blinded to treatment assignment adjudicated the efficacy and safety outcomes. RESULTS: Of 605 patients randomized between 29 July 2020 and 4 April 2021 for statin randomization in the INSPIRATION-S trial, 343 were co-randomized to intermediate dose versus standard dose prophylactic anticoagulation with heparin based regimens, whereas 262 were randomized after completion of the anticoagulation study. 587 of the 605 participants were included in the primary analysis of INSPIRATION-S, reported here: 290 were assigned to atorvastatin and 297 to placebo (median age 57 years (interquartile range 45-68 years); 256 (44%) women). The primary outcome occurred in 95 (33%) patients assigned to atorvastatin and 108 (36%) assigned to placebo (odds ratio 0.84, 95% confidence interval 0.58 to 1.21). Death occurred in 90 (31%) patients in the atorvastatin group and 103 (35%) in the placebo group (odds ratio 0.84, 95% confidence interval 0.58 to 1.22). Rates for venous thromboembolism were 2% (n=6) in the atorvastatin group and 3% (n=9) in the placebo group (odds ratio 0.71, 95% confidence interval 0.24 to 2.06). Myopathy was not clinically diagnosed in either group. Liver enzyme levels were increased in five (2%) patients assigned to atorvastatin and six (2%) assigned to placebo (odds ratio 0.85, 95% confidence interval 0.25 to 2.81). CONCLUSIONS: In adults with covid-19 admitted to the ICU, atorvastatin was not associated with a significant reduction in the composite of venous or arterial thrombosis, treatment with extracorporeal membrane oxygenation, or all cause mortality compared with placebo. Treatment was, however, found to be safe. As the overall event rates were lower than expected, a clinically important treatment effect cannot be excluded. TRIAL REGISTRATION: ClinicalTrials.gov NCT04486508.


Subject(s)
Anticoagulants/therapeutic use , Atorvastatin/therapeutic use , COVID-19/complications , Critical Care/methods , Venous Thromboembolism/epidemiology , Adolescent , Adult , Aged , COVID-19/mortality , Critical Care/statistics & numerical data , Double-Blind Method , Extracorporeal Membrane Oxygenation/statistics & numerical data , Female , Heparin/therapeutic use , Humans , Intensive Care Units , Iran/epidemiology , Male , Middle Aged , Odds Ratio , SARS-CoV-2 , Treatment Outcome , Venous Thromboembolism/prevention & control , Venous Thromboembolism/virology , Young Adult
3.
MMWR Morb Mortal Wkly Rep ; 71(1): 19-25, 2022 Jan 07.
Article in English | MEDLINE | ID: covidwho-1608771

ABSTRACT

Vaccination against SARS-CoV-2, the virus that causes COVID-19, is highly effective at preventing COVID-19-associated hospitalization and death; however, some vaccinated persons might develop COVID-19 with severe outcomes† (1,2). Using data from 465 facilities in a large U.S. health care database, this study assessed the frequency of and risk factors for developing a severe COVID-19 outcome after completing a primary COVID-19 vaccination series (primary vaccination), defined as receipt of 2 doses of an mRNA vaccine (BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) or a single dose of JNJ-78436735 [Janssen (Johnson & Johnson)] ≥14 days before illness onset. Severe COVID-19 outcomes were defined as hospitalization with a diagnosis of acute respiratory failure, need for noninvasive ventilation (NIV), admission to an intensive care unit (ICU) including all persons requiring invasive mechanical ventilation, or death (including discharge to hospice). Among 1,228,664 persons who completed primary vaccination during December 2020-October 2021, a total of 2,246 (18.0 per 10,000 vaccinated persons) developed COVID-19 and 189 (1.5 per 10,000) had a severe outcome, including 36 who died (0.3 deaths per 10,000). Risk for severe outcomes was higher among persons who were aged ≥65 years, were immunosuppressed, or had at least one of six other underlying conditions. All persons with severe outcomes had at least one of these risk factors, and 77.8% of those who died had four or more risk factors. Severe COVID-19 outcomes after primary vaccination are rare; however, vaccinated persons who are aged ≥65 years, are immunosuppressed, or have other underlying conditions might be at increased risk. These persons should receive targeted interventions including chronic disease management, precautions to reduce exposure, additional primary and booster vaccine doses, and effective pharmaceutical therapy as indicated to reduce risk for severe COVID-19 outcomes. Increasing COVID-19 vaccination coverage is a public health priority.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/complications , COVID-19/prevention & control , Hospitalization/statistics & numerical data , Vaccination/statistics & numerical data , Adult , Aged , Critical Care/statistics & numerical data , Databases, Factual , Death , Female , Humans , Male , Middle Aged , Respiration, Artificial , Respiratory Insufficiency/complications , Risk Factors , SARS-CoV-2/immunology , United States/epidemiology , Young Adult
4.
J Med Internet Res ; 23(2): e26257, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1574035

ABSTRACT

BACKGROUND: As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. OBJECTIVE: In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. METHODS: We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. RESULTS: Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). CONCLUSIONS: The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Age Factors , Aged , Critical Care/statistics & numerical data , Dementia/epidemiology , Female , Humans , Kidney Failure, Chronic/epidemiology , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Prognosis , Proportional Hazards Models , ROC Curve , Republic of Korea/epidemiology , Retrospective Studies , Risk Factors , Severity of Illness Index , Survival Rate
5.
J Med Virol ; 94(1): 291-297, 2022 01.
Article in English | MEDLINE | ID: covidwho-1544344

ABSTRACT

Due to current advances and growing experience in the management of coronavirus Disease 2019 (COVID-19), the outcome of COVID-19 patients with severe/critical illness would be expected to be better in the second wave compared with the first wave. As our hospitalization criteria changed in the second wave, we aimed to investigate whether a favorable outcome occurred in hospitalized COVID-19 patients with only severe/critical illness. Among 642 laboratory-confirmed hospitalized COVID-19 patients in the first wave and 1121 in the second wave, those who met World Health Organization (WHO) definitions for severe or critical illness on admission or during follow-up were surveyed. Data on demographics, comorbidities, C-reactive protein (CRP) levels on admission, and outcomes were obtained from an electronic hospital database. Univariate analysis was performed to compare the characteristics of patients in the first and second waves. There were 228 (35.5%) patients with severe/critical illness in the first wave and 681 (60.7%) in the second wave. Both groups were similar in terms of age, gender, and comorbidities, other than chronic kidney disease. Median serum CRP levels were significantly higher in patients in the second wave compared with those in the first wave [109 mg/L (interquartile range [IQR]: 65-157) vs. 87 mg/L (IQR: 39-140); p < 0.001]. However, intensive care unit admission and mortality rates were similar among the waves. Even though a lower mortality rate in the second wave has been reported in previous studies, including all hospitalized COVID-19 patients, we found similar demographics and outcomes among hospitalized COVID-19 patients with severe/critical illness in the first and second wave.


Subject(s)
COVID-19/drug therapy , COVID-19/mortality , Critical Care/statistics & numerical data , Severity of Illness Index , Aged , Amides/therapeutic use , Antibodies, Monoclonal, Humanized/therapeutic use , Azithromycin/therapeutic use , C-Reactive Protein/analysis , COVID-19/epidemiology , COVID-19/pathology , Comorbidity , Drug Combinations , Enoxaparin/therapeutic use , Female , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Hydroxychloroquine/therapeutic use , Interleukin 1 Receptor Antagonist Protein/therapeutic use , Lopinavir/therapeutic use , Male , Methylprednisolone/therapeutic use , Middle Aged , Pyrazines/therapeutic use , Retrospective Studies , Ritonavir/therapeutic use , SARS-CoV-2 , Treatment Outcome , Turkey/epidemiology
6.
BMC Pulm Med ; 21(1): 338, 2021 Oct 29.
Article in English | MEDLINE | ID: covidwho-1486570

ABSTRACT

Severe coronavirus disease 2019 (COVID-19) accompanies hypercytokinemia, similar to secondary hemophagocytic lymphohistiocytosis (sHLH). We aimed to find if HScore could predict disease severity in COVID-19. HScore was calculated in hospitalized children and adult patients with a proven diagnosis of COVID-19. The need for intensive care unit (ICU), hospital length of stay (LOS), and in-hospital mortality were recorded. The median HScore was 43.0 (IQR 0.0-63.0), which was higher in those who needed ICU care (59.7, 95% CI 46.4-72.7) compared to those admitted to non-ICU medical wards (38.8, 95% CI 32.2-45.4; P = 0.003). It was also significantly higher in patients who died of COVID-19 (105.1, 95% CI 53.7-156.5) than individuals who survived (41.5, 95% CI 35.8-47.1; P = 0.005). Multivariable logistic regression analysis revealed that higher HScore was associated with a higher risk of ICU admission (adjusted OR = 4.93, 95% CI 1.5-16.17, P = 0.008). The risk of death increased by 20% for every ten units increase in HScore (adjusted OR 1.02, 95% CI 1.00-1.04, P = 0.009). Time to discharge was statistically longer in high HScore levels than low levels (HR = 0.41, 95% CI 0.24-0.69). HScore is much lower in patients with severe COVID-19 than sHLH. Higher HScore is associated with more ICU admission, more extended hospitalization, and a higher mortality rate. A modified HScore with a new cut-off seems more practical in predicting disease severity in patients with severe COVID-19.


Subject(s)
COVID-19/diagnosis , Severity of Illness Index , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/pathology , COVID-19/therapy , COVID-19 Testing , Child , Child, Preschool , Critical Care/statistics & numerical data , Cytokine Release Syndrome/diagnosis , Cytokine Release Syndrome/virology , Female , Hospital Mortality , Hospitalization , Humans , Infant , Iran/epidemiology , Length of Stay/statistics & numerical data , Logistic Models , Male , Middle Aged , Predictive Value of Tests , Prognosis , Prospective Studies , Survival Analysis , Young Adult
7.
Eur J Endocrinol ; 185(1): 137-144, 2021 May 28.
Article in English | MEDLINE | ID: covidwho-1477604

ABSTRACT

Objective: Hyponatremia is the most common electrolyte disorder in hospitalized patients and occurs in about 30% of patients with pneumonia. Hyponatremia has been associated with a worse outcome in several pathologic conditions The main objective of this study was to determine whether serum sodium alterations may be independent predictors of the outcome of hospitalized COVID-19 patients. Design and methods: In this observational study, data from 441 laboratory-confirmed COVID-19 patients admitted to a University Hospital were collected. After excluding 61 patients (no serum sodium at admission available, saline solution infusion before sodium assessment, transfer from another hospital), data from 380 patients were analyzed. Results: 274 (72.1%) patients had normonatremia at admission, 87 (22.9%) patients had hyponatremia and 19 (5%) patients had hypernatremia. We found an inverse correlation between serum sodium and IL-6, whereas a direct correlation between serum sodium and PaO2/FiO2 ratio was observed. Patients with hyponatremia had a higher prevalence of non-invasive ventilation and ICU transfer than those with normonatremia or hypernatremia. Hyponatremia was an independent predictor of in-hospital mortality (2.7-fold increase vs normonatremia) and each mEq/L of serum sodium reduction was associated with a 14.4% increased risk of death. Conclusions: These results suggest that serum sodium at admission may be considered as an early prognostic marker of disease severity in hospitalized COVID-19 patients.


Subject(s)
COVID-19/blood , SARS-CoV-2 , Severity of Illness Index , Sodium/blood , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/mortality , Comorbidity , Critical Care/statistics & numerical data , Female , Fluorocarbons/blood , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Hydrocarbons, Brominated/blood , Hypernatremia/epidemiology , Hyponatremia/epidemiology , Interleukin-6/blood , Male , Middle Aged , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS Virus
8.
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
9.
Anaesthesist ; 69(10): 717-725, 2020 10.
Article in German | MEDLINE | ID: covidwho-1453673

ABSTRACT

BACKGROUND: Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients. OBJECTIVE: In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources. MATERIAL AND METHODS: For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources. RESULTS: Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19 at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month. CONCLUSION: Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.


Subject(s)
Coronavirus Infections/therapy , Critical Care/organization & administration , Hospital Bed Capacity , Hospitals, University/organization & administration , Intensive Care Units/organization & administration , Pneumonia, Viral/therapy , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Critical Care/statistics & numerical data , Germany , Hospitals, University/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Pandemics , Pneumonia, Viral/epidemiology , Prognosis , SARS-CoV-2
10.
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
11.
Sci Rep ; 11(1): 18959, 2021 09 23.
Article in English | MEDLINE | ID: covidwho-1437695

ABSTRACT

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Intensive Care Units/trends , Area Under Curve , Computational Biology/methods , Critical Care/statistics & numerical data , Critical Care/trends , Denmark/epidemiology , Hospitalization/trends , Hospitals/trends , Humans , Machine Learning , Pandemics , ROC Curve , Respiration, Artificial/statistics & numerical data , Respiration, Artificial/trends , Retrospective Studies , Risk Assessment/methods , Risk Factors , SARS-CoV-2/pathogenicity , Ventilators, Mechanical/trends
12.
Epidemiol Infect ; 149: e210, 2021 09 16.
Article in English | MEDLINE | ID: covidwho-1411625

ABSTRACT

Little is known about the impact of COVID-19 on the outcomes of patients undergoing surgery and intervention. This study was conducted between 20 March and 20 May 2020 in six hospitals in Istanbul, and aimed to investigate the effects of surgery and intervention on COVID-19 disease progression, intensive care (ICU) need, mortality and virus transmission to patients and healthcare workers. Patients were examined in three groups: group I underwent emergency surgery, group II had an emergency non-operating room intervention, and group III received inpatient COVID-19 treatment but did not have surgery or undergo intervention. Mortality rates, mechanical ventilation needs and rates of admission to the ICU were compared between the three groups. During this period, patient and healthcare worker transmissions were recorded. In total, 1273 surgical, 476 non-operating room intervention patients and 1884 COVID-19 inpatients were examined. The rate of ICU requirement among patients who had surgery was nearly twice that for inpatients and intervention patients, but there was no difference in mortality between the groups. The overall mortality rates were 2.3% in surgical patients, 3.3% in intervention patients and 3% in inpatients. COVID-19 polymerase chain reaction positivity among hospital workers was 2.4%. Only 3.3% of infected frontline healthcare workers were anaesthesiologists. No deaths occurred among infected healthcare workers. We conclude that emergency surgery and non-operating room interventions during the pandemic period do not increase postoperative mortality and can be performed with low transmission rates.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , General Surgery/statistics & numerical data , Adult , COVID-19/diagnosis , Critical Care/statistics & numerical data , Cross Infection/diagnosis , Cross Infection/epidemiology , Cross Infection/transmission , Female , Health Personnel/statistics & numerical data , Hospital Mortality , Hospitalization , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Turkey/epidemiology
13.
Br J Radiol ; 94(1126): 20210221, 2021 Oct 01.
Article in English | MEDLINE | ID: covidwho-1406740

ABSTRACT

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. METHODS: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. RESULTS: Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. CONCLUSIONS: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. ADVANCES IN KNOWLEDGE: We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.


Subject(s)
COVID-19/diagnostic imaging , Machine Learning , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Adult , Aged , COVID-19/therapy , Critical Care/statistics & numerical data , Early Diagnosis , Female , Health Services Needs and Demand , Humans , Male , Middle Aged , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Predictive Value of Tests , Prognosis , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2
14.
J Infect Dev Ctries ; 15(8): 1094-1103, 2021 08 31.
Article in English | MEDLINE | ID: covidwho-1405472

ABSTRACT

INTRODUCTION: About 14% of COVID-19 patients experience severe symptoms and require hospitalization. Managing these patients could be challenging for limited-resource countries, such as Palestine. This study aimed to evaluate hospitalized severe COVID-19 patients' treatment outcomes managed with supportive care and steroids. METHODOLOGY: This was a single-center observational retrospective cohort study that enrolled COVID-19 patients admitted to the "Martyrs medical military complex- COVID Hospital" in Palestine. The managing physicians manually collected data through chart reviews, including patients' characteristics, complications, outcomes, and different management modalities. Continuous and categorical variables between those who were discharged alive and who died were compared using t-test and Chi-squares test, respectively. RESULTS: Overall, 334 patients were included in this study. Median (IQR) age was 62(11) years, 49.1% were males, and 29.6% were ICU status patients. The median (IQR) PaO2/FiO2 ratio was 76 (67), and 67.6% of these patients had moderate to severe acute respiratory distress syndrome, and 4.8% of the patients received invasive mechanical ventilation. Most of the patients (78.7%) had at least one comorbidity, and 18.3% developed at least one complication. The overall mortality was 12.3% (95% CI 8.9-16.2%), and the median (IQR) length of hospital stay was 11 (8) days. Age (aOR 1.05, p = 0.08), smoking (aOR 4.12, p = 0.019), IMV (aOR 27.4, p < 0.001) and PaO2/FiO2 ratio (aOR 1.03, p < 0.001) were found to predict higher mortality. CONCLUSIONS: Supportive care for patients with severe COVID-19 pneumonia in a Palestinian hospital with limited resources was associated with in-hospital mortality of 12.3%.


Subject(s)
COVID-19/mortality , COVID-19/therapy , Developing Countries , Health Resources , Hospital Mortality , Palliative Care/statistics & numerical data , Aged , COVID-19/epidemiology , Comorbidity , Critical Care/methods , Critical Care/standards , Critical Care/statistics & numerical data , Female , Hospitalization/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Palliative Care/methods , Palliative Care/standards , Respiration, Artificial , Retrospective Studies
15.
Sci Rep ; 11(1): 17968, 2021 09 09.
Article in English | MEDLINE | ID: covidwho-1402115

ABSTRACT

The impact of overlapping risk factors on coronavirus disease (COVID-19) severity is unclear. To evaluate the impact of type 2 diabetes (T2D) and obesity on COVID-19 severity, we conducted a cohort study with 28,095 anonymized COVID-19 patients using data from the COVID-19 Research Database from January 1, 2020 to November 30, 2020. The mean age was 50.8 ± 17.5 years, and 11,802 (42%) patients were male. Data on age, race, sex, T2D complications, antidiabetic medication prescription, and body mass index ≥ 30 kg/m2 (obesity) were analysed using Cox proportional hazard models, with hospitalization risk and critical care within 30 days of COVID-19 diagnosis as the main outcomes. The risk scores were 0-4 for age ≥ 65 years, male sex, T2D, and obesity. Among the participants, 11,294 (61.9%) had obesity, and 4445 (15.8%) had T2D. T2D, obesity, and male sex were significantly associated with COVID-19 hospitalization risk. Regarding hospitalization risk scores, compared with those for hospitalization risk score 0 and critical care risk score 0, hazard ratios [95% confidence intervals] were 19.034 [10.470-34.600] and 55.803 [12.761-244.015] (P < 0.001) (P < 0.001), respectively, for risk score 4. Complications from diabetes and obesity increased hospitalization and critical care risks for COVID-19 patients.


Subject(s)
COVID-19/pathology , Critical Care/statistics & numerical data , Diabetes Mellitus, Type 2/pathology , Obesity/pathology , Severity of Illness Index , Aged , Aging/pathology , COVID-19/drug therapy , Diabetes Complications/pathology , Female , Hospitalization/statistics & numerical data , Humans , Hypoglycemic Agents/therapeutic use , Intensive Care Units/statistics & numerical data , Male , Metformin/therapeutic use , Middle Aged , Risk Factors , SARS-CoV-2 , Sodium-Glucose Transporter 2 Inhibitors/therapeutic use , United States
16.
Acta Obstet Gynecol Scand ; 99(7): 819-822, 2020 07.
Article in English | MEDLINE | ID: covidwho-1388175

ABSTRACT

The Public Health Agency of Sweden has analyzed how many pregnant and postpartum women with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been treated in intensive care units (ICU) in Sweden between 19 March and 20 April 2020 compared with non-pregnant women of similar age. Cases were identified in a special reporting module within the Swedish Intensive Care Registry (SIR). Fifty-three women aged 20-45 years with SARS-CoV-2 were reported in SIR, and 13 of these women were either pregnant or postpartum (<1 week). The results indicate that the risk of being admitted to ICU may be higher in pregnant and postpartum women with laboratory-confirmed SARS-CoV-2 in Sweden, compared with non-pregnant women of similar age.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections , Critical Care , Hospitalization/statistics & numerical data , Pandemics , Pneumonia, Viral , Pregnancy Complications, Infectious , Puerperal Infection , Adult , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/physiopathology , Coronavirus Infections/therapy , Critical Care/methods , Critical Care/statistics & numerical data , Female , Humans , Pneumonia, Viral/epidemiology , Pneumonia, Viral/physiopathology , Pneumonia, Viral/therapy , Pregnancy , Pregnancy Complications, Infectious/epidemiology , Pregnancy Complications, Infectious/physiopathology , Pregnancy Complications, Infectious/therapy , Pregnancy Complications, Infectious/virology , Puerperal Infection/epidemiology , Puerperal Infection/physiopathology , Puerperal Infection/therapy , Puerperal Infection/virology , Registries/statistics & numerical data , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Sweden/epidemiology
17.
BMC Med ; 19(1): 213, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379790

ABSTRACT

BACKGROUND: The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. METHODS: A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). RESULTS: One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)]. CONCLUSION: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19/mortality , Cause of Death , Critical Care/statistics & numerical data , Hospital Mortality , Intensive Care Units , Ventilators, Mechanical , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Young Adult
18.
Biomolecules ; 11(8)2021 08 01.
Article in English | MEDLINE | ID: covidwho-1334994

ABSTRACT

Severe coronavirus disease 2019 (COVID-19) is associated with hyperinflammation leading to organ injury, including respiratory failure. Galectin-3 was implicated in innate immunological response to infections and in chronic fibrosis. The aim of our preliminary study was the assessment of the diagnostic utility of serum galectin-3 in patients with COVID-19. The prospective observational study included adult patients admitted with active COVID-19 and treated in tertiary hospital between June and July 2020. The diagnosis was confirmed by the quantitative detection of nucleic acid of severe acute respiratory syndrome coronavirus 2 in nasopharyngeal swabs. Galectin-3 was measured by enzyme immunoassay in serum samples obtained during the first five days of hospital stay. We included 70 patients aged 25 to 73 years; 90% had at least one comorbidity. During the hospital stay, 32.9% were diagnosed with COVID-19 pneumonia and 12.9% required treatment in the intensive care unit (ICU). Serum galectin-3 was significantly increased in patients who developed pneumonia, particularly those who required ICU admission. Positive correlations were found between galectin-3 and inflammatory markers (interleukin-6, C-reactive protein, ferritin, pentraxin-3), a marker of endothelial injury (soluble fms-like tyrosine kinase-1), and a range of tissue injury markers. Serum galectin-3 enabled the diagnosis of pneumonia with moderate diagnostic accuracy and the need for ICU treatment with high diagnostic accuracy. Our findings strengthen the hypothesis that galectin-3 may be involved in severe COVID-19. Further studies are planned to confirm the preliminary results and to verify possible associations of galectin-3 with long-term consequences of COVID-19, including pulmonary fibrosis.


Subject(s)
COVID-19/blood , Galectin 3/blood , Adult , Biomarkers/blood , C-Reactive Protein/analysis , COVID-19/epidemiology , COVID-19/pathology , COVID-19/therapy , Comorbidity , Critical Care/statistics & numerical data , Female , Ferritins/blood , Humans , Interleukin-6/blood , Male , Middle Aged , Serum Amyloid P-Component/analysis , Vascular Endothelial Growth Factor Receptor-1/blood
19.
J Prim Care Community Health ; 11: 2150132720954687, 2020.
Article in English | MEDLINE | ID: covidwho-1318263

ABSTRACT

BACKGROUND: COVID-19 is a highly infectious disease which usually presents with respiratory symptoms. This virus is disseminated through respiratory droplets, and, therefore, individuals residing in close quarters are at a higher risk for the acquisition of infection. The prison population is at a significantly increased risk for infection. METHODS: Prisoners from the Montford Correctional facility in Lubbock, Texas, hospitalized in the medical intensive care unit at University Medical Center between March 1, 2020 and May 15, 2020 were compared to community-based patients hospitalized in the same medical intensive care unit. Clinical information, laboratory results, radiographic results, management requirements, and outcomes were compared. RESULTS: A total of 15 community-based patients with a mean age of 67.4 ± 15.5 years were compared to 5 prisoners with a mean age of 56.0 ± 9.0 years. All prisoners were men; 10 community-based patients were men. Prisoners presented with fever, dyspnea, and GI symptoms. The mean number of comorbidities in prisoners was 2.4 compared to 1.8 in community-based patients. Prisoners had significantly lower heart rates and respiratory rates at presentation than community-based patients. The mean length of stay in prisoners was 12.6 ± 8.9 days; the mean length of stay in community-based patients was 8.6 ± 6.5. The case fatality rate was 60% in both groups. CONCLUSIONS: Prisoners were younger than community-based patients but required longer lengths of stay and had the same mortality rate. This study provides a basis for comparisons with future studies which could involve new treatment options currently under study.


Subject(s)
Coronavirus Infections/therapy , Critical Care/statistics & numerical data , Pandemics , Patients/statistics & numerical data , Pneumonia, Viral/therapy , Prisoners/statistics & numerical data , Academic Medical Centers , Age Distribution , Aged , Aged, 80 and over , COVID-19 , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Female , Hospitalization , Humans , Intensive Care Units , Length of Stay/statistics & numerical data , Male , Middle Aged , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Retrospective Studies , Texas/epidemiology , Treatment Outcome
20.
Intern Emerg Med ; 16(7): 1975-1985, 2021 10.
Article in English | MEDLINE | ID: covidwho-1316333

ABSTRACT

Contrasting data have been published about the impact of cardiovascular disease on Covid-19. A comprehensive synthesis and pooled analysis of the available evidence is needed to guide prioritization of prevention strategies. To clarify the association of cardiovascular disease with Covid-19 outcomes, we searched PubMed up to 26 October 2020, for studies reporting the prevalence of cardiovascular disease among inpatients with Covid-19 in relation to their outcomes. Pooled odds-ratios (OR) for death, for mechanical ventilation or admission in an intensive care unit (ICU) and for composite outcomes were calculated using random effect models overall and in the subgroup of people with comorbid diabetes. Thirty-three studies enrolling 52,857 inpatients were included. Cardiovascular disease was associated with a higher risk of death both overall (OR 2.58, 95% confidence intervals, CI 2.12-3.14, p < 0.001, number of studies 24) and in the subgroup of people with diabetes (OR 2.91, 95% CI 2.13-3.97, p < 0.001, number of studies 4), but not with higher risk of ICU admission or mechanical ventilation (OR 1.35, 95% CI 0.73-2.50, p = 0.34, number of studies 4). Four out of five studies reporting OR adjusted for confounders failed to show independent association of cardiovascular disease with Covid-19 deaths. Accordingly, the adjusted-OR for Covid-19 death in people with cardiovascular disease dropped to 1.31 (95% CI 1.01-1.70, p = 0.041). Among patients hospitalized for Covid-19, cardiovascular disease confers higher risk of death, which was highly mitigated when adjusting the association for confounders.


Subject(s)
COVID-19/mortality , Cardiovascular Diseases/mortality , Critical Care/statistics & numerical data , COVID-19/complications , Cardiovascular Diseases/complications , Comorbidity , Humans , Intensive Care Units , Respiration, Artificial/mortality
SELECTION OF CITATIONS
SEARCH DETAIL
...