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3.
J Med Ethics ; 46(8): 514-525, 2020 08.
Article in English | MEDLINE | ID: covidwho-1467726

ABSTRACT

BACKGROUND: Humanitarian crises and emergencies, events often marked by high mortality, have until recently excluded palliative care-a specialty focusing on supporting people with serious or terminal illness or those nearing death. In the COVID-19 pandemic, palliative care has received unprecedented levels of societal attention. Unfortunately, this has not been enough to prevent patients dying alone, relatives not being able to say goodbye and palliative care being used instead of intensive care due to resource limitations. Yet global guidance was available. In 2018, the WHO released a guide on 'Integrating palliative care and symptom relief into the response to humanitarian emergencies and crises'-the first guidance on the topic by an international body. AIMS: This paper argues that while a landmark document, the WHO guide took a narrowly clinical bioethics perspective and missed crucial moral dilemmas. We argue for adding a population-level bioethics lens, which draws forth complex moral dilemmas arising from the fact that groups having differential innate and acquired resources in the context of social and historical determinants of health. We discuss dilemmas concerning: limitations of material and human resources; patient prioritisation; euthanasia; and legacy inequalities, discrimination and power imbalances. IMPLICATIONS: In parts of the world where opportunity for preparation still exists, and as countries emerge from COVID-19, planners must consider care for the dying. Immediate steps to support better resolutions to ethical dilemmas of the provision of palliative care in humanitarian and emergency contexts will require honest debate; concerted research effort; and international, national and local ethical guidance.


Subject(s)
Bioethical Issues , Delivery of Health Care/ethics , Disaster Planning , Palliative Care/ethics , Pandemics/ethics , Terminal Care/ethics , Altruism , Betacoronavirus , Bioethics , COVID-19 , Coronavirus Infections/therapy , Coronavirus Infections/virology , Critical Care , Decision Making/ethics , Emergencies , Ethics, Clinical , Global Health , Health Care Rationing , Health Equity , Health Resources , Humans , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Practice Guidelines as Topic , SARS-CoV-2 , Socioeconomic Factors , Stress, Psychological
4.
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
5.
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
6.
JAMA ; 323(16): 1582-1589, 2020 04 28.
Article in English | MEDLINE | ID: covidwho-1453469

ABSTRACT

Importance: Coronavirus disease 2019 (COVID-19) is a pandemic with no specific therapeutic agents and substantial mortality. It is critical to find new treatments. Objective: To determine whether convalescent plasma transfusion may be beneficial in the treatment of critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design, Setting, and Participants: Case series of 5 critically ill patients with laboratory-confirmed COVID-19 and acute respiratory distress syndrome (ARDS) who met the following criteria: severe pneumonia with rapid progression and continuously high viral load despite antiviral treatment; Pao2/Fio2 <300; and mechanical ventilation. All 5 were treated with convalescent plasma transfusion. The study was conducted at the infectious disease department, Shenzhen Third People's Hospital in Shenzhen, China, from January 20, 2020, to March 25, 2020; final date of follow-up was March 25, 2020. Clinical outcomes were compared before and after convalescent plasma transfusion. Exposures: Patients received transfusion with convalescent plasma with a SARS-CoV-2-specific antibody (IgG) binding titer greater than 1:1000 (end point dilution titer, by enzyme-linked immunosorbent assay [ELISA]) and a neutralization titer greater than 40 (end point dilution titer) that had been obtained from 5 patients who recovered from COVID-19. Convalescent plasma was administered between 10 and 22 days after admission. Main Outcomes and Measures: Changes of body temperature, Sequential Organ Failure Assessment (SOFA) score (range 0-24, with higher scores indicating more severe illness), Pao2/Fio2, viral load, serum antibody titer, routine blood biochemical index, ARDS, and ventilatory and extracorporeal membrane oxygenation (ECMO) supports before and after convalescent plasma transfusion. Results: All 5 patients (age range, 36-65 years; 2 women) were receiving mechanical ventilation at the time of treatment and all had received antiviral agents and methylprednisolone. Following plasma transfusion, body temperature normalized within 3 days in 4 of 5 patients, the SOFA score decreased, and Pao2/Fio2 increased within 12 days (range, 172-276 before and 284-366 after). Viral loads also decreased and became negative within 12 days after the transfusion, and SARS-CoV-2-specific ELISA and neutralizing antibody titers increased following the transfusion (range, 40-60 before and 80-320 on day 7). ARDS resolved in 4 patients at 12 days after transfusion, and 3 patients were weaned from mechanical ventilation within 2 weeks of treatment. Of the 5 patients, 3 have been discharged from the hospital (length of stay: 53, 51, and 55 days), and 2 are in stable condition at 37 days after transfusion. Conclusions and Relevance: In this preliminary uncontrolled case series of 5 critically ill patients with COVID-19 and ARDS, administration of convalescent plasma containing neutralizing antibody was followed by improvement in their clinical status. The limited sample size and study design preclude a definitive statement about the potential effectiveness of this treatment, and these observations require evaluation in clinical trials.


Subject(s)
Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/therapeutic use , Betacoronavirus/immunology , Coronavirus Infections/therapy , Pneumonia, Viral/therapy , Respiratory Distress Syndrome/therapy , Adult , Aged , Antibodies, Viral/blood , Antiviral Agents/therapeutic use , Blood Donors , COVID-19 , Coronavirus Infections/drug therapy , Coronavirus Infections/physiopathology , Critical Illness , Female , Glucocorticoids/therapeutic use , Humans , Immunization, Passive , Immunoglobulin G/blood , Immunoglobulin M/blood , Male , Methylprednisolone/therapeutic use , Middle Aged , Organ Dysfunction Scores , Pandemics , Pneumonia, Viral/drug therapy , Pneumonia, Viral/physiopathology , SARS-CoV-2
10.
J Ambul Care Manage ; 44(4): 293-303, 2021.
Article in English | MEDLINE | ID: covidwho-1447660

ABSTRACT

COVID-19 necessitated significant care redesign, including new ambulatory workflows to handle surge volumes, protect patients and staff, and ensure timely reliable care. Opportunities also exist to harvest lessons from workflow innovations to benefit routine care. We describe a dedicated COVID-19 ambulatory unit for closing testing and follow-up loops characterized by standardized workflows and electronic communication, documentation, and order placement. More than 85% of follow-ups were completed within 24 hours, with no observed staff, nor patient infections associated with unit operations. Identified issues include role confusion, staffing and gatekeeping bottlenecks, and patient reluctance to visit in person or discuss concerns with phone screeners.


Subject(s)
Ambulatory Care Facilities/organization & administration , COVID-19/therapy , Continuity of Patient Care/organization & administration , Pneumonia, Viral/therapy , Respiratory Care Units/organization & administration , Adult , Aged , Boston/epidemiology , COVID-19/epidemiology , Female , Humans , Male , Middle Aged , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Referral and Consultation/statistics & numerical data , SARS-CoV-2 , Systems Analysis , Workflow
15.
N Engl J Med ; 382(18): 1708-1720, 2020 04 30.
Article in English | MEDLINE | ID: covidwho-1428982

ABSTRACT

BACKGROUND: Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. METHODS: We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. RESULTS: The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. CONCLUSIONS: During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.).


Subject(s)
Betacoronavirus , Coronavirus Infections , Disease Outbreaks , Pandemics , Pneumonia, Viral , Adolescent , Adult , Aged , COVID-19 , Child , China/epidemiology , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Female , Fever/etiology , Humans , Male , Middle Aged , Patient Acuity , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , SARS-CoV-2 , Young Adult
17.
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
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