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4.
J Med Internet Res ; 23(2): e23026, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575588

ABSTRACT

BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE: This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS: Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS: Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Machine Learning , Pneumonia, Viral/diagnosis , Aged , Area Under Curve , Cohort Studies , Comorbidity , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Predictive Value of Tests , Prognosis , ROC Curve , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2 , Treatment Outcome
5.
Ann Med ; 53(1): 151-159, 2021 12.
Article in English | MEDLINE | ID: covidwho-1574907

ABSTRACT

OBJECTIVE: To utilize publicly reported, state-level data to identify factors associated with the frequency of cases, tests, and mortality in the USA. MATERIALS AND METHODS: Retrospective study using publicly reported data collected included the number of COVID-19 cases, tests and mortality from March 14th through April 30th. Publicly available state-level data was collected which included: demographics comorbidities, state characteristics and environmental factors. Univariate and multivariate regression analyses were performed to identify the significantly associated factors with percent mortality, case and testing frequency. All analyses were state-level analyses and not patient-level analyses. RESULTS: A total of 1,090,500 COVID-19 cases were reported during the study period. The calculated case and testing frequency were 3332 and 19,193 per 1,000,000 patients. There were 63,642 deaths during this period which resulted in a mortality of 5.8%. Factors including to but not limited to population density (beta coefficient 7.5, p < .01), transportation volume (beta coefficient 0.1, p < .01), tourism index (beta coefficient -0.1, p = .02) and older age (beta coefficient 0.2, p = .01) are associated with case frequency and percent mortality. CONCLUSIONS: There were wide variations in testing and case frequencies of COVID-19 among different states in the US. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality. Key messages There were wide variations in testing and case frequencies of COVID-19 among different states in the USA. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality.


Subject(s)
Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/mortality , Pneumonia, Viral/mortality , COVID-19 , COVID-19 Testing , Comorbidity , Coronavirus Infections/diagnosis , Female , Healthcare Disparities , Humans , Male , Pandemics , Pneumonia, Viral/diagnosis , United States/epidemiology
6.
Epidemiol Infect ; 148: e168, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-1537262

ABSTRACT

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/therapy , Logistic Models , Machine Learning , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Adolescent , Adult , Aged , COVID-19 , China/epidemiology , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Prognosis , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment/methods , Young Adult
7.
Can J Anaesth ; 67(9): 1217-1248, 2020 09.
Article in English | MEDLINE | ID: covidwho-1536371

ABSTRACT

PURPOSE: We conducted two World Health Organization-commissioned reviews to inform use of high-flow nasal cannula (HFNC) in patients with coronavirus disease (COVID-19). We synthesized the evidence regarding efficacy and safety (review 1), as well as risks of droplet dispersion, aerosol generation, and associated transmission (review 2) of viral products. SOURCE: Literature searches were performed in Ovid MEDLINE, Embase, Web of Science, Chinese databases, and medRxiv. Review 1: we synthesized results from randomized-controlled trials (RCTs) comparing HFNC to conventional oxygen therapy (COT) in critically ill patients with acute hypoxemic respiratory failure. Review 2: we narratively summarized findings from studies evaluating droplet dispersion, aerosol generation, or infection transmission associated with HFNC. For both reviews, paired reviewers independently conducted screening, data extraction, and risk of bias assessment. We evaluated certainty of evidence using GRADE methodology. PRINCIPAL FINDINGS: No eligible studies included COVID-19 patients. Review 1: 12 RCTs (n = 1,989 patients) provided low-certainty evidence that HFNC may reduce invasive ventilation (relative risk [RR], 0.85; 95% confidence interval [CI], 0.74 to 0.99) and escalation of oxygen therapy (RR, 0.71; 95% CI, 0.51 to 0.98) in patients with respiratory failure. Results provided no support for differences in mortality (moderate certainty), or in-hospital or intensive care length of stay (moderate and low certainty, respectively). Review 2: four studies evaluating droplet dispersion and three evaluating aerosol generation and dispersion provided very low certainty evidence. Two simulation studies and a crossover study showed mixed findings regarding the effect of HFNC on droplet dispersion. Although two simulation studies reported no associated increase in aerosol dispersion, one reported that higher flow rates were associated with increased regions of aerosol density. CONCLUSIONS: High-flow nasal cannula may reduce the need for invasive ventilation and escalation of therapy compared with COT in COVID-19 patients with acute hypoxemic respiratory failure. This benefit must be balanced against the unknown risk of airborne transmission.


Subject(s)
Coronavirus Infections/therapy , Oxygen Inhalation Therapy/methods , Pneumonia, Viral/therapy , Respiratory Insufficiency/therapy , Aerosols , COVID-19 , Cannula , Coronavirus Infections/complications , Coronavirus Infections/mortality , Humans , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/mortality , Randomized Controlled Trials as Topic , Respiratory Insufficiency/physiopathology , Respiratory Insufficiency/virology
10.
Sci Rep ; 11(1): 13587, 2021 06 30.
Article in English | MEDLINE | ID: covidwho-1500741

ABSTRACT

Influenza is an important cause of severe illness and death among patients with underlying medical conditions and in the elderly. The aim of this study was to investigate factors associated with ICU admission and death in patients hospitalized with severe laboratory-confirmed influenza during the 2017-2018 season in Catalonia. An observational epidemiological case-to-case study was carried out. Reported cases of severe laboratory-confirmed influenza requiring hospitalization in 2017-2018 influenza season were included. Mixed-effects regression analysis was used to estimate the factors associated with ICU admission and death. A total of 1306 cases of hospitalized severe influenza cases were included, of whom 175 (13.4%) died and 217 (16.6%) were ICU admitted. Age 65-74 years and ≥ 75 years and having ≥ 2 comorbidities were positively associated with death (aOR 3.19; 95%CI 1.19-8.50, aOR 6.95, 95%CI 2.76-1.80 and aOR 1.99; 95%CI 1.12-3.52, respectively). Neuraminidase inhibitor treatment and pneumonia were negatively associated with death. The 65-74 years and ≥ 75 years age groups were negatively associated with ICU admission (aOR 0.41; 95%CI 0.23-0.74 and aOR 0.30; 95%CI 0.17-0.53, respectively). A factor positively associated with ICU admission was neuraminidase inhibitor treatment. Our results support the need to investigate the worst outcomes of hospitalized severe cases, distinguishing between death and ICU admission.


Subject(s)
Antiviral Agents/administration & dosage , Influenza, Human , Intensive Care Units , Aged , Aged, 80 and over , Female , Humans , Influenza, Human/drug therapy , Influenza, Human/mortality , Male , Middle Aged , Neuraminidase/antagonists & inhibitors , Pneumonia, Viral/drug therapy , Pneumonia, Viral/mortality , Retrospective Studies , Severity of Illness Index , Spain/epidemiology
11.
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
15.
Crit Care ; 25(1): 344, 2021 09 23.
Article in English | MEDLINE | ID: covidwho-1438302

ABSTRACT

BACKGROUND: The primary aim of this study was to assess the outcome of elderly intensive care unit (ICU) patients treated during the spring and autumn COVID-19 surges in Europe. METHODS: This was a prospective European observational study (the COVIP study) in ICU patients aged 70 years and older admitted with COVID-19 disease from March to December 2020 to 159 ICUs in 14 European countries. An electronic database was used to register a number of parameters including: SOFA score, Clinical Frailty Scale, co-morbidities, usual ICU procedures and survival at 90 days. The study was registered at ClinicalTrials.gov (NCT04321265). RESULTS: In total, 2625 patients were included, 1327 from the first and 1298 from the second surge. Median age was 74 and 75 years in surge 1 and 2, respectively. SOFA score was higher in the first surge (median 6 versus 5, p < 0.0001). The PaO2/FiO2 ratio at admission was higher during surge 1, and more patients received invasive mechanical ventilation (78% versus 68%, p < 0.0001). During the first 15 days of treatment, survival was similar during the first and the second surge. Survival was lower in the second surge after day 15 and differed after 30 days (57% vs 50%) as well as after 90 days (51% vs 40%). CONCLUSION: An unexpected, but significant, decrease in 30-day and 90-day survival was observed during the second surge in our cohort of elderly ICU patients. The reason for this is unclear. Our main concern is whether the widespread changes in practice and treatment of COVID-19 between the two surges have contributed to this increased mortality in elderly patients. Further studies are urgently warranted to provide more evidence for current practice in elderly patients. TRIAL REGISTRATION NUMBER: NCT04321265 , registered March 19th, 2020.


Subject(s)
COVID-19/mortality , Critical Illness/mortality , Pneumonia, Viral/mortality , Aged , Aged, 80 and over , Comorbidity , Europe/epidemiology , Female , Frail Elderly , Humans , Intensive Care Units , Male , Organ Dysfunction Scores , Pandemics , Pneumonia, Viral/virology , Prospective Studies , SARS-CoV-2 , Survival Analysis
19.
Am J Otolaryngol ; 43(1): 103230, 2022.
Article in English | MEDLINE | ID: covidwho-1427502

ABSTRACT

PURPOSE: Tracheostomy is an aerosol-generating procedure, thus performing it during the COVID-19 pandemic arises considerations such as the most appropriate timing and the patients to whom it is suitable. Medical teams lack sufficient data to assist determining whether or not to conduct tracheostomy, its short- and long-term implications are not fully understood. This study aims to shed light on the critically ill COVID-19 patients that require tracheostomy, and to investigate its value. METHODS: A retrospective multicentral case-control study of 157 hospitalized critically ill COVID-19 patients, among whom 30 patients went through tracheostomy and consisted of our study group. RESULTS: The mean age was similar between study and control groups (68.9 ± 12.7 years vs 70.5 ± 15.8 years, p = 0.57), as well as comorbidity prevalence (56.7% vs 67.7%, p = 0.25). Patients in the study group were hospitalized for longer duration until defined critically ill (5 ± 4.3 vs 3 ± 3.9 days; p = 0.01), until admitted to the intensive care unit (6 ± 6.6 vs 2.5 ± 3.7 days respectively; p = 0.005), and until discharged (24 ± 9.7 vs 10.7 ± 9.1 days, p < 0.001). Mortality rate was lower in the study group (30% vs 59.8%, p = 0.003). Kaplan Meier survival analysis revealed a statistically significant difference in survival time between groups (Log rank chi-sq = 20.91, p < 0.001) with mean survival time of 41 ± 3.1 days vs 21 ± 2.2 days. Survival was significantly longer in the study group (OR = 0.37, p = 0.004). CONCLUSION: Tracheostomy allows for more prolonged survival for gradually deteriorating critically ill COVID-19 patients. This should be integrated into the medical teams' considerations when debating whether or not to conduct tracheostomy.


Subject(s)
COVID-19/therapy , Critical Illness/therapy , Pneumonia, Viral/therapy , Tracheostomy , Aged , COVID-19/mortality , Critical Illness/mortality , Female , Humans , Length of Stay/statistics & numerical data , Male , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , SARS-CoV-2 , Survival Rate
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