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Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China.

ABSTRACT: An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 10⁹/L versus (4-10) × 10⁹/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 10⁹/L versus (0.8-4) × 10⁹/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1-4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.

SUBMITTER: Wu R

PROVIDER: S-EPMC7832941 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNeurological involvement in Coronavirus disease-2019 (COVID-19) is widely recognized. However, the role of pre-existing neurological comorbidities in modulating COVID-19-related mortality still remains unclear. This cohort study evaluates the COVID-19-related case fatality rate (CFR) of patients with pre-existing neurological diseases.MethodsWe retrospectively evaluated all patients consecutively admitted to our hospital with a diagnosis of COVID-19 between March and April 2020. We used a multivariate regression analysis to estimate the association between pre-existing neurological diseases and COVID-19-related mortality. Then, we compared the CFR and survival curves of two cohorts (patients suffering vs. those not suffering from pre-existing neurological disease), matched trough the propensity score (PS). Age and other comorbidities were considered for PS calculation. We applied a 1:1 matching for the entire neurological cohort and, separately, for cerebrovascular, neurodegenerative, and other neurological diseases.ResultsAmong 332 patients, 75 (22.6%) were affected by pre-existing neurological disease (n = 29 cerebrovascular, n = 26 neurodegenerative, n = 20 others). From the multivariate regression analysis, they resulted with a significant increase of COVID-19-related mortality (OR:2.559; 95%CI 1.181-5.545; p < 0.017). From the cohort analysis, CFR resulted 2-fold higher in patients with neurological disease (48.0% vs. 24.0%; p = 0.002). CFR was significantly higher in patients with neurodegenerative diseases compared to matched individuals (73.9% vs. 39.1%; p = 0.017), while CFR increase in patients with cerebrovascular diseases did not reach statistical significance (48.3% vs. 41.4%; p = 0.597).ConclusionsPre-existing neurological comorbidities, in particular neurodegenerative diseases, increase significantly COVID-19-related case fatality, indicating a clear priority for viral screening, access to care facilities and vaccination in these populations.

Project description:The evidence for the effects of environmental factors on COVID-19 case fatality remains controversial, and it is crucial to understand the role of preventable environmental factors in driving COVID-19 fatality. We thus conducted a nationwide cohort study to estimate the effects of environmental factors (temperature, particulate matter [PM2.5, PM10], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]) on COVID-19 case fatality. A total of 71,808 confirmed COVID-19 cases were identified and followed up for their vital status through April 25, 2020. Exposures to ambient air pollution and temperature were estimated by linking the city- and county-level monitoring data to the residential community of each participant. For each participant, two windows were defined: the period from symptom onset to diagnosis (exposure window I) and the period from diagnosis date to date of death/recovery or end of the study period (exposure window II). Cox proportional hazards models were used to estimate the associations between these environmental factors and COVID-19 case fatality. COVID-19 case fatality increased in association with environmental factors for the two exposure windows. For example, each 10 μg/m3 increase in PM2.5, PM10, O3, and NO2 in window I was associated with a hazard ratio of 1.11 (95% CI 1.09, 1.13), 1.10 (95% CI 1.08, 1.13), 1.09 (95 CI 1.03, 1.14), and 1.27 (95% CI 1.19, 1.35) for COVID-19 fatality, respectively. A significant effect was also observed for low temperature, with a hazard ratio of 1.03 (95% CI 1.01, 1.04) for COVID-19 case fatality per 1°C decrease. Subgroup analysis indicated that these effects were stronger in the elderly, as well as in those with mild symptoms and living in Wuhan or Hubei. Overall, the sensitivity analyses also yielded consistent estimates. Short-term exposure to ambient air pollution and low temperature during the illness would play a nonnegligible part in causing case fatality due to COVID-19. Reduced exposures to high concentrations of PM2.5, PM10, O3, SO2, and NO2 and low temperature would help improve the prognosis and reduce public health burden.

Project description:The new coronavirus (COVID-19) infection reported in China in December 2019 has become a pandemic in a few weeks, affecting the entire world. In this respect, it is crucial to determine the case-increase, case-fatality, and case-recovery rates to control COVID-19. In this study, the case-increase, case-fatality, and case-recovery rates of COVID-19 in 36 European countries were analyzed with the meta-analysis method using data released by the health organizations and WHO. The data were obtained from the website of health organizations of 36 European countries and the website of WHO until 11 May 2020. The analyses were carried out on 1 744 704 COVID-19-diagnosed cases in 36 European countries. The case-increase, case-fatality and case-recovery rates of COVID-19 were calculated using 95% confidence intervals (95% CI), single-arm meta-analysis, cross-temporal meta-analysis, and meta-regression random-effects model. The standardized case-increase rate of COVID-19 is 5% (95% CI [0.040, 0.063]) and the average case-increase rate in European countries has started to decline by around 3% (95% CI [0.047, 0.083]) weekly. The countries with the highest rate of case increase are Belgium, Sweden, Russia, the Netherlands and the United Kingdom. Although the case-fatality rate of COVID-19 patients was 4.5% as of May 11 (95% CI [0.037-0.055]), this rate is 6.3% (95% CI [0.047, 0.083]) in standardized time (6th week). The case-recovery rates of patients are 46% (95% CI [0.376-0.547]). This study presents important results regarding the COVID-19 pandemic in Europe. Although the rate of increase in new COVID-19 cases has dropped, there is not much decline in the case-fatality rates and no increase in case-recovery rates. The case-fatality rate of COVID-19 in Europe was estimated to be in the range of 4% to 4.5% and a minimum of 4 weeks (as of 11 May) is expected to have the figure below 1% in a country with an average case-increase rate. Monitoring case fatalities in Belgium, the Netherlands and Sweden, and treatment successes in Germany and Austria play a role of utmost importance.

Project description:BACKGROUND:Patients with cancer are a high-risk population in the COVID-19 pandemic. We aimed to describe clinical characteristics and outcomes of patients with cancer and COVID-19, and examined risk factors for mortality in this population. METHODS:We did a retrospective, multicentre, cohort study of 205 patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and with a pathological diagnosis of a malignant tumour in nine hospitals within Hubei, China, from Jan 13 to March 18, 2020. All patients were either discharged from hospitals or had died by April 20, 2020. Clinical characteristics, laboratory data, and cancer histories were compared between survivors and non-survivors by use of ?2 test. Risk factors for mortality were identified by univariable and multivariable logistic regression models. FINDINGS:Between Jan 13 and Mar 18, 2020, 205 patients with cancer and laboratory-confirmed SARS-CoV-2 infection were enrolled (median age 63 years [IQR 56-70; range 14-96]; 109 [53%] women). 183 (89%) had solid tumours and 22 (11%) had haematological malignancies. The median duration of follow-up was 68 days (IQR 59-78). The most common solid tumour types were breast (40 [20%] patients), colorectal (28 [14%]), and lung cancer (24 [12%]). 54 (30%) of 182 patients received antitumour therapies within 4 weeks before symptom onset. 30 (15%) of 205 patients were transferred to an intensive care unit and 40 (20%) died during hospital admission. Patients with haematological malignancies had poorer prognoses than did those with solid tumours: nine (41%) of 22 patients with haematological malignancies died versus 31 (17%) of 183 patients with solid tumours (hazard ratio for death 3·28 [95% CI 1·56-6·91]; log rank p=0·0009). Multivariable regression analysis showed that receiving chemotherapy within 4 weeks before symptom onset (odds ratio [OR] 3·51 [95% CI 1·16-10·59]; p=0·026) and male sex (OR 3·86 [95% CI 1·57-9·50]; p=0·0033) were risk factors for death during admission to hospital. INTERPRETATION:Patients with cancer and COVID-19 who were admitted to hospital had a high case-fatality rate. Unfavourable prognostic factors, including receiving chemotherapy within 4 weeks before symptom onset and male sex, might help clinicians to identify patients at high risk of fatal outcomes. FUNDING:National Natural Science Foundation of China.

Project description:While surveillance can identify changes in COVID-19 transmission patterns over time and space, sections of the population at risk, and the efficacy of public health measures, reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases. Our primary objective was to estimate the percentage of cases reported in the general community, considered as those that occurred outside of long-term care facilities (LTCFs), in specific provinces and Canada as a whole. We applied a methodology using the delay-adjusted case fatality ratio (CFR) to all cases and deaths, as well as those representing the general community. Our second objective was to assess whether the assumed CFR (mean = 1.38%) was appropriate for calculating underestimation of cases in Canada. Estimates were developed for the period from March 11th, 2020 to September 16th, 2020. Estimates of the percentage of cases reported (PrCR) and CFR varied spatially and temporally across Canada. For the majority of provinces, and for Canada as a whole, the PrCR increased through the early stages of the pandemic. The estimated PrCR in general community settings for all of Canada increased from 18.1% to 69.0% throughout the entire study period. Estimates were greater when considering only those data from outside of LTCFs. The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07% on March 11th, 2020 to 2.00% on September 16th, 2020. Therefore, the true CFR in the general community in Canada was likely less than 2% on September 16th. According to our analysis, some provinces, such as Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatchewan reported a greater percentage of cases as of September 16th, compared to British Columbia, Ontario, and Québec. This could be due to differences in testing rates and criteria, demographics, socioeconomic factors, race, and access to healthcare among the provinces. Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study. The estimates provide context to the summative state of the pandemic in Canada, and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

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Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China.

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