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Clinical predictors of COVID-19 mortality.

ABSTRACT: BACKGROUND:The coronavirus disease 2019 (COVID-19) pandemic has affected over millions of individuals and caused hundreds of thousands of deaths worldwide. It can be difficult to accurately predict mortality among COVID-19 patients presenting with a spectrum of complications, hindering the prognostication and management of the disease. METHODS:We applied machine learning techniques to clinical data from a large cohort of 5,051 COVID-19 patients treated at the Mount Sinai Health System in New York City, the global COVID-19 epicenter, to predict mortality. Predictors were designed to classify patients into Deceased or Alive mortality classes and were evaluated in terms of the area under the receiver operating characteristic (ROC) curve (AUC score). FINDINGS:Using a development cohort (n=3,841) and a systematic machine learning framework, we identified a COVID-19 mortality predictor that demonstrated high accuracy (AUC=0.91) when applied to test sets of retrospective (n= 961) and prospective (n=249) patients. This mortality predictor was based on five clinical features: age, minimum O2 saturation during encounter, type of patient encounter (inpatient vs. various types of outpatient and telehealth encounters), hydroxychloroquine use, and maximum body temperature. INTERPRETATION:An accurate and parsimonious COVID-19 mortality predictor based on five features may have utility in clinical settings to guide the management and prognostication of patients affected by this disease.

SUBMITTER: Yadaw AS

PROVIDER: S-EPMC7273288 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Clinical predictors of COVID-19 mortality.

Yadaw Arjun S AS Li Yan-Chak YC Bose Sonali S Iyengar Ravi R Bunyavanich Supinda S Pandey Gaurav G

medRxiv : the preprint server for health sciences 20200522

<h4>Background</h4>The coronavirus disease 2019 (COVID-19) pandemic has affected over millions of individuals and caused hundreds of thousands of deaths worldwide. It can be difficult to accurately predict mortality among COVID-19 patients presenting with a spectrum of complications, hindering the prognostication and management of the disease.<h4>Methods</h4>We applied machine learning techniques to clinical data from a large cohort of 5,051 COVID-19 patients treated at the Mount Sinai Health Sy ...[more]

PMID: 32511520

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Project description:BackgroundStudying the characteristics of hospitalized Coronavirus Disease 2019 (COVID-19) patients is vital for understanding the disease and preparing for future outbreaks. The aim of this study was to analyze and describe the clinical profiles and factors associated with mortality among COVID-19 patients admitted to Jimma Medical Center COVID-19 Treatment Center (JMC CTC) in Ethiopia.MethodsAll confirmed COVID-19 patients admitted to JMC CTC between 17 April 2020 and 05 March 2022 were included in this study. Socio-demographic data, clinical information, and outcome variables were collected retrospectively from medical records and COVID-19 database at the hospital. Bivariable and multivariable analyses were performed to determine factors associated with COVID-19 severity and mortality. A P-value < 0.05 was considered statistically significant.ResultsA total of 542 confirmed COVID-19 patients were admitted to JMC CTC, of which 322 (59.4%) were male. Their median age was 48 years (IQR 32-64). About 51% (n = 277) of them had severe COVID-19 upon admission. Patients with hypertension [AOR: 2.8 (95% CI: 1.02-7.7, p = 0.046)], diabetes [AOR: 8.8 (95% CI: 1.2-17.3, p = 0.039)], and underlying respiratory diseases [AOR: 18.8 (95% CI: 2.06-71.51, p = 0.009)] were more likely to present with severe COVID-19 cases. Overall, 129 (23.8%) died in the hospital. Death rate was higher among patients admitted with severe disease [AHR = 5.5 (3.07-9.9) p < 0.001)] and those with comorbidities such as hypertension [AHR = 3.5 (2.28-5.41), p < 0.001], underlying respiratory disease [AHR = 3.4 (1.97-5.94), p < 0.001], cardiovascular disease (CVDs) [AHR = 2.8 (1.73-4.55), p < 0.001], and kidney diseases [AHR = 3.7 (2.3-5.96), p < 0.001].ConclusionAbout half of COVID-19 cases admitted to the hospital had severe disease upon admission. Comorbidities such as hypertension, diabetes, and respiratory diseases were linked to severe illness. COVID-19 admissions were associated with high inpatient mortality, particularly among those with severe disease and comorbidities.

Project description:Background and aimsOlder people have higher rates of comorbidities and may experience more severe inflammatory responses; therefore, are at higher risk of death. Herein, we aimed to systematically review the mortality in coronavirus disease 2019 (COVID-19) patients and its predictors in this age group.MethodsWe searched PubMed, Web of Science, and Science Direct using relevant keywords. Retrieved records underwent a two-step screening process consisting of title/abstract and full-text screenings to identify the eligible studies.ResultsSummarizing findings of 35 studies demonstrated that older patients have higher mortality rates compared to the younger population. A review of articles revealed that increasing age, body mass index, a male gender, dementia, impairment or dependency in daily activities, presence of consolidations on chest X-ray, hypoxemic respiratory failure, and lower oxygen saturation at admission were risk factors for death. High d-dimer levels, 25-hydroxy vitamin D serum deficiencies, high C-reactive protein (≥5 mg/L) levels plus any other abnormalities of lymphocyte, higher blood urea nitrogen or lactate dehydrogenase, and higher platelet count were predictors of poor prognosis and mortality in the elderly. Studies have also shown that previous treatment with renin-angiotensin-aldosterone system inhibitors, pharmacological treatments of respiratory disorders, antibiotics, corticosteroids, vitamin K antagonist, antihistamines, azithromycin, Itolizumab (an anti-CD6 monoclonal antibody) in combination with other antivirals reduces COVID-19 worsening and mortality. Vaccination against seasonal influenza might also reduce COVID-19 mortality.ConclusionOverall, a critical consideration is necessary for the care and management of COVID-19 in the aged population considering the drastic contrasts in manifestation and prognosis compared to other age groups. Mortality from COVID-19 is independently associated with the patient's age. Elderly patients with COVID-19 are more vulnerable to poor outcomes. Thus, strict preventive measures, timely diagnosis, and aggressive therapeutic/nontherapeutic care are of great importance to reduce acute respiratory distress syndrome and severe complications in older people.

Project description:ObjectiveThe coronavirus disease 2019 (COVID-19) has become a global pandemic. Timely and effective predictors of survival and death rates are crucial for improving the management of COVID-19 patients. In this study, we evaluated the predictors of mortality based on the demographics, comorbidities, clinical characteristics, laboratory findings, and vital signs of 500 patients with COVID-19 admitted at Imam Khomeini Hospital Complex, the biggest hospital in Tehran, Iran.MethodsFive hundred hospitalized laboratory-confirmed COVID-19 patients were included in this study. Subsequently, electronic medical records, including patient demographics, clinical manifestation, comorbidities, and laboratory test results were collected and analyzed. They were divided into two groups: expired and discharged. Demographics, clinical, and laboratory data were compared among the two groups. The related factors with death in the patients were determined using univariate and multivariate logistic regression approaches.ResultsAmong the 500 hospitalized patients, most patients were male (66.4% versus 33.6%). The expired group had more patients ⩾70 years of age compared with the discharged group (32.9% versus 16.3%, respectively). Almost 66% of the expired patients were hospitalized for ⩾5 days which was higher than the discharge group (26.9%). Patients with a history of opium use in the expired group were significantly higher compared to the discharged group (14.8% versus 8.6%, p = 0.04) as well as a history of cancer (15.5% versus 4.7%, p < 0.001). Out of the 500 patients with COVID-19, four patients (2.6%) were HIV positive, all of whom expired. Dyspnea (76.4%), fever (56.6%), myalgia (59.9%), and dry cough (67%) were the most common chief complaints of hospitalized patients. Age ⩾70 years (adjusted odds ratio = 2.49; 95% confidence interval, 1.02-6.04), being female (adjusted odds ratio = 2.06; 95% confidence interval, 1.25-3.41), days of hospitalization (adjusted odds ratio = 5.73; 95% confidence interval, 3.49-9.41), and having cancer (adjusted odds ratio = 3.23; 95% confidence interval, 1.42-7.39) were identified as independent predictors of mortality among COVID-19 patients.ConclusionDischarged and expired COVID-19 patients had distinct clinical and laboratory characteristics, which were separated by principal component analysis. The mortality risk factors for severe patients identified in this study using a multivariate logistic regression model included elderly age (⩾70 years), being female, days of hospitalization, and having cancer.

Project description:BackgroundEstimating the risk of intubation and mortality among COVID-19 patients can help clinicians triage these patients and allocate resources more efficiently. Thus, here we sought to identify the risk factors associated with intubation and intra-hospital mortality in a cohort of COVID-19 patients hospitalized due to hypoxemic acute respiratory failure (ARF).ResultsWe included retrospectively a total of 187 patients admitted to the subintensive and intensive care units of the University Hospital "Maggiore della Carità" of Novara between March 1st and April 30th, 2020. Based on these patients' demographic characteristics, early clinical and laboratory variables, and quantitative chest computerized tomography (CT) findings, we developed two random forest (RF) models able to predict intubation and intra-hospital mortality. Variables independently associated with intubation were C-reactive protein (p < 0.001), lactate dehydrogenase level (p = 0.018) and white blood cell count (p = 0.026), while variables independently associated with mortality were age (p < 0.001), other cardiovascular diseases (p = 0.029), C-reactive protein (p = 0.002), lactate dehydrogenase level (p = 0.018), and invasive mechanical ventilation (p = 0.001). On quantitative chest CT analysis, ground glass opacity, consolidation, and fibrosis resulted significantly associated with patient intubation and mortality. The major predictors for both models were the ratio between partial pressure of arterial oxygen and fraction of inspired oxygen, age, lactate dehydrogenase, C-reactive protein, glycemia, CT quantitative parameters, lymphocyte count, and symptom onset.ConclusionsAltogether, our findings confirm previously reported demographic, clinical, hemato-chemical, and radiologic predictors of adverse outcome among COVID-19-associated hypoxemic ARF patients. The two newly developed RF models herein described show an overall good level of accuracy in predicting intra-hospital mortality and intubation in our study population. Thus, their future development and implementation may help not only identify patients at higher risk of deterioration more effectively but also rebalance the disproportion between resources and demand.

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Clinical predictors of COVID-19 mortality.

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Clinical predictors of COVID-19 mortality.

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