Figure
The effective explanatory factors on hospitalization time in a competing risk regression model for death versus (discharge (backward fitted model
Source publication
Introduction: In the present study, the goal was to estimate the hospital length of stay among patients admitted with COVID-19 in a hospital in Tehran.
Methods: We used retrospective data on 446 hospitalized patients with COVID-19 who admitted from 7 March to 8 Oct 2020 in a referral hospital in Tehran, Iran. The prognostic effects of variables, i...
Contexts in source publication
Context 1
... effective variables on the survival time of patients were respiratory distress, cancer, and SPo2. The adjusted Hazard Ratio is shown in Table 4. According to this table that Adjusted HR in Patients with SPo2, ≥93% for discharge event was 2.05 times more comparing reference group with SPo2<93%. ...Context 2
... effective variables on the survival time of patients were respiratory distress, cancer, and SPo2. The adjusted Hazard Ratio is shown in Table 4. According to this table that Adjusted HR in Patients with SPo2, ≥93% for discharge event was 2.05 times more comparing reference group with SPo2<93%. ...Similar publications
In recent years, online shopping has benefited great importance and popularity across the world. The purpose of this study is to identify factors affecting consumers online shopping intention. A paper-based survey questionnaire was distributed among the residents of Tehran and ultimately 355 questionnaires were used for modeling. The results of the...
Citations
... 32 Also, several studies have shown a higher risk of death, and LoS in COVID-19 patients with a history of chronic comorbidities such as hypertension, diabetes, and acute respiratory distress syndrome than others. [33][34][35][36][37][38][39][40] Although these studies have yielded interesting results, some of them used standard biostatistics methods for their calculations, leaving room for ML approaches. [34][35][36] In addition, some studies just included a specific type of inpatients, such as patients in ICU. ...
... The names of the variables were extracted from the patient medical records. Based on the review of various studies 32,33,41,42 and the approval of two infectiologist, factors essential for predicting mortality and LoS of patients with COVID-19 with chronic comorbidities were identified. Data of all patients were extracted from the hospital information system and based on their Electronic Health Records (EHRs). ...
Background
The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality.
Objective
The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms.
Methods
This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting.
Results
This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath.
Conclusion
The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
This study was designed and implemented to analyze and establish documents related to the above cases in the first to third COVID-19 epidemic waves for the use of researchers and doctors during and after the epidemic. The current case series study was conducted on 24,563 thousand hospitalized COVID-19 patients by examining their clinical characteristics within a one-year period from the beginning of the pandemic on 02.22.2020 to 02.14.2021, which included the first to the third waves, based on gender and severity of COVID-19. The mean age of the participants was 56 ± 20.71, and 51.8% were male. Out of a total of 24,563 thousand hospitalized COVID-19 patients until February 2021, there were 2185 mortalities (9.8%) and 2559 cases of severe COVID-19 (13.1%). The median length of hospitalization from the time of admission to discharge or death in the hospital (IQR: 13–41) was estimated to be 21 days. The rate of hospital mortality was higher in severe (37.8%) than in non-severe (4.8%) cases of COVID-19, While the risk of severe cases increased significantly in the third (HR = 1.65, 95% CI: 1.46–1.87, P < 0.001) and early fourth waves (HR = 2.145, 95% CI: 1.7–2.71, P < 0.001). Also, the risk of contracting severe COVID-19 increased significantly in patients aged ≥ 65 years old (HR = 2.1, 95% CI 1.1.93–2.72, P < 0.001). As shown by the results, the rates of hospital mortality (9.3% vs. 8.5%) and severe cases of COVID-19 (13.6% vs. 12.5%) were higher among men than women (P < 0.01). In our study, the mortality rate and severity of COVID-19 were within the scope of global studies. Men experienced higher severity and mortality than women. The was a significantly higher prevalence of old age and underlying diseases in individuals with severe COVID-19. Our data also showed that patients with a previous history of COVID-19 had a more severe experience of COVID-19, while most of these patients were also significantly older and had an underlying disease.
Objective:
Identifying the epidemiological characteristics of COVID-19 could help to control the pandemic. The aim of this study was to characterize the epidemiological features of hospitalized COVID-19 patients in Iran.
Methods:
Data were collected on patients admitted to a military referral hospital in Tehran, Iran, from February 8, 2020 to July 28, 2021. Gender, age, clinical symptoms, outcome, type of comorbidities, level of blood Spo2, time of admission, and time of discharge were investigated. Sex ratio, case fatality rate (CFR), and daily trends of hospital admissions and deaths were also determined. Descriptive statistics and multiple logistic regression with 95% confidence intervals were used for data analysis. The statistical significance level was set at 0.05. STATA16.0 and Excel 2010 were used for data analysis.
Results:
The median hospital length of stay (LOS) was 6 days. The following symptoms were most common: cough (63.5%), fever (50%), respiratory distress (46.1%), and muscular pain (40.8%). Hypertension (29.5%), diabetes (24.7%), and cardiovascular diseases (21.8%) were the most prevalent comorbidities. The CFR was calculated at 8.30%. Respiratory symptoms increased the odds of death by 45% (OR 1.45, 95% CI 1.03-2.06). Gastrointestinal symptoms were associated with a reduction in the mortality of COVID-19 cases, but this association was not statistically significant (OR 0.94, 95% CI 0.73-1.21).
Conclusions:
The results of this study emphasize higher mortality rates among older age groups, male patients, and patients with underlying diseases.
