Establishment of prediction models for COVID-19 patients in different age groups based on Random Forest algorithm

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Random forest model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression model

OBJECTIVE

To explore if random forest (RF) model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression(LR) model.

METHODS

A total of 254 cases of hospital-acquired Klebsiella pneumoniae infection in a tertiary hospital in Beijing from January 2016 to December 2020 were retrospectively collected. Appropriate influencing factors were selected by referring to relevant articles from the aspects of basic clinical information and contact history before infection, and divided into a training set and a test set. Both the RF and LR models were trained by the training set, and using testing set to compare these two models.

RESULTS

The prediction accuracy of the LR model was 87.0%, the true positive rate of the LR model was 94.7%; the false negative rate of the LR model was 5.3%; the false positive rate of the LR model was 35%; the true negative rate of the LR model was 65%; the sensitivity of the LR model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 94.7%; and the specificity was 65%. The prediction accuracy of the RF model was 89.6%; the true positive rate of the RF model was 92.1%; the false negative rate of the RF model was 7.9%; the false positive rate of the RF model was 21.4%; the true negative rate of the RF model was 78.6%; the sensitivity of the RF model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 92.1%; and the specificity was 78.6%. ROC curve shows that the area under curve(AUC) of the LR model was 0.91, and that of the RF model was 0.95.

CONCLUSION

The RF model has higher specificity, sensitivity, and accuracy for the prognostic prediction of hospital-acquired Klebsiella pneumoniae infection than the LR model and has greater clinical application prospects.

How Does Vaccination against SARS-CoV-2 Affect Hospitalized Patients with COVID-19?

(1) Background: The development of effective COVID-19 vaccines has reduced the impact of COVID-19 on the general population. Our study aims to analyze how vaccination modifies the likelihood of death and length of stay in hospitalized patients with COVID-19; (2) Methods: A retrospective cohort study of 1927 hospitalized patients infected with COVID-19 was conducted. Information was gathered on vaccination status, hospitalization episode, and clinical profile of the patients. The effect of vaccination on mortality was analyzed using a multiple logistic regression model, and length of stay was analyzed using linear regression. The performance and fit of the models were evaluated; (3) Results: In hospitalized patients with COVID-19, the risk of dying during admission in vaccinated patients was half that of non-vaccinated (OR: 0.45; CI 95%: 0.25 to 0.84). In patients who were discharged due to improvement, the reduction in hospital stay in vaccinated patients was 3.17 days (CI 95%: 5.88 to 0.47); (4) Conclusions: Patients who, despite having been vaccinated, acquire the infection by SARS-CoV-2, have a significant reduction of the risk of death during admission and a reduction of hospital stay compared with unvaccinated patients.

The geriatric nutritional risk index mediated the relationship between serum uric acid and hypertension: a mediation analysis

Background

The elevated serum uric acid (SUA) is associated with an increased risk of hypertension and nutritional status. Malnutrition might modify the association of SUA with hypertension. Therefore, the aims of this study were to examine the mediation effect of malnutrition on the association of SUA with the risk of hypertension in Chinese population.

Methods

The study was based on the China Health and Nutrition Survey in 2009. Participants aged ≥ 60 years with complete analyzed data were eligible. The Geriatric Nutritional Risk Index (GNRI) was calculated by serum albumin (ALB) and BMI. Participants were identified as hypertension if systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg or receiving antihypertensive drug.

Results

There were 2371 participants included in the final analysis. In total, there was a significant mediation effect of the GNRI on the relationship between SUA level with hypertension (P < 0.001; OR: 1.096; and 95 % CI: 1.048–1.146). And the proportion mediated was 17.77 %. The results stratified by sex were consistent with those of total population. The significant mediation effects of the GNRI were found in the 60–69 years and 70–79 years groups (P = 0.002 and 0.032; OR: 1.099 and 1.075; and 95 % CI: 1.036–1.165 and 1.006–1.148, respectively) but not in the 80–99 years group (P = 0.303). The proportions mediated were16.22 % and 18.36 %, respectively.

Conclusions

The GNRI can mediate and account for approximately 17.77 % of the relationship between SUA level and the risk of hypertension. And this mediation effect was fully observed in both males and females, especially in the 60–79 years population.