Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records

Objective. Short-term or long-term connections between different diseases have not been fully acknowledged. This study was aimed at exploring the network association pattern between disorders that occurred in the same individual by using the association rule mining technique. Methods. Raw data were extracted from the large-scale electronic medical record database of the affiliated hospital of Xuzhou Medical University. 1551732 pieces of diagnosis information from 144207 patients were collected from 2015 to 2020. Clinic diagnoses were categorized according to “International Classification of Diseases, 10th revision”. The Apriori algorithm was used to explore the association patterns among those diagnoses. Results. 12889 rules were generated after running the algorithm at first. After threshold filtering and manual examination, 110 disease combinations (support≥0.001, confidence≥60%, lift>1) with strong association strength were obtained eventually. Association rules about the circulatory system and metabolic diseases accounted for a significant part of the results. Conclusion. This research elucidated the network associations between disorders from different body systems in the same individual and demonstrated the usefulness of the Apriori algorithm in comorbidity or multimorbidity studies. The mined combinations will be helpful in improving prevention strategies, early identification of high-risk populations, and reducing mortality.

Impact of chemotherapy and immunotherapy on the composition and function of immune cells in COVID-19 convalescent with gynecological tumors.

Ongoing pandemic and potential resurgence of Coronavirus disease 2019 (COVID-19) has prompted urgent efforts to investigate the immunological memory of convalescent patients, especially in patients with active cancers. Here we performed single-cell RNA sequencing in peripheral blood samples of 3 healthy donors (HDs), 4 COVID-19 patients (Covs) and 4 COVID-19 patients with active gynecological tumor (TCs) pre- and post- anti-tumor treatment. All Covs patients had recovered from their acute infection. Interestingly, the molecular features of PBMCs in TCs are similar to that in Covs, suggesting that convalescent COVID-19 with gynecologic tumors do not have major immunological changes and may be protected against reinfection similar to COVID-19 patients without tumors. Moreover, the chemotherapy given to these patients mainly caused neutropenia, while having little effect on the proportion and functional phenotype of T and B cells, and T cell clonal expansion. Notably, anti-PD-L1 treatment massively increased cytotoxic scores of NK cells, and T cells, and facilitated clonal expansion of T cells in these patients. It is likely that T cells could protect patients from SARS-CoV-2 virus reinfection and anti-PD-L1 treatment can enhance the anti-viral activity of the T cells.

Single-cell RNA sequencing identify SDCBP in ACE2-positive bronchial epithelial cells negatively correlates with COVID-19 severity.

The coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in many deaths throughout the world. It is vital to identify the novel prognostic biomarkers and therapeutic targets to assist with the subsequent diagnosis and treatment plan to mitigate the expansion of COVID-19. Since angiotensin-converting enzyme 2 (ACE2)-positive cells are hosts for COVID-19, we focussed on this cell type to explore the underlying mechanisms of COVID-19. In this study, we identified that ACE2-positive cells from the bronchoalveolar lavage fluid (BALF) of patients with COVID-19 belong to bronchial epithelial cells. Comparing with patients of COVID-19 showing severe symptoms, the antigen processing and presentation pathway was increased and 12 typical genes, HLA-DRB5, HLA-DRB1, CD74, HLA-DRA, HLA-DPA1, HLA-DQA1, HSP90AA1, HSP90AB1, HLA-DPB1, HLA-DQB1, HLA-DQA2, and HLA-DMA, particularly HLA-DPB1, were obviously up-regulated in ACE2-positive bronchial epithelial cells of patients with mild disease. We further discovered SDCBP was positively correlated with above 12 genes particularly with HLA-DPB1 in ACE2-positive bronchial epithelial cells of COVID-19 patients. Moreover, SDCBP may increase the immune infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells in different lung carcinoma. Moreover, we found the expression of SDCBP was positively correlated with the expression of antigen processing and presentation genes in post-mortem lung biopsies tissues, which is consistent with previous discoveries. These results suggest that SDCBP has good potential to be further developed as a novel diagnostic and therapeutic target in the treatment of COVID-19.

Dynamics and Correlation Among Viral Positivity, Seroconversion, and Disease Severity in COVID-19 : A Retrospective Study.

BACKGROUND: The understanding of viral positivity and seroconversion during the course of coronavirus disease 2019 (COVID-19) is limited. OBJECTIVE: To describe patterns of viral polymerase chain reaction (PCR) positivity and evaluate their correlations with seroconversion and disease severity. DESIGN: Retrospective cohort study. SETTING: 3 designated specialty care centers for COVID-19 in Wuhan, China. PARTICIPANTS: 3192 adult patients with COVID-19. MEASUREMENTS: Demographic, clinical, and laboratory data. RESULTS: Among 12 780 reverse transcriptase PCR tests for severe acute respiratory syndrome coronavirus 2 that were done, 24.0% had positive results. In 2142 patients with laboratory-confirmed COVID-19, the viral positivity rate peaked within the first 3 days. The median duration of viral positivity was 24.0 days (95% CI, 18.9 to 29.1 days) in critically ill patients and 18.0 days (CI, 16.8 to 19.1 days) in noncritically ill patients. Being critically ill was an independent risk factor for longer viral positivity (hazard ratio, 0.700 [CI, 0.595 to 0.824]; P < 0.001). In patients with laboratory-confirmed COVID-19, the IgM-positive rate was 19.3% in the first week, peaked in the fifth week (81.5%), and then decreased steadily to around 55% within 9 to 10 weeks. The IgG-positive rate was 44.6% in the first week, reached 93.3% in the fourth week, and then remained high. Similar antibody responses were seen in clinically diagnosed cases. Serum inflammatory markers remained higher in critically ill patients. Among noncritically ill patients, a higher proportion of those with persistent viral positivity had low IgM titers (<100 AU/mL) during the entire course compared with those with short viral positivity. LIMITATION: Retrospective study and irregular viral and serology testing. CONCLUSION: The rate of viral PCR positivity peaked within the initial few days. Seroconversion rates peaked within 4 to 5 weeks. Dynamic laboratory index changes corresponded well to clinical signs, the recovery process, and disease severity. Low IgM titers (<100 AU/mL) are an independent risk factor for persistent viral positivity. PRIMARY FUNDING SOURCE: None.

Longitudinal multi-omics transition associated with fatality in critically ill COVID-19 patients.

PURPOSE: Critically ill COVID-19 patients have significantly increased risk of death. Although several circulating biomarkers are thought to be related to COVID-19 severity, few studies have focused on the characteristics of critically ill patients with different outcomes. The objective of this study was to perform a longitudinal investigation of the potential mechanisms affecting the prognosis of critically ill COVID-19 patients. METHODS: In addition to clinical data, 113 whole blood samples and 85 serum samples were collected from 33 severe and critical COVID-19 patients without selected comorbidities. Multi-omics analysis was then performed using longitudinal samples. RESULTS: Obvious transcriptional transitions were more frequent in critical survivors than in critical non-survivors, indicating that phase transition may be related to survival. Based on analysis of differentially expressed genes during transition, the erythrocyte differentiation pathway was significantly enriched. Furthermore, clinical data indicated that red blood cell counts showed greater fluctuation in survivors than in non-survivors. Moreover, declining red blood cell counts and hemoglobin levels were validated as prognostic markers of poor outcome in an independent cohort of 114 critical COVID-19 patients. Protein-metabolite-lipid network analysis indicated that tryptophan metabolism and melatonin may contribute to molecular transitions in critical COVID-19 patients with different outcomes. CONCLUSIONS: This study systematically and comprehensively depicted the longitudinal hallmarks of critical COVID-19 patients and indicated that multi-omics transition may impact the prognosis. TAKE HOME MESSAGE: Frequent transcriptional phase transitions may contribute to outcome in critically ill COVID-19 patients. Furthermore, fluctuation in red blood cell and hemoglobin levels may relate to poor prognosis. The biological function of melatonin was suppressed in COVID-19 non-survivors, which may provide a potential theoretical basis for clinical administration.

Distinct Clinical Characteristics and Risk Factors for Mortality in Female Inpatients With Coronavirus Disease 2019 (COVID-19): A Sex-stratified, Large-scale Cohort Study in Wuhan, China.

BACKGROUND: As the coronavirus disease 2019 (COVID-19) outbreak accelerates worldwide, it is important to evaluate sex-specific clinical characteristics and outcomes, which may affect public health policies. METHODS: Patients with COVID-19 admitted to Tongji Hospital between 18 January and 27 March 2020 were evaluated. Clinical features, laboratory data, complications, and outcomes were compared between females and males. Risk factors for mortality in the whole population, females, and males were determined respectively. RESULTS: There were 1667 (50.38%) females among the 3309 patients. The mortality rate was 5.9% in females but 12.7% in males. Compared with males, more females had no initial symptoms (11.1% vs 8.3%, P = .008). Complications including acute respiratory distress syndrome, acute kidney injury, septic shock, cardiac injury, and coagulation disorder were less common in females; critical illness was also significantly less common in females (31.1% vs 39.4%, P < .0001). Significantly fewer females received antibiotic treatment (P = .001), antiviral therapy (P = .025), glucocorticoids treatment (P < .0001), mechanical ventilation (P < .0001), and had intensive care unit admission (P < .0001). A lower risk of death was found in females (OR, .44; 95% CI, .34-.58) after adjusting for age and coexisting diseases. Among females, age, malignancy, chronic kidney disease, and days from onset to admission were significantly associated with mortality, while chronic kidney disease was not a risk factor in males. CONCLUSIONS: Significantly milder illness and fewer deaths were found in female COVID-19 inpatients and risk factors associated with mortality varied among males and females.

A retrospective comparison of drugs against COVID-19.

Coronavirus disease 19 (COVID-19) has posed serious threats to the general population. To relieve the crisis, a comparison of drug effects against COVID-19 is instructive. Between January 27, 2020 and March 21, 2020, a total of 333 patients treated with arbidol, corticosteroids, hydroxychloroquine, lopinavir/ritonavir, or oseltamivir monotherapy, having definite outcomes and serological antibody detection results, were retrospectively analyzed. The hydroxychloroquine group had a significantly reduced duration of hospital stay than the arbidol and corticosteroids groups. The oseltamivir group had a significantly shorter length of hospital stay than the arbidol, corticosteroids, and lopinavir/ritonavir groups. The hydroxychloroquine group had a significantly higher IgM titer than the other four groups and exhibited significantly higher IgG levels than the arbidol, lopinavir/ritonavir, and oseltamivir groups. Our findings indicated that hydroxychloroquine might have the potential for efficient COVID-19 management, while oseltamivir should be prudently considered in combination therapy.

Clinical Management of Gynecological Cancer Patients During COVID-19 Epidemic

During the epidemic of COVID-19, the routine clinical treatment for gynecological cancer patients has been disturbed due to the redistribution of medical resource. Due to the systemic immunosuppression caused by the malignancy and anticancer treatments, gynecological cancer patients are more susceptible to COVID-19. With the improvement of the epidemic, the treatment needs of gynecological cancer patients are extremely strong. During this special period, it should carefully identify fever and respiratory symptoms of gynecological cancer patients receiving chemotherapy, immunotherapy and operations. Therefore, it is quite necessary to carry out comprehensive clinical management. We introduce a clinical management of gynecological cancer patients in three aspects of outpatient, in-hospital and out-of-hospital management during this period, in order to maximize the treatment of tumors and effectively prevent COVID-19.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Cancer history is an independent risk factor for mortality in hospitalized COVID-19 patients: a propensity score-matched analysis.

BACKGROUND: Although research on the effects of comorbidities on coronavirus disease 2019 (COVID-19) patients is increasing, the risk of cancer history has not been evaluated for the mortality of patients with COVID-19. METHODS: In this retrospective study, we included 3232 patients with pathogen-confirmed COVID-19 who were hospitalized between January 18th and March 27th, 2020, at Tongji Hospital in Wuhan, China. Propensity score matching was used to minimize selection bias. RESULTS: In total, 2665 patients with complete clinical outcomes were analyzed. The impacts of age, sex, and comorbidities were evaluated separately using binary logistic regression analysis. The results showed that age, sex, and cancer history are independent risk factors for mortality in hospitalized COVID-19 patients. COVID-19 patients with cancer exhibited a significant increase in mortality rate (29.4% vs. 10.2%, P < 0.0001). Furthermore, the clinical outcomes of patients with hematological malignancies were worse, with a mortality rate twice that of patients with solid tumors (50% vs. 26.1%). Importantly, cancer patients with complications had a significantly higher risk of poor outcomes. One hundred nine cancer patients were matched to noncancer controls in a 1:3 ratio by propensity score matching. After propensity score matching, the cancer patients still had a higher risk of mortality than the matched noncancer patients (odds ratio (OR) 2.98, 95% confidence interval (95% CI) 1.76-5.06). Additionally, elevations in ferritin, high-sensitivity C-reactive protein, erythrocyte sedimentation rate, procalcitonin, prothrombin time, interleukin-2 (IL-2) receptor, and interleukin-6 (IL-6) were observed in cancer patients. CONCLUSIONS: We evaluated prognostic factors with epidemiological analysis and highlighted a higher risk of mortality for cancer patients with COVID-19. Importantly, cancer history was the only independent risk factor for COVID-19 among common comorbidities, while other comorbidities may act through other factors. Moreover, several laboratory parameters were significantly different between cancer patients and matched noncancer patients, which may indicate specific immune and inflammatory reactions in COVID-19 patients with cancer.

Experience of Clinical Management for Pregnant Women and Newborns with Novel Coronavirus Pneumonia in Tongji Hospital, China.

Based on the New Diagnosis and Treatment Scheme for Novel Coronavirus Infected Pneumonia (Trial Edition 5), combined with our current clinical treatment experience, we recently proposed a revision of the first edition of "Guidance for maternal and fetal management during pneumonia epidemics of novel coronavirus infection in the Wuhan Tongji Hospital". This article focused on the issues of greatest concern of pregnant women including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection diagnostic criteria, inspection precautions, drug treatment options, indications and methods of termination of pregnancy, postpartum fever, breastfeeding considerations, mode of mother-to-child transmission, neonatal isolation and advice on neonatal nursing, to provide valuable experience for better management of SARS-CoV-2 infection in pregnant women and newborns.