The Efficacy of Computerized Cognitive Behavioral Therapy for Depressive and Anxiety Symptoms in Patients With COVID-19: Randomized Controlled Trial.

BACKGROUND: The prevalence of depressive and anxiety symptoms in patients with COVID-19 is higher than usual. Previous studies have shown that there are drug-to-drug interactions between antiretroviral drugs and antidepressants. Therefore, an effective and safe treatment method was needed. Cognitive behavioral therapy (CBT) is the first-line psychological therapy in clinical treatment. Computerized CBT (cCBT) was proven to be an effective alternative to CBT and does not require face-to-face therapy between a therapist and the patient, which suited the COVID-19 pandemic response. OBJECTIVE: This study aims to evaluate the efficacy of the cCBT program we developed in improving depressive and anxiety symptoms among patients with COVID-19. METHODS: We customized a cCBT program focused on improving depressive and anxiety symptoms among patients with COVID-19, and then, we assessed its effectiveness. Screening was based on symptoms of depression or anxiety for patients who scored ≥7 on the Hamilton Depression Rating Scale (HAMD17) or the Hamilton Anxiety Scale (HAMA). A total of 252 patients with COVID-19 at five sites were randomized into two groups: cCBT + treatment as usual (TAU; n=126) and TAU without cCBT (n=126). The cCBT + TAU group received the cCBT intervention program for 1 week. The primary efficacy measures were the HAMD17 and HAMA scores. The secondary outcome measures were the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Athens Insomnia Scale (AIS). Assessments were carried out pre- and postintervention. The patients' symptoms of anxiety and depression in one of the centers were assessed again within 1 month after the postintervention assessment. RESULTS: The cCBT + TAU group displayed a significantly decreased score on the HAMD17, HAMA, SDS, SAS, and AIS after the intervention compared to the TAU group (all P<.001). A mixed-effects repeated measures model revealed significant improvement in symptoms of depression (HAMD17 and SDS scores, both P<.001), anxiety (HAMA and SAS scores, both P<.001), and insomnia (AIS score, P=.002) during the postintervention and follow-up periods in the cCBT + TAU group. Additionally, the improvement of insomnia among females (P=.14) and those with middle school education (P=.48) in the cCBT + TAU group showed no significant differences when compared to the TAU group. CONCLUSIONS: The findings of this study suggest that the cCBT program we developed was an effective nonpharmacological treatment for symptoms of anxiety, depression, and insomnia among patients with COVID-19. Further research is warranted to investigate the long-term effects of cCBT for symptoms of anxiety, depression, and insomnia in patients with COVID-19. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000030084; http://www.chictr.org.cn/showprojen.aspx?proj=49952.

Acute Kidney Injury in Hospitalized Patients Infected with COVID-19 from Wuhan, China: A Retrospective Study.

BACKGROUND: Since the first diagnosed case of infection with the novel coronavirus (SARS-CoV-2), there has been a rapid spread of the disease with an increasing number of cases confirmed every day, as well as a rising death toll. An association has been reported between acute kidney injury (AKI) and mortality in patients infected with SARS-CoV-2. Therefore, our study was conducted to explore possible risk factors of AKI as well as whether AKI was a risk factor for worse outcome, especially mortality among patients with coronavirus disease (COVID-19). METHODS: We included all hospital admissions with confirmed or clinically diagnosed COVID-19 from January 29 to February 25, 2020. We collected demographic and epidemiological information, past medical history, symptoms, laboratory tests, treatments, and outcome data from electronic medical records. A total of 492 patients with diagnosed or clinically diagnosed COVID-19 were included in this study. RESULTS: The prevalence rate of AKI was 7.32%. Among the factors associated with AKI, males versus females (aOR 2.73), chronic kidney disease (aOR 42.2), hypertension (aOR 2.82), increased leucocytes (aOR 6.08), and diuretic use (aOR 7.89) were identified as independent risk factors for AKI among patients infected by SARS-CoV-2. There was a significant difference in hospital fees and death in patients with and without AKI (p < 0.05). The mortality rate in patients with AKI was 63.9%. CONCLUSIONS: AKI was widespread among patients with COVID-19. The risk factors of AKI in COVID-19 patients included sex, chronic kidney disease, hypertension, infection, and diuretic use. AKI may be associated with a worse outcome, especially mortality in COVID-19 patients.

Characteristics and outcomes of hemodialysis patients with COVID-19: a retrospective single center study.

BACKGROUND: The coronavirus 19 (COVID-19) pandemic has heightened the threat to the health and lives of patients with comorbid diseases. Infection by COVID-19 is especially detrimental to patients on hemodialysis. In this study, we evaluated the clinical characteristics, laboratory findings, treatments and prognoses of hemodialysis patients with COVID-19. METHODS: A total of 16 hemodialysis patients with COVID-19 were recruited from Wuhan Fourth Hospital from 5 February to 20 March 2020 for a retrospective, single-center study. A total of 62 non-dialysis patients with COVID-19 were the control group. We collected data on the clinical characteristics, laboratory findings, treatments, and clinical outcomes of patients affected by the virus. RESULTS: Hemodialysis patients with COVID-19 had a lower incidence of fever (P = 0.001) and relatively higher incidence of pre-admission comorbidities and shortness of breath than non-dialysis patients with COVID-19 (75% vs. 61%, P = 0.467 50% vs. 33.87%, P = 0.248 ). Hemodialysis patients had lower levels of hemoglobin (P < 0.001), white blood cell counts (P = 0.015), neutrophils (P = 0.016), AST (P = 0.037), ALT (P < 0.001) and procalcitonin (P < 0.001), and higher levels of D-dimer (P < 0.001) and thrombin time (P < 0.001). Hemodialysis patients had a higher incidence of pulmonary effusion, cord-like high-density shadows, pleural thickening, and atelectasis (P < 0.05). Hemodialysis patients also had relatively higher rates of mortality and prolonged hospital stays compared with the control group. CONCLUSIONS: Hemodialysis patients typically present with multiple comorbidities and are considered to be a high-risk group for COVID-19 infections. Hemodialysis patients with COVID-19 may have prolonged hospital stays and unfavorable prognoses and should be closely monitored.

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. METHODS: The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses. RESULTS: Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42-16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13-11.25]). CONCLUSION: We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.