Nomogram to Predict the Outcome of Ventriculoperitoneal Shunt Among Patients with Non-HIV Cryptococcal Meningitis.

BACKGROUND: The ventriculoperitoneal (VP) shunt is widely acknowledged as a treatment option for managing intracranial hypertension resulting from non-human immunodeficiency virus (HIV) cryptococcal meningitis (CM). Nonetheless, there is currently no consensus on the appropriate surgical indications for this procedure. Therefore, it is crucial to conduct a preoperative evaluation of patient characteristics and predict the outcome of the VP shunt to guide clinical treatment effectively. METHODS: A retrospective analysis was conducted on data from 85 patients with non-HIV CM who underwent VP shunt surgery at our hospital. The analysis involved studying demographic data, preoperative clinical manifestations, cerebrospinal fluid (CSF) characteristics, and surgical outcomes and comparisons between before and after surgery. A nomogram was developed and evaluated. RESULTS: The therapy outcomes of 71 patients improved, whereas 14 cases had worse outcomes. Age, preoperative cryptococcus count, and preoperative CSF protein levels were found to influence the surgical outcome. The nomogram exhibited exceptional predictive performance (area under the curve = 0.896, 95% confidence interval: 0.8292-0.9635). Internal validation confirmed the nomogram's excellent predictive capabilities. Moreover, decision curve analysis demonstrated the nomogram's practical clinical utility. CONCLUSIONS: The surgical outcome of VP shunt procedures patients with non-HIV CM was associated with age, preoperative cryptococcal count, and preoperative CSF protein levels. We developed a nomogram that can be used to predict surgical outcomes in patients with non-HIV CM.

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.

Clinical Characteristics and Outcomes of 74 Patients With Severe or Critical COVID-19.

BACKGROUND: The outbreak of the coronavirus disease (COVID-19) has led to a major concern and caused a pandemic globally. The goal of this study was to clarify the clinical characteristics of recovery and death in patients with severe or critical COVID-19. MATERIALS AND METHODS: In this retrospective single-center study, clinical data were collected from 74 severe or critical COVID-19 patients in Wuhan Fourth Hospital between Jan. 25th and Feb. 26th, 2020. All patients were divided into a recovery group or a death group according to clinical outcomes, and the differences between the groups were compared. RESULTS: Of the 74 patients enrolled in the study, 48 (64.9%) were severe cases and 26 (35.1%) were critical cases. Sixty (81.1%) patients were recovered and 14 (18.9%) died. Compared with recovery patients, patients in the death group were older, and had higher incidences of hypertension, coronary disease and dyspnea at admission. Laboratory tests for lactate dehydrogenase, creatine kinase, myoglobin, brain natriuretic peptide and D-dimer indicated higher levels in the death group. The PaO2:FiO2 ratio and minimum SpO2 were lower in the death group, and a higher proportion of these patients received noninvasive mechanical ventilation, invasive mechanical ventilation and extracorporeal membrane oxygenation treatment. CONCLUSIONS: Elderly patients with comorbidities are at higher risk of severe COVID-19 or death. Patients with a low blood gas index and poor coagulation function at admission had a high mortality rate. For such patients, comprehensive treatment should be performed as soon as possible to improve the prognosis and reduce mortality.