The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection.

Background: SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods: This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results: Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion: Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection

Background SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities;lower Ct value at recurrence;more persistent cough;and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Clinical outcomes of COVID-19 patients with rheumatic diseases: a retrospective cohort study and synthesis analysis in Wuhan, China.

BACKGROUND: The clinical outcomes of patients with rheumatic diseases infected with COVID-19 were inconsistent characteristics across regions and time periods. We need to revisit and sort out the clinical characteristics of these patients at the beginning of the global COVID-19 epidemic. METHODS: We collected data from confirmed COVID-19 patients from two military-run field hospitals and classified them into the rheumatic disease group and no rheumatic disease groups, and the latter was further distinguished by ARD and non-ARD. We compared the primary outcome, which we defined as mortality, and the secondary outcome, which we defined as the ICU occupancy rate, the duration of hospitalization and the duration of viral clearance, between the patients with and without rheumatic diseases after PSM. A study-level meta-analysis of four studies was conducted on the mortality of the COVID-19 patients with and without rheumatic diseases. RESULTS: A total of 4353 COVID-19 patients were included in our cohort study; 91 had rheumatic diseases. The mean age of the entire cohort was 59.37, and 2281 (52.40%) patients were female. The mortalities after PSM were 1.11% and 3.46% in the rheumatic diseases and no rheumatic disease groups, respectively. The ICU occupancy rates after PSM were 2.22% and 4.61% in the rheumatic diseases and no rheumatic disease groups. The duration of hospitalization and viral clearance in the rheumatic disease group were 15.97 and 43.69, respectively; moreover, the same parameters in the no rheumatic diseases after PSM were 15.48 and 45.48. No significant differences were found in either the primary or secondary outcomes. After excluding the gout cases, the results were still similar. However, there was a significant difference between the two groups upon meta-analysis (RR = 1.70, 95% CI 1.35-2.13). CONCLUSIONS: Rheumatic diseases seemed to aggravate the course of COVID-19 infection. However, the poor outcomes of COVID-19 seemed to be unassociated with rheumatic diseases undergoing an adequate medical intervention. KEY POINTS: • We compared the outcomes and prognosis of COVID-19 patients in China at the beginning of the outbreak regarding the presence or absence of rheumatic disease patients and made some meaningful conclusions for future outbreaks of similar infectious diseases. • We compared similar recent studies from other countries and explored the changes and differences in patient outcomes associated with COVID-19 as it continued to spread worldwide during the year, providing clinical evidence to further explore the role rheumatic diseases play in COVID-19 patient outcomes. • We provided evidence for the treatment of relevant patients and made rationalized recommendations for treatment strategy.

Calcium channel blockers improve prognosis of patients with coronavirus disease 2019 and hypertension.

BACKGROUND: Hypertension is considered an important risk factor for the coronavirus disease 2019 (COVID-19). The commonly anti-hypertensive drugs are the renin-angiotensin-aldosterone system (RAAS) inhibitors, calcium channel blockers (CCBs), and beta-blockers. The association between commonly used anti-hypertensive medications and the clinical outcome of COVID-19 patients with hypertension has not been well studied. METHODS: We conducted a retrospective cohort study that included all patients admitted with COVID-19 to Huo Shen Shan Hospital and Guanggu District of the Maternal and Child Health Hospital of Hubei Province, Wuhan, China. Clinical and laboratory characteristics were extracted from electronic medical records. Hypertension and anti-hypertensive treatment were confirmed by medical history and clinical records. The primary clinical endpoint was all-cause mortality. Secondary endpoints included the rates of patients in common wards transferred to the intensive care unit and hospital stay duration. Logistic regression was used to explore the risk factors associated with mortality and prognosis. Propensity score matching was used to balance the confounders between different anti-hypertensive treatments. Kaplan-Meier curves were used to compare the cumulative recovery rate. Log-rank tests were performed to test for differences in Kaplan-Meier curves between different groups. RESULTS: Among 4569 hospitalized patients with COVID-19, 31.7% (1449/4569) had a history of hypertension. There were significant differences in mortality rates between hypertensive patients with CCBs (7/359) and those without (21/359) (1.95% vs. 5.85%, risk ratio [RR]: 0.32, 95% confidence interval [CI]: 0.13-0.76, χ2 = 7.61, P = 0.0058). After matching for confounders, the mortality rates were similar between the RAAS inhibitor (4/236) and non-RAAS inhibitor (9/236) cohorts (1.69% vs. 3.81%, RR: 0.43, 95% CI: 0.13-1.43, χ2 = 1.98, P = 0.1596). Hypertensive patients with beta-blockers (13/340) showed no statistical difference in mortality compared with those without (11/340) (3.82% vs. 3.24%, RR: 1.19, 95% CI: 0.53-2.69, χ2 = 0.17, P = 0.6777). CONCLUSIONS: In our study, we did not find any positive or negative effects of RAAS inhibitors or beta-blockers in COVID-19 patients with hypertension, while CCBs could improve prognosis.

Prediction of in-hospital clinical outcomes of coronavirus disease 2019 patients based on machine learning algorithms

Objective: To construct prediction models for the clinical outcomes of coronavirus disease 2019 (COVID-19) patients using machine learning algorithms, and explore the outcome-related factors.