Restless legs syndrome is associated with long-COVID in women.

STUDY OBJECTIVES: Sleep disturbance is common in long-COVID (LC). Restless legs syndrome (RLS) is characterized by sleep disturbance and has been reported after viral infections. Therefore, we evaluated RLS symptoms cross-sectionally in individuals with LC at both current and pre-coronavirus disease 2019 (pre-COVID-19) time points. METHODS: Adults on LC-focused Facebook pages were recruited for an online assessment of symptoms before COVID-19 infection and during their present LC state in a cross-sectional manner. The LC group documented baseline symptoms retrospectively. Questions were included about the presence/severity of RLS symptoms and assessments of fatigue, quality of life, and sleep apnea. A control group was recruited and included individuals ≥ 18 years of age who never had overt symptoms of COVID-19. Pregnancy was an exclusion criterion for both groups. RESULTS: There were 136 participants with LC (89.7% females, age 46.9 ± 12.9 years) and 136 controls (65.4% females, age 49.2 ± 15.5). RLS prevalence in females with LC was 5.7% pre-COVID-19 and 14.8% post-COVID-19 (P < .01) vs 6.7% in control females. Severity of RLS was moderate in both groups. Logistic regression predicting post-COVID-19 RLS among females with LC failed to find significant effects of hospitalization, sleep apnea, neuropathic pain severity, or use of antihistamines and antidepressants. CONCLUSIONS: The baseline prevalence of RLS in females with LC was similar to the general population group as well as to patients in epidemiological studies. The prevalence significantly increased in the LC state. Postinfectious immunological mechanisms may be at play in the production for RLS symptoms. CITATION: Weinstock LB, Brook JB, Walters AS, Goris A, Afrin LB, Molderings GJ. Restless legs syndrome is associated with long-COVID in women. J Clin Sleep Med. 2022;18(5):1413-1418.

Mast cell activation symptoms are prevalent in Long-COVID.

OBJECTIVES: Hyper-inflammation caused by COVID-19 may be mediated by mast cell activation (MCA) which has also been hypothesized to cause Long-COVID (LC) symptoms. We determined prevalence/severity of MCA symptoms in LC. METHODS: Adults in LC-focused Facebook support groups were recruited for online assessment of symptoms before and after COVID-19. Questions included presence and severity of known MCA and LC symptoms and validated assessments of fatigue and quality of life. General population controls and mast cell activation syndrome (MCAS) patients were recruited for comparison if they were ≥18 years of age and never had overt COVID-19 symptoms. RESULTS: There were 136 LC subjects (89.7% females, age 46.9 ±12.9 years), 136 controls (65.4% females, age 49.2 ±15.5), and 80 MCAS patients (85.0% females, age 47.7 ±16.4). Pre-COVID-19 LC subjects and controls had virtually identical MCA symptom and severity analysis. Post-COVID-19 LC subjects and MCAS patients prior to treatment had virtually identical MCA symptom and severity analysis. CONCLUSIONS: MCA symptoms were increased in LC and mimicked the symptoms and severity reported by patients who have MCAS. Increased activation of aberrant mast cells induced by SARS-CoV-2 infection by various mechanisms may underlie part of the pathophysiology of LC, possibly suggesting routes to effective therapy.

How clean is clean enough? An observational pilot study to assess central sterilization processing efficacy with adenosine triphosphate levels.

BACKGROUND: There is strong evidence showing the need for quantitative assessment of the efficacy of cleaning medical devices. Studies comparing visual and microscopic analysis have demonstrated that visual inspection alone is insufficient to determine cleanliness. OUR OBJECTIVE WAS TO: collect baseline adenosine triphosphate testing data for surgical instruments to inform future quality improvement programs, and to develop a process to comply with standards recommended by the Association for the Advancement of Medical Instrumentation. METHODS: The central sterilization processing department (CSPD) identified difficult to clean tools in hip, spinal fusion, and colon procedures. Instruments used during the procedures were swabbed at arrival to the CSPD, after manual washing, and after a standard 24-minute machine wash cycle. Each instrument was swabbed in a uniform manner on surface areas including hinged joints and teeth, consistent with manufacturer recommendations. Instruments were tagged to ensure accuracy of tracking. RESULTS: The mean relative light unit (RLU) readout of all instruments on entry to the CSPD, postmanual wash, and postmachine wash was 240,416, 3,352, and 47, respectively. A total of 97.7% of all instruments had a passable final RLU readout. One Rongeur had a failed final RLU readout of 171. CONCLUSIONS: This study suggests that the current cleaning process is adequate for the instruments tested. Ongoing quality assessment would be beneficial to validate other instruments.

Increasing the Reliability of Fully Automated Surveillance for Central Line-Associated Bloodstream Infections.

OBJECTIVE: To increase reliability of the algorithm used in our fully automated electronic surveillance system by adding rules to better identify bloodstream infections secondary to other hospital-acquired infections. METHODS: Intensive care unit (ICU) patients with positive blood cultures were reviewed. Central line-associated bloodstream infection (CLABSI) determinations were based on 2 sources: routine surveillance by infection preventionists, and fully automated surveillance. Discrepancies between the 2 sources were evaluated to determine root causes. Secondary infection sites were identified in most discrepant cases. New rules to identify secondary sites were added to the algorithm and applied to this ICU population and a non-ICU population. Sensitivity, specificity, predictive values, and kappa were calculated for the new models. RESULTS: Of 643 positive ICU blood cultures reviewed, 68 (10.6%) were identified as central line-associated bloodstream infections by fully automated electronic surveillance, whereas 38 (5.9%) were confirmed by routine surveillance. New rules were tested to identify organisms as central line-associated bloodstream infections if they did not meet one, or a combination of, the following: (I) matching organisms (by genus and species) cultured from any other site; (II) any organisms cultured from sterile site; (III) any organisms cultured from skin/wound; (IV) any organisms cultured from respiratory tract. The best-fit model included new rules I and II when applied to positive blood cultures in an ICU population. However, they didn't improve performance of the algorithm when applied to positive blood cultures in a non-ICU population. CONCLUSION: Electronic surveillance system algorithms may need adjustment for specific populations.

Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit.

BACKGROUND: Manual surveillance for central line-associated bloodstream infections (CLABSIs) by infection prevention practitioners is time-consuming and often limited to intensive care units (ICUs). An automated surveillance system using existing databases with patient-level variables and microbiology data was investigated. METHODS: Patients with a positive blood culture in 4 non-ICU wards at Barnes-Jewish Hospital between July 1, 2005, and December 31, 2006, were evaluated. CLABSI determination for these patients was made via 2 sources; a manual chart review and an automated review from electronically available data. Agreement between these 2 sources was used to develop the best-fit electronic algorithm that used a set of rules to identify a CLABSI. Sensitivity, specificity, predictive values, and Pearson's correlation were calculated for the various rule sets, using manual chart review as the reference standard. RESULTS: During the study period, 391 positive blood cultures from 331 patients were evaluated. Eighty-five (22%) of these were confirmed to be CLABSI by manual chart review. The best-fit model included presence of a catheter, blood culture positive for known pathogen or blood culture with a common skin contaminant confirmed by a second positive culture and the presence of fever, and no positive cultures with the same organism from another sterile site. The best-performing rule set had an overall sensitivity of 95.2%, specificity of 97.5%, positive predictive value of 90%, and negative predictive value of 99.2% compared with intensive manual surveillance. CONCLUSIONS: Although CLABSIs were slightly overpredicted by electronic surveillance compared with manual chart review, the method offers the possibility of performing acceptably good surveillance in areas where resources do not allow for traditional manual surveillance.

Automated surveillance for central line-associated bloodstream infection in intensive care units.

OBJECTIVE: To develop and evaluate computer algorithms with high negative predictive values that augment traditional surveillance for central line-associated bloodstream infection (CLABSI). SETTING: Barnes-Jewish Hospital, a 1,250-bed tertiary care academic hospital in Saint Louis, Missouri. METHODS: We evaluated all adult patients in intensive care units who had blood samples collected during the period from July 1, 2005, to June 30, 2006, that were positive for a recognized pathogen on culture. Each isolate recovered from culture was evaluated using the definitions for nosocomial CLABSI provided by the National Healthcare Safety Network of the Centers for Disease Control and Prevention. Using manual surveillance by infection prevention specialists as the gold standard, we assessed the ability of various combinations of dichotomous rules to determine whether an isolate was associated with a CLABSI. Sensitivity, specificity, and predictive values were calculated. RESULTS: Infection prevention specialists identified 67 cases of CLABSI associated with 771 isolates recovered from blood samples. The algorithms excluded approximately 40%-62% of the isolates from consideration as possible causes of CLABSI. The simplest algorithm, with 2 dichotomous rules (ie, the collection of blood samples more than 48 hours after admission and the presence of a central venous catheter within 48 hours before collection of blood samples), had the highest negative predictive value (99.4%) and the lowest specificity (44.2%) for CLABSI. Augmentation of this algorithm with rules for common skin contaminants confirmed by another positive blood culture result yielded in a negative predictive value of 99.2% and a specificity of 68.0%. CONCLUSIONS: An automated approach to surveillance for CLABSI that is characterized by a high negative predictive value can accurately identify and exclude positive culture results not representing CLABSI from further manual surveillance.