Symptom Clusters and Functional Impairment in Individuals Treated for Lyme Borreliosis.

Context: Persistent fatigue, pain, and neurocognitive impairment are common in individuals following treatment for Lyme borreliosis (LB). Poor sleep, depression, visual disturbance, and sensory neuropathies have also been reported. The cause of these symptoms is unclear, and widely accepted effective treatment strategies are lacking. Objectives: To identify symptom clusters in people with persistent symptoms previously treated for LB and to examine the relationship between symptom severity and perceived disability. Methods: This was a retrospective chart review of individuals with a history of treatment of LB referred to The Dean Center for Tick-Borne Illness at Spaulding Rehabilitation Hospital between 2015 and 2018 (n = 270) because of persistent symptoms. Symptoms and functional impairment were collected using the General Symptom Questionnaire-30 (GSQ-30), and the Sheehan Disability Scale. Clinical tests were conducted to evaluate for tick-borne co-infections and to rule out medical disorders that could mimic LB symptomatology. Exploratory factor analysis was performed to identify symptom clusters. Results: Five symptom clusters were identified. Each cluster was assigned a name to reflect the possible underlying etiology and was based on the majority of the symptoms in the cluster: the neuropathy symptom cluster, sleep-fatigue symptom cluster, migraine symptom cluster, cognitive symptom cluster, and mood symptom cluster. Symptom severity for each symptom cluster was positively associated with global functional impairment (p < 0.001). Conclusion: Identifying the interrelationship between symptoms in post-treatment LB in a cluster can aid in the identification of the etiological basis of these symptoms and could lead to more effective symptom management strategies. Key Message: This article describes symptom clusters in individuals with a history of Lyme borreliosis. Five clusters were identified: sleep-fatigue, neuropathy, migraine-like, cognition, and mood clusters. Identifying the interrelationship between symptoms in each of the identified clusters could aid in more effective symptom management through identifying triggering symptoms or an underlying etiology.

Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change.

Cardiorespiratory instability (CRI) in monitored step-down unit (SDU) patients has a variety of etiologies, and likely manifests in patterns of vital signs (VS) changes. We explored use of clustering techniques to identify patterns in the initial CRI epoch (CRI1; first exceedances of VS beyond stability thresholds after SDU admission) of unstable patients, and inter-cluster differences in admission characteristics and outcomes. Continuous noninvasive monitoring of heart rate (HR), respiratory rate (RR), and pulse oximetry (SpO2) were sampled at 1/20 Hz. We identified CRI1 in 165 patients, employed hierarchical and k-means clustering, tested several clustering solutions, used 10-fold cross validation to establish the best solution and assessed inter-cluster differences in admission characteristics and outcomes. Three clusters (C) were derived: C1) normal/high HR and RR, normal SpO2 (n = 30); C2) normal HR and RR, low SpO2 (n = 103); and C3) low/normal HR, low RR and normal SpO2 (n = 32). Clusters were significantly different based on age (p < 0.001; older patients in C2), number of comorbidities (p = 0.008; more C2 patients had ≥ 2) and hospital length of stay (p = 0.006; C1 patients stayed longer). There were no between-cluster differences in SDU length of stay, or mortality. Three different clusters of VS presentations for CRI1 were identified. Clusters varied on age, number of comorbidities and hospital length of stay. Future study is needed to determine if there are common physiologic underpinnings of VS clusters which might inform clinical decision-making when CRI first manifests.

The interface between monitoring and physiology at the bedside.

Hemodynamic instability as a clinical state represents either a perfusion failure with clinical manifestations of circulatory shock or heart failure or 1 or more out-of-threshold hemodynamic monitoring values, which may not necessarily be pathologic. Different types of causes of circulatory shock require different types of treatment modalities, making these distinctions important. Diagnostic approaches or therapies based on data derived from hemodynamic monitoring assume that specific patterns of derangements reflect specific disease processes, which respond to appropriate interventions. Hemodynamic monitoring at the bedside improves patient outcomes when used to make treatment decisions at the right time for patients experiencing hemodynamic instability.