ABSTRACT
BACKGROUND: Neuroinflammatory responses are closely associated with poststroke prognosis severity. This study aimed to develop a predictive model, combining inflammation-derived markers and clinical indicators, for distinguishing functional outcomes in patients with subacute ischemic stroke. METHODS AND RESULTS: Based on activities of daily living assessments, ischemic stroke participants were categorized into groups with little effective (LE) recovery and obvious effective (OE) recovery. Initial biocandidates were identified by overlapping differentially expressed proteins from proteomics of clinical serum samples (5 LE, 5 OE, and 6 healthy controls) and differentially expressed genes from an RNA sequence of the ischemic cortex in middle cerebral artery occlusion mice (n=3). Multidimensional validations were conducted in ischemia-reperfusion models and a clinical cohort (15 LE, 11 OE, and 18 healthy controls). Models of robust biocandidates combined with clinical indicators were developed with machine learning in the training data set and prediction in another test data set (15 LE and 11 OE). We identified 194 differentially expressed proteins (LE versus healthy controls) and 174 differentially expressed proteins (OE versus healthy controls) in human serum, and 5121 differentially expressed genes (day 3) and 5906 differentially expressed genes (day 7) in middle cerebral artery occlusion mice cortex. Inflammation-derived biomarkers TIMP1 (tissue inhibitor metalloproteinase-1) and galactosidase-binding protein LGLAS3 (galectin-3) exhibited robust increases under ischemic injury in mice and humans. TIMP1 and LGALS3 coupled with clinical indicators (hemoglobin, low-density lipoprotein cholesterol, and uric acid) were developed into a combined model for differentiating functional outcome with high accuracy (area under the curve, 0.8). CONCLUSIONS: The combined model is a valuable tool for evaluating prognostic outcomes, and the predictive factors can facilitate development of better treatment strategies.
ABSTRACT
OBJECTIVE: The aim of this study was to validate a novel pictorial-based Longshi Scale for evaluating a patient's disability by healthcare professionals and non-professionals. DESIGN: Prospective study. SETTING: Rehabilitation departments from a grade A, class 3 public hospital, a grade B, class 2 public hospital, and a private hospital and seven community rehabilitation centers. SUBJECTS: A total of 618 patients and 251 patients with functional disabilities were recruited in a two-phase study, respectively. MAIN MEASURES: Outcome measure: pictorial scale of activities of daily living (ADLs, Longshi Scale). Reference measure: Barthel Index. The Spearman correlation coefficient was used to analyze the validity of Longshi Scale against Barthel Index. RESULTS: In phase 1 study, from March 2016 to August 2016, the results demonstrated that the Longshi Scale was both reliable and valid (intraclass correlation coefficient based on two-way random effect (ICC2,1) = 0.877-0.974 for intra-rater reliability; ICC2,1 = 0.928-0.979; κ = 0.679-1.000 for inter-rater reliability; intraclass correlation coefficient based on one-way random effect (ICC1,1) = 0.921-0.984 for test-retest reliability and Spearman correlation coefficient = 0.836-0.899). In the second phase, in March 2018, results further demonstrated that the Longshi Scale had good inter-rater and intra-rater reliability among healthcare professionals and non-professionals including therapists, interns, and personal care aids (ICC1,1 = 0.822-0.882 on Day 1; ICC1,1 = 0.842-0.899 on Day 7 for inter-rater reliability). In addition, the Longshi Scale decreased assessment time significantly, compared with the Barthel Index assessment (P < 0.01). CONCLUSION: The Longshi Scale could potentially provide an efficient way for healthcare professionals and non-professionals who may have minimal training to assess the ADLs of functionally disabled patients.