Dataset Information

Gait pattern analysis and clinical subgroup identification: a retrospective observational study.

ABSTRACT: To identify basic gait features and abnormal gait patterns that are common to different neurological or musculoskeletal conditions, such as cerebral stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.In this retrospective study, temporal-spatial, kinematic, and kinetic gait parameters were analyzed in 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal controls (total, 1328 subjects). We assessed differences according to the condition and used a community detection algorithm to identify subgroups within each condition. Additionally, we developed a prediction model for subgroup classification according to gait speed and maximal hip extension in the stance phase.The main findings can be summarized as follows. First, there was an asymmetric decrease of the knee/ankle flexion angles in hemiplegia and a marked reduction of the hip/knee range of motion with increased moment in Parkinsonian disorders. Second, three abnormal gait patterns, including fast gait speed with adequate maximal hip extension, fast gait speed with inadequate maximal hip extension, and slow gait speed, were found throughout the conditions examined. Third, our simple prediction model based on gait speed and maximal hip extension angle was characterized by a high degree of accuracy in predicting subgroups within a condition.Our findings suggest the existence of specific gait patterns within and across conditions. Our novel subgrouping algorithm can be employed in routine clinical settings to classify abnormal gait patterns in various neurological disorders and guide the therapeutic approach and monitoring.

SUBMITTER: Kyeong S

PROVIDER: S-EPMC7440325 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gait pattern analysis and clinical subgroup identification: a retrospective observational study.

Kyeong Sunghyon S Kim Seung Min SM Jung Suk S Kim Dae Hyun DH

Medicine 20200401 15

To identify basic gait features and abnormal gait patterns that are common to different neurological or musculoskeletal conditions, such as cerebral stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.In this retrospective study, temporal-spatial, kinematic, and kinetic gait parameters were analyzed in 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal con ...[more]

PMID: 32282704

Similar Datasets

Project description:BackgroundFew studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.ObjectiveThis study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records.MethodsIn this study, 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to the intensive care unit within 7 days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International Edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on their meaning by 2 experts with more than 5 years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.ResultsAll nursing statements were semantically mapped to SNOMED CT concepts for "clinical finding," "situation with explicit context," and "procedure" hierarchies. The interrater reliability of the mapping results was 87.7%. The most important features calculated by random forest were "oxygen saturation below reference range," "dyspnea," "tachypnea," and "cough" in "clinical finding," and "oxygen therapy," "pulse oximetry monitoring," "temperature taking," "notification of physician," and "education about isolation for infection control" in "procedure." Among these, "dyspnea" and "inadequate food diet" in "clinical finding" increased clinical deterioration risk (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84), and "oxygen therapy" and "notification of physician" in "procedure" also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).ConclusionsThe study used SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.

Project description:IntroductionPrimary angle-closure glaucoma (PACG) is a leading cause of irreversible blindness globally, and the number of patients with PACG rises every year. Yet, there is a lack of knowledge about the clinical characteristics, therapeutic options and profile of patients with PACG in China. Hence, we design the China Glaucoma Treatment Pattern Study Ⅰ-Primary Angle-Closure Glaucoma (Ch-GTPⅠ). The objective of this paper is to describe the design and methodology of Ch-GTP. The aim of this study is to characterise the profile and trend associated with initial PACG treatment for the last 10 years in China.MethodsCh-GTPⅠ is a national multicentre retrospective observational study that will randomly sample from 50 hospitals throughout China. Over 7000 patient records hospitalised for initial PACG treatment from 2011 to 2020 will be selected randomly. The data from electronic medical records will be uploaded to an encrypted online platform that will receive and collate data from all collaborating hospitals. Data abstraction and monitoring will be performed in a standardised manner by trained statisticians to ensure consistency. Systematic data cleaning will also be conducted by statisticians to ensure data integrity before final data storage. The outcomes will include four broad categories: (1) demographics, (2) clinical characteristics, (3) therapeutic strategies and procedures and (4) early outcomes at discharge. The demographic characteristics and early outcomes will be summarised using descriptive statistics. Comparative analyses of characteristics and treatment pattern changing trends for different regions and years will be used to test for significant differences (t-test or Mann-Whitney U test).Ethics and disseminationThe collaborating hospitals obtained local approval based on a standard ethics application from internal ethics committees or acknowledged an existent ethics approval of the leading institution with approval from internal ethics committees. Due to the retrospective nature, written informed consent from patients was waived by the ethics committee. The results will be published in academic journals and presented at national and international academic conferences.Trial registration numberChiCTR2100054643.

Dataset Information

Gait pattern analysis and clinical subgroup identification: a retrospective observational study.

Publications

Gait pattern analysis and clinical subgroup identification: a retrospective observational study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets