Objective Criteria for Judging Walking Independence in a Convalescent Rehabilitation Ward for Hemiparetic Stroke：A Study Using Decision Tree Analysis / The Japanese Journal of Rehabilitation Medicine

Objective：This study aimed to clarify the objective criteria for assessing walking independence using cane in patients with stroke in the convalescent rehabilitation ward.Methods：Participants were in-patients with hemiparetic stroke who could walk with a cane, and they were categorized into the independent (ID) and supervised (SV) walking groups. Stroke impairment assessment set-motor for lower extremity (SIAS-LE), trunk control test (TCT), Berg balance scale (BBS), 10-m walking speed (m/s), and functional independence measure-cognitive (FIM-C) were assessed. ID and SV used the scores at the time of independent walking and at the discharge time, respectively. Additionally, falls after independence were investigated. Statistical analysis was performed using univariate analysis and decision tree analysis.Results：In total, 148 patients (ID：n=101, 68±13 years, SV：n=47, 79±12) were included. Significant differences were observed in walking speed, TCT score, BBS score, and FIM-C score between the groups. Moreover, walking speed, FIM-C score, and BBS score were selected in the decision tree analysis in this order and divided into five groups namely：1) walking speed ≥ 0.42 and FIM-C ≥ 22 (percentage of independent patients 97％/percentage of fallers 5％), 2.) walking speed ≥ 0.42, FIM-C＜22, and BBS ≥ 50 (100％/0％), 3.) walking speed ≥ 0.42, FIM-C＜22, and BBS＜50 (52％/8％), 4.) walking speed＜0.42, and BBS ≥ 28 (49％/28％), and 5) walking speed＜0.42 and BBS＜28 (0％/0％). The overall percentage of fallers was 8.9％, with group 4 having the highest number of fallers.Conclusion：Walking speed, FIM-C, and BBS, in decreasing order, were involved in walking independence. Patients with low walking speed were more likely to fall. Therefore, careful assessment of walking independence is particularly required.

Characteristics and Early Diagnosis of Gastric Cancer Discovered after Helicobacter pylori Eradication

The prevalence of gastric cancer after eradication (GCAE) is increasing dramatically in Japan. GCAE has characteristic features, and we must understand these features in endoscopic examinations. Differentiated cancer types were frequently found after eradication and included characteristic endoscopic features such as reddish depression (RD). However, benign RD can be difficult to distinguish from gastric cancer because of histological alterations in the surface structures (nonneoplastic epithelium or epithelium with low-grade atypia [ELA]) as well as multiple appearances of RD. Recently, we clarified similar alterations in genetic mutations between ELA and gastric cancer, suggesting that ELA is derived from gastric cancer. Clinically, submucosal invasive cancer was frequently found in patients after eradication therapy even if they received annual endoscopic surveillance. We can improve the diagnostic ability using image-enhanced endoscopy with magnified observation.

Characteristics and Early Diagnosis of Gastric Cancer Discovered after Helicobacter pylori Eradication

The prevalence of gastric cancer after eradication (GCAE) is increasing dramatically in Japan. GCAE has characteristic features, and we must understand these features in endoscopic examinations. Differentiated cancer types were frequently found after eradication and included characteristic endoscopic features such as reddish depression (RD). However, benign RD can be difficult to distinguish from gastric cancer because of histological alterations in the surface structures (nonneoplastic epithelium or epithelium with low-grade atypia [ELA]) as well as multiple appearances of RD. Recently, we clarified similar alterations in genetic mutations between ELA and gastric cancer, suggesting that ELA is derived from gastric cancer. Clinically, submucosal invasive cancer was frequently found in patients after eradication therapy even if they received annual endoscopic surveillance. We can improve the diagnostic ability using image-enhanced endoscopy with magnified observation.

Clinical Usefulness of Dual Red Imaging in Gastric Endoscopic Submucosal Dissection: A Pilot Study

Background/Aims@#Dual red imaging (DRI) is a new, image-enhanced endoscopy technique. There are few reports about the usefulness of DRI during gastric endoscopic submucosal dissection (ESD). We aimed to examine the usefulness of DRI in endoscopic hemostasis during gastric ESD. @*Methods@#We enrolled a total of 20 consecutive patients who underwent gastric ESD. Five endoscopists compared DRI with white light imaging (WLI) for the visibility of blood vessels and bleeding points while performing endoscopic hemostasis. @*Results@#The visibility of blood vessels was increased in 56% (19/34) of the cases, and the visibility of bleeding points was improved in 55% (11/20) of the cases with the use of DRI compared with the use of WLI. @*Conclusions@#DRI improved the visibility of blood vessels and bleeding points in cases with oozing bleeding, blood pooling around the bleeding points, and multiple bleeding points.

Validation and Recalibration of Charlson and Elixhauser Comorbidity Indices to Predict In-hospital Mortality in Hospitalized Patients in a Japanese Hospital-Based Administrative Database / 薬剤疫学

Objective: To validate and recalibrate Charlson and Elixhauser comorbidity indices (CCI and ECI, respectively) in a Japanese hospital-based administrative database.Methods: In this retrospective, cohort study, derivation and validation cohorts were developed to include all hospitalizations for patients aged ≥ 18 years at admission and discharged in 2015 or 2016, respectively, from an administrative database based on 287 hospitals. Seventeen CCI and 30 ECI conditions were identified using the International Classification of Diseases (ICD) -10 codes at admission or during the stay. Predictability for hospital death was evaluated using C statistics from multivariable logistic regression models including age, sex, and individual CCI/ECI conditions or the CCI/ECI score in the derivation cohort. After stepwise selection, weighted risk scores were re-assigned to each condition based on the odds ratios (CCI) or beta-coefficient (ECI), and these modified models were evaluated in the validation cohort.Results: The original CCI/ECI had good predictive abilities for hospital death: C statistics (95% confidence interval) for individual comorbidities and score models were 0.764 (0.762-0.765) and 0.731 (0.729-0.733) for CCI, and 0.783 (0.781-0.784) and 0.750 (0.748-0.752) for ECI, respectively. Modified CCI and ECI had 13 and 27 conditions, respectively, but maintained comparable predictive abilities: C statistics for modified individual comorbidities and score models were 0.761 (0.759-0.763) and 0.759 (0.757-0.760) for CCI, and 0.784 (0.782-0.785) and 0.783 (0.781-0.785) for ECI, respectively.Conclusions: The original and modified CCI/ECI models, with reduced numbers of conditions, had sufficient and comparable predictive abilities for hospital death and can be used in future studies using this administrative database.

Validation and Recalibration of Charlson and Elixhauser Comorbidity Indices to Predict In-hospital Mortality in Hospitalized Patients in a Japanese Hospital-Based Administrative Database / 薬剤疫学

Objective: To validate and recalibrate Charlson and Elixhauser comorbidity indices (CCI and ECI, respectively) in a Japanese hospital-based administrative database.Methods: In this retrospective, cohort study, derivation and validation cohorts were developed to include all hospitalizations for patients aged ≥ 18 years at admission and discharged in 2015 or 2016, respectively, from an administrative database based on 287 hospitals. Seventeen CCI and 30 ECI conditions were identified using the International Classification of Diseases (ICD) -10 codes at admission or during the stay. Predictability for hospital death was evaluated using C statistics from multivariable logistic regression models including age, sex, and individual CCI/ECI conditions or the CCI/ECI score in the derivation cohort. After stepwise selection, weighted risk scores were re-assigned to each condition based on the odds ratios (CCI) or beta-coefficient (ECI), and these modified models were evaluated in the validation cohort.Results: The original CCI/ECI had good predictive abilities for hospital death: C statistics (95% confidence interval) for individual comorbidities and score models were 0.764 (0.762-0.765) and 0.731 (0.729-0.733) for CCI, and 0.783 (0.781-0.784) and 0.750 (0.748-0.752) for ECI, respectively. Modified CCI and ECI had 13 and 27 conditions, respectively, but maintained comparable predictive abilities: C statistics for modified individual comorbidities and score models were 0.761 (0.759-0.763) and 0.759 (0.757-0.760) for CCI, and 0.784 (0.782-0.785) and 0.783 (0.781-0.785) for ECI, respectively.Conclusions: The original and modified CCI/ECI models, with reduced numbers of conditions, had sufficient and comparable predictive abilities for hospital death and can be used in future studies using this administrative database.

Validation and Recalibration of Charlson and Elixhauser Comorbidity Indices Based on Data From a Japanese Insurance Claims Database / 薬剤疫学

Objective: The Charlson and Elixhauser comorbidity indices (CCI and ECI, respectively) are widely used to study comorbid conditions but these indices have not been validated in Japanese datasets. In this study, our objective was to validate and recalibrate CCI and ECI in a Japanese insurance claims database.Methods: All hospitalizations for patients aged≥18 years discharged between January 2011 and December 2016 were randomly allocated to derivation and validation cohorts. Predictability for hospital death and re-admission was evaluated using C statistics from multivariable logistic regression models including age, sex, and individual CCI/ECI conditions at admission month or the derived score in the derivation cohort. After stepwise variable selection, weighted risk scores for each condition were re-assigned using odds ratios (CCI) or beta coefficients (ECI). The modified models were evaluated in the validation cohort.Results: The original CCI/ECI had good discriminatory power for hospital death: C statistics (95％ confidence interval) for individual comorbidities and score models were 0.845 (0.835-0.855) and 0.823 (0.813-0.834) for CCI, and 0.839 (0.828-0.850) and 0.801 (0.790-0.812) for ECI, respectively. Modified CCI and ECI had reduced numbers of comorbidities (17 to 10 and 30 to 21, respectively) but maintained comparable discriminatory abilities: C statistics for modified individual comorbidities and score models were 0.843 (0.833-0.854) and 0.838 (0.827-0.848) for CCI, and 0.840 (0.828-0.852) and 0.839 (0.827-0.851) for ECI, respectively.Conclusions: The original and modified models showed comparable discriminatory abilities and both can be used in future studies using insurance claims databases.

Validation and Recalibration of Charlson and Elixhauser Comorbidity Indices Based on Data From a Japanese Insurance Claims Database / 薬剤疫学

Objective: The Charlson and Elixhauser comorbidity indices (CCI and ECI, respectively) are widely used to study comorbid conditions but these indices have not been validated in Japanese datasets. In this study, our objective was to validate and recalibrate CCI and ECI in a Japanese insurance claims database.Methods: All hospitalizations for patients aged≥18 years discharged between January 2011 and December 2016 were randomly allocated to derivation and validation cohorts. Predictability for hospital death and re-admission was evaluated using C statistics from multivariable logistic regression models including age, sex, and individual CCI/ECI conditions at admission month or the derived score in the derivation cohort. After stepwise variable selection, weighted risk scores for each condition were re-assigned using odds ratios (CCI) or beta coefficients (ECI). The modified models were evaluated in the validation cohort.Results: The original CCI/ECI had good discriminatory power for hospital death: C statistics (95％ confidence interval) for individual comorbidities and score models were 0.845 (0.835-0.855) and 0.823 (0.813-0.834) for CCI, and 0.839 (0.828-0.850) and 0.801 (0.790-0.812) for ECI, respectively. Modified CCI and ECI had reduced numbers of comorbidities (17 to 10 and 30 to 21, respectively) but maintained comparable discriminatory abilities: C statistics for modified individual comorbidities and score models were 0.843 (0.833-0.854) and 0.838 (0.827-0.848) for CCI, and 0.840 (0.828-0.852) and 0.839 (0.827-0.851) for ECI, respectively.Conclusions: The original and modified models showed comparable discriminatory abilities and both can be used in future studies using insurance claims databases.