Effects of small community walking intervention on physical activity, well-being, and social capital among older patients with cardiovascular disease in the maintenance phase: a randomized controlled trial.

[Purpose] Older patients with cardiovascular disease should increase their physical activity and prioritize positive psychological and social approaches in the maintenance phase of their cardiac rehabilitation. This study aimed to clarify the effect of small community walking on physical activity, well-being, and social capital in older patients with cardiovascular disease in the maintenance phase. [Participants and Methods] We conducted a multicenter study in Kumamoto, Japan. We randomly divided 55 patients with cardiovascular disease into two groups: small community walking and walking alone. For three months, a registered cardiac rehabilitation instructor provided walking guidance to both groups using a wearable device. We measured physical activity, social capital, and subjective happiness before and after the intervention. [Results] Results revealed a statistically significant main effect of time on physical activity and social participation. In the subjective happiness scale, there was an association between group and time. [Conclusion] Our results suggest that walking guidance using a wearable device was beneficial in improving overall physical activity, regardless of whether the individual did small community walking or walking alone. Furthermore, small community walking intervention may effectively enhance well-being. The relationship between physical activity and social participation needs to be further investigated.

A machine learning-based scoring system and ten factors associated with hip fracture occurrence in the elderly.

Hip fractures are fragility fractures frequently seen in persons over 80-years-old. Although various factors, including decreased bone mineral density and a history of falls, are reported as hip fracture risks, few large-scale studies have confirmed their relevance to individuals older than 80, and tools to assess contributions of various risks to fracture development and the degree of risk are lacking. We recruited 1395 fresh hip fracture patients and 1075 controls without hip fractures and comprehensively evaluated various reported risk factors and their association with hip fracture development. We initially constructed a predictive model using Extreme Gradient Boosting (XGBoost), a machine learning algorithm, incorporating all 40 variables and evaluated the model's performance using the area under the receiver operating characteristic curve (AUC), yielding a value of 0.87. We also employed SHapley Additive exPlanation (SHAP) values to evaluate each feature importance and ranked the top 20. We then used a stepwise selection method to determine key factors sequentially until the AUC reached a plateau nearly equal to that of all variables and identified the top 10 sufficient to evaluate hip fracture risk. For each, we determined the cutoff value for hip fracture occurrence and calculated scores of each variable based on the respective feature importance. Individual scores were: serum 25(OH)D levels (<10 ng/ml, score 7), femoral neck T-score (<-3, score 5), Barthel index score (<100, score 3), maximal handgrip strength (<18 kg, score 3), GLFS-25 score (≥24, score 2), number of falls in previous 12 months (≥3, score 2), serum IGF-1 levels (<50 ng/ml, score 2), cups of tea/day (≥5, score -2), use of anti-osteoporosis drugs (yes, score -2), and BMI (<18.5 kg/m2, score 1). Using these scores, we performed receiver operating characteristic (ROC) analysis and the resultant optimal cutoff value was 7, with a specificity of 0.78, sensitivity of 0.75, and AUC of 0.85. These ten factors and the scoring system may represent tools useful to predict hip fracture.

An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records.

BACKGROUND: Owing to the linguistic situation, Japanese natural language processing (NLP) requires morphological analyses for word segmentation using dictionary techniques. OBJECTIVE: We aimed to clarify whether it can be substituted with an open-end discovery-based NLP (OD-NLP), which does not use any dictionary techniques. METHODS: Clinical texts at the first medical visit were collected for comparison of OD-NLP with word dictionary-based-NLP (WD-NLP). Topics were generated in each document using a topic model, which later corresponded to the respective diseases determined in International Statistical Classification of Diseases and Related Health Problems 10 revision. The prediction accuracy and expressivity of each disease were examined in equivalent number of entities/words after filtration with either term frequency and inverse document frequency (TF-IDF) or dominance value (DMV). RESULTS: In documents from 10,520 observed patients, 169,913 entities and 44,758 words were segmented using OD-NLP and WD-NLP, simultaneously. Without filtering, accuracy and recall levels were low, and there was no difference in the harmonic mean of the F-measure between NLPs. However, physicians reported OD-NLP contained more meaningful words than WD-NLP. When datasets were created in an equivalent number of entities/words with TF-IDF, F-measure in OD-NLP was higher than WD-NLP at lower thresholds. When the threshold increased, the number of datasets created decreased, resulting in increased values of F-measure, although the differences disappeared. Two datasets near the maximum threshold showing differences in F-measure were examined whether their topics were associated with diseases. The results showed that more diseases were found in OD-NLP at lower thresholds, indicating that the topics described characteristics of diseases. The superiority remained as much as that of TF-IDF when filtration was changed to DMV. CONCLUSION: The current findings prefer the use of OD-NLP to express characteristics of diseases from Japanese clinical texts and may help in the construction of document summaries and retrieval in clinical settings.

Increased numbers of pre-operative circulating monocytes predict risk of developing cardiac surgery-associated acute kidney injury in conditions requiring cardio pulmonary bypass.

BACKGROUND: Evaluating patients' risk for acute kidney injury (AKI) is crucial for positive outcomes following cardiac surgery. Our aims were first to select candidate risk factors from pre- or intra-operative real-world parameters collected from routine medical care and then evaluate potential associations between those parameters and risk of onset of post-operative cardiac surgery-associated AKI (CSA-AKI). METHOD: We conducted two cohort studies in Japan. The first was a single-center prospective cohort study (n = 145) to assess potential association between 115 clinical parameters collected from routine medical care and CSA-AKI (≥ Stage1) risk in the population of patients undergoing cardiac surgery involving cardiopulmonary bypass (CPB). To select candidate risk factors, we employed random forest analysis and applied survival analyses to evaluate association strength. In a second retrospective cohort study, we targeted patients undergoing cardiac surgery with CPB (n = 619) and evaluated potential positive associations between CSA-AKI incidence and risk factors suggested by the first cohort study. RESULTS: Variable selection analysis revealed that parameters in clinical categories such as circulating inflammatory cells, CPB-related parameters, ventilation, or aging were potential CSA-AKI risk factors. Survival analyses revealed that increased counts of pre-operative circulating monocytes and neutrophils were associated with CSA-AKI incidence. Finally, in the second cohort study, we found that increased pre-operative circulating monocyte counts were associated with increased CSA-AKI incidence. CONCLUSIONS: Circulating monocyte counts in the pre-operative state are associated with increased risk of CSA-AKI development. This finding may be useful in stratifying patients for risk of developing CSA-AKI in routine clinical practice.

Questionnaire Survey Regarding Troubles and Concerns Related to Clinical Research Based on the Clinical Trial Act for Clinicians and Academics.

The Clinical Trial Act came into force in 2018 in Japan. A questionnaire survey was conducted with personnel at Kumamoto University Hospital engaged in research and development, to explore their perceptions of troubles and concerns about clinical research related to the Clinical Trial Act. We collected 127 comments about troubles and 149 about concerns. Text mining (co-occurrence network and hierarchical cluster analysis) was used to extract the characteristics or tendencies in these comments. The analysis extracted 18 key terms for troubles and 21 for concerns. Most troubles and concerns had to do with concrete examples of clinical research or protocols and biostatistics information. Our results emphasized the importance of clinical research support organizations, and suggested that appropriate workshops and information covering specific situations are necessary to perform clinical research under the new regime.

Associations of cardiovascular risk factors with survival outcomes in a cancer registration: Findings from the KUMAMON registry.

ABSTRACT: Although the relationship between cardiovascular diseases and malignant diseases has recently attracted attention, the associations of cardiovascular risk factors and clinical outcomes in cancer patients remain to be elucidated. We performed a retrospective, observational study that explored the clinical outcomes of patients with cancer or with a history of cancer.We enrolled 30,706 consecutive adult cancer patients from Kumamoto University Hospital. We investigated mortality and morbidity, including cardiovascular conditions (dyslipidemia [DL]/diabetes mellitus [DM]/hypertension [HT]). The primary endpoint was all-cause mortality.Of the enrolled patients, 9032 patients (29.4%) died within the follow-up period. The Kaplan-Meier analysis demonstrated that in the groups classified according to the number of DL/DM/HT (LDH) factors, the LDH1 and LDH2 groups had a significantly higher probability of the primary endpoint than the LDH0 group (P < .001 and P < .001, respectively), whereas there were no significant differences between the LDH0 group and LDH3 group (P = .963). Univariate Cox proportional hazards regression analyses of mortality complemented by the multiple imputation method including various factors demonstrated that the presence of DL in cancer patients was a significant negative predictor of mortality (hazard ratio = 0.79, P < .01).The all-cause mortality rate did not always increase as the number of LDH factors increased. The present study revealed that the presence of DL is a negative risk factor for all-cause mortality in cancer patients.

Development of an algorithm for assessing fall risk in a Japanese inpatient population.

Falling is a representative incident in hospitalization and can cause serious complications. In this study, we constructed an algorithm that nurses can use to easily recognize essential fall risk factors and appropriately perform an assessment. A total of 56,911 inpatients (non-fall, 56,673; fall; 238) hospitalized between October 2017 and September 2018 were used for the training dataset. Correlation coefficients, multivariable logistic regression analysis, and decision tree analysis were performed using 36 fall risk factors identified from inpatients. An algorithm was generated combining nine essential fall risk factors (delirium, fall history, use of a walking aid, stagger, impaired judgment/comprehension, muscle weakness of the lower limbs, night urination, use of sleeping drug, and presence of infusion route/tube). Moreover, fall risk level was conveniently classified into four groups (extra-high, high, moderate, and low) according to the priority of fall risk. Finally, we confirmed the reliability of the algorithm using a validation dataset that comprised 57,929 inpatients (non-fall, 57,695; fall, 234) hospitalized between October 2018 and September 2019. Using the newly created algorithm, clinical staff including nurses may be able to appropriately evaluate fall risk level and provide preventive interventions for individual inpatients.

Pre-hospital Diagnosis of Large Vessel Occlusion in Patients Being Transported by Helicopter.

BACKGROUND: During the helicopter transportation of patients suspected of large vessel occlusion (LVO), an accurate and rapid decision-making process is required. AIMS: We attempted to create an algorithm for the pre-hospital diagnosis of the presence of LVO in patients suspected of stroke using data from patients transported urgently by helicopter. METHODS: One hundred and sixty-five patients transported by helicopter were divided into two subgroups: a training dataset and a validation dataset. We extracted clinical information obtained on site, the unadjusted score of the National Institutes of Health Stroke Scale, and previously reported pre-hospital scales as an LVO screen. On the basis of the analyses of these factors, an algorithm was devised to predict the presence of LVO and its predictive accuracy was evaluated using the validation dataset. RESULTS: Ischemic stroke with LVO was diagnosed in 36 out of 121 cases (29.8%) in the training dataset and in 10 out of 44 cases (22.7%) in the validation dataset. Combining five factors (conjugate deviation, upper limb paresis, atrial fibrillation, Japan Coma Scale ≥ 200, and systolic blood pressure ≥ 180), an algorithm was created to classify cases into six groups with different likelihoods of LVO presence. The algorithm predicted correctly 6 out of 10 cases in the validation dataset. Furthermore, it definitively ruled out 17 out of 34 cases in the validation dataset. CONCLUSIONS: Using the newly created algorithm, emergency staff could easily and accurately distinguish patients suitable for urgent endovascular thrombectomy from patients with non-LVO or stroke mimics.

A Randomized Phase 2 Trial of Antibiotic Prophylaxis Versus No Intervention for Muscle Biopsy in A Neurology Department.

Muscle biopsy can be used to confirm the diagnosis of neuromuscular diseases. However, it is unclear whether antibiotic prophylaxis prior to muscle biopsy is needed to prevent surgical site infection (SSI). We are conducting a phase 2, single-center, open-labeled, prospective randomized trial to clarify the need for antibiotic prophylaxis in patients at low risk for SSI undergoing muscle biopsy. Patients will be randomized to an antibiotic prophylaxis group or a control group, and the incidence of SSI will be compared between the groups. Our findings will clarify the need for antibiotic prophylaxis in this patient population.

The jPOST environment: an integrated proteomics data repository and database.

Rapid progress is being made in mass spectrometry (MS)-based proteomics, yielding an increasing number of larger datasets with higher quality and higher throughput. To integrate proteomics datasets generated from various projects and institutions, we launched a project named jPOST (Japan ProteOme STandard Repository/Database, https://jpostdb.org/) in 2015. Its proteomics data repository, jPOSTrepo, began operations in 2016 and has accepted more than 10 TB of MS-based proteomics datasets in the past two years. In addition, we have developed a new proteomics database named jPOSTdb in which the published raw datasets in jPOSTrepo are reanalyzed using standardized protocol. jPOSTdb provides viewers showing the frequency of detected post-translational modifications, the co-occurrence of phosphorylation sites on a peptide and peptide sharing among proteoforms. jPOSTdb also provides basic statistical analysis tools to compare proteomics datasets.