Usefulness of a pluralistic approach in sentinel surveillance: seasonal influenza activity based on case counts per sentinel site in the National Epidemiological Surveillance of Infectious Diseases Program and test counts, case counts, and test positivity from the National Hospital Organization.

In Japan, based on the National Epidemiological Surveillance of Infectious Diseases (NESID) Program, influenza cases from ~5,000 sentinel sites are monitored weekly as part of influenza surveillance (as number of influenza cases/sentinel site). One limitation is that the number of influenza tests conducted is not reported. Separately, the National Hospital Organization (NHO), with ~140 hospitals, routinely publishes three indicators: number of influenza tests, influenza-positive case counts, and test positivity. We used NESID and NHO data from April 2011 to June 2022 to assess the usefulness of multiple indicators to monitor influenza activity. Temporal trends of the NHO and NESID indicators were similar, and NHO indicator levels well-correlated with those of the NESID indicator. Influenza positivity in the NHO data, however, showed an earlier rise and peak time compared to the NESID indicator. Importantly, through the non-epidemic summer periods and the coronavirus disease 2019 pandemic, a sizable number of influenza tests continued to be done at NHO hospitals, with results showing considerably low case counts and test positivity. These data show that a relatively small number of sentinel sites is sufficient to monitor influenza activity nationally, and, that utilizing multiple indicators can increase our confidence in situational awareness and data interpretations.

Differences in asthma-related outcomes by anti-IL-5 biologics, omalizumab, and dupilumab based on blood eosinophil counts.

BACKGROUND: Selecting optimal biologics based on type 2 biomarkers has been of interest in severe asthma treatment. However, few direct biomarker stratification-based comparisons have been made. OBJECTIVE: To compare the effectiveness of anti-IL-5 (mepolizumab, benralizumab), omalizumab, and dupilumab in reducing the number of hospitalizations from asthma and exacerbations across all and eosinophil-stratified subgroups. METHODS: A retrospective cohort study using the National Hospital Organization database (2016-2020) was performed. Asthmatic patients using biologics were selected, and the baseline backgrounds of the groups were balanced using inverse probability treatment weighting for propensity scores. Weighted rate ratios (RRs) were obtained using a Poisson regression model. RESULTS: Among the 320 patients with asthma using biologics, 205 (64.1%), 75 (23.4%), and 40 (12.5%) were categorized into the anti-IL-5, omalizumab, and dupilumab groups, respectively. After weighting, there were 47.1, 30.0, and 62.6 hospitalizations per 100 person-years [omalizumab vs. anti-IL-5: weighted RR, 0.61 (0.34-1.08); dupilumab vs. anti-IL-5: 1.48 (0.81-2.72)], and 117.0, 134.6, and 287.3 exacerbations per 100 person-years [omalizumab vs. anti-IL-5: 1.13 (0.83-1.54); dupilumab vs. anti-IL-5: 2.69 (1.91-3.78)] in these respective groups. In patients with eosinophil of ≥ 300/µL, the dupilumab group had more exacerbations compared with the anti-IL-5 group [weighted RR, 2.85 (1.82-4.46)]. In patients with eosinophil of < 300/µL, the omalizumab group had fewer hospitalizations compared with the anti-IL-5 group [weighted RR, 0.32 (0.13-0.51)]. CONCLUSION: Anti-IL-5 biologics may be more effective than dupilumab in patients with high blood eosinophil counts, while less effective than omalizumab in patients with low eosinophil counts.

Microbiological findings in patients with community-acquired pneumonia: An analysis using the National Hospital Organization Clinical Data Archives.

Although large-scale administrative databases may be useful for studies of infectious diseases, conventional databases lack microbiological data. To illustrate the applicability of the National Hospital Organization Clinical Data Archives, a novel database of electronic medical records in Japan, we conducted a descriptive study of the microbiological findings in patients with community-acquired pneumonia using the database. We identified patients aged ≥18 years who were hospitalized for community-acquired bacterial pneumonia between April 2016 and March 2019. We evaluated the results of bacterial culture and antibacterial susceptibility of specimens obtained on the first day of hospitalization, in addition to patient characteristics, diagnosis codes, and intravenous antibiotics administered. The analysis identified 2200 eligible patients from 15 hospitals. Sulbactam-ampicillin was the most frequently used initial antibiotic (32 %), followed by ceftriaxone (22 %) and tazobactam-piperacillin (19 %). Overall, 56 %, 95 %, 56 %, and 73 % of patients with pathogen-specific diagnosis codes in the database for Streptococcus pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, and Pseudomonas aeruginosa, respectively, also tested positive for the corresponding pathogen in their sputum or blood cultures. Antibacterial susceptibilities were consistent with a previous report from Japan; 81 % of S. pneumoniae cases were resistant to azithromycin, and 48 % of H. influenzae cases were resistant to ampicillin. These microbiological characteristics warrant the future use of this database for detailed real-world research on infectious diseases.

Bevacizumab-induced proteinuria and its association with antihypertensive drugs: A retrospective cohort study using a Japanese administrative database.

Proteinuria is a major side-effect of the anti-tumor drug bevacizumab, although its incidence and risk factors in the real world are still unclear. Although renin-angiotensin-aldosterone system inhibitors are used clinically to prevent proteinuria, their efficacy remains unclear. The aim of the present study was to reveal the incidence and risk factors of bevacizumab-induced proteinuria and examine the effectiveness of antihypertensive drugs in preventing proteinuria. We conducted a retrospective cohort study using the National Hospital Organization Clinical Data Archives and Medical Information Analysis Databank. Hospitalized patients who received bevacizumab between January 1, 2016, and June 30, 2019, were included. The study outcome was proteinuria within 12 months of bevacizumab administration. Patient characteristics, laboratory tests, and medications were compared between patients with and without proteinuria using multivariable logistic regression analysis. Among the 2,458 patients, 27% developed proteinuria after bevacizumab administration. Nursing dependence (odds ratio [OR], 2.40; 95% confidence interval [CI], 1.89-3.05; P<0.001) and systolic blood pressure ≥140 mmHg (OR, 1.44; 95% CI, 1.17-1.79; P<0.001) were identified as risk factors. Patients with an estimated glomerular filtration rate (eGFR) of 60-89, 45-59, and <45 mL/min/1.73 m2 had 29.7%, 76.8%, and 66.0% higher odds of proteinuria, respectively, than those with an eGFR ≥90 mL/min/1.73 m2. No significant relationship was observed between antihypertensive drugs and the occurrence of proteinuria. More patients may suffer from proteinuria after bevacizumab administration than previously reported. Nursing dependence and systolic blood pressure are predictive risk factors for bevacizumab-induced proteinuria. Patients at risk of proteinuria should be closely monitored.

Risk of Underlying Diseases and Effectiveness of Drugs on COVID-19 Inpatients Assessed Using Medical Claims in Japan: Retrospective Observational Study.

Background: Results of earlier studies have demonstrated underlying diseases such as cancer, diabetes mellitus, immunodeficiency, hypertension and heart failure to be risk factors for severe outcomes and mortality. Furthermore, clinical trials have shown that drugs such as antiviral drugs, antibody cocktails, steroids and anti-inflammatory drugs can be expected to prevent severe COVID-19 outcomes and death. Methods: This study, using inpatient records from the Medical Information Analysis Databank covering national hospital organizations in Japan, was conducted to evaluate the effects of underlying diseases and/or administered drugs on mortality. Subjects were all inpatients receiving oxygen administration and inpatients using respiratory ventilators, categorized by three age classes: all ages, patients 65 years old or older, and patients younger than 65 years old. We used logistic regression to analyze outcomes for underlying diseases, administered drugs, age, sex, the proportion of the mutated strains, and vaccine coverage. Results: Patients with hypertension, except for younger inpatients, have a lower risk of mortality (estimated coefficient 0.67 among all inpatients (p < 0.01): 0.77 among inpatients with oxygen therapy (p = 0.02) and 0.57 among inpatients with respiratory ventilation w (p = 0.01)). Except for younger inpatients, antibody cocktail (casirivimab/imdevimab or sotrovimab) administration was associated with a higher probability of survival (estimated coefficient 0.27 among all inpatients (p < 0.01)). It raised the survival probability consistently, although other drugs might have reduced the probability of survival. Conclusion: These findings suggest that antiviral drugs (remdesivir, estimated coefficient 1.44 (p < 0.01)), steroids (dexamethasone, estimated coefficient 1.85 (p < 0.01)), and anti-inflammatory drugs (baricitinib, estimated coefficient 1.62 (p < 0.01), and tocilizumab, estimated coefficient 2.73 (p < 0.01)) might not contribute to survival. These results have not been reported from earlier studies. More sophisticated estimation procedures, such as treatment effect models, are necessary to obtain conclusive results.

Implementation of Rehabilitation and Patient Outcomes During the Initial COVID-19 Pandemic.

Objectives: This study investigated the impact of the initial outbreak of coronavirus disease (COVID-19) on rehabilitation and functional outcomes of patients in Japanese hospitals. Methods: The study subjects were hospitals belonging to Japan's National Hospital Organization that provided inpatient care for patients with coronavirus COVID-19 during March-May 2020. We specifically focused on patients who were hospitalized for acute diseases, such as stroke, hip fracture, acute myocardial infarction, congestive heart failure, or chronic obstructive pulmonary disease, and received rehabilitation during hospitalization. Data were sourced from Japanese administrative data. The primary outcome was rehabilitation provision time in the target hospitals. The secondary outcomes were patient outcomes: rehabilitation participation time, length of hospital stay, 30-day readmission rate, and improvement of activities of daily living. Interrupted time series analysis was performed to evaluate the trend of rehabilitation provision time. Patient outcomes were compared with those for 2019. Results: The rehabilitation provision time for outpatients declined by 62% during the pandemic, while that for inpatients declined temporarily, and then increased. Compared with 2019 outcomes, rehabilitation participation time was longer and hospital stay length was shorter for stroke and hip-fracture patients, the 30-day readmission rate was increased for hip-fracture patients, and improvement of activities in daily living was less for patients with congestive heart failure who were totally dependent at admission. Other outcomes did not change. Conclusions: The findings suggest that during the initial COVID-19 pandemic, resources for rehabilitation were quickly reallocated to inpatient care, and the impact on inpatient outcomes was minimized.

Existing Data Sources for Clinical Epidemiology: Database of the National Hospital Organization in Japan.

This review introduces the National Hospital Organization (NHO) database in Japan. The NHO has maintained two databases through a system of data collection from 140 hospitals in the NHO. National Hospital Organization Clinical Data Archives (NCDA) is collecting clinical information in real time from the electronic medical records since January 2016, and Medical Information Analysis (MIA) databank is collecting daily insurance claims data since April 2010. The NHO database covers more than 8 million patients in 140 hospitals throughout Japan. The database consists of the information of patient profiles, hospital admission and discharge, diagnosis with ICD-10 codes, text data from medical chart, daily health insurance claims such as medical procedures, medications or surgeries, vital signs and laboratory data, and so on. The NHO database includes a wide variety of diseases and settings, including acute, chronic and intractable diseases, emergency medical services, disaster medicine, response to emerging infectious disease outbreaks, medical care according to health policies such as psychiatry, tuberculosis, or muscular dystrophy, and health systems in sparsely populated non-urban areas. Among several common diseases, the database has representativeness in terms of age distribution compared with the Patient Survey 2017 by the Ministry of Health, Labour and Welfare. Interested researchers can contact (700-dbproject@mail.hosp.go.jp) the NHO database division to obtain more information about the NHO database for utilization.

Aspiration pneumonia and anesthesia techniques in hip fracture surgery in elderly patients: A retrospective cohort study using administrative data.

Purpose: Aspiration pneumonia is a critical issue. General anesthesia may suppress the airway's protective reflex. However, aspiration pneumonia is also observed in patients who undergo hip fracture surgery under spinal anesthesia. The aim of this study was to investigate the relationship between anesthesia methods and aspiration pneumonia as well as the predictive factors of aspiration pneumonia in elderly patients undergoing hip fracture surgery. Methods: The medical records of 19,809 patients aged ≥60 years who underwent hip fracture surgery under general or spinal anesthesia were reviewed. After propensity score matching, the anesthesia methods affecting the occurrences of aspiration pneumonia and other complications were investigated via logistic regression and instrumental variable analyses. Predictive factors of aspiration pneumonia were also investigated in all subjects using a multivariable logistic regression analysis. Results: Among the 11,673 general anesthesia patients and 8136 spinal anesthesia patients, aspiration pneumonia occurred in 356 patients (1.8%). Post-propensity score matching the incidences of aspiration pneumonia with general and spinal anesthesia were 1.8% and 1.5%, respectively (p = 0.158); other pulmonary complications were 1.5% and 1.5%, respectively (p = 0.893); and the mortality rates were 1.4% and 1.2%, respectively (p = 0.219). The predictive factors of aspiration pneumonia were advanced age, male sex, lean body, cerebrovascular disease, dementia, and dependency for activities of daily living (eating). Conclusion: Spinal and general anesthesia showed similar incidences of aspiration pneumonia in elderly hip fracture surgery. Regardless of the anesthesia method, great care should be taken, especially in elderly patients with the identified predictive factors.

Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan.

Automated summarization of clinical texts can reduce the burden of medical professionals. "Discharge summaries" are one promising application of the summarization, because they can be generated from daily inpatient records. Our preliminary experiment suggests that 20-31% of the descriptions in discharge summaries overlap with the content of the inpatient records. However, it remains unclear how the summaries should be generated from the unstructured source. To decompose the physician's summarization process, this study aimed to identify the optimal granularity in summarization. We first defined three types of summarization units with different granularities to compare the performance of the discharge summary generation: whole sentences, clinical segments, and clauses. We defined clinical segments in this study, aiming to express the smallest medically meaningful concepts. To obtain the clinical segments, it was necessary to automatically split the texts in the first stage of the pipeline. Accordingly, we compared rule-based methods and a machine learning method, and the latter outperformed the formers with an F1 score of 0.846 in the splitting task. Next, we experimentally measured the accuracy of extractive summarization using the three types of units, based on the ROUGE-1 metric, on a multi-institutional national archive of health records in Japan. The measured accuracies of extractive summarization using whole sentences, clinical segments, and clauses were 31.91, 36.15, and 25.18, respectively. We found that the clinical segments yielded higher accuracy than sentences and clauses. This result indicates that summarization of inpatient records demands finer granularity than sentence-oriented processing. Although we used only Japanese health records, it can be interpreted as follows: physicians extract "concepts of medical significance" from patient records and recombine them in new contexts when summarizing chronological clinical records, rather than simply copying and pasting topic sentences. This observation suggests that a discharge summary is created by higher-order information processing over concepts on sub-sentence level, which may guide future research in this field.

Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?

Medical professionals have been burdened by clerical work, and artificial intelligence may efficiently support physicians by generating clinical summaries. However, whether hospital discharge summaries can be generated automatically from inpatient records stored in electronic health records remains unclear. Therefore, this study investigated the sources of information in discharge summaries. First, the discharge summaries were automatically split into fine-grained segments, such as those representing medical expressions, using a machine learning model from a previous study. Second, these segments in the discharge summaries that did not originate from inpatient records were filtered out. This was performed by calculating the n-gram overlap between inpatient records and discharge summaries. The final source origin decision was made manually. Finally, to reveal the specific sources (e.g., referral documents, prescriptions, and physician's memory) from which the segments originated, they were manually classified by consulting medical professionals. For further and deeper analysis, this study designed and annotated clinical role labels that represent the subjectivity of the expressions and builds a machine learning model to assign them automatically. The analysis results revealed the following: First, 39% of the information in the discharge summary originated from external sources other than inpatient records. Second, patient's past clinical records constituted 43%, and patient referral documents constituted 18% of the expressions derived from external sources. Third, 11% of the missing information was not derived from any documents. These are possibly derived from physicians' memories or reasoning. According to these results, end-to-end summarization using machine learning is considered infeasible. Machine summarization with an assisted post-editing process is the best fit for this problem domain.

Validity of a model using routinely collected data for identifying infections following gastric, colon, and liver cancer surgeries.

PURPOSE: Validating outcome measures is a prerequisite for using administrative databases for comparative effectiveness research. Although the Japanese Diagnosis Procedure Combination database is widely used in surgical studies, the outcome measure for postsurgical infection has not been validated. We developed a model to identify postsurgical infections using the routinely collected Diagnosis Procedure Combination data. METHODS: We retrospectively identified inpatients who underwent surgery for gastric, colon, or liver cancer between April 2016 and March 2018 at four hospitals. Chart reviews were conducted to identify postsurgical infections. We used bootstrap analysis with backwards variable elimination to select independent variables from routinely collected diagnosis and procedure data. Selected variables were used to create a score predicting the chart review-identified infections, and the performance of the score was tested. RESULTS: Among the 746 eligible patients, 96 patients (13%) had postoperative infections. Three variables were identified as predictors: diagnosis of infectious disease recorded as a complication arising after admission, addition of an intravenous antibiotic, and bacterial microscopy or culture. The prediction model had a C-statistic of 0.885 and pseudo-R2 of 0.358. A cut-off of one point of the score showed a sensitivity of 92% and specificity of 72%, and a cut-off of two points showed a sensitivity of 75% and specificity of 91%. CONCLUSIONS: Our model using routinely collected administrative data accurately identified postoperative infections. Further external validation would lead to the application of the model for research using administrative databases.

Prognosis of patients with liver cirrhosis: A multi-center retrospective observational study.

AIM: Despite advances in the management of liver diseases and changes in the etiology of cirrhosis, few studies have updated the prognosis of cirrhosis. This study aimed to clarify the recent prognosis of cirrhosis and identify risk factors for death. METHODS: In this retrospective observational study by the Hepatic Disease Network of the National Hospital Organization in Japan, chart reviews were performed to follow patients with cirrhosis beginning in 2011. We conducted Kaplan-Meier survival time analyses stratified by Child-Pugh classification and albumin-bilirubin grade. Cox regression analysis was used to identify risk factors for death. RESULTS: We identified 444 eligible patients from 25 hospitals, including 303 (68%), 110 (25%), and 31 (7%) patients with Child-Pugh classes A, B, and C, respectively. Hepatitis C virus infection was the cause of cirrhosis for 63% of the patients. The 1-year and 5-year cumulative survival rates of patients with Child-Pugh classes A, B, and C were 90% and 61%, 78% and 42%, and 65% and 25%, respectively. The 1-year and 5-year cumulative survival rates of patients with albumin-bilirubin grades 1, 2, and 3 were 98% and 80%, 91% and 56%, and 58% and 23%, respectively. Cirrhosis classification (Child-Pugh and albumin-bilirubin), age, liver cancer, and untreated esophageal varices were associated with increased hazard of death. CONCLUSIONS: Little improvement was observed in the prognosis of cirrhosis compared with previous reports, and the prognosis of Child-Pugh class C cirrhosis remained poor. Untreated esophageal varices were identified as a risk factor for death.

De-identifying free text of Japanese electronic health records.

BACKGROUND: Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identification is necessary to protect personal information, automatic de-identification of Japanese language EHRs has not been studied sufficiently. This study was conducted to raise de-identification performance for Japanese EHRs through classic machine learning, deep learning, and rule-based methods, depending on the dataset. RESULTS: Using three datasets, we implemented de-identification systems for Japanese EHRs and compared the de-identification performances found for rule-based, Conditional Random Fields (CRF), and Long-Short Term Memory (LSTM)-based methods. Gold standard tags for de-identification are annotated manually for age, hospital, person, sex, and time. We used different combinations of our datasets to train and evaluate our three methods. Our best F1-scores were 84.23, 68.19, and 81.67 points, respectively, for evaluations of the MedNLP dataset, a dummy EHR dataset that was virtually written by a medical doctor, and a Pathology Report dataset. Our LSTM-based method was the best performing, except for the MedNLP dataset. The rule-based method was best for the MedNLP dataset. The LSTM-based method achieved a good score of 83.07 points for this MedNLP dataset, which differs by 1.16 points from the best score obtained using the rule-based method. Results suggest that LSTM adapted well to different characteristics of our datasets. Our LSTM-based method performed better than our CRF-based method, yielding a 7.41 point F1-score, when applied to our Pathology Report dataset. This report is the first of study applying this LSTM-based method to any de-identification task of a Japanese EHR. CONCLUSIONS: Our LSTM-based machine learning method was able to extract named entities to be de-identified with better performance, in general, than that of our rule-based methods. However, machine learning methods are inadequate for processing expressions with low occurrence. Our future work will specifically examine the combination of LSTM and rule-based methods to achieve better performance. Our currently achieved level of performance is sufficiently higher than that of publicly available Japanese de-identification tools. Therefore, our system will be applied to actual de-identification tasks in hospitals.

Comparison of peripheral nerve block with local infiltration analgesia regarding walking ability after total knee replacement: A retrospective, propensity-score matched-pair cohort study.

PURPOSE: It is unclear whether perioperative analgesic techniques affect the functional outcome of total knee replacement (TKR). We investigated the effects of peripheral nerve block (PNB) and local infiltration (LI) on walking ability after TKR. METHODS: The medical records of 7143 patients who underwent TKR using general anesthesia with PNB or LI techniques were reviewed. Factors affecting independence and/or improvement of walking after surgery were investigated using multivariate regression analysis. To adjust for baseline differences and minimize selection bias for the chosen analgesic technique, patients were matched by propensity scores. RESULTS: The multivariate regression analysis showed that PNB was associated with independence and/or improvement of walking. Of the 7143 patients, 2755 (39%) received PNB analgesia and 4388 (61%) LI analgesia. After the propensity score matching, the analgesic types were not associated with walking ability. Independence reflected by the total score of daily living activities was higher in the PNB group than in the LI group. The PNB group started rehabilitation later but performed rehabilitation for longer in the initial period than the LI group. Consumption levels of fentanyl, pentazocine, and antiemetics were lower in the PNB group than in the LI group. The PNB group had fewer hypertensive episodes during surgery than the LI group. There was no significant difference in total hospitalization costs between the two groups. CONCLUSIONS: No significant difference in postoperative walking ability was found between PNB and LI groups. However, PNB offered some advantages over LI. Future detailed investigations to improve TKR surgery are needed.

Risk stratification for physical morbidity using factors associated with atypical antipsychotic treatment in Parkinson's disease: A retrospective observational study using administrative claims data.

Evidence shows that atypical antipsychotics (AAPs), a treatment of psychosis in Parkinson's disease (PD), are associated with factors reflecting the severity of the disease. Therefore, we evaluated the applicability of these factors in risk stratification for physical morbidity in PD patients requiring AAPs. We implemented a nested case-control analysis using administrative claims data derived from PD inpatients in 143 National Hospitals in Japan between April 2012 and March 2017. The analysis compared PD patients exposed to AAPs with unexposed matched controls using conditional logistic regression. The cases were then stratified by the weighted score using the partial regression coefficients of extracted factors or the number of factors that they had. Physical morbidity was evaluated using length of stay (LOS) and readmission. After comparing the cases (n = 829) with the matched controls (n = 3316), 10 factors were extracted. The cases were stratified into four level groups using the weighted score, or five level groups using the number of factors. LOS was prolonged with increasing score (49.7; 58.5; 72.7; and 83.3 days) and number of factors (52.1; 52.9; 63.9; 80.7; and 79.1 days). Readmission within 30 days increased along with increasing score (5.7; 10.2; 10.2 and 12.9%) and number of factors (5.9; 9.3; 8.9; 11.3; and 14.3%). We confirmed two stratification manners for physical morbidity in PD patients requiring AAPs. These manners would be useful for considering management plan for these patients.

Validity of administrative database detection of previously resolved hepatitis B virus in Japan.

The risk of hepatitis B virus (HBV) reactivation has increased owing to advances in the immunosuppressive therapy field. However, the HBV reactivation incidence among patients with previously resolved HBV (prHBV) infection during immunosuppressive therapy for rheumatoid arthritis (RA) remains unclear. The objective of this work is to describe the validity of detecting prHBV infection from administrative data through comparisons with chart abstraction and determine the incidence of HBV reactivation during immunosuppressive therapy for RA in Japan. In this retrospective cohort study, data on selected patients were extracted from administrative claims data. To identify patients with prHBV infection and de novo hepatitis, and HBsAg carriers, we conducted chart abstraction. The incidence rate of de novo hepatitis was 1.23 of 100 person-years. The positive predictive value (PPV) and its 95% confidence interval (CI) of administrative data for the identification of suspected prHBV infections was 85.8% (95% CI: 81.7%-89.3%). This study evaluated the PPV of the algorithm of HBV-DNA testing with immunosuppressive therapy performed four times or more per year for the detection of prHBV infection from administrative data. Additionally, we determined the incidence rate of HBV reactivation among preHBV infections during immunosuppressive therapy for RA to be 1.23 of 100 person-years.

Comparison of the association of risperidone and quetiapine with deteriorating performance in walking and dressing in subjects with Parkinson's disease: a retrospective cohort study using administrative claims data.

This retrospective cohort study was performed to investigate the association between risperidone and deteriorating performance in walking and dressing in subjects with Parkinson's disease using the Japanese Diagnosis Procedure Combination data. These data include inpatient claims including information from the time of admission to discharge from 89 acute phase National Hospitals in Japan. The data were evaluated by implementing the inverse probability of treatment weighting, using propensity scores estimated from the clinical characteristics of subjects prescribed risperidone or quetiapine. The generalized estimation equation was used to estimate the adjusted risk ratios (aRRs) and 95% confidence intervals (CIs). In total, 304 subjects were eligible for participation, and were hospitalized between April 2012 and March 2017 (108 and 196 for risperidone and quetiapine groups, respectively). The performance of walking deteriorated at discharge, with 22.2% and 10.2% recorded at admission for the risperidone and quetiapine groups (aRR, 1.7; 95% CI, 0.9 to 3.4), respectively. The performance of dressing also deteriorated: 24.1% and 10.7% in the risperidone and quetiapine groups (aRR, 1.9; 95% CI, 1.04 to 3.7), respectively. These results suggest an association between risperidone and deteriorating performance in dressing in subjects with Parkinson's disease in comparison with quetiapine.

End-of-life care for cancer patients in Japanese acute care hospitals: A nationwide retrospective administrative database survey.

BACKGROUND: End-of-life (EOL) cancer care in Japanese acute care hospitals has not been well described. METHODS: We aimed to assess the aggressiveness of EOL care and examine common treatments administered to cancer patients using a health administrative database. Subjects are adult cancer patients who died at acute care hospitals between April 2011 and March 2014. Data from the Japanese Diagnosis Procedure Combination database were analysed to measure the aggressiveness of care (chemotherapy, intensive care unit [ICU] admission and cardiopulmonary resuscitation [CPR]) and describe procedures and prescriptions administered in the last 14 and 30 days of life, disaggregated by hospital case volume: high, intermediate and low volumes. RESULTS: Of 248,978 cancer decedents, 170,024 died in high-, 70,231 in intermediate- and 8,723 in low-volume hospitals. Aggressive treatment in the last 14 days of life included chemotherapy (9.4%, 7.3%, and 5.4%, respectively), ICU admission (3.0%, 2.0%, and 2.4%) and CPR (5.8%, 6.4%, and 8.3%). Opioids were administered to 66.0%, 59.0% and 49.4% patients, while Palliative Care Team intervention was performed for 8.5%, 2.2% and 2.0% of patients, respectively in the last 30 days. In high-volume hospitals, radiotherapy and certified outpatient chemotherapy fees were more frequent. Catecholamines and hyperalimentation were more frequently administered in low-volume hospitals. CONCLUSION: This is the first study to assess EOL care among Japanese acute care hospitals. More frequent use of chemotherapy at high-volume hospitals may reflect a well-established cancer treatment system. The approach for low-volume hospitals might improve the EOL care for all cancer patients in Japan.

Postoperative daily living activities of geriatric patients administered general or spinal anesthesia for hip fracture surgery: A retrospective cohort study.

PURPOSE: Maintaining independence after hip fracture repair is important for geriatric patients and general welfare. We investigated the effects of anesthetic methods on postoperative activities of daily living (ADLs) following hip fracture surgery in elderly patients. METHODS: The medical records of 12,342 patients aged ≥65 years who underwent typical surgeries for hip fracture using either general anesthesia or spinal anesthesia were reviewed. To adjust for baseline differences and minimize selection bias for the chosen method of anesthesia, patients were matched by propensity scores. Factors affecting the deterioration in ADLs during hospital stay were also investigated in all subjects using a multivariate logistic regression analysis. Eating, grooming, toileting, bathing, and walking were selected as the ADL parameters, as they are considered important for an independent life. RESULTS: Of the 12,342 patients, 6918 (56.1%) received general anesthesia and 5424 (43.9%) received spinal anesthesia. After the propensity score matching, the anesthesia types were not associated with ADL scores except toileting at discharge. Results from the multivariate logistic regression analysis showed that the types of anesthesia were not associated with deterioration in ADL scores. Advanced age, male sex, high Charlson Comorbidity Index scores, psychiatric disease, no administration of nonsteroidal anti-inflammatory drugs, and short length of hospital stay were associated with deterioration in ADL scores. CONCLUSION: The anesthesia types were not associated with ADL dependency except toileting at discharge. Spinal anesthesia adversely affected toilet use at hospital discharge. However, anesthesia types were not factors that affected deterioration in ADL during hospital stay in elderly patients who underwent hip fracture surgery.

Coexisting infectious diseases on admission as a risk factor for mechanical ventilation in patients with Guillain-Barré syndrome.

BACKGROUND: The aim of this study was to investigate patient characteristics on admission to hospital that increase the risk of subsequent mechanical ventilation (MV) use for patients with Guillain-Barré syndrome (GBS). METHODS: We extracted data from the Japanese Diagnosis Procedure Combination (DPC) database for 4132 GBS patients admitted to hospital. Clinical characteristics of GBS patients with and without MV were compared. Multivariate logistic regression analyses were performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of requirement for MV with coexisting infectious diseases, after adjustment for potential confounding variables, age, sex, hospital type, and ambulance transportation. RESULTS: In total, 281 patients required MV, and 493 patients had coexisting respiratory diseases on admission. After adjustment for covariates and stratification by coexisting respiratory diseases, multivariate logistic regression analysis revealed that coexisting cytomegaloviral (CMV) disease (OR 8.81; 95% CI, 2.34-33.1) and herpes simplex viral (HSV) infections (OR 4.83; 95% CI, 1.16-20.1) were significantly associated with the requirement for MV in the group without coexisting respiratory diseases. CONCLUSION: Our findings suggest that coexisting CMV and HSV infections on admission might be significantly associated with increased risk of respiratory failure in GBS patients.