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Objectives: To determine the most reliable comorbidity measure, we adapted and validated outcome-specific comorbidity scores to predict mortality and hospital charges using the comorbidities composing the Charlson and Elixhauser measures and the combination of these two used in developing Gagne's combined comorbidity scores (CC, EC, and GC, respectively)., Study Design and Setting: We divided cases of patients discharged in 2016-17 from the Diagnosis Procedure Combination database (n = 2,671,749) into two: one to derive weights for the scores, and the other for validation. We further validated them in subgroups, such as that with a selected diagnosis., Results: The c-statistics of the models predicting in-hospital mortality using new mortality scores using the CC, EC, and GC were 0.780, 0.795, and 0.794, respectively. Among them, that using the EC showed the best calibration. To predict hospital charges and the length of hospital stay (LOS), the models using variables indicating the GC performed the best. The performances of the mortality and expenditure scores were considerably different in predicting each outcome., Conclusion: The new score using the EC performed the best in predicting in-hospital mortality for most situations. For hospital charges and the LOS, the binary variables of the GC showed the best results. The outcome-specific comorbidity scores should be considered for different outcomes., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The prognosis of pneumonia in patients with advanced stage chronic kidney disease (CKD) remains unimproved for years. We attempt to develop a simple and more useful scoring system for predicting in-hospital mortality for advanced CKD patients with pneumonia., Methods: Using the Diagnosis Procedure Combination database, we identified the in-hospital adult patients both with a record of pneumonia and stage 5 or 5D CKD as a comorbidity on admission between April 1, 2012 and March 31, 2016. Predictive variable selection was analyzed by multivariable logistic regression analysis, stepwise method, LASSO method and random forest method, and then develop a new simple scoring system seeking for highest c-statistics combination of variables in one sample data set for model development. Finally, we compared c-statistics of univariate logistic regression about new scoring system with c-statistics about "A-DROP" in the other sample data set., Result: We identified 8402 patients in 707 hospitals, and the total in-hospital mortality was 11.0% (437 patients) in development data set. Seven variables were selected, which includes age (male ≥ 70 years, female ≥ 75 years), respiratory failure, orientation disturbance, low blood pressure, the need of assistance in feeding or bowel control, severe or moderate thinness and CRP 200 mg/L or extent of consolidation on chest X-ray ≥ 2/3 of one lung. The c-statistics of univariate logistic regression was 0.8017 using seven variables, while that was 0.7372 using "A-DROP" CONCLUSION: In advanced CKD patients, if we select appropriate variables for predicting in-hospital mortality, simple scoring system may have better discrimination than "A-DROP".

Background: Some clinicians keep patients in stage 5 chronic kidney disease (CKD) without hemodialysis for a while. This study investigated whether previously-initiated hemodialysis in stage 5 CKD patients may become a prognostic factor for in-hospital mortality due to pneumonia., Methods: Patient data were obtained from the multi-institutional diagnosis procedure combination database between April 1, 2012 and March 31, 2016. The patients had records of pneumonia as both trigger and major diagnoses and records of end stage renal disease (ESRD) or stage 5 CKD as a comorbidity or other diagnoses on admission and aged 18 years or older. The following factors were adjusted: age, sex, body mass index, Barthel index, orientation disturbance, arterial oxygen saturation, systolic blood pressure, C-reactive protein level or the extent of consolidation on chest radiography, ambulance use, hospitalization within 90 days, and comorbidities upon admission. The primary outcome measure was all-cause in-hospital mortality obtained via multivariable logistic regression analysis using four Models. Model 1 involved complete case analysis with overlapping; one hospitalization per patient was counted as one. Model 2 involved a complete case analysis without overlapping; only the first hospitalization per patient was counted. Model 3 involved multilevel analysis clustered by hospital codes. Model 4 was created after multiple imputation for lacking adjusted factors., Results: A total of 907 hospitals and 7,726 patients were identified. Hemodialysis was significantly associated with lower in-hospital mortality in all models (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.54-0.87 in Model 1; OR = 0.71, 95% CI: 0.55-0.91 in Model 2; OR = 0.67, 95% CI: 0.52-0.86 in Model 3; and OR = 0.68, 95% CI: 0.54-0.87 in Model 4)., Conclusion: Previously-initiated hemodialysis may be an independent prognostic factor for in-hospital mortality in pneumonia patients with end-stage renal disease. This should be borne in mind when considering the time of initiation of dialysis., Competing Interests: The authors have declared that no competing interests exist.

Purpose: To comparatively examine in-hospital mortality among different underweight body mass index (BMI) categories in pancreatic cancer patients after pancreatectomy in Japan., Methods: We conducted a large-scale multi-center retrospective cohort study of adult patients with pancreatic cancer who underwent pancreatectomy between April 1, 2010 and March 31, 2016. Patients were classified according to BMI as follows: normal BMI (18.50-24.99 kg/m 2 ), mild thinness (17.00-18.49 kg/m 2 ), moderate thinness (16.00-16.99 kg/m 2 ), and severe thinness (< 16.00 kg/m 2 ). A multivariable logistic regression analysis was performed with in-hospital mortality as the dependent variable and BMI groups as the main independent variable of interest., Results: We analyzed 6173 patients from 332 hospitals. The results showed that the severe thinness group had a longer postoperative hospital stay (34.4 ± 25.6 days) and higher incidence of postoperative pneumonia (5.5%) than the other groups. The generalized estimating equations accounted for patient demographics, surgical procedure, anesthetic technique, activities of daily living score, and Charlson comorbidity index as covariates. Relative to the normal BMI group, the odds ratios for in-hospital mortality were 0.57 (95% confidence interval: 0.26-1.24; P = 0.16) in the mild thinness group, 1.49 (0.64-3.48; P = 0.36) in the moderate thinness group, and 2.54 (1.05-6.08; P = 0.04) in the severe thinness group., Conclusion: Severe thinness was significantly associated with a higher risk of mortality, and extremely low BMI should be considered a risk factor in pancreatectomy patients.

Objectives: Although current case-mix classifications in prospective payment systems were developed to estimate patient resource usage, whether these classifications reflect clinical outcomes remains unknown. The efficient management of acute heart failure (AHF) with high mortality is becoming more important in many countries as its prevalence and associated costs are rapidly increasing. Here, we investigate the determinants of in-hospital mortality and hospitalisation costs to clarify the impact of severity factors on these outcomes in patients with AHF, and examine the level of agreement between the predicted values of mortality and costs., Design: Cross-sectional observational study., Setting and Participants: A total of 19 926 patients with AHF from 261 acute care hospitals in Japan were analysed using administrative claims data., Main Outcome Measures: Multivariable logistic regression analysis and linear regression analysis were performed to examine the determinants of in-hospital mortality and hospitalisation costs, respectively. The independent variables were grouped into patient condition on admission, postadmission procedures indicating disease severity (eg, intra-aortic balloon pumping) and other high-cost procedures (eg, single-photon emission CT). These groups of independent variables were cumulatively added to the models, and their effects on the models' abilities to predict the respective outcomes were examined. The level of agreement between the quartiles of predicted mortality and predicted costs was analysed using Cohen's κ coefficient., Results: In-hospital mortality was associated with patient's condition on admission and severity-indicating procedures (C-statistics 0.870), whereas hospitalisation costs were associated with severity-indicating procedures and high-cost procedures (R 2 0.32). There were substantial differences in determinants between the outcomes. In addition, there was no consistent relationship observed (κ=0.016, p<0.0001) between the quartiles of in-hospital mortality and hospitalisation costs., Conclusions: The determinants of mortality and costs for hospitalised patients with AHF were generally different. Our results indicate that the same case-mix classifications should not be used to predict both these outcomes., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Background: Many hospitals experience a reduction in the number of available physicians on days when national scientific meetings are conducted. This study investigates the relationship between in-hospital mortality in acute myocardial infarction (AMI) patients and admission during national cardiology meeting dates., Methods: Using an administrative database, we analyzed patients with AMI admitted to acute care hospitals in Japan from 2011 to 2013. There were 3 major national cardiology meetings held each year. A hierarchical logistic regression model was used to compare in-hospital mortality and treatment patterns between patients admitted on meeting dates and those admitted on identical days during the week before and after the meeting dates., Results: We identified 6,332 eligible patients, with 1,985 patients admitted during 26 meeting days and 4,347 patients admitted during 52 non-meeting days. No significant differences between meeting and non-meeting dates were observed for in-hospital mortality (7.4% vs. 8.5%, respectively; p=0.151, unadjusted odds ratio: 0.861, 95% confidence interval: 0.704-1.054) and the proportion of percutaneous coronary intervention (PCI) performed on the day of admission (75.9% vs. 76.2%, respectively; p=0.824). We also found that some low-staffed hospitals did not treat AMI patients during meeting dates., Conclusion: Little or no "national meeting effect" was observed on in-hospital mortality in AMI patients, and PCI rates were similar for both meeting and non-meeting dates. Our findings also indicated that during meeting dates, AMI patients may have been consolidated to high-performance and sufficiently staffed hospitals., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Objectives: Despite the increasing burden of acute heart failure (AHF) on healthcare systems, the association between centralised cardiovascular specialist care and the quality of AHF care remains unknown. We examine the relationship between the number of cardiologists per hospital and hospital practice variations., Design, Setting and Participants: In a retrospective observational study, we analysed 38,668 patients with AHF admitted to 546 Japanese acute care hospitals between 2010 and 2011 using the Diagnosis Procedure Combination administrative claims database. Sample hospitals were categorised into four groups according to the number of cardiologists per facility (none, 1-4, 5-9 and ≥10). To confirm the capability of administrative data to identify patients with AHF, the ≥10 cardiologists group was compared with two recent clinical registries in Japan., Main Outcome Measures: Using multivariable logistic regression models, patient risk-adjusted in-hospital mortality rates and age-sex-adjusted ORs of various AHF therapies were calculated and compared among four hospital groups., Results: The ≥10 cardiologists group of hospitals from the administrative database had similar major underlying disease incidence and therapeutic practices to those of the clinical registry hospitals. Age-adjusted and sex-adjusted ORs of various AHF therapies in the four hospital groups revealed wide practice variations associated with the number of cardiologists. Adjusted in-hospital mortality demonstrated a negative association with the number of cardiologists. In addition, the different hospital-level distribution patterns of specific therapeutic practices illustrated the diffusion process of therapies across facilities., Conclusions: Wide practice variations in AHF care were associated with the number of cardiologists per facility, indicating a possible relationship between the quality of AHF care and manpower resources. The provision of recommended therapies increased together with the number of cardiologists., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: Community-acquired pneumonia (CAP) is a common cause of patient hospitalization and death, and its burden on the healthcare system is increasing in aging societies. Here, we develop and internally validate risk-adjustment models and scoring systems for predicting mortality in CAP patients to enable more precise measurements of hospital performance., Methods: Using a multicenter administrative claims database, we analyzed 35,297 patients hospitalized for CAP who had been discharged between April 1, 2012 and September 30, 2013 from 303 acute care hospitals in Japan. We developed hierarchical logistic regression models to analyze predictors of in-hospital mortality, and validated the models using the bootstrap method. Discrimination of the models was assessed using c-statistics. Additionally, we developed scoring systems based on predictors identified in the regression models., Results: The 30-day in-hospital mortality rate was 5.8%. Predictors of in-hospital mortality included advanced age, high blood urea nitrogen level or dehydration, orientation disturbance, respiratory failure, low blood pressure, high C-reactive protein levels or high degree of pneumonic infiltration, cancer, and use of mechanical ventilation or vasopressors. Our models showed high levels of discrimination for mortality prediction, with a c-statistic of 0.89 (95% confidence interval: 0.89-0.90) in the bootstrap-corrected model. The scoring system based on 8 selected variables also showed good discrimination, with a c-statistic of 0.87 (95% confidence interval: 0.86-0.88)., Conclusions: Our mortality prediction models using administrative data showed good discriminatory power in CAP patients. These risk-adjustment models may support improvements in quality of care through accurate hospital evaluations and inter-hospital comparisons.

Background: Acute heart failure (AHF) with its high in-hospital mortality is an increasing burden on healthcare systems worldwide, and comparing hospital performance is required for improving hospital management efficiency. However, it is difficult to distinguish patient severity from individual hospital care effects. The aim of this study was to develop a risk adjustment model to predict in-hospital mortality for AHF using routinely available administrative data., Methods: Administrative data were extracted from 86 acute care hospitals in Japan. We identified 8620 hospitalized patients with AHF from April 2010 to March 2011. Multivariable logistic regression analyses were conducted to analyze various patient factors that might affect mortality. Two predictive models (models 1 and 2; without and with New York Heart Association functional class, respectively) were developed and bootstrapping was used for internal validation. Expected mortality rates were then calculated for each hospital by applying model 2., Results: The overall in-hospital mortality rate was 7.1%. Factors independently associated with higher in-hospital mortality included advanced age, New York Heart Association class, and severe respiratory failure. In contrast, comorbid hypertension, ischemic heart disease, and atrial fibrillation/flutter were found to be associated with lower in-hospital mortality. Both model 1 and model 2 demonstrated good discrimination with c-statistics of 0.76 (95% confidence interval, 0.74-0.78) and 0.80 (95% confidence interval, 0.78-0.82), respectively, and good calibration after bootstrap correction, with better results in model 2., Conclusions: Factors identifiable from administrative data were able to accurately predict in-hospital mortality. Application of our model might facilitate risk adjustment for AHF and can contribute to hospital evaluations., (Copyright © 2013 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

Background: Stroke and other cerebrovascular diseases are a major cause of death and disability. Predicting in-hospital mortality in ischaemic stroke patients can help to identify high-risk patients and guide treatment approaches. Chart reviews provide important clinical information for mortality prediction, but are laborious and limiting in sample sizes. Administrative data allow for large-scale multi-institutional analyses but lack the necessary clinical information for outcome research. However, administrative claims data in Japan has seen the recent inclusion of patient consciousness and disability information, which may allow more accurate mortality prediction using administrative data alone. The aim of this study was to derive and validate models to predict in-hospital mortality in patients admitted for ischaemic stroke using administrative data., Methods: The sample consisted of 21,445 patients from 176 Japanese hospitals, who were randomly divided into derivation and validation subgroups. Multivariable logistic regression models were developed using 7- and 30-day and overall in-hospital mortality as dependent variables. Independent variables included patient age, sex, comorbidities upon admission, Japan Coma Scale (JCS) score, Barthel Index score, modified Rankin Scale (mRS) score, and admissions after hours and on weekends/public holidays. Models were developed in the derivation subgroup, and coefficients from these models were applied to the validation subgroup. Predictive ability was analysed using C-statistics; calibration was evaluated with Hosmer-Lemeshow χ(2) tests., Results: All three models showed predictive abilities similar or surpassing that of chart review-based models. The C-statistics were highest in the 7-day in-hospital mortality prediction model, at 0.906 and 0.901 in the derivation and validation subgroups, respectively. For the 30-day in-hospital mortality prediction models, the C-statistics for the derivation and validation subgroups were 0.893 and 0.872, respectively; in overall in-hospital mortality prediction these values were 0.883 and 0.876., Conclusions: In this study, we have derived and validated in-hospital mortality prediction models for three different time spans using a large population of ischaemic stroke patients in a multi-institutional analysis. The recent inclusion of JCS, Barthel Index, and mRS scores in Japanese administrative data has allowed the prediction of in-hospital mortality with accuracy comparable to that of chart review analyses. The models developed using administrative data had consistently high predictive abilities for all models in both the derivation and validation subgroups. These results have implications in the role of administrative data in future mortality prediction analyses., (Copyright © 2013 S. Karger AG, Basel.)

Introduction: The present study aimed to investigate the postoperative mortality due to all surgeries at the prefectural level using a nationwide diagnosis procedure combination (DPC) database in Japan and to evaluate the data according to temporal changes and regional differences. Methods: Data were provided in accordance with the guidelines indicated on the Ministry of Health, Labor and Welfare, Japan. The number of cases and in-hospital mortality were calculated for each representative surgery for each hospitalization according to fiscal year of discharge from 2011 to 2018 and according to prefecture. Values of ≥10 in each aggregated data cell were presented. Results and discussion: The aggregated result data contain 474,154 records, with about 2,000 different surgical codes. More than 10 mortalities were recorded in only 16,890 data cells, which can be used in the mortality analysis. In the analyses of artificial head insertion, cerebral aneurysm neck clipping, coronary artery and aortic bypass grafting, and tracheotomy, regional differences and a declining trend were observed in some categories. Conclusion: In addition to considering categories that can be used in the analysis, careful consideration must be given to the inclusion of background context such as the quality of care. [ABSTRACT FROM AUTHOR]

Septic patients can develop disseminated intravascular coagulation (DIC), which is characterized by systemic blood coagulation and an increased risk of life-threatening haemorrhage. Although antithrombin (AT) and thrombomodulin (TM) combination anticoagulant therapy is frequently used to treat septic patients with DIC in Japan, its effectiveness in improving patient outcomes remains unclear. In this large-scale multicentre retrospective study of adult septic patients with DIC treated at Japanese hospitals between February 2010 and March 2016, we compared in-hospital mortality between AT monotherapy and AT + TM combination therapy. We performed logistic regression analysis with in-hospital mortality as the dependent variable and anticoagulant therapy as the main independent variable of interest. Covariates included patient demographics, disease severity, and body surface area. The AT group and AT + TM group comprised 1,017 patients from 352 hospitals and 1,205 patients from 349 hospitals, respectively. AT + TM combination therapy was not significantly associated with lower mortality when compared with AT monotherapy (odds ratio: 0.97, 95% confidence interval: 0.78–1.21; P = 0.81). AT + TM combination therapy was also not superior to AT monotherapy in reducing mechanical ventilation or hospitalization durations. Despite its widespread use for treating sepsis with DIC, AT + TM combination therapy is not more effective in improving prognoses than the simpler AT monotherapy. [ABSTRACT FROM AUTHOR]

Background: Patients admitted to hospital during off-hours may experience poorer quality of care and clinical outcomes. However, few studies have examined the variations in clinical processes and outcomes across admission times and days of the week in acute myocardial infarction (AMI) patients. This study aimed to comparatively analyze the effect of weekend and weekday admissions stratified by admission time on in-hospital mortality in AMI patients. Methods and results: Using a large nationwide administrative database, we analyzed 103,908 AMI patients admitted to 639 Japanese acute care hospitals between April 2011 and March 2015. We divided patients into the following 4 groups: weekday daytime admissions, weekday night-time admissions, weekend daytime admissions, and weekend night-time admissions. A hierarchical logistic regression model was used to comparatively examine in-hospital mortality among the groups after adjusting for age, sex, ambulance use, Killip class, comorbidities, and the number of cardiologists in the admitting hospital. In addition, we also calculated and compared the adjusted odds ratios of various AMI therapies among the groups. The in-hospital mortality rate of weekend daytime admissions was higher than those admitted during other times (weekday daytime: 6.8%; weekday night-time; 6.5%, weekend daytime; 7.6%; weekend night-time: 6.6%; P < 0.001), even after adjusting for the covariates (adjusted odds ratio for weekend daytime admissions: 1.10; 95% confidence interval: 1.03–1.19). The prescription rates of guideline-based medications provided on the first day of admission were higher in night-time admissions than in daytime admissions. Conclusions: In-hospital mortality rates were higher in AMI patients admitted during weekend daytime hours when compared with patients admitted during other times. Furthermore, patients admitted during daytime hours had lower prescription rates of guideline-based medications. Our findings indicate that weekend daytime admissions may be a potential target for improvement in the Japanese healthcare system. [ABSTRACT FROM AUTHOR]

Background and objective Little is known about the consequences of weekend admission on the quality of care in patients with severe community-acquired pneumonia. We compared the outcomes of weekend versus weekdays' admission for these patients on risk-adjusted mortality. Methods Using a large nationwide administrative database, we analysed patients with severe pneumonia who had been hospitalized in 1044 acute care hospitals between 2012 and 2013. We compared risk-adjusted in-hospital mortality of guideline-concordant care between patients admitted weekdays and patients admitted on weekends. Results The study sample comprised 17 342 patients admitted on weekdays and 6190 patients admitted on weekends. The mortality rate of the weekend admission group was significantly higher than that of the weekday admission group (23.7% vs 20.5%; P < 0.001). Even after adjusting for baseline patient severity and need for urgent care, weekend admissions were associated with higher mortality (odds ratio: 1.10; 95% confidence interval: 1.02-1.19). The implementation rates of guideline-concordant microbiological tests (including sputum cultures and urine antigen tests) were significantly lower in the weekend admission group. These tests were found to be associated with lower in-hospital mortality. Conclusion Our findings showed that weekend admission was associated with increased mortality in patients with severe community-acquired pneumonia in Japan. This may have been influenced by lower implementation of microbiological testing. [ABSTRACT FROM AUTHOR]