Hiroshi Ikai

Erratum to: Incidence and Prevention of Postoperative Venous Thromboembolism: Are They Meaningful Quality Indicators in Japanese Health Care Settings?

BackgroundVenous thromboembolism (VTE) epidemiology varies widely across surgical procedures. At present, there are few epidemiologic reports regarding VTE in Japan. Japanese VTE prophylaxis guidelines recommend a risk-based approach based on previous epidemiologic statistics. VTE includes deep vein thrombosis (DVT) and pulmonary embolism (PE). PE prevention is the main goal, although the relation between PE and DVT is still controversial.MethodsWe collected administrative data for 1,016,496 surgical patients from 260 hospitals. We analyzed DVT and PE incidence and selected two subgroups for further analysis: gastroenterologic surgery and specific orthopedic surgery (high-frequency group).ResultsOverall DVT incidence was 1947 (0.19%); and the PE incidence was 538 (0.05%). DVT case fatality rate was 3.44% (67/1947); that for PE was 22.86% (123/538). Both overall and subgroup incidences were comparable to those in previous reports. Subgroup analyses in the high-frequency group did not show a relation between DVT and PE. VTE prophylaxis did not show a relation between DVT and PE despite 99% adherence.ConclusionsOur results are consistent with established data regarding DVT and PE incidence. Administrative data available in Japan provides a powerful epidemiologic tool to characterize rare diseases such as DVT and PE. DVT is not a suitable quality indicator in Japan. However, PE is too rare to be considered a rate-based outcome indicator, and VTE prophylaxis is too widely applied to be used as a process indicator. VTE measurement is not a useful quality indicator in Japan to compare hospitals but provides a longitudinal self-survey.

The impact of acute organ dysfunction on patients' mortality with severe sepsis

Background: Severe sepsis leads to organ failure and results in high mortality. Organ dysfunction is an independent prognostic factor for intensive care unit (ICU) mortality. The objective of the present study was to determine the effect of acute organ dysfunction for ICU mortality in patients with severe sepsis using administrative data. Materials and Methods: A multicenter cross-sectional study was performed in 2008. The study was conducted in 112 teaching hospitals in Japan. All cases with severe sepsis in ICU were identified from administrative data. Results: Administrative data acquired for 4196 severe septic cases of 75,069 cases entered in the ICU were used to assess patient outcomes. Cardiovascular dysfunction was identified as the most major organ dysfunction (73.0%), and the followings were respiratory dysfunction (69.4%) and renal dysfunction (39.0%), respectively. The ICU mortality and 28-day means 28-day from ICU entry. were 18.8% and 27.7%, respectively. After adjustment for age, gender, and severity of illness, the hazard ratio of 2, 3, and ≥4, the organ dysfunctions for one organ failure on ICU mortality was 1.6, 2.0, and 2.7, respectively. Conclusions: We showed that the number of organ dysfunction was a useful indicator for ICU mortality on administrative data. The hepatic dysfunction was the highest mortality among organ dysfunctions. The hazard ratio of ICU death in severe septic patients with multiple organ dysfunctions was average 2.2 times higher than severe septic patients with single organ dysfunction.

Influence of Verification Bias on the Assessment of MRI in the Diagnosis of Meniscal Tear

OBJECTIVE Previous studies of the sensitivity and specificity of MRI in the diagnosis of meniscal tear have not included correction for verification bias. The purpose of this study was to investigate the extent to which verification bias affected assessment of the utility of MRI in the diagnosis of meniscal tear. MATERIALS AND METHODS The patients included in the study were outpatients who from April 2006 through July 2008 consecutively visited a single institution for MRI of the meniscus for evaluation of knee pain. For patients who underwent arthroscopy in addition to MRI, the sensitivity and specificity of MRI were calculated. Global sensitivity analysis of data on patients who did not undergo arthroscopy was performed to estimate the influence of verification bias. Global sensitivity analysis is a method for graphically determining whether a particular pair of sensitivity and specificity estimates is compatible with observed data. RESULTS Eighty-two patients (23%) underwent arthroscopic verification. The sensitivity and specificity of MRI were 85% and 31%. When the possibility of meniscal tears in patients who did not undergo arthroscopy was subjected to global sensitivity analysis, the sensitivity of MRI ranged from 29% to 95% and the specificity ranged from 3% to 92%. All combinations of sensitivity and specificity produced a butterfly-shaped curve, but the base case was not inside the curve. CONCLUSION Verification bias greatly affected assessment of the utility of MRI in the diagnosis of meniscal tear. Sensitivity and specificity from previous studies may be incompatible with our data owing to verification bias.

Impact of hospital case volume on quality of end-of-life care in terminal cancer patients

BACKGROUND Quality of end-of-life (EOL) care is gaining increasing attention. However, the relationship between hospital case volume and performance of benchmark quality indicators is not well characterized. The aim of this study was to determine whether hospital case volume affects EOL care for terminal cancer patients. METHODS We conducted a retrospective cross-sectional study using claims data of patients who died of cancer at acute-care hospitals in Kyoto prefecture, Japan, between March 2009 and May 2010. Hospitals were grouped into tertiles based on the number of terminal cancer cases. We used multilevel logistic regression models to examine the association of the following quality indicators with the tertiles: opioid use during the last 2 months of life (indicating good quality of care), provision of intensive care unit (ICU) service or life-sustaining treatments during the last month of life (poor quality), and chemotherapy during the last month of life (poor quality). RESULTS The final sample for analysis consisted of 3294 decedents from 88 hospitals. Significant associations between hospital case volume and quality of EOL care were identified after adjusting for patient and hospital characteristics. Small- and medium-volume hospitals were found to be less likely to administer opioids, and medium-volume hospitals were more likely to provide ICU service or life-sustaining treatments when compared with large-volume hospitals. No significant association between chemotherapy use and case volume was observed. CONCLUSIONS The results showed that the case volume of terminally ill cancer patients was associated with several aspects of quality of EOL care.

Derivation and Validation of In-Hospital Mortality Prediction Models in Ischaemic Stroke Patients Using Administrative Data

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 χ2 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.

Association of Geographical Factors With Administration of Tissue Plasminogen Activator for Acute Ischemic Stroke

Background Intravenous tissue plasminogen activator (tPA) is an effective treatment for acute ischemic stroke if administered within a few hours of stroke onset. Because of this time restriction, tPA administration remains infrequent. Ambulance use is an effective strategy for increasing tPA administration but may be influenced by geographical factors. The objectives of this study are to investigate the relationship between tPA administration and ambulance use and to examine how patient travel distance and population density affect tPA utilization. Methods and Results We analyzed administrative claims data from 114 194 acute ischemic stroke cases admitted to 603 hospitals between July 2010 and March 2012. Mixed‐effects logistic regression models of patients nested within hospitals with a random intercept were generated to analyze possible predictive factors (including patient characteristics, ambulance use, and driving time from home to hospital) of tPA administration for different population density categories to investigate differences in these factors in various regional backgrounds. Approximately 5.1% (5797/114 194) of patients received tPA. The composition of baseline characteristics varied among the population density categories, but adjustment for covariates resulted in all factors having similar associations with tPA administration in every category. The administration of tPA was associated with patient age and severity of stroke symptoms, but driving time showed no association. Ambulance use was significantly associated with tPA administration even after adjustment for covariates. Conclusion The association between ambulance use and tPA administration suggests the importance of calling an ambulance for suspected stroke. Promoting ambulance use for acute ischemic stroke patients may increase tPA use.

Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database

BackgroundCommunity-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.MethodsUsing 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.ResultsThe 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).ConclusionsOur 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.

Quality of care and in-hospital resource use in acute myocardial infarction: Evidence from Japan

OBJECTIVES To determine the association between quality of care in process and outcome measures and in-hospital resource use among patients admitted for acute myocardial infarction (AMI) in Japan. METHODS We analyzed 23,512 AMI patients across 150 hospitals in Japan between April 2008 and March 2011. The exposure measure was inpatient hospital resource use, which was calculated from the sum of all hospital fees for healthcare services provided to AMI patients. Hospitals were then categorized into quartiles based on a risk-adjusted in-hospital resource use index. Quality of care was assessed using three process measures (in-hospital prescription of aspirin, β-blockers, and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers) and two outcome measures (7-day and 30-day in-hospital mortality). Process and outcome measures were analyzed with multilevel logistic regression models that adjusted for patient and hospital characteristics. RESULTS No significant differences in process measures were observed across the quartiles of in-hospital resource use. In contrast, hospitals with the lowest resource use were significantly associated with poorer outcomes (7-day in-hospital mortality OR: 1.851 [95% CI 1.327-2.582]; 30-day in-hospital mortality OR: 1.706 [95% CI 1.259-2.312]) than hospitals with higher resource use. CONCLUSION Poorer quality of care in outcome measures was significantly associated with lower resource utilization among AMI patients in Japanese hospitals, but process measures did not show similar associations.

Decentralization and centralization of healthcare resources: Investigating the associations of hospital competition and number of cardiologists per hospital with mortality and resource utilization in Japan

OBJECTIVE To investigate the associations of hospital competition and number of cardiologists per hospital (indicating the decentralization and centralization of healthcare resources, respectively) with 30-day in-hospital mortality, healthcare spending, and length of stay (LOS) among patients with acute myocardial infarction (AMI) in Japan. METHODS We collected data from 23,197 AMI patients admitted to 172 hospitals between 2008 and 2011. Hospital competition and number of cardiologists per hospital were analyzed as exposure variables in multilevel regression models for in-hospital mortality, healthcare spending, and LOS. Other covariates included patient, hospital, and regional variables; as well as the use of percutaneous coronary intervention (PCI). RESULTS Hospitals in competitive regions and hospitals with a higher number of cardiologists were both associated lower in-hospital mortality. Additionally, hospitals in competition regions were also associated with longer LOS durations, whereas hospitals with more cardiologists had higher spending. The use of PCI was also associated with reduced mortality, increased spending and increased LOS. CONCLUSIONS Centralization of cardiologists at the hospital level and decentralization of acute hospitals at the regional level may be contributing factors for improving the quality of care in Japan. Policymakers need to strike a balance between these two approaches to improve healthcare provision and quality.

Validation of a novel method to identify healthcare-associated infections.

Despite its potential for use in large-scale analyses, previous attempts to utilise administrative data to identify healthcare-associated infections (HAI) have been shown to be unsuccessful. In this study, we validate the accuracy of a novel method of HAI identification based on antibiotic utilisation patterns derived from administrative data. We contemporaneously and independently identified HAIs using both chart review analysis and our method from four Japanese hospitals (N=584). The accuracy of our method was quantified using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) relative to chart review analysis. We also analysed the inter-rater agreement between both identification methods using Cohens kappa coefficient. Our method showed a sensitivity of 0.93 (95% CI: 0.87-0.96), specificity of 0.91 (0.89-0.94), PPV of 0.75 (0.68-0.81) and NPV of 0.98 (0.96-0.99). A kappa coefficient of 0.78 indicated a relatively high level of agreement between the two methods. Our results show that our method has sufficient validity for identification of HAIs in large groups of patients, though the relatively lower PPV may imply limited utilisation in the pinpointing of individual infections. Our method may have applications in large-scale HAI identification, risk-adjusted multicentre studies involving cost of illness, or even as the starting point of future cost-effectiveness analyses of HAI control measures.