Background: Pre-operative kidney function is known to be associated with surgical outcomes. However, in emergency surgery, the pre-operative kidney function may reflect chronic kidney disease (CKD) or acute kidney injury (AKI). We examined the association of pre-operative CKD and/or AKI with in-hospital outcomes of emergency colorectal surgery. Methods: We conducted a retrospective cohort study including adult patients undergoing emergency colorectal surgery in 38 Japanese hospitals between 2010 and 2017. We classified patients into five groups according to the pre-operative status of CKD (defined as baseline estimated glomerular filtration rate < 60 mL/min/1.73 m 2 or recorded diagnosis of CKD), AKI (defined as admission serum creatinine value/baseline serum creatinine value ≥ 1.5), and end-stage renal disease (ESRD): (i) CKD(-)AKI(-), (ii) CKD(-)AKI(+), (iii) CKD(+)AKI(-), (iv) CKD(+)AKI(+), and (v) ESRD groups. The primary outcome was in-hospital mortality, while secondary outcomes included use of vasoactive drugs, mechanical ventilation, blood transfusion, post-operative renal replacement therapy, and length of hospital stay. We compared these outcomes among the five groups, followed by a multivariable logistic regression analysis for in-hospital mortality. Results: We identified 3002 patients with emergency colorectal surgery (mean age 70.3 ± 15.4 years, male 54.5%). The in-hospital mortality was 8.6% (169/1963), 23.8% (129/541), 15.3% (52/340), 28.8% (17/59), and 32.3% (32/99) for CKD(-)AKI(-), CKD(-)AKI(+), CKD(+)AKI(-), CKD(+)AKI(+), and ESRD, respectively. Other outcomes such as blood transfusion and post-operative renal replacement therapy showed similar trends. Compared to the CKD(-)AKI(-) group, the adjusted odds ratio (95% confidence interval) for in-hospital mortality was 2.54 (1.90-3.40), 1.29 (0.90-1.85), 2.86 (1.54-5.32), and 2.76 (1.55-4.93) for CKD(-)AKI(+), CKD(+)AKI(-), CKD(+)AKI(+), and ESRD groups,

The authors produced a dual task (DT) that provided a dynamic balance task and a cognitive task in a game system using motion sensors and virtual images. There had been no DT where a cognitive task needs a dynamic balance task that requires full-body motions. We developed and evaluated a game system to assess the performance of the DT. The DT was to solve a Sudoku puzzle using full-body motions like Tái Chi. An ability to perform a DT is intimately related to risk of falls. To evaluate the developed system, we compared the performance of elderly people and young people. Generally, elderly people are at a higher risk of falls. Twenty elderly community-dwelling adults (mean age, 73.0 ± 6.2 years) and 16 young adults (mean age, 21.8 ± 1.0 years) participated in this study. To compare the two groups, we applied an independent-samples t-test. The time taken for the elderly people was 60.6 ± 43.2 s, whereas the time taken for the young people was 16.0 ± 4.8 s. The difference is statistically significant (p < 0.05). This result suggests that the developed game system is useful for the evaluation of the DT performance.

Objectives: The authors developed and evaluated a method of selecting accurate diagnosis procedure combination (DPC) codes based on standardized treatment information relative to the number of hospitalization days. Methods: The authors used machine learning methods to generate DPC codes based on treatment data. The machine learning methods utilized were the Naïve Bayes method, the SVM method, and a combined method of the two methods. We prepared DPC code data and standardized treatment data corresponding to cases occurring in fiscal year 2008 at Kyoto University Hospital. To produce classification models, machine learning methods require a moderate amount of data corresponding to each DPC code; accordingly, we selected 166 DPC codes that were each related to at least 20 cases. The number of cases with these DPC codes was 10,123. Results: DPC code selection was attempted using the Naïve Bayes method, the SVM method, and the combined method of the two; of these, the combined method yielded the best results, producing accurate DPC codes in 73.8% of cases. In addition, we were able to improve the precision of DPC code selection to 76.5% by utilizing partial treatment data gathered up to the 11th day of each hospitalization. Conclusion: The present study confirmed the feasibility of automatic DPC code selection through machine learning methods based on treatment information. Our future work will include the construction of a system to select DPC codes automatically and the evaluation of this system to determine whether it can reduce doctors' workloads.

The movement of create medical information systems that is now taking place involves both progress in EMR (Electronic Medical Records)-computerization of records at hospitals and clinics, and also in EHR (Electronic Health Records) in which information is shared with individual regions. However, the geographical coming and going of people in modern society is extremely active. Naturally the places these people move to are not necessarily within the same region. For this reason, even if the basic unit for the health care supply system is in practical terms limited to the local level, if services are restricted to only one region, many persons may be unable to receive the benefits of health care cooperation. In this study, we constructed a mechanism for a medical cooperation system which links the EHR systems of individual regions and is able to create a one-patient, one-record system on the national level. In this paper, we will provide a report of this mechanism and of the 4-year operational trial.

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