Background:
Chronic subdural hematoma (CSDH) is usually associated with good recovery with burr hole irrigation and postoperative drainage under local anesthesia. In Japan, traffic accidents by the elderly drivers over 65 years old are severely increasing, and there is no consensus on whether or not to return to driving after CSDH treatment. We perform a postoperative cognitive assessment. We retrospectively investigated the return-to-driving rate and associated factors.
Methods:
Of the 45 patients over 65 y.o. and who had usually driven, 30 patients wished to drive again. We performed tests composed of Mini-Mental State Examination (MMSE), line cancellation and line bisection task, Kohs block design test, trail making test (TMT)-A and B, Kana-hiroi test, Rey-Osterrieth complex figure test, and behavioral assessment of the dysexecutive syndrome, in order. When all tests’ scores were better than the cutoff values, we let patients drive again. When some of the scores were worse than the cutoff values, we reevaluated the patients at the outpatient every month. If the patients’ scores could not improve at the outpatient, we recommended them to stop driving.
Results:
Nineteen of 30 patients could return to driving. Worse MMSE, Kohs block design test, TMT-A, TMT-B scores, higher age, dementia, or consciousness disturbance as chief complaints were associated with driving disability.
Conclusion:
CSDH is known as treatable dementia. However, we should perform an objective cognitive assessment before discharge because only 63% of the patients over 65 y.o. who wished to drive could return to driving.
IntroductionPrediction models of functional independent measure (FIM) score after kaifukuki (convalescent) rehabilitation ward (KRW) are needed to decide the treatment strategies and save medical resources. Statistical models were reported, but their accuracies were not satisfactory. We made such prediction models using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan).
MethodsOf the 559 consecutive stroke patients, 122 patients were transferred to our KRW. We divided our 122 patients' data randomly into halves of training and validation datasets. Prediction One made three prediction models from the training dataset using (1) variables at the acute care ward admission, (2) those at the KRW admission, and (3) those combined (1) and (2). The models' determination coefficients (R 2 ), correlation coefficients (rs), and residuals were calculated using the validation dataset.
ResultsOf the 122 patients, the median age was 71, length of stay (LOS) in acute care ward 23 (17-30) days, LOS in KRW 53 days, total FIM scores at the admission of KRW 85, those at discharge 108. The mean FIM gain and FIM efficiency were 19 and 0.417. All patients were discharged home. Model (1), (2), and (3)'s R 2 were 0.794, 0.970, and 0.972. Their mean residuals between the predicted and actual total FIM scores were -1.56±24.6, -4.49±17.1, and -2.69±15.7.
ConclusionOur FIM gain and efficiency were better than national averages of FIM gain 17.1 and FIM efficiency 0.187. We made DL-based total FIM score prediction models, and their accuracies were superior to those of previous statistically calculated ones. The DL-based FIM score prediction models would save medical costs and perform efficient stroke and rehabilitation medicine.
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