Yoshifumi Wakata

Prehospital Epinephrine Use and Survival Among Patients With Out-of-Hospital Cardiac Arrest

CONTEXT Epinephrine is widely used in cardiopulmonary resuscitation for out-of-hospital cardiac arrest (OHCA). However, the effectiveness of epinephrine use before hospital arrival has not been established. OBJECTIVE To evaluate the association between epinephrine use before hospital arrival and short- and long-term mortality in patients with cardiac arrest. DESIGN, SETTING, AND PARTICIPANTS Prospective, nonrandomized, observational propensity analysis of data from 417 188 OHCAs occurring in 2005-2008 in Japan in which patients aged 18 years or older had an OHCA before arrival of emergency medical service (EMS) personnel, were treated by EMS personnel, and were transported to the hospital. MAIN OUTCOME MEASURES Return of spontaneous circulation before hospital arrival, survival at 1 month after cardiac arrest, survival with good or moderate cerebral performance (Cerebral Performance Category [CPC] 1 or 2), and survival with no, mild, or moderate neurological disability (Overall Performance Category [OPC] 1 or 2). RESULTS Return of spontaneous circulation before hospital arrival was observed in 2786 of 15,030 patients (18.5%) in the epinephrine group and 23,042 of 402,158 patients (5.7%) in the no-epinephrine group (P < .001); it was observed in 2446 (18.3%) and 1400 (10.5%) of 13,401 propensity-matched patients, respectively (P < .001). In the total sample, the numbers of patients with 1-month survival and survival with CPC 1 or 2 and OPC 1 or 2, respectively, were 805 (5.4%), 205 (1.4%), and 211 (1.4%) with epinephrine and 18,906 (4.7%), 8903 (2.2%), and 8831 (2.2%) without epinephrine (all P <.001). Corresponding numbers in propensity-matched patients were 687 (5.1%), 173 (1.3%), and 178 (1.3%) with epinephrine and 944 (7.0%), 413 (3.1%), and 410 (3.1%) without epinephrine (all P <.001). In all patients, a positive association was observed between prehospital epinephrine and return of spontaneous circulation before hospital arrival (adjusted odds ratio [OR], 2.36; 95% CI, 2.22-2.50; P < .001). In propensity-matched patients, a positive association was also observed (adjusted OR, 2.51; 95% CI, 2.24-2.80; P < .001). In contrast, among all patients, negative associations were observed between prehospital epinephrine and long-term outcome measures (adjusted ORs: 1-month survival, 0.46 [95% CI, 0.42-0.51]; CPC 1-2, 0.31 [95% CI, 0.26-0.36]; and OPC 1-2, 0.32 [95% CI, 0.27-0.38]; all P < .001). Similar negative associations were observed among propensity-matched patients (adjusted ORs: 1-month survival, 0.54 [95% CI, 0.43-0.68]; CPC 1-2, 0.21 [95% CI, 0.10-0.44]; and OPC 1-2, 0.23 [95% CI, 0.11-0.45]; all P < .001). CONCLUSION Among patients with OHCA in Japan, use of prehospital epinephrine was significantly associated with increased chance of return of spontaneous circulation before hospital arrival but decreased chance of survival and good functional outcomes 1 month after the event.

Prehospital Lactated Ringer's Solution Treatment and Survival in Out-of-Hospital Cardiac Arrest: A Prospective Cohort Analysis

In a cohort of more than 500,000 individuals who experienced out-of-hospital cardiac arrest in Japan, Akihito Hagihara and colleagues studied whether administration of lactated Ringers solution was associated with survival and functional outcomes.

Assessing a clinical pathway to improve the quality of care in pulmonary resections

PurposeTo evaluate the efficacy of the current clinical pathway for pulmonary resections.MethodsThis study examined variances from expected clinical pathway outcomes for pulmonary resections performed between 2005 and 2009. Data on a total of 383 patients were retrospectively analyzed.ResultsThe median length of hospital stay (LOS) using the clinical pathway was 12 days (range: 1–188 days); the mean LOS was 15.5 days. The cost per day with use of the clinical pathway was 102 726 yen. Poor control of pain from intercostal neuralgia was the most frequently observed variance from expected outcomes. It affected 119 of 168 electronic clinical pathway patients (70.8%). The clinical pathway was terminated in 3.9% of patients (15/383) due to serious or life-threatening complications.ConclusionsThis study showed the single institutional experience of the clinical pathway for pulmonary resections. These findings indicate a need to revise certain aspects of the pathway, based on data from our analysis of variances.

Clinical characteristics of atrial fibrillation-related cardioembolic stroke in patients aged 80 years or older.

Atrial fibrillation (AF)‐related cardioembolic stroke is a serious problem in the aging society. The present study examined the clinical characteristics and outcomes of AF‐related cardioembolic stroke in patients aged ≥80 years.

Predictive Factors for Excellent or Extremely Poor Functional Outcome in Initial Atrial Fibrillation-Related Cardioembolic Stroke

Background: We aimed to determine the predictive factors for excellent or extremely poor functional outcome in patients with first-ever atrial fibrillation (AF)-related cardioembolic stroke. Methods: Retrospective observational study from a database. Patients with AF-related cardioembolic stroke with a premorbid modified Rankin Scale (mRS) score of 0 or 1 and without a previous history of stroke were included. Results: Factors associated with excellent functional outcome (mRS scores of 0 or 1; n = 77; 30.4% of patients) included age >78 years (OR 0.31, 95% CI 0.15-0.61), male sex (OR 2.16, 95% CI 1.04-4.60), absence of hypertension (OR 0.46, 95% CI 0.22-0.94) and initial National Institutes of Health Stroke Scale (NIHSS) score of >9 (OR 0.08, 95% CI 0.03-0.16). Factors associated with extremely poor functional outcome (mRS scores of 5 or 6; n = 63; 24.9%) included age >78 years (OR 3.30, 95% CI 1.54-7.39), initial NIHSS score of >9 (OR 12.38, 95% CI 5.40-32.56), congestive heart failure (OR 4.82, 95% CI 2.00-12.19) and ischemic heart disease (OR 4.02, 95% CI 1.18-14.69). Conclusions: Predictive factors exist to delineate excellent and extremely poor functional outcomes after a first-time stroke associated with AF.

Interpreting Medical Information Using Machine Learning and Individual Conditional Expectation

Recently, machine-learning techniques have spread many fields. However, machine-learning is still not popular in medical research field due to difficulty of interpreting. In this paper, we introduce a method of interpreting medical information using machine learning technique. The method gave new explanation of partial dependence plot and individual conditional expectation plot from medical research field.

Temporal Relation Extraction in Outcome Variances of Clinical Pathways.

Recently the clinical pathway has progressed with digitalization and the analysis of activity. There are many previous studies on the clinical pathway but not many feed directly into medical practice. We constructed a mind map system that applies the spanning tree. This system can visualize temporal relations in outcome variances, and indicate outcomes that affect long-term hospitalization.

Visualization of Key Factor Relation in Clinical Pathway

Abstract The secondary use of medical data to improve medical care is gaining much attention. We have analyzed electronic clinical pathways for improving the medical process. The analysis of clinical pathways so far has used statistics analysis models, however as issue remains that the order, and multistory spatial and time relations of the each factor could not be analyzed. We constructed an Outcome tree system that shows the greatest significant relation for each factor. The Hip replacement arthroplasty clinical pathway was analyzed by the system, and the outcome variance of the clinical pathway was visualized. The results indicate the path of patients who have a long hospitalization stay and extracted four critical indicators.

Construction of Dominant Factor Presumption Model for Postoperative Hospital Days from Operation Records

The secondary use of clinical text data to improve the quality and the efficiency of medical care is gaining much attention. However, there are few previous researches that have given feedback to clinical situations. The present paper analyzes the words that appear in operation records to predict the postoperative length of stay. SVM (support vector machine) and feature selection are applied to predict if a stay is longer than the standard length of 25 days. It was confirmed that with less than 20 feature words we can predict if a stay is longer or not with almost the optimal prediction performance.

Extraction of determinants of postoperative length of stay from operation records

Secondary use of clinical text data are gaining much attention in improving the quality and the efficiency of medical treatment. Although there is some case studies of medical-examination text data, there are not many examples fed back to the medical-examination spot. The present paper analyses the operation records of total hip arthroplasty. We extracted feature words that characterize the two peaks which appeared in distribution of postoperative hospital days using SVM (support vector machine) and FS (feature selection). The models gained by optimal FS attained 60% accuracy as prediction performance. We applied logistic regression analysis to estimate postoperative length of stay from the extracted feature words. Most words were not statistically significant except two words.