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This work proposes a learning model to predict the outcome of electrical defibrillation from ECG signals in ventricular fibrillation (VF) periods, which is a lethal situation happening when a patient is suffering cardiac arrest. An animal experiment of rats is conducted to obtain the ECG signals and necessary information for this study. This proposed model only extracts one feature from the ECG signals and enjoys low computational complexity at both training and testing stages. The statistics of this extracted single feature is further analyzed, and mathematical closed-form formulas for several interesting performance indices including the sensitivity, specificity, accuracy, precision and Area Under the Curve (AUC) are obtained to gain more insights of the proposed system. Moreover, the extracted feature can be treated as a linear combination of individual frequency components of the ECG signal, where the combining coefficients of the linear combination may show informative clinical inference. Frequencies corresponding to large trained combining coefficients imply that they contribute more in distinguishing the defibrillation outcome, and vice versa. As a result, important frequencies of the ECG signals can be identified and insignificant frequencies can also be filtered out by the proposed training. Simulation results corroborate the analytical results, and show that the proposed scheme greatly outperforms several competitive learning models and traditional methods in terms of testing accuracy and computational complexity.

Background: Little is known about the risk of in-hospital cardiac arrest (IHCA) among patients with sepsis. We aimed to characterize the incidence and outcome of IHCA among patients with sepsis in a national database. We then determined the major risk factors associated with IHCA among sepsis patients.Methods: We used data from a population-based cohort study based on the National Health Insurance Research Database of Taiwan (NHRID) between 2000 and 2013. We used Martin's implementation that combined the explicit ICD-9 CM codes for sepsis and six major organ dysfunction categories. IHCA among sepsis patients was identified by the presence of cardiopulmonary resuscitation procedures. The survival impact was analyzed with the Cox proportional-hazards model using inverse probability of treatment weighting (IPTW). The risk factors were identified by logistic regression models with 10-fold cross-validation, adjusting for competing risks.Results: We identified a total of 20,022 patients with sepsis, among whom 2,168 developed in-hospital cardiac arrest. Sepsis patients with a higher burden of comorbidities and organ dysfunction were more likely to develop in-hospital cardiac arrest. Acute respiratory failure, hematological dysfunction, renal dysfunction, and hepatic dysfunction were associated with increased risk of IHCA. Regarding the source of infection, patients with respiratory tract infections were at the highest risk, whereas patients with urinary tract infections and primary bacteremia were less likely to develop IHCA. The risk of IHCA correlated well with age and revised cardiac risk index (RCRI). The final competing risk model concluded that acute respiratory failure, male gender, and diabetes are the three strongest predictors for IHCA. The effect of IHCA on survival can last 1 year after hospital discharge, with an IPTW-weighted hazard ratio of 5.19 (95% CI: 5.06, 5.35) compared to patients who did not develop IHCA.Conclusion: IHCA in sepsis patients had a negative effect on both short- and long-term survival. The risk of IHCA among hospitalized sepsis patients was strongly correlated with age and cardiac risk index. The three identified risk factors can help clinicians to identify patients at higher risk for IHCA.

Introduction: Emergency department (ED) revisits are traditionally used to measure potential lapses in emergency care. However, recent studies on in-hospital outcomes following ED revisits have begun to challenge this notion. We aimed to examine inpatient outcomes and resource use among patients who were hospitalized following a return visit to the ED using a national database. Methods: This was a retrospective cohort study using the National Health Insurance Research Database in Taiwan. One-third of ED visits from 2012–2013 were randomly selected and their subsequent hospitalizations included. We analyzed the inpatient outcomes (mortality and intensive care unit [ICU] admission) and resource use (length of stay [LOS] and costs). Comparisons were made between patients who were hospitalized after a return visit to the ED and those who were hospitalized during the index ED visit. Results: Of the 3,019,416 index ED visits, 477,326 patients (16%) were directly admitted to the hospital. Among the 2,504,972 patients who were discharged during the index ED visit, 229,059 (9.1%) returned to the ED within three days. Of them, 37,118 (16%) were hospitalized. In multivariable analyses, the inpatient mortality rates and hospital LOS were similar between the two groups. Compared with the direct-admission group, the return-admission group had a lower ICU admission rate (adjusted odds ratio, 0.78; 95% confidence interval [CI], 0.72-0.84), and lower costs (adjusted difference, -5,198 New Taiwan dollars, 95% CI, -6,224 to -4,172). Conclusion: Patients who were hospitalized after a return visit to the ED had a lower ICU admission rate and lower costs, compared to those who were directly admitted. Our findings suggest that ED revisits do not necessarily translate to poor initial care and that subsequent inpatient outcomes should also be considered for better assessment.

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Objectives Emergency department (ED) revisits had traditionally been used as a quality indicator, but focused more on the same hospital revisit (SHRV). Our study investigated the 72-hour ED revisits on SHRV and different hospital revisits (DHRV), and explored the predictors of DHRV. Methods 72-hour ED revisits were analyzed using Taiwan's National Health Insurance Research Database that contained one-third patient records from 2012 to 2013. Rates of SHRV and DHRV were calculated and compared among levels of hospital accreditation. Linear regression analyses were used to measure the correlation between revisit rates and average monthly volumes of the index ED. Multilevel logistic analyses were performed to evaluate the predictors of DHRV. Results There were 4 065 215 index ED visits. Of them, 234 826 (5.8%) were associated with 72-hour revisits and 36.7% of them occurred at different institutions. The revisit rates showed differences across distinct hospital levels. DHRV rates had significant inverse correlation with the average monthly volume of the index ED. Independent predictors associated with the increased odds of DHRV were: ED visits on weekend or holiday, or the index ED being at a local hospital. Conclusions About one in three ED revisits occurred in another hospital. SHRV rate alone would inevitably underestimate the true revisit rate. DHRV is associated with the hospital level and annual ED volume, and there is increased likelihood of DHRV if patient's index ED visit being a local hospital.

Treatment of locally advanced penile squamous cell carcinoma (pSCC) remains highly controversial secondary to disease rarity and lack of prospective randomized controlled trials. The current mainstays of care are multi-modality treatment with neoadjuvant chemotherapy and surgery. However, clinicians often have difficulty making recommendations for patients unable to tolerate chemotherapy or surgery due to scarcity of data to guide clinical decision-making. We report two cases of locally advanced pSCC that achieved complete remission after treatment with cisplatin-based neoadjuvant chemotherapy and surgery in one case, and concurrent cisplatin chemoradiation in a second, supporting the use of chemotherapy as part of first-line multimodal therapy. We also discuss additional treatment options for patients unable to tolerate traditional chemotherapy regimens.