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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.

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BACKGROUND:Multiple trauma deserves early prognostication and stratification. Copeptin, a precursor of vasopressin, is produced in response to stress. We examined the association between serum levels of copeptin and mortality risk in patients with multiple trauma. We aimed to also enhance the previously established Trauma-Related Injury Severity Score (TRISS) and Mechanism, GCS, Age, and Arterial Pressure (MGAP) score with the additional consideration of copeptin levels. METHODS:This single-center prospective cohort study enrolled patients who presented to the emergency department with potential major injuries. The serum levels of copeptin were measured, and the correlation to clinical severity in terms of 30-day mortality and requirement of intensive care management was analyzed. By combining copeptin levels with TRISS or MGAP, comparison between performance of the original models with the copeptin-enhanced models was performed via discrimination, calibration, and reclassification analyses. RESULTS:There was a significant increase in copeptin levels in patients who died within 30 days (median 644.4 pg/L, interquartile range [472.5, 785.9]) or were admitted to intensive care units (233.8 pg/L, [105.7, 366.4]), compared with those who survived (37.49 pg/L, [17.88, 77.68]). Adding the natural log of copeptin levels to the established TRISS and MGAP models improved the AUC of TRISS from 0.89 to 0.96, and that of MGAP from 0.82 to 0.95. Both calibrations as measured by Brier's scores and reclassification as measured by net reclassification improvement or integrated discrimination improvement demonstrated significant improvements. A Web-based calculator was built to generate predicted mortality rates of various models for convenient clinical use. CONCLUSION:Admission serum copeptin levels were correlated with clinical severity in multiple trauma. Coupling copeptin with preexisting trauma severity scores improved prediction accuracy. Copeptin shows promise as a novel biomarker for the prediction of trauma outcome.

BACKGROUND Early identification of impending in-hospital cardiac arrest (IHCA) improves clinical outcomes but remains elusive for practicing clinicians. OBJECTIVE We aimed to develop a multimodal machine learning algorithm based on ensemble techniques to predict the occurrence of IHCA. METHODS Our model was developed by the MIMIC-IV database and validated in the eICU-CRD database. Baseline features consisting of patient demographics, presenting illness, and comorbidities were collected to train a Random Forest (RF) model. Next, vital signs were extracted to train a long short-term memory (LSTM) model. A Support Vector Machine (SVM) algorithm then stacked the results to form the final prediction model. RESULTS Of 23,909 patients in the MIMIC-IV database and 10,049 patients in the eICU database, 452 and 85 patients had incident IHCA. Up to 13 hours in advance of an IHCA event, our algorithm maintained an area under the ROC curve above 0.78. Satisfactory results were also seen in validation from two external databases and comparison to existing warning systems. CONCLUSIONS Using only vital signs and information available in the electronic medical record, our model demonstrates it is possible to detect a trajectory of clinical deterioration up to 13 hours in advance. This predictive tool, which has undergone external validation, could forewarn and help clinicians identify patients in need of assessment to improve their overall prognosis.