Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring.

PURPOSE: Cardiac arrest (CA) is the most serious death-related event in critically ill patients and the early detection of CA is beneficial to reduce mortality according to clinical research. This study aims to develop and verify a real-time, interpretable machine learning model, namely cardiac arrest prediction index (CAPI), to predict CA of critically ill patients based on bedside vital signs monitoring. METHODS: A total of 1,860 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Based on vital signs, we extracted a total of 43 features for building machine learning model. Extreme Gradient Boosting (XGBoost) was used to develop a real-time prediction model. Three-fold cross validation determined the consistency of model accuracy. SHAP value was used to capture the overall and real-time interpretability of the model. RESULTS: On the test set, CAPI predicted 95% of CA events, 80% of which were identified more than 25 min in advance, resulting in an area under the receiver operating characteristic curve (AUROC) of 0.94. The sensitivity, specificity, area under the precision-recall curve (AUPRC) and F1-score were 0.86, 0.85, 0.12 and 0.05, respectively. CONCLUSION: CAPI can help predict patients with CA in the vital signs monitoring at bedside. Compared with previous studies, CAPI can give more timely notifications to doctors for CA events. However, current performance was at the cost of alarm fatigue. Future research is still needed to achieve better clinical application.

Research Progress of Physiologic Parameters Monitoring Technology for Critical Neonates / 中国医疗器械杂志

Physiological parameter monitoring is essential to medical staff to evaluate, diagnose and treat patients in neonatal intensive care unit (NICU). Monitoring in NICU includes basic vital signal monitoring and functional monitoring. Basic vital signal monitoring (including ECG, respiration, SpO2, blood pressure, temperature) is advanced and focus on study of usability, continuity and anti-interference. Functional monitoring (including respiratory function, circulatory function, cerebral function) still focus on study of monitoring precision and reliability. Meanwhile, video monitoring and artifact intelligence have presented well performance on improving monitoring precision and anti-interference. In this article, the main parameters and relevant measurement technology for monitoring critical neonates were described.

Study on the value of pulse oximetry plethysmographic waveform derived parameters on the quality of cardiopulmonary resuscitation / 中华急诊医学杂志

Objective:Pulse oximetry plethysmographic (POP) waveform to indicate the patient's perfusion status and the quality of resuscitation has been affirmed. The POP waveform is obtained by a non-invasive monitoring method, and its clinical feasibility during CPR is better than that of invasive monitoring technologies. This study aimed to analyze the three parameters derived from POP waveform: CPR quality index (CQI), perfusion index (PI), and chest compression fraction (CCF) in evaluating the CPR quality and ROSC possibility.Methods:A prospective descriptive study was conducted on 74 CPR patients who were divided into the ROSC group and non-ROSC group according to their resuscitation results. The clinical data were extracted from patient monitor, the distribution and changes of the three parameters during CPR were collected, and their value of evaluating resuscitation outcome were analyzed.Results:At the end stage of resuscitation, there were statistically significant differences in the three parameters between the two groups ( P<0.05). In addition, CQI was significantly more capable in evaluating the possibility of ROSC than PI and CCF ( P<0.05). Conclusions:CQI, PI and CCF derived from POP waveform can all be applied to evaluate CPR quality and ROSC possibility. CQI has higher prognosis value than PI and CCF.

Review of Physiological Parameters Monitoring Technology in the ICU / 中国医疗器械杂志

Physiological parameters monitoring is essential to direct medical staff to evaluate, diagnose and treat critical patients quantitatively. ECG, blood pressure, SpO2, respiratory rate and body temperature are the basic vital signs of patients in the ICU. The measuring methods are relatively mature at present, and the trend is to be wireless and more accurate and comfortable. Hemodynamics, oxygen metabolism and microcirculation should be taken seriously during the treatment of acute critical patients. The related monitoring technology has made significant progress in recent years, the trend is to reduce the trauma and improve the accuracy and usability. With the development of machine vision and data fusion technology, the identification of patient behavior and deterioration has become hot topics. This review is focused on current parameters monitoring technologies, aims to provide reference for future related research.

Research on Patient Monitoring Alarms in ICU and NICU / 中国医疗器械杂志

OBJECTIVE@#The patient monitors were used to explore the alarm actuality in a ICU and NICU to investigate the awareness and reaction of medical staff to alarms.@*METHODS@#A series of surveys and interviews were taken to acquire clinicians' feelings and attitudes to monitoring alarms. The researchers were scheduled to track the alarms with annotations, and collect the alarm data of patient monitors using central monitoring system.@*RESULTS@#A total of 235 387 and 67 783 alarms occurred in ICU and NICU respectively. The average alarm rate was about 142 alarms/patient-day in ICU and 96 alarms/patient-day in NICU.@*CONCLUSIONS@#There remains alarm fatigue in ICU and NICU, the main reason is the large number of false alarms and clinically irrelevant alarms. In addition, patient monitor is still in the level of threshold alarms or combined alarms, the data integrity and intelligence level need to be improved in future.

Research of Methods to Reduce Alarm Fatigue of Monitoring System / 中国医疗器械杂志

OBJECTIVE@#In order to solve alarm fatigue, the algorithm optimization strategies were researched to reduce false and worthless alarms.@*METHODS@#A four-lead arrhythmia analysis algorithm, a multiparameter fusion analysis algorithm, an intelligent threshold reminder, a refractory period delay technique were proposed and tested with collected 28 679 alarms in multi-center study.@*RESULTS@#The sampling survey indicate that the 80.8% of arrhythmia false alarms were reduced by the four-lead analysis, the 55.9% of arrhythmia and pulse false alarms were reduced by the multi-parameter fusion analysis, the 28.0% and 29.8% of clinical worthless alarms were reduced by the intelligent threshold and refractory period delay techniques respectively. Finally, the total quantity of alarms decreased to 12 724.@*CONCLUSIONS@#To increase the dimensionality of parametric analysis and control the alarm limits and delay time are conducive to reduce alarm fatigue in intensive care units.