Identification of Molecular Biomarkers Associated With Stroke Progression Using WGCNA and SVM-RFE

Background: Stroke is the second most common cause of death worldwide and the leading cause of long-term severe disability with neurological impairment worsening within hours after stroke onset and being especially involved with motor function. So far, there are no established and reliable biomarkers to prognose stroke. Early detection of biomarkers that can prognose stroke is of great importance for clinical intervention and prevention of clinical deterioration of stroke.Methods: TGSE119121 dataset was retrieved from the Gene Expression Integrated Database (Gene Expression Omnibus, GEO) and weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules that could regulate disease progression. Moreover, functional enrichment analysis was conducted to study the biological functions of the key module genes. The GSE16561 dataset was further analyzed by the Support Vector Machines coupled with Recursive Feature Elimination (SVM-RFE )algorithm to identify the top genes regulating disease progression. The hub genes revealed by WGCNA were associated with disease progression using the receiver operating characteristic curve (ROC) analysis. Subsequently, functional enrichment of the hub genes was performed by deploying gene set variation analysis (GSVA). The changes at gene level were transformed into the changes at pathway level to identify the biological function of each sample. Finally, the expression level of the hub gene in the rat infarction model of MCAO was measured using RT-qPCR for validation. Results: WGCNA analysis revealed four hub genes: DEGS1, HSDL2, ST8SIA4 and STK3. The result of GSVA showed that the hub genes were involved in stroke progression by regulating the p53 signal pathway, the PI3K signal pathway, and the inflammatory response pathway. The results of RT-qPCR indicated that the expression of the four HUB genes was increased significantly in the rat model of MCAO.Conclusion: Several genes, such as DEGS, HSDL2, ST8SIA4 and STK3, were identified and associated with the progression of the disease. Moreover, it was hypothesized that these genes may be involved in the progression stroke by regulating the P53 signal, the PI3K signal, and the inflammatory response pathway, respectively. These genes have potential prognostic value and may serve as biomarkers for predicting stroke progression. The early identification of the patients at risk of progression is essential to prevent clinical deterioration and provide a reference for future research.

Race/Ethnicity Is Not Independently Associated with Risk of Adverse Waitlist Removal among Patients with HCC Exception Points

(1) Background: Since 2015, exception points have been awarded to appropriate candidates after six months of waitlist time to allow more equitable access to liver transplants regardless of hepatocellular carcinoma status. However, it remains unknown whether racial disparities in outcomes among waitlisted patients remain after the introduction of a 6-month waiting period for exception points. (2) Methods: Using the United Network for Organ Sharing database, we identified 2311 patients diagnosed with hepatocellular carcinoma listed for liver transplant who received exception points from 2015 to 2019. The outcome of interest was waitlist survival defined as the composite outcome of death or removal for clinical deterioration. Competing risk analysis was used to identify factors associated with death or removal for clinical deterioration. The final model adjusted for age, sex, race/ethnicity, blood type, diabetes, obesity, laboratory MELD score, tumor size, AFP, locoregional therapies, UNOS region, and college education. (3) Results: No difference was found in the risk of adverse waitlist removal among ethnic/racial groups.

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

Epidemiology and Prognostic Significance of Rapid Response System Activation in Patients Undergoing Liver Transplantation

Patients undergoing liver transplantation have a high risk of perioperative clinical deterioration. The Rapid Response System is an intensive care unit-based approach for the early recognition and management of hospitalized patients identified as high-risk for clinical deterioration by a medical emergency team (MET). The etiology and prognostic significance of clinical deterioration events is poorly understood in liver transplant patients. We conducted a cohort study of 381 consecutive adult liver transplant recipients from a prospectively collected transplant database (2011–2017). Medical records identified patients who received MET activation pre- and post-transplantation. MET activation was recorded in 131 (34%) patients, with 266 MET activations in total. The commonest triggers for MET activation were tachypnea and hypotension pre-transplantation, and tachycardia post-transplantation. In multivariable analysis, female sex, increasing Model for End-Stage Liver Disease score and hepatorenal syndrome were independently associated with MET activation. The unplanned intensive care unit admission rate following MET activation was 24.1%. Inpatient mortality was 4.2% and did not differ by MET activation status; however, patients requiring MET activation had significantly longer intensive care unit and hospital length of stay and were more likely to require inpatient rehabilitation. In conclusion, liver transplant patients with perioperative complications requiring MET activation represent a high-risk group with increased morbidity and length of stay.

Failure To Rescue, What Can Be Done To Prevent It?

Introduction: Failure to Rescue (FTR) is the failure to prevent a patient’s death after a complication. It measures the ability of a hospital to prevent the death of patients who develop one or more complication that was not present at the time of admission. Therefore, the aim of this study is to review the factors that contribute to FTR, and the measures and strategies that can be applied to prevent the FTR events, in order to discuss the best way to improve patient outcomes in the hospital setting. Methods: A search was conducted on PUBMED retrieving a total of 464 articles. A review of the selected articles’ bibliography was conducted to find other relevant articles. Sixty studies were reviewed in this paper. Results: Patient factors as increasing age, comorbidities and frailty increase the risk of FTR, as well as an increasing number of complications. Several hospital factors, nursing care, and microsystem also influence FTR. Some track and Trigger Systems (TTS) and Early Warning Scores (EWS) have been shown to predict clinical deterioration. On the other hand, machine learning systems have outperformed EWS. Rapid response teams have become the standard approach to delivery and escalation of care, and cognitive aids and crisis checklists also have potential to help reduce FTR. Conclusion: Patient and hospital factors are often non-modifiable; thus, microsystem factors could be a target for improvement. Creating clinical pathways can improve surveillance, and communication tools like SBAR can help relay information. EWS, machine learning models and continuous monitoring are strategies that can help detect clinical deterioration. In the efferent limb rapid response teams have shown to reduce FTR.

Family-Assisted Severity of Illness Monitoring for Hospitalized Children in Low-resource settings – a two-arm interventional pilot study

IntroductionPediatric mortality remains unacceptably high in many low-resource settings, with inpatient deaths often associated with delayed recognition of clinical deterioration. The Family-Assisted Severe Febrile Illness ThERapy (FASTER) tool has been developed for caregivers to assist in monitoring their hospitalized children and alert clinicians. While utilization of the tool is feasible, the impact on outcomes in low-resource settings has not been studied.MethodsRandomized controlled pilot study at Kenyatta National Hospital, Kenya. Children hospitalized with acute febrile illness with a caregiver at the bedside for 24 hours were enrolled. Caregivers were trained using the FASTER tool (monitors work of breathing, mental status, perfusion, producing color-coded flags to signal illness severity). The primary outcome was the frequency of clinician reassessments between intervention (FASTER) and control (standard care) arms. Secondary outcomes included survey assessments of clinician and caregiver experiences with the tool. The study was registered with ClinicalTrials.govNCT03513861.Results150 patient/caregiver pairs were enrolled, 139 included in the analysis, 74 in the intervention, 65 in the control arm. Patients’ median age was 0.9 (range 0.2-10) and 1.1 years (range 0.2-12) in intervention versus control arms. The most common diagnoses were pneumonia (80[58%]), meningitis (58[38%]) and malaria (34[24%]). 134(96%) caregivers were patients’ mothers. Clinician visits/hour increased with patients’ illness severity in both arms, but without difference in frequency between arms (point estimate for the difference -0.2%, p=0.99). Of the 16 deaths, 8 (four/arm) occurred within 2 days of enrollment. Forty clinicians were surveyed, 33(82%) reporting that FASTER could improve outcomes of very sick children in low-resource settings; 26(65%) rating caregivers as able to adequately capture patients’ severity of illness. Of 70 caregivers surveyed, 63(90%) reported that FASTER training was easy to understand; all(100%) agreed that the intervention would improve care of hospitalized children and help identify sick children in their community.DiscussionAlthough we observed no difference in recorded frequency of clinician visits with FASTER monitoring, the tool was rated positively by caregivers and clinicians. Further research to refine implementation with additional input from all stakeholders might increase the effectiveness of FASTER in detecting and responding to clinical deterioration in low-resource settings.

Abstract 12134: Risk of In-Hospital Deterioration for Children With Single Ventricle Physiology

Introduction: Children with single ventricle physiology (SV) are at high risk of in-hospital morbidity and mortality, with much of that increased risk coming in the first year of life. Understanding which children are at the highest risk for clinical deterioration may allow for increased monitoring and earlier escalation of care, with associated decreased mortality. Methods: We conducted a retrospective chart review of all admissions to the pediatric cardiology non-ICU inpatient service from 2014 - 2018 for children < 18 years old. Clinical deterioration was defined as an unplanned transfer to the ICU or inpatient mortality. Children with SV were selected by diagnosis codes. Results: From the entire cohort of 1612 pediatric cardiology admissions (56 % male, 25% SV), 288 admissions had a deterioration event including 26 deaths. Infants less than one year with SV (n = 197 admissions) were significantly more likely to have a deterioration event (107 events over 62 admissions with an event) than the overall pediatric cardiology cohort (OR 2.11, 95% CI 1.52-2.93). Among infants with SV, those with a deterioration event were significantly younger (median 1.7 v 4.3 months, p < 0.001). Further, at baseline they had significantly lower oxygen saturation (84% v 87%, p < 0.01), lower systolic blood pressure (85mmHg v 90mmHg, p< 0.02), higher respiration rate (48 v 44, p < 0.01), and higher hematocrit (44.0 v 40.2, p < 0.005) compared to those who remained stable. Mean Pediatric Early Warning Scores (PEWS) were significantly higher for infants with SV who had a deterioration event (1.4 v 0.9, p < 0.001) and PEWS scores significantly increased in the 48 hours prior to an event (p < 0.001). Of the 104 non-death events, 61 required an increase in oxygen support and 51 required a fluid bolus prior to the event (p < 0.001). Conclusions: Infants with SV are at high risk for clinical deterioration. There are baseline differences in vital signs and lab work between those that remain stable and those that have a deterioration event. PEWS scores and oxygen and fluid treatment significantly increase prior to deterioration events. Leveraging data from the Electronic Medical Record to identify the highest risk patients may allow for earlier detection and intervention to prevent clinical deterioration.

The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

Background Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients. Methods A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission. Results Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death. Conclusion Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.