Serum substance P levels are associated with severity and mortality in patients with severe traumatic brain injury

IntroductionSubstance P (SP) is a member of the tachykinin family of neuropeptides, which are widely distributed throughout the central nervous system (CNS) and actively involved in inflammatory processes. SP is released early following acute injury to the CNS, promoting a neurogenic inflammatory response characterized by an increase in the permeability of the blood–brain barrier and the development of vasogenic edema. High levels of SP could lead to an exacerbated inflammatory response that could be fatal for patients with traumatic brain injury (TBI). Thus, the main goal of the present study was to determine whether serum SP levels are associated with injury severity and mortality in patients with severe TBI.MethodsThis multicenter, observational, prospective study was carried out in six Spanish intensive care units and included patients with Glasgow Coma Scale (GCS) scores ≤8. Patients with an Injury Severity Score ≥10 in non-cranial aspects were excluded. Blood samples were collected on day 1 of TBI to measure serum SP levels. The endpoint was 30-day mortality.ResultsWe found higher serum SP levels (P =0.002) in non-surviving patients (n =27) than in surviving patients (n =73). The area under the curve for serum SP levels with regard to predicting 30-day mortality was 0.70 (95% confidence interval (CI), 0.60 to 0.79; P <0.001). Survival analysis showed that patients with serum SP levels >299 pg/ml had higher 30-day mortality than patients with lower levels (hazard ratio =3.7; 95% CI, 1.75 to 7.94; P <0.001). Multiple binomial logistic regression analysis showed that serum SP levels >299 pg/ml were associated with 30-day mortality when we controlled for APACHE II score and Marshall computed tomography lesion classification (odds ratio (OR) =5.97; 95% CI, 1.432 to 24.851; P =0.01) and for GCS score and age (OR =5.71; 95% CI, 1.461 to 22.280; P =0.01). We found a negative association between serum SP levels and GCS score (Spearman’s ρ = −0.22; P =0.03).ConclusionsWe report, for the first time to our knowledge, that serum SP levels were associated with injury severity and mortality in patients with severe TBI. These results open the possibility that SP antagonists may be useful in the treatment of patients with severe TBI.     

Resistin is associated with mortality in patients with traumatic brain injury

Introduction  

Recently, we reported that high levels of resistin are present in the peripheral blood of patients with intracerebral hemorrhage and are associated with a poor outcome. However, not much is known regarding the change in plasma resistin and its relation with mortality after traumatic brain injury (TBI). Thus, we sought to investigate change in plasma resistin level after TBI and to evaluate its relation with disease outcome.     

Early detection of intensive care needs and mortality risk by use of five early warning scores in patients with traumatic injuries: An observational study

ObjectiveThe purpose of this research was to evaluate the predictive capacity of five Early Warning Scores in relation to the clinical evolution of adult patients with different types of trauma.Research MethodologyWe conducted a longitudinal, prospective, observational study, calculating the Early Warning Scores [Modified Early Warning Score (MEWS), National Early Warning Score 2 (NEWS-2), VitalPAC Early Warning Score (ViEWS), Modified Rapid Emergency Medicine Score (MREMS), and Rapid Acute Physiology Score (RAPS)] upon arrival of patients to the emergency department.SettingIn total, 445 cases of traumatic injuries were included in the study.Main Outcome MeasuresThe predictive capacity was verified with the data on admission to intensive care units (ICU) and mortality at two, seven and 30 days.Results201 patients were hospitalized and 244 were discharged after being attended in the emergency department. 91 cases (20.4%) required ICU care and 4.7% of patients died (21 patients) within two days, 6.5% (29 patients) within seven days and 9.7% (43 patients) within 30 days. The highest area under the curve for predicting the need for ICU care was obtained by the National Early Warning Score 2 and the VitalPAC Early Warning Score. For predicting mortality, the Modified Rapid Emergency Medicine Score obtained the best scores for two-day mortality, seven-day mortality and 30-day mortality.ConclusionsEvery Early Warning Score analyzed in this study obtained good results in predicting adverse effects in adult patients with traumatic injuries, creating an opportunity for new clinical applications in the emergency department.     

Predicting mortality in intensive care unit patients infected with Klebsiella pneumoniae: A retrospective cohort study

IntroductionAlthough several models to predict intensive care unit (ICU) mortality are available, their performance decreases in certain subpopulations because specific factors are not included. Moreover, these models often involve complex techniques and are not applicable in low-resource settings. We developed a prediction model and simplified risk score to predict 14-day mortality in ICU patients infected with Klebsiella pneumoniae.MethodologyA retrospective cohort study was conducted using data of ICU patients infected with Klebsiella pneumoniae at the largest tertiary hospital in Northern Vietnam during 2016–2018. Logistic regression was used to develop our prediction model. Model performance was assessed by calibration (area under the receiver operating characteristic curve-AUC) and discrimination (Hosmer-Lemeshow goodness-of-fit test). A simplified risk score was also constructed.ResultsTwo hundred forty-nine patients were included, with an overall 14-day mortality of 28.9%. The final prediction model comprised six predictors: age, referral route, SOFA score, central venous catheter, intracerebral haemorrhage surgery and absence of adjunctive therapy. The model showed high predictive accuracy (AUC = 0.83; p-value Hosmer-Lemeshow test = 0.92). The risk score has a range of 0–12 corresponding to mortality risk 0–100%, which produced similar predictive performance as the original model.ConclusionsThe developed prediction model and risk score provide an objective quantitative estimation of individual 14-day mortality in ICU patients infected with Klebsiella pneumoniae. The tool is highly applicable in practice to help facilitate patient stratification and management, evaluation of further interventions and allocation of resources and care, especially in low-resource settings where electronic systems to support complex models are missing.     

Association of skull fracture with in-hospital mortality in severe traumatic brain injury patients

IntroductionTo identify the association between skull fracture (SF) and in-hospital mortality in patients with severe traumatic brain injury (TBI).Materials and methodsThis multicenter cohort study included a retrospective analysis of data from the Japan Trauma Data Bank (JTDB). JTDB is a nationwide, prospective, observational trauma registry with data from 235 hospitals. Adult patients with severe TBI (Glasgow Coma Scale <9, head Abbreviated Injury Scale (AIS) ≥ 3, and any other AIS < 3) who were registered in the JTDB between January 2004 and December 2017 were included in the study. Patients who (a) were < 16 years old, (b) developed cardiac arrest before or at hospital arrival, and (c) had burns and penetrating injuries were excluded from the study. In-hospital mortality was the primary outcome assessed. Multivariable logistic regression analyses were performed to calculate the adjusted odds ratios (ORs) of SF and their 95% confidence intervals (CIs) for in-hospital mortality.ResultsA total of 9607 patients were enrolled [median age: 67 (interquartile range: 50–78) years] in the study. Among those patients, 3574 (37.2%) and 6033 (62.8%) were included in the SF and non-SF groups, respectively. The overall in-hospital mortality rate was 44.1% (4238/9607). A multivariate analysis of the association between SF and in-hospital mortality yielded a crude OR of 1.63 (95% CI: 1.47–1.80). A subgroup analysis of the association of skull vault fractures, skull base fractures, and both fractures together with in-hospital mortality yielded adjusted ORs of 1.60 (95% CI: 1.42–1.98), 1.40 (95% CI: 1.16–1.70), and 2.14 (95% CI: 1.74–2.64), respectively, relative to the non-SF group.ConclusionsThis observational study showed that SF is associated with in-hospital mortality among patients with severe TBI. Furthermore, patients with both skull base and skull vault fractures were associated with higher in-hospital mortality than those with only one of these injuries.     

Brain injury severity and autonomic dysregulation accurately predict heterotopic ossification in patients with traumatic brain injury

OBJECTIVE: To assess brain injury severity, autonomic dysregulation and systemic infection as risk factors for the occurrence of heterotopic ossification in patients with severe traumatic brain injury. DESIGN: Historic cohort study. SETTING: Radboud University Medical Centre. SUBJECTS: All consecutively admitted patients with severe traumatic brain injury (admission Glasgow Coma Scale score 8 or less) during the years 2002-2003. MAIN MEASURES: The development of clinically relevant heterotopic ossification, defined as painful swelling of joints with redness and decreased range of motion, confirmed radiographically. RESULTS: Seventy-six (64%) of the 119 patients survived and were eligible for further follow-up. Nine patients (12%) developed 20 symptomatic heterotopic ossifications, in one or more joints. Patients with heterotopic ossification had sustained more severe brain injuries, compared to the group without heterotopic ossification. The mean coma duration in the heterotopic ossification group was 28.11 days (SD 20.20) versus 7.54 days (SD 7.47) in the patients without heterotopic ossification (P < 0.001). The occurrence of autonomic dysregulation (relative risk (RR) 59.55, 95% confidence interval (CI) 8.39-422.36), diffuse axonal injury (RR 20.68, 95% CI 4.92-86.91), spasticity (RR 16.96, 95% CI 3.96-72.57) and systemic infection (RR 13.12, 95% CI 3.01-57.17) were all associated with an increased risk of developing symptomatic heterotopic ossification. However, only autonomic dysregulation had a high positive (88.9%, 95% CI 51.7-99.7) and negative (98.5%, 95% CI 91.9-99.9) predictive value with regard to heterotopic ossification. CONCLUSIONS: The occurrence of autonomic dysregulation may predict the chance of developing heterotopic ossification in patients with severe head injury.     

One-year mortality among Danish intensive care patients with acute kidney injury: a cohort study

ABSTRACT: INTRODUCTION: There are few studies on long-term mortality among intensive care unit (ICU) patients with acute kidney injury (AKI). We assessed the prevalence of AKI at ICU admission, its impact on mortality during one year of follow-up, and whether the influence of AKI varied in subgroups of ICU patients. METHODS: We identified all adults admitted to any ICU in Northern Denmark (approximately 1.15 million inhabitants) from 2005 through 2010 using population-based medical registries. AKI was defined at ICU admission based on the risk, injury, failure, loss of kidney function, and end-stage kidney disease (RIFLE) classification, using plasma creatinine changes. We included four severity levels: AKI-risk, AKI-injury, AKI-failure, and without AKI. We estimated cumulative mortality by the Kaplan-Meier method and hazard ratios (HRs) using a Cox model adjusted for potential confounders. We computed estimates for all ICU patients and for subgroups with different comorbidity levels, chronic kidney disease status, surgical status, primary hospital diagnosis, and treatment with mechanical ventilation or with inotropes/vasopressors. RESULTS: We identified 30,762 ICU patients, of which 4,793 (15.6%) had AKI at ICU admission. Thirty-day mortality was 35.5% for the AKI-risk group, 44.2% for the AKI-injury group, and 41.0% for the AKI-failure group, compared with 12.8% for patients without AKI. The corresponding adjusted HRs were 1.96 (95% confidence interval (CI) 1.80-2.13), 2.60 (95% CI 2.38 to 2.85) and 2.41 (95% CI 2.21 to 2.64), compared to patients without AKI. Among patients surviving 30 days (n = 25,539), 31- to 365 day mortality was 20.5% for the AKI-risk group, 23.8% for the AKI-injury group, and 23.2% for the AKI-failure group, compared with 10.7% for patients without AKI, corresponding to adjusted HRs of 1.33 (95% CI 1.17 to 1.51), 1.60 (95% CI 1.37 to1.87), and 1.64 (95% CI 1.42 to 1.90), respectively. The association between AKI and 30-day mortality was evident in subgroups of the ICU population, with associations persisting in most subgroups during the 31- to 365-day follow-up period, although to a lesser extent than for the 30-day period. CONCLUSIONS: AKI at ICU admission is an important prognostic factor for mortality throughout the subsequent year.     

The value of trauma scores: predicting discharge after traumatic brain injury

OBJECTIVE: To determine the association of acute variables with disposition after acute hospitalization. DESIGN: Revised Trauma Score (RTS), Injury Severity Score (ISS), and the Combined Trauma Score Injury Severity Score (TRISS(RTS)) were compared with discharge disposition after acute hospitalization of 378 consecutive patients who sustained a traumatic brain injury (TBI) and were treated at a level 1 trauma center between September 1997 and May 1998. RESULTS: Logistic regression modeling found TRISS(RTS) to predict discharge to home with or without home health assistance or inpatient rehabilitation vs. nursing home placement or death. Subsequent modeling, excluding patients who died or went to nursing homes, identified RTS and ISS as predictors of discharge to home with or without home health vs. inpatient rehabilitation. A sensitivity of 97.78% and 93.91% were achieved with these two models when tested on a population of 4,625 patients with TBI treated during the last 10 yr at the same facility. CONCLUSIONS: The results suggest that RTS, ISS, and TRISS(RTS) are predictors of discharge disposition after acute hospitalization with TBI and may be useful measures of rehabilitation services resource planning early in the course of TBI management.     

前降钙素在创伤性脑损伤患者病情评估和预后判断中的应用价值

目的：探讨前降钙素在创伤性脑损伤患者病情评估和预后判断中的应用价值。方法选择2010年3月-2013年3月期间我院收治的创伤性脑损伤患者92例,根据入院时格拉斯哥昏迷指数评分分为轻度脑损伤组（53例）和重度脑损伤组（39例）。同时,根据患者入院1周内有无发生感染分为感染组（39例）和无感染组（53例）。所有患者均在入院后第1、3、5、7天检测前降钙素水平,比较轻度脑损伤组与重度脑损伤组、感染组与无感染组患者的前降钙素水平;同时采用 Kaplan-Meiers 生存分析比较入院前降钙素水平正常和升高患者的28天生存情况。结果重度脑损伤组患者入院第1、3、5、7天的前降钙素水平均显著高于轻度脑损伤患者（均 P ＜0．05）;患者入院时格拉斯哥昏迷评分与前降钙素水平呈负相关（r＝0．532,P ＜0．05）;感染组患者入院第3、5、7天的前降钙素水平均显著高于无感染组患者（均 P ＜0．05）;入院时前降钙素水平正常的患者28天生存率显著高于入院时前降钙素水平升高的患者（χ2＝5．821,P ＝0．016）。结论动态监测前降钙素水平变化情况,对创伤性脑损伤患者病情的评估、感染的诊断和预后的判断均具有重要的临床意义。     

Effectiveness of seatbelts in mitigating traumatic brain injury severity

INTRODUCTION Over the past few decades,traumatic brain injuries(TBIs)have become one of the leading causes of death and the leading cause of injury-related death in the USA.[1,2]It is estimated that 1.70 million people are subject to TBIs each year.[2]Males are more likely to sustain TBIs(59%);the most common age groups are 0–5 years,15–19 years,and>65 years.[2]Approximately 1.36 million people present to the emergency department(ED),275,000 are admitted to the hospital,and 52,000 people die from TBIs.[2]The leading causes of TBIs are falling(35.2%),motor vehicle collisions(MVCs,17.3%),struck by/against an object(16.5%),and assault(10.0%).[2]These statistics combine to make TBIs the leading cause of injury-related death in the USA at 30.5%.[2]It has been estimated that,with specifi c guidelines from the Brain Trauma Foundation,up to 50.0%of the 52,000 TBIrelated deaths may be prevented.[3]     

颅内压监测在颅脑损伤患者中的应用与护理

目的探讨经颅内引流管行颅内压（ICP）监测对颅脑损伤疾病治疗的应用价值与护理。方法对2009年2月至2011年1月入住ICU的因颅脑损伤已行颅内引流术的患者,在常规监护、治疗基础上,在颅内引流管外侧端接三通管,行ICP监测。对ICP〉2.0 kPa者予加强脱水,ICP〈0.5 kPa者提高引流袋水平,减少脱水剂用量及使用时间。结果共53例患者行ICP监测,死亡6例,死亡率11.32%;较同期未行ICP监测病例死亡率26.53%明显下降。监测结果及表现图谱与病程发展相符。结论颅脑损伤术后患者经颅内引流管行ICP监测操作简便,达到ICP监测技术要求,具有明显的临床意义。     

Evaluation of patients with mild traumatic brain injury

Care of patients who have sustained a mild traumatic brain injury (MTBI) requires collaboration between the case manager, other disciplines, the patients, and their significant other. The case manager's knowledge of the evaluation tools that may be used to assess patient status in various phases of care is essential in planning and dealing with possible variances of care, which could affect outcomes of care. An overview of the etiology of MTBI, diagnosis, recovery, evaluation at various stages, outcome measurement, and examples of valid and reliable tools are presented.     

Simple translational equations to compare illness severity scores in intensive care trials

Purpose

Comparison of illness severity for intensive care unit populations assessed according to different scoring systems should increase our ability to compare and meta-analyze past and future trials but is currently not possible. Accordingly, we aimed to establish a methodology to translate illness severity scores obtained from one system into another.

Materials and methods

Using the Australian and New-Zealand intensive care adult patient database, we obtained simultaneous admission Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III scores and Simplified Acute Physiology Score (SAPS) II in 634 428 patients admitted to 153 units between 2001 and 2010. We applied linear regression analyses to create models enabling translation of one score into another. Sensitivity analyses were performed after removal of diagnostic categories excluded from the original APACHE database, after matching for similar risk of death, after splitting data according to country of origin (Australia or New Zealand) and after splitting admissions occurring before or after 2006.

Results

The translational models were APACHE III = 3.08 × APACHE II + 5.75; APACHE III = 1.47 × SAPS II + 8.6; and APACHE II = 0.36 × SAPS II + 4.4. The area under the receiver operating curve for mortality prediction was 0.853 (95% confidence interval, 0.851-0.855) for the “APACHE II derived APACHE III” score and 0.854 (0.852-0.855) for the “SAPS II derived APACHE III” vs 0.854 (0.852-0.855) for the original APACHE III score. Similarly, it was 0.841 (0.839-0.843) for the “SAPS II derived APACHE II score” vs 0.842 (0.840-0.843) for the original APACHE II score. Correlation coefficients as well as intercepts remained very similar in all subgroups analyses.

Conclusions

Simple and robust translational formulas can be developed to allow clinicians to compare illness severity between studies involving critically ill patients. Further studies in other countries and health care systems are needed to confirm the generalizability of these results.