Mortality following congenital diaphragmatic hernia repair: the role of anesthesia.

BACKGROUND: Mortality following surgical repair of congenital diaphragmatic hernia (CDH) remains high. The volume and type of perioperative intravenous fluid administered, baro-trauma, oxygen toxicity, and the duration of anesthesia are thought to affect outcome in surgical populations. AIMS: The aim of this retrospective observational study was to determine whether the perioperative volume or type of fluids and/or the duration of anesthesia were associated with postoperative mortality and if mortality was predicted by the oxygenation index (OI) prior to or following CDH surgical repair. METHODS: The records of infants with a left-sided CDH and without other congenital anomalies, who underwent surgical repair between April 2009 and March 2015, were examined. The oxygenation index was used to "quantify" the severity of lung function abnormality and reported as the best OI on day 1 after birth (OIBEST ), the OI immediately prior to surgery (OIPRE ) and at 1, 6, 12, and 24 h postsurgery (OI1h , OI6h , OI12h , OI24h ), respectively. The change in the OI index (delta OI) was calculated by subtracting OIPRE from postoperative OIs. RESULTS: The records of 37 CDH infants (median gestational age 35.8, range 31.5-41.4 weeks) were assessed; six died postoperatively. Neither the duration of anesthesia, the volume of crystalloids or colloids administered, nor the peak inflation pressures used during surgical repair were significantly correlated with postoperative mortality. Neither fetal tracheal occlusion nor use of a parietal patch significantly influenced mortality. The postoperative OI1h , OI6h , OI12h showed weak evidence for a difference between survivors and nonsurvivors. An OI24h of ≥5.5 predicted mortality with 100% sensitivity (95% CI, confidence intervals (CI) 40-100) and 93.1% specificity (95% CI, 77-99). CONCLUSION: Neither the volume of intraoperative fluids administered nor the duration of anesthesia was associated with postoperative death. The OI 24 h postsurgery was the best predictor of an increased risk of mortality.

Exploring the role of soil types on defense and fitness traits of silverleaf nightshade (Solanum elaeagnifolium), a worldwide invasive species through a field survey in the native range.

Silverleaf nightshade (Solanum elaeagnifolium) is a highly successful invasive weed that has caused agricultural losses both in its home and invaded ranges. Surveying 50 sub-populations over 36,000 km2 in its native range in South Texas, we investigated the interactions among soil type, population size, plant height, herbivory, and plant defenses in its home range with the expectation that populations growing in the plant's preferred sandier soils would host larger colonies of healthier and better defended plants. At each sampling location, on randomly selected plants, we measured height, insect herbivore damage, and presence, and density of internode spines. Soil type was determined using the NRCS Web Soil Survey and primarily grouped into sand, clay, or urban. Our results show a tradeoff between growth and defense with larger colonies and taller plants in clay soils, but smaller colonies of shorter, spinier plants in sandy soils. We also observed decreased herbivory in urban soils, further confirming the plant's ability to survive and even be strengthened by highly disturbed conditions. This study is a starting point for a better understanding of silverleaf nightshade's ecology in its home range and complicates the assumption that it thrives best in sandy soils.

Prognostic value of the oxygenation index to predict survival and timing of surgery in infants with congenital diaphragmatic hernia.

AIMS: To compare the mean oxygenation index on day1 (MOId1) with the best OI on day1 (BOId1) in predicting 30-day mortality in congenital diaphragmatic hernia (CDH). In addition, to determine whether serial OIs in the first 48 h after birth and preoperative OI (PreOp-OI), were associated with optimal timing of surgery, ventilation requirement and hospital stay in infants with CDH. METHODS: The medical records of infants with antenatally diagnosed CDH during 2009-2015 were retrospectively reviewed. Receiver operator characteristic curves were constructed to identify MOId1 and BOId1 cutoff levels to predict 30-day mortality. In those who underwent surgery, the mean OI on each of the first two days (MOId1, MOId2) and PreOp-OI were correlated using Spearman (rs) with the age at surgery, the duration of ventilation and length of stay. Statistical significance was P < 0.05* and < 0.001**. RESULTS: Survivors (n = 44) and nonsurvivors (n = 24) were comparable in gestational age, birth weight and defect laterality. Nonsurvivors had higher median BOId1 (15.4 vs 2.9; P < 0.01) and MOId1 (48 vs 7.5; p < 0.01) than survivors. Mortality was best predicted by two cutoffs [BOId1 > 6 (sensitivity 92%, specificity 89%); and MOId1 > 17 (sensitivity 96%, specificity 96%)]. Forty-four infants underwent surgery at a median postnatal age of five (range 2-19) days. MOId1 and MOId2 both correlated significantly with the age at surgery (rs = 0.4**, rs 0.5**) but not ventilation period and length of stay. PreOp-OI correlated significantly with age at surgery, duration of ventilation and length of stay (rs = 0.32*, rs = 0.47**, rs = 0.37*). A PreOp-OI <3 was predictive of optimal timing for surgery with improved duration of ventilation (8 vs 22, P = 0.001) and length of stay (26 vs 47, P = 0.004). However, 11/44 (25%) patients would not achieve a PreOp-OI < 3 by day 7+ of life and might still require surgery, one of them died. CONCLUSION: Both MOId1 and BOId1 are highly predictive of mortality in CDH. Oxygenation indices in the first 48 h poorly predicted the timing of surgery. PreOp-OI <3 may be a cutoff for optimal timing for surgery in infants for CDH repair. TYPE OF STUDY: Prognostic Study. LEVEL OF EVIDENCE: Level III.

Crash sequence based risk matrix for motorcycle crashes.

Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers' age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011-2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-off-road crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.

Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland.

One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury severities and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses.

Partial proportional odds model-an alternate choice for analyzing pedestrian crash injury severities.

The conventional methods for crash injury severity analyses include either treating the severity data as ordered (e.g. ordered logit/probit models) or non-ordered (e.g. multinomial models). The ordered models require the data to meet proportional odds assumption, according to which the predictors can only have the same effect on different levels of the dependent variable, which is often not the case with crash injury severities. On the other hand, non-ordered analyses completely ignore the inherent hierarchical nature of crash injury severities. Therefore, treating the crash severity data as either ordered or non-ordered results in violating some of the key principles. To address these concerns, this paper explores the application of a partial proportional odds (PPO) model to bridge the gap between ordered and non-ordered severity modeling frameworks. The PPO model allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. This study is based on a five-year (2008-2012) national pedestrian safety dataset for Switzerland. A comparison between the application of PPO models, ordered logit models, and multinomial logit models for pedestrian injury severity evaluation is also included here. The study shows that PPO models outperform the other models considered based on different evaluation criteria. Hence, it is a viable method for analyzing pedestrian crash injury severities.

Propensity scores-potential outcomes framework to incorporate severity probabilities in the highway safety manual crash prediction algorithm.

Accurate estimation of the expected number of crashes at different severity levels for entities with and without countermeasures plays a vital role in selecting countermeasures in the framework of the safety management process. The current practice is to use the American Association of State Highway and Transportation Officials' Highway Safety Manual crash prediction algorithms, which combine safety performance functions and crash modification factors, to estimate the effects of safety countermeasures on different highway and street facility types. Many of these crash prediction algorithms are based solely on crash frequency, or assume that severity outcomes are unchanged when planning for, or implementing, safety countermeasures. Failing to account for the uncertainty associated with crash severity outcomes, and assuming crash severity distributions remain unchanged in safety performance evaluations, limits the utility of the Highway Safety Manual crash prediction algorithms in assessing the effect of safety countermeasures on crash severity. This study demonstrates the application of a propensity scores-potential outcomes framework to estimate the probability distribution for the occurrence of different crash severity levels by accounting for the uncertainties associated with them. The probability of fatal and severe injury crash occurrence at lighted and unlighted intersections is estimated in this paper using data from Minnesota. The results show that the expected probability of occurrence of fatal and severe injury crashes at a lighted intersection was 1 in 35 crashes and the estimated risk ratio indicates that the respective probabilities at an unlighted intersection was 1.14 times higher compared to lighted intersections. The results from the potential outcomes-propensity scores framework are compared to results obtained from traditional binary logit models, without application of propensity scores matching. Traditional binary logit analysis suggests that the probability of occurrence of severe injury crashes is higher at lighted intersections compared to unlighted intersections, which contradicts the findings obtained from the propensity scores-potential outcomes framework. This finding underscores the importance of having comparable treated and untreated entities in traffic safety countermeasure evaluations.

Application of propensity scores and potential outcomes to estimate effectiveness of traffic safety countermeasures: Exploratory analysis using intersection lighting data.

More than 5.5 million police-reported traffic crashes occurred in the United States in 2009, resulting in 33,808 fatalities and more than 2.2 million injuries. Significant funds are expended annually by federal, state, and local transportation agencies in an effort to reduce traffic crashes. Effective safety management involves selecting highway and street locations with potential for safety improvements; correctly diagnosing safety problems; identifying appropriate countermeasures; prioritizing countermeasure implementation at selected sites; and, evaluating the effectiveness of implemented countermeasures. Accurate estimation of countermeasure effectiveness is a critical component of the safety management process. In this study, a statistical modeling framework, based on propensity scores and potential outcomes, is described to estimate countermeasure effectiveness from non-randomized observational data. Average treatment effects are estimated using semi-parametric estimation methods. To demonstrate the framework, the average treatment effect of fixed roadway lighting at intersections in Minnesota is estimated. The results indicate that fixed roadway lighting reduces expected nighttime crashes by approximately 6%, which compares favorably to other, recent lighting-safety research findings.