Prognosis in moderate-severe traumatic brain injury in a Swedish cohort and external validation of the IMPACT models

Background A major challenge in management of traumatic brain injury (TBI) is to assess the heterogeneity of TBI pathology and outcome prediction. A reliable outcome prediction would have both great value for the healthcare provider, but also for the patients and their relatives. A well-known prediction model is the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic calculator. The aim of this study was to externally validate all three modules of the IMPACT calculator on TBI patients admitted to Uppsala University hospital (UUH). Method TBI patients admitted to UUH are continuously enrolled into the Uppsala neurointensive care unit (NICU) TBI Uppsala Clinical Research (UCR) quality register. The register contains both clinical and demographic data, radiological evaluations, and outcome assessments based on the extended Glasgow outcome scale extended (GOSE) performed at 6 months to 1 year. In this study, we included 635 patients with severe TBI admitted during 2008–2020. We used IMPACT core parameters: age, motor score, and pupillary reaction. Results The patients had a median age of 56 (range 18–93), 142 female and 478 male. Using the IMPACT Core model to predict outcome resulted in an AUC of 0.85 for mortality and 0.79 for unfavorable outcome. The CT module did not increase AUC for mortality and slightly decreased AUC for unfavorable outcome to 0.78. However, the lab module increased AUC for mortality to 0.89 but slightly decreased for unfavorable outcome to 0.76. Comparing the predicted risk to actual outcomes, we found that all three models correctly predicted low risk of mortality in the surviving group of GOSE 2–8. However, it produced a greater variance of predicted risk in the GOSE 1 group, denoting general underprediction of risk. Regarding unfavorable outcome, all models once again underestimated the risk in the GOSE 3–4 groups, but correctly predicts low risk in GOSE 5–8. Conclusions The results of our study are in line with previous findings from centers with modern TBI care using the IMPACT model, in that the model provides adequate prediction for mortality and unfavorable outcome. However, it should be noted that the prediction is limited to 6 months outcome and not longer time interval.

Dark Triad traits as predictors of adherence to traditional masculine norms in men

The present study examined whether Dark Triad traits explain variance in men’s adherence to traditional masculine norms (Playboy, Self-Reliance, Emotional Control, Winning, Violence, Heterosexual Self-Presentation, Risk-Taking, and Power over Women). Two-hundred and thirty-seven English speaking men (aged 18 to 62 years) completed online versions of the Self-Report Psychopathy Scale-III, the Narcissistic Personality Inventory, the Mach-IV, and the Conformity to Masculine Norms Inventory-29. Results from regression analyses showed that the psychopathic trait Callous Affect positively predicted men’s Need to Win, Emotional Control, Violence, and Power Over Women; Erratic Lifestyle was a positive predictor of Risk-Taking; and Antisocial Behaviour was a positive predictor of Playboy. Machiavellianism predicted only Violence. The Narcissistic sub-trait Leadership positively predicted Risk-Taking; Manipulativeness predicted Risk-Taking and Violence; Superiority predicted Risk-Taking and Power over Women; Vanity predicted Self-Reliance; and Exhibitionism predicted Emotional Control. We conclude that whilst Callous Affect appears to hold the highest predictive validity, the Dark Triad traits differentially predict adherence to specific masculine norms.

A Prediction Model for Pediatric Radiographic Pneumonia

BACKGROUND: Chest radiographs (CXRs) are frequently used in the diagnosis of community-acquired pneumonia (CAP). We sought to construct a predictive model for radiographic CAP based on clinical features to decrease CXR use. METHODS: We performed a single-center prospective study of patients 3 months to 18 years of age with signs of lower respiratory infection who received a CXR for suspicion of CAP. We used penalized multivariable logistic regression to develop a full model and bootstrapped backward selection models to develop a parsimonious reduced model. We evaluated model performance at different thresholds of predicted risk. RESULTS: Radiographic CAP was identified in 253 (22.2%) of 1142 patients. In multivariable analysis, increasing age, prolonged fever duration, tachypnea, and focal decreased breath sounds were positively associated with CAP. Rhinorrhea and wheezing were negatively associated with CAP. The bootstrapped reduced model retained 3 variables: age, fever duration, and decreased breath sounds. The area under the receiver operating characteristic for the reduced model was 0.80 (95% confidence interval: 0.77–0.84). Of 229 children with a predicted risk of <4%, 13 (5.7%) had radiographic CAP (sensitivity of 94.9% at a 4% risk threshold). Conversely, of 229 children with a predicted risk of >39%, 140 (61.1%) had CAP (specificity of 90% at a 39% risk threshold). CONCLUSIONS: A predictive model including age, fever duration, and decreased breath sounds has excellent discrimination for radiographic CAP. After external validation, this model may facilitate decisions around CXR or antibiotic use in CAP.

Lean Yet Unhealthy: Asian American Adults Had Higher Risks for Metabolic Syndrome than Non-Hispanic White Adults with the Same Body Mass Index: Evidence from NHANES 2011–2016

(1) Background: Despite having consistently lower rates of obesity than other ethnic groups, Asian Americans (AAs) are more likely to be identified as metabolically obese, suggesting an ethnic-specific association between BMI and cardiometabolic outcomes. The goal of this study was to provide an estimate of metabolic syndrome (MetS) prevalence among AAs using national survey data and to compare this rate to that of non-Hispanic Whites (NHWs) over the BMI continuum. (2) Methods: Using the NHANES 2011–2016 data, we computed age-adjusted, gender-specific prevalence of MetS and its individual components for three BMI categories. Furthermore, we conducted multivariate binary logistic regression to examine the risk of MetS in AAs compared to NHWs, controlling for sociodemographic and lifestyle factors. The analysis sample consisted of 2121 AAs and 6318 NHWs. (3) Results: Among AAs, the prevalence of MetS and its components increased with higher BMI levels, with overall prevalence being 5.23% for BMI < 23, 38.23% for BMI of 23–27.4, and 77.68% for BMI ≥ 27.5 in men; and 18.61% for BMI < 23, 47.82% for BMI of 23–27.4, and 67.73% for BMI ≥ 27.5 in women. We also found that for those with a BMI > 23, AAs had a higher predicted risk of MetS than their NHW counterparts of the same BMI level, in both men and women. (4) Conclusions: Our findings support the use of lower BMI ranges for defining overweight and obesity in Asian populations, which would allow for earlier and more appropriate screening for MetS and may better facilitate prevention efforts.

A Comparison between Endostatin and Conventional Biomarkers on 30-Day Mortality and Renal Replacement Therapy in Unselected Intensive Care Patients

Endostatin may predict mortality and kidney impairment in general populations as well as in critically ill patients. We decided to explore the possible role of endostatin as a predictor of 30-day mortality, acute kidney injury (AKI), and renal replacement therapy (RRT) in a cohort of unselected intensive care unit (ICU) patients. Endostatin and creatinine in plasma were analyzed and SAPS3 was determined in 278 patients on ICU arrival at admission to a Swedish medium-sized hospital. SAPS3 had the highest predictive value, 0.85 (95% C.I.: 0.8–0.90), for 30-day mortality. Endostatin, in combination with age, predicted 30-day mortality by 0.76 (95% C.I.: 0.70–0.82). Endostatin, together with age and creatinine, predicted AKI with 0.87 (95% C.I.: 0.83–0.91). Endostatin predicted AKI with [0.68 (0.62–0.74)]. Endostatin predicted RRT, either alone [0.82 (95% C.I.: 0.72–0.91)] or together with age [0.81 (95% C.I.: 0.71–0.91)]. The predicted risk for 30-day mortality, AKI, or RRT during the ICU stay, predicted by plasma endostatin, was not influenced by age. Compared to the complex severity score SAPS3, circulating endostatin, combined with age, offers an easily managed option to predict 30-day mortality. Additionally, circulating endostatin combined with creatinine was closely associated with AKI development.

Numerical Model for Prediction of Indoor COVID-19 Infection Risk Based on Sensor Data

In addition to infection with SARS-CoV-2 via direct droplet transmission or contact with contaminated surfaces, infection via aerosol transport is a predominant pathway in indoor environments. The developed numerical model evaluates the risk of a COVID-19 infection in a particular room based on measurements of temperature, humidity, CO2 and particle concentration, the number of people and instances of speech, coughs and sneezing using a dedicated low-cost sensor system. The model can dynamically provide the predicted risk of infection to the building management system or people in the room. The effect of temperature, humidity and ventilation intensity on the infection risk is shown. Coughing and especially sneezing greatly increase the probability of infection in the room; therefore distinguishing these events is crucial for the applied measurement system.

Perceptions of Singaporeans towards informed consent: a cross-sectional survey

Introduction: In a patient-centric health system, it is essential to know patients’ views about informed consent. The objective of this study was to understand the perceptions of the local population regarding informed consent. Methods: Spanning across six weeks from January 2016 to March 2016, a cross-sectional survey of adults attending General Surgery outpatient clinics at Tan Tock Seng Hospital was performed. Sociodemographic data, lifestyle and health-related information, perception and purpose of consent forms, and decision-making preferences were studied. Results: 445 adults participated in the survey. Most participants were below 40 years old (n = 265, 60.1%), female (n = 309, 70.1%) and degree holders (n = 196, 44.4%). 56.9% of participants wanted to know every possible risk, while 28.3% wanted to know common and serious risks. On multivariate analysis, age (age 61–74 years: odds ratio [OR] 11.1, 95% confidence interval [CI] 2.2–56.1, p = 0.004; age > 75 years: OR 22.2, 95% CI 1.8–279.1, p = 0.017) was a predictor of not wanting to know any risks. Age also predicted risk of disclosure for death (age 61–74 years: OR 13.4, 95% CI 4.2–42.6, p < 0.001; age > 75 years: OR 32.0, 95% CI 4.5–228.0, p = 0.001). Most participants (48.1%) preferred making shared decisions with doctors, and an important predictor was employment status (OR = 4.8, 95% CI 1.9–12.2, p = 0.001). Conclusion: Sociodemographic factors and educational level influence decision-making, and therefore, the informed consent process should be tailored for each patient.

Non-Small-Cell Lung Cancer Patients with a High Predicted Risk of Irradical Resection: Can Chemoradiotherapy Offer Similar Survival?

Purpose Irradical resection of non-small-cell lung cancer (NSCLC) is a detrimental prognostic factor. Recently, Rasing et al. presented an internationally validated risk score for pre-treatment prediction of irradical resection. We hypothesized that chemoradiation therapy (CRT) could serve as an alternative approach in patients with a high risk score and compared overall survival (OS) outcomes between surgery and CRT. Methods Patients from a population-based cohort with stage IIB–III NSCLC between 2015 and 2018 in The Netherlands were selected. Patients with a ‘Rasing score’ > 4 who underwent surgery were matched with patients who underwent CRT using 1:1 nearest-neighbor propensity score matching. The primary endpoint of OS was compared using a Kaplan–Meier analysis. Results In total, 2582 CRT and 638 surgery patients were eligible. After matching, 523 well-balanced pairs remained. Median OS in the CRT group was 27.5 months, compared with 45.6 months in the surgery group (HR 1.44, 95% CI 1.23–1.70, p < 0.001). The 114 surgical patients who underwent an R1–2 resection (21.8%) had a worse median OS than the CRT group (20.2 versus 27.5 months, HR 0.77, 95% CI 0.61–0.99, p = 0.039). Conclusion In NSCLC patients at high predicted risk of irradical resection, CRT appears to yield inferior survival compared with surgery. Therefore, choosing CRT instead of surgery cannot solely be based on the Rasing score. Since patients receiving an R1–2 resection do have detrimental outcomes compared with primary CRT, the treatment decision should be based on additional information, such as imaging features, comorbidities, patient preference, and the surgeon’s confidence in achieving an R0 resection.

Selection of eligible participants for screening for lung cancer using primary care data

Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.MethodThe Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLPv2) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCOm2012) models. Lung cancer occurrence over 5–6 years was measured in ever-smokers aged 50–80 years and compared with 5-year (LLPv2) and 6-year (PLCOm2012) predicted risk.ResultsOver 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLPV2 produced a c-statistic of 0.700 (0.694–0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCOm2012 showed similar performance (c-statistic: 0.679 (0.673–0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLPv2) and 0.15% (PLCOm2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers.ConclusionRisk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.