Circulating micro ribonucleic acids in cardiovascular disease: a look beyond myocardial injury

Disease progression modelling reveals heterogeneity in trajectories of Lewy-type α-synuclein pathology

Lewy body (LB) diseases, characterized by the aggregation of misfolded α-synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. Here we apply a data-driven disease progression model to regional neuropathological LB density scores from 814 brain donors with Lewy pathology. We describe three inferred trajectories of LB pathology that are characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) show earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) initially exhibit abnormalities in brainstem regions. Early limbic pathology is associated with Alzheimer's disease-associated characteristics while early brainstem pathology is associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in Lewy body disease.

Association of stress hyperglycemia ratio and mortality in patients with sepsis: results from 13,199 patients

BACKGROUND

The stress hyperglycemia ratio (SHR), adjusted for average glycemic status, is suggested for assessing actual blood glucose levels. Its link with adverse outcomes is known in certain populations, yet its impact on sepsis patients' prognosis is unclear. This study explores the association between SHR and mortality in sepsis.

METHODS

We included 13,199 sepsis patients in this study and categorized SHR into distinct groups. Additionally, we utilized restricted cubic spline analysis to evaluate the correlation between SHR as a continuous variable and mortality. The primary outcome was 1-year all-cause mortality. Logistic regression and Cox proportional hazards models were employed to assess the associations between the SHR and both in-hospital mortality and 1-year mortality, respectively.

RESULTS

Among the study participants, 4,690 (35.5%) patients died during the 1-year follow-up. After adjusting for confounding variables, we identified a U-shaped correlation between SHR and 1-year mortality. Using an SHR of 0.99 as the reference point, the hazard ratio for predicted 1-year mortality increased by 1.17 (95% CI 1.08 to 1.27) per standard deviation above 0.99, whereas each standard deviation increase predicted the hazard ratio of 0.52 (95% CI 0.39 to 0.69) below 0.99. Furthermore, we found that SHR could enhance the predictive performance of conventional severity scores.

CONCLUSION

There exists a U shaped association between SHR and mortality in sepsis patients, where both low and high SHR values are associated with an increased risk of poor outcomes.

Association of dietary overall antioxidant intake with all-cause and cause-specific mortality among adults with depression: evidence from NHANES 2005-2018

Higher intakes of individual antioxidants such as vitamins A, C, and E have been linked to mortality in the general population, but the association of overall antioxidant intake with mortality especially in depressed adults remains unclear. We aimed to investigate whether the dietary overall antioxidant intake is associated with all-cause and cause-specific mortality among depressed adults. This study included 3051 US adults with depression, who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Patient Health Questionnaire-9 (PHQ-9) was used to define depression and evaluate depression severity. The dietary antioxidant quality score (DAQS) and dietary antioxidant index (DAI) were calculated based on the intakes of vitamins A, C, and E, zinc, selenium, and magnesium. A higher DAQS and DAI were significantly associated with lower depression scores (PHQ-9) (all P-trend < 0.05). For individual antioxidants, significant negative associations of vitamins A and E with all-cause mortality were observed. For overall antioxidant intake, the DAQS and DAI were inversely associated with all-cause and cancer mortality. Compared with participants in the lowest categories of DAQS and DAI, the corresponding HRs (95% CIs) in the highest categories were 0.63 (0.42-0.93) and 0.70 (0.49-0.98) for all-cause mortality and 0.39 (0.17-0.87) and 0.43 (0.21-0.88) for cancer mortality, respectively. The overall dietary antioxidant intake was beneficially associated with all-cause and cancer mortality in depressed adults. These findings suggest that comprehensive dietary antioxidant intake may improve depressive symptoms and lower mortality risk among adults with depression.

Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study

This study explores the bidirectional association between multimorbidity and falls in Chinese middle-aged and elderly adults. Participants aged 45 and above from the China Health and Retirement Longitudinal Study were included. Binary logistic regression assessed the impact of chronic conditions on fall incidence (stage I), while multinomial logistic regression examined the relationship between baseline falls and multimorbidity (stage II). The fully adjusted odds ratios (ORs) for one, two, or three or more chronic conditions were 1.34, 1.65, and 2.02, respectively. Among participants without baseline falls, 28.61% developed two or more chronic conditions during follow-up, compared to 37.4% of those with a history of falls. Fully adjusted ORs for one, two, or three or more chronic conditions in those with a history of falls were 1.21, 1.38 and 1.70, respectively. The bidirectional relationship held in sensitivity and subgroup analyses. A bidirectional relationship exists between multimorbidity and falls in Chinese middle-aged and elderly adults. Strengthening chronic condition screening and treatment in primary healthcare may reduce falls risk, and prioritizing fall prevention and intervention in daily life is recommended.

Machine Learning-Based Model for Predicting Prolonged Mechanical Ventilation in Patients with Congestive Heart Failure

BACKGROUND

Mechanical ventilation (MV) is widely used to relieve respiratory failure in patients with congestive heart failure (CHF). Prolonged MV (PMV) is associated with a poor prognosis. We aimed to establish a prediction model based on machine learning (ML) algorithms for the early identification of patients with CHF requiring PMV.

METHODS

Twelve commonly used ML algorithms were used to build the prediction model. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the key features. We examined the area under the curve (AUC) statistics to evaluate the prediction performance. Data from another database were used to conduct external validation.

RESULTS

We screened out 10 key features from the initial 65 variables via LASSO regression to improve the practicability of the model. The CatBoost model showed the best performance for predicting PMV among the 12 commonly used ML algorithms, with favorable discrimination (AUC = 0.790) and calibration (Brier score = 0.154). Moreover, hospital mortality could be accurately predicted using the CatBoost model as well (AUC = 0.844). In the external validation, the CatBoost model also showed satisfactory prediction performance (AUC = 0.780), suggesting certain generalizability of the model. Finally, a nomogram with risk classification of PMV was shown in this study.

CONCLUSION

The present study developed and validated a CatBoost model, which could accurately predict PMV in mechanically ventilated patients with CHF. Moreover, this model has a favorable performance in predicting hospital mortality in these patients.

Systemic inflammation response index as a prognostic factor for patients with sepsis-associated acute kidney injury: a retrospective observational study

OBJECTIVE

To assess the association between the systemic inflammation response index (SIRI) and the prognosis in patients with sepsis-associated acute kidney injury (SA-AKI).

METHODS

In this observational study, adult patients with SA-AKI were categorized into three groups based on SIRI tertiles. Survival outcomes were compared across the three groups using Kaplan-Meier survival curves. Various Cox proportional hazards regression models were developed to determine the association between the SIRI and mortality in patients with SA-AKI. Subgroup analyses were also performed to explore the association between different SIRI tertiles and all-cause mortality.

RESULTS

After adjusting for several confounders, the second SIRI tertile (2.5 < SIRI < 7.6) was found to be an independent risk factor for 30-day mortality [hazard ratio (95% confidence interval): 1.19 (1.01-1.40)], 90-day mortality [1.22 (1.06-1.41)], and 365-day mortality [1.24 (1.09-1.40)]. Furthermore, high SIRI values were associated with increased risks of 30-day, 90-day, and 365-day mortality in patients with SA-AKI across all three models. The third tertile showed a significant association with adverse outcomes in most subgroups.

CONCLUSIONS

The SIRI serves as a comprehensive biomarker for predicting all-cause mortality of critically ill patients with SA-AKI.

Patient Decisions Regarding Rescheduling Colonoscopies Postponed Due to the COVID-19 Pandemic

BACKGROUND

Due to the COVID-19 pandemic, elective colonoscopies were postponed in Ohio from 3/17/2020 to 5/1/2020. When the ban was lifted, canceled patients determined whether to reschedule their colonoscopy in the midst of the ongoing pandemic.

AIMS

We aim to determine whether demographic, colorectal cancer (CRC) risk, and COVID-19 morbidity and mortality risk factors are associated with rescheduling of colonoscopies canceled by the COVID-19 pandemic.

METHODS

A medical record review of 420 participants ages 40-74 at a midwestern academic health system with elective colonoscopies canceled from 3/17/2020 to 5/1/2020 due to the COVID-19 pandemic was performed.

RESULTS

More than half of participants (71.0%) rescheduled their colonoscopy within the next 8 months. Indication for colonoscopy being 'surveillance following adenoma', colonoscopy ordered by primary care provider rather than gastroenterologist, and dyslipidemia were independently associated with rescheduling colonoscopy. Higher body mass index, indication for colonoscopy being simply 'screening for CRC,' and stool testing were associated with not rescheduling. Diagnoses associated with colorectal cancer risk such as adenomas, personal or family history of colorectal cancer, and inflammatory bowel disease were not associated with rescheduling, nor were other comorbidities associated with increased COVID-19 severity. 4.5% (19/420) opted for stool fecal immunochemical test or Cologuard testing.

CONCLUSIONS

Most patients rescheduled their colonoscopy despite the risk of virus exposure, suggesting that concern of missed colorectal cancer diagnosis outweighed coronavirus concerns. Patient trust in referring providers may be important for rescheduling, and colonoscopy indications were independently associated with rescheduling status.

Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features

BACKGROUND

To build prediction models with radiomics features, clinical/conventional radiographic signs and combined scores for the discrimination of micropapillary or solid subtypes (high-risk subtypes) of lung adenocarcinoma.

METHODS

This retrospective study enrolled 351 patients with and without high-risk subtypes. Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was performed to determine the optimal features of radiomics model. Missing clinical data were imputed by Multiple Imputation with Chain Equations (MICE). Clinical model with radiographic signs was built and scores of both models were integrated to establish combined model. Receiver operating characteristics (ROC) curves, area under ROC curves and decision curve analysis (DCA) were plotted to evaluate the model performance and clinical application.

RESULTS

Stratified splitting allocated 246 patients into training set. MICE for missing values obtained complete and unbiased data for the following analysis. Ninety radiomic features and four clinical/conventional radiographic signs were used to predict the high-risk subtypes. The radiomic model, clinical model and combined model achieved AUCs of 0.863 (95%CI: 0.817-0.909), 0.771 (95%CI: 0.713-0.713) and 0.872 (95%CI: 0.829-0.916) in the training set, and 0.849 (95%CI: 0.774-0.924), 0.778 (95%CI: 0.687-0.868) and 0.853 (95%CI: 0.782-0.925) in the test set. Decision curve showed that the radiomic and combined models were more clinically useful when the threshold reached 37.5%.

CONCLUSIONS

Radiomics features could facilitate the prediction of subtypes of lung adenocarcinoma. A simple combination of radiomics and clinical scores generated a robust model with high performance for the discrimination of micropapillary or solid subtype of lung adenocarcinoma.

Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database

BACKGROUND

This study aimed to explore the association between the triglyceride-glucose (TyG) index and the risk of in-hospital mortality in critically ill patients with sepsis.

METHODS

This was a retrospective observational cohort study and data were obtained from the Medical Information Mart for Intensive Care-IV (MIMIC IV2.2) database. The participants were grouped into three groups according to the TyG index tertiles. The primary outcome was in-hospital all-cause mortality. Multivariable logistics proportional regression analysis and restricted cubic spline regression was used to evaluate the association between the TyG index and in-hospital mortality in patients with sepsis. In sensitivity analysis, the feature importance of the TyG index was initially determined using machine learning algorithms and subgroup analysis based on different subgroups was also performed.

RESULTS

1,257 patients (56.88% men) were included in the study. The in-hospital, 28-day and intensive care unit (ICU) mortality were 21.40%, 26.17%, and 15.43% respectively. Multivariate logistics regression analysis showed that the TyG index was independently associated with an elevated risk of in-hospital mortality (OR 1.440 [95% CI 1.106-1.875]; P = 0.00673), 28-day mortality (OR 1.391; [95% CI 1.52-1.678]; P = 0.01414) and ICU mortality (OR 1.597; [95% CI 1.188-2.147]; P = 0.00266). The restricted cubic spline regression model revealed that the risks of in-hospital, 28-day, and ICU mortality increased linearly with increasing TyG index. Sensitivity analysis indicate that the effect size and direction in different subgroups are consistent, the results is stability. Additionally, the machine learning results suggest that TyG index is an important feature for the outcomes of sepsis.

CONCLUSION

Our study indicates that a high TyG index is associated with an increased in-hospital mortality in critically ill sepsis patients. Larger prospective studies are required to confirm these findings.

Ideal vitamin D and handgrip strength counteracts the risk effect of APOE genotype on dementia: a population-based longitudinal study

BACKGROUND

Higher vitamin D concentrations and grip strength contribute to lower individual-level risk of dementia, while apolipoprotein 4 (APOE e4) genotype carries increases dementia risk, but whether combination of ideal vitamin D and grip strength counteracts the risk effect of dementia related to APOE e4 genotype remains unclear. We aimed to investigate the interactions between vitamin D/grip strength and APOE e4 genotype and their association with dementia.

METHODS

The UK Biobank cohort comprised 165,688 dementia-free participants (aged at least 60 years) for the dementia analysis. Dementia was ascertained using hospital inpatient, mortality, and self-reported data until 2021. Vitamin D and grip strength were collected at baseline and divided into tertiles. APOE genotype was coded as APOE e4 non-carries and APOE e4 carries. Data were analyzed using Cox proportional hazard models and restricted cubic regression splines, with adjusted for known confounders.

RESULTS

Over the follow-up (median: 12.0 years), 3917 participants developed dementia. In women and men, respectively, compared with to the lowest tertile of vitamin D, the HRs (95% CIs) of dementia were lower in the middle [0.86 (0.76-0.97)/0.80 (0.72-0.90)] and the highest tertile [0.81 (0.72-0.90)/0.73 (0.66-0.81)]. Tertiles of grip strength showed similar patterns. In women and men, respectively, participants who had both highest tertile of vitamin D and grip strength was associated with a lower risk of dementia compared to those with both lowest tertile of these two exposures among APOE e4 genotype carries (HR = 0.56, 95% CI 0.42-0.76, and HR = 0.48, 95% CI 0.36-0.64) and APOE e4 genotype non-carries (HR = 0.56, 95% CI 0.38-0.81, and HR = 0.34, 95% CI 0.24-0.47). There were significant additive interactions between lower vitamin D/grip strength and APOE e4 genotype on dementia among women and men.

CONCLUSIONS

Higher vitamin D and grip strength were associated with a lower risk of dementia, and seemed to halve the adverse effects of APOE e4 genotype on dementia. Our findings suggested that vitamin D and grip strength may be imperative for estimating the risks of dementia, especially among APOE e4 genotype carries.

Associations of systolic blood pressure and in-hospital mortality in critically ill patients with acute kidney injury

PURPOSE

Although systolic blood pressure (SBP) is associated with acute renal injury (AKI), the relationship between baseline SBP and prognosis in critically ill patients with AKI is unclear. We aimed to assess the linearity and profile of the relationship between SBP at intensive care unit (ICU) admission and in-hospital mortality in these patients.

METHODS

Data of AKI patients in the ICU settings were extracted from the Medical Information Mart for Intensive Care III database. The association between seven SBP categories (< 100, 100-109, 110-119, 120-129, 130-139, 140-149, and ≥ 150 mmHg) and all-cause in-hospital mortality was assessed by Cox proportional hazard models. Restricted cubic spline analysis for the multivariate Cox model was performed to explore the shape of the relationship between SBP and mortality.

RESULTS

A total of 24,202 patients with AKI were included in this study. A typically U-shaped relationship was found between SBP at admission and in-hospital mortality. Among all SBP categories, the lowest risk of death was observed in patients with SBP around 110-119 mmHg, whereas the highest was noted in patients with extremely low SBP (< 100 mmHg), followed by those with extremely high SBP (≥ 150 mmHg). SBP showed a significant interaction with vasopressor use and AKI stage in relation to the risk of in-hospital mortality.

CONCLUSIONS

SBP upon admission showed a non-linear association with all-cause in-hospital mortality in critically ill patients with AKI. Patients with low or high SBP show an increased risk of mortality compared to patients with normal SBP.

Machine learning-based colorectal cancer prediction using global dietary data

BACKGROUND

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active health screening for CRC yielded detection of an increasingly younger adults. However, current machine learning algorithms that are trained using older adults and smaller datasets, may not perform well in practice for large populations.

AIM

To evaluate machine learning algorithms using large datasets accounting for both younger and older adults from multiple regions and diverse sociodemographics.

METHODS

A large dataset including 109,343 participants in a dietary-based colorectal cancer ase study from Canada, India, Italy, South Korea, Mexico, Sweden, and the United States was collected by the Center for Disease Control and Prevention. This global dietary database was augmented with other publicly accessible information from multiple sources. Nine supervised and unsupervised machine learning algorithms were evaluated on the aggregated dataset.

RESULTS

Both supervised and unsupervised models performed well in predicting CRC and non-CRC phenotypes. A prediction model based on an artificial neural network (ANN) was found to be the optimal algorithm with CRC misclassification of 1% and non-CRC misclassification of 3%.

CONCLUSIONS

ANN models trained on large heterogeneous datasets may be applicable for both younger and older adults. Such models provide a solid foundation for building effective clinical decision support systems assisting healthcare providers in dietary-related, non-invasive screening that can be applied in large studies. Using optimal algorithms coupled with high compliance to cancer screening is expected to significantly improve early diagnoses and boost the success rate of timely and appropriate cancer interventions.

Techniques for mesoappendix transection and appendix resection: insights from the ESTES SnapAppy study

INTRODUCTION

Surgically managed appendicitis exhibits great heterogeneity in techniques for mesoappendix transection and appendix amputation from its base. It is unclear whether a particular surgical technique provides outcome benefit or reduces complications.

MATERIAL AND METHODS

We undertook a pre-specified subgroup analysis of all patients who underwent laparoscopic appendectomy at index admission during SnapAppy (ClinicalTrials.gov Registration: NCT04365491). We collected routine, anonymized observational data regarding surgical technique, patient demographics and indices of disease severity, without change to clinical care pathway or usual surgeon preference. Outcome measures of interest were the incidence of complications, unplanned reoperation, readmission, admission to the ICU, death, hospital length of stay, and procedure duration. We used Poisson regression models with robust standard errors to calculate incident rate ratios (IRRs) and 95% confidence intervals (CIs).

RESULTS

Three-thousand seven hundred sixty-eight consecutive adult patients, included from 71 centers in 14 countries, were followed up from date of admission for 90 days. The mesoappendix was divided hemostatically using electrocautery in 1564(69.4%) and an energy device in 688(30.5%). The appendix was amputated by division of its base between looped ligatures in 1379(37.0%), with a stapler in 1421(38.1%) and between clips in 929(24.9%). The technique for securely dividing the appendix at its base in acutely inflamed (AAST Grade 1) appendicitis was equally divided between division between looped ligatures, clips and stapled transection. However, the technique used differed in complicated appendicitis (AAST Grade 2 +) compared with uncomplicated (Grade 1), with a shift toward transection of the appendix base by stapler (58% vs. 38%; p < 0.001). While no statistical difference in outcomes could be detected between different techniques for division of appendix base, decreased risk of any [adjusted IRR (95% CI): 0.58 (0.41-0.82), p = 0.002] and severe [adjusted IRR (95% CI): 0.33 (0.11-0.96), p = 0.045] complications could be detected when using energy devices.

CONCLUSIONS

Safe mesoappendix transection and appendix resection are accomplished using heterogeneous techniques. Technique selection for both mesoappendix transection and appendix resection correlates with AAST grade. Higher grade led to more ultrasonic tissue transection and stapled appendix resection. Higher AAST appendicitis grade also correlated with infection-related complication occurrence. Despite the overall well-tolerated heterogeneity of approaches to acute appendicitis, increasing disease acuity or complexity appears to encourage homogeneity of intraoperative surgical technique toward advanced adjuncts.

Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

Sepsis-associated encephalopathy (SAE) is a major complication of sepsis and is associated with high mortality and poor long-term prognosis. The purpose of this study is to develop interpretable machine learning models to predict the occurrence of SAE after ICU admission and implement the individual prediction and analysis. Patients with sepsis admitted to ICU were included. SAE was diagnosed as glasgow coma score (GCS) less than 15. Statistical analysis at baseline was performed between SAE and non-SAE. Six machine learning classifiers were employed to predict the occurrence of SAE, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to the prediction efficiency. In addition, professional physicians were invited to evaluate our model prediction results for further quantitative assessment of the model interpretability. The preliminary analysis of variance showed significant differences in the incidence of SAE among patients with pathogen infection. There were significant differences in physical indicators like respiratory rate, temperature, SpO₂ and mean arterial pressure (P < 0.001). In addition, the laboratory results were also significantly different. The optimal classification model (XGBoost) indicated that the best risk factors (cut-off points) were creatinine (1.1 mg/dl), mean respiratory rate (18), pH (7.38), age (72), chlorine (101 mmol/L), sodium (138.5 k/ul), SAPSII score (23), platelet count (160), and phosphorus (2.4 and 5.0 mg/dL). The ranked features derived from the best model (AUC is 0.8837) were mechanical ventilation, duration of mechanical ventilation, phosphorus, SOFA score, and vasopressin usage. The SAE risk prediction model based on XGBoost created here can make very accurate predictions using simple indicators and support the visual explanation. The interpretable model was effectively evaluated by professional physicians and can help them predict the occurrence of SAE more intuitively.

Association of cardiovascular health with diabetic complications, all-cause mortality, and life expectancy among people with type 2 diabetes

BACKGROUND

We aimed to assess the impact of healthy cardiovascular health (CVH) on diabetic complications, mortality, and life expectancy among people with type 2 diabetes and to explore whether inflammation marker mediate these associations.

METHODS

This prospective cohort study included 33,236 participants (aged 40-72) with type 2 diabetes from the UK Biobank with annual follow-up from 2006 to 2010 to 2020. Type 2 diabetes was ascertained from self-report, glycated hemoglobin ≥ 6.5%, hospital inpatient registry, or glucose-lowering medication use. Information on mortality was derived from the national death registry. Favorable CVH metrics consisted of non-smoker, regular physical activity, a healthy diet, non-overweight, untreated resting blood pressure < 120/<80 mm Hg, and untreated total cholesterol < 200 mg/dL. Participants were categorized into three groups according to the number of favorable CVH metrics: unfavorable (0 or 1); intermediate (any 2 or 3); and favorable (4 or more). Inflammation marker, as measured by C-reactive protein (CRP), was assessed at baseline and categorized as low (≤ 3 mg/L) and high (> 3 mg/L). Data were analyzed using Cox regression models, flexible parametric survival models, and mediation models.

RESULTS

During the follow-up (median: 11.7 years), 3133 (9.4%) cases of diabetes complications and 4701 (14.1%) deaths occurred. Compared to unfavorable CVH, favorable CVH was associated with a reduced risk of diabetes complications (HR, 0.35; 95% CI, 0.26-0.47) and all-cause mortality (HR, 0.53; 95% CI, 0.43-0.65). In participants with unfavorable CVH, life expectancy at age 45 had a significantly reduction of 7.20 (95% CI, 5.48-8.92) years compared to those with a favorable CVH. Among people with type 2 diabetes, the proportions of diabetes complications and all-cause mortality that would be reduced by promoting the favorable CVH was 61.5% and 39.1%, respectively. CRP level mediated 14.3% and 29.7% of the associations between CVH and diabetic complication and all-cause mortality, respectively.

CONCLUSION

A favorable CVH was associated with lower risk of diabetes complications and mortality risk, and was associated with a longer life expectancy among people with type 2 diabetes. This association may be in part accounted for by inflammatory processes. Our findings highlight the importance of favorable CVH for the prevention of diabetic complications and all-cause mortality among people with type 2 diabetes, and underscores the need to monitor inflammation among people with unfavorable CVH.

The clinical impact of frailty on the postoperative outcomes of patients undergoing appendectomy: propensity score-matched analysis of 2011-2017 US hospitals

BACKGROUND

The presence of clinical frailty can pose an escalated risk toward surgical outcomes including in cases that involve minimally invasive procedures. Given this premise, we evaluate the effects of frailty on post-appendectomy outcomes using a national in-hospital registry.

METHODS

2011-2017 National Inpatient Sample was used to isolate inpatient appendectomy cases; the population as stratified using Johns Hopkins ACG clinical frailty, expressed as either binary or ternary (prefrailty, frailty, and without frailty) indicators. The controls were matched to frailty-present groups using propensity score matching and compared to various endpoints, including mortality, length of stay (LOS), hospitalization costs, and postoperative complications.

RESULTS

Post-match, there were 11,758 with and without frailty per binary; and 1236 frail, 10,522 pre-frail with respective equal number controls per ternary indicator. Using binary term, frail patients had higher mortality (4.22 vs 1.49% OR 2.92 95%CI 2.45-3.47), LOS (14.3 vs 5.35d p < 0.001), and costs ($160,700 vs $64,141 p < 0.001). In multivariate, frail patients had higher mortality (aOR 2.77 95%CI 2.32-3.31), as well as higher rates of postoperative complications. Using ternary term, frail patients had higher mortality (5.02 vs 2.27% OR 2.28 95%CI 1.45-3.59), LOS (18.9 vs 5.66 day p < 0.001) and costs ($200,517 vs $66,193 p < 0.001). In multivariate, frail patients had higher mortality (aOR 2.16 95%CI 1.35-3.43) and complications. Those with pre-frailty had higher mortality (4.12 vs 1.47% OR 2.88 95%CI 2.39-3.46), LOS (13.8 vs 5.34 day p < 0.001) and costs ($156,022 vs $63,772 p < 0.001). In multivariate, pre-frailty patients had higher mortality (aOR 2.79 95%CI 2.31-3.37) and complications.

CONCLUSIONS

Frailty and prefrailty (using the ternary indicator) are associated with increased postoperative mortality and complication in patients who undergo appendectomy; given this finding, it is imperative that these vulnerable patients are identified early in the preoperative phase and are provided risk-modifying measures to ameliorate risks and optimize outcomes.

The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality globally. Compared with traditional diagnostic methods, microRNAs (miRNAs) were novel biomarkers with higher accuracy.

OBJECTIVE

We aimed to identify combinatorial biomarkers of miRNAs to construct a classification model for the diagnosis of HCC.

METHOD

The mature miRNAs expression profile data of six cancers (liver, lung, gastric, breast, prostate and colon) were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database with accession number GSE36915, GSE29250, GSE99417, GSE41970, GSE64333 and GSE35982. The messenger RNA (mRNA) expression profile data of these six cancers were obtained from TCGA. Three R software packages, student's t-test and a normalized fold-change method were utilized to identify HCC-specific differentially expressed miRNAs (DEMs). Using all combinations of obtained HCC- specific DEMs as input features, we construct a classification model by support vector machine searching for the optimal combination. Furthermore, target genes prediction was conducted on the miRWalk 2.0 website to obtain differentially expressed mRNAs (DEmRNAs), and KEGG pathway enrichment was analyzed on the DAVID website.

RESULTS

The optimal combination consisted of four miRNAs (hsa-miR-130a-3p, hsa-miR-450b-5p, hsa-miR-136-5p and hsa-miR-24-1-5p), of which the last one has not been currently reported to be relevant to HCC. The target genes of hsa-miR-24-1-5p (CDC7, ACACA, CTNNA1, and NF2) were involved in the cell cycle, AMPK signaling pathway, Hippo signaling pathway and insulin signaling pathway, which affect the proliferation, metastasis, and apoptosis of cancer cells. Moreover, the area under the receiver operating characteristic curves of the four miRNAs were all higher than 0.85.

CONCLUSION

These results suggest that the miRNAs combined biomarkers were reliable for the diagnosis of HCC. Hsa-miR-24-1-5p was a novel biomarker for HCC diagnosis identified in this study.

Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites

Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model’s outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783–0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784–0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive variables were total protein, C-reactive protein, prothrombin activity, cholinesterase, lymphocyte ratio and apolipoprotein A1. For binary classifier, the sensitivity and NPV of MODEL-1 were 0.894 and 0.885, respectively, while for MODEL-2 they were 0.927 and 0.904, respectively. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. We also identified 6 important variables closely related to the occurrence of SBP.

Clinical impact of visually assessed right ventricular dysfunction in patients with septic shock

We retrospectively analyzed data from the Medical Information Mart for Intensive Care-III critical care database to determine whether visually-assessed right ventricular (RV) dysfunction was associated with clinical outcomes in septic shock patients. Associations between visually-assessed RV dysfunction by echocardiography and in-hospital mortality, lethal arrhythmia, and hemodynamic indicators to determine the prognostic value of RV dysfunction in patients with septic shock were analyzed. Propensity score analysis showed RV dysfunction was associated with increased risk of in-hospital death in patients with septic shock (adjusted odds ratio [OR] 2.15; 95% confidence interval [CI] 1.99–2.32; P < 0.001). In multivariate logistic regression analysis, RV dysfunction was associated with in-hospital death (OR 2.19; 95% CI 1.91–2.53; P < 0.001), lethal arrhythmia (OR 2.19; 95% CI 1.34–3.57; P < 0.001), and tendency for increased blood lactate levels (OR 1.31; 95% CI 1.14–1.50; P < 0.001) independent of left ventricular (LV) dysfunction. RV dysfunction was associated with lower cardiac output, pulmonary artery pressure index, and RV stroke work index. In patients with septic shock, visually-assessed RV dysfunction was associated with in-hospital mortality, lethal arrhythmia, and circulatory insufficiency independent of LV dysfunction. Visual assessment of RV dysfunction using echocardiography might help to identify the short-term prognosis of patients with septic shock by reflecting hemodynamic status.

Relative platelet reductions provide better pathophysiologic signatures of coagulopathies in sepsis

In sepsis-associated coagulopathies and disseminated intravascular coagulation, relative platelet reductions may reflect coagulopathy severity. However, limited evidence supports their clinical significance and most sepsis-associated coagulopathy criteria focus on the absolute platelet counts. To estimate the impact of relative platelet reductions and absolute platelet counts on sepsis outcomes. A multicenter retrospective observational study was performed using the eICU Collaborative Research Database, comprising 335 intensive care units (ICUs) in the United States. Patients with sepsis and an ICU stay > 2 days were included. Estimated effects of relative platelet reductions and absolute platelet counts on mortality and coagulopathy-related complications were evaluated. Overall, 26,176 patients were included. Multivariate mixed-effect logistic regression analysis revealed marked in-hospital mortality risk with larger platelet reductions between days one and two, independent from the resultant absolute platelet counts. The adjusted odds ratio (OR) [95% confidence intervals (CI)] for in-hospital mortality was 1.28[1.23–1.32], 1.86[1.75–1.97], 2.99[2.66–3.36], and 6.05[4.40–8.31] for 20–40%, 40–60%, 60–80%, and > 80% reductions, respectively, when compared with a < 20% decrease in platelets (P < 0.001 for each). In the multivariate logistic regression analysis, platelet reductions ≥ 11% and platelet counts ≤ 100,000/μL on day 2 were associated with high coagulopathy-related complications (OR [95%CI], 2.03 and 1.18; P < 0.001 and P < 0.001), while only platelet reduction was associated with thromboembolic complications (OR [95%CI], 1.43 [1.03–1.98], P < 0.001). The magnitude of platelet reductions represent mortality risk and provides a better signature of coagulopathies in sepsis; therefore, it is a plausible criterion for sepsis-associated coagulopathies.

C-factor: a summary measure for systemic arterial calcifications

Background

Arterial calcification, the hallmark of arteriosclerosis, has a widespread distribution in the human body with only moderate correlation among sites. Hitherto, a single measure capturing the systemic burden of arterial calcification was lacking. In this paper, we propose the C-factor as an overall measure of calcification burden.

Methods

To quantify calcification in the coronary arteries, aortic arch, extra- and intracranial carotid arteries, and vertebrobasilar arteries, 2384 Rotterdam Study participants underwent cardiac and extra-cardiac non-enhanced CT. We performed principal component analyses on the calcification volumes of all twenty-six possible combinations of these vessel beds. Each analysis’ first principal component represents the C-factor. Subsequently, we determined the correlation between the C-factor derived from all vessel beds and the other C-factors with intraclass correlation coefficient (ICC) analyses. Finally, we examined the association of the C-factor and calcification in the separate vessel beds with cardiovascular, non-cardiovascular, and overall mortality using Cox–regression analyses.

Results

The ICCs ranged from 0.80 to 0.99. Larger calcification volumes and a higher C-factor were all individually associated with higher risk of cardiovascular, non-cardiovascular, and overall mortality. When included simultaneously in a model, the C-factor was still associated with all three mortality types (adjusted hazard ratio per standard deviation increase (HR) > 1.52), whereas associations of the separate vessel beds with mortality attenuated substantially (HR < 1.26).

Conclusions

The C-factor summarizes the systemic component of arterial calcification on an individual level and appears robust among different combinations of vessel beds. Importantly, when mutually adjusted, the C-factor retains its strength of association with mortality while the site-specific associations attenuate.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02126-y.

Association Between Troponin I Levels During Sepsis and Post-Sepsis Cardiovascular Complications

RATIONALE

Sepsis commonly results in elevated serum troponin I levels and increased risk for post-sepsis cardiovascular complications; however, the association between troponin I level during sepsis and cardiovascular complications after sepsis is unclear.

OBJECTIVES

To evaluate the association between serum troponin levels during sepsis and 1-year post-sepsis cardiovascular risks.

METHODS

We included patients aged >40 years without a prior diagnosis of cardiovascular disease within 5-years, admitted with sepsis across 21 hospitals from 2011 to 2017. Peak serum troponin I levels during sepsis were grouped as normal (<0.04ng/mL) or tertiles of abnormal (>0.04 to <0.09ng/mL, >0.09 to <0.42ng/mL, or >0.42ng/mL). Multivariable adjusted, cause-specific, Cox proportional hazards models that treated death as a competing risk were used to assess associations between peak sepsis troponin I levels and a composite cardiovascular outcome (atherosclerotic cardiovascular disease, atrial fibrillation, and heart failure) in the year following sepsis. Models were adjusted for pre-sepsis and intra-sepsis factors considered potential confounders.

MEASUREMENTS AND MAIN RESULTS

Among 14,046 patients with troponin I measured during sepsis, 2,012 (14.3%) patients experienced the composite cardiovascular outcome in the year following sepsis hospitalization. Compared with patients with normal troponin levels, those with elevated troponins had increased risks of adverse cardiovascular events (adjusted Hazard Ratiotroponin 0.04-0.09=1.37 (95% CI 1.20-1.55), aHRtroponin 0.09-0.42=1.44 (95% CI 1.27-1.63), and aHRtroponin > 0.42=1.77 (95% CI 1.56-2.00)).

CONCLUSIONS

Among patients without pre-existing cardiovascular disease, troponin elevation during sepsis identified patients at increased risk for post-sepsis cardiovascular complications. Strategies to mitigate cardiovascular complications among this high-risk subset of patients is warranted.

Proposition of a corrected measure of the Lawton instrumental activities of daily living score

Background

We aimed to propose a correction of the Lawton instrumental activity of daily living (IADL) score to take into account the possibility to have never done some activities, and measured its agreement and reliability with the usual IADL score.

Methods

A cross-sectional study was conducted in outpatients attending French memory clinics between 2014 and 2017. Lawton IADL, cognitive performance, diagnosis, neuropsychiatric symptoms, and sociodemographics characteristics were collected. A corrected IADL was calculated and its agreement with the usual IADL was assessed.

Results

The study included of 2391 patients (79.9 years old, 61.7% female). Based on the usual IADL, 36.9% of patients had never carried out at least one of the activities. This proportion reached 68.8% for men and 17.7% for women. Women had a mean IADL higher than men: 4.72 vs 3.49, this difference decreased when considering the corrected IADL: 4.82 vs 4.26 respectively. Based on Bland-Altman method, 93.5% of observations lied within the limits agreement. The ICC between the 2 scores was 0.98. The relationships between patients’ characteristics and the IADL scores were similar, regardless the usual or corrected version.

Conclusions

This corrected IADL score had an excellent degree of agreement with the usual version based the ICC. This simple correction could benefit both for the clinical practice by providing a more accurate description of the real clinical state of the patients allowing to manage them more precisely, and for research involving the evaluation of the functional abilities of patients.

Performance of a Severity Score on Admission Chest Radiography in Predicting Clinical Outcomes in Hospitalized Patients With Coronavirus Disease (COVID-19)

BACKGROUND. Chest radiographs (CXRs) are typically obtained early in patients admitted with coronavirus disease (COVID-19) and may help guide prognosis and initial management decisions. OBJECTIVE. The purpose of this study was to assess the performance of an admission CXR severity scoring system in predicting hospital outcomes in patients admitted with COVID-19. METHODS. This retrospective study included 240 patients (142 men, 98 women; median age, 65 [range, 50-80] years) admitted to the hospital from March 16 to April 13, 2020, with COVID-19 confirmed by real-time reverse-transcriptase polymerase chain reaction who underwent chest radiography within 24 hours of admission. Three attending chest radiologists and three radiology residents independently scored patients' admission CXRs using a 0- to 24-point composite scale (sum of scores that range from 0 to 3 for extent and severity of disease in upper and lower zones of left and right lungs). Interrater reliability of the score was assessed using the Kendall W coefficient. The mean score was obtained from the six readers' scores for further analyses. Demographic variables, clinical characteristics, and admission laboratory values were collected from electronic medical records. ROC analysis was performed to assess the association between CXR severity and mortality. Additional univariable and multivariable logistic regression models incorporating patient characteristics and laboratory values were tested for associations between CXR severity and clinical outcomes. RESULTS. Interrater reliability of CXR scores ranged from 0.687 to 0.737 for attending radiologists, from 0.653 to 0.762 for residents, and from 0.575 to 0.666 for all readers. A composite CXR score of 10 or higher on admission achieved 53.0% (35/66) sensitivity and 75.3% (131/174) specificity for predicting hospital mortality. Hospital mortality occurred in 44.9% (35/78) of patients with a high-risk admission CXR score (≥ 10) versus 19.1% (31/162) of patients with a low-risk CXR score (< 10) (p < .001). Admission composite CXR score was an independent predictor of death (odds ratio [OR], 1.17; 95% CI, 1.10-1.24; p < .001). composite CXR score was a univariable predictor of intubation (OR, 1.23; 95% CI, 1.12-1.34; p < .001) and continuous renal replacement therapy (CRRT) (OR, 1.15; 95% CI, 1.04-1.27; p = .007) but was not associated with these in multivariable models (p > .05). CONCLUSION. For patients admitted with COVID-19, an admission CXR severity score may help predict hospital mortality, intubation, and CRRT. CLINICAL IMPACT. CXR may assist risk assessment and clinical decision-making early in the course of COVID-19.

Defining persistent critical illness based on growth trajectories in patients with sepsis

BACKGROUND

Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients.

METHODS

This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class.

RESULTS

The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15 days after intensive care unit admission in the overall population. Only a minority of the study subjects (n = 643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27 days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8 days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness.

CONCLUSIONS

While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.

Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database

Cardiac arrest (CA) may occur due to a variety of causes with heterogeneity in their clinical presentation and outcomes. This study aimed to identify clinical patterns or subphenotypes of CA patients admitted to the intensive care unit (ICU). The clinical and laboratory data of CA patients in a large electronic healthcare database were analyzed by latent profile analysis (LPA) to identify whether subphenotypes existed. Multivariable Logistic regression was used to assess whether mortality outcome was different between subphenotypes. A total of 1,352 CA patients fulfilled the eligibility criteria were included. The LPA identified three distinct subphenotypes: Profile 1 (13%) was characterized by evidence of significant neurological injury (low GCS). Profile 2 (15%) was characterized by multiple organ dysfunction with evidence of coagulopathy (prolonged aPTT and INR, decreased platelet count), hepatic injury (high bilirubin), circulatory shock (low mean blood pressure and elevated serum lactate); Profile 3 was the largest proportion (72%) of all CA patients without substantial derangement in major organ function. Profile 2 was associated with a significantly higher risk of death (OR: 2.09; 95% CI: 1.30 to 3.38) whilst the mortality rates of Profiles 3 was not significantly different from Profile 1 in multivariable model. LPA using routinely collected clinical data could identify three distinct subphenotypes of CA; those with multiple organ failure were associated with a significantly higher risk of mortality than other subphenotypes. LPA profiling may help researchers to identify the most appropriate subphenotypes of CA patients for testing effectiveness of a new intervention in a clinical trial.

Mechanical power normalized to predicted body weight as a predictor of mortality in patients with acute respiratory distress syndrome

PURPOSE

Protective mechanical ventilation based on multiple ventilator parameters such as tidal volume, plateau pressure, and driving pressure has been widely used in acute respiratory distress syndrome (ARDS). More recently, mechanical power (MP) was found to be associated with mortality. The study aimed to investigate whether MP normalized to predicted body weight (norMP) was superior to other ventilator variables and to prove that the discrimination power cannot be further improved with a sophisticated machine learning method.

METHODS

The study included individual patient data from eight randomized controlled trials conducted by the ARDSNet. The data was split 3:1 into training and testing subsamples. The discrimination of each ventilator variable was calculated in the testing subsample using the area under receiver operating characteristic curve. The gradient boosting machine was used to examine whether the discrimination could be further improved.

RESULTS

A total of 5159 patients with acute onset ARDS were included for analysis. The discrimination of norMP in predicting mortality was significantly better than the absolute MP (p = 0.011 for DeLong's test). The gradient boosting machine was not able to improve the discrimination as compared to norMP (p = 0.913 for DeLong's test). The multivariable regression model showed a significant interaction between norMP and ARDS severity (p < 0.05). While the norMP was not significantly associated with mortality outcome (OR 0.99; 95% CI 0.91-1.07; p = 0.862) in patients with mild ARDS, it was associated with increased risk of mortality in moderate (OR 1.11; 95% CI 1.02-1.23; p = 0.021) and severe (OR 1.13; 95% CI 1.03-1.24; p < 0.008) ARDS.

CONCLUSIONS

The study showed that norMP was a good ventilator variable associated with mortality, and its predictive discrimination cannot be further improved with a sophisticated machine learning method. Further experimental trials are needed to investigate whether adjusting ventilator variables according to norMP will significantly improve clinical outcomes.

Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care

BACKGROUND AND OBJECTIVES

Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI.

METHODS

AKI patients with urine output < 0.5 ml/kg/h for the first 6 h after ICU admission and fluid intake > 5 l in the following 6 h in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-III)) were considered. Patients who received diuretics and renal replacement on day 1 were excluded. Two predictive models, using either machine learning extreme gradient boosting (XGBoost) or logistic regression, were developed to predict urine output > 0.65 ml/kg/h during 18 h succeeding the initial 6 h for assessing oliguria. Established models were assessed by using out-of-sample validation. The whole sample was split into training and testing samples by the ratio of 3:1.

MAIN RESULTS

Of the 6682 patients included in the analysis, 2456 (36.8%) patients were volume responsive with an increase in urine output after receiving > 5 l fluid. Urinary creatinine, blood urea nitrogen (BUN), age, and albumin were the important predictors of VR. The machine learning XGBoost model outperformed the traditional logistic regression model in differentiating between the VR and VU groups (AU-ROC, 0.860; 95% CI, 0.842 to 0.878 vs. 0.728; 95% CI 0.703 to 0.753, respectively).

CONCLUSIONS

The XGBoost model was able to differentiate between patients who would and would not respond to fluid intake in urine output better than a traditional logistic regression model. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in critical care research.

Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis

BACKGROUND AND OBJECTIVE

Sepsis is a heterogeneous disease and identification of its subclasses may facilitate and optimize clinical management. This study aimed to identify subclasses of sepsis and its responses to different amounts of fluid resuscitation.

METHODS

This was a retrospective study conducted in an intensive care unit at a large tertiary care hospital. The patients fulfilling the diagnostic criteria of sepsis from June 1, 2001 to October 31, 2012 were included. Clinical and laboratory variables were used to perform the latent profile analysis (LPA). A multivariable logistic regression model was used to explore the independent association of fluid input and mortality outcome.

RESULTS

In total, 14,993 patients were included in the study. The LPA identified four subclasses of sepsis: profile 1 was characterized by the lowest mortality rate and having the largest proportion and was considered the baseline type; profile 2 was characterized by respiratory dysfunction; profile 3 was characterized by multiple organ dysfunction (kidney, coagulation, liver, and shock), and profile 4 was characterized by neurological dysfunction. Profile 3 showed the highest mortality rate (45.4%), followed by profile 4 (27.4%), 2 (18.2%), and 1 (16.9%). Overall, the amount of fluid needed for resuscitation was the largest on day 1 (median 5115 mL, interquartile range (IQR) 2662 to 8800 mL) and decreased rapidly on day 2 (median 2140 mL, IQR 900 to 3872 mL). Higher cumulative fluid input in the first 48 h was associated with reduced risk of hospital mortality for profile 3 (odds ratio (OR) 0.89, 95% CI 0.83 to 0.95 for each 1000 mL increase in fluid input) and with increased risk of death for profile 4 (OR 1.20, 95% CI 1.11 to 1.30).

CONCLUSION

The study identified four subphenotypes of sepsis, which showed different mortality outcomes and responses to fluid resuscitation. Prospective trials are needed to validate our findings.

Evaluation of the effectiveness of a WHO-5A model based comprehensive tobacco control program among migrant workers in Guangdong, China: a pilot study

BACKGROUND

As a vulnerable population in China, migrant workers have a higher smoking rate than the general population. This study aims to assess the effectiveness of a WHO-5A based comprehensive tobacco control program in workplaces aggregated with migrants.

METHODS

Using a controlled before and after design, four purposely selected manufacturing factories were assigned to either intervention or control groups. Participants in the intervention arm received adapted 5A group counseling regularly supported by social-media and traditional health education approaches. The primary outcome was the change of smoking rate based on salivary cotinine concentration at three-month follow-up as compared to the control arm. Secondary outcomes were changes in smoking-related knowledge and attitudes assessed using questionnaires. Difference-in-differences approach (DID) and generalized estimating equations (GEE) models were used to conduct the effectiveness analysis.

RESULTS

149 and 166 workers were enrolled in the intervention and control arm respectively. The multiple imputed and adjusted GEE models demonstrated that, compared to those in the control arm, participants in the intervention arm had nearly 2.4 times odds of improving smoking-related knowledge (OR = 2.40, 95% CI = 1.32-4.36, P = 0.02) and three times the odds of improving smoking-related attitude (OR = 3.07, 95% CI = 1.28-7.41, P = 0.03). However, no significant difference was found regarding the change of smoking rate between the two arms (P > 0.05). The regression analysis showed that attendance at the 5A group counseling sections was an important determinant of stopping smoking or improving smoking-related knowledge and attitudes in the intervention group.

CONCLUSIONS

This WHO-5A comprehensive intervention was effective in improving migrant workers' knowledge of smoking and anti-smoking attitudes. A large-scale, long-term trial is recommended to determine the effectiveness of this intervention.

TRIAL REGISTRATION

ChiCTR-OPC-17011637 at Chinese Clinical Trial Registry. Retrospectively registered on 12th June 2017.

Germline MC1R status influences somatic mutation burden in melanoma

The major genetic determinants of cutaneous melanoma risk in the general population are disruptive variants (R alleles) in the melanocortin 1 receptor (MC1R) gene. These alleles are also linked to red hair, freckling, and sun sensitivity, all of which are known melanoma phenotypic risk factors. Here we report that in melanomas and for somatic C>T mutations, a signature linked to sun exposure, the expected single-nucleotide variant count associated with the presence of an R allele is estimated to be 42% (95% CI, 15-76%) higher than that among persons without an R allele. This figure is comparable to the expected mutational burden associated with an additional 21 years of age. We also find significant and similar enrichment of non-C>T mutation classes supporting a role for additional mutagenic processes in melanoma development in individuals carrying R alleles.