Analysis of 567,758 randomized controlled trials published over 30 years reveals trends in phrases used to discuss results that do not reach statistical significance.

The power of language to modify the reader's perception of interpreting biomedical results cannot be underestimated. Misreporting and misinterpretation are pressing problems in randomized controlled trials (RCT) output. This may be partially related to the statistical significance paradigm used in clinical trials centered around a P value below 0.05 cutoff. Strict use of this P value may lead to strategies of clinical researchers to describe their clinical results with P values approaching but not reaching the threshold to be "almost significant." The question is how phrases expressing nonsignificant results have been reported in RCTs over the past 30 years. To this end, we conducted a quantitative analysis of English full texts containing 567,758 RCTs recorded in PubMed between 1990 and 2020 (81.5% of all published RCTs in PubMed). We determined the exact presence of 505 predefined phrases denoting results that approach but do not cross the line of formal statistical significance (P < 0.05). We modeled temporal trends in phrase data with Bayesian linear regression. Evidence for temporal change was obtained through Bayes factor (BF) analysis. In a randomly sampled subset, the associated P values were manually extracted. We identified 61,741 phrases in 49,134 RCTs indicating almost significant results (8.65%; 95% confidence interval (CI): 8.58% to 8.73%). The overall prevalence of these phrases remained stable over time, with the most prevalent phrases being "marginally significant" (in 7,735 RCTs), "all but significant" (7,015), "a nonsignificant trend" (3,442), "failed to reach statistical significance" (2,578), and "a strong trend" (1,700). The strongest evidence for an increased temporal prevalence was found for "a numerical trend," "a positive trend," "an increasing trend," and "nominally significant." In contrast, the phrases "all but significant," "approaches statistical significance," "did not quite reach statistical significance," "difference was apparent," "failed to reach statistical significance," and "not quite significant" decreased over time. In a random sampled subset of 29,000 phrases, the manually identified and corresponding 11,926 P values, 68,1% ranged between 0.05 and 0.15 (CI: 67. to 69.0; median 0.06). Our results show that RCT reports regularly contain specific phrases describing marginally nonsignificant results to report P values close to but above the dominant 0.05 cutoff. The fact that the prevalence of the phrases remained stable over time indicates that this practice of broadly interpreting P values close to a predefined threshold remains prevalent. To enhance responsible and transparent interpretation of RCT results, researchers, clinicians, reviewers, and editors may reduce the focus on formal statistical significance thresholds and stimulate reporting of P values with corresponding effect sizes and CIs and focus on the clinical relevance of the statistical difference found in RCTs.

Hospital variation of outcomes in status epilepticus.

OBJECTIVE: Understanding factors driving variation in status epilepticus outcomes would be critical to improve care. We evaluated the degree to which patient and hospital characteristics explained hospital-to-hospital variability in intubation and postacute outcomes. METHODS: This was a retrospective cohort study of Medicare beneficiaries admitted with status epilepticus between 2009 and 2019. Outcomes included intubation, discharge to a facility, and 30- and 90-day readmissions and mortality. Multilevel models calculated percent variation in each outcome due to hospital-to-hospital differences. RESULTS: We included 29 150 beneficiaries. The median age was 68 years (interquartile range [IQR] = 57-78), and 18 084 (62%) were eligible for Medicare due to disability. The median (IQR) percentages of each outcome across hospitals were: 30-day mortality 25% (0%-38%), any 30-day readmission 14% (0%-25%), 30-day status epilepticus readmission 0% (0%-3%), 30-day facility stay 40% (25%-53%), and intubation 46% (20%-61%). However, after accounting for many hospitals with small sample size, hospital-to-hospital differences accounted for 2%-6% of variation in all unadjusted outcomes, and approximately 1%-5% (maximally 8% for 30-day readmission for status epilepticus) after adjusting for patient, hospitalization, and/or hospital characteristics. Although many characteristics significantly predicted outcomes, the largest effect size was cardiac arrest predicting death (odds ratio = 10.1, 95% confidence interval = 8.8-11.7), whereas hospital characteristics (e.g., staffing, accreditation, volume, setting, services) all had lesser effects. SIGNIFICANCE: Hospital-to-hospital variation explained little variation in studied outcomes. Rather, certain patient characteristics (e.g., cardiac arrest) had greater effects. Interventions to improve outcomes after status epilepticus may be better focused on individual or prehospital factors, rather than at the inpatient systems level.

Substance use outcomes from the Child/Adolescent Anxiety Multimodal Extended Long-term Study (CAMELS).

BACKGROUND: Substance use problems and anxiety disorders are both highly prevalent and frequently cooccur in youth. The present study examined the benefits of successful anxiety treatment at 3-12 years after treatment completion on substance use outcomes (i.e. diagnoses and lifetime expected use). METHODS: The sample was from the Child/Adolescent Anxiety Multimodal Extended Long-term Study (CAMELS), a naturalistic follow-up study to the Child/Adolescent Anxiety Multimodal Study (CAMS) which randomized youth to cognitive behavioral therapy (CBT; Coping cat), medication (sertraline), their combination, or pill placebo. The first CAMELS visit occurred an average of 6.5 years following CAMS randomization. Participants were 319 youth (65.4% of the CAMS sample), aged 7-17 years at CAMS baseline assessment with a mean age of 17.6 years (range: 11-26 years) at the time of the first CAMELS follow-up. Substance use outcomes included diagnoses as well as lifetime substance use (i.e. alcohol and tobacco use). RESULTS: Eleven of 319 (3.4%) CAMELS participants were diagnosed with a substance use disorder at the initial follow-up visit. When compared to the population lifetime rate of 11.4%, the rate of diagnoses in the posttreated sample was significantly lower. Additionally, rates of lifetime alcohol use were lower than population rates at the initial and final follow-up visits. Rates of lifetime tobacco use were similarly lower than lifetime population rates at the initial visit (driven by significantly lower rates in the CBT treatment condition), but higher by the final visit. Furthermore, treatment remission (but not treatment response) was associated with a lower rate of substance use diagnoses at the initial follow-up visit, although rates of lifetime alcohol and tobacco use did not differ by treatment outcome. CONCLUSIONS: Anxiety treatments confer a beneficial impact on problematic substance use (i.e. diagnoses) as well as on expected substance use (i.e. alcohol and tobacco use) for on average, a period of 6.5 years.

Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level.

BACKGROUND: Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes. METHODS: We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years. RESULTS: Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]). CONCLUSIONS: In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

Composite outcome measures in high-impact critical care randomised controlled trials: a systematic review.

BACKGROUND: The use of composite outcome measures (COM) in clinical trials is increasing. Whilst their use is associated with benefits, several limitations have been highlighted and there is limited literature exploring their use within critical care. The primary aim of this study was to evaluate the use of COM in high-impact critical care trials, and compare study parameters (including sample size, statistical significance, and consistency of effect estimates) in trials using composite versus non-composite outcomes. METHODS: A systematic review of 16 high-impact journals was conducted. Randomised controlled trials published between 2012 and 2022 reporting a patient important outcome and involving critical care patients, were included. RESULTS: 8271 trials were screened, and 194 included. 39.1% of all trials used a COM and this increased over time. Of those using a COM, only 52.6% explicitly described the outcome as composite. The median number of components was 2 (IQR 2-3). Trials using a COM recruited fewer participants (409 (198.8-851.5) vs 584 (300-1566, p = 0.004), and their use was not associated with increased rates of statistical significance (19.7% vs 17.8%, p = 0.380). Predicted effect sizes were overestimated in all but 6 trials. For studies using a COM the effect estimates were consistent across all components in 43.4% of trials. 93% of COM included components that were not patient important. CONCLUSIONS: COM are increasingly used in critical care trials; however effect estimates are frequently inconsistent across COM components confounding outcome interpretations. The use of COM was associated with smaller sample sizes, and no increased likelihood of statistically significant results. Many of the limitations inherent to the use of COM are relevant to critical care research.

Predicting treatment response using machine learning: A registered report.

OBJECTIVE: Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively screening for patients at risk of low treatment response, gain more knowledge about risk factors and to evaluate treatments, accurate insights about predictors of treatment response in naturalistic inpatient samples are needed. METHODS: We compared the performance of different machine learning algorithms in predicting treatment response, operationalized as a substantial reduction in symptom severity as expressed in the Patient Health Questionnaire Anxiety and Depression Scale. To achieve this goal, we used different sets of variables-(a) demographics, (b) physical indicators, (c) psychological indicators and (d) treatment-related variables-in a naturalistic inpatient sample (N = 723) to specify their joint and unique contribution to treatment success. RESULTS: There was a strong link between symptom severity at baseline and post-treatment (R2 = .32). When using all available variables, both machine learning algorithms outperformed the linear regressions and led to an increment in predictive performance of R2 = .12. Treatment-related variables were the most predictive, followed psychological indicators. Physical indicators and demographics were negligible. CONCLUSIONS: Treatment response in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators. Regularization via machine learning algorithms leads to higher predictive performances as opposed to including nonlinear and interaction effects. Heterogenous aspects of mental health have incremental predictive value and should be considered as prognostic markers when modelling treatment processes.

Outcome Evaluation of a Public Health Intervention on Cardiovascular Disease (CVD) Prevention Among Women Aged 40-64 with Low Incomes in Nebraska.

OBJECTIVES: To evaluate the effectiveness of the program interventions on cardiovascular disease in Nebraska women who are low income and have no health insurance. DESIGN: This evaluation used pre- and post-comparison approach. Paired t test and McNemar's test were used to examine the changes after the program interventions. PARTICIPANTS: Nebraska women aged 40 to 64 years, without health insurance, and with household incomes up to 225% Federal Poverty Level. SETTING AND INTERVENTION: A network of community-clinical linkages in which medical providers provided preventive screening services and risk reduction counseling in clinical settings and community health workers provided lifestyle interventions in community settings either over the phone or in person. MAIN OUTCOME MEASURE: The data included weight, blood pressure measures, self-blood pressure monitoring and management, total cholesterol, fasting glucose or A1C, smoking status, nutrition, and physical activities. RESULTS: Among 2649 participants, 82.2% were overweight, 50.3% had hypertension, 52.7% had high cholesterol, 20.7% had diabetes, 22.5% were current smokers, and 56.4% had more than 1 risk factor. A total of 1312 participants (57.3%) participated in at least 1 lifestyle intervention session, and among them, 65.8% completed at least 3 sessions. Paired t test and McNemar's test indicated significant improvement in hypertension control and self-management; a significant amount of weight loss with 24.1% losing at least 5 pounds; and an increase in healthy eating and physical activity. CONCLUSIONS: These participants benefited from the Nebraska program. Utilizing a statewide clinical network and participating in lifestyle interventions through local health departments, participants improved some chronic health conditions and decreased their risks of developing cardiovascular diseases.

Disparities in Socioeconomic Factors Mediate the Impact of Racial Segregation Among Patients With Hepatopancreaticobiliary Cancer.

BACKGROUND: Structural racism within the U.S. health care system contributes to disparities in oncologic care. This study sought to examine the socioeconomic factors that underlie the impact of racial segregation on hepatopancreaticobiliary (HPB) cancer inequities. METHODS: Both Black and White patients who presented with HPB cancer were identified from the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database (2005-2015) and 2010 Census data. The Index of Dissimilarity (IoD), a validated measure of segregation, was examined relative to cancer stage at diagnosis, surgical resection, and overall mortality. Principal component analysis and structural equation modeling were used to determine the mediating effect of socioeconomic factors. RESULTS: Among 39,063 patients, 86.4 % (n = 33,749) were White and 13.6 % (n = 5314) were Black. Black patients were more likely to reside in segregated areas than White patients (IoD, 0.62 vs. 0.52; p < 0.05). Black patients in highly segregated areas were less likely to present with early-stage disease (relative risk [RR], 0.89; 95 % confidence interval [CI] 0.82-0.95) or undergo surgery for localized disease (RR, 0.81; 95% CI 0.70-0.91), and had greater mortality hazards (hazard ratio 1.12, 95% CI 1.06-1.17) than White patients in low segregation areas (all p < 0.05). Mediation analysis identified poverty, lack of insurance, education level, crowded living conditions, commute time, and supportive income as contributing to 25 % of the disparities in early-stage presentation. Average income, house price, and income mobility explained 17 % of the disparities in surgical resection. Notably, average income, house price, and income mobility mediated 59 % of the effect that racial segregation had on long-term survival. CONCLUSION: Racial segregation, mediated through underlying socioeconomic factors, accounted for marked disparities in access to surgical care and outcomes for patients with HPB cancer.

Changes in Hospital Adverse Events and Patient Outcomes Associated With Private Equity Acquisition.

Importance: The effects of private equity acquisitions of US hospitals on the clinical quality of inpatient care and patient outcomes remain largely unknown. Objective: To examine changes in hospital-acquired adverse events and hospitalization outcomes associated with private equity acquisitions of US hospitals. Design, Setting, and Participants: Data from 100% Medicare Part A claims for 662 095 hospitalizations at 51 private equity-acquired hospitals were compared with data for 4 160 720 hospitalizations at 259 matched control hospitals (not acquired by private equity) for hospital stays between 2009 and 2019. An event study, difference-in-differences design was used to assess hospitalizations from 3 years before to 3 years after private equity acquisition using a linear model that was adjusted for patient and hospital attributes. Main Outcomes and Measures: Hospital-acquired adverse events (synonymous with hospital-acquired conditions; the individual conditions were defined by the US Centers for Medicare & Medicaid Services as falls, infections, and other adverse events), patient mix, and hospitalization outcomes (including mortality, discharge disposition, length of stay, and readmissions). Results: Hospital-acquired adverse events (or conditions) were observed within 10 091 hospitalizations. After private equity acquisition, Medicare beneficiaries admitted to private equity hospitals experienced a 25.4% increase in hospital-acquired conditions compared with those treated at control hospitals (4.6 [95% CI, 2.0-7.2] additional hospital-acquired conditions per 10 000 hospitalizations, P = .004). This increase in hospital-acquired conditions was driven by a 27.3% increase in falls (P = .02) and a 37.7% increase in central line-associated bloodstream infections (P = .04) at private equity hospitals, despite placing 16.2% fewer central lines. Surgical site infections doubled from 10.8 to 21.6 per 10 000 hospitalizations at private equity hospitals despite an 8.1% reduction in surgical volume; meanwhile, such infections decreased at control hospitals, though statistical precision of the between-group comparison was limited by the smaller sample size of surgical hospitalizations. Compared with Medicare beneficiaries treated at control hospitals, those treated at private equity hospitals were modestly younger, less likely to be dually eligible for Medicare and Medicaid, and more often transferred to other acute care hospitals after shorter lengths of stay. In-hospital mortality (n = 162 652 in the population or 3.4% on average) decreased slightly at private equity hospitals compared with the control hospitals; there was no differential change in mortality by 30 days after hospital discharge. Conclusions and Relevance: Private equity acquisition was associated with increased hospital-acquired adverse events, including falls and central line-associated bloodstream infections, along with a larger but less statistically precise increase in surgical site infections. Shifts in patient mix toward younger and fewer dually eligible beneficiaries admitted and increased transfers to other hospitals may explain the small decrease in in-hospital mortality at private equity hospitals relative to the control hospitals, which was no longer evident 30 days after discharge. These findings heighten concerns about the implications of private equity on health care delivery.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models.

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

Long-term first-in-man Phase I/II study of an adjuvant dendritic cell vaccine in patients with high-risk prostate cancer after radical prostatectomy.

BACKGROUND: Patients with high-risk prostate cancer (PC) can experience biochemical relapse (BCR), despite surgery, and develop noncurative disease. The present study aimed to reduce the risk of BCR with a personalized dendritic cell (DC) vaccine, given as adjuvant therapy, after robot-assisted laparoscopic prostatectomy (RALP). METHODS: Twelve weeks after RALP, 20 patients with high-risk PC and undetectable PSA received DC vaccinations for 3 years or until BCR. The primary endpoint was the time to BCR. The immune response was assessed 7 weeks after surgery (baseline) and at one-time point during the vaccination period. RESULTS: Among 20 patients, 11 were BCR-free over a median of 96 months (range: 84-99). The median time from the end of vaccinations to the last follow-up was 57 months (range: 45-60). Nine patients developed BCR, either during (n = 4) or after (n = 5) the vaccination period. Among five patients diagnosed with intraductal carcinoma, three experienced early BCR during the vaccination period. All patients that developed BCR remained in stable disease within a median of 99 months (range: 74-99). The baseline immune response was significantly associated with the immune response during the vaccination period (p = 0.015). For patients diagnosed with extraprostatic extension (EPE), time to BCR was longer in vaccine responders than in non-responders (p = 0.09). Among 12 patients with the International Society of Urological Pathology (ISUP) grade 5 PC, five achieved remission after 84 months, and all mounted immune responses. CONCLUSION: Patients diagnosed with EPE and ISUP grade 5 PC were at particularly high risk of developing postsurgical BCR. In this subgroup, the vaccine response was related to a reduced BCR incidence. The vaccine was safe, without side effects. This adjuvant first-in-man Phase I/II DC vaccine study showed promising results. DC vaccines after curative surgery should be investigated further in a larger cohort of patients with high-risk PC.

Incidence of liver- and non-liver-related outcomes in patients with HCV-cirrhosis after SVR.

BACKGROUND & AIMS: As the long-term benefits of a sustained virological response (SVR) in HCV-related cirrhosis following direct-acting antiviral (DAA) treatment remain undefined, we assessed the incidence and predictors of liver-related events (LREs), non-liver-related events (NLREs) and mortality in DAA-treated patients with cirrhosis. METHODS: Consecutive patients with cirrhosis and SVR were enrolled in a longitudinal, single-center study, and divided into 3 cohorts: Cohort A (Child-Pugh A without a previous LRE), Cohort B (Child-Pugh B or Child-Pugh A with prior non-hepatocellular carcinoma [HCC] LREs), Cohort C (previous HCC). RESULTS: A total of 636 patients with cirrhosis (median 65 years-old, 58% males, 89% Child-Pugh A) were followed for 51 (8-68) months (Cohort A n = 480, Cohort B n = 89, Cohort C n = 67). The 5-year estimated cumulative incidences of LREs were 10.4% in Cohort A vs. 32.0% in Cohort B (HCC 7.7% vs. 19.7%; ascites 1.4% vs. 8.6%; variceal bleeding 1.3% vs. 7.8%; encephalopathy 0 vs. 2.5%) vs. 71% in Cohort C (HCC only) (p <0.0001). The corresponding figures for NLREs were 11.7% in Cohort A vs. 17.9% in Cohort B vs. 17.5% in Cohort C (p = 0.32). The 5-year estimated probabilities of liver-related vs. non-liver-related deaths were 0.5% vs. 4.5% in Cohort A, 16.2% vs. 8.8% in Cohort B and 12.1% vs. 7.7% in Cohort C. The all-cause mortality rate in Cohort A was similar to the rate expected for the general population stratified by age, sex and calendar year according to the Human Mortality Database, while it was significantly higher in Cohort B. CONCLUSIONS: Patients with cirrhosis and an SVR on DAAs face risks of liver-related and non-liver-related events and mortality; however, their incidence is strongly influenced by pre-DAA patient history. LAY SUMMARY: In this large single-center study enrolling patients with hepatitis C virus (HCV)-related cirrhosis cured by direct-acting antivirals, pre-treatment liver disease history strongly influenced long-term outcomes. In patients with HCV-related cirrhosis, hepatocellular carcinoma was the most frequent liver-related complication after viral cure. Due to improved long-term outcomes, patients with cirrhosis after HCV cure are exposed to a significant proportion of non-liver-related events.

Outcomes of living liver donors are worse than those of matched healthy controls.

BACKGROUND & AIMS: Donor death is the most serious complication of living liver donation but is reported rarely. We investigated the actual mortality of living liver donors (LLDs) compared with matched control groups based on analysis of the Korean National Health Insurance Services (NHIS) database. METHODS: This cohort study included 12,372 LLDs who donated a liver graft between 2002 and 2018, and were registered in the Korean Network for Organ Sharing. They were compared to 3 matched control groups selected from the Korean NHIS and comprising a total of 123,710 individuals: healthy population (Group I); general population without comorbidities (Group II); and general population with comorbidities (Group III). RESULTS: In this population, 78.5% of living liver donors were 20-39 years old, and 64.7% of all donors were male. Eighty-nine donors (0.7%) in the LLD group died (68 males and 21 females), a mortality rate (/1,000 person-years) of 0.91 (0.74-1.12). Mortality rate ratio and the adjusted hazard ratio of the LLD group was 2.03 (1.61-2.55) and 1.71 (1.31-2.25) compared to Control Group I, 0.75 (0.60-0.93) and 0.63 (0.49-0.82) compared to Control Group II, and 0.58 (0.46-0.71) and 0.49 (0.39-0.60) compared to Control Group III. LLD group, depression, and lower income were risk factors for adjusted mortality. The incidence of liver failure, depression, cancer, diabetes, hypertension, brain infarction, brain hemorrhage, and end-stage renal disease in the LLD group was significantly higher than in Control Group I. CONCLUSIONS: Outcomes of the LLD group were worse than those of the matched healthy control group despite the small number of deaths and medical morbidities in this group. LLDs should receive careful medical attention for an extended period after donation. LAY SUMMARY: The incidence of mortality, liver failure, depression, cancer, diabetes, hypertension, brain infarction, brain hemorrhage, and end-stage renal disease in the living liver donor group was significantly higher than in the matched healthy group. Careful donor evaluation and selection processes can improve donor safety and enable safe living donor liver transplantation.

Distribution and density of tertiary lymphoid structures predict clinical outcome in intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: The prognostic value and clinical relevance of tertiary lymphoid structures (TLSs) in intrahepatic cholangiocarcinoma (iCCA) remain unclear. Thus, we aimed to investigate the prognostic value and functional involvement of TLSs in iCCA. METHODS: We retrospectively included 962 patients from 3 cancer centers across China. The TLSs at different anatomic subregions were quantified and correlated with overall survival (OS) by Cox regression and Kaplan-Meier analyses. Multiplex immunohistochemistry (mIHC) was applied to characterize the composition of TLSs in 39 iCCA samples. RESULTS: A quaternary TLS scoring system was established for the intra-tumor region (T score) and peri-tumor region (P score) respectively. T scores positively correlated with favorable prognosis (p <0.001), whereas a high P score signified worse survival (p <0.001). mIHC demonstrated that both T follicular helper and regulatory T cells were significantly increased in intra-tumoral TLSs compared to peri-tumoral counterparts (p <0.05), and regulatory T cell frequencies within intra-tumoral TLSs were positively associated with P score (p <0.05) rather than T score. Collectively, the combination of T and P scores stratified iCCAs into 4 immune classes with distinct prognoses (p <0.001) that differed in the abundance and distribution pattern of TLSs. Patients displaying an immune-active pattern had the lowest risk, with 5-year OS rates of 68.8%, whereas only 3.4% of patients with an immune-excluded pattern survived at 5 years (p <0.001). The C-index of the immune class was statistically higher than the TNM staging system (0.73 vs. 0.63, p <0.001). These results were validated in an internal and 2 external cohorts. CONCLUSIONS: The spatial distribution and abundance of TLSs significantly correlated with prognosis and provided a useful immune classification for iCCA. T follicular helper and regulatory T cells may play a critical role in determining the functional orientation of spatially different TLSs. LAY SUMMARY: Tertiary lymphoid structures (TLSs) are associated with favorable prognosis in a number of cancers. However, their role in intrahepatic cholangiocarcinoma (iCCA) remains unclear. Herein, we comprehensively evaluated the spatial distribution, abundance, and cellular composition of TLSs in iCCA, and revealed the opposite prognostic impacts of TLSs located within or outside the tumor. This difference could be mediated by the different immune cell subsets present within the spatially distinct TLSs. Based on our analysis, we were able to stratify iCCAs into 4 immune subclasses associated with varying prognoses.

A multicentre outcome analysis to define global benchmarks for donation after circulatory death liver transplantation.

BACKGROUND & AIMS: The concept of benchmarking is established in the field of transplant surgery; however, benchmark values for donation after circulatory death (DCD) liver transplantation are not available. Thus, we aimed to identify the best possible outcomes in DCD liver transplantation and to propose outcome reference values. METHODS: Based on 2,219 controlled DCD liver transplantations, collected from 17 centres in North America and Europe, we identified 1,012 low-risk, primary, adult liver transplantations with a laboratory MELD score of ≤20 points, receiving a DCD liver with a total donor warm ischemia time of ≤30 minutes and asystolic donor warm ischemia time of ≤15 minutes. Clinically relevant outcomes were selected and complications were reported according to the Clavien-Dindo-Grading and the comprehensive complication index (CCI). Corresponding benchmark cut-offs were based on median values of each centre, where the 75th-percentile was considered. RESULTS: Benchmark cases represented between 19.7% and 75% of DCD transplantations in participating centres. The 1-year retransplant and mortality rates were 4.5% and 8.4% in the benchmark group, respectively. Within the first year of follow-up, 51.1% of recipients developed at least 1 major complication (≥Clavien-Dindo-Grade III). Benchmark cut-offs were ≤3 days and ≤16 days for ICU and hospital stay, ≤66% for severe recipient complications (≥Grade III), ≤16.8% for ischemic cholangiopathy, and ≤38.9 CCI points 1 year after transplant. Comparisons with higher risk groups showed more complications and impaired graft survival outside the benchmark cut-offs. Organ perfusion techniques reduced the complications to values below benchmark cut-offs, despite higher graft risk. CONCLUSIONS: Despite excellent 1-year survival, morbidity in benchmark cases remains high. Benchmark cut-offs targeting morbidity parameters offer a valid tool to assess the protective value of new preservation technologies in higher risk groups and to provide a valid comparator cohort for future clinical trials. LAY SUMMARY: The best possible outcomes after liver transplantation of grafts donated after circulatory death (DCD) were defined using the concept of benchmarking. These were based on 2,219 liver transplantations following controlled DCD donation in 17 centres worldwide. Donor and recipient combinations with higher risk had significantly worse outcomes. However, the use of novel organ perfusion technology helped high-risk patients achieve similar outcomes as the benchmark cohort.

Impact of MELD 30-allocation policy on liver transplant outcomes in Italy.

BACKGROUND & AIMS: In Italy, since August 2014, liver transplant (LT) candidates with model for end-stage liver disease (MELD) scores ≥30 receive national allocation priority. This multicenter cohort study aims to evaluate time on the waiting list, dropout rate, and graft survival before and after introducing the macro-area sharing policy. METHODS: A total of 4,238 patients registered from 2010 to 2018 were enrolled and categorized into an ERA-1 Group (n = 2,013; before August 2014) and an ERA-2 Group (n = 2,225; during and after August 2014). A Cox proportional hazards model was used to estimate the hazard ratio (HR) of receiving a LT or death between the two eras. The Fine-Gray model was used to estimate the HR for dropout from the waiting list and graft loss, considering death as a competing risk event. A Fine-Gray model was also used to estimate risk factors of graft loss. RESULTS: Patients with MELD ≥30 had a lower median time on the waiting list (4 vs.12 days, p <0.001) and a higher probability of being transplanted (HR 2.27; 95% CI 1.78-2.90; p = 0.001) in ERA-2 compared to ERA-1. The subgroup analysis on 3,515 LTs confirmed ERA-2 (odds ratio 0.56; 95% CI 0.46-0.68; p = 0.001) as a protective factor for better graft survival rate. The protective variables for lower dropouts on the waiting list were: ERA-2, high-volume centers, no competition centers, male recipients, and hepatocellular carcinoma. The protective variables for graft loss were high-volume center and ERA-2, while MELD ≥30 remained related to a higher risk of graft loss. CONCLUSIONS: The national MELD ≥30 priority allocation was associated with improved patient outcomes, although MELD ≥30 was associated with a higher risk of graft loss. Transplant center volumes and competition among centers may have a role in recipient prioritization and outcomes. CLINICAL TRIAL NUMBER: NCT04530240 LAY SUMMARY: Italy introduced a new policy in 2014 to give national allocation priority to patients with a model for end-stage liver disease (MELD) score ≥30 (i.e. very sick patients). This policy has led to more liver transplants, fewer dropouts, and shorter waiting times for patients with MELD ≥30. However, a higher risk of graft loss still burdens these cases. Transplant center volumes and competition among centers may have a role in recipient prioritization and outcomes.

NK cell frequencies, function and correlates to vaccine outcome in BNT162b2 mRNA anti-SARS-CoV-2 vaccinated healthy and immunocompromised individuals.

Adaptive immune responses have been studied extensively in the course of mRNA vaccination against COVID-19. Considerably fewer studies have assessed the effects on innate immune cells. Here, we characterized NK cells in healthy individuals and immunocompromised patients in the course of an anti-SARS-CoV-2 BNT162b2 mRNA prospective, open-label clinical vaccine trial. See trial registration description in notes. Results revealed preserved NK cell numbers, frequencies, subsets, phenotypes, and function as assessed through consecutive peripheral blood samplings at 0, 10, 21, and 35 days following vaccination. A positive correlation was observed between the frequency of NKG2C+ NK cells at baseline (Day 0) and anti-SARS-CoV-2 Ab titers following BNT162b2 mRNA vaccination at Day 35. The present results provide basic insights in regards to NK cells in the context of mRNA vaccination, and have relevance for future mRNA-based vaccinations against COVID-19, other viral infections, and cancer.Trial registration: The current study is based on clinical material from the COVAXID open-label, non-randomized prospective clinical trial registered at EudraCT and clinicaltrials.gov (no. 2021-000175-37). Description: https://clinicaltrials.gov/ct2/show/NCT04780659?term=2021-000175-37&draw=2&rank=1 .

Disparities in Hypoxemia Detection by Pulse Oximetry Across Self-Identified Racial Groups and Associations With Clinical Outcomes.

OBJECTIVES: To assess disparities in hypoxemia detection by pulse oximetry across self-identified racial groups and associations with clinical outcomes. DESIGN: Observational cohort study from May 5, 2018, to December 31, 2020. SETTING: Three academic medical centers in the United States. PATIENTS: Adults greater than or equal to 18 years who self-identified as White, Black, Asian, or American Indian admitted to the ICU or undergoing surgery during inpatient hospitalization with simultaneous measurements of pulse oximetry-estimated oxygen saturation and arterial blood gas-derived oxygen saturation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Multivariable models were employed to assess the relationships between race, occult hypoxemia (i.e., arterial blood gas-derived oxygen saturation < 88% despite pulse oximetry-estimated oxygen saturation ≥ 92%), and clinical outcomes of hospital mortality and hospital-free days. One-hundred twenty-eight-thousand two-hundred eighty-five paired pulse oximetry-estimated oxygen saturation-arterial blood gas-derived oxygen saturation measurements were included from 26,603 patients. Pulse oximetry-estimated oxygen saturation on average overestimated arterial blood gas-derived oxygen saturation by 1.57% (1.54-1.61%). Black, Asian, and American Indian patients were more likely to experience occult hypoxemia during hospitalization (estimated probability 6.2% [5.1-7.6%], 6.6% [4.9-8.8%], and 6.6% [4.4-10.0%], respectively) compared with White patients (3.6% [3.4-3.8%]). Black patients had increased odds of occult hypoxemia compared with White patients after adjustment (odds ratio, 1.65; 1.28-2.14; p < 0.001). Differences in occult hypoxemia between Asian and American Indian patients compared with White patients were not significant after adjustment (odds ratio, 1.53; 0.95-2.47; p = 0.077 and odds ratio, 1.31; 0.80-2.16; p = 0.288, respectively). Occult hypoxemia was associated with increased odds of mortality in surgical (odds ratio, 2.96; 1.20-7.28; p = 0.019) and ICU patients (1.36; 1.03-1.80; p = 0.033). Occult hypoxemia was associated with fewer hospital-free days in surgical (-2.5 d [-3.9 to -1.2 d]; p < 0.001) but not ICU patients (0.4 d [-0.7 to 1.4 d]; p = 0.500). CONCLUSIONS: Occult hypoxemia is more common in Black patients compared with White patients and is associated with increased mortality, suggesting potentially important outcome implications for undetected hypoxemia. It is imperative to validate pulse oximetry with expanded racial inclusion.

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

OBJECTIVES: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation. DESIGN: Analysis of the Get With The Guidelines-Resuscitation registry. SETTING: Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017. PATIENTS: Adult in-hospital cardiac arrest survivors. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age. CONCLUSIONS: The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.

Continuous Versus Routine Standardized Electroencephalogram for Outcome Prediction in Critically Ill Adults: Analysis From a Randomized Trial.

OBJECTIVES: To investigate electroencephalogram (EEG) features' relation with mortality or functional outcome after disorder of consciousness, stratifying patients between continuous EEG and routine EEG. DESIGN: Retrospective analysis of data from a randomized controlled trial. SETTING: Multiple adult ICUs. PATIENTS: Data from 364 adults with acute disorder of consciousness, randomized to continuous EEG (30-48 hr; n = 182) or repeated 20-minute routine electroencephalogram (n = 182). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Correlations between electrographic features and mortality and modified Rankin scale at 6 months (good 0-2) were assessed. Background continuity, higher frequency, and reactivity correlated with survival and good modified Rankin scale. Rhythmic and periodic patterns carried dual prognostic information: lateralized periodic discharges were associated with mortality and bad modified Rankin scale. Generalized rhythmic delta activity correlated with survival, good modified Rankin scale, and lower occurrence of status epilepticus. Presence of sleep-spindles and continuous EEG background was associated with good outcome in the continuous EEG subgroup. In the routine EEG group, a model combining background frequency, continuity, reactivity, sleep-spindles, and lateralized periodic discharges was associated with mortality at 70.91% (95% CI, 59.62-80.10%) positive predictive value and 63.93% (95% CI, 58.67-68.89%) negative predictive value. In the continuous EEG group, a model combining background continuity, reactivity, generalized rhythmic delta activity, and lateralized periodic discharges was associated with mortality at 84.62% (95%CI, 75.02-90.97) positive predictive value and 74.77% (95% CI, 68.50-80.16) negative predictive value. CONCLUSIONS: Standardized EEG interpretation provides reliable prognostic information. Continuous EEG provides more information than routine EEG.