Survival in metastatic microsatellite-stable colorectal cancer correlated with tumor mutation burden and mutations identified by next-generation sequencing.

Background: Next-generation sequencing (NGS) identifies mutations and molecular abnormalities within tumors, including tumor mutation burden (TMB). If a solid tumor has high TMB, immune checkpoint inhibitors (ICIs) are approved as an option for treatment. Studies have been inconclusive regarding how effective ICI are in treating patients with colorectal cancer (CRC), and it is unclear if high TMB is a good prognostic marker for CRC. We collected data from NGS of CRC and correlated survival to both TMB and mutations of interest, as well as investigated the efficacy of ICI. Methods: This was a retrospective cohort analysis at a single institution, collecting NGS data from January 2018 to December 2020 in patients with CRC who were microsatellite-stable (MSS), n=161. Demographics, clinical data, and results from NGS were collected, and a survival analysis looking at TMB and selected mutations of interest was performed. Patients who were treated with ICI were assessed in a descriptive subset analysis. Results: Patients with CRC who were MSS and had high TMB trended towards worse survival [hazard ratio (HR) =1.38] though the result was not significant (P=0.28). Survival was significantly worse in patients with a KRAS mutation (HR =1.71, P=0.04) and/or a CDKN2A mutation (HR =4.45, P<0.001). In this study population, 12 patients with high TMB had treatment with ICI, with nine of these patients having shorter progression-free survival (PFS) between 0.7 and 4.1 months, and three patients having longer PFS of 26.3, 24.7, and 13.2 months. Conclusions: High TMB in MSS CRC did not show statistical difference in outcome. Mutations in KRAS and/or CDKN2A correlated with worse prognosis. Some patients with MSS CRC and high TMB responded to ICI, though there is a need to identify a better biomarker to predict which patients will have a good response to ICI therapy.

Postpartum Breast Cancer and Survival in Women With Germline BRCA Pathogenic Variants.

Importance: In young-onset breast cancer (YOBC), a diagnosis within 5 to 10 years of childbirth is associated with increased mortality. Women with germline BRCA1/2 pathogenic variants (PVs) are more likely to be diagnosed with BC at younger ages, but the impact of childbirth on mortality is unknown. Objective: To determine whether time between most recent childbirth and BC diagnosis is associated with mortality among patients with YOBC and germline BRCA1/2 PVs. Design, Setting, and Participants: This prospective cohort study included women with germline BRCA1/2 PVs diagnosed with stage I to III BC at age 45 years or younger between 1950 and 2021 in the United Kingdom, who were followed up until November 2021. Data were analyzed from December 3, 2021, to November 29, 2023. Exposure: Time between most recent childbirth and subsequent BC diagnosis, with recent childbirth defined as 0 to less than 10 years, further delineated to 0 to less than 5 years and 5 to less than 10 years. Main Outcomes and Measures: The primary outcome was all-cause mortality, censored at 20 years after YOBC diagnosis. Mortality of nulliparous women was compared with the recent post partum groups and the 10 or more years post partum group. Cox proportional hazards regression analyses were adjusted for age, tumor stage, and further stratified by tumor estrogen receptor (ER) and BRCA gene status. Results: Among 903 women with BRCA PVs (mean [SD] age at diagnosis, 34.7 [6.1] years; mean [SD] follow-up, 10.8 [9.8] years), 419 received a BC diagnosis 0 to less than 10 years after childbirth, including 228 women diagnosed less than 5 years after childbirth and 191 women diagnosed 5 to less than 10 years after childbirth. Increased all-cause mortality was observed in women diagnosed within 5 to less than 10 years post partum (hazard ratio [HR], 1.56 [95% CI, 1.05-2.30]) compared with nulliparous women and women diagnosed 10 or more years after childbirth, suggesting a transient duration of postpartum risk. Risk of mortality was greater for women with ER-positive BC in the less than 5 years post partum group (HR, 2.35 [95% CI, 1.02-5.42]) and ER-negative BC in the 5 to less than 10 years post partum group (HR, 3.12 [95% CI, 1.22-7.97]) compared with the nulliparous group. Delineated by BRCA1 or BRCA2, mortality in the 5 to less than 10 years post partum group was significantly increased, but only for BRCA1 carriers (HR, 2.03 [95% CI, 1.15-3.58]). Conclusions and Relevance: These findings suggest that YOBC with germline BRCA PVs was associated with increased risk for all-cause mortality if diagnosed within 10 years after last childbirth, with risk highest for ER-positive BC diagnosed less than 5 years post partum, and for ER-negative BC diagnosed 5 to less than 10 years post partum. BRCA1 carriers were at highest risk for poor prognosis when diagnosed at 5 to less than 10 years post partum. No such associations were observed for BRCA2 carriers. These results should inform genetic counseling, prevention, and treatment strategies for BRCA PV carriers.

Frequency and Characteristics of Postoperative Neuropathy in Individuals on Gender-Affirming Hormone Therapy Undergoing Gender Affirmation Surgery: A Retrospective Cohort Study.

Introduction Gender affirmation surgery includes procedures of the face, larynx, chest, reproductive system, external genitalia, and adipose tissue performed to ameliorate incongruence between gender identity and phenotype. The annual number of gender affirmation surgeries performed in the United States has increased significantly. There have been no investigations into the frequency of peripheral neuropathy after gender affirmation surgery, which is an important topic, given transgender individuals are at increased risk for delaying necessary medical care. After appreciating a number of cases of postoperative neuropathy in our clinical practice, we hypothesized that gender affirmation surgery is a high-risk procedure for postoperative neuropathy.  Methods We conducted a one-year, monocentric, retrospective cohort study utilizing clinical data of individuals on gender-affirming hormone therapy undergoing gender-affirmation surgery under general anesthesia. The study included transgender women, assigned male at birth, receiving antiandrogen, progesterone, or estrogen therapy (target range plasma estradiol concentration 90-200 pg/ml) and transgender men, assigned female at birth, receiving antiestrogen or testosterone therapy (target range plasma testosterone concentration 320-1000 ng/dl). The primary objective was to estimate the incidence of postoperative peripheral neuropathy, defined as new numbness, paresthesia, neuropathic pain, or muscle weakness occurring in a peripheral innervation territory. Secondary objectives were to summarize the clinical presentation of neuropathy and investigate for associations between procedure characteristics and neuropathy. Results We identified nine cases of postoperative peripheral nerve injury in 232 consecutive gender affirmation procedures establishing an incidence of 3.9%. All injuries were associated with surgery longer than six hours and were characterized by sensory deficits including numbness 89% (8/9) and tingling 56% (5/9), which were diagnosed by postoperative day one. Conclusions Our results suggest that gender affirmation surgery is a high-risk procedure for postoperative neuropathy, with an incidence similar to other high-risk procedures, and an incidence that is higher than the general surgical population. However, given this has not been previously reported and our study includes a heterogenous population from a single institution, our results should be considered hypothesis generating. Additional studies that include multiple institutions are needed to confirm our findings and identify modifiable risk factors for postoperative neuropathy.

Association between negative-pressure room utilization and hospital-acquired Aspergillus rates in patients with coronavirus disease 2019 (COVID-19) in two academic hospitals.

Hospital-acquired Aspergillus rates among coronavirus disease 2019 (COVID-19) patients were initially higher at a hospital with high negative-pressure room utilization compared to a similar hospital with low utilization but with otherwise identical infection control policies. After the index hospital decreased negative-pressure utilization, hospital-acquired Aspergillus case rates at the 2 hospitals converged.

The impact of surgical volume on hospital ranking using the standardized infection ratio.

The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014-2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability.

Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers.

Quantitative imaging biomarkers (QIBs) are increasingly used in clinical studies. Because many QIBs are derived through multiple steps in image data acquisition and data analysis, QIB measurements can produce large variabilities, posing a significant challenge in translating QIBs into clinical trials, and ultimately, clinical practice. Both repeatability and reproducibility constitute the reliability of a QIB measurement. In this article, we review the statistical aspects of repeatability and reproducibility of QIB measurements by introducing methods and metrics for assessments of QIB repeatability and reproducibility and illustrating the impact of QIB measurement error on sample size and statistical power calculations, as well as predictive performance with a QIB as a predictive biomarker.

Calibrated Regression Models Based on the Risk of Clinical Nodal Metastasis Should be Used as Decision Aids for Prostate Cancer Staging to Reduce Unnecessary Imaging.

INTRODUCTION: Radionuclide imaging will change the role of computed tomography and magnetic resonance imaging (CT/MRI) for prostate cancer (CaP) staging. Current guidelines recommend abdominopelvic imaging for new cases of CaP categorized as unfavorable intermediate risk (UIR) or higher. We assessed the performance characteristics of CT/MRI based on the National Comprehensive Cancer Network (NCCN) guidelines and developed a model that predicts cN1 disease using conventional imaging. PATIENTS AND METHODS: We selected patients in the National Cancer Database diagnosed with CaP from 2010 to 2016 with available age, prostate specific antigen, clinical locoregional staging, biopsy Gleason grading, and core information. Multivariate logistic regression (MLR) was used on a undersampled training dataset using cN1 as the outcome. Performance characteristics were compared to those of the three most recent versions of the NCCN guidelines. RESULTS: A total of 443,640 men were included, and 2.5% had cN1 disease. Using CT/MRI only, the current NCCN guidelines have a sensitivity of 99%, and the number needed to image (NNI) is 24. At the same sensitivity, the cN1 risk was 1.6% using the MLR. The NNI for UIR alone is 341. Using the MLR model and a threshold of 10%, the PPV is 10.3% and 64% of CTs/MRIs could be saved at a cost of missing 6% of cN1 patients (or 0.15% of all patients). CONCLUSION: The NCCN guidelines are sensitive for detecting cN1 with CT/MRI, however, the number needed to image is 24. Obtaining CT/MRI for nodal staging when patients have a cN1 risk of 10% would reduce total imaging while still remaining sensitive. As novel PET tracers becomes increasingly used for initial CaP staging, well calibrated prediction models trained on the outcome of interest should be developed as decision aids for obtaining imaging.

Association Between Airborne Infection Isolation Room Utilization Rates and Healthcare Worker Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infections in 2 Academic Hospitals.

We compared healthcare worker severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates between March and August 2020 in 2 similar hospitals with high vs low airborne infection isolation room utilization rates but otherwise identical infection control policies. We found no difference in healthcare worker infection rates between the 2 hospitals, nor between patient-facing vs non-patient-facing providers.

Therapy with PTH 1-34 or calcitriol and calcium in diverse etiologies of hypoparathyroidism over 27 years at a single tertiary care center.

OBJECTIVE: Hypoparathyroidism has heterogeneous genetic and acquired etiologies with a broad spectrum of severity. Herein we describe the clinical outcomes of the largest cohort of hypoparathyroid patients reported to date, who were followed over 27-years. DESIGN: Pooled analysis of current and past studies describing the differential responses to PTH 1-34 injections vs conventional therapy among the varied hypoPT etiologies. METHODS: 192 participants (ages 2-74 years) with hypoparathyroidism who received either calcitriol and calcium or PTH 1-34 by subcutaneous injection. RESULTS: Among the 4 main etiologic categories of hypoparathyroidism (autoimmune polyglandular failure type 1, activating mutation of the calcium receptor, surgical, and idiopathic hypoparathyroidism), we reveal significant differences in PTH 1-34 dose requirements, prevalence of nephrocalcinosis, biomarkers of mineral homeostasis, and pharmacodynamic profiles. Serum 1,25-dihydroxyvitamin D3 increased significantly (P < 0.001) and 25-hydroxyvitamin D levels decreased during PTH 1-34 injections compared to calcitriol therapy (P < 0.01). Post-surgical patients achieved consistently lower urine calcium excretion over long-term PTH 1-34 therapy compared to conventional therapy (p < 0.001), but this was not achieved in the other etiologies. At study entry, patients had a high prevalence of renal insufficiency and nephrocalcinosis which were directly related to the duration of hypoparathyroidism (P < 0.03). Renal function remained stable during participation in our studies for both PTH 1-34 and conventional therapies. CONCLUSIONS: We conclude that the effects and dose-response of PTH 1-34 treatment differ according to the etiology of hypoparathyroidism. Postsurgical hypoPT maintained mean serum calcium levels in the mid- to low-normal range while concurrently maintaining normal mean urine calcium during long-term twice-daily PTH 1-34 therapy.

Healthcare Worker's Mental Health and Their Associated Predictors During the Epidemic Peak of COVID-19.

INTRODUCTION: The outbreak of the coronavirus disease 2019 (COVID-19) poses an unprecedented challenge to healthcare workers (HCWs) globally. This study investigated potential factors related to depression, anxiety, and stress in a sample of Chinese HCWs during the peak of the COVID-19 epidemic. METHODS: An online survey was distributed to Chinese HCWs using respondent-driven sampling. Data were collected between February 13th and February 20th, 2020, immediately following the COVID-19 contagion peak in Hubei. A total of 1208 respondents were eligible for analysis. Mental health problems and social support were measured by the Depression Anxiety Stress Scales-21 (DASS-21) and the Perceived Social Support Scale (PSS). RESULTS: The prevalence rates of depression, (DASS-depression > 9) anxiety (DASS-anxiety > 7) and stress (DASS-stress > 14) were 37.8%, 43.0% and 38.5%, respectively. Multivariate logistic regressions revealed that stress, anxiety, and depression were positively related to lower levels of social support, longer working hours, discrimination experience and workplace violence. The scarcity of medical equipment was correlated with increased stress and depression. Chinese HCWs working at COVID 19 designated hospitals were more likely to report anxiety. Additionally, volunteering to work in the frontline health facilities was inversely associated with depression. CONCLUSION: Mental health problems among Chinese HCWs were alarming during the peak of the COVID-19 epidemic. Health facilities require appropriate and standing services that address the mental health of healthcare workers, particularly during epidemic outbreaks.

A Segmented Logistic Regression Approach to Evaluating Change in Caesarean Section Rate with Reform of Birth Planning Policy in Two Regions in China from 2012 to 2016.

OBJECTIVE: This study evaluated change in caesarean section rate with reform of birth planning policy in China from one-child to two-child policy. METHODS: Study data were collected from patient-level hospital records of 59,668 pregnant women who visited three major urban hospitals in Jiangsu Province and Inner Mongolia Autonomous Region of China between January 2012 and December 2016. A segmented logistic regression approach was developed to evaluate the changes in caesarean section rate in these regions with the launch of China's new partial and universal two-child policies in January 2014 and January 2016, respectively. RESULTS: Jiangsu Province had a significantly lower non-emergency caesarean rate (Jiangsu 8.15% vs Inner Mongolia 34.03%, p < 0.001) and a much lower percentage of minority population (Jiangsu 6.99% vs Inner Mongolia 21.76%, p < 0.001) than Inner Mongolia Autonomous Region. In Jiangsu Province, no change in caesarean section rate was detected with the two-child policies (all p-values > 0.05), although the unadjusted trend change (0.038, 95% confidence interval or CI: [0.016, 0.060], p < 0.001) in log odds after the implementation of the partial two-child policy was statistically significant. In Inner Mongolia Autonomous Region, an immediate jump in caesarean section rate was discovered by the segmented logistic regression with the implementation of both the partial (unadjusted level change 0.297, CI: [0.105, 0.489], p = 0.002) and universal two-child policies (unadjusted level change 1.945, CI: [1.277, 2.614], p < 0.001); but the rate reverted to the previous level thereafter. Ethnicity, maternal age, maternal reproduction history, insurance coverage type, infant weight, and infant gender were the significant factors associated with caesarean section rate (for odds ratios, all p-values < 0.05). However, the significance of infant gender may stem from the large sample size of the study and is not clinically meaningful. CONCLUSION: Change in caesarean section rate was not observed with the launch of two-child policy in China.

Ensemble Learning to Improve the Prediction of Fetal Macrosomia and Large-for-Gestational Age.

BACKGROUND: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. METHODS: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. RESULTS: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. CONCLUSIONS: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.

Rural-urban disparities in caesarean section rates in minority areas in China: evidence from electronic health records.

OBJECTIVE: To assess the rural-urban disparity in caesarean section rates using electronic health records from hospitals located in the Province of Inner Mongolia, which is a minority area in Northeastern China. METHODS: The study examined the electronic health records of women that gave birth in three major public hospitals between January 2012 and December 2016. Multinomial regression analyses were used to estimate rural-urban disparities in caesarean section rates. RESULTS: Data from 61 903 women were examined. Caesarean section rates increased slightly over the study period and the rate was significantly higher in rural compared with urban hospitals (48% versus 38%). This disparity consistently increased over time. Multinomial regression analyses showed that maternal age, ethnicity, health insurance type, employment status, reproductive history and the newborn's sex were significant risk factors associated with caesarean section rate. Furthermore, stratified analysis of first-time pregnancies, minorities and different age groups showed that the odds of undergoing an emergency caesarean section was lower in the rural hospital, but the odds of undergoing a planned caesarean section was higher in the rural hospital. CONCLUSIONS: Caesarean section rates have increased in rural areas and the disparity in rural-urban areas has increased substantially over the years.

Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions.

OBJECTIVE: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies. METHODS: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. RESULTS: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models. CONCLUSION: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented.

Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes.

BACKGROUND: This study proposes the use of machine learning algorithms to improve the accuracy of type 2 diabetes predictions using non-invasive risk score systems. METHODS: We evaluated and compared the prediction accuracies of existing non-invasive risk score systems using the data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Two simple risk scores were established on the bases of logistic regression. Machine learning techniques (ensemble methods) were used to improve prediction accuracies by combining the individual score systems. RESULTS: Existing score systems from Western populations performed worse than the scores from Eastern populations in general. The two newly established score systems performed better than most existing scores systems but a little worse than the Chinese score system. Using ensemble methods with model selection algorithms yielded better prediction accuracy than all the simple score systems. CONCLUSION: Our proposed machine learning methods can be used to improve the accuracy of screening the undiagnosed type 2 diabetes and identifying the high-risk patients.

Association of diabetes mellitus with hepatitis B and hepatitis C virus infection: evidence from an epidemiological study.

OBJECTIVE: To study the association between glucose metabolism disorders and hepatotropic virus infection. METHODS: A cross-sectional analysis was performed using data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Outcomes of the analysis were test results of kidney function, liver function, lipid metabolism, and the prevalence of hepatitis B virus (HBV) infection and potential hepatitis C virus (HCV) infection (positive hepatitis C virus antibody) among individuals with and without diabetes mellitus (DM) or pre-diabetes mellitus (pre-DM). RESULTS: Of the 10,080 patients who participated in the study, 7665 eligible subjects were included in the analysis. There was no significant difference in the prevalence of HBV infection between DM and normal subjects, pre-DM and normal subjects, and DM or pre-DM and normal subjects (p-values of 0.9180, 0.8154, and 0.6448, respectively). There was also no significant difference in the prevalence of potential HCV infection between DM and normal subjects, pre-DM and normal subjects, and DM or pre-DM and normal subjects (p-values of 0.1190, 0.0591, and 0.5591, respectively). Lipid metabolism showed a significant difference between DM or pre-DM subjects and normal subjects (p-values were less than 0.0221 in all cases). Multiple logistic regression analysis revealed hypertension as the leading significant variable associated with DM, pre-DM, and both. Other significant factors included gender, body mass index, age, and alanine aminotransferase. CONCLUSION: No significant association was detected between DM or pre-DM and HBV or potential HCV infection. Significant association was detected between lipid metabolism disorders and DM, but this association was absent in pre-DM patients when adjusting for other factors.

Neutral diagnosis: An innovative concept for medical device clinical trials.

Study design and statistical analysis are crucial in pivotal clinical trials to evaluate the effectiveness and safety of new medical devices under investigation. In recent years, innovative intraoperative in vivo breast tumor diagnostic devices have been proposed to improve the accuracy and surgical outcomes of breast tumor patients undergoing resection. Although such technologies are promising, investigators need to obtain statistical evidence for the effectiveness and safety of these devices by conducting valid clinical trials. However, the study design and statistical analysis for these clinical trials are complicated. While these trials are designed to provide real-time intraoperative diagnosis of cancerous tissue, they also have clear therapeutic objectives to lower the reoperation rate of breast cancer surgery. This research article introduces the new concept of neutral diagnosis (ND), and the ND clinical trial design as an innovative study design to evaluate the effectiveness and safety of diagnostic devices with direct therapeutic purposes. A joint modeling approach is adopted to make inferences on the effectiveness and safety of these devices for non-neutral diagnosis (non-ND) clinical trials. Simulation studies were conducted to show the efficiency of the ND trials and strength of the joint modeling approach in the non-ND clinical trials. An example on a diagnostic medical device that provides real-time, intraoperative diagnosis of breast cancer tumor tissues during breast cancer surgeries is comprehensively discussed and analyzed.