CDKN2A/B Homozygous Deletion Sensitizes IDH-Mutant Glioma to CDK4/6 Inhibition.

PURPOSE: Treatment paradigms for isocitrate dehydrogenase (IDH)-mutant gliomas are rapidly evolving. Although typically indolent and responsive to initial treatment, these tumors invariably recur at a higher grade and require salvage treatment. Homozygous deletion of the tumor suppressor gene CDKN2A/B frequently emerges at recurrence in these tumors, driving poor patient outcomes. We investigated the effect of CDK-Rb pathway blockade on IDH-mutant glioma growth in vitro and in vivo using CDK4/6 inhibitors (CDKi). EXPERIMENTAL DESIGN: Cell viability, proliferation assays, and flow cytometry were used to examine the pharmacologic effect of two distinct CDKi, palbociclib and abemaciclib, in multiple patient-derived IDH-mutant glioma lines. Isogenic models were used to directly investigate the influence of CDKN2A/B status on CDKi sensitivity. Orthotopic xenograft tumor models were used to examine the efficacy and tolerability of CDKi in vivo. RESULTS: CDKi treatment leads to decreased cell viability and proliferative capacity in patient-derived IDH-mutant glioma lines, coupled with enrichment of cells in the G1 phase. CDKN2A inactivation sensitizes IDH-mutant glioma to CDKi in both endogenous and isogenic models with engineered CDKN2A deletion. CDK4/6 inhibitor administration improves survival in orthotopically implanted IDH-mutant glioma models. CONCLUSIONS: IDH-mutant gliomas with deletion of CDKN2A/B are sensitized to CDK4/6 inhibitors. These results support the investigation of the use of these agents in a clinical setting.

Risk of Severe COVID-19 Outcomes Among Patients with Rheumatoid Arthritis in the United States

IntroductionCoronavirus disease 2019 (COVID-19) has infected over 22 million individuals worldwide. It remains unclear whether patients with COVID-19 and Rheumatoid Arthritis (RA) experience worse clinical outcomes compared to similar patients with COVID-19 without RA. AimThe aim of this study is to provide insights on how COVID-19 impacted patients with RA given the nature of the disease and medication used. MethodsRA cases were identified via International Classification of Diseases (ICD) codes and COVID-19 cases by laboratory results in the U.S. based TriNetX network. Patients with COVID-19 and RA were propensity-score matched based on demographics with patients with COVID-19 without RA at a 1:3 ratio. A hospitalized sub-population was defined by procedure codes. ResultsWe identified 1,014 COVID-19 patients with RA and 3,042 non-RA matches selected from 137,757 patients. The odds of hospitalization (non-RA:23%, RA:24.6%, OR:1.08, 95% CI: 0.88 to 1.33) or mortality (non-RA:5.4%, RA:6%, OR:0.93, 95% CI: 0.65 to 1.34) were not significantly different. The hospitalized sub-population included 249 patients with COVID-19 and RA and 745 non-RA matches selected from 21,435 patients. The risk of intensive care unit (ICU) admission (non-RA:18.8%, RA:18.1%, OR:0.94, 95% CI: 0.60 to 1.45), and inpatient mortality (non-RA:14.4%, RA:14.5%, OR:0.86, 95% CI: 0.53 to 1.40) were not significantly different. ConclusionWe didnt find evidence suggesting patients with COVID-19 and RA are more likely to have severe outcomes than patients with COVID-19 without RA. Key Messages- Patients with Rheumatoid Arthritis (RA) tend to be older, and often have co-morbidities which could put them at greater risk of severe COVID-19 outcomes. - This study is one of the largest studies of COVID-19 infected RA populations to date. We did not find increased risk of hospitalization, ICU admission, or mortality among RA patients vs. matched non-RA patients. - Patients previously exposed to anti-coagulants experienced higher risks of hospitalization and overall mortality. Extra attention is needed for treating such patients.

Risk factors for mortality among hospitalized patients with COVID-19

ObjectivesTo develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19. DesignRetrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores, and calibration plots in the test set. SettingOptum(R) de-identified COVID-19 Electronic Health Record dataset. Participants17,086 patients hospitalized with COVID-19 between February 20, 2020 and June 5, 2020. Main outcome measureAll-cause mortality during hospital stay. ResultsThe full model that included information on demographics, comorbidities, laboratory results and vital signs had good discrimination (C-index = 0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were generally similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index = 0.79) was only slightly better than a model that only included age (C-index = 0.76). Across the study period, predicted mortality was 1.2% for 18-year olds, 8.4% for 55-year olds, and 28.6% for 85-year olds. Predicted mortality across all ages declined over the study period from 21.7% by March to 13.3% by May. ConclusionAge was the most important predictor of all-cause mortality although vital signs and laboratory results added considerable prognostic information with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase, and white blood cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

Descriptive epidemiology of 16,780 hospitalized COVID-19 patients in the United States

1.BackgroundDespite the significant morbidity and mortality caused by the 2019 novel coronavirus disease (COVID-19), our understanding of basic disease epidemiology remains limited. This study aimed to describe key patient characteristics, comorbidities, treatments, and outcomes of a large U.S.-based cohort of patients hospitalized with COVD-19 using electronic health records (EHR). MethodsWe identified patients in the Optum De-identified COVID-19 EHR database who had laboratory-confirmed COVID-19 or a presumptive diagnosis between 20 February 2020 and 6 June 2020. We included hospitalizations that occurred 7 days prior to, or within 21 days after, COVID-19 diagnosis. Among hospitalized patients we describe the following: vital statistics and laboratory results on admission, relevant comorbidities (using diagnostic, procedural, and revenue codes), medications (NDC, HCPC codes), ventilation, intensive care unit (ICU) stay, length of stay (LOS), and mortality. FindingsWe identified 76,819 patients diagnosed with COVID-19, 16,780 of whom met inclusion criteria for COVID-related hospitalization. Over half the cohort was over age 50 (74.5%), overweight or obese (77.2%), or had hypertension (56.6%). At admission, 30.3% of patients presented with fever (>38{degrees} C) and 32.3% had low oxygen saturation (<90%). Among the 16,099 patients with complete hospital records, we observed that 58.9% had hypoxia, 23.4% had an ICU stay during hospitalization, 18.1% were ventilated, and 16.2% died. The median LOS was 6 days (IQR: 4, 11). InterpretationTo our knowledge, this is the largest descriptive study of patients hospitalized with COVID-19 in the United States. We report summary statistics of key clinical outcomes that provide insights to better understand COVID-19 disease epidemiology.

Orthopaedic Surgery in Dystonic Cerebral Palsy.

BACKGROUND: Outcomes after orthopaedic interventions in patients with dystonic cerebral palsy (DCP) are historically regarded as unpredictable. This study aims to evaluate the overall outcome of orthopaedic surgery in children with DCP. METHOD: Children with DCP who underwent lower limb orthopaedic surgery with a minimum follow-up of 12 months were included. Data collected included age at time of surgery, surgical procedures performed, Gross Motor Function Classification System (GMFCS) level, and Barry Albright Dystonia Scale (BADS) score. The cohort was divided into 2 groups. Group 1 (GMFCS levels I to III), mean age 12 years 7 months and group 2 (GMFCS levels IV to V), mean age 10 years 7 months. Group 1 had surgery aimed at deformity correction to improve gait and mobility, and group 2 for the management or prevention of hip displacement. Outcome measures analyzed were: the incidence of unpredictable results related to surgery and early recurrence of deformity in both groups. Functional mobility scale scores were evaluated for group 1 and hip migration percentage for group 2. Linear mixed models were used to take into account repeated measures over time and correlations between measurements from the same patient. RESULTS: Group 1 (n=18); had low BADS scores and were considered to have mild dystonia. Three children experienced unpredictable results, 2 had early recurrence of deformity, 3 had a decline, and 1 child improved in the functional mobility scale.Group 2 (n=19); had high BADS scores and were considered to have moderate to severe dystonia. Nine surgical events involved bony procedures and 15 were soft tissue surgery only. One surgical event lead to unpredictable results and 2 children had early recurrence of deformity. Postoperatively, a linear trend of increasing migration percentage [0.49% (95% confidence interval, 0.23-0.74; P=0.0002)] was seen up to 21 months. There was no significant change after 21 months [-0.08% (95% confidence interval, -0.24 to +0.041; P=0.18)]. CONCLUSIONS: This study suggests that unpredictable results and early recurrence of deformity following orthopaedic surgery in children with DCP are not as common as previously regarded. Furthermore, functional mobility and hip morphology can be improved. LEVEL OF EVIDENCE: Level IV-this is a case-series.

Orthopedic surgery and mobility goals for children with cerebral palsy GMFCS level IV: what are we setting out to achieve?

BACKGROUND: Multilevel orthopedic surgery is considered to be the gold standard treatment for ambulatory children with cerebral palsy (CP), classified at levels I, II, or III according to the Gross Motor Function Classification System (GMFCS). Hip enlocation and stability are the main goals of orthopedic intervention in the GMFCS level IV subgroup and are well researched; however, there is no evidence to date to support or challenge the effectiveness of orthopedic treatment to preserve functional mobility in this patient group. The aim of this study was to evaluate the results of orthopedic surgery to maintain or restore standing transfers and supported walking in children with CP at GMFCS level IV. METHODS: Twenty-two children with CP GMFCS level IV who underwent orthopedic surgery to improve mobility between the years 2004 and 2008 were included in this study. A retrospective chart review was performed and a satisfaction questionnaire sent to all patients. The primary outcome measure was the attainment and maintenance of mobility goals 2 years post-surgery. The secondary outcome measures were family/patient satisfaction, Functional Mobility Scale (FMS), and complications. RESULTS: The two goals identified by the patients and carers were standing transfers and supported walking. At the 2-year post-surgery assessment, 14 children (63.6 %) did not reach their pre-determined goals. In the questionnaire, 21.4 % of the families reported that surgery was not beneficial. The FMS score remained unchanged in 95.4 % of the patients. Fourteen patients (63.6 %) had at least one complication that prolonged their post-operative rehabilitation (e.g., neuropraxia). CONCLUSION: This study suggests that orthopedic surgery in children with CP at GMFCS level IV is unlikely to maintain or restore mobility. Furthermore, it carries a significant risk of complications. LEVEL OF EVIDENCE: Case series, Level IV.

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer.

Estimating the natural history parameters of breast cancer not only elucidates the disease progression but also make contributions to assessing the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancer. We applied three-state and five-state Markov models to data on a two-yearly routine mammography screening in Finland between 1988 and 2000. The mean sojourn time (MST) was computed from estimated transition parameters. Computer simulation was implemented to examine the effect of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancers. In three-state model, the MST was 2.02 years, and the sensitivity for detecting preclinical breast cancer was 84.83%. In five-state model, the MST was 2.21 years for localized tumor and 0.82 year for non-localized tumor. Annual, biennial, and triennial screening programs can reduce 53, 37, and 28% of advanced cancer. The effectiveness of intensive screening with poor attendance is the same as that of infrequent screening with high attendance rate. We demonstrated how to estimate the natural history parameters using a service screening program and applied these parameters to assess the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced cancer. The proposed method makes contribution to further cost-effectiveness analysis. However, these findings had better be validated by using a further long-term follow-up data.

Evaluation of breast cancer service screening programme with a Bayesian approach: mortality analysis in a Finnish region.

Evaluation of long-term effectiveness of population-based breast cancer service screening program in a small geographic area may suffer from self-selection bias and small samples. Under a prospective cohort design with exposed and non-exposed groups classified by whether women attended the screen upon invitation, we proposed a Bayesian acyclic graphic model for correcting self-selection bias with or without incorporation of prior information derived from previous studies with an identical screening program in Sweden by chronological order and applied it to an organized breast cancer service screening program in Pirkanmaa center of Finland. The relative mortality rate of breast cancer was 0.27 (95% CI 0.12-0.61) for the exposed group versus the non-exposed group without adjusting for self-selection bias. With adjustment for selection-bias, the adjusted relative mortality rate without using previous data was 0.76 (95% CI 0.49-1.15), whereas a statistically significant result was achieved [0.73 (95% CI 0.57-0.93)] with incorporation of previous information. With the incorporation of external data sources from Sweden in chronological order, adjusted relative mortality rate was 0.67 (0.55-0.80). We demonstrated how to apply a Bayesian acyclic graphic model with self-selection bias adjustment to evaluating an organized but non-randomized breast cancer screening program in a small geographic area with a significant 27% mortality reduction that is consistent with the previous result but more precise. Around 33% mortality was estimated by taking previous randomized controlled data from Sweden.