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Background: Poly (ADP-ribose) polymerases (PARPs) are a highly conserved family of enzymes whose main function is to preserve genomic integrity following DNA damage. PARP inhibitors (PARPi) are increasingly used in cancers with deficiencies in homologous recombination. Clinical trials in breast and ovarian cancers have led to several FDA approvals in recent years, and their use in clinical practice is continuing to rise. It is thus necessary to assess their adverse event (AE) profile. Method: Literature search was performed using Ovid MEDLINE, EMBASE, CENTRAL, and Scopus (inception through October 26, 2018). Eligible studies were phase 3, randomized, controlled trials that compared single agent PARPi to placebo or standard treatment. Number of patients treated and AEs reported were recorded. Observed incidence of AE was reported with 95% CI. Heterogeneity was evaluated using Cochran Q statistic and I2 statistics quantified the proportion of heterogeneity not due to chance. Results: Databases revealed 869 references. Of these, 6 were eligible: 2 in breast cancer (OlympiAD, EMBRACA) and 4 in ovarian cancer (NOVA, ARIEL3, SOLO1, SOLO2). PARPi included niraparib (NOVA), olaparib (OlympiAD, SOLO1, SOLO2), rucaparib (ARIEL3), and talazoparib (EMBRACA). Of 1,685 patients who received PARPi, incidence of any AE, regardless of grade, was 98.5% (95% CI, 97.2–99.2%). Common AEs were: nausea (incidence rate, 68.9% and 95% CI, 58.7%–77.5%), fatigue (56.3%, 45.3%–66.8%), anemia (46.3%, 37.2%–55.8%), vomiting (33.7%, 29.5%–38.3%), neutropenia (24.7%, 15.3%–37.4%), headache (23.9%, 19.9%–28.4%), and reduced appetite (21.7%, 19.3%–24.3%). Myeloid neoplasms were rare (1.2%, 0.7%–1.9%). Incidence of grade 3 or higher AE was 44.3% (30.2%–59.5%) and often related to myelosuppression, specifically anemia (24.7%, 15.3%–37.4%), neutropenia (10.7%, 6.6%–16.9%), and thrombocytopenia (5.0%, 1.7%–14.0%). Incidence of serious AE was 24.3% (19.4%–29.9%); dose interruption occurred in 53.3% (41.2%–65.0%) and dose reduction occurred in 39.2% (23.6%–57.4%). 10% (7.4%–13.6%) of patients discontinued therapy due to AE. Death due to AE was rare; less than 1% (0.4%, 0.2%–0.8%) in all trials. Conclusion: Myelosuppression and gastrointestinal toxicities were the most commonly reported AE in 6 randomized phase 3 trials of PARPi for breast and ovarian cancers. Therapy was rarely discontinued due to AE. It remains to be seen whether these results will be reflected in clinical practice.

Background: Four large RCTs (ASSURE, S-TARC, PROTECT, ATLAS) tested adjuvant VEGF-TKI therapy in high risk RCC. The results were variable for efficacy and there were concerns for increased toxicity and decline in quality of life (QoL). We performed an updated meta-analysis including results of ATLAS trial to asses a risk-benefit for adjuvant post-operative treatments in high risk RCC patients by assessing reported disease-free survival (DFS), overall survival (OS), and toxicity endpoints. Methods: Literature search was done using Medline, CENTRAL, and Embase. The DerSimonian and Laird random effects model was used to pool estimates for DFS, OS, and common side effects across the 4 trials. A subgroup analysis was performed for sunitinib alone because of its FDA approval. Heterogeneity was assessed with Cochrane Q statistic and was quantified with I2 test. Risk for bias was assessed using the Cochrane Collaboration’s tool. Results: The 4 RCTs included 4,820 patients. Adjuvant therapy with TKIs yielded no significant improvement in DFS or OS as compared to placebo (DFS HR=0.916; 95% CI, 0.832–1.009 and OS HR=1.09; 95% CI, 0.886–1.150). Separate analysis of DFS in sunitinib vs placebo did not show any benefit (2 studies, N=1,909; HR=0.90; 95% CI, 0.67–1.19). Use of TKIs was associated with significantly increased risk of drug toxicity. Increased risk of grade 3 or 4 adverse events (RR=5.110; 95% CI, 3.765–6.935), diarrhea (RR=10.725; 95% CI, 4.672–24.622), fatigue (RR=3.310; 95% CI, 1.879–5.829), hypertension (RR=4.274; 95% CI, 3.452–5.292) and palmar/plantar dysesthesia (RR=20.53; 95% CI, 9.006–46.801) was observed. There was no statistically significant heterogeneity amongst included trials. QoL endpoints were inconsistently reported. Risk of bias was low. Conclusions: This pooled analysis provides further evidence that there is no OS or DFS benefit associated with adjuvant TKI treatment. There was a significantly increased risk of grade 3 or 4 toxicity in greater than half of the patient population leading to decline in QoL during TKI therapy. Carefully selected very high-risk patients who can tolerate these agents without dose modifications may benefit from adjuvant TKI approach.

Abstract Background Osteoporosis and osteopenia are associated with increased fracture incidence in postmenopausal women. We aimed to determine the comparative effectiveness of various available pharmacological therapies. Methods We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, ISI Web of Science, and Scopus for randomized controlled trials that enrolled postmenopausal women with primary osteoporosis and evaluated the risk of hip, vertebral, or nonvertebral fractures. A network meta-analysis was conducted using the multivariate random effects method. Results We included 107 trials (193,987 postmenopausal women; mean age, 66 years; 55% white; median follow-up, 28 months). A significant reduction in hip fractures was observed with romosozumab, alendronate, zoledronate, risedronate, denosumab, estrogen with progesterone, and calcium in combination with vitamin D. A significant reduction in nonvertebral fractures was observed with abaloparatide, romosozumab, denosumab, teriparatide, alendronate, risedronate, zoledronate, lasofoxifene, tibolone, estrogen with progesterone, and vitamin D. A significant reduction in vertebral fractures was observed with abaloparatide, teriparatide, parathyroid hormone 1-84, romosozumab, strontium ranelate, denosumab, zoledronate, risedronate, alendronate, ibandronate, raloxifene, bazedoxifene, lasofoxifene, estrogen with progesterone, tibolone, and calcitonin. Teriparatide, abaloparatide, denosumab, and romosozumab were associated with the highest relative risk reductions, whereas ibandronate and selective estrogen receptor modulators had lower efficacy. The evidence for the treatment of fractures with vitamin D and calcium remains limited despite numerous large trials. Conclusions This network meta-analysis provides comparative effective estimates for the various available treatments to reduce the risk of fragility fractures in postmenopausal women.

Background. This systematic review was commissioned to identify new variables associated with transplant outcomes that are not currently collected by the Organ Procurement and Transplantation Network (OPTN). Methods. We identified 81 unique studies including 1 193 410 patients with median follow-up of 36 months posttransplant, reporting 108 unique risk factors. Results. Most risk factors (104) were recipient related; few (4) were donor related. Most risk factors were judged to be practical and feasible to routinely collect. Relative association measures were small to moderate for most risk factors (ranging between 1.0 and 2.0). The strongest relative association measure for a heart transplant outcome with a risk factor was 8.6 (recipient with the previous Fontan operation), for a kidney transplant 2.8 (sickle cell nephropathy as primary cause of end-stage renal disease), for a liver transplant 14.3 (recipient serum ferritin >500 µg/L), and for a lung transplant 6.3 (Burkholderia cepacia complex infection for 1 y or less). OPTN may consider some of these 108 variables for future collection to enhance transplant research and clinical care. Conclusions. Evidence-based approaches can be used to determine variables collected in databases and registries. Several candidate variables have been identified for OPTN.

TPS462 Background: Dysregulation of fibroblast growth factor receptor (FGFR) signaling by FGFR genetic alterations is implicated in many cancers, including cholangiocarcinoma (CCA). FGFR2 translocations with fusion partners occur in ≈10% to 20% of intrahepatic CCA tumors. Pemigatinib is a selective oral inhibitor of FGFR-1, 2, 3. Preliminary data from the ongoing phase 2 study show efficacy and tolerable safety in patients (pts) with CCA with FGFR2 translocations. We present the design for a phase 3, open-label, randomized trial investigating pemigatinib monotherapy versus gemcitabine plus cisplatin chemotherapy in the first-line treatment of pts with advanced/metastatic or unresectable CCA with FGFR2 rearrangement. Methods: Eligible pts (target, N = 432) are ≥ 18 years (≥ 20 years for Japanese pts) and have ECOG performance status ≤ 1 and histologically confirmed advanced (locally advanced, metastatic, or recurrent) CCA with a documented FGFR2 rearrangement. Key exclusion criteria include prior systemic therapy, excluding adjuvant/neoadjuvant treatment completed ≥ 6 months before enrollment; current evidence of clinically significant corneal or retinal disorder; history of calcium and phosphate homeostasis disorder or systemic mineral imbalance with ectopic calcification of soft tissues; and known, untreated CNS metastases or history of uncontrolled seizures. Pts are randomized 1:1 and stratified by geographic region and tumor burden into 2 treatment groups: pemigatinib starting dose 13.5 mg once daily continuously on a 3-week cycle; or gemcitabine (1000 mg/m2) and cisplatin (25 mg/m2) administered intravenously on days 1 and 8 of every 3-week cycle for up to 8 cycles until disease progression or unacceptable toxicity. Crossover to pemigatinib may be allowed once progressive disease is confirmed. The primary endpoint is progression-free survival (based on independent central review using RECIST v1.1). Secondary endpoints include overall response rate, overall survival, duration of response, disease control rate, safety and tolerability, and impact on quality of life. Clinical trial information: NCT03656536.

619 Background: Regorafenib (at a starting dose of 160 mg/day, rego 160), regorafenib (with a weekly dose escalation, rego 80+) and TAS-102, are suggested treatment options for refractory metastatic colorectal cancer (mCRC). We aimed to evaluate the comparative effectiveness evidence supporting these 3 strategies. Methods: We searched PubMed, Embase, and Cochrane CENTRAL, for randomized controlled trials evaluating TAS 102 or regorafenib in refractory mCRC patients who progressed on/intolerant of previous oxaliplatin, irinotecan, and fluoropyrimidine. Outcomes of interest included OS and PFS. The overall effect was pooled using the DerSimonian random effects model. We conducted network meta-analysis based on White’s multivariate meta-regression to pool evidence from direct and indirect comparisons. Results: Six trials (3 of regorafenib and 3 of TAS-102) at low risk of bias (2,445 patients) were included. Direct comparisons showed that Rego 160 and TAS-102 as monotherapy were superior to BSC in terms of PFS (Rego 160: HR = 0.4, CI 0.26 to 0.63; TAS-102: HR = 0.46, CI 0.40 to 0.52) and OS (Rego 160: HR = 0.67, CI 0.48 to 0.93; TAS-102: HR = 0.67, CI 0.57 to 0.80). Network analysis showed that there was no difference in PFS or OS between Rego 160 and TAS-102. Rego 80+ was superior to BSC in terms of OS (HR = 0.44, CI 0.23 to 0.84) and PFS (HR = 0.37, CI 0.21 to 0.66). There was a numerical advantage for Rego 80+ compared to TAS-102 and Rego 160 (see table). Conclusions: Regorafenib 160 and TAS-102 appear to have similar efficacy. Rego 80+ is shown to be superior to BSC. A trend for improved OS was observed with Rego 80+ versus Rego 160 or TAS 102. [Table: see text]

Abstract Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the ‘black-box’ criticism), its need for extensive adjudicated (‘labelled’) data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.