IMPORTANCEThe addition of a claims-based frailty metric to traditional comorbidity-based risk-adjustment models for acute myocardial infarction (AMI), heart failure (HF), and pneumonia improves the prediction of 30-day mortality and readmission. This may have important implications for hospitals that tend to care for frail populations and participate in Centers for Medicare & Medicaid Services value-based payment programs, which use these risk-adjusted metrics to determine reimbursement.OBJECTIVE To determine whether the addition of frailty measures to traditional comorbidity-based risk-adjustment models improved prediction of outcomes for patients with AMI, HF, and pneumonia. DESIGN, SETTING, AND PARTICIPANTSA nationwide cohort study included Medicare fee-for-service beneficiaries 65 years and older in the United States between January 1 and December 1, 2016. Analysis began August 2018. MAIN OUTCOMES AND MEASURESRates of mortality within 30 days of admission and 30 days of discharge, as well as 30-day readmission rates by frailty group. We evaluated the incremental effect of adding the Hospital Frailty Risk Score (HFRS) to current comorbidity-based risk-adjustment models for 30-day outcomes across all conditions.RESULTS For 785 127 participants, there were 166 200 hospitalizations [21.2%] for AMI, 348 619 [44.4%] for HF, and 270 308 [34.4%] for pneumonia. The mean (SD) age at the time of hospitalization was 79.2 (8.9) years; 656 315 (83.6%) were white and 402 639 (51.3%) were women. The mean (SD) HFRS was 7.3 (7.4) for patients with AMI, 10.8 (8.3) for patients with HF, and 8.2 (5.7) for patients with pneumonia. Among patients hospitalized for AMI, an HFRS more than 15 (compared with an HFRS <5) was associated with a higher risk of 30-day postadmission mortality (adjusted odds ratio [aOR], 3.6; 95% CI, 3.4-3.8), 30-day postdischarge mortality (aOR, 4.0; 95% CI, 3.7-4.3), and 30-day readmission (aOR, 3.0; 95% CI, 2.9-3.1) after multivariable adjustment for age, sex, race, and comorbidities. Similar patterns were observed for patients hospitalized with HF (30-day postadmission mortality: aOR, 3.5; 95% CI, 3.4-3.7; 30-day postdischarge mortality: aOR, 3.5; 95% CI, 3.3-3.6; and 30-day readmission: aOR, 2.9; 95% CI, 2.8-3.0) and among patients with pneumonia (30-day postadmission mortality: aOR, 2.5; 95% CI, 2.3-2.6; 30-day postdischarge mortality: aOR, 3.0; 95% CI, 2.9-3.2; and 30-day readmission: aOR, 2.8; 95% CI, 2.7-2.9). The addition of HFRS to traditional comorbidity-based risk-prediction models improved discrimination to predict outcomes for all 3 conditions. CONCLUSIONS AND RELEVANCE Among Medicare fee-for-service beneficiaries, frailty as measured by the HFRS was associated with mortality and readmissions among patients hospitalized for AMI, HF, or pneumonia. The addition of HFRS to traditional comorbidity-based risk-prediction models improved the prediction of outcomes for all 3 conditions.

Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

ObjectiveTo determine if using a parachute prevents death or major traumatic injury when jumping from an aircraft.DesignRandomized controlled trial.SettingPrivate or commercial aircraft between September 2017 and August 2018.Participants92 aircraft passengers aged 18 and over were screened for participation. 23 agreed to be enrolled and were randomized.InterventionJumping from an aircraft (airplane or helicopter) with a parachute versus an empty backpack (unblinded).Main outcome measuresComposite of death or major traumatic injury (defined by an Injury Severity Score over 15) upon impact with the ground measured immediately after landing.ResultsParachute use did not significantly reduce death or major injury (0% for parachute v 0% for control; P>0.9). This finding was consistent across multiple subgroups. Compared with individuals screened but not enrolled, participants included in the study were on aircraft at significantly lower altitude (mean of 0.6 m for participants v mean of 9146 m for non-participants; P<0.001) and lower velocity (mean of 0 km/h v mean of 800 km/h; P<0.001).ConclusionsParachute use did not reduce death or major traumatic injury when jumping from aircraft in the first randomized evaluation of this intervention. However, the trial was only able to enroll participants on small stationary aircraft on the ground, suggesting cautious extrapolation to high altitude jumps. When beliefs regarding the effectiveness of an intervention exist in the community, randomized trials might selectively enroll individuals with a lower perceived likelihood of benefit, thus diminishing the applicability of the results to clinical practice.

Aims We sought to identify the prevalence and related outcomes of frail individuals undergoing transcatheter mitral valve repair and transcatheter aortic valve replacement (TAVR). Methods and results Patients aged 65 and older were included in the study if they had at least one procedural code for transcatheter mitral valve repair or TAVR between 1 January 2016 and 31 December 2016 in the Centers for Medicare and Medicaid Services Medicare Provider and Review database. The Hospital Frailty Risk Score, an International Classification of Diseases, Tenth Revision (ICD-10) claims-based score, was used to identify frailty and the primary outcome was all-cause 1-year mortality. A total of 3746 (11.6%) patients underwent transcatheter mitral valve repair and 28 531 (88.4%) underwent TAVR. In the transcatheter mitral valve repair and TAVR populations, respectively, there were 1903 (50.8%) and 14 938 (52.4%) patients defined as low risk for frailty (score <5), 1476 (39.4%) and 11 268 (39.5%) defined as intermediate risk (score 5–15), and 367 (9.8%) and 2325 (8.1%) defined as high risk (score >15). One-year mortality was 12.8% in low-risk patients, 29.7% in intermediate-risk patients, and 40.9% in high-risk patients undergoing transcatheter mitral valve repair (log rank P < 0.001). In patients undergoing TAVR, 1-year mortality rates were 7.6% in low-risk patients, 17.6% in intermediate-risk patients, and 30.1% in high-risk patients (log rank P < 0.001). Conclusions This study successfully identified individuals at greater risk of short- and long-term mortality after undergoing transcatheter valve therapies in an elderly population in the USA using the ICD-10 claims-based Hospital Frailty Risk Score.

Current PCI public reporting programs can foster risk-averse clinical practice patterns, which do not vary significantly between interventional cardiologists in New York and Massachusetts. Coordinated efforts by policy makers, health systems leadership, and the interventional cardiology community are needed to mitigate these unintended consequences.

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Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center.

Background: Prospectively collected frailty markers are associated with an incremental 1-year mortality risk after transcatheter aortic valve replacement (TAVR) compared to comorbidities alone. Whether information on frailty markers captured retrospectively in administrative billing data is similarly predictive of long-term mortality after TAVR is unknown. We sought to characterize the prognostic importance of frailty factors as identified in healthcare billing records in comparison to validated measures of frailty for the prediction of long-term mortality after TAVR. Methods and Results: Adult patients undergoing TAVR between August 25, 2011 and September 29, 2015 were identified among Medicare fee-for-service beneficiaries. The Johns Hopkins Claims-based Frailty Indicator was used to identify frail patients. We used nested Cox regression models to identify claims-based predictors of mortality up to 4 years post-procedure. Four groups of variables including cardiac risk factors, non-cardiac risk factors, patient procedural risk factors, and non-traditional markers of frailty were introduced sequentially, and their integrated discrimination improvement (IDI) was assessed. A total of 52,338 TAVR patients from 558 clinical sites were identified, with a mean follow-up time period of 16 months. In total, 14,174 (27.1%) patients died within the study period. The mortality rate was 53.9% at 4-years post TAVR. A total of 34,863 (66.6%) patients were defined as frail. The discrimination of each of the 4 models was 0.60 (95% CI: 0.59–60), 0.65 (95% CI: 0.64–0.65), 0.68 (95% CI: 0.67–0.68) and 0.70 (95% CI: 0.69–0.70), respectively. The addition of non-traditional frailty markers as identified in claims improved mortality prediction above and beyond traditional risk factors (IDI: 0.019, p < 0.001). Conclusions: Risk prediction models that include frailty as identified in claims data can be used to predict long-term mortality risk after TAVR. Linkage to claims data may allow enhanced mortality risk prediction for studies that do not collect information on frailty.