Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type.

AIMS: Previous studies have shown that patients with stress (Takotsubo) cardiomyopathy (SC) and cancer have higher in-hospital mortality than patients with SC alone. No studies have examined outcomes in patients with active cancer and SC compared to patients with active cancer without SC. We aimed to assess the potential association between primary malignancy type and SC and their shared interaction with inpatient mortality. METHODS AND RESULTS: We analysed SC by primary malignancy type with propensity score adjusted multivariable regression and machine learning analysis using the 2016 United States National Inpatient Sample. Of 30 195 722 adult hospitalized patients, 4 719 591 had active cancer, of whom 568 239 had SC. The mean age of patients with cancer and SC was 69.1, of which 74.7% were women. Among patients with cancer, those with SC were more likely to be female and have white race, Medicare insurance, hypertension, heart failure with reduced ejection fraction, obesity, cerebrovascular disease, anaemia, and chronic obstructive pulmonary disease (P < 0.003 for all). In machine learning-augmented, propensity score multivariable regression adjusted for age, race, and income, only lung cancer [OR 1.25; 95% CI: 1.08-1.46; P = 0.003] and breast cancer [OR 1.81; 95% CI: 1.62-2.02; P < 0.001] were associated with a significantly increased likelihood of SC. Neither SC alone nor having both SC and cancer was significantly associated with in-hospital mortality. The presence of concomitant SC and breast cancer was significantly associated with reduced mortality (OR 0.48; 95% CI: 0.25-0.94; P = 0.032). CONCLUSIONS: This analysis demonstrates that primary malignancy type influences the likelihood of developing SC. Further studies will be necessary to delineate characteristics in patients with lung cancer and breast cancer which contribute to development of SC. Additional investigation should confirm lower mortality in patients with SC and breast cancer and determine possible explanations and protective factors.

Thirty-Day Readmission Rates, Timing, Causes, and Costs after ST-Segment-Elevation Myocardial Infarction in the United States: A National Readmission Database Analysis 2010-2014.

Background Readmission after ST-segment-elevation myocardial infarction ( STEMI ) poses an enormous economic burden to the US healthcare system. Efforts to prevent readmissions should be based on understanding the timing and causes of these readmissions. This study aimed to investigate contemporary causes, timing, and cost of 30-day readmissions after STEMI . Methods and Results All STEMI hospitalizations were selected in the Nationwide Readmissions Database ( NRD ) from 2010 to 2014. The 30-day readmission rate as well as the primary cause and cost of readmission were examined. Multivariate regression analysis was performed to identify the predictors of 30-day readmission and increased cumulative cost. From 2010 to 2014, the 30-day readmission rate after STEMI was 12.3%. Within 7 days of discharge, 43.9% were readmitted, and 67.3% were readmitted within 14 days. The annual rate of 30-day readmission decreased by 19% from 2010 to 2014 ( P<0.001). Female sex, AIDS , anemia, chronic kidney disease , collagen vascular disease, diabetes mellitus, hypertension, pulmonary hypertension, congestive heart failure , atrial fibrillation, and increased length of stay were independent predictors of 30-day readmission. A large proportion of patients (41.6%) were readmitted for noncardiac reasons. After multivariate adjustment, 30-day readmission was associated with a 47.9% increase in cumulative cost ( P<0.001). Conclusions Two thirds of patients were readmitted within the first 14 days after STEMI , and a large proportion of patients were readmitted for noncardiac reasons. Thirty-day readmission was associated with an ≈50% increase in cumulative hospitalization costs. These findings highlight the importance of closer surveillance of both cardiac and general medical conditions in the first several weeks after STEMI discharge.

Trends in hospital treatments for peripheral arterial disease in the United States and association between payer status and quality of care/outcomes, 2007-2011.

OBJECTIVE: This study sought to identify the temporal trends of presenting diagnoses and vascular procedures performed for peripheral arterial disease (PAD) along with the rates of procedures and in-hospital outcomes by payer status. BACKGROUND: Previous studies suggest that patients with Medicare, Medicaid, or lack of insurance receive poorer quality of care leading to worse outcomes. METHODS: We analyzed 196,461,055 discharge records to identify all hospitalized patients with PAD records (n=1,687,724) from January 2007 through December 2011 in the Nationwide Inpatient Sample database. RESULTS: The annual frequency of vascular procedures remained unchanged during the study period. Patients with Medicaid were more likely to present with gangrenes, whereas patients with Medicare were more likely to present with ulcers. After adjustment, patients with Medicare and Medicaid were more likely to undergo amputations when compared with private insurance/HMO (OR=1.13, 95% CI=1.10-1.16 and OR=1.24, 95% CI=1.20-1.29, respectively). Patients with both Medicare and Medicaid were less likely to undergo bypass surgery (OR=0.82, 95% CI=0.81-0.84 and OR=0.87, 95% CI=0.85-0.90, respectively), but more likely to undergo endovascular procedures (OR=1.18, 95% CI=1.17-1.20 and OR=1.03, 95% CI=1.01-1.06, respectively). Medicare and Medicaid status versus private insurance/HMO was associated with worse adjusted odds of in-hospital outcomes, including mortality after amputations, endovascular procedures, and bypass surgeries. CONCLUSIONS: In this analysis, patients with Medicare and Medicaid had more comorbid conditions at baseline when compared with private insurance/HMO cohorts, were more likely to present with advanced stages of PAD, undergo amputations, and develop in-hospital complications. These data unveil a critical gap and an opportunity for quality improvement in the elderly and those with poor socioeconomic status.