Risk Prediction Model for 6-Month Mortality for Patients Discharged to Skilled Nursing Facilities.

OBJECTIVE: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Patients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016. METHODS: Gradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation. RESULTS: We identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82). CONCLUSION AND IMPLICATIONS: We derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations.

Use of a Computerized Algorithm to Evaluate the Proportion and Causes of Potentially Preventable Readmissions Among Patients Discharged to Skilled Nursing Facilities.

OBJECTIVES: Older patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for hospital readmission. Yet, as in the community setting, some readmissions may be preventable with optimal transitional care. This study examined the proportion of 30-day hospital readmissions from SNFs that could be considered potentially preventable readmissions (PPRs) and evaluated the reasons for these readmissions. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Post-acute practice of an integrated health care delivery system serving 11 SNFs in the US Midwest. Patients discharged from the hospital to an SNF and subsequently readmitted to the hospital within 30 days from January 1, 2009, through November 31, 2016. METHODS: A computerized algorithm evaluated the relationship between initial and repeat hospitalizations to determine whether the repeat hospitalization was a PPR. We assessed for changes in PPR rates across the system over the study period and evaluated the readmission categories to identify the most prevalent PPR categories. RESULTS: Of 11,976 discharges to SNFs for post-acute care among 8041 patients over the study period, 16.6% resulted in rehospitalization within 30 days, and 64.8% of these rehospitalizations were considered PPRs. Annual proportion of PPRs ranged from 58.2% to 66.4% [mean (standard deviation) 0.65 (0.03); 95% confidence interval CI 0.63-0.67; P = .36], with no discernable trend. Nearly one-half (46.2%) of all 30-day readmissions were classified as potentially preventable medical readmissions related to recurrence or continuation of the reason for initial admission or to complications from the initial hospitalization. CONCLUSIONS AND IMPLICATIONS: For this cohort of patients discharged to SNFs, a computerized algorithm categorized a large proportion of 30-day hospital readmissions as potentially preventable, with nearly one-half of those linked to the reason for the initial hospitalization. These findings indicate the importance of improvement in postdischarge transitional care for patients discharged to SNFs.

Plasma marine n-3 polyunsaturated fatty acids and cardiovascular risk factors: data from the ACE 1950 study.

PURPOSE: A high intake of marine n-3 polyunsaturated fatty acids (PUFAs) might improve cardiovascular (CV) health. We conducted a cross-sectional study to investigate associations between plasma phospholipid levels of marine n-3 PUFAs and CV risk factors, educational level, physical activity and smoking habits. METHODS: A total of 3706 individuals from a general population, all born in 1950 and residing in Akershus County, Norway, were included in this study. The main statistical approach was multivariable adjusted linear regression. RESULTS: Plasma marine n-3 PUFA levels ranged from 2.7 to 20.3 wt%, with a median level of 7.7 wt% (interquartile range 4.3-11.1 wt%). High levels of plasma marine n-3 PUFAs were associated with lower serum triglycerides [Standardized regression coefficient (Std.ß-coeff.) - 0.14, p < 0.001], body mass index (Std. ß-coeff. -0.08, p < 0.001), serum creatinine (Std. ß-coeff. -0.03, p = 0.05), C-reactive protein levels (Std. ß-coeff. - 0.03, p = 0.04), higher levels of serum high-density lipoprotein cholesterol (Std. ß-coeff. 0.08, p < 0.001) and low-density lipoprotein cholesterol (Std. ß-coeff. 0.04, p = 0.003). High levels of plasma marine n-3 PUFAs were also associated with lower glycated hemoglobin (Std. ß-coeff. - 0.04, p = 0.01), however, only in individuals without diabetes. We found no associations between plasma marine n-3 PUFA levels and fasting plasma glucose or carotid intima-media thickness. High levels of plasma marine n-3 PUFAs were associated with higher educational level, more physical activity and lower prevalence of smoking. CONCLUSION: In this cross-sectional study of Norwegian individuals born in 1950, high levels of plasma marine n-3 PUFAs were favourably associated with several CV risk factors, suggesting that fish consumption might improve CV health.

Risk of 30-Day Hospital Readmission Among Patients Discharged to Skilled Nursing Facilities: Development and Validation of a Risk-Prediction Model.

OBJECTIVES: Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF. DESIGN: Retrospective cohort study. SETTING: Ten independent SNFs affiliated with the post-acute care practice of an integrated health care delivery system. PARTICIPANTS: We evaluated 6032 patients who were discharged to SNFs for post-acute care after hospitalization. MEASUREMENTS: The primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver operating characteristic curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation. RESULTS: Among 8616 discharges to SNFs from January 1, 2009, through June 30, 2014, a total of 1568 (18.2%) were readmitted to the hospital within 30 days. The 30-day hospital readmission prediction model had an AUC of 0.69, a 16% improvement over risk assessment using the Charlson Comorbidity Index alone. The final model included length of stay, abnormal laboratory parameters, and need for intensive care during the index hospitalization; comorbid status; and number of emergency department and hospital visits within the preceding 6 months. CONCLUSIONS AND IMPLICATIONS: We developed and validated a risk-prediction model for 30-day hospital readmission in patients discharged to a SNF for post-acute care. This prediction tool can be used to risk stratify the complex population of hospitalized patients who are discharged to SNFs to prioritize interventions and potentially improve the quality, safety, and cost-effectiveness of care.

Care Transitions Program for High-Risk Frail Older Adults is Most Beneficial for Patients with Cognitive Impairment.

BACKGROUND: Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success. OBJECTIVE: To identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30-day readmissions. DESIGN: Retrospective cohort. PATIENTS: A total of 315 community-dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013. SETTING: Midwest primary care practice in an integrated health system. INTERVENTION: Enrollment in a CTP during acute hospitalization. MEASUREMENTS: The primary outcome was all-cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare. RESULTS: The overall 30-day readmission rate was 17.1%. The intensity of follow-up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission. CONCLUSIONS: No single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.