Development of an automated model to predict the risk of elderly emergency medical admissions within a month following an index hospital visit: A Hong Kong experience

To define a set of screening criteria that identifies elders who are at high risk for repeated hospital admission in the future. Longitudinal cohort study. Logistic regression analysis of data from half of the subjects was used to identify risk factors for repeated hospital admission. The ability of these risk factors to identify elders who are at high risk for repeated hospitalization in the future was then tested using data from the other half of the subjects. United States. A subsample (n = 5876) of a multistage probability sample of all non-institutionalized U.S. civilians who were 70 years or older in 1984. At baseline (1984), elderly subjects were asked about their demographic, socioeconomic, medical, and functional characteristics and about their recent use of health services. Their subsequent hospital admissions and mortality were then monitored through the records of the Medicare program and the National Death Index (1985-88). Among the subjects in the first half of the sample, eight factors emerged as risk factors for repeated admission: older age, male sex, poor self-rated general health, availability of an informal caregiver, having ever had coronary artery disease, and having had, during the previous year, a hospital admission, more than six doctor visits, or diabetes. Based on the presence or absence of these factors in 1984, 7.2% of the subjects in the second half of the sample were estimated to have a high probability of repeated admission (Pra > or = 0.5) during 1985-1988. In comparison with subjects estimated to have a low risk (Pra < 0.5), this high-risk group's actual experiences during 1985-1988 included a higher cumulative incidence of repeated admission (41.8% vs 26.2%, P < 0.0001), a higher cumulative rate of mortality (44.2% vs 19.0%, P < 0.0001), more hospital days per person-year survived (5.2 vs 2.6), and higher hospital charges per person-year survived ($3731 vs $1841). Eight easily ascertained risk factors affect elders' probability of being hospitalized repeatedly within four years. In the future, brief surveys about the presence of these factors could be used to estimate elders' risk of future hospitalization and, thereby, to identify some of those who may derive the greatest benefit from interventions designed to avert the need for hospitalization.

With 1-2% of patients leaving the hospital against medical advice (AMA), the potential for these patients to suffer adverse health outcomes is of major concern. To examine 30-day hospital readmission and mortality rates for medical patients who left the hospital AMA and identify independent risk factors associated with these outcomes. A 5-year retrospective cohort of all patients discharged from a Veterans Administration (VA) hospital. The final study sample included 1,930,947 medical admissions to 129 VA hospitals from 2004 to 2008; 32,819 patients (1.70%) were discharged AMA. Primary outcomes of interest were 30-day mortality and 30-day all-cause hospital readmission. Compared to discharges home, AMA patients were more likely to be black, have low income, and have co-morbid alcohol abuse (for all, Chi(2) df = 1, p < 0.001). AMA patients had a higher 30-day readmission rate (17.7% vs. 11.0%, p < 0.001) and higher 30-day mortality rate (0.75% vs. 0.61%, p = 0.001). In Cox proportional hazard modeling controlling for demographics and co-morbidity, the largest hazard for patients having a 30-day readmission is leaving AMA (HR = 1.35, 95% CI 1.32-1.39). Similar modeling for 30-day mortality reveals a nearly significant increased hazard rate for patients discharged AMA (HR = 1.10, 95% CI 0.98-1.24). Due to the higher risk of adverse outcomes, hospitals should target AMA patients for post-discharge interventions, such as phone follow-up, home visits, or mental health counseling to improve outcomes.

The objective of the study was to develop and validate predictors of 30-day hospital readmission using readily available administrative data and to compare prediction models that use alternate comorbidity classifications. A retrospective cohort study was designed; the models were developed in a two-thirds random sample and validated in the remaining one-third sample. The study cohort consisted of 29,292 adults aged 65 or older who were admitted from July 2002 to June 2004 to any of seven acute care hospitals in the Dallas-Fort Worth metropolitan area affiliated with the Baylor Health Care System. Demographic variables (age, sex, race), health system variables (insurance, discharge location, medical vs surgical service), comorbidity (classified by the Elixhauser classification or the High-Risk Diagnoses in the Elderly Scale), and geographic variables (distance from patient's residence to hospital and median income) were assessed by estimating relative risk and risk difference for 30-day readmission. Population-attributable risk was calculated. Results showed that age 75 or older, male sex, African American race, medical vs surgical service, Medicare with no other insurance, discharge to a skilled nursing facility, and specific comorbidities predicted 30-day readmission. Models with demographic, health system, and either comorbidity classification covariates performed similarly, with modest discrimination (C statistic, 0.65) and acceptable calibration (Hosmer-Lemeshow χ² = 6.08; P > 0.24). Models with demographic variables, health system variables, and number of comorbid conditions also performed adequately. Discharge to long-term care (relative risk, 1.94; 95% confidence interval, 1.80- 2.09) had the highest population-attributable risk of 30-day readmission (12.86%). A 25% threshold of predicted probability of 30-day readmission identified 4.1 % of patients ≥65 years old as priority patients for improved discharge planning. We conclude that elders with a high risk of 30-day hospital readmission can be identified early in their hospital course.