Risk factors for 30-day readmission following hypoglycemia-related emergency room and inpatient admissions.

OBJECTIVE: Hypoglycemia is a serious complication of diabetes treatment. This retrospective observational study characterized hypoglycemia-related hospital emergency room (ER) and inpatient (in-pt) admissions and identified risk factors for 30-day all-cause and hypoglycemia-related readmission. RESEARCH DESIGN AND METHODS: 4476 hypoglycemia-related ER and in-pt encounters with discharge dates from 1/1/2009 to 3/31/2014 were identified in a large, multicenter electronic health record database. Outcomes were 30-day all-cause ER/hospital readmission and hypoglycemia-related readmission. Multivariable logistic regression methods identified risk factors for both outcomes. RESULTS: 1095 (24.5%) encounters had ER/hospital all-cause readmission within 30 days and 158 (14.4%) of these were hypoglycemia-related. Predictors of all-cause 30-day readmission included recent exposure to a hospital/nursing home (NH)/skilled nursing facility (SNF; OR 1.985, p<0.001); age 25-34 and 35-44 (OR 2.334 and 1.996, respectively, compared with age 65-74, both p<0.001); and African-American (AA) race versus all other race categories (OR 1.427, p=0.011). Other factors positively associated with readmission include chronic obstructive pulmonary disease, cerebrovascular disease, cardiac dysrhythmias, congestive heart disease, hypertension, and mood disorders. Predictors of readmissions attributable to hypoglycemia included recent exposure to a hospital/NH/SNF (OR 2.299, p<0.001), AA race (OR 1.722, p=0.002), age 35-44 (OR 3.484, compared with age 65-74, p<0.001), hypertension (OR 1.891, p=0.019), and delirium/dementia and other cognitive disorders (OR 1.794, p=0.038). Obesity was protective against 30-day hypoglycemia-related readmission (OR 0.505, p=0.017). CONCLUSIONS: Factors associated with 30-day all-cause and hypoglycemia-related readmission among patients with diabetic hypoglycemia include recent exposure to hospital/SNF/NH, adults <45 years, AAs, and several cardiovascular and respiratory-related comorbid conditions.

Incidence of anaemia among HIV-infected patients treated with highly active antiretroviral therapy.

OBJECTIVE: The aim of the study was to compare the incidence of anaemia in patients treated with zidovudine (ZDV) with that in patients treated with highly active antiretroviral therapy (HAART) not including ZDV. METHODS: Using HIV Insight, a database of abstracted US HIV care centre medical charts, ZDV-naïve patients starting ZDV-containing HAART were compared with those starting non-ZDV, nucleoside reverse transcriptase inhibitor-containing HAART. Cohorts were divided as follows: group 1: without baseline anaemia [haemoglobin (Hb) >or=11 g/dL]; group 2: with baseline anaemia (Hb <11 g/dL). The incidence of anaemia (anaemia diagnosis, Hb <11 g/dL, erythropoietic therapy or blood transfusion) was computed for group 1. The anaemia hazard ratio (HR) was adjusted using Cox regression. The rate of worsening anaemia (Hb decrease >or=1.0 g/dL) was computed for group 2. RESULTS: In group 1, the incidence of anaemia was 24.3 and 8.1 per 100 person-years in the ZDV and non-ZDV cohorts, respectively, after 6 months of follow-up, and 12.5 and 5.3 per 100 person-years after 24 months. Significant predictors of anaemia were ZDV, low initial Hb, injecting drug use, CD4 count <200 cells/microL and AIDS. The adjusted HR for ZDV was 1.6 (P=0.005). In group 2, the ZDV/non-ZDV risk ratio for worsening anaemia was 2.2 (95% confidence interval 1.1-4.3). CONCLUSIONS: Patients initiating ZDV-containing HAART are at greater risk of developing new anaemia or worsening anaemia than patients initiating non-ZDV-containing HAART.

Integrated patient data for optimal patient management: the value of laboratory data in quality improvement.

Managed care organizations are shifting from traditional utilization management programs to focus on initiatives that improve the health of an insured population. This strategy requires sophisticated data integration to identify at-risk individuals and track outcomes. Laboratory data are becoming increasingly valuable tools for managed care organizations and healthcare providers. The HEDIS Effectiveness of Care measures have incorporated laboratory data into several key performance indicators. By building a comprehensive repository of laboratory data that includes both procedure codes and laboratory values, managed care organizations can realize substantial savings by avoiding the costly medical record reviews required when administrative data are incomplete. In addition to tracking clinical outcomes, laboratory data provide the ability to risk-stratify a population to target high-risk individuals for case management and disease management interventions. Healthcare organizations face several challenges in the integration of laboratory data into medical databases and practice management software. Confidentiality is a key consideration in view of recent healthcare regulations. Providers of laboratory services should work collaboratively with organizations setting standards for healthcare informatics to facilitate the pooling of data for quality improvement and outcomes research. Health Level Seven, Inc. (HL7), Logical Observation Identifier Names and Codes (LOINC), and Systematized Nomenclature of Medicine (SNOMED) will likely play a key role in this process.