Opioid Use and Outcomes in Hospitalized Older Patients With Heart Failure Receiving and Not Receiving Hospice Referrals.

BACKGROUND: The use of opioids is associated with poor outcomes. Less is known about this association in patients with heart failure (HF) and whether it varies by the receipt of hospice care. METHODS: Of the 7467 patients hospitalized for HF without previous opioid use, 124 received discharge opioids. We matched 123 of these patients with 123 not receiving opioids based on their propensity scores for opioid use, thus assembling a matched cohort of 246 patients balanced on 30 baseline characteristics (mean age, 76 years, 60% women, and 11% African American). We repeated the process in hospice (n = 155; 20 received opioids) and nonhospice (n = 7298; 104 received opioids) subgroups, thus assembling 2 matched cohorts of 22 and 208 patients, respectively. Hazard ratios (HRs) and 95% confidence intervals (CIs) associated with opioid use were estimated from matched cohorts. RESULTS: During 8.6 (median, 1.4) years of follow-up, all-cause mortality occurred in 80% and 68% of matched patients in the opioid and nonopioid groups, respectively (HR, 1.49; 95% CI, 1.11-1.99; P = 0.008). There was evidence of heterogeneity in this association between hospice and nonhospice patients (P for interaction, 0.027). Among matched hospice and nonhospice patients, HRs (95% CIs) for mortality were 6.37 (2.06-19.69; P = 0.001) and 1.42 (1.03-1.96; P = 0.035), respectively. HRs (95% CIs) for 30-day and 1-year mortality were 1.98 (1.06-3.70; P = 0.033) and 1.72 (1.18-2.49; P = 0.004), respectively. HRs (95% CIs) for all-cause, HF, and non-HF readmissions were 1.31 (0.97-1.76; P = 0.079), 1.03 (0.71-1.49; P = 0.866), and 1.75 (1.05-2.91; P = 0.031), respectively. Readmission associations were similar among matched nonhospice patients. There was no readmission among matched hospice patients receiving opioids. CONCLUSIONS: In older patients with HF, opioid use is associated with a higher risk of mortality, which is greater in the hospice subgroup, and a higher risk of non-HF readmission in the nonhospice subgroup.

Advantages and limitations of anticipating laboratory test results from regression- and tree-based rules derived from electronic health-record data.

Laboratory testing is the single highest-volume medical activity, making it useful to ask how well one can anticipate whether a given test result will be high, low, or within the reference interval ("normal"). We analyzed 10 years of electronic health records--a total of 69.4 million blood tests--to see how well standard rule-mining techniques can anticipate test results based on patient age and gender, recent diagnoses, and recent laboratory test results. We evaluated rules according to their positive and negative predictive value (PPV and NPV) and area under the receiver-operator characteristic curve (ROC AUCs). Using a stringent cutoff of PPV and/or NPV≥0.95, standard techniques yield few rules for sendout tests but several for in-house tests, mostly for repeat laboratory tests that are part of the complete blood count and basic metabolic panel. Most rules were clinically and pathophysiologically plausible, and several seemed clinically useful for informing pre-test probability of a given result. But overall, rules were unlikely to be able to function as a general substitute for actually ordering a test. Improving laboratory utilization will likely require different input data and/or alternative methods.