Two hundred and eight patients were hospitalized 304 times. Characteristics of patients and their hospital stays are shown in . Forty-two percent of hospitalizations were in the VA setting. The most common reasons for hospital admission were pneumonia, urinary tract infection, dehydration, and exacerbations of congestive heart failure and chronic obstructive pulmonary disease. Median hospital length of stay was 7 days (range 1–296). Median length of follow-up for ADE ascertainment was 63 days (range 1–120).
Characteristics of patients and their hospital stays.
Patients received a mean of 6.5 (s.d. 2.9) medications prior to hospital admission and had a mean of 2.8 (s.d. 2.1) prescribing discrepancies associated with nursing home-to-hospital transfer. Patients received a mean of 6.1 (s.d. 3.2) medications prior to hospital discharge and had a mean of 1.5 (s.d. 1.7) prescribing discrepancies associated with hospital-to-nursing home transfer. The total number of prescribing discrepancies observed in the study sample was 1350 and the total number of discrepancy-associated ADEs observed was 65. Of these, 51%, 39%, and 9% were possible, probable, and definite ADEs. Forty-six percent were asymptomatic ADEs, 42% were associated with temporary symptoms, 10% caused temporary disability, and 3% caused a prolonged or an additional hospital stay. No ADE caused permanent disability or death. Finally, 48% of prescribing discrepancies that caused ADEs were considered to be prescribing errors; 46% of these errors were wrong omissions, 46% were errors in dosing frequency, and 8% were errors in dosage.
Overall, 65 of 1350 prescribing discrepancies caused ADEs for a PPV of .048 (95%CI .037–.061). Positive predictive values of prescribing discrepancies by drug class are shown in ; they ranged from 0 – .28. The drug classes with the highest PPVs were opioid analgesics, metronidazole, and non-opioid analgesics. Among episodes in which medical incidents commonly captured by automated data systems and suspect prescribing discrepancies both occurred, episode PPVs ranged from .07–.37, as shown in .
Predictive value of co-occurrence of a medical incident commonly captured by automated data systems and a suspect prescribing discrepancy.
Examination of the discrimination of 3 indices of transition drug prescribing – number of drugs prescribed prior to transfer, number of drug discrepancies after transfer, and number of high-risk drug discrepancies after transfer – is shown in . Number of high-risk drug discrepancies demonstrated the best discrimination, as demonstrated by the highest c-statistic and best risk gradient. Patients with 0, 1–2, and ≥ 3 high-risk discrepancies (representing first quartile, second and third quartiles together, and fourth quartile) had 13%, 23%, and 47% chance of experiencing a discrepancy-related ADE, respectively. In a multivariable logistic regression model that included relevant demographic (gender, age), clinical (comorbidity score, APACHE score, number of medications at baseline), and circumstantial (off-hours admission, duration of follow-up) variables as predictors, number of high-risk discrepancies was the only statistically significant predictor of ADE with an odds ratio (OR) of 1.71 (95%CI 1.28–2.28; p=.0003), indicating an additional 71% risk of ADE with each additional high-risk discrepancy. Bootstrap validation resulted in an OR of 1.71 (95%CI 1.16–2.28) and c-statistic of .713 (95%CI .654–.774).
Table 4 Association of ADE from a medication discrepancy with 3 prescribing indices: number of medications prescribed prior to transfer, number of medication discrepancies that occurred after transfer, and number of high-risk medication discrepancies that occurred (more ...)