Neal Biddick, MD

Clinical question: What are the differences between factors associated with early (zero to seven days after discharge) and late (eight to 30 days after discharge) readmission?

Background: Thirty-day readmission rates are a quality metric; however, recent evidence challenges the notion that readmissions represent unnecessary and preventable healthcare use. It remains unclear whether the 30-day window post-discharge represents a homogenous period or if there are factors that contribute to readmission during that time.

Study design: Retrospective, single-center, cohort study.

Setting: Large, urban teaching hospital.

Synopsis: Based on 13,355 admissions representing 8,078 patients over a two-year period, the overall readmission rate was 19.7%, with 7.8% early (zero to seven days post-discharge) readmissions, and 11.9% late (eight to 30 days post-discharge) readmissions. Variables were categorized as indicators of acute illness burden, chronic illness burden, patient care process factors, and social determinants of health.

Several markers of acute illness burden were associated with early readmission only. Some markers of chronic illness burden were associated with late readmissions only (e.g. hemodialysis), while others were associated with readmissions throughout the 30-day period. Worse social determinants of health increased odds of readmission in both periods.

The single-center study was able to examine detailed clinical variables; however, this approach limited the generalizability of the the results.

Bottom line: Policies to reduce 30-day readmissions should reflect the different risk factors at play across that time frame.

Citation: Graham KL, Wilker EH, Howell MD, Davis RB, Marcantonio ER. Differences between early and late readmissions among patients: A cohort study. Ann Intern Med. 2015;162(11):741-749.

Clinical question: What are the differences between factors associated with early (zero to seven days after discharge) and late (eight to 30 days after discharge) readmission?

Background: Thirty-day readmission rates are a quality metric; however, recent evidence challenges the notion that readmissions represent unnecessary and preventable healthcare use. It remains unclear whether the 30-day window post-discharge represents a homogenous period or if there are factors that contribute to readmission during that time.

Study design: Retrospective, single-center, cohort study.

Setting: Large, urban teaching hospital.

Synopsis: Based on 13,355 admissions representing 8,078 patients over a two-year period, the overall readmission rate was 19.7%, with 7.8% early (zero to seven days post-discharge) readmissions, and 11.9% late (eight to 30 days post-discharge) readmissions. Variables were categorized as indicators of acute illness burden, chronic illness burden, patient care process factors, and social determinants of health.

Several markers of acute illness burden were associated with early readmission only. Some markers of chronic illness burden were associated with late readmissions only (e.g. hemodialysis), while others were associated with readmissions throughout the 30-day period. Worse social determinants of health increased odds of readmission in both periods.

The single-center study was able to examine detailed clinical variables; however, this approach limited the generalizability of the the results.

Bottom line: Policies to reduce 30-day readmissions should reflect the different risk factors at play across that time frame.

Citation: Graham KL, Wilker EH, Howell MD, Davis RB, Marcantonio ER. Differences between early and late readmissions among patients: A cohort study. Ann Intern Med. 2015;162(11):741-749.

Clinical question: What are the differences between factors associated with early (zero to seven days after discharge) and late (eight to 30 days after discharge) readmission?

Background: Thirty-day readmission rates are a quality metric; however, recent evidence challenges the notion that readmissions represent unnecessary and preventable healthcare use. It remains unclear whether the 30-day window post-discharge represents a homogenous period or if there are factors that contribute to readmission during that time.

Study design: Retrospective, single-center, cohort study.

Setting: Large, urban teaching hospital.

Synopsis: Based on 13,355 admissions representing 8,078 patients over a two-year period, the overall readmission rate was 19.7%, with 7.8% early (zero to seven days post-discharge) readmissions, and 11.9% late (eight to 30 days post-discharge) readmissions. Variables were categorized as indicators of acute illness burden, chronic illness burden, patient care process factors, and social determinants of health.

Several markers of acute illness burden were associated with early readmission only. Some markers of chronic illness burden were associated with late readmissions only (e.g. hemodialysis), while others were associated with readmissions throughout the 30-day period. Worse social determinants of health increased odds of readmission in both periods.

The single-center study was able to examine detailed clinical variables; however, this approach limited the generalizability of the the results.

Bottom line: Policies to reduce 30-day readmissions should reflect the different risk factors at play across that time frame.

Citation: Graham KL, Wilker EH, Howell MD, Davis RB, Marcantonio ER. Differences between early and late readmissions among patients: A cohort study. Ann Intern Med. 2015;162(11):741-749.

Clinical question: How can patient adherence to pharmacological thromboprophylaxis be improved?

Background: Prior studies suggest that the hospital-wide prevalence of nonadministration of VTE thromboprophylaxis orders ranges from 5% to 13%, with patient refusal listed as the most common reason for nonadministration.

Study design: Quasi-experimental, pre-post intervention, with intervention and control units.

Setting: Academic medical center in Philadelphia.

Synopsis: Researchers identified 20,208 admissions for the study; 8,293 (41%) admissions occurred prior to the intervention and 11,915 (59%) after. The three-part intervention, which was composed of (1) standardized nurse response to patient refusal, (2) integration of daily assessment of VTE into rounds, and (3) regular audit with feedback, resulted in a decrease in nonadministration rates during the intervention. Rates continued to decline in the 21-month follow-up period.

After the intervention, the rate of missed doses of pharmacological thromboprophylaxis decreased from 24.7% to 14.7% (P<0.01). This was due to a decrease in patient refusal from 18.3% to 9.4% (P<0.01).

Although there was a decrease in the missed doses of thromboprophylaxis, there was no statistically significant change in the rate of hospital-associated VTE.

Bottom line: A multifaceted intervention resulted in a decrease in the proportion of missed and refused doses of pharmacological VTE thromboprophylaxis, but this was not associated with a statistically significant change in VTE rates.

Citation: Baillie CA, Guevara JP, Boston RC, Hecht TE. A unit-based intervention aimed at improving patient adherence to pharmacological thromboprophylaxis [published online ahead of print June 2, 2015]. BMJ Qual Saf. doi:10.1136/bmjqs-2015-003992.

Clinical question: How can patient adherence to pharmacological thromboprophylaxis be improved?

Background: Prior studies suggest that the hospital-wide prevalence of nonadministration of VTE thromboprophylaxis orders ranges from 5% to 13%, with patient refusal listed as the most common reason for nonadministration.

Study design: Quasi-experimental, pre-post intervention, with intervention and control units.

Setting: Academic medical center in Philadelphia.

Synopsis: Researchers identified 20,208 admissions for the study; 8,293 (41%) admissions occurred prior to the intervention and 11,915 (59%) after. The three-part intervention, which was composed of (1) standardized nurse response to patient refusal, (2) integration of daily assessment of VTE into rounds, and (3) regular audit with feedback, resulted in a decrease in nonadministration rates during the intervention. Rates continued to decline in the 21-month follow-up period.

After the intervention, the rate of missed doses of pharmacological thromboprophylaxis decreased from 24.7% to 14.7% (P<0.01). This was due to a decrease in patient refusal from 18.3% to 9.4% (P<0.01).

Although there was a decrease in the missed doses of thromboprophylaxis, there was no statistically significant change in the rate of hospital-associated VTE.

Bottom line: A multifaceted intervention resulted in a decrease in the proportion of missed and refused doses of pharmacological VTE thromboprophylaxis, but this was not associated with a statistically significant change in VTE rates.

Citation: Baillie CA, Guevara JP, Boston RC, Hecht TE. A unit-based intervention aimed at improving patient adherence to pharmacological thromboprophylaxis [published online ahead of print June 2, 2015]. BMJ Qual Saf. doi:10.1136/bmjqs-2015-003992.

Clinical question: How can patient adherence to pharmacological thromboprophylaxis be improved?

Background: Prior studies suggest that the hospital-wide prevalence of nonadministration of VTE thromboprophylaxis orders ranges from 5% to 13%, with patient refusal listed as the most common reason for nonadministration.

Study design: Quasi-experimental, pre-post intervention, with intervention and control units.

Setting: Academic medical center in Philadelphia.

Synopsis: Researchers identified 20,208 admissions for the study; 8,293 (41%) admissions occurred prior to the intervention and 11,915 (59%) after. The three-part intervention, which was composed of (1) standardized nurse response to patient refusal, (2) integration of daily assessment of VTE into rounds, and (3) regular audit with feedback, resulted in a decrease in nonadministration rates during the intervention. Rates continued to decline in the 21-month follow-up period.

After the intervention, the rate of missed doses of pharmacological thromboprophylaxis decreased from 24.7% to 14.7% (P<0.01). This was due to a decrease in patient refusal from 18.3% to 9.4% (P<0.01).

Although there was a decrease in the missed doses of thromboprophylaxis, there was no statistically significant change in the rate of hospital-associated VTE.

Bottom line: A multifaceted intervention resulted in a decrease in the proportion of missed and refused doses of pharmacological VTE thromboprophylaxis, but this was not associated with a statistically significant change in VTE rates.

Citation: Baillie CA, Guevara JP, Boston RC, Hecht TE. A unit-based intervention aimed at improving patient adherence to pharmacological thromboprophylaxis [published online ahead of print June 2, 2015]. BMJ Qual Saf. doi:10.1136/bmjqs-2015-003992.

Clinical question: Is there a score that will predict the mortality rate in elderly patients presenting with a non-ST-elevation myocardial infarction (NSTEMI)?

Background: Although they represent only 9% of patients in clinical trials, patients over the age of 75 make up one third of patients with NSTEMI, accounting for more than half of NSTEMI-related mortality.

Study design: Retrospective cohort analysis for score calculator design, with prospective cohort validation.

Setting: The retrospective cohort was derived from a meta-analysis of 55 papers. The prospective validation arm used a cohort of patients from a randomized multicenter Italian trial.

Synopsis: The authors developed and validated a mortality predictor for patients 75 and older who present with an NSTEMI. The calculator: hemoglobin less than 10 g/dl (two points), elevated troponin levels, ECG ischemic changes, estimated glomerular filtration rate (eGFR) less than 45, previous vascular event (one point each two). The calculator predicted probabilities of death in one year ranging from 2% (score of zero) to 75% (score of six). The calculator allowed stratification into low (score: zero to one), intermediate (score: two), or high (score: three or greater) risk. High-risk patients appeared to benefit from intervention with significantly reduced risk for mortality (odds ratio 0.44).

Bottom line: A simple risk calculator stratifies elderly patients into low, intermediate, or high risk to predict mortality from NSTEMI. High-risk patients appear to achieve a mortality benefit from intervention.

Citation: Angeli F, Cavallini C, Verdecchia P, et al. A risk score for predicting 1-year mortality in patients ≥75 years of age presenting with non-ST-elevation acute coronary syndrome. Am J Cardiol. 2015;116(2):208-213.

Clinical question: Is there a score that will predict the mortality rate in elderly patients presenting with a non-ST-elevation myocardial infarction (NSTEMI)?

Background: Although they represent only 9% of patients in clinical trials, patients over the age of 75 make up one third of patients with NSTEMI, accounting for more than half of NSTEMI-related mortality.

Study design: Retrospective cohort analysis for score calculator design, with prospective cohort validation.

Setting: The retrospective cohort was derived from a meta-analysis of 55 papers. The prospective validation arm used a cohort of patients from a randomized multicenter Italian trial.

Synopsis: The authors developed and validated a mortality predictor for patients 75 and older who present with an NSTEMI. The calculator: hemoglobin less than 10 g/dl (two points), elevated troponin levels, ECG ischemic changes, estimated glomerular filtration rate (eGFR) less than 45, previous vascular event (one point each two). The calculator predicted probabilities of death in one year ranging from 2% (score of zero) to 75% (score of six). The calculator allowed stratification into low (score: zero to one), intermediate (score: two), or high (score: three or greater) risk. High-risk patients appeared to benefit from intervention with significantly reduced risk for mortality (odds ratio 0.44).

Bottom line: A simple risk calculator stratifies elderly patients into low, intermediate, or high risk to predict mortality from NSTEMI. High-risk patients appear to achieve a mortality benefit from intervention.

Citation: Angeli F, Cavallini C, Verdecchia P, et al. A risk score for predicting 1-year mortality in patients ≥75 years of age presenting with non-ST-elevation acute coronary syndrome. Am J Cardiol. 2015;116(2):208-213.

Clinical question: Is there a score that will predict the mortality rate in elderly patients presenting with a non-ST-elevation myocardial infarction (NSTEMI)?

Background: Although they represent only 9% of patients in clinical trials, patients over the age of 75 make up one third of patients with NSTEMI, accounting for more than half of NSTEMI-related mortality.

Study design: Retrospective cohort analysis for score calculator design, with prospective cohort validation.

Setting: The retrospective cohort was derived from a meta-analysis of 55 papers. The prospective validation arm used a cohort of patients from a randomized multicenter Italian trial.

Synopsis: The authors developed and validated a mortality predictor for patients 75 and older who present with an NSTEMI. The calculator: hemoglobin less than 10 g/dl (two points), elevated troponin levels, ECG ischemic changes, estimated glomerular filtration rate (eGFR) less than 45, previous vascular event (one point each two). The calculator predicted probabilities of death in one year ranging from 2% (score of zero) to 75% (score of six). The calculator allowed stratification into low (score: zero to one), intermediate (score: two), or high (score: three or greater) risk. High-risk patients appeared to benefit from intervention with significantly reduced risk for mortality (odds ratio 0.44).

Bottom line: A simple risk calculator stratifies elderly patients into low, intermediate, or high risk to predict mortality from NSTEMI. High-risk patients appear to achieve a mortality benefit from intervention.

Citation: Angeli F, Cavallini C, Verdecchia P, et al. A risk score for predicting 1-year mortality in patients ≥75 years of age presenting with non-ST-elevation acute coronary syndrome. Am J Cardiol. 2015;116(2):208-213.