Advertisement

Journal of General Internal Medicine

, Volume 32, Issue 1, pp 20–20 | Cite as

Care Transitions Measure and Readmissions

  • Amanda S. Mixon
  • Sunil Kripalani
Letter to the Editor

To the Editor: We echo the concerns explicated in Goldstein et al.’s recent article regarding the inconsistent association between the Care Transitions Measure (CTM-3) and readmission risk.1 Their findings add to the mounting evidence questioning the predictive ability of the CTM-3 and its use as a quality indicator of care transitions.

We recently compared two measures of preparedness for discharge and readmission: the CTM-3 and B-PREPARED (Brief-Prescriptions, Ready to re-enter community, Education, Placement, Assurance of safety, Realistic expectations, Empowerment, Directed to appropriate services).2 These measures were assessed in an observational cohort of 1,239 adult patients recently hospitalized for acute coronary syndrome and/or acute decompensated heart failure. Additionally, for all patients we calculated a commonly used, administratively derived readmission index, LACE (Length of stay, Acuity, Comorbidity, and Emergency department use). For our main outcome, we determined all-cause readmission or death at 30 and 90 days post-discharge. Then, we employed Cox regression models to determine the association of the CTM-3, B-PREPARED, and LACE with time to readmission or death at 30 and 90 days after discharge.

Our results showed no association of CTM-3 with readmission/death in simple bivariate and multivariable analyses at either time point. Moreover, CTM-3 alone had a discriminative ability only slightly better than a coin toss in differentiating patients who were or were not readmitted (C statistic 0.523 at 30 days). Interestingly, the B-PREPARED and LACE indices were individually associated with 30-day readmission or death; however, in multivariable models that included CTM-3, B-PREPARED, LACE plus demographics and diagnosis, only LACE remained significantly associated with readmission. In contrast to our study, Goldstein et al. found an independent association of CTM-3 with readmission even when controlling for two factors similar to those in the LACE-Elixhauser count (Comorbidity) and prior hospitalization (Emergency department use). It would be interesting to know whether, in Goldstein’s study, the CTM-3 offers any benefit beyond the LACE index.

Like Goldstein et al. we have reservations about the widespread use of CTM-3 as a basis for improving care transitions and hospital reimbursements. Namely, the CTM-3 appears to perform differently in diverse populations, demonstrates a ceiling effect, and may not reflect mutable aspects of care transitions.3 Future research is needed to assess how well the CTM-3 and other measures of care transitions quality predict readmission risk adequately across broad populations to be rigorous enough for comparing hospitals nationwide.4 , 5

Notes

Compliance with Ethical Standards

Conflict of Interest

Dr. Kripalani is a consultant to SAI Interactive and Verustat. Dr. Mixon is a Veterans Affairs Health Services Research and Development Service Career Development awardee at the Tennessee Valley Department of Veterans Affairs. The contents do not represent the views of the US Department of Veterans Affairs or the US government.

References

  1. 1.
    Goldstein JN, Hicks LS, Kolm P, Weintraub WS, Elliott DJ. Is the Care Transitions Measure associated with readmission risk? Analysis from a single academic center. J Gen Intern Med. 2016;31(7):732–8.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Mixon AS, Goggins K, Bell SP, Vasilevskis EE, Nwosu S, Schildcrout JS, Kripalani S. Preparedness for hospital discharge and prediction of readmission. J Hosp Med. 2016. doi: 10.1002/jhm.2572.Google Scholar
  3. 3.
    Atreya AR, Pack QR. Care Transition Measure score and coronary revascularization related readmission: ready for primetime use? J Gen Intern Med. 2016;31(7):707–9.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Meyers AG, Salanitro A, Wallston KA, Cawthon C, Vasilevskis EE, Goggins KM, Davis CM, Rothman RL L, Castel LD, Donato KM, Schnelle JF, Bell SP, Schildcrout JS, Osborn CY, Harrell FE, Kripalani S. Determinants of health after hospital discharge: rationale and design of the Vanderbilt Inpatient Cohort Study (VICS). BMC Health Serv Res. 2014;14:10.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society of General Internal Medicine 2016

Authors and Affiliations

  1. 1.Vanderbilt University School of MedicineNashvilleUSA

Personalised recommendations