Comparing the predictive ability of the Revised Minimum Dataset Mortality Risk Index (MMRI-R) with nurses' predictions of mortality among frail older people: a cohort study. (26th February 2019) - Cole, Andy; Arthur, Antony; Seymour, Jane
Abstract: Objectives: to establish the accuracy of community nurses' predictions of mortality among older people with multiple long-term conditions, to compare these with a mortality rating index and to assess the incremental value of nurses' predictions to the prognostic tool. Design: a prospective cohort study using questionnaires to gather clinical information about patients case managed by community nurses. Nurses estimated likelihood of mortality for each patient on a 5-point rating scale. The dataset was randomly split into derivation and validation cohorts. Cox proportional hazard models were used to estimate risk equations for the Revised Minimum Dataset Mortality Risk Index (MMRI-R) and nurses' predictions of mortality individually and combined. Measures of discrimination and calibration were calculated and compared within the validation cohort. Setting: two NHS Trusts in England providing case-management services by nurses for frail older people with multiple long-term conditions. Participants: 867 patients on the caseload of 35 case-management nurses. 433 and 434 patients were assigned to the derivation and validation cohorts, respectively. Patients were followed up for 12 months. Results: 249 patients died (28.72%). In the validation cohort, MMRI-R demonstrated good discrimination (Harrell's c-index 0.71) and nurses' predictions similar discrimination (Harrell's c-index 0.70). There was no evidence of superiority in performance of either method individually ( P = 0.83) but the MMRI-R and nurses' predictions together were superior to nurses' predictions alone ( P = 0.01). Conclusions: patient mortality is associated with higher MMRI-R scores and nurses' predictions of 12-month mortality. The MMRI-R enhanced nurses' predictions and may improve nurses' confidence in initiating anticipatory care interventions.