High rates of cancer screening among dialysis patients seen in primary care a cohort study

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis

RATIONALE & OBJECTIVE

The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD.

STUDY DESIGN

Predictive modeling study.

SETTING & PARTICIPANTS

42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers.

NEW PREDICTORS & ESTABLISHED PREDICTORS

Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR.

OUTCOME

For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival).

ANALYTICAL APPROACH

We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points.

RESULTS

All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%.

LIMITATIONS

The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids.

CONCLUSIONS

A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality.

PLAIN-LANGUAGE SUMMARY

Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

Improving Primary Care Delivery for Patients Receiving Maintenance Hemodialysis

The beneficial impact of primary care, focused on all aspects of a patient's health (rather than a disease-specific focus) is well established. Recognized benefits include greater receipt of preventive care and counseling, lower use of emergency care and hospitalization for ambulatory care-sensitive conditions, and decreased early mortality. Although the importance of primary care and care coordination at the primary care/specialty interface is well recognized, the role of primary care within traditional and emerging care models for patients receiving in-center maintenance hemodialysis remains ill-defined. In this perspective article, we will describe: (1) the role of primary care for patients receiving maintenance hemodialysis and the current evidence regarding the receipt of primary care among these patients; (2) the key challenges to delivery of primary care in these complex cases, including suboptimal care coordination between nephrology and primary care providers, the intensity of dialysis care, and the limited capacity of nephrologists and primary care providers to meet the broad health needs of hemodialysis patients; (3) potential strategies for improving the delivery of primary care for patients receiving hemodialysis; and (4) future research requirements to improve primary care delivery for this high-risk population.

Preventive care for patients with end-stage kidney disease: crossroads between nephrology and primary care

Patients with end-stage kidney disease (ESKD) undergoing maintenance hemodialysis (HD) might expect their nephrologists to coordinate all their healthcare needs. We performed a survey among adult patients with ESKD undergoing HD in two outpatient dialysis centers at the University of Florida to identify differences in characteristics between patients with and without primary care providers (PCP) and to explore the association of PCP utilization with adherence to preventive health measures. Of the 132 participants, 89.4% reported having a PCP. This group was more likely to be female, older, and with higher education level. Having a PCP was associated with influenza, pneumococcal, and tetanus/Tdap vaccinations as well as screening for tuberculosis, depression, hypertension, and dyslipidemia. The PCP group had statistically significant higher rates of influenza immunization (89.8% vs 71.4%, P = .04) as well as screening for hypertension (93.2% vs 64.3%, P = .04) and depression (78.8% vs 42.9%, P = .004), compared to the group without PCP, in the multivariable analysis. Having a PCP is associated with higher rates of influenza vaccination and screening for depression and hypertension. These findings could have important implications as far as identifying patients with ESKD at risk for fragmented care and potential gaps in optimal preventive care.

When ESKD complicates cancer screening and cancer treatment

End-stage kidney disease (ESKD) affects the recommended screening, incidence, treatment, and mortality of cancer. Cancer occurring in a patient with ESKD can influence candidacy for kidney transplantation as well as dialysis decision-making and cancer treatment. Certain cancers are more common among ESKD patients, notably, viral-mediated cancers that are associated with human papilloma or hepatitis viruses, and urothelial cancers associated with analgesic and Balkan nephropathies. Solid tumors are not believed to occur more frequently in ESKD patients. The presence of ESKD may confer a higher risk of post-surgical complications as well as mortality. The cost-effectiveness of cancer screening depends upon individual cancer risk and estimated overall survival. The high mortality associated with ESKD argues against routine cancer screening in dialysis patients. Cancer treatment in ESKD may be complicated by the need to avoid, adjust doses of and/or coordinate the timing of administration of imaging contrast, chemotherapy, and immunotherapy with dialysis treatments. There is a general dearth of information on the treatment of cancer in ESKD patients. These issues will be discussed, and some general guidelines presented based upon the current literature.