A Markov multistate analysis of the relationship between performance status and death among an ambulatory population of cancer patients

Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure

AIMS

Risk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF).

METHODS AND RESULTS

We described the pattern of behaviour among 7496 consecutive patients assessed for suspected HF. The following mutually exclusive health states were defined and assessed every 4 months: death, hospitalization, outpatient visit, no event, and leaving the service altogether (defined as no event at any point following assessment). The observed figures at the first transition (4 months) weres 427 (6%), 1559 (21%), 2254 (30%), 1414 (19%), and 1842 (25%), respectively. The probabilities derived from the first two transitions (i.e. from baseline to 4 months and from 4 to 8 months) were used to construct the model. An example of the model's prediction is that at cycle 4, the cumulative probability of death was 14%; leaving the system, 37%; being hospitalized between 12 and 16 months, 10%; having an outpatient visit, 8%; and having no event, 31%. The corresponding observed figures were 14%, 41%, 10%, 15%, and 21%, respectively. The model predicted that during the first 2 years, a patient had a probability of dying of 0.19, and the observed value was 0.18.

CONCLUSIONS

A model derived from the first 8 months of follow-up is strongly predictive of future events in a population of patients with chronic heart failure. The course of CHF is more linear than is commonly supposed, and thus more predictable.

Quantifying Dynamic Flow of Emergency Department (ED) Patient Managements: A Multistate Model Approach

Background

Emergency department (ED) crowding and prolonged lengths of stay continue to be important medical issues. It is difficult to apply traditional methods to analyze multiple streams of the ED patient management process simultaneously. The aim of this study was to develop a statistical model to delineate the dynamic patient flow within the ED and to analyze the effects of relevant factors on different patient movement rates.

Methods

This study used a retrospective cohort available with electronic medical data. Important time points and relevant covariates of all patients between January and December 2013 were collected. A new five-state Markov model was constructed by an expert panel, including three intermediate states: triage, physician management, and observation room and two final states: admission and discharge. A day was further divided into four six-hour periods to evaluate dynamics of patient movement over time.

Results

A total of 149,468 patient records were analyzed with a median total length of stay being 2.12 (interquartile range = 6.51) hours. The patient movement rates between states were estimated, and the effects of the age group and triage level on these movements were also measured. Patients with lower acuity go home more quickly (relative rate (RR): 1.891, 95% CI: 1.881–1.900) but have to wait longer for physicians (RR: 0.962, 95% CI: 0.956–0.967) and admission beds (RR: 0.673, 95% CI: 0.666–0.679). While older patients were seen more quickly by physicians (RR: 1.134, 95% CI: 1.131–1.139), they spent more time waiting for the final state (for admission RR: 0.830, 95% CI: 0.821–0.839; for discharge RR: 0.773, 95% CI: 0.769–0.776). Comparing the differences in patient movement rates over a 24-hour day revealed that patients wait longer before seen by physicians during the evening and that they usually move from the ED to admission afternoon. Predictive dynamic illustrations show that six hours after the patients' entry, the probability of still in the ED system ranges from 28% in the evening to 38% in the morning.

Conclusions

The five-state model well described the dynamic ED patient flow and analyzed the effects of relevant influential factors at different states. The model can be used in similar medical settings or incorporate different important covariates to develop individually tailored approaches for the improvement of efficiency within the health professions.

Symptom Distress in Patients With Hepatocellular Carcinoma Toward the End of Life

PURPOSE/OBJECTIVES

To describe the presence, frequency, severity, and distress of symptoms in outpatients with advanced hepatocellular carcinoma toward the end of life, and the variability in psychological and physical symptom distress between and within patients over time. 
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DESIGN

A prospective, longitudinal, descriptive design. 
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SETTING

Outpatient clinics at two healthcare institutions. 
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SAMPLE

18 patients (15 men and 3 women) with hepatocellular carcinoma and a mean age of 63.3 years (range = 54-81 years).
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METHODS

Data were collected monthly for six months. Patients completed the Memorial Symptom Assessment Scale, which reports a total score, and three subscales that provide global distress, psychological distress, and physical distress scores.
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MAIN RESEARCH VARIABLES

Global, psychological, and physical distress.
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FINDINGS

Patients reported lack of energy and pain as the most frequent and distressing symptoms. Problems with sexual interest or activity was the fourth most present symptom after drowsiness. Global Distress Index mean scores had notable variability between and within patients over time. During data collection, six patients died. None were referred to palliative care.
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CONCLUSIONS

Gaining knowledge about symptom distress and prevalent symptoms experienced by patients with advanced hepatocellular carcinoma is critical for designing symptom management strategies that are comprehensive and tailored to patients to optimize their quality of life as they approach death.
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IMPLICATIONS FOR NURSING

Nurses play a vital role in advocating for, initiating, and providing comprehensive holistic care based on individual patient needs by facilitating discussions about apparent and less apparent distressing symptoms, including those related to sexuality.

Alternative Design and Analytical Techniques for Longitudinal Rheumatology Studies: Improved Understanding of Outcomes

Longitudinal cohort designs (with three or more measurement occasions) are invaluable to investigate between- and within-individual variation in outcomes. However, traditional longitudinal designs require a lengthy implementation and data collection period and impose a substantial burden on participants and investigators. We discuss alternative longitudinal designs, including planned missing data designs and retrospective cohort studies with secondary data, which require a shorter period for data accrual and reduce participant burden while maintaining statistical power. We also discuss analysis strategies to maximize data use and produce unbiased estimates of treatment effectiveness, including models for recurrent or multistate events and time-varying covariates.

Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study

BACKGROUND

Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death.

AIM

To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis.

DESIGN

This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients.

RESULTS

Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis.

CONCLUSION

When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status.

Variations in intensity of end-of-life cancer therapy by cancer type at a Canadian tertiary cancer centre between 2003 and 2010

Background

Aggressive medical management of cancer patients at the end of life (EOL) is an indicator of health services quality. We evaluated the variations in EOL cancer therapy utilization and in acute care hospital deaths across different types of cancer within the setting of a regionalized cancer program.

Methods

Intravenous chemotherapy and radiotherapy use within the last 14 and 30 days of life was identified through the Alberta Cancer Registry and then verified by chart review for cancer decedents residing within 50 km of the Tom Baker Cancer Centre between 2003 and 2010. Multivariable logistic regression was used to examine variations in outcomes of interest by cancer, adjusting for age and other factors in prespecified models.

Results

Of the 9863 decedents included in the study, 3.0 and 6.3 % received chemotherapy within the final 14 and 30 days of life, respectively. In multivariable model, breast, hematological, and gynecological cancers were at least 2.5 times more likely than other cancers to undergo EOL chemotherapy. Radiotherapy was given to 4.6 % of decedents within 14 days of death, but only 66 % (359/542 courses) were completed as prescribed. Acute care admission within 14 days of death was seen in 44 % of decedents and 34 % died in the hospital.

Conclusions

In our regional cancer program, the intensity of cancer therapies near the end of life varied considerably across different cancer types. Such variations may be unwarranted. A substantial proportion of cancer deaths occurred in the acute care setting. Greater efforts to integrate palliative care in outpatient cancer services are needed.

Electronic supplementary material

The online version of this article (doi:10.1007/s00520-015-2676-y) contains supplementary material, which is available to authorized users.