A markov model to evaluate hospital readmission

<p>Abstract</p> <p>Background</p> <p>The analysis of non-fatal recurring events is frequently found in studies on chronic-degenerative diseases. The aim of this paper is to estimate the probability of readmission of patients with Chronic Obstructive Pulmonary Disease (COPD) or with Respiratory Failure (RF).</p> <p>Methods</p> <p>The Repeated hospital admissions of a patient are considered as a Markov Chain. The transitions between the states are estimated using the Nelson-Aalen estimator. The analysis was carried out using the Puglia Region hospital patient discharge database for the years 1998–2005. Patients were selected on the basis of first admission between 01/01/2001 and 31/12/2005 with ICD-9-CM code of COPD or RF as principal and/or secondary diagnosis. For those selected two possible transitions were considered in the case they had the second and third admission with an ICD-9-CM code of COPD or RF as principal diagnosis.</p> <p>Results</p> <p>The probability of readmission is increased in patients with a diagnosis of RF (OR = 1.618 in the first transition and 1.279 in the second) and also in those with a diagnosis of COPD or RF as the principal diagnosis at first admission (OR = 1.615 in the first transition and 1.193 in the second). The clinical gravity and the ward from which they were discharged did not significantly influence the probability of readmission.</p> <p>Conclusion</p> <p>The time to readmission depends on the gravity of the pathology at onset. In patients with a grave clinical picture, either COPD or Respiratory Failure, when treated and controlled after the first admission, they become minor problems and they are indicated among secondary diagnoses in any further admission.</p

A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring.

This paper presents a parametric method of fitting semi-Markov models with piecewise-constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three-state illness-death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time-varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation-Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study

Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality

Background: Several models have been designed to predict survival of patients with heart failure. These, while available and widely used for both stratifying and deciding upon different treatment options on the individual level, have several limitations. Specifically, some clinical variables that may influence prognosis may have an influence that change over time. Statistical models that include such characteristic may help in evaluating prognosis. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting. Methodology: Survival data from an inception cohort of five hundred patients diagnosed with heart failure functional class III and IV between 2002 and 2004 and followed-up to 2006 were analyzed by using the proportional hazards Cox model and variations of the Cox's model and also of the Aalen's additive model. Principal Findings: One-hundred and eighty eight (188) patients died during follow-up. For patients under study, age, serum sodium, hemoglobin, serum creatinine, and left ventricular ejection fraction were significantly associated with mortality. Evidence of time-varying effect was suggested for the last three. Both high hemoglobin and high LV ejection fraction were associated with a reduced risk of dying with a stronger initial effect. High creatinine, associated with an increased risk of dying, also presented an initial stronger effect. The impact of age and sodium were constant over time. Conclusions: The current study points to the importance of evaluating covariates with time-varying effects in heart failure models. The analysis performed suggests that variations of Cox and Aalen models constitute a valuable tool for identifying these variables. The implementation of covariates with time-varying effects into heart failure prognostication models may reduce bias and increase the specificity of such models.CNPq Brazilian Foundation for Scientific and Technological DevelopmentCNPq - Brazilian Foundation for Scientific and Technological Development [150653/2008-5

Different competing risks models applied to data from the Australian Orthopaedic Association National Joint Replacement Registry

Purpose: Here we describe some available statistical models and illustrate their use for analysis of arthroplasty registry data in the presence of the competing risk of death, when the influence of covariates on the revision rate may be different to the influence on the probability (that is, risk) of the occurrence of revision. Patients and methods: Records of 12,525 patients aged 75–84 years who had received hemiarthroplasty for fractured neck of femur were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. The covariates whose effects we investigated were: age, sex, type of prosthesis, and type of fixation (cementless or cemented). Extensions of competing risk regression models were implemented, allowing the effects of some covariates to vary with time. Results: The revision rate was significantly higher for patients with unipolar than bipolar prostheses (HR = 1.38, 95% CI: 1.01–1.89) or with monoblock than bipolar prostheses (HR = 1.45, 95% CI: 1.08–1.94). It was significantly higher for the younger age group (75–79 years) than for the older one (80–84 years) (HR = 1.28, 95% CI: 1.05–1.56) and higher for males than for females (HR = 1.37, 95% CI: 1.09–1.71). The probability of revision, after correction for the competing risk of death, was only significantly higher for unipolar prostheses than for bipolar prostheses, and higher for the younger age group. The effect of fixation type varied with time; initially, there was a higher probability of revision for cementless prostheses than for cemented prostheses, which disappeared after approximately 1.5 years. Interpretation: When accounting for the competing risk of death, the covariates type of prosthesis and sex influenced the rate of revision differently to the probability of revision. We advocate the use of appropriate analysis tools in the presence of competing risks and when covariates have time-dependent effects.Marianne H Gillam, Amy Salter, Philip Ryan, and Stephen E Grave

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

The impact of population-based faecal occult blood test screening on colorectal cancer mortality:a matched cohort study

BACKGROUND: Randomised trials show reduced colorectal cancer (CRC) mortality with faecal occult blood testing (FOBT). This outcome is now examined in a routine, population-based, screening programme. METHODS: Three biennial rounds of the UK CRC screening pilot were completed in Scotland (2000–2007) before the roll out of a national programme. All residents (50–69 years) in the three pilot Health Boards were invited for screening. They received a FOBT test by post to complete at home and return for analysis. Positive tests were followed up with colonoscopy. Controls, selected from non-pilot Health Boards, were matched by age, gender, and deprivation and assigned the invitation date of matched invitee. Follow-up was from invitation date to 31 December 2009 or date of death if earlier. RESULTS: There were 379 655 people in each group (median age 55.6 years, 51.6% male). Participation was 60.6%. There were 961 (0.25%) CRC deaths in invitees, 1056 (0.28%) in controls, rate ratio (RR) 0.90 (95% confidence interval (CI) 0.83–0.99) overall and 0.73 (95% CI 0.65–0.82) for participants. Non-participants had increased CRC mortality compared with controls, RR 1.21 (95% CI 1.06–1.38). CONCLUSION: There was a 10% relative reduction in CRC mortality in a routine screening programme, rising to 27% in participants

Development of a decision support tool to facilitate primary care management of patients with abnormal liver function tests without clinically apparent liver disease [HTA03/38/02]. Abnormal Liver Function Investigations Evaluation (ALFIE)

Liver function tests (LFTs) are routinely performed in primary care, and are often the gateway to further invasive and/or expensive investigations. Little is known of the consequences in people with an initial abnormal liver function (ALF) test in primary care and with no obvious liver disease. Further investigations may be dangerous for the patient and expensive for Health Services. The aims of this study are to determine the natural history of abnormalities in LFTs before overt liver disease presents in the population and identify those who require minimal further investigations with the potential for reduction in NHS costs