Laparoscopically-assisted resection rectopexy for rectal prolapse: Ten years' experience

PURPOSE: This study has been undertaken to audit a single-center experience with laparoscopically-assisted resection rectopexy for full-thickness rectal prolapse. The clinical Outcomes and long-term results were evaluated. METHODS: The data were prospectively collected for the duration of the operation, time to passage of flatus postoperatively, hospital stay, morbidity, and mortality. For follow-up, patients received a questionnaire or were contacted. The data were divided into quartiles over the study period, and the differences in operating time and length of hospital stay were tested using the Kruskal-Wallis test. RESULTS: Between March 1992 and October 2003, a total of 117 patients underwent laparoscopic resection rectopexy for rectal prolapse. The median operating time during the first quartile (representing the early experience) was 180 minutes compared with 110 minutes for the fourth quartile (Kruskal-Wallis test for operating time = 35.523, 3 df, P < 0.0001). Overall morbidity was 9 percent (ten patients), with one death (< 1 percent). One patient had a ureteric injury requiring conversion. One minor anastomotic leak Occurred, necessitating laparoscopic evacuation of a pelvic abscess. Altogether, 77 patients were available for follow-up. The median follow-up was 62 months. Eighty percent of the patients reported alleviation of their symptoms after the operation. Sixty-nine percent of the constipated patients experienced an improvement in bowel frequency. No patient had new or worsening symptoms of constipation after Surgery. Two (2.5 percent) patients had full-thickness rectal prolapse recurrence. Mucosal prolapse recurred in 14 (18 percent) patients. Anastomotic dilation was performed for stricture in five (4 percent) patients. CONCLUSIONS: Laparoscopically-assisted resection rectopexy for rectal prolapse provides a favorable functional outcome and low recurrence rate. Shorter operating time is achieved with experience. The minimally invasive technique benefits should be considered when offering rectal prolapse patients a transabdominal approach for repair, and emphasis should now be on advanced training in the laparoscopic approach.

Pitch jump probability measures for the analysis of snoring sounds in apnea

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The gold standard of diagnosis, called polysomnography (PSG), requires a full-night hospital stay connected to over ten channels of measurements requiring physical contact with sensors. PSG is inconvenient, expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. Diagnostic systems intent on using snore-related sounds (SRS) face the tough problem of how to define a snore. In this paper, we present a working definition of a snore, and propose algorithms to segment SRS into classes of pure breathing, silence and voiced/unvoiced snores. We propose a novel feature termed the 'intra-snore-pitch-jump' (ISPJ) to diagnose OSA. Working on clinical data, we show that ISPJ delivers OSA detection sensitivities of 86-100% while holding specificity at 50-80%. These numbers indicate that snore sounds and the ISPJ have the potential to be good candidates for a take-home device for OSA screening. Snore sounds have the significant advantage in that they can be conveniently acquired with low-cost non-contact equipment. The segmentation results presented in this paper have been derived using data from eight patients as the training set and another eight patients as the testing set. ISPJ-based OSA detection results have been derived using training data from 16 subjects and testing data from 29 subjects.

Predicting functional capacity outcome 12 months after hospitalized injury

Background: There is a recognized need to move from mortality to morbidity outcome predictions following traumatic injury. However, there are few morbidity outcome prediction scoring methods and these fail to incorporate important comorbidities or cofactors. This study aims to develop and evaluate a method that includes such variables. Methods: This was a consecutive case series registered in the Queensland Trauma Registry that consented to a prospective 12-month telephone conducted follow-up study. A multivariable statistical model was developed relating Trauma Registry data to trichotomized 12-month post-injury outcome (categories: no limitations, minor limitations and major limitations). Cross-validation techniques using successive single hold-out samples were then conducted to evaluate the model's predictive capabilities. Results: In total, 619 participated, with 337 (54%) experiencing no limitations, 101 (16%) experiencing minor limitations and 181 (29%) experiencing major limitations 12 months after injury. The final parsimonious multivariable statistical model included whether the injury was in the lower extremity body region, injury severity, age, length of hospital stay, pulse at admission and whether the participant was admitted to an intensive care unit. This model explained 21% of the variability in post-injury outcome. Predictively, 64% of those with no limitations, 18% of those with minor limitations and 37% of those with major limitations were correctly identified. Conclusion: Although carefully developed, this statistical model lacks the predictive power necessary for its use as a basis of a useful prognostic tool. Further research is required to identify variables other than those routinely used in the Trauma Registry to develop a model with the necessary predictive utility.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Type of housing predicts rate of readmission to hospital but not length of stay in people with schizophrenia on the Gold Coast in Queensland

Accommodation is considered to be important by institutions interested in mental health care both in Australia and internationally. Some authorities assert that no component of a community mental health system is more important than decent affordable housing. Unfortunately there has been little research in Australia into the consequences of discharging people with a primary diagnosis of schizophrenia to different types of accommodation. This paper uses archival data to investigate the outcomes for people with schizophrenia discharged to two types of accommodation. The types of accommodation chosen are the person's own home and for-profit boarding house. These two were chosen because the literature suggests that they are respectively the most and least desirable types of accommodation. Results suggest that people with schizophrenia who were discharged to boarding houses are significantly more likely to be readmitted to the psychiatric unit of Gold Coast Hospital although their length of stay in hospital is not significantly different. (author abstract)

Rehabilitation outcomes for brain injured patients in Australia: functional status, length of stay and discharge destination

This study describes the rehabilitation length of stay (LOS), discharge destination and discharge functional status of 149 patients admitted with traumatic brain injury (TBI) to an Australian hospital over a 5-year period. Hospital charts of patients admitted between 1993-1998 were reviewed. Average LOS over the 5-year time period was 61.8 days and only decreased nominally over this time. Longer LOS was predicted by lower admission motor FIM scores and presence of comorbidities. Mean admission and discharge motor FIM scores were 58 and 79, which represented a gain of 21 points. Higher discharge motor FIM scores were predicted by higher admission motor FIM scores and younger age. FIM gain was predicted by cognitive status and age. Most patients, 88%, were discharged back to the community, with 30% changing their living setting or situation. Changing living status was predicted by living alone and having poorer functional status on admission.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic ( or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction ( AMI) ( n = 3427), or stroke ( n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals ( long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups ( same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided. Conclusions: Diagnosis-outcome indicators based on administrative data require validation as markers of significant risk adjusted SV. Validated indicators allow quantification of realisable outcome benefits if all hospitals achieved best performer levels. The overall level of quality of care within single institutions cannot be inferred from the results of one or a few indicators.

Trends in the use of hospital beds by older people in Australia: 1993-2002

Objective: To determine trends in use of Australian acute hospital inpatient services by older patients. Design and data sources: Secondary analysis of hospital data from the Australian Institute of Health and Welfare in the period 1993-94 to 2001-02, with population data for this period from the Australian Bureau of Statistics. Outcome measures: Population-based rates of hospital separations and bed utilisation. Results: The Australian aged population (65 years and older) increased by 18% compared with total population growth of 10%, yet the proportion of hospital beds occupied by older patients remained stable at 47%. The most substantial changes were observed in the population aged 75 years and older, with separations increasing by 89%, length of stay reducing by 35% and bed utilisation increasing by 23%. However, rates of bed utilisation (in relation to population) declined among older groups (10% decline in per capita use in population 75 years and older), but increased in the younger population (1% increase in per capita use in people younger than 65 years). Conclusion: Important trends in use of inpatient services were identified in this study. These trends are contrary to common perception. Ageing of the Australian population was not associated with an increase in the proportion of hospital beds used by older patients.

The use of cusum analysis in the early detection and management of hospital bed occupancy crises

Objective: To assess the value of cusum analysis in hospital bed management. Design: Comparative analysis of medical patient flows, bed occupancy, and emergency department admission rates and access block over 2 years. Setting: Internal Medicine Services and Emergency Department in a teaching hospital. Interventions: Improvements in bed use and changes in the level of available beds. Main outcome measures: Average length of stay; percentage occupancy of available beds; number of patients waiting more than 8 hours for admission (access block); number of medical patients occupying beds in non-medical wards; and number of elective surgical admissions. Results: Cusum analysis provided a simple means of revealing important trends in patient flows that were not obvious in conventional time-series data. This prompted improvements in bed use that resulted in a decrease of 9500 occupied bed-days over a year. Unfortunately and unexpectedly, after some initial improvement, the levels of access block, medical ward congestion and elective surgical admissions all then deteriorated significantly. This was probably caused by excessive bed closures in response to the initial improvement in bed use. Conclusion: Cusum analysis is a useful technique for the early detection of significant changes in patient flows and bed use, and in determining the appropriate number of beds required for a given rate of patient flow.

Length of Stay, Discharge Destination, and Functional Improvement: Utility of the Australian National Subacute and Nonacute Patient Casemix Classification

Background and Purpose - Although implemented in 1998, no research has examined how well the Australian National Subacute and Nonacute Patient (AN-SNAP) Casemix Classification predicts length of stay (LOS), discharge destination, and functional improvement in public hospital stroke rehabilitation units in Australia. Methods - 406 consecutive admissions to 3 stroke rehabilitation units in Queensland, Australia were studied. Sociode-mographic, clinical, and functional data were collected. General linear modeling and logistic regression were used to assess the ability of AN-SNAP to predict outcomes. Results - AN-SNAP significantly predicted each outcome. There were clear relationships between the outcomes of longer LOS, poorer functional improvement and discharge into care, and the AN-SNAP classes that reflected poorer functional ability and older age. Other predictors included living situation, acute LOS, comorbidity, and stroke type. Conclusions - AN-SNAP is a consistent predictor of LOS, functional change and discharge destination, and has utility in assisting clinicians to set rehabilitation goals and plan discharge.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Family orientated delivery of routine nursing care in hospital

Hospital nursing may be better deployed to acute clinical patient care. The recruitment of family assistance will facilitate this process in patients in hospital awaiting placement and without acute care issues.