Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model

BACKGROUND: Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients. METHODOLOGY AND PRINCIPAL FINDINGS: This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745-0.769) and calibration of this prognostic model were acceptable. SIGNIFICANCE: Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients

Method for evaluating prediction models that apply the results of randomized trials to individual patients

<p>Abstract</p> <p>Introduction</p> <p>The clinical significance of a treatment effect demonstrated in a randomized trial is typically assessed by reference to differences in event rates at the group level. An alternative is to make individualized predictions for each patient based on a prediction model. This approach is growing in popularity, particularly for cancer. Despite its intuitive advantages, it remains plausible that some prediction models may do more harm than good. Here we present a novel method for determining whether predictions from a model should be used to apply the results of a randomized trial to individual patients, as opposed to using group level results.</p> <p>Methods</p> <p>We propose applying the prediction model to a data set from a randomized trial and examining the results of patients for whom the treatment arm recommended by a prediction model is congruent with allocation. These results are compared with the strategy of treating all patients through use of a net benefit function that incorporates both the number of patients treated and the outcome. We examined models developed using data sets regarding adjuvant chemotherapy for colorectal cancer and Dutasteride for benign prostatic hypertrophy.</p> <p>Results</p> <p>For adjuvant chemotherapy, we found that patients who would opt for chemotherapy even for small risk reductions, and, conversely, those who would require a very large risk reduction, would on average be harmed by using a prediction model; those with intermediate preferences would on average benefit by allowing such information to help their decision making. Use of prediction could, at worst, lead to the equivalent of an additional death or recurrence per 143 patients; at best it could lead to the equivalent of a reduction in the number of treatments of 25% without an increase in event rates. In the Dutasteride case, where the average benefit of treatment is more modest, there is a small benefit of prediction modelling, equivalent to a reduction of one event for every 100 patients given an individualized prediction.</p> <p>Conclusion</p> <p>The size of the benefit associated with appropriate clinical implementation of a good prediction model is sufficient to warrant development of further models. However, care is advised in the implementation of prediction modelling, especially for patients who would opt for treatment even if it was of relatively little benefit.</p

A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients

<p>Abstract</p> <p>Background</p> <p>Risk adjustment and mortality prediction in studies of critical care are usually performed using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), which emphasize physiological derangement. Common risk adjustment systems used in administrative datasets, like the Charlson index, are entirely based on the presence of co-morbid illnesses. The purpose of this study was to compare the discriminative ability of the Charlson index to the APACHE II in predicting hospital mortality in adult multisystem ICU patients.</p> <p>Methods</p> <p>This was a population-based cohort design. The study sample consisted of adult (>17 years of age) residents of the Calgary Health Region admitted to a multisystem ICU between April 2002 and March 2004. Clinical data were collected prospectively and linked to hospital outcome data. Multiple regression analyses were used to compare the performance of APACHE II and the Charlson index.</p> <p>Results</p> <p>The Charlson index was a poor predictor of mortality (C = 0.626). There was minimal difference between a baseline model containing age, sex and acute physiology score (C = 0.74) and models containing either chronic health points (C = 0.76) or Charlson index variations (C = 0.75, 0.76, 0.77). No important improvement in prediction occurred when the Charlson index was added to the full APACHE II model (C = 0.808 to C = 0.813).</p> <p>Conclusion</p> <p>The Charlson index does not perform as well as the APACHE II in predicting hospital mortality in ICU patients. However, when acuity of illness scores are unavailable or are not recorded in a standard way, the Charlson index might be considered as an alternative method of risk adjustment and therefore facilitate comparisons between intensive care units.</p

Predicting the outcome of conservative treatment with physiotherapy in adults with shoulder pain associated with partial-thickness rotator cuff tears – a prognostic model development study

Model coefficient statistics (DOCX 21 kb

The impact of hyperactivity and leptin on recovery from anorexia nervosa

In anorexia nervosa (AN), hyperactivity is observed in about 80% of patients and has been associated with low leptin levels in the acute stage of AN and in anorexia animal models. To further understand the importance of this correlation in AN, we investigated the relationship between hypoleptinaemia and hyperactivity in AN patients longitudinally and assessed their predictive value for recovery

Temporal trends in hospitalisation for stroke recurrence following incident hospitalisation for stroke in Scotland

&lt;p&gt;Background: There are few studies that have investigated temporal trends in risk of recurrent stroke. The aim of this study was to examine temporal trends in hospitalisation for stroke recurrence following incident hospitalisation for stroke in Scotland during 1986 to 2001.&lt;/p&gt; &lt;p&gt;Methods: Unadjusted survival analysis of time to first event, hospitalisation for recurrent stroke or death, was undertaken using the cumulative incidence method which takes into account competing risks. Regression on cumulative incidence functions was used to model the temporal trends of first recurrent stroke with adjustment for age, sex, socioeconomic status and comorbidity. Complete five year follow-up was obtained for all patients. Restricted cubic splines were used to determine the best fitting relationship between the survival events and study year.&lt;/p&gt; &lt;p&gt;Results: There were 128,511 incident hospitalisations for stroke in Scotland between 1986 and 2001, 57,351 (45%) in men. A total of 13,835 (10.8%) patients had a recurrent hospitalisation for stroke within five years of their incident hospitalisation. Another 74,220 (57.8%) patients died within five years of their incident hospitalisation without first having a recurrent hospitalisation for stroke. Comparing incident stroke hospitalisations in 2001 with 1986, the adjusted risk of recurrent stroke hospitalisation decreased by 27%, HR = 0.73 95% CI (0.67 to 0.78), and the adjusted risk of death being the first event decreased by 28%, HR = 0.72 (0.70 to 0.75).&lt;/p&gt; &lt;p&gt;Conclusions: Over the 15-year period approximately 1 in 10 patients with an incident hospitalisation for stroke in Scotland went on to have a hospitalisation for recurrent stroke within five years. Approximately 6 in 10 patients died within five years without first having a recurrent stroke hospitalisation. Using hospitalisation and death data from an entire country over a 20-year period we have been able to demonstrate not only an improvement in survival following an incident stroke, but also a reduction in the risk of a recurrent event.&lt;/p&gt

Imaging of Bubonic Plague Dynamics by In Vivo Tracking of Bioluminescent Yersinia pestis

Yersinia pestis dissemination in a host is usually studied by enumerating bacteria in the tissues of animals sacrificed at different times. This laborious methodology gives only snapshots of the infection, as the infectious process is not synchronized. In this work we used in vivo bioluminescence imaging (BLI) to follow Y. pestis dissemination during bubonic plague. We first demonstrated that Y. pestis CO92 transformed with pGEN-luxCDABE stably emitted bioluminescence in vitro and in vivo, while retaining full virulence. The light produced from live animals allowed to delineate the infected organs and correlated with bacterial loads, thus validating the BLI tool. We then showed that the first step of the infectious process is a bacterial multiplication at the injection site (linea alba), followed by a colonization of the draining inguinal lymph node(s), and subsequently of the ipsilateral axillary lymph node through a direct connection between the two nodes. A mild bacteremia and an effective filtering of the blood stream by the liver and spleen probably accounted for the early bacterial blood clearance and the simultaneous development of bacterial foci within these organs. The saturation of the filtering capacity of the spleen and liver subsequently led to terminal septicemia. Our results also indicate that secondary lymphoid tissues are the main targets of Y. pestis multiplication and that colonization of other organs occurs essentially at the terminal phase of the disease. Finally, our analysis reveals that the high variability in the kinetics of infection is attributable to the time the bacteria remain confined at the injection site. However, once Y. pestis has reached the draining lymph nodes, the disease progresses extremely rapidly, leading to the invasion of the entire body within two days and to death of the animals. This highlights the extraordinary capacity of Y. pestis to annihilate the host innate immune response

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

Peak bone mineral density in Vietnamese women

While the prevalence of osteoporosis and risk factors for low bone mineral density (BMD) has been well documented in Caucasian populations, there is a lack of data from Asia. This work was designed to clarify to what extent osteoporosis could be regarded as a major public health problem in Vietnam. Furthermore, to elucidate the prevalence of certain risk factors, such as vitamin D deficiency and other determinants of bone mass as a basis to indentify high-risk individuals among the Vietnamese women and men. The clinical studies were designed as cross-sectional investigations using a multistage sampling scheme. Within the setting of northern Vietnam (latitude 21°N), districts were selected to represent urban and rural areas. In total 612 healthy women and 222 men aged 13-83 years were investigated. BMD was measured at the lumbar spine, femoral neck and total hip in all qualified subjects with dual energy X-ray absortiometry. Serum concentrations of 25(OH)D, parathyroid hormone, estrogen and testosterone were quantified by electrochemiluminescence immunoassay. Data on clinical history and lifestyle were collected by individual face-to-face interviews. Reference values for peak BMD were defined. These data allowed the calculation of T-scores and thus for the first time, an accurate identification of osteoporosis in a Vietnamese population. As determined at the femoral neck, the prevalence of osteoporosis was 17-23% in women and 9% in men. The results clearly suggest that osteoporosis is an important public health problem. Postmenopausal women living in urban areas experienced osteoporosis more than rural residents. Serum levels of 25(OH)D and estrogen were significantly associated with bone mass in both women and men. The prevalence of vitamin D deficiency (<20 ng/mL) was very high, 30% in women and 16% in men. An experimental study on the isoflavone content of different soymilk preparations was performed by HPLC (high pressure liquid chromatography). Values of isoflavones were very low, around 60-80 mg/L, and there were only 10-20% of bioactive aglycones. This is far below the reported threshold levels to exert significant effects on bone. In the future these data will be useful as a valuable reference base to diagnose osteoporosis and for the clinical management of its consequences. The high prevalence of vitamin D deficiency should raise the awareness of potentially important health issues such as osteoporosis but also about other serious diseases within the Vietnamese society