Development of a multivariable prediction model for early revision of total knee arthroplasty - The effect of including patient-reported outcome measures.

BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As revision is becoming increasingly common in patients under 65 years, the need for improved preoperative patient selection is imminently needed. Therefore, this study aimed to identify the most important factors of early revision and to develop a prediction model of early revision including assessment of the effect of incorporating data on patient-reported outcome measures (PROMs). MATERIAL AND METHODS: A cohort of 538 patients undergoing primary TKA was included. Multiple logistic regression using forward selection of variables was applied to identify the best predictors of early revision and to develop a prediction model. The model was internally validated with stratified 5-fold cross-validation. This procedure was repeated without including data on PROMs to develop a model for comparison. The models were evaluated on their discriminative capacity using area under the receiver operating characteristic curve (AUC). RESULTS: The most important factors of early revision were age (OR 0.63 [0.42, 0.95]; P = 0.03), preoperative EQ-5D (OR 0.07 [0.01, 0.51]; P = 0.01), and number of comorbidities (OR 1.01 [0.97, 1.25]; P = 0.15). The AUCs of the models with and without PROMs were 0.65 and 0.61, respectively. The difference between the AUCs was not statistically significant (P = 0.32). CONCLUSIONS: Although more work is needed in order to reach a clinically meaningful quality of the predictions, our results show that the inclusion of PROMs seems to improve the quality of the prediction model.

Spontaneous and iatrogenic hypoglycaemia related to mortality in the ICU.

AIM: Our study investigated the relationship between both spontaneous and iatrogenic hypoglycaemia and mortality. METHODS: A total of 200,859 patients from the eICU Collaborative Research Database were grouped by whether they had registered episodes of hypoglycaemia or not. Patients with hypoglycaemia were then further divided into subgroups according to type of hypoglycaemia-whether spontaneous or iatrogenic. Spontaneous hypoglycaemia during an ICU stay was defined as one or more registered blood glucose measurements < 70 mg/dL (3.9 mmol/L) with no evidence of insulin therapy. RESULTS: Evidence of at least one hypoglycaemic episode during ICU hospitalization was associated with a significant increase in mortality: the observed mortality rate in such patients was 15.6% compared with 8% in patients without hypoglycaemia (P < 0.001). Multivariate logistic regression analysis was performed with APACHE Scores, hypoglycaemia and baseline data (age, gender, and ethnicity). Spontaneous hypoglycaemia remained a statistically significant predictor of mortality with an adjusted odds ratio (OR) of 1.61 (95% CI: 1.38-1.88; P < 0.001), whereas iatrogenic hypoglycaemia was not a significant predictor with an adjusted OR of 0.97 (95% CI: 0.82-1.14; P = 0.71). CONCLUSION: Spontaneous hypoglycaemia observed in ICU patients was associated with increased mortality and increased length of ICU stay. Although the present study, given its observational design, cannot provide a definitive answer, the clear difference between spontaneous and iatrogenic hypoglycaemia does not support a causal relationship between (short-lasting) hypoglycaemia and adverse outcomes, but instead indicates that (short-lasting) hypoglycaemia may be a marker of illness severity.

The long-term effects of using telehomecare technology on functional health literacy: results from a randomized trial.

OBJECTIVES: The association between the use of telehomecare technology and functional health literacy is rather unexplored in the current literature. This relationship could prove important in the future management of chronic diseases, as technology has become a more integrated part of modern healthcare systems. Therefore, the purpose of this study was to explore how the use of telehomecare technology affects the level of functional health literacy over a period of 10 months. STUDY DESIGN: Randomized controlled trial. METHODS: Our sample comprised 116 patients diagnosed with chronic obstructive pulmonary disease. A face-to-face interview and an objective test of functional health literacy were conducted with each patient at baseline and again at follow-up after 10 months. Twenty-six patients were lost to follow-up and thus, providing a total of 47 chronic obstructive pulmonary disease patients in the intervention group and 43 in the control group for this follow-up study. The level of functional health literacy was assessed with the Danish Test of Functional Health Literacy in Adults. The difference from baseline to follow-up, in both the functional health literacy score and the mean response time to the entire Danish Test of Functional Health Literacy in Adults (TOFHLA), was tested for statistical significance between the intervention group and the control group. RESULTS: A significant increase in functional health literacy is observed in both the groups from baseline to follow-up, but there is no statistical difference between groups (P-value = 0.62). CONCLUSIONS: A significant increase in the functional health literacy score was observed in both groups, but the findings of this present study provide no information on what causes the increase, so further research is needed to explore the increase in functional health literacy score more thoroughly and establish if the use of telehomecare technology is a part of the explanation.

Hypoglycaemia and QT interval prolongation in type 1 diabetes--bridging the gap between clamp studies and spontaneous episodes.

AIMS: We propose a study design with controlled hypoglycaemia induced by subcutaneous injection of insulin and matched control episodes to bridge the gap between clamp studies and studies of spontaneous hypoglycaemia. The observed prolongation of the heart rate corrected QT interval (QTc) during hypoglycaemia varies greatly between studies. METHODS: We studied ten adults with type 1 diabetes (age 41±15years) without cardiovascular disease or neuropathy. Single-blinded hypoglycaemia was induced by a subcutaneous insulin bolus followed by a control episode on two occasions separated by 4weeks. QT intervals were measured using the semi-automatic tangent approach, and QTc was derived by Bazett's (QTcB) and Fridericia's (QTcF) formulas. RESULTS: QTcB increased from baseline to hypoglycaemia (403±20 vs. 433±39ms, p<0.001). On the euglycaemia day, QTcB also increased (398±20 vs. 410±27ms, p<0.01), but the increase was less than during hypoglycaemia (p<0.001). The same pattern was seen for QTcF. Plasma adrenaline levels increased significantly during hypoglycaemia compared to euglycaemia (p<0.01). Serum potassium levels decreased similarly after insulin injection during both hypoglycaemia and euglycaemia. CONCLUSIONS: Hypoglycaemia as experienced after a subcutaneous injection of insulin may cause QTc prolongation in type 1 diabetes. However, the magnitude of prolongation is less than typically reported during glucose clamp studies, possible because of the study design with focus on minimizing unwanted study effects.

QT interval prolongation during spontaneous episodes of hypoglycaemia in type 1 diabetes: the impact of heart rate correction.

AIMS/HYPOTHESIS: Prolongation of the heart rate corrected QT interval (QTc) is seen during episodes of hypoglycaemia in type 1 diabetes. We studied the relationship between spontaneous hypoglycaemia and the QT interval and hypothesised that the choice of heart rate correction affects the observed change in QTc. METHODS: Twenty-one participants with type 1 diabetes (aged 58 +/- 10 years with duration of diabetes 34 +/- 12 years) had continuous glucose and ECG monitoring for 72 h. QT and RR intervals were measured during hypoglycaemia (blood glucose or continuous glucose measurements

Use of the Internet to optimize self-management of type 1 diabetes: preliminary experience with DiasNet.

Six patients with type 1 diabetes participated in a pilot trial. Their median age was 36 years (range 29-61) and the median duration of diabetes was 10 years (range 3-29). They were asked to enter, from their home or work PC, blood glucose values, insulin doses and a food diary. From the data entered, a computer model generated a simulation of the blood glucose concentration for the data collection period. It could then suggest alternative insulin doses (or regimes), or meal sizes, to reduce the risk of hypo- and hyperglycaemia. During a six-month study, patients entered a median of five sets of data (range two to eight). Feedback from participants revealed that while the system was helpful, difficulties with data entry hindered its use. Information gained from this exercise is shaping further development of the system.

DiasNet--a diabetes advisory system for communication and education via the internet.

Intensive diabetes treatment can lead to a substantial reduction of the rate of the complications associated with diabetes. However, a number of patients may have poor control despite specialist care, and this along with devolution of care to non-specialists suggests that alternative interventions should be developed. The present paper describes an Internet based system where more emphasis is put on patient empowerment, the keywords being education and communication. The DiasNet system is based on a well documented decision support system, Dias, designed for use by clinicians. The scope of DiasNet has been widened from being used by clinicians to give advice on insulin dose, to also being used by patients as a tool for education and communication. Patients can experiment with their own data, adjusting insulin doses or meal sizes. In this way different therapeutic and dietary alternatives can be tried out, allowing the patient to gain experience in achieving glycaemic control. DiasNet is implemented in JAVA according to the client/server principle, enabling a new way of communication between patient and clinician: in case of any problems, the patient simply phones the clinician, who immediately, using his or her office PC, can take a look at the data the patient has entered.

Using the internet in patient-centred diabetes care for communication, education, and decision support.

The New England Journal of Medicine 329 (1993) 977- The present paper describes two systems for communication, education, and decision support in patient-centred diabetes care. Both systems are developed under the assumption that while the clinical resources in the health care sector are limited, patients' resources and new information technology may be able to play a much more central role. With DiasNet patients can experiment with their own data. They can, retrospectively, adjust insulin doses or meals sizes, and thereby learn how to cope with various situations. DiabVision, brings together algorithms for detection of retinal lesions in digital images and for aligning time series of retinal images. While the former algorithms are aimed at automated procedures for screening for diabetic retinopathy, the latter can be used to enable motivated patients to see the actual retinal lesions in their own eyes, illustrating how the changes appear, and perhaps disappear, as a consequence of changes in lifestyle and glycaemic control. One of the long-term goals of our work is to assess the potential of integrating all health related information for patients with diabetes and other major chronic diseases. One solution to these problems would be to organise data and information as one virtual database, which then could be accessed by both health professionals and by patients. This structure would facilitate easy access, a clear overview, and quality control. A web site taking the first step in this process has been launched.

Impaired absorption and omission of insulin: a novel method of detection using the diabetes advisory system computer model.

The Diabetes Advisory System (DIAS) is a decision-support program developed to assist insulin dose adjustment in type 1 diabetes. In this paper, we show how it might be used to identify impaired absorption or omission of insulin in patients with poorly controlled blood glucose. An evaluation of glucose results from four outpatients with persistent hyperglycemia is presented (age 19-48 years with type 1 diabetes for 13-18 years of duration, HbA1c 9.4-13.6%). Each had completed a 4-day record of blood glucose (BG, pre-meal and bedtime), dietary (carbohydrate) intake, and insulin doses (with injection sites). From these data, DIAS modeled a glucose profile (simulated glucose, SG) for the same period. Qualitative assessments were made of differences between BG and SG, and selective reduction or complete removal of insulin doses where BG >> SG. Large improvements in modeling were attributed to either impaired absorption or omission of insulin. Confirmation of these problems was sought from the patients by detailed consultation and physical examination. Impaired insulin absorption was suspected in two patients, both having significant injection site abnormalities. Insulin omission was suspected in the other two subjects. Both had normal injection sites, and one admitted to missing doses. Following retraining, data from three patients showed noticeable improvements in overall modeling as well as glucose control. Using DIAS in the evaluation of patients with type 1 diabetes may highlight previously unrecognized injection site abnormalities or insulin dose omission. This could assist rational optimization of insulin therapy in cases of persistently poor glucose control.

Screening for diabetic retinopathy using computer based image analysis and statistical classification.

Diabetic retinopathy is one of the most common causes of blindness in Europe. However, efficient therapies do exist. An accurate and early diagnosis and correct application of treatment can prevent blindness in more than 50% of all cases. Digital imaging is becoming available as a means of screening for diabetic retinopathy. As well as providing a high quality permanent record of the retinal appearance, which can be used for monitoring of progression or response to treatment, and which can be reviewed by an ophthalmologist, digital images have the potential to be processed by automatic analysis systems. We have described the preliminary development of a tool to provide automatic analysis of digital images taken as part of routine monitoring of diabetic retinopathy in our clinic. Various statistical classifiers, a Bayesian, a Mahalanobis, and a KNN classifier were tested. The system was tested on 134 retinal images. The Mahalanobis classifier had the best results: microaneurysms, haemorrhages, exudates, and cotton wool spots were detected with a sensitivity of 69, 83, 99, and 80%, respectively.

DiasNet--an Internet tool for communication and education in diabetes.

Although studies have shown that intensive diabetes treatment with the goal of maintaining blood glucose concentrations close to the normal range lead to a substantial reduction of the rate of the complications (e.g. blindness, kidney failure, amputations and circulatory diseases), this can be difficult to achieve using conventional means. It is recognised that a number of patients may have poor control despite specialist care, and this along with devolution of care to non-specialists suggests that alternative interventions should be developed. DiasNet, which is used by patients as a tool for education and communication, is based on a well documented decision support system used by clinicians to give advise on insulin dose. In DiasNet the patients can experiment with their own data, adjusting insulin doses or meals sizes, and thereby learning how to cope with various situations. The system can run both as an ordinary application on a standard PC, and as an Internet application using a standard web browser. The latter enables a new way of communication between patient and clinician. DiasNet is currently being tested on a small group of diabetes patients.

Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks.

Diabetes advisory system (DIAS) is a decision support system, which has been developed to provide advice on the amount of insulin injected by subjects with insulin-dependent diabetes mellitus (IDDM). DIAS employs a temporal causal probabilistic network (CPN) to implement a stochastic model of carbohydrate metabolism. The CPN network has recently been extended to provide also advice to subjects with noninsulin-dependent diabetes mellitus (NIDDM). However, due to increased complexity and size of the extended CPN the calculations became unfeasible. The CPN network was, therefore, simplified and a novel approach employed to generate conditional probability tables. The principles of dynamic CPN's were adopted and, in combination with the method of conditioning, learning, and forecasting, were implemented in a time- and memory-efficient way. An evaluation using experimental data was carried out to compare the original and revised DIAS implementations employing data collected by patients with IDDM, and to assess the a posteriori identifiability of model parameters in patients with NIDDM.

Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes.

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism which predicts hourly blood glucose concentrations and provides advice on insulin dose. Its ability to provide appropriate advice was assessed in 20 well-controlled IDDM patients (mean (SD) age 38 (11), duration 17 (9) years; HbA1 8.8 (0.9)%, reference range 5.4-7.6%). Patients recorded blood glucose measurements, insulin dose and food intake for 4 days. These data were used to generate insulin dose advice by both DIAS and a diabetes specialist nurse. Patients were then allocated to follow either DIAS or nurse advice for a further 4 days. There was no significant difference in mean recorded blood glucose values or frequency of reported hypoglycaemia between the DIAS and nurse groups either before or after insulin dose adjustment. The DIAS model, however, generated significantly lower insulin dose advice than the nurse (median (range)% change in insulin dose: DIAS group -13.3% (-25.0 to +11.6) versus nurse group 0% (-8.7 to +2.5), P < 0.05). We conclude that, in the patients studied, DIAS provided insulin dose advice which maintained good short term control of diabetes, despite significant reductions in dose in some cases.

Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?

This paper assesses the feasibility of using a double blind controlled clinical trial to evaluate the function of a decision support system by applying such a design to the evaluation of a Diabetes Advisory System (DIAS). DIAS is based on a model of the human carbohydrate metabolism and is designed an interactive clinical tool, which can be used to predict the effects of changes in insulin dose or food intake on the blood glucose concentration in patients with insulin dependent diabetes. It can also be used to identify risk periods for hypoglycaemia. and to provide advice on insulin dose. The latter feature was evaluated in the present study. We believe double blind controlled clinical trials are prerequisites for clinical application of many decision support systems, and conclude that the present double blind controlled clinical trial is a suitable evaluation method for the function of DIAS.

DIAS-NIDDM--a model-based decision support system for insulin dose adjustment in insulin-treated subjects with NIDDM.

A decision support system has been developed, Diabetes Insulin Advisory System for patients with non-insulin dependent diabetes mellitus (DIAS-NIDDM), assisting in the adjustment of insulin doses in insulin-treated subjects. DIAS-NIDDM uses a causal probabilistic network (CPN) model of carbohydrate metabolism to make stochastic predictions of blood glucose (BG) excursions. The CPN model is an extension of an existing model with an added component representing endogenous insulin secretion. A linear relationship between BG and insulin concentration due to BG stimulated insulin secretion is assumed. Model parameters (pancreatic sensitivity, insulin sensitivity, and time-to-peak of NPH insulin) are estimated by Bayesian probability updating from patient's specific data (food intake, insulin doses, BG measurements) recorded over a period of 4 days. The estimated parameters allow the system to be potentially used as a diagnostic tool to identify abnormalities of carbohydrate metabolism: impaired insulin secretion, insulin resistance and the severity of the impairments. DIAS-NIDDM was used to predict patient-specific BG profiles and advise on insulin doses during a pilot study in eight patients with NIDDM of whom five were treated with insulin. Compared to the administered insulin amount, daily insulin amount advised by DIAS-NIDDM was similar (within 4 U) in three patients, higher by 20% (19 U) in one patient and lower by 40% (18 U) and 50% (11 U) in two patients, respectively. The inter-day coefficient of variation of the daily insulin advice suggests that, at least according to DIAS-NIDDM criteria, day-to-day adjustment of insulin doses is necessary to maintain optimum control.

DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far.

The present paper gives a description of the Diabetes Advisory System (DIAS), and the evaluation results obtained so far. DIAS is a decision support system for the management of insulin dependent diabetes. The core of the system is a compartment model of the human carbohydrate metabolism implemented as a causal probabilistic network (CPN or Bayesian network), which gives it the ability to handle the uncertainty, for example, in blood glucose measurements or physiological variations in glucose metabolism. The evaluation results suggest that, at least in our hands, DIAS can generate advice that is safe and of a quality that is at least comparable to what is available from experienced clinicians.

Analysing the hypoglycaemic counter-regulation: a clinically relevant phenomenon?

This paper describes an analysis of the temporal relation between episodes of low blood glucose (hypoglycaemia) and counter-regulations, i.e., episodes of elevated blood glucose (hyperglycaemia), in patients with insulin dependent diabetes. The relation was assessed by statistical methods based on a metabolic computer model of the human glucose metabolism. The study material was standard collected clinical data on meals, insulin injections, and measured blood glucose from hospitalised patients. We have found that a typical hypoglycaemic counter-regulation begins 6-8 h after the hypoglycaemia, that it lasts 16-18 h, giving a total duration of 24 h, and that it elevates the blood glucose by 4-10 mmol/l. The phenomenon was demonstrated in the data from more than half of the patients with hypoglycaemic episodes.

Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes.

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism implemented in a causal probabilistic network. It handles data on insulin dose, carbohydrate intake and blood glucose concentration to predict hourly blood glucose concentrations and thus provide an indication of blood glucose values between home blood tests. DIAS was used to predict blood glucose profiles in eight patients with well-controlled insulin-dependent diabetes, who are at increased risk of hypoglycaemia (abnormally low blood glucose levels). DIAS predicted nocturnal hypoglycaemia in six patients and daytime hypoglycaemia in one patient. The occurrence of nocturnal hypoglycaemia was not recognised by the patient or suspected by their doctor but was subsequently confirmed by blood testing in five patients. It is known that unrecognised nocturnal hypoglycaemia is common in patients with well-controlled diabetes. The ability of DIAS to identify such periods of hypoglycaemia with reasonable accuracy illustrates how the advanced technology it employs may provide reliable decision support to clinicians.