Summarising salient information on historical controls: A structured assessment of validity and comparability across studies.

BACKGROUND: While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. METHODS: We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. RESULTS: A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. CONCLUSION: Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

Noninferiority Clinical Trials: The Good, the Bad, and the Ugly.

For decades, the superiority trial has been the most popular design to assess the efficacy of newly developed drugs in a randomized controlled clinical trial. In a superiority trial, the aim is to show that the new (experimental) treatment is better than the standard treatment or placebo. However, it becomes increasingly difficult to improve the efficacy upon that of existing drugs. For this reason, noninferiority designs have been suggested. In a noninferiority study, one aims to show that the experimental treatment does not lower the efficacy of the standard treatment too much, but this loss of efficacy should be compensated by other better properties. In this article, the design, aims, and properties of the superiority and the noninferiority trial are contrasted and illustrated on recently published studies to treat patients with advanced hepatocellular carcinoma. The author discusses the reasons why noninferiority studies are becoming popular, but also why the results of noninferiority studies may be difficult to interpret and can be easily misused. Since only a few noninferiority studies in hepatocellular cancer have been organized, also examples from other therapeutic areas were taken. Finally, it is indicated how to appreciate the qualities of published noninferiority studies.

Including historical data in the analysis of clinical trials: Is it worth the effort?

Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data. The motivating data set consists of randomised controlled trials for acute myeloid leukaemia. A simulation study was used to compare the methods in terms of bias, precision, power and type I error rate. Methods that estimate parameters for the between-trial heterogeneity generally offer the best trade-off of power, precision and type I error, with the meta-analytic-predictive prior being the most promising method. The results show that it can be feasible to include historical data in the analysis of clinical trials, if an appropriate method is used to estimate the heterogeneity between trials, and the historical data satisfy criteria for comparability.

Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking.

A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting, it is of interest to optimally utilize the recorded information and provide medically relevant summary measures, such as survival probabilities, which will aid in decision making. In this work, we present and compare two statistical techniques that provide dynamically updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time-to-event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.

An Application of the Multivariate Linear Mixed Model to the Analysis of Shoulder Complexity in Breast Cancer Patients.

In this study, four major muscles acting on the scapula were investigated in patients who had been treated in the last six years for unilateral carcinoma of the breast. Muscle activity was assessed by electromyography during abduction and adduction of the affected and unaffected arms. The main principal aim of the study was to compare shoulder muscle activity in the affected and unaffected shoulder during elevation of the arm. A multivariate linear mixed model was introduced and applied to address the principal aims. The result of fitting this model to the data shows a huge improvement as compared to the alternatives.

Diagnostic accuracy for apical and chronic periodontitis biomarkers in gingival crevicular fluid: an exploratory study.

AIM: The aim of this study was to assess the levels and diagnostic accuracy of a set of potential biomarkers of periodontal tissue metabolism in gingival crevicular fluid (GCF) from patients with chronic periodontitis (CP) and asymptomatic apical periodontitis ( AAP). MATERIALS AND METHODS: Thirty one GCF samples from 11 CP patients, 44 GCF samples from 38 AAP patients and 31 GCF samples from 13 healthy volunteers were obtained (N = 106). Matrix metalloproteinases (MMPs) -2 and -9 were determined by zymography; levels of MMP-8 by ELISA and IFMA and MPO by ELISA. IL-1, IL-6, TNFα, DKK-1, Osteonectin, Periostin, TRAP-5 and OPG were determined by a multiplex quantitative panel. Statistical analysis was performed using linear mixed-effects models. RESULTS: The MMP-9 and MMP-8 were higher in CP, followed by AAP, versus healthy individuals (p < 0.05). ProMMP-2, MPO, IL-1, IL-6, PTN, TRAP-5 and OPG were significantly higher in CP when compared with AAP and healthy patients (p < 0.05). The highest diagnostic accuracies were observed for ProMMP-2, ProMMP-9, MMP-8 and TRAP-5 (AUC > 0.97) in CP, and for the active form of MMP-9 and MMP-8 (AUC > 0.90) in AAP. CONCLUSION: Gingival crevicular fluid composition is modified by CP and AAP. MMP-9 and MMP-8 show diagnostic potential for CP and AAP, whereas MMP-2 and TRAP-5 are useful only for CP.

Modeling agreement on categorical scales in the presence of random scorers.

Kappa coefficients are often used to assess agreement between two fixed scorers on categorical scales. Cohen's version is popular for nominal scales and the weighted version for ordinal scales. In the present paper, similar agreement coefficients are defined for random scorers. A partial-Bayesian methodology is then developed to directly relate these agreement coefficients to predictors through a multilevel model. Statistical properties of the proposed approach are studied using simulations. Finally, the approach is applied to gynecological and medical imaging data.

A multivariate Bayesian model for embryonic growth.

Most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately. When applied to a 'reference population', this exercise leads to univariate reference curves against which new individuals can be evaluated. However, growth should be evaluated in totality, that is, by evaluating all body characteristics jointly. Recently, Cole et al. suggested the Superimposition by Translation and Rotation (SITAR) model, which expresses individual growth curves by three subject-specific parameters indicating their deviation from a flexible overall growth curve. This model allows the characterization of normal growth in a flexible though compact manner. In this paper, we generalize the SITAR model in a Bayesian way to multiple dimensions. The multivariate SITAR model allows us to create multivariate reference regions, which is advantageous for prediction. The usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy, collected in the ongoing Rotterdam Predict study. Further, we demonstrate how the model can be used to find determinants of embryonic growth.

Reduced trigeminovascular cyclicity in patients with menstrually related migraine.

OBJECTIVE: A case-control study to investigate the effect of the menstrual cycle on trigeminal nerve-induced vasodilation in healthy women and patients with menstrually related migraine (MRM). METHODS: Using a laser-Doppler imager, we compared the vasodilator effects of capsaicin application and electrical stimulation (ES) on the forehead skin, a trigeminal nerve-innervated dermatome, in premenopausal patients with MRM (n = 22), healthy controls (n = 20), and postmenopausal women without migraine (n = 22). Blood samples were collected for female sex hormone measurements. RESULTS: Dermal blood flow (DBF) responses to capsaicin were higher in controls during days 1-2 than during days 19-21 of their menstruation cycle (mean Emax ± SEM: 203 ± 28 AU vs 156 ± 27 AU [p = 0.031] for 0.06 mg/mL capsaicin and 497 ± 25 AU vs 456 ± 24 AU [p = 0.009] for 6.0 mg/mL capsaicin). In contrast, patients with MRM demonstrated DBF responses without significant cycle-dependent variability (days 1-2 vs days 19-21: Emax 148 ± 20 AU vs 154 ± 20 AU [p = 0.788] for 0.06 mg/mL capsaicin and 470 ± 17 AU vs 465 ± 20 AU [p = 0.679] for 6.0 mg/mL capsaicin). DBF responses to ES were not different between either patients with MRM or controls, at either occasion. Estradiol levels on days 19-21 of the menstrual cycle were higher in healthy controls (mean ± SEM: 75 ± 8 pg/mL) than in patients with MRM (52 ± 4 pg/mL, p = 0.014). In postmenopausal women, DBF responses to capsaicin and ES, as well as estradiol levels at both visits, were all significantly reduced compared to patients with MRM and controls (in all cases, p < 0.05). CONCLUSIONS: Our study provides evidence for a reduced menstrual cyclicity of both estradiol levels and the trigeminovascular vasodilator system in patients with MRM.

A randomized phase I Bayesian dose escalation design for the combination of anti-cancer drugs.

Nowadays, treatment regimens for cancer often involve a combination of drugs. The determination of the doses of each of the combined drugs in phase I dose escalation studies poses methodological challenges. The most common phase I design, the classic '3+3' design, has been criticized for poorly estimating the maximum tolerated dose (MTD) and for treating too many subjects at doses below the MTD. In addition, the classic '3+3' is not able to address the challenges posed by combinations of drugs. Here, we assume that a control drug (commonly used and well-studied) is administered at a fixed dose in combination with a new agent (the experimental drug) of which the appropriate dose has to be determined. We propose a randomized design in which subjects are assigned to the control or to the combination of the control and experimental. The MTD is determined using a model-based Bayesian technique based on the difference of probability of dose limiting toxicities (DLT) between the control and the combination arm. We show, through a simulation study, that this approach provides better and more accurate estimates of the MTD. We argue that this approach may differentiate between an extreme high probability of DLT observed from the control and a high probability of DLT of the combination. We also report on a fictive (simulation) analysis based on published data of a phase I trial of ifosfamide combined with sunitinib.

Joint modeling of two longitudinal outcomes and competing risk data.

Aortic gradient and aortic regurgitation are echocardiographic markers of aortic valve function. Both are biomarkers repeatedly measured in patients with valve abnormalities, and thus, it is expected that they are biologically interrelated. Loss of follow-up could be caused by multiple reasons, including valve progression related, such as an intervention or even the death of the patient. In that case, it would be of interest and appropriate to analyze these outcomes jointly. Joint models have recently received much attention because they cover a wide range of clinical applications and have promising results. We propose a joint model consisting of two longitudinal outcomes, one continuous (aortic gradient) and one ordinal (aortic regurgitation), and two time-to-events (death and reoperation). Moreover, we allow for more flexibility for the average evolution and the subject-specific profiles of the continuous repeated outcome by using B-splines. A disadvantage, however, is that when adopting a non-linear structure for the model, we may have difficulties when interpreting the results. To overcome this problem, we propose a graphical approach. In this paper, we apply the proposed joint models under the Bayesian framework, using a data set including serial echocardiographic measurements of aortic gradient and aortic regurgitation and measurements of the occurrence of death and reoperation in patients who received a human tissue valve in the aortic position. The interpretation of the results will be discussed.

Nurse staffing and education and hospital mortality in nine European countries: a retrospective observational study.

BACKGROUND: Austerity measures and health-system redesign to minimise hospital expenditures risk adversely affecting patient outcomes. The RN4CAST study was designed to inform decision making about nursing, one of the largest components of hospital operating expenses. We aimed to assess whether differences in patient to nurse ratios and nurses' educational qualifications in nine of the 12 RN4CAST countries with similar patient discharge data were associated with variation in hospital mortality after common surgical procedures. METHODS: For this observational study, we obtained discharge data for 422,730 patients aged 50 years or older who underwent common surgeries in 300 hospitals in nine European countries. Administrative data were coded with a standard protocol (variants of the ninth or tenth versions of the International Classification of Diseases) to estimate 30 day in-hospital mortality by use of risk adjustment measures including age, sex, admission type, 43 dummy variables suggesting surgery type, and 17 dummy variables suggesting comorbidities present at admission. Surveys of 26,516 nurses practising in study hospitals were used to measure nurse staffing and nurse education. We used generalised estimating equations to assess the effects of nursing factors on the likelihood of surgical patients dying within 30 days of admission, before and after adjusting for other hospital and patient characteristics. FINDINGS: An increase in a nurses' workload by one patient increased the likelihood of an inpatient dying within 30 days of admission by 7% (odds ratio 1·068, 95% CI 1·031-1·106), and every 10% increase in bachelor's degree nurses was associated with a decrease in this likelihood by 7% (0·929, 0·886-0·973). These associations imply that patients in hospitals in which 60% of nurses had bachelor's degrees and nurses cared for an average of six patients would have almost 30% lower mortality than patients in hospitals in which only 30% of nurses had bachelor's degrees and nurses cared for an average of eight patients. INTERPRETATION: Nurse staffing cuts to save money might adversely affect patient outcomes. An increased emphasis on bachelor's education for nurses could reduce preventable hospital deaths. FUNDING: European Union's Seventh Framework Programme, National Institute of Nursing Research, National Institutes of Health, the Norwegian Nurses Organisation and the Norwegian Knowledge Centre for the Health Services, Swedish Association of Health Professionals, the regional agreement on medical training and clinical research between Stockholm County Council and Karolinska Institutet, Committee for Health and Caring Sciences and Strategic Research Program in Care Sciences at Karolinska Institutet, Spanish Ministry of Science and Innovation.

Síntomas laringofaríngeos posoperatorios en cirugía electiva. Incidencia y factores asociados / Postoperative laringo-pharyngeal symptoms in elective surgery - Incidence and related factors

Introducción: Los síntomas laringofaríngeos (SLF) son comunes en anestesia. La incidencia de morbilidad laringofaríngea varía en la literatura. Objetivos: Determinar la incidencia de SLF al usar máscara laríngea y tubo endotraqueal en la primera y a las 24h posoperatorias y estimar la asociación de factores de riesgo. Métodos: Estudio de cohorte cerrada que incluyó 451 pacientes. Se indagó la presencia de odinofagia, disfonía y disfagia. Se utilizaron modelos marginales para estimar asociación con variables en estudio. Resultados: La incidencia de SLF durante la primera y 24h posoperatorias fue del 26 y del 13%, respectivamente. A las 24 h, la incidencia disminuyó significativamente. Conclusiones: La incidencia en un centro hospitalario colombiano de SLF en cirugía ambulatoria es importante. Existen diferencias en la reducción con el tubo endotraqueal y la máscara laríngea en el tiempo.

Introduction: To determine cumulative incidence of sore throat complaints (STCs) which occur with the insertion of the laryngeal mask (LM) and endotracheal tube (ETT) during the first hour and 24 hours after elective surgery. In addition, to establish risk factors associated with its occurrence. Methods: In a cohort study, a total of 451 patients scheduled for elective non-cardiac surgery were included consecutively for 6 months (ASA I-II-III, >18 years old) who underwent LM or ETT airway management for general anesthesia. Through a questionnaire with indirect and direct questions the presence of sore throat, hoarseness, dysphagia and the composite endpoint STCs were assessed one and 24 hours after surgery. Marginal models were used to identify risk factors. Results:We found an incidence of STCs of 26.8% and 13.5% at first and 24 postoperative hours respectively. At first hour, they were classified as sore throat (23.9%), hoarseness (6.7%) and dysphagia (6.4%). Each compound was not mutually exclusive. At 24 hours of follow up, incidence of STCs and its compounds decreases significantly but differently to ETT and LM. STCs were associated with female gender (OR=1.53 95%CI 1.00-2.37, p=0.05), ETT intubation (OR=4.20 95%CI 2.19-8.04, p<0.01) and bloodstain on airway device at extubation (OR=2.00 95%CI 1.18-3.36, p<0.01). Conclusions: The incidence of STCs remains important. There are differences in the pattern of reduction between ETT and LM over time and this study confirms risk factors for postoperative STCs like use of ETT, presence of blood during the airway device extraction and female gender.

Stochastic EM algorithm for doubly interval-censored data.

In clinical trials, it is frequently of interest to estimate the time between the onset of two events (e.g. duration of response in oncology). Here, we consider the case where subjects are assessed at fixed visits but the initial event and the terminating event occur in between visits. This type of data, called doubly interval censored, is often analyzed with standard survival techniques, assuming either that the survival time (between initial and terminating event) is known exactly or is single interval censored. We introduce a motivating dataset in which the interest is to evaluate the impact of the treatment on the duration of response endpoint. We review the existing approaches and discuss their limitations with respect to the characteristics of our motivating dataset. Furthermore, we propose a stochastic EM algorithm that overcomes the problems in the existing approaches. We show by simulations the finite sample properties of our approach.

Autograft and pulmonary allograft performance in the second post-operative decade after the Ross procedure: insights from the Rotterdam Prospective Cohort Study.

AIMS: The objective of the present study was to report our ongoing prospective cohort of autograft recipients with up to 21 years of follow-up. METHODS AND RESULTS: All consecutive patients (n = 161), operated between 1988 and 2010, were analysed. Mixed-effects models were used to assess changes in echocardiographic measurements (n = 1023) over time in both the autograft and the pulmonary allograft. The mean patient age was 20.9 years (range 0.05-52.7)-66.5% were male. Early mortality was 2.5% (n = 4), and eight additional patients died during a mean follow-up of 11.6 ± 5.7 years (range 0-21.5). Patient survival was 90% [95% confidence interval (CI), 78-95] up to 18 years. During the follow-up, 57 patients required a re-intervention related to the Ross operation. Freedom from autograft reoperation and allograft re-intervention was 51% (95% CI 38-63) and 82% (95% CI 71-89) after 18 years, respectively. No major changes were observed over time in autograft gradient, and allograft gradient and regurgitation. An initial increase of sinotubular junction and aortic anulus diameter was observed in the first 5 years after surgery. The only factor associated with an increased autograft reoperation rate was pre-operative pure aortic regurgitation (AR) (hazard ratio 1.88; 95% CI 1.04-3.39; P= 0.037). CONCLUSION: We observed good late survival in patients undergoing autograft procedure without reinforcement techniques. However, over half of the autografts failed prior to the end of the second decade. The reoperation rate and the results of echocardiographic measurements over time underline the importance of careful monitoring especially in the second decade after the initial autograft operation and in particular in patients with pre-operative AR.

An introduction to mixed models and joint modeling: analysis of valve function over time.

An important target of many clinical studies is to identify biomarkers, including risk scores, with strong prognostic capabilities. While biomarker evaluations are commonly utilized to predict the progress of the disease at single time points, appropriate statistical tools to assess the prognostic value of serial biomarker evaluation are rarely used. The goal of this paper is to demonstrate flexible and appropriate statistical methodology to assess the predictive capability of serial echocardiographic measurements of allograft aortic valve function. Moreover, the concept of joint modeling of longitudinal and survival data to optimally utilize the relationship between repeated valve function measurements and time-to-death or time-to-reoperation, is introduced and illustrated. Optimal and suboptimal methods are illustrated using a prospective cohort of patients who survived aortic valve or root replacement with an allograft valve and who were followed clinically and echocardiographically over time.

Hierarchical Bayesian formulations for selecting variables in regression models.

The objective of finding a parsimonious representation of the observed data by a statistical model that is also capable of accurate prediction is commonplace in all domains of statistical applications. The parsimony of the solutions obtained by variable selection is usually counterbalanced by a limited prediction capacity. On the other hand, methodologies that assure high prediction accuracy usually lead to models that are neither simple nor easily interpretable. Regularization methodologies have proven to be useful in addressing both prediction and variable selection problems. The Bayesian approach to regularization constitutes a particularly attractive alternative as it is suitable for high-dimensional modeling, offers valid standard errors, and enables simultaneous estimation of regression coefficients and complexity parameters via computationally efficient MCMC techniques. Bayesian regularization falls within the versatile framework of Bayesian hierarchical models, which encompasses a variety of other approaches suited for variable selection such as spike and slab models and the MC(3) approach. In this article, we review these Bayesian developments and evaluate their variable selection performance in a simulation study for the classical small p large n setting. The majority of the existing Bayesian methodology for variable selection deals only with classical linear regression. Here, we present two applications in the contexts of binary and survival regression, where the Bayesian approach was applied to select markers prognostically relevant for the development of rheumatoid arthritis and for overall survival in acute myeloid leukemia patients.

Examiner performance in calibration exercises compared with field conditions when scoring caries experience.

The objective of this study was to verify how valid misclassification measurements obtained from a 'pre-survey' calibration exercise are by comparing them to validation scores obtained in 'field' conditions. Validation data were collected from the 'Smile for Life' project, an oral health intervention study in Flemish children. A calibration exercise was organized under 'pre-survey' conditions (32 age-matched children examined by eight examiners and the benchmark scorer). In addition, using a pre-determined sampling scheme blinded to the examiners, the benchmark scorer re-examined between six and 11 children screened by each of the dentists during the survey. Factors influencing sensitivity and specificity for scoring caries experience (CE) were investigated, including examiner, tooth type, surface type, tooth position (upper/lower jaw, right/left side) and validation setting (pre-survey versus field). In order to account for the clustering effect in the data, a generalized estimating equations approach was applied. Sensitivity scores were influenced not only by the calibration setting (lower sensitivity in field conditions, p < 0.01), but also by examiner, tooth type (lower sensitivity in molar teeth, p < 0.01) and tooth position (lower sensitivity in the lower jaw, p < 0.01). Factors influencing specificity were examiner, tooth type (lower specificity in molar teeth, p < 0.01) and surface type (the occlusal surface with a lower specificity than other surfaces) but not the validation setting. Misclassification measurements for scoring CE are influenced by several factors. In this study, the validation setting influenced sensitivity, with lower scores obtained when measuring data validity in 'field' conditions. Results obtained in a pre-survey calibration setting need to be interpreted with caution and do not (always) reflect the actual performance of examiners during the field work.

The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels.

AIM: The aim of this article was to assess the relationship between (1) in-hospital mortality and/or (2) unplanned readmission to intensive care units or operating theatre and nurse staffing variables. BACKGROUND: Adverse events are used as surrogates for patient safety in nurse staffing and patient safety research. A single adverse event cannot adequately capture the multi-dimensional attributes of patient safety; hence, there is a need to consider composite measures. Unplanned readmission into the postoperative Intensive Care nursing unit and/or operating Theatre and in-hospital mortality can be viewed as measures that incorporate the effects of several adverse events. METHODS: We conducted a Bayesian multilevel analysis on a subset of the 2003 Belgian Hospital Discharge and Nursing Minimum Data sets. The sample included 9054 patients who underwent coronary artery bypass surgery or heart valve procedures from 28 Belgian acute hospitals. Two proxies of patient safety were considered, namely postoperative in-hospital mortality in the first postoperative intensive care unit and unplanned readmission into the intensive care and/or operating theatre (including mortality beyond the first postoperative intensive care unit) after the first-operative intensive care nursing unit. RESULTS: There is an association between in-hospital mortality and/or unplanned readmissions and nurse staffing levels, but the relationship is moderated by volume and severity of illness respectively. In addition, the relationship differs between the two endpoints. CONCLUSION: Higher nurse staffing levels on postoperative general nursing cardiac surgery units protected patients from unplanned readmission to intensive care units or operating theatre and in-hospital mortality.

Incident somatic comorbidity after psychosis: results from a retrospective cohort study based on Flemish general practice data.

BACKGROUND: Psychotic conditions and especially schizophrenia, have been associated with increased morbidity and mortality. Many studies are performed in specialized settings with a strong focus on schizophrenia. Somatic comorbidity after psychosis is studied, using a general practice comorbidity registration network. METHODS: Hazard ratios are presented resulting from frailty models to assess the risk of subsequent somatic disease after a diagnosis of psychosis compared to people without psychosis matched on practice, age and gender. Diseases studied are cancer, physical trauma, diabetes mellitus, gastrointestinal disorders, joint disorders, irritable bowel syndrome, general infections, metabolic disorders other than diabetes, hearing and vision problems, anemia, cardiovascular disease, alcohol abuse, lung disorders, mouth and teeth problems, sexually transmitted diseases. RESULTS: Significant higher risks after a diagnosis of psychosis were found for the emergence of diabetes, physical trauma, gastrointestinal disorders, alcohol abuse, chronic lung disease and teeth and mouth problems. With regard to diabetes, by including the type of antipsychotic medication it is clear that the significant overall effect was largely due to the use of atypical antipsychotic medication. No significant higher risk was seen for cancer, joint conditions, irritable bowel syndrome, general infections, other metabolic conditions, hearing/vision problems, anaemia, cardiovascular disease or diabetes, in case no atypical antipsychotic medication was used. CONCLUSION: Significantly higher morbidity rates for some somatic conditions in patients with psychosis are apparent. People with a diagnosis of psychosis benefit from regular assessments for the emergence of somatic disorders and risk factors, including diabetes in case of atypical antipsychotic medication.