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International audience; OBJECTIVE: To identify and critically appraise risk prediction models for living donor solid organ transplant counselling.STUDY DESIGN AND SETTING: We systematically reviewed articles describing the development or validation of prognostic risk prediction models about living donor solid organ (kidney and liver) transplantation indexed in Medline until April 4(th) 2021. Models were eligible if intended to predict, at transplant counselling, any outcome occurring after transplantation or donation in recipients or donors. Duplicate study selection, data extraction, assessment for risk of bias and quality of reporting was done using the CHARMS checklist, PRISMA recommendations, PROBAST tool, and TRIPOD Statement.RESULTS: We screened 4691 titles and included 49 studies describing 68 models (35 kidney, 33 liver transplantation). We identified 49 new risk prediction models and 19 external validations of existing models. Most models predicted recipients outcomes (n=38, 75%), e.g., kidney graft loss (29%), or mortality of liver transplant recipients (55%). Many new models (n= 46, 94%) and external validations (n=17, 89%) had a high risk of bias because of methodological weaknesses. The quality of reporting was generally poor.CONCLUSION: We advise against applying poorly developed, reported or validated prediction models. Future studies could validate or update the few identified methodologically appropriate models.

ImportanceKidney transplant is considered beneficial in terms of survival compared with continued dialysis for patients with kidney failure. However, randomized clinical trials are infeasible, and available evidence from cohort studies is at high risk of bias.ObjectiveTo compare restricted mean survival times (RMSTs) between patients who underwent transplant and patients continuing dialysis across transplant candidate ages and depending on waiting time, applying target trial emulation methods.Design, Setting, and ParticipantsIn this retrospective cohort study, patients aged 18 years or older appearing on the wait list for their first single-organ deceased donor kidney transplant between January 1, 2000, and December 31, 2018, in Austria were evaluated. Available data were obtained from the Austrian Dialysis and Transplant Registry and Eurotransplant and included repeated updates on wait-listing status and relevant covariates. Data were analyzed between August 1, 2019, and December 23, 2021.ExposuresA target trial was emulated in which patients were randomized to either receive the transplant immediately (treatment group) or to continue dialysis and never receive a transplant (control group) at each time an organ became available.Main Outcomes and MeasuresThe primary outcome was time from transplant allocation to death. Effect sizes in terms of RMSTs were obtained using a sequential Cox approach.ResultsAmong the 4445 included patients (2974 men [66.9%]; mean [SD] age, 52.2 [13.2] years), transplant was associated with increased survival time across all considered ages compared with continuing dialysis and remaining on the wait list within a 10-year follow-up. The estimated RMST differences were 0.57 years (95% CI, –0.14 to 1.84 years) at age 20 years, 3.01 years (95% CI, 2.50 to 3.54 years) at age 60 years, and 2.48 years (95% CI, 1.88 to 3.04 years) at age 70 years. The survival benefit for patients who underwent transplant across ages was independent of waiting time.Conclusions and RelevanceThe findings of this study suggest that kidney transplant prolongs the survival time of persons with kidney failure across all candidate ages and waiting times.

Intraoperative hypothermia increases perioperative morbidity and identifying patients at risk preoperatively is challenging. The aim of this study was to develop and internally validate prediction models for intraoperative hypothermia occurring despite active warming and to implement the algorithm in an online risk estimation tool. The final dataset included 36,371 surgery cases between September 2013 and May 2019 at the Vienna General Hospital. The primary outcome was minimum temperature measured during surgery. Preoperative data, initial vital signs measured before induction of anesthesia, and known comorbidities recorded in the preanesthetic clinic (PAC) were available, and the final predictors were selected by forward selection and backward elimination. Three models with different levels of information were developed and their predictive performance for minimum temperature below 36 °C and 35.5 °C was assessed using discrimination and calibration. Moderate hypothermia (below 35.5 °C) was observed in 18.2% of cases. The algorithm to predict inadvertent intraoperative hypothermia performed well with concordance statistics of 0.71 (36 °C) and 0.70 (35.5 °C) for the model including data from the preanesthetic clinic. All models were well-calibrated for 36 °C and 35.5 °C. Finally, a web-based implementation of the algorithm was programmed to facilitate the calculation of the probabilistic prediction of a patient’s core temperature to fall below 35.5 °C during surgery. The results indicate that inadvertent intraoperative hypothermia still occurs frequently despite active warming. Additional thermoregulatory measures may be needed to increase the rate of perioperative normothermia. The developed prediction models can support clinical decision-makers in identifying the patients at risk for intraoperative hypothermia and help optimize allocation of additional thermoregulatory interventions.

Equations predicting the risk of occurrence of cardiovascular disease (CVD) are used in primary care to identify high-risk individuals among the general population. To improve the predictive performance of such equations, we updated the Framingham general CVD 1991 and 2008 equations and the Pooled Cohort equations for atherosclerotic CVD within five years in a contemporary cohort of individuals who participated in the Austrian health-screening program from 2009–2014. The cohort comprised 1.7 M individuals aged 30–79 without documented CVD history. CVD was defined by hospitalization or death from cardiovascular cause. Using baseline and follow-up data, we recalibrated and re-estimated the equations. We evaluated the gain in discrimination and calibration and assessed explained variation. A five-year general CVD risk of 4.61% was observed. As expected, discrimination c-statistics increased only slightly and ranged from 0.73–0.79. The two original Framingham equations overestimated the CVD risk, whereas the original Pooled Cohort equations underestimated it. Re-estimation improved calibration of all equations adequately, especially for high-risk individuals. Half of the individuals were reclassified into another risk category using the re-estimated equations. Predictors in the re-estimated Framingham equations explained 7.37% of the variation, whereas the Pooled Cohort equations explained 5.81%. Age was the most important predictor.

Objectives Neurologic outcome prediction in out-of-hospital cardiac arrest survivors is highly limited due to the lack of consistent predictors of clinically relevant brain damage. The present study aimed to identify novel biomarkers of neurologic recovery to improve early prediction of neurologic outcome. Design Prospective, single-center study, SETTING:: University-affiliated tertiary care center. Patients We prospectively enrolled 96 out-of-hospital cardiac arrest survivors into our study. Interventions None. Measurements and main results Neurologic outcome was assessed by the Cerebral Performance Categories score. To identify plasma biomarkers for poor neurologic outcome (Cerebral Performance Categories score ≥ 3), we performed a three-step proteomics strategy of preselection by shotgun analyses, crosschecking in brain tissue samples, and verification by targeted proteomic analyses using a multistep statistical modeling approach. Sixty-three patients (66%) had a poor neurologic outcome. Out of a total of 299 proteins, we identified α-enolase, 14-3-3 protein ζ/δ, cofilin-1, and heat shock cognate 71 kDa protein as novel biomarkers for poor neurologic outcome. The implementation of these biomarkers into a clinical multimarker model, consisting of previously identified covariates associated to outcome, resulted in a significant improvement of neurologic outcome prediction (C-index, 0.70; explained variation, 11.9%; p for added value, 0.019). Conclusions This study identified four novel biomarkers for the prediction of poor neurologic outcome in out-of-hospital cardiac arrest survivors. The implementation of α-enolase, 14-3-3 protein ζ/δ, cofilin-1, and heat shock cognate 71 kDa protein into a multimarker predictive model along with previously identified risk factors significantly improved neurologic outcome prediction. Each of the proteomically identified biomarkers did not only outperform current risk stratification models but may also reflect important pathophysiologic pathways undergoing during cerebral ischemia.

Background and Aims Kidney transplantation is considered to be the optimal treatment strategy for eligible end stage renal disease patients. However, the body of evidence to underpin the anticipated survival advantage for kidney transplant recipients is weak, as random treatment allocation to either kidney transplantation or remaining on dialysis is not feasible and previously reported results obtained from observational studies did not allow for causal interpretation. The aim of this study is to investigate survival differences of kidney transplantation compared to remaining waitlisted on dialysis across different transplant candidate ages applying causal inference methodology. Method We conducted a retrospective cohort study using the Austrian Dialysis and Transplant Registry. We included all maintenance dialysis patients who were waitlisted for their first kidney transplant between January 2000 and December 2018 and utilized repeated updates on waitlisting status and relevant covariates. To estimate the causal effect of kidney transplantation compared to remaining waitlisted on all-cause survival we applied a sequential Cox approach mimicking a series of target trials, where each trial started at the time of a transplantation. In each of these emulated trials transplanted patients were classified as treated and patients with current active waitlisting status as controls, and the groups were balanced for covariates by inverse probability weighting. Controls who were transplanted at later times were censored but assigned to the treated group in a later target trial of the series. All trials were combined into a single data set and analyzed by a Cox proportional hazards model using inverse probability weighting also to adjust for artificial censoring. Additionally, we evaluated potential effect modifications by age at trial initiation (continuous) and stratified our analyses by time on waitlist before trial initiation (up to 1 year, between 1 and 2 years, and more than 2 years). Results are reported as hazard ratios (HRs), 5-year survival probabilities and restricted mean survival time together with respective bootstrap confidence intervals (CIs). Results The study cohort consisted of 4206 patients, of whom one third were women and the mean age was 52 years. In total, 3399 patients (81%) received a transplant and 1256 patients died. The median time from waitlisting to transplantation was 1.8 years. Overall, patients who received a kidney transplant had a significant survival benefit compared to patients who remained waitlisted (HR 0.36, 95% CI 0.29 to 0.43). Assessing survival across different ages showed a significant benefit for kidney transplantation for patients between 32 and 77 years of age at time of transplantation (e.g. HR at age of 70: 0.43, 95% CI 0.33 to 0.54). For older and younger patients our analysis did not provide definitive conclusions due to limited sample sizes. Transplanted patients had higher predicted survival and longer restricted mean survival time compared to patients remaining waitlisted. For example, within 5 years after engraftment, a transplanted patient 70 years of age at trial initiation had a 0.28 higher survival probability (95% CI 0.20-0.37) and was expected to gain 0.75 years of survival time. Our stratified analyses showed a survival benefit for kidney transplantation regardless of time on waitlist before trial initiation across all ages. Conclusion Our study provides robust evidence based on state-of-the-art causal inference methodology for increased survival after kidney transplantation across different transplant candidate ages and irrespective of time on waiting list.

Background Cardiovascular prevention guidelines advocate the use of statistical risk equations to predict individual cardiovascular risk. However, predictive accuracy and clinical value of existing equations may differ in populations other than the one used for their development. Using baseline and follow-up data of the Austrian health-screening program, we assessed discrimination, calibration, and clinical utility of three widely recommended equations—the Framingham 1991 and 2008 general cardiovascular disease (CVD) equations, and the Pooled Cohort equations predicting atherosclerotic CVD. Methods The validation cohort comprised 1.7 M individuals aged 30–79, without documented CVD history who participated in the program from 2009 to 2014. CVD events were defined by a cardiovascular cause of hospitalization or death. Results The observed five-year general CVD risk was 4.66%. Discrimination c-indices (0.72–0.78) were slightly lower than those reported for the development cohorts. C-indices for women were always higher than for men. CVD risk was overestimated by the Framingham 2008 equation, but underestimated by the Pooled Cohort equations. The Framingham 1991 equation was well-calibrated, especially for individuals up to 64 years. If applied to recommend health interventions at a predicted five-year risk between 5 and 10%, the equations were clinically useful with their net benefits, weighting true positives against false positives, ranging from 0.13 to 3.43%. Conclusion The equations can discriminate high-risk from low-risk individuals, but predictive accuracy (especially for high-risk individuals) might be improved by recalibration. The Framingham 1991 equation yielded the most accurate predictions.

Equations predicting the risk of occurrence of cardiovascular disease (CVD) are used in primary care to identify high-risk individuals among the general population. To improve the predictive performance of such equations, we updated the Framingham general CVD 1991 and 2008 equations and the Pooled Cohort equations for atherosclerotic CVD within five years in a contemporary cohort of individuals who participated in the Austrian health-screening program from 2009-2014. The cohort comprised 1.7 M individuals aged 30-79 without documented CVD history. CVD was defined by hospitalization or death from cardiovascular cause. Using baseline and follow-up data, we recalibrated and re-estimated the equations. We evaluated the gain in discrimination and calibration and assessed explained variation. A five-year general CVD risk of 4.61% was observed. As expected, discrimination c-statistics increased only slightly and ranged from 0.73-0.79. The two original Framingham equations overestimated the CVD risk, whereas the original Pooled Cohort equations underestimated it. Re-estimation improved calibration of all equations adequately, especially for high-risk individuals. Half of the individuals were reclassified into another risk category using the re-estimated equations. Predictors in the re-estimated Framingham equations explained 7.37% of the variation, whereas the Pooled Cohort equations explained 5.81%. Age was the most important predictor.

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital or dying with the disease.Design Living systematic review and critical appraisal by the covid-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.Data sources PubMed and Embase through Ovid, up to 17 February 2021, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.Study selection Studies that developed or validated a multivariable covid-19 related prediction model.Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).Results 126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/.Conclusion Prediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. Methodological guidance, as provided in this paper, should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction modellers should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.Readers’ note This article is the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

ObjectiveTo review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.DesignRapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection.Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24thMarch 2020.Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text.Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count.Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed.ConclusionCOVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models.Systematic review registration protocolosf.io/ehc47/,registration: osf.io/wy245Summary boxesWhat is already known on this topic-The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases.-Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming.-Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection.What this study adds-We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population.-We identified 18 diagnostic models for COVID-19 detection in symptomatic patients.-13 of these were machine learning models based on CT images.-We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission.-Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable.

Summary Although separate prediction models for donors and recipients were previously published, we identified a need to predict outcomes of donor/recipient simultaneously, as they are clearly not independent of each other. We used characteristics from transplantations performed at the Oslo University Hospital from 1854 live donors and from 837 recipients of a live donor kidney transplant to derive Cox models for predicting donor mortality up to 20 years, and recipient death, and graft loss up to 10 years. The models were developed using the multivariable fractional polynomials algorithm optimizing Akaike’s information criterion, and optimism‐corrected performance was assessed. Age, year of donation, smoking status, cholesterol and creatinine were selected to predict donor mortality (C‐statistic of 0.81). Linear predictors for donor mortality served as summary of donor prognosis in recipient models. Age, sex, year of transplantation, dialysis vintage, primary renal disease, cerebrovascular disease, peripheral vascular disease and HLA mismatch were selected to predict recipient mortality (C‐statistic of 0.77). Age, dialysis vintage, linear predictor of donor mortality, HLA mismatch, peripheral vascular disease and heart disease were selected to predict graft loss (C‐statistic of 0.66). Our prediction models inform decision‐making at the time of transplant counselling and are implemented as online calculators.

Statistical models are often fitted to obtain a concise description of the association of an outcome variable with some covariates. Even if background knowledge is available to guide preselection of covariates, stepwise variable selection is commonly applied to remove irrelevant ones. This practice may introduce additional variability and selection is rarely certain. However, these issues are often ignored and model stability is not questioned. Several resampling‐based measures were proposed to describe model stability, including variable inclusion frequencies (VIFs), model selection frequencies, relative conditional bias (RCB), and root mean squared difference ratio (RMSDR). The latter two were recently proposed to assess bias and variance inflation induced by variable selection. Here, we study the consistency and accuracy of resampling estimates of these measures and the optimal choice of the resampling technique. In particular, we compare subsampling and bootstrapping for assessing stability of linear, logistic, and Cox models obtained by backward elimination in a simulation study. Moreover, we exemplify the estimation and interpretation of all suggested measures in a study on cardiovascular risk. The VIF and the model selection frequency are only consistently estimated in the subsampling approach. By contrast, the bootstrap is advantageous in terms of bias and precision for estimating the RCB as well as the RMSDR. Though, unbiased estimation of the latter quantity requires independence of covariates, which is rarely encountered in practice. Our study stresses the importance of addressing model stability after variable selection and shows how to cope with it.

Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10–30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change‐in‐estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p‐values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low‐dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms.

PURPOSE: Although the discovery of prions was rewarded with a Nobel Prize, their existence was only attributed to a limited number of diseases. Recent evidence suggests that their role has been underestimated and several other proteins carry prion-like properties, like ß-amyloid, and most recently p53. High-grade serous ovarian cancers (HGSOC) harbor TP53 mutations in about 96% of cases. These mutations promote p53 aggregation, which might be responsible for complete abrogation of tumor suppressor functions, resulting in dominant-negative activity and oncogenic gain-of-function. Here, we describe the use of an ELISA-based technology for p53 prion detection in fresh-frozen tumor tissue and their clinical relevance in ovarian cancer. EXPERIMENTAL PROCEDURES: Fresh-frozen tumor tissue specimens of 81 HGSOC patients, who previously had been included in the EU-funded OVCAD study, were analyzed. For each of these patients at least 5-year follow-up data were available. For the detection of p53 prions the Seprion-ELISA, previously designed for the detection of BSE and scrapie was adapted and optimized. To investigate the impact of p53 aggregation on clinical outcomes (overall survival and progression-free survival), we performed a complete-case analysis. In a subset of patients the Ki67 proliferation index and a homologous recombination deficiency (HRD) score, based on the Myriad score, were available. Correlations were analyzed using ANOVA and t-test. RESULTS: In 39 of 46 (84.8%) patients with missense mutated cancers a p53 prion specific signal was observed. The aggregation propensity varied considerably within samples carrying the same mutations. Multivariable Cox regression models, with respect to other prognostic factors significantly associated with overall survival in patients with late-stage serous epithelial ovarian cancer (age, FIGO stage and presence of a residual tumor), show superiority of the group with extensive p53 aggregation in overall survival and in progression-free survival. No and moderate p53 aggregation are associated with a worse overall survival in contrast to high p53 aggregation (P values 0.025 and 0.011). Similar results in these groups are assessed for progression-free survival (P values 0.030 and 0.008). Interestingly, the group with extensive p53 aggregation was associated with a non-significant trend toward higher HR deficiency. Furthermore, this group had a significantly higher Ki67 index compared to patients with moderate p53 aggregation (P value 0.033). CONCLUSIONS: This study reports the first specific and quantitative screening for p53 prions in patient material. We were able to demonstrate that the p53-Seprion-ELISA is a robust and highly sensitive detection tool for p53 prions. Our data show that the aggregation propensity is not only depending on the TP53 mutation and that other cofactors may be involved. Moreover, we show that p53 aggregation is an independent prognostic marker for survival. The higher Ki67 proliferation index and the trend towards higher genomic instability in patients with extensive p53 aggregation suggest that these tumors have an increased likelihood of response to platinum-containing therapy. To conclude, we demonstrated the high potential of p53 aggregation as a biomarker for patients' survival, suggesting that classification of patients based on the amount of aggregated p53 could allow therapy decisions. Citation Format: Nicole Heinzl, Elisabeth Maritschnegg, Katarzyna Koziel, Stuart Wilson, Georg Heinze, Christine Wallisch, Reinhard Horvat, Jalid Sehouli, Ioana Braicu, Ignace Vergote, Els van Nieuwenhuysen, Sven Mahner, Eva Obermayr, Eva Schuster, Barbara Holzer, Nicole Concin, Robert Zeillinger. IDENTIFICATION OF P53 PRIONS AS AN INDEPENDENT PROGNOSTIC MARKER FOR SURVIVAL IN HIGH-GRADE SEROUS OVARIAN CANCER [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr AP15.

BACKGROUND Collections of electronic medical records (EMRs) can provide a rich source of information for renal health care research. However, their use in statistical analyses requires many preparatory steps, including coding of freetext entries and clear definitions of time windows for harvesting prognostic factors and outcomes. We analyse a large collection of EMRs to identify prognostic factors of adequate health care in diabetic patients at risk for chronic kidney disease, and discuss benefits and risks of such re-use of routine data. METHODS In a representative sample of 695 068 patient records collected in 58 Austrian general practitioners' offices, we could identify 31 374 patients with diabetes mellitus. As outcomes, we investigated whether a patient received a serum creatinine measurement, and the time elapsing between two consecutive serum creatinine measurements. Prognostic factors were defined by extracting previous diagnoses, laboratory measurements, drug prescriptions and demographic characteristics from the records. RESULTS Serum creatinine was measured annually in 44.4% of diabetic patients with previous signs of reduced kidney function and in 20.5% of the patients without such signs. Within 1 year after the first measurement, a follow-up measurement was made in 79.4 and 68.4% of the patients, respectively. Previous diagnoses, laboratory measurements, drug prescriptions and demographic characteristics explained 41% of the observed variance of kidney function monitoring. With 24% explained variance, previous referrals to laboratories were identified as the most important prognostic factor group. CONCLUSIONS The analysis of large routine data collections poses various challenges, among which the need for coding free text into variables and various sources of biases are most demanding. However, routine data collections represent the daily practice of health care and offer many chances for renal health services and outcomes research.