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As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID-19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly under-represent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high-quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR-based CER for COVID-19-related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics: (1) study population identification based on exposure status; (2) ascertainment of outcomes; (3) common biases and potential solutions; and (iv) data operational challenges specific to COVID-19 CER using EHRs. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This paper is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Background Heart failure (HF) is a major public health problem with rising prevalence, especially in elderly. Survival rates for advanced HF patients are worse than those for breast or prostate cancer. Two decades of biomarker research highlighted the prognostic ability of certain markers, and informed the development of new or updated prognostic models. Despite numerous published models and NICE’s recognition of the need for prognosis information, no risk stratification models have been adequately established, nor has the quality of the models and the evidence they present being systematically brought together and tested. Objectives We hypothesise that HF–related biomarkers may offer an added value to the traditional prognostic factors for HF clinical outcomes, independent of other present co-morbidities. We aim to test this hypothesis through a systematic reviews series assessing the evidence of HF prognostic models using novel meta-analysis (MA) methodology and relevant critical appraisal tools. Methods We follow Cochrane methodology. Published search filters were combined for a sensitive literature search. Prognostic models including at least one HF-related biomarker were eligible. Independent pairs of co-authors carried out screening and data extraction. Based on the CHARMS and PROBAST checklists we considered model development studies with and without external validation in independent data, and model updating studies. MA will be carried out using recently published novel methodology. Results Searches yielded over 40,000 titles, highlighting the need for tighter, updated prognostic search filters. A pilot screening of 10% of these (ie 4000) returned only a 2% for full text screening, with an ultimate estimate of 150 included models for evaluation. Conclusions This is a complex time constrained project with potential to advise on future HF prognostic model design; contribute to improved HF clinical management; apply recently developed MA methodology for combining prognostic model data, and inform the project for developing Cochrane methodology standards of prognostic model reviews. Acknowledgements Project funded by the British Heart Foundation (grant no. PG/17/49/33099)

Background: Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor neuron disease with a variable natural history. There are no accurate models that predict the disease course and outcomes, which complicates risk assessment and counselling for individual patients, stratification of patients for trials, and timing of interventions. We therefore aimed to develop and validate a model for predicting a composite survival endpoint for individual patients with ALS. Methods: We obtained data for patients from 14 specialised ALS centres (each one designated as a cohort) in Belgium, France, the Netherlands, Germany, Ireland, Italy, Portugal, Switzerland, and the UK. All patients were diagnosed in the centres after excluding other diagnoses and classified according to revised El Escorial criteria. We assessed 16 patient characteristics as potential predictors of a composite survival outcome (time between onset of symptoms and noninvasive ventilation for more than 23 h per day, tracheostomy, or death) and applied backward elimination with bootstrapping in the largest population-based dataset for predictor selection. Data were gathered on the day of diagnosis or as soon as possible thereafter. Predictors that were selected in more than 70% of the bootstrap resamples were used to develop a multivariable Royston-Parmar model for predicting the composite survival outcome in individual patients. We assessed the generalisability of the model by estimating heterogeneity of predictive accuracy across external populations (ie, populations not used to develop the model) using internal–external cross-validation, and quantified the discrimination using the concordance (c) statistic (area under the receiver operator characteristic curve) and calibration using a calibration slope. Findings: Data were collected between Jan 1, 1992, and Sept 22, 2016 (the largest data-set included data from 1936 patients). The median follow-up time was 97·5 months (IQR 52·9–168·5). Eight candidate predictors entered the prediction model: bulbar versus non-bulbar onset (univariable hazard ratio [HR] 1·71, 95% CI 1·63–1·79), age at onset (1·03, 1·03–1·03), definite versus probable or possible ALS (1·47, 1·39–1·55), diagnostic delay (0·52, 0·51–0·53), forced vital capacity (HR 0·99, 0·99–0·99), progression rate (6·33, 5·92–6·76), frontotemporal dementia (1·34, 1·20–1·50), and presence of a C9orf72 repeat expansion (1·45, 1·31–1·61), all p

Context While urinary incontinence (UI) commonly occurs after radical prostatectomy (RP), it is unclear what factors increase the risk of UI development. Objective To perform a systematic review of patient- and tumour-related prognostic factors for post-RP UI. The primary outcome was UI within 3 mo after RP. Secondary outcomes included UI at 3–12 mo and ≥12 mo after RP. Evidence acquisition Databases including Medline, EMBASE, and CENTRAL were searched between January 1990 and May 2020. All studies reporting patient- and tumour-related prognostic factors in univariable or multivariable analyses were included. Surgical factors were excluded. Risk of bias (RoB) and confounding assessments were performed using the Quality In Prognosis Studies (QUIPS) tool. Random-effects meta-analyses were performed for all prognostic factor, where possible. Evidence synthesis A total of 119 studies (5 randomised controlled trials, 24 prospective, 88 retrospective, and 2 case-control studies) with 131 379 patients were included. RoB was high for study participation and confounding; moderate to high for statistical analysis, study attrition, and prognostic factor measurement; and low for outcome measurements. Significant prognostic factors for postoperative UI within 3 mo after RP were age (odds ratio [OR] per yearly increase 1.04, 95% confidence interval [CI] 1.03–1.05), membranous urethral length (MUL; OR per 1-mm increase 0.81, 95% CI 0.74–0.88), prostate volume (PV; OR per 1-ml increase 1.005, 95% CI 1.000–1.011), and Charlson comorbidity index (CCI; OR 1.28, 95% CI 1.09–1.50). Conclusions Increasing age, shorter MUL, greater PV, and higher CCI are independent prognostic factors for UI within 3 mo after RP, with all except CCI remaining prognostic at 3–12 mo. Patient summary We reviewed the literature to identify patient and disease factors associated with urinary incontinence after surgery for prostate cancer. We found increasing age, larger prostate volume, shorter length of a section of the urethra (membranous urethra), and lower fitness were associated with worse urinary incontinence for the first 3 mo after surgery, with all except lower fitness remaining predictive at 3–12 mo.

This is a protocol for a Cochrane Review (Prognosis). The objectives are as follows: The primary objective is to synthesise available evidence from external validation studies evaluating the predictive accuracy of clinical scoring systems (measured on admission and up to 48 hours following admission) for severity and mortality within six months in people with acute pancreatitis. The secondary objective is to compare different risk thresholds of available scoring systems (i.e. the level at which the risk of severe acute pancreatitis or mortality is considered to be high) to predict severity and mortality within six months in people with acute pancreatitis. For both objectives, we will explore differences in patient populations, length of follow‐up, and study design as potential sources of between‐study heterogeneity.