Basic host response parameters to classify mortality risk in COVID-19 and community-acquired pneumonia.

Improved phenotyping in pneumonia is necessary to strengthen risk assessment. Via a feasible and multidimensional approach with basic parameters, we aimed to evaluate the effect of host response at admission on severity stratification in COVID-19 and community-acquired pneumonia (CAP). Three COVID-19 and one CAP multicenter cohorts including hospitalized patients were recruited. Three easily available variables reflecting different pathophysiologic mechanisms-immune, inflammation, and respiratory-were selected (absolute lymphocyte count [ALC], C-reactive protein [CRP] and, SpO2/FiO2). In-hospital mortality and intensive care unit (ICU) admission were analyzed as outcomes. A multivariable, penalized maximum likelihood logistic regression was performed with ALC (< 724 lymphocytes/mm3), CRP (> 60 mg/L), and, SpO2/FiO2 (< 450). A total of 1452, 1222 and 462 patients were included in the three COVID-19 and 1292 in the CAP cohort for the analysis. Mortality ranged between 4 and 32% (0 to 3 abnormal biomarkers) and 0-9% in SARS-CoV-2 pneumonia and CAP, respectively. In the first COVID-19 cohort, adjusted for age and sex, we observed an increased odds ratio for in-hospital mortality in COVID-19 with elevated biomarkers altered (OR 1.8, 3, and 6.3 with 1, 2, and 3 abnormal biomarkers, respectively). The model had an AUROC of 0.83. Comparable findings were found for ICU admission, with an AUROC of 0.76. These results were confirmed in the other COVID-19 cohorts Similar OR trends were reported in the CAP cohort; however, results were not statistically significant. Assessing the host response via accessible biomarkers is a simple and rapidly applicable approach for pneumonia.

Contribution of Low CD4 Cell Counts and High Human Immunodeficiency Virus (HIV) Viral Load to the Efficacy of Preferred First-Line Antiretroviral Regimens for Treating HIV Infection: A Systematic Review and Meta-Analysis.

We assessed whether low CD4 count and high viral load (VL) affect the response to currently preferred ART. We performed a systematic review of randomized, controlled clinical trials that analyzed preferred first-line ART and a subgroup analysis by CD4 count (≤ or >200 CD4/µL) or VL (≤ or >100 000 copies/mL). We computed the odds ratio (OR) of treatment failure (TF) for each subgroup and individual treatment arm. Patients with ≤200 CD4 cells or VL ≥100 000 copies/mL showed an increased likelihood of TF at 48 weeks: OR, 1.94; 95% confidence interval (CI): 1.45-2.61 and OR, 1.75; 95% CI: 1.30-2.35, respectively. A similar increase in the risk of TF was observed at 96 weeks. There was no significant heterogeneity regarding integrase strand transfer inhibitor or nucleoside reverse transcriptase inhibitor backbone. Our results show that CD4 <200 cells/µL and VL ≥100,000 copies/mL impair ART efficacy in all preferred regimens.

CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models.

BACKGROUND: Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models. RESULTS: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication. CONCLUSION: We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews.

Prediction of significant coronary artery disease in acute chest pain without infarction in emergency department: MAPAC Cardio-PreTest model.

Acute nontraumatic chest pain is a frequent reaso n for consultation in emergency departments and represents a diagnostic challenge. The objective is to estimate the risk of significant coronary artery disease (CAD) in patients with cardiogenic acute chest pain for whom the diagnosis of infarction was ruled out in the emergency department with a nondiagnostic ECG and negative high-sensitivity troponins. We prospectively recruited 1625 patients from emergency departments of seven Spanish hospitals. The outcome was presence of significant CAD determined by presence of ischaemia in functional tests or more than 70% stenosis in imaging tests. In this study, we developed a predictive model and evaluated its performance and clinical utility. The prevalence of significant CAD was 14% [227/1625; 95% confidence interval (CI), 12-16]. MAPAC Cardio-PreTest model included seven predictors: age, sex, smoking, history of hypertension, family history of CAD, history of hyperuricaemia, and type of chest pain. The optimism-adjusted model discrimination was C-statistic 0.654 (95% CI, 0.618-0.693). Calibration plot showed good agreement between the predicted and observed risks, and calibration slope was 0.880 (95% CI, 0.731-1.108) and calibration-in-the-large -0.001 (95% CI, -0.141 to 0.132). The model increased net benefit and improved risk classification over the recommended approach by the European Society of Cardiology [Net Reclassification Index (NRI) of events = 5.3%, NRI of nonevents = 7.0%]. MAPAC Cardio-PreTest model is an online prediction tool to estimate the individualised probability of significant CAD in patients with acute chest pain without a diagnosis of infarction in emergency department. The model was more useful than the current alternatives in helping patients and clinicians make individually tailored choices about the intensity of monitoring or additional coronary tests.

Performance and validation of the Healthy Heart Score model for predicting 12-year cardiovascular mortality in a nationwide Mediterranean population.

INTRODUCTION AND OBJECTIVES: The Healthy Heart Score (HHS) is a lifestyle-based equation for predicting cardiovascular disease (CVD) risk and may serve as a tool in primordial prevention. However, its performance outside North American populations is unknown. This study assessed the performance of the HHS for estimating CVD mortality in the adult population of Spain. METHODS: We analyzed data from the ENRICA cohort, comprising 10 228 participants free of chronic disease and representative of the Spanish population aged ≥ 18-years, who were recruited from 2008 to 2010 and were followed up to 2020. The HHS includes body mass index, alcohol, physical activity, smoking, and a 5-component dietary score. The HHS was calculated at baseline using the sex-specific beta coefficients from the original development cohorts. Model discrimination was assessed using the Harrell c-statistic and Gonen-Heller c-statistic for survival data, and calibration was evaluated through calibration plots. RESULTS: After a median follow-up of 11.8 years, 110 CVD deaths were ascertained. The discrimination of the HHS was similar for women (Harrell c, 0.91; 95%CI, 0.87-0.95; Gonen-Heller-c, 0.85; 95%CI, 0.83-0.88) and men (Harrell c, 0.91; 95%CI, 0.88-0.94; Gonen-Heller c: 0.85; 95%CI, 0.83-0.88). After recalibration by the sex-specific baseline survival function, the calibration became optimal for: a) all deciles of predicted CVD risk except the highest decile, where HHS underestimated the risk, and b) all age groups except 70 years and older, where there was an underestimation. CONCLUSIONS: In this Spanish cohort, the HHS showed good discrimination and calibration for predicting CVD death. The performance of HHS in other European populations and its implementation in the clinical setting warrants further investigation.

Effects of race and ethnicity on perinatal outcomes in high-income and upper-middle-income countries: an individual participant data meta-analysis of 2 198 655 pregnancies.

BACKGROUND: Existing evidence on the effects of race and ethnicity on pregnancy outcomes is restricted to individual studies done within specific countries and health systems. We aimed to assess the impact of race and ethnicity on perinatal outcomes in high-income and upper-middle-income countries, and to ascertain whether the magnitude of disparities, if any, varied across geographical regions. METHODS: For this individual participant data (IPD) meta-analysis we used data from the International Prediction of Pregnancy Complications (IPPIC) Network of studies on pregnancy complications; the full dataset comprised 94 studies, 53 countries, and 4 539 640 pregnancies. We included studies that reported perinatal outcomes (neonatal death, stillbirth, preterm birth, and small-for-gestational-age babies) in at least two racial or ethnic groups (White, Black, south Asian, Hispanic, or other). For our two-step random-effects IPD meta-analysis, we did multiple imputations for confounder variables (maternal age, BMI, parity, and level of maternal education) selected with a directed acyclic graph. The primary outcomes were neonatal mortality and stillbirth. Secondary outcomes were preterm birth and a small-for-gestational-age baby. We estimated the association of race and ethnicity with perinatal outcomes using a multivariate logistic regression model and reported this association with odds ratios (ORs) and 95% CIs. We also did a subgroup analysis of studies by geographical region. FINDINGS: 51 studies from 20 high-income and upper-middle-income countries, comprising 2 198 655 pregnancies, were eligible for inclusion in this IPD meta-analysis. Neonatal death was twice as likely in babies born to Black women than in babies born to White women (OR 2·00, 95% CI 1·44-2·78), as was stillbirth (2·16, 1·46-3·19), and babies born to Black women were at increased risk of preterm birth (1·65, 1·46-1·88) and being small for gestational age (1·39, 1·13-1·72). Babies of women categorised as Hispanic had a three-times increased risk of neonatal death (OR 3·34, 95% CI 2·77-4·02) than did those born to White women, and those born to south Asian women were at increased risk of preterm birth (OR 1·26, 95% CI 1·07-1·48) and being small for gestational age (1·61, 1·32-1·95). The effects of race and ethnicity on preterm birth and small-for-gestational-age babies did not vary across regions. INTERPRETATION: Globally, among underserved groups, babies born to Black women had consistently poorer perinatal outcomes than White women after adjusting for maternal characteristics, although the risks varied for other groups. The effects of race and ethnicity on adverse perinatal outcomes did not vary by region. FUNDING: National Institute for Health and Care Research, Wellbeing of Women.

Sample size requirement in trials that use the composite endpoint major adverse cardiovascular events (MACE): new insights.

BACKGROUND: The real impact of the degree of association (DoA) between endpoint components of a composite endpoint (CE) on sample size requirement (SSR) has not been explored. We estimate the impact of the DoA between death and acute myocardial infarction (AMI) on SSR of trials using use the CE of major adverse cardiac events (MACE). METHODS: A systematic review and quantitative synthesis of trials that include MACE as the primary outcome through search strategies in MEDLINE and EMBASE electronic databases. We limited to articles published in journals indexed in the first quartile of the Cardiac & Cardiovascular Systems category (Journal Citation Reports, 2015-2020). The authors were contacted to estimate the DoA between death and AMI using joint probability and correlation. We analyzed the SSR variation using the DoA estimated from RCTs. RESULTS: Sixty-three of 134 publications that reported event rates and the therapy effect in all component endpoints were included in the quantitative synthesis. The most frequent combination was death, AMI, and revascularization (n = 20; 31.8%). The correlation between death and AMI, estimated from 5 trials¸ oscillated between - 0.02 and 0.31. SSR varied from 14,602 in the scenario with the strongest correlation to 12,259 in the scenario with the weakest correlation; the relative impact was 16%. CONCLUSIONS: The DoA between death and AMI is highly variable and may lead to a considerable SSR variation in a trial including MACE.

Predicting major complications in patients undergoing laparoscopic and open hysterectomy for benign indications.

BACKGROUND: Hysterectomy, the most common gynecological operation, requires surgeons to counsel women about their operative risks. We aimed to develop and validate multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions. METHODS: We obtained routinely collected health administrative data from the English National Health Service (NHS) from 2011 to 2018. We defined major complications based on core outcomes for postoperative complications including ureteric, gastrointestinal and vascular injury, and wound complications. We specified 11 predictors a priori. We used internal-external cross-validation to evaluate discrimination and calibration across 7 NHS regions in the development cohort. We validated the final models using data from an additional NHS region. RESULTS: We found that major complications occurred in 4.4% (3037/68 599) of laparoscopic and 4.9% (6201/125 971) of abdominal hysterectomies. Our models showed consistent discrimination in the development cohort (laparoscopic, C-statistic 0.61, 95% confidence interval [CI] 0.60 to 0.62; abdominal, C-statistic 0.67, 95% CI 0.64 to 0.70) and similar or better discrimination in the validation cohort (laparoscopic, C-statistic 0.67, 95% CI 0.65 to 0.69; abdominal, C-statistic 0.67, 95% CI 0.65 to 0.69). Adhesions were most predictive of complications in both models (laparoscopic, odds ratio [OR] 1.92, 95% CI 1.73 to 2.13; abdominal, OR 2.46, 95% CI 2.27 to 2.66). Other factors predictive of complications included adenomyosis in the laparoscopic model, and Asian ethnicity and diabetes in the abdominal model. Protective factors included age and diagnoses of menstrual disorders or benign adnexal mass in both models and diagnosis of fibroids in the abdominal model. INTERPRETATION: Personalized risk estimates from these models, which showed moderate discrimination, can inform clinical decision-making for people with benign conditions who may require hysterectomy.

The COVID-19 pandemic effect on the prehospital Madrid stroke code metrics and diagnostic accuracy.

BACKGROUND: Only very few studies have investigated the effect of the COVID-19 pandemic on the pre-hospital stroke code protocol. During the first wave, Spain was one of the most affected countries by the SARS-CoV-2 coronavirus disease pandemic. This health catastrophe overshadowed other pathologies, such as acute stroke, the leading cause of death among women and the leading cause of disability among adults. Any interference in the stroke code protocol can delay the administration of reperfusion treatment for acute ischemic strokes, leading to a worse patient prognosis. We aimed to compare the performance of the stroke code during the first wave of the pandemic with the same period of the previous year. METHODS: This was a multicentre interrupted time-series observational study of the cohort of stroke codes of SUMMA 112 and of the ten hospitals with a stroke unit in the Community of Madrid. We established two groups according to the date on which they were attended: the first during the dates with the highest daily cumulative incidence of the first wave of the COVID-19 (from February 27 to June 15, 2020), and the second, the same period of the previous year (from February 27 to June 15, 2019). To assess the performance of the stroke code, we compared each of the pre-hospital emergency service time periods, the diagnostic accuracy (proportion of stroke codes with a final diagnosis of acute stroke out of the total), the proportion of patients treated with reperfusion therapies, and the in-hospital mortality. RESULTS: SUMMA 112 activated the stroke code in 966 patients (514 in the pre-pandemic group and 452 pandemic). The call management time increased by 9% (95% CI: -0.11; 0.91; p value = 0.02), and the time on scene increased by 12% (95% CI: 2.49; 5.93; p value = <0.01). Diagnostic accuracy, and the proportion of patients treated with reperfusion therapies remained stable. In-hospital mortality decreased by 4% (p = 0.05). CONCLUSIONS: During the first wave, a prolongation of the time "on the scene" of the management of the 112 calls, and of the hospital admission was observed. Prehospital diagnostic accuracy and the proportion of patients treated at the hospital level with intravenous thrombolysis or mechanical thrombectomy were not altered with respect to the previous year, showing the resilience of the stroke network and the emergency medical service.

Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model.

Background: The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. Methods: A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis. Findings: The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome. Interpretation: The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria. Funding: This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.

Personalising the risk of conversion from laparoscopic to open hysterectomy in benign conditions: Development and external validation of risk prediction models.

OBJECTIVE: To develop and validate novel prediction models to personalise the risk of conversion from laparoscopic to open hysterectomy in benign conditions. DESIGN: Retrospective cohort study. SETTINGS: English NHS Hospitals between 2011 and 2018. POPULATION: 68 752 women undergoing laparoscopic hysterectomy for benign conditions. METHODS: We developed two multivariable logistic models using readily available clinical information, one for the pre-operative setting and another for operative decision-making using additional surgical information, using 2011-2016 data in five regions (24 806 women). We validated them (a) temporally in the same regions using 2017-2018 data (12 438 women); (b) geographically in the same time-period using data from three different regions (22 024 women); and (c) temporally and geographically using 2017-2018 data in three different regions (9484 women). MAIN OUTCOME MEASURES: Conversion from laparoscopic to open hysterectomy. RESULTS: Conversions occurred in 6.8% (1687/24 806) of hysterectomies in the development group, and in 5.5% (681/12 438) in the temporal, 5.9% (1297/22 204) in the geographical and 5.2% (488/9484) in the temporal and geographical validation groups. In the development cohort, the area under the receiver operating characteristic curve values for the pre-operative and operative models were 0.65 and 0.67, respectively. In the validation cohorts the corresponding values were 0.65 and 0.66 (temporal), 0.66 and 0.68 (geographical) and 0.65 and 0.68 (temporal and geographical), respectively. Factors predictive of conversion included age, Asian ethnicity, obesity, fibroids, adenomyosis, endometriosis and adhesions. Adhesions were the most predictive (odds ratio 2.54, 95% confidence interval 2.22-2.90). CONCLUSION: The models for predicting conversions showed acceptable performance and transferability. TWEETABLE ABSTRACT: Novel tool to personalise the risk of conversion from laparoscopic to open hysterectomy in benign conditions.

Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models.

BACKGROUND: There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. OBJECTIVE: We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. DATA SOURCES: We searched Medline and EMBASE databases from inception to June 2020. STUDY ELIGIBILITY CRITERIA: We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. METHODS: We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. RESULTS: We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76-0.82), calibration slope 0.98 (95% CI 0.86-1.13) and calibration-in-the-large -0.05 (95% CI -0.20 to 0.11). CONCLUSIONS: The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE. PROTOCOL REGISTRATION: PROSPERO (registration number CRD42020192602).

Worldwide Prevalence of Intimate Partner Violence in Pregnancy. A Systematic Review and Meta-Analysis.

Background: Intimate partner violence (IPV) affects outcomes of mothers and their offspring. This systematic review collated the worldwide literature on the prevalence rates of different types of IPV in pregnancy. Methods: Two reviewers independently identified cross sectional and cohort studies of IPV prevalence in pregnancy in online databases (PubMed, WOS and Scopus), selected and extracted data [participants' country, study quality, measurement tool (validation and purpose) and rates of IPV in pregnancy]. We considered a high quality study if it had a prospective design, an adequate sampling method, a sample size estimation, a response rate > 90%, a contemporary ascertainment of IPV in the index pregnancy, and a well-developed detailed IPV tool. We performed random effects meta-analysis and explored reasons for heterogeneity of rates. Results: One hundred fifty-five studies were included, of which 44 (28%) met two-thirds of the quality criteria. Worldwide prevalence of physical (126 studies, 220,462 participants), psychological (113 studies, 189,630 participants) and sexual (98 studies, 155,324 participants) IPV in pregnancy was 9.2% (95% CI 7.7-11.1%, I2 95.9%), 18.7% (15.1-22.9%, I2 98.2%), 5.5% (4.0-7.5%, I2 93.4%), respectively. Where several types of IPV were reported combined, the prevalence of any kind of IPV (118 studies, 124,838 participants) was 25.0% (20.3, 30.5%, I2 98.6%). IPV rates varied within and between continents, being the highest in Africa and the lowest in Europe (p < 0.001). Rates also varied according to measurement purpose, being higher for diagnosis than for screening, in physical (p = 0.022) and sexual (p = 0.014) IPV. Conclusions: IPV prevalence in pregnancy varies across countries, with one-quarter of mothers exposed on average globally. Routine systematic antenatal detection should be applied worldwide. Systematic Review Registration: identifier: CRD42020176131.

Propensity-Score Analysis Reveals that Sex is Not a Prognostic Factor for Mortality in Intensive Care Unit-Admitted Patients with Septic Bacteremia.

OBJECTIVE: Men have been considered to have a higher incidence of infectious diseases, with controversy over the possibility that sex could influence the prognosis of the infection. This study aimed to explore this assumption in patients admitted to the intensive care unit (ICU) with septic bacteremia. METHODS: A retrospective analysis (2006-2017) of septic patients with microbiologically confirmed bacteremia (n=440) was performed. Risk of ICU and in-hospital mortality in males versus females was compared by univariate analysis and a propensity score analysis integrating their clinical characteristics. RESULTS: Sepsis more frequently occurred in males (80.2% vs 76.1%) as well as in-hospital (48.0% vs 41.3%) and ICU (39.9% vs 36.5%) mortality. Univariate analyses showed that males had a higher Charlson comorbidity index and worse McCabe prognostic score. However, the propensity score in 296 matched patients demonstrated that females had higher risk of both ICU (OR 1.39; 95% CI 0.89-2.19) and in-hospital mortality (OR 1.18; 95% CI 0.77-1.83), but without statistical significance. CONCLUSION: Males with sepsis had worse clinical characteristics when admitted to the ICU, but sex had no influence on mortality. These data contribute to helping reduce the sex-dependent gap present in healthcare provision.

Physician-Related Variability in the Outcomes of an Invasive Treatment for Neck and Back Pain: A Multi-Level Analysis of Data Gathered in Routine Clinical Practice.

Neuro-reflexotherapy (NRT) is a proven effective, invasive treatment for neck and back pain. To assess physician-related variability in results, data from post-implementation surveillance of 9023 patients treated within the Spanish National Health Service by 12 physicians were analyzed. Separate multi-level logistic regression models were developed for spinal pain (SP), referred pain (RP), and disability. The models included all patient-related variables predicting response to NRT and physician-related variables. The Intraclass Correlation Coefficient (ICC) and the Median Odds Ratio (MOR) were calculated. Adjusted MOR (95% CI) was 1.70 (1.47; 2.09) for SP, 1.60 (1.38; 1.99) for RP, and 1.65 (1.42; 2.03) for disability. Adjusted ICC (95%CI) values were 0.08 (0.05; 0.15) for SP, 0.07 (0.03; 0.14) for RP, and 0.08 (0.04; 0.14) for disability. In the sensitivity analysis, in which the 6920 patients treated during the physicians' training period were excluded, adjusted MOR was 1.38 (1.17; 1.98) for SP, 1.37 (1.12; 2.31) for RP, and 1.25 (1.09; 1.79) for disability, while ICCs were 0.03 (0.01; 0.14) for SP, 0.03 (0.00; 0.19) for RP, and 0.02 (0.00; 0.10) for disability. In conclusion, the variability in results obtained by different NRT-certified specialists is reasonable. This suggests that current training standards are appropriate.

Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes.

INTRODUCTION: Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS AND ANALYSIS: We undertook formative research to conceptualise and design the prediction model. Informed by these findings, we will conduct a model development and validation study using a retrospective cohort design with participant data collected as part of routine clinical care across three hospitals. The study will include all pregnancies resulting in births from 1 July 2017 to 31 December 2018 coded for a diagnosis of GDM (estimated sample size 2430 pregnancies). We will use a temporal split-sample development and validation strategy. A multivariable logistic regression model will be fitted. The performance of this model will be assessed, and the validated model will also be evaluated using decision curve analysis. Finally, we will explore modes of model presentation suited to clinical use, including electronic risk calculators. ETHICS AND DISSEMINATION: This study was approved by the Human Research Ethics Committee of Monash Health (RES-19-0000713 L). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals. TRIAL REGISTRATION DETAILS: Systematic review proceeding this work was registered on PROSPERO (CRD42019115223) and the study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results.

Effects of first-line antiretroviral therapy on the CD4/CD8 ratio and CD8 cell counts in CoRIS: a prospective multicentre cohort study.

BACKGROUND: A low CD4/CD8 ratio during antiretroviral therapy (ART) identifies people with heightened immunosenescence and increased risk of mortality. We aimed to assess the effects of integrase strand transfer inhibitor (INSTI)-based, protease inhibitor-based, or non-nucleoside reverse transcriptase inhibitor (NNRTI)-based first-line ART on long-term CD4/CD8 ratio recovery. METHODS: This prospective cohort study included 13 026 individuals with HIV registered in the Spanish HIV Research Network (CoRIS) cohort recruited from 45 Spanish hospitals. We included HIV-positive people who started triple ART (two nucleoside reverse transcriptase inhibitors [NRTI] with a NNRTI, protease inhibitor, or INSTI) and had HIV RNA suppression within 48 weeks. We used piecewise linear mixed models adjusted for potential confounders to compare longitudinal changes in the CD4/CD8 ratio between people receiving three different types of ART. We used Cox proportional-hazard models to compare the times to CD4/CD8 normalisation between the treatment groups, using cutoff ratios of 0·4, 1·0, and 1·5. FINDINGS: 6804 individuals contributing 37 149 persons-years and 37 680 observations were analysed; median follow-up was 49 months (IQR 22-89). INSTI-based ART was associated with greater CD4/CD8 gain (change per year compared with INSTI was coefficient -0·07 [95% CI -0·08 to -0·06] for NNRTI and was -0·08 [-0·09 to -0·08] for protease inhibitors). Differences were observed from the first year of therapy and were driven by changes in both CD4 and CD8 cell counts. Subanalyses at different time periods suggested that these differences were driven by changes during the first year of ART without significant differences in the adjusted CD4/CD8 ratio trajectories after the second year of ART (change per year compared with INSTI was coefficient -0·03 [95% CI -0·05 to -0·13] for NNRTI and was -0·06 [95% CI -0·08 to -0·04] for protease inhibitors). Although no differences in the time until CD4/CD8 normalisation at a cutoff ratio of no less than 0·4 were reported between any of the groups, compared with the INSTI group, both the NNRTI and protease inhibitor groups showed lower rates of normalisation at cutoff ratios of 1·0 or more (adjusted hazard ratio 0·80 [95% CI 0·72-0·89] for the NNRTI group and 0·79 [0·69-0·89] for the protease inhibitor group), and 1·5 or more (0·79 [0·65-0·95] for the NNRTI group and 0·78 [0·64-0·97] for the protease inhibitor group). No differences were found between the different integrases in the time until CD4/CD8 normalisation. Subanalyses adjusted for the backbone NRTIs and allowing observations after virological failure yielded similar results. INTERPRETATION: This study provides new evidence that reinforces the positioning of INSTI-based therapies as a first choice and underlines the importance of analysing the effects of therapeutic interventions on biomarkers linked with morbidity and mortality beyond the plasma HIV RNA and the CD4 cell counts. FUNDING: Spanish AIDS Research Network (Instituto de Salud Carlos III), European Development Regional Fund "A way to achieve Europe".

Impact of first-line antiretroviral therapy regimens on the restoration of the CD4/CD8 ratio in the CNICS cohort.

BACKGROUND: The CD4/CD8 ratio is an indicator of immunosenescence and a predictor of all-cause mortality in HIV-infected patients. The effects of different ART regimens on CD4/CD8 ratio recovery remain unclear. METHODS: Clinical cohort study of ART-treated patients from the CFAR Network of Integrated Clinical Systems (CNICS). We included ART-naive adults with HIV infection who achieved undetectable HIV RNA during the first 48 weeks of treatment and had additional follow-up 48 weeks after virological suppression (VS). Primary endpoints included increase in CD4/CD8 ratio at both timepoints and secondary endpoints were CD4/CD8 ratio recovery at cut-offs of ≥0.5 or ≥1.0. RESULTS: Of 3971 subjects who met the study criteria, 1876 started ART with an NNRTI, 1804 with a PI and 291 with an integrase strand transfer inhibitor (INSTI). After adjusting for age, sex, race, year of entry, risk group, HCV serostatus, baseline viral load and baseline CD4/CD8 ratio, subjects on an NNRTI showed a significantly greater CD4/CD8 ratio gain compared with those on a PI, either 48 weeks after ART initiation or after 48 weeks of HIV RNA VS. The greater CD4/CD8 ratio improvement in the NNRTI arm was driven by a higher decline in CD8 counts. The INSTI group showed increased rates of CD4/CD8 ratio normalization at the ≥1.0 cut-off compared with the PI group. CONCLUSIONS: NNRTI therapy was associated with a greater increase in the CD4/CD8 ratio compared with PIs. NNRTI- and INSTI-based first-line ART were associated with higher rates of CD4/CD8 ratio normalization at a cut-off of 1.0 than a PI-based regimen, which might have clinical implications.