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.

Sex-Specific Obesity and Cardiometabolic Disease Risks in Low- and Middle-Income Countries: A Meta-Analysis Involving 3 916 276 Individuals.

CONTEXT: There is limited knowledge about the disparities between the sexes in obesity prevalence and associated cardiovascular complications in low- and middle-income countries (LMICs). OBJECTIVE: We undertook a systematic review and meta-analysis to assess sex-specific disparities in the prevalence of obesity and cardiometabolic diseases in LMICs, the burden in women, and variations by region, country's income status, setting, and time. METHODS: We searched major databases from inception to March 2023. Two independent reviewers selected the studies, assessed their quality, and extracted data. We used DerSimonian and Laird random-effects models to obtain pooled estimates of odds ratios and 95% CI for the association between sex and obesity and cardiometabolic diseases, and multilevel random-effects logistic regression models to estimate the prevalence of relevant outcomes (PROSPERO CRD42019132609). RESULTS: We included 345 studies (3 916 276 individuals). The odds of obesity were 2.72-fold higher in women than men (OR 2.72; 95% CI, 2.54-2.91). The sex-specific disparities varied by region, with the greatest disparities in Sub-Saharan Africa (OR 3.91; 95% CI, 3.49-4.39). Among women in LMICs, 23% (95% CI, 21%-25%) had obesity, 27% (95% CI, 24%-29%) had hypertension, and 7% (95% CI, 6%-9%) had type 2 diabetes. The prevalence of obesity and type 2 diabetes in women varied by region, country's income, and setting, with the highest prevalence in the Middle East and North Africa, upper-middle-income countries and urban settings. The odds of hypertension (OR 2.41; 95% CI, 1.89-3.08) and type 2 diabetes (OR 2.65; 95% CI, 1.76-3.98) were doubled in women with vs without obesity. CONCLUSION: There is an urgent need for a women-centred and region-stratified approach to tackle obesity awareness, treatment, and prevention in women in LMICs.

Basal procalcitonin, C-reactive protein, interleukin-6, and presepsin for prediction of mortality in critically ill septic patients: a systematic review and meta-analysis.

BACKGROUND: Numerous biomarkers have been proposed for diagnosis, therapeutic, and prognosis in sepsis. Previous evaluations of the value of biomarkers for predicting mortality due to this life-threatening condition fail to address the complexity of this condition and the risk of bias associated with prognostic studies. We evaluate the predictive performance of four of these biomarkers in the prognosis of mortality through a methodologically sound evaluation. METHODS: We conducted a systematic review a systematic review and meta-analysis to determine, in critically ill adults with sepsis, whether procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and presepsin (sCD14) are independent prognostic factors for mortality. We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials up to March 2023. Only Phase-2 confirmatory prognostic factor studies among critically ill septic adults were included. Random effects meta-analyses pooled the prognostic association estimates. RESULTS: We included 60 studies (15,681 patients) with 99 biomarker assessments. Quality of the statistical analysis and reporting domains using the QUIPS tool showed high risk of bias in > 60% assessments. The biomarker measurement as a continuous variable in models adjusted by key covariates (age and severity score) for predicting mortality at 28-30 days showed a null or near to null association for basal PCT (pooled OR = 0.99, 95% CI = 0.99-1.003), CRP (OR = 1.01, 95% CI = 0.87 to 1.17), and IL-6 (OR = 1.02, 95% CI = 1.01-1.03) and sCD14 (pooled HR = 1.003, 95% CI = 1.000 to 1.006). Additional meta-analyses accounting for other prognostic covariates had similarly null findings. CONCLUSION: Baseline, isolated measurement of PCT, CRP, IL-6, and sCD14 has not been shown to help predict mortality in critically ill patients with sepsis. The role of these biomarkers should be evaluated in new studies where the patient selection would be standardized and the measurement of biomarker results. TRIAL REGISTRATION: PROSPERO (CRD42019128790).

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.

Plasma CoQ10 Status in Patients with Propionic Acidaemia and Possible Benefit of Treatment with Ubiquinol.

Propionic acidaemia (PA) is an innate error of metabolism involving a deficiency in the enzyme propionyl-CoA carboxylase. Better control of acute decompensation episodes together with better treatment and monitoring have improved the prognosis of patients with this problem. However, long-term complications can arise in those in whom good metabolic control is achieved, the result of mitochondrial dysfunction caused by deficient anaplerosis, increased oxidative stress, and reduced antioxidative capacity. Coenzyme Q10 (CoQ10) is a nutritional supplement that has a notable antioxidative effect and has been shown to improve mitochondrial function. The present prospective, interventional study examines the plasma concentration of CoQ10 in patients with PA, their tolerance of such supplementation with ubiquinol, and its benefits. Seven patients with PA (aged 2.5 to 20 years, 4 males) received supplements of CoQ10 in the form of ubiquinol (10 mg/kg/day for 6 months). A total of 6/7 patients showed reduced plasma CoQ10 concentrations that normalized after supplementation with ubiquinol (p-value < 0.001), which was well tolerated. Urinary citrate levels markedly increased during the study (p-value: 0.001), together with elevation of citrate/methlycitrate ratio (p-value: 0.03). No other significant changes were seen in plasma or urine biomarkers of PA. PA patients showed a deficiency of plasma CoQ10, which supplementation with ubiquinol corrected. The urinary excretion of Krebs cycle intermediate citrate and the citrate/methylcitrate ratio significantly increased compared to the baseline, suggesting improvement in anaplerosis. This treatment was well tolerated and should be further investigated as a means of preventing the chronic complications associated with likely multifactorial mitochondrial dysfunction in PA.

An Easy-to-Implement Risk Score for Targeted Hepatitis C Virus Testing in the General Population.

Despite the effectiveness of available treatments, hepatitis C virus (HCV) remains a major public health problem, mainly due to the high percentage of undiagnosed individuals. We aim to create an easy-to-implement risk score to facilitate targeted HCV testing in the general population. This is a substudy derived from a prospective study in primary care in Madrid (Spain). Participants completed a 21-question risk assessment questionnaire, followed by HCV testing for those with at least one positive response and those >50 years of age, even if they did not answer positively. We used the population >50 years of age to fit a logistic regression model to create a score predicting the risk of a positive test result. We then performed a sensitivity analysis by applying the score obtained to the population <50 years of age, to assess its diagnostic accuracy. Data collected from 2,302 participants were included in the analysis. The prevalence of HCV infection was 1.3%. Five items were selected, showing a C-statistic of 0.896, i.e., male sex, Eastern European origin, use of intravenous drugs, self-perceived risk of acquired HCV infection, and past hepatitis or unexplained liver disease. The sensitivity was 98%, and the negative likelihood ratio was 0.05 for participants with scores of 0 (49.8% in our sample), ruling out HCV infection with high probability. We obtained similar estimates in the population <50 years of age. This tool achieved high diagnostic accuracy to target HCV testing. This could help optimize resources when universal screening is not feasible. IMPORTANCE Despite the highly effective treatments currently available, HCV remains one of the major public health problems related to an infectious agent, mainly because a high percentage of individuals remain undiagnosed. Universal screening has been proposed as a way to end this epidemic; however, it is not feasible in all settings due to different implementation barriers. With this work, we aim to collaborate in improving the diagnosis of HCV infection by creating a simple 5-item score that rules out HCV infection with a very high probability. Almost one-half of the participants in our sample did not present any affirmative answers to these questions, and their probability of being infected was close to 0%. This tool could be a useful strategy and could be considered a cost-effective alternative to optimize resources when universal screening is not feasible.

Interorgan amino acid interchange in propionic acidemia: the missing key to understanding its physiopathology.

BACKGROUND: Propionic acidemia is an inborn error of metabolism caused by a deficiency in the mitochondrial enzyme propionyl-CoA carboxylase that converts the propionyl CoA to methyl malonyl CoA. This leads to profound changes in distinct metabolic pathways, including the urea cycle, with consequences in ammonia detoxification. The implication of the tricarboxylic acid cycle is less well known, but its repercussions could explain both some of the acute and long-term symptoms of this disease. MATERIALS AND METHODS: The present observational study investigates the amino acid profiles of patients with propionic acidemia being monitored at the Hospital Ramón y Cajal (Madrid, Spain), between January 2015 and September 2017, comparing periods of metabolic stability with those of decompensation with ketosis and/or hyperammonemia. RESULTS: The concentrations of 19 amino acids were determined in 188 samples provided by 10 patients. We identified 40 metabolic decompensation episodes (22 only with ketosis and 18 with hyperammonemia). Plasma glutamine and alanine levels were reduced during these metabolic crises, probably indicating deficiency of anaplerosis (p < 0.001 for both alanine and glutamine). Hypocitrulllinemia and hypoprolinemia were also detected during hyperammonemia (p < 0.001 and 0.03, respectively). CONCLUSIONS: The amino acid profile detected during decompensation episodes suggests deficient anaplerosis from propionyl-CoA and its precursors, with implications in other metabolic pathways like synthesis of urea cycle amino acids and ammonia detoxification.

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).

Sex as a prognostic factor for mortality in critically ill adults with sepsis: a systematic review and meta-analysis.

OBJECTIVE: To assess the role of sex as an independent prognostic factor for mortality in patients with sepsis admitted to intensive care units (ICUs). DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, Web of Science, ClinicalTrials.gov and the WHO Clinical Trials Registry from inception to 17 July 2020. STUDY SELECTION: Studies evaluating independent associations between sex and mortality in critically ill adults with sepsis controlling for at least one of five core covariate domains prespecified following a literature search and consensus among experts. DATA EXTRACTION AND SYNTHESIS: Two authors independently extracted and assessed the risk of bias using Quality In Prognosis Studies tool. Meta-analysis was performed by pooling adjusted estimates. The Grades of Recommendations, Assessment, Development and Evaluation approach was used to rate the certainty of evidence. RESULTS: From 14 304 records, 13 studies (80 520 participants) were included. Meta-analysis did not find sex-based differences in all-cause hospital mortality (OR 1.02, 95% CI 0.79 to 1.32; very low-certainty evidence) and all-cause ICU mortality (OR 1.19, 95% CI 0.79 to 1.78; very low-certainty evidence). However, females presented higher 28-day all-cause mortality (OR 1.18, 95% CI 1.05 to 1.32; very low-certainty evidence) and lower 1-year all-cause mortality (OR 0.83, 95% CI 0.68 to 0.98; low-certainty evidence). There was a moderate risk of bias in the domain adjustment for other prognostic factors in six studies, and the certainty of evidence was further affected by inconsistency and imprecision. CONCLUSION: The prognostic independent effect of sex on all-cause hospital mortality, 28-day all-cause mortality and all-cause ICU mortality for critically ill adults with sepsis was uncertain. Female sex may be associated with decreased 1-year all-cause mortality. PROSPERO REGISTRATION NUMBER: CRD42019145054.