Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.

The Transformative Potential of AI in Obstetrics and Gynaecology.

The transformative power of artificial intelligence (AI) is reshaping diverse domains of medicine. Recent progress, catalyzed by computing advancements, has seen commensurate adoption of AI technologies within obstetrics and gynaecology. We explore the use and potential of AI in three focus areas: predictive modelling for pregnancy complications, Deep learning-based image interpretation for precise diagnoses, and large language models enabling intelligent health care assistants. We also provide recommendations for the ethical implementation, governance of AI, and promote research into AI explainability, which are crucial for responsible AI integration and deployment. AI promises a revolutionary era of personalized health care in obstetrics and gynaecology.

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.

BACKGROUND: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data. METHODS: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. RESULTS: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh). CONCLUSIONS: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.

Validation of gestational age determination from ultrasound or a metabolic gestational age algorithm using exact date of conception in a cohort of newborns conceived using assisted reproduction technologies.

BACKGROUND: Accurate estimates of gestational age in pregnancy are important for the provision of optimal care. Although current guidelines generally recommend estimating gestational age via first-trimester ultrasound measurement of crown-rump length, error associated with this method can range from 3 to 8 days of gestation. In pregnancies resulting from assisted reproductive technology, estimated due date can be calculated on the basis of the age of the embryo and the date of embryo transfer, arguably providing the most accurate estimates possible. We have developed and extensively validated statistical models to estimate gestational age postnatally using metabolomic markers from blood samples in combination with clinical and demographic data. These models have shown high accuracy compared with first-trimester ultrasound, the recommended method for estimating gestational age in spontaneous pregnancies. We hypothesized that gestational age derived from date and stage of embryo at transfer in newborns conceived using assisted reproduction therapy would provide the most accurate reference standard possible to evaluate and compare the accuracy of both first-trimester ultrasound and metabolomic model-based gestational dating. OBJECTIVE: This study aimed to validate both first-trimester ultrasound dating and postnatal metabolomic gestational age estimation models against gestational age derived from date and stage of embryo at transfer in a cohort of newborns conceived via assisted reproductive technology, both overall and in important subgroups of interest (preterm birth, small for gestational age, and multiple birth). STUDY DESIGN: This was a retrospective cohort study of infants born in Ontario, Canada between 2015 and 2017 and captured in the provincial birth registry. Spontaneous conceptions were randomly partitioned into a model derivation sample (80%) and a test sample (20%) for model validation. A cohort of assisted conceptions resulting from fresh embryo transfers was derived to evaluate the accuracy of both ultrasound and model-based gestational dating. Postnatal gestational age estimation models were developed with multivariable linear regression using elastic-net regularization. Gestational age estimates from dating ultrasound and from postnatal metabolomic models were compared with date of embryo transfer reference gestational age in the independent test cohorts. Accuracy was quantified by calculating mean absolute error and the square root of mean squared error. RESULTS: Our model derivation cohort included 202,300 spontaneous conceptions, and the testing cohorts included 50,735 spontaneous conceptions and 1924 assisted conceptions. In the assisted conception cohort, first-trimester dating ultrasound was accurate to within approximately ±1.5 days compared with date of embryo transfer reference overall (mean absolute error, 0.21 [95% confidence interval, 0.20-0.23]). When compared with gestational age derived from date of embryo transfer, the metabolomic estimation models were accurate to within approximately ±5 days overall (0.79 [0.76-0.81] weeks). When ultrasound was used as the reference in validating the metabolomic model, the mean absolute error was slightly higher overall (0.81 [0.78-0.84] weeks). In general, the accuracy of gestational age estimates derived from ultrasound or metabolomic models was highest in term infants and lower in preterm and small-for-gestational-age newborns. CONCLUSION: Our findings support the accuracy of ultrasound as a gestational age dating tool. They also support the potential utility of metabolic gestational age dating algorithms in settings where ultrasound or other accurate methods of estimating gestational age are not available because of lack of infrastructure or specialized training (eg, low-income countries). However, the accuracy of metabolomic model-based dating was generally lower than that of ultrasound.

The psychological impact of the COVID-19 pandemic and a SARS-CoV-2 testing programme on obstetric patients and healthcare workers.

OBJECTIVES: The aim of this study was to describe the psychological impact of the COVID-19 pandemic and the specific impact of a universal SARS-CoV-2 testing programme on obstetric patients and healthcare workers at The Ottawa Hospital. METHODS: This was a follow-up survey study of obstetric healthcare workers and then-pregnant patients who participated in a SARS-CoV-2 testing programme conducted in The Ottawa Hospital obstetrical triage units from 19 October to 17 November 2020. Surveys explored the effects of the COVID-19 pandemic and the testing programme on participants' psychological well-being. Responses were collected from April to September 2021. Descriptive summary statistics were calculated for both groups. RESULTS: During hospitalization for delivery, obstetric patients (n = 143) worried about giving COVID-19 to their new baby (88.11%), catching COVID-19 (83.22%), and giving COVID-19 to their partner (76.22%). Patients felt relief at being tested for COVID-19 during the universal testing programme (24.65%) and at getting their results (28.87%). Patients also believed that universal SARS-CoV-2 testing was a good way to slow COVID-19 spread (79.72%), reduce anxiety (75.52%), and increase relief (76.22%). In addition, patients felt good about participating in research that could help others (91.61%). Among obstetric healthcare workers (n = 94), job satisfaction decreased and job stress increased during the COVID-19 pandemic. The universal testing programme led to minor increases in healthcare worker job stress and burden, particularly among nurses, but the majority (85.23%) believed it was a valuable research initiative. CONCLUSION: The COVID-19 pandemic has had a negative psychological impact on obstetric patients and healthcare workers. Universal SARS-CoV-2 testing was generally viewed favourably and may serve as an effective strategy for estimating COVID-19 prevalence without adding undue stress onto patients and healthcare workers during the pandemic.

Exposure to Intrapartum Epidural Analgesia and Risk of Autism Spectrum Disorder in Offspring.

Importance: There is conflicting evidence on the association between intrapartum epidural analgesia and risk of autism spectrum disorder (ASD) in offspring. Objective: To evaluate the association between intrapartum epidural analgesia and the risk of ASD in offspring. Design, Setting, and Participants: This population-based cohort study was conducted in Ontario, Canada, using the health and administrative records of singleton live births by vaginal delivery between April 1, 2006, and March 31, 2014. Neonates with less than 24 weeks' gestation or weighing less than 500 g were excluded. Offspring were followed up from 18 months of age until ASD diagnosis, loss to follow-up, or the end of the study (December 31, 2020), whichever occurred first. Exposure, covariate, and outcome data were obtained using provincial health administrative databases. Exposures: Any intrapartum exposure to epidural or combined spinal-epidural analgesia. Main Outcomes and Measures: The primary outcome was ASD diagnosis after 18 months of age. Inverse probability of treatment weighting (IPTW) of Cox proportional hazards regression models was used to estimate the hazard ratio (HR) of intrapartum epidural analgesia and ASD in offspring. Offspring head injury was used as a control outcome. Models were adjusted for maternal sociodemographic factors, health behaviors, and medical and obstetrical history as well as labor, delivery, and offspring characteristics. Post hoc analyses included restriction to term neonates, a conditional within-mother analysis, exclusion of records with concomitant intrapartum pain management exposures, a complete case analysis, use of an alternative ASD definition, and estimation of the average treatment effect in the treated group. Results: Among the 650 373 mother-offspring pairs included in the study, 418 761 (64.4%) were exposed to intrapartum epidural analgesia. The mean (SD) maternal age at delivery was 29.7 (5.5) years; the offspring had a mean (SD) gestational age at delivery of 39.1 (1.6) weeks and included 329 808 male newborns (50.7%). The exposed and unexposed groups were similar in all maternal and newborn characteristics after IPTW (standardized difference <0.10). Autism spectrum disorder was diagnosed in 7546 offspring (1.8%) of mothers who received intrapartum epidural analgesia (incidence rate, 18.8 [95% CI, 18.4-19.3] per 10 000 person-years) compared with 3234 offspring (1.4%) who were unexposed (incidence rate, 14.4 [95% CI, 13.9-14.9] per 10 000 person-years). The crude HR for ASD associated with intrapartum epidural analgesia was 1.30 (95% CI, 1.25-1.36), and the IPTW-adjusted HR was 1.14 (95% CI, 1.08-1.21). Results did not qualitatively differ in post hoc analyses. Conclusions and Relevance: Results of this study showed that intrapartum epidural analgesia was associated with a small increase in risk for ASD in offspring. The biological plausibility of this association, however, remains unclear, and the finding must be interpreted with caution.

Real world external validation of metabolic gestational age assessment in Kenya.

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Health services use by children identified as heterozygous hemoglobinopathy mutation carriers via newborn screening.

BACKGROUND: Newborn screening (NBS) for sickle cell disease incidentally identifies heterozygous carriers of hemoglobinopathy mutations. In Ontario, Canada, these carrier results are not routinely disclosed, presenting an opportunity to investigate the potential health implications of carrier status. We aimed to compare rates of health services use among children identified as carriers of hemoglobinopathy mutations and those who received negative NBS results. METHODS: Eligible children underwent NBS in Ontario from October 2006 to March 2010 and were identified as carriers or as screen-negative controls, matched to carriers 5:1 based on neighbourhood and timing of birth. We used health care administrative data to determine frequencies of inpatient hospitalizations, emergency department (ED) visits, and physician encounters through March 2012, using multivariable negative binomial regression to compare rates of service use in the two cohorts. We analyzed data from 4987 carriers and 24,935 controls. RESULTS: Adjusted incidence rate ratios (95% CI) for service use in carriers versus controls among children < 1 year of age were: 1.11 (1.06-1.17) for ED visits; 0.97 (0.89-1.06) for inpatient hospitalization; and 1.02 (1.00-1.04) for physician encounters. Among children ≥1 year of age, adjusted rate ratios were: 1.03 (0.98-1.07) for ED visits; 1.14 (1.03-1.25) for inpatient hospitalization and 0.92 (0.90-0.94) for physician encounters. CONCLUSIONS: While we identified statistically significant differences in health services use among carriers of hemoglobinopathy mutations relative to controls, effect sizes were small and directions of association inconsistent across age groups and health service types. Our findings are consistent with the assumption that carrier status is likely benign in early childhood.

External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants.

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China.  Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.

Metabolic gestational age assessment in low resource settings: a validation protocol.

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

Postnatal gestational age estimation via newborn screening analysis: application and potential.

Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.

Effects of sex and birth weight on non-specific health services use following whole-cell pertussis vaccination: a self-controlled case series analysis.

Previous studies from low-resource countries have highlighted concerns surrounding non-specific effects of whole-cell pertussis vaccination, particularly in females. We sought to examine the effects of sex and birth weight on health services utilization following first exposure to whole-cell pertussis vaccine. Using a self-controlled case series design and by calculating relative incidence ratios (RIRs), we compared the relative incidence of emergency department visits and/or hospital admissions between sexes and between birth weight quintiles. Females had a higher relative incidence of events following vaccination compared to males (RIR = 1.13, 95% CI: 0.99, 1.30), which persisted after adjustment for birth weight (RIR = 1.12, 95% CI: 0.97, 1.28). We also observed a trend of increasing relative incidence of events over decreasing quintiles of birth weight; infants in the lowest quintile had a 26% higher relative event rate compared to the highest quintile, which was robust to adjustment for sex (Unadjusted RIR = 1.26, 95% CI: 1.01, 1.56; Adjusted RIR = 1.23, 95% CI: 0.99, 1.53). The risk of all-cause health services utilization immediately following vaccination, was elevated in female infants and infants having lower birth weight. Further study is warranted to determine if vaccine dosing should take infant weight into account.

Temporal trends in postcolonoscopy colorectal cancer rates in 50- to 74-year-old persons: a population-based study.

BACKGROUND AND AIMS: Colorectal cancers (CRCs) diagnosed between 6 and 36 months after colonoscopy, termed postcolonoscopy CRCs (PCCRCs), arise primarily due to missed or inadequately treated neoplasms during colonoscopy. Introduction of multiple quality indicators and technological advances to colonoscopy practice should have reduced the PCCRC rate over time. We assessed temporal trends in the population rate of PCCRC as a measure of changing colonoscopy quality. METHODS: We conducted a population-based retrospective cohort study of persons aged 50 to 74 years without advanced risk factors for CRC who underwent complete colonoscopy in Ontario, Canada between 1996 and 2010. We defined the PCCRC rate as the proportion of individuals diagnosed with CRC within 36 months of colonoscopy that had PCCRC. We compared age-adjusted and sex-adjusted rates of PCCRC over time based on 3 periods (1996-2001, 2001-2006 and 2006-2010) and assessed the independent association between time period and PCCRC risk through multivariable regression, with respect to all PCCRCs, proximal PCCRC and distal PCCRC. RESULTS: There was a marked increase in colonoscopy volumes over the study period, particularly in younger age groups and non-hospital settings. Among 1,093,658 eligible persons the PCCRC rate remained stable at approximately 8% over the 15-year study period. The adjusted odds of PCCRC, distal PCCRC and proximal PCCRC, comparing the 2006 to 2010 period with the 1996 to 2001 period, were 1.14 (95% confidence interval [CI], 1.0-1.31), 1.11 (95% CI, 0.91-1.34), and 1.14 (95% CI, 0.94-1.38), respectively. Temporal trends in PCCRC risk did not differ by endoscopist specialty or institutional setting after covariate adjustment. CONCLUSION: The PCCRC rate in Ontario has remained consistently high over time. Widespread initiatives are needed to improve colonoscopy quality.

Risk of colorectal cancer after a negative colonoscopy in low-to-moderate risk individuals: impact of a 10-year colonoscopy.

Background and study aims National societies recommend colorectal cancer (CRC) screening 10 years after a normal ("negative") colonoscopy in low-risk individuals. We studied the impact of a 10-year repeat colonoscopy on the risk of early incident CRC. Patients and methods We used health administrative data from Ontario, Canada, to conduct a population-based retrospective cohort study in 50 - 74-year-old individuals at low-to-moderate risk of CRC who had a negative colonoscopy between 1996 and 2001. We approximated exposure to repeat colonoscopy using an 8 - 12-year window. We excluded individuals who underwent lower endoscopy or colectomy, developed CRC, or were lost to follow-up between the baseline and repeat colonoscopies. We matched exposed individuals 1:1 to individuals who did not undergo lower endoscopy within 12 years for age, sex, and calendar year of baseline colonoscopy, and followed matched pairs for incident CRC. The primary analysis was multivariable hazards regression, adjusting for competing risks. Results A total of 13 350 matched pairs were observed for a median of 4.5 years (interquartile range 3.2 - 5.9 years). The cumulative probability of CRC following the matching date was 0.70 % (95 % confidence interval [CI] 0.42 % - 1.11 %) in individuals who underwent repeat colonoscopy and 0.77 % (95 %CI 0.48 % - 1.2 %) in individuals who did not undergo repeat colonoscopy. The adjusted hazard ratio for CRC was 0.91 (95 %CI 0.68 - 1.22). Conclusions We did not find an association between a second colonoscopy performed 10 years after a negative colonoscopy and early incident CRC. Our findings support the need for further studies on the utility of 10-year re-screening with colonoscopy in this setting.

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada.

OBJECTIVES: Biological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity. DESIGN: We conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011. SETTING: Provincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences. PARTICIPANTS: The dataset included 230 034 infants born to non-landed immigrants and 70 098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10 038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada's Landed Immigrant Database. PRIMARY AND SECONDARY OUTCOME MEASURES: Performance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations. RESULTS: Root mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (≥37 vs <37 weeks gestational age or >34 vs ≤34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups. CONCLUSIONS: Algorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.

Association of Proteinuria and Incident Atrial Fibrillation in Patients With Intact and Reduced Kidney Function.

BACKGROUND: Early evidence suggests proteinuria is independently associated with incident atrial fibrillation (AF). We sought to investigate whether the association of proteinuria with incident AF is altered by kidney function. METHODS AND RESULTS: Retrospective cohort study using administrative healthcare databases in Ontario, Canada (2002-2015). A total of 736 666 patients aged ≥40 years not receiving dialysis and with no previous history of AF were included. Proteinuria was defined using the urine albumin-to-creatinine ratio (ACR) and kidney function by the estimated glomerular filtration rate (eGFR). The primary outcome was time to AF. Cox proportional models were used to determine the hazard ratio for AF censored for death, dialysis, kidney transplant, or end of follow-up. Fine and Grey models were used to determine the subdistribution hazard ratio for AF, with death as a competing event. Median follow-up was 6 years and 44 809 patients developed AF. In adjusted models, ACR and eGFR were associated with AF (P<0.0001). The association of proteinuria with AF differed based on kidney function (ACR × eGFR interaction, P<0.0001). Overt proteinuria (ACR, 120 mg/mmol) was associated with greater AF risk in patients with intact (eGFR, 120) versus reduced (eGFR, 30) kidney function (adjusted hazard ratios, 4.5 [95% CI, 4.0-5.1] and 2.6 [95% CI, 2.4-2.8], respectively; referent ACR 0 and eGFR 120). Results were similar in competing risk analyses. CONCLUSIONS: Proteinuria increases the risk of incident AF markedly in patients with intact kidney function compared with those with decreased kidney function. Screening and preventative strategies should consider proteinuria as an independent risk factor for AF.

Risks and trends of red blood cell transfusion in obstetric patients: a retrospective study of 45,213 deliveries using administrative data.

BACKGROUND: Transfusion data for obstetric patients are scarce. Identifying characteristics associated with red blood cell transfusion (RBCT) is of importance to better identify patients who would benefit from blood conservation strategies as the risk of alloimmunization from RBCT has the potential to affect the fetus and newborn. STUDY DESIGN AND METHODS: We conducted a retrospective cohort study using hospital administrative data to identify trends and risk factors of RBCT in obstetric patients. Data were analyzed according to the mode of delivery. RESULTS: A total of 45,213 deliveries were captured between January 1, 2007, and December 31, 2013. A higher proportion of patients undergoing cesarean sections (C/Ss) received an RBCT (2.3%) compared to other modes of delivery (0.7% for spontaneous vaginal delivery, 1.5% for instrumental delivery; p < 0.001). In addition, the risk of RBCT increased over the 7-year period for those patients undergoing C/S (relative risk [RR], 1.56; 95% confidence interval [CI], 1.14-2.15). An unavailable hemoglobin (Hb) level (RR, 12.94; 95% CI, 7.39-22.66) and Hb level of 70 to 80 g/L (RR, 7.78; 95% CI = 5.21-11.60) were strongly associated with RBCT among women undergoing C/S. Earlier gestational age at induction increased the risk of RBCT across all modes of delivery. CONCLUSIONS: The higher frequency of RBCT for unknown and low Hb supports the need for predelivery patient blood management at the time of delivery. The additional risk factors associated with RBCT identified may be used to develop risk stratification tools by mode of delivery to assist in the identification of patients at the highest risk of requiring RBCT.

Assessing the risk of intussusception and rotavirus vaccine safety in Canada.

BACKGROUND: Intussusception has been identified as a rare adverse event following rotavirus immunization. We sought to determine the incidence of intussusception among infants in Canada both before and after introduction of rotavirus immunization programs. METHODS: We used Canadian Institute for Health Information (CIHI) Discharge Abstract Database (DAD) to identify infants under 1 y of age who were admitted to a Canadian hospital, which the exception of Quebec, which does not submit data to CIHI, with a diagnosis of intussusception (ICD-10 code K56.1, and ICD-9 code 560) between January 1st, 2003 and December 31, 2013. We compared rates of intussusception hospitalization before and after rotavirus vaccine program introduction. Rates were adjusted for calendar year, age (in months), sex and region using Poisson regression models. Denominator data for infants under 1 year, stratified by age in months, were obtained from Statistics Canada. RESULTS: Annual intussusception hospitalization rates ranged from 20-30 per 100,000 infants over the study period, with no evidence of a trend over time. Intussusception hospitalization rates were highest in infants 4 to <8 months and lowest in those under 2 months or between 10 and <12 months. Males had higher rates than females both overall and within each age group. The rate of intussusception hospitalization after rotavirus vaccine program introduction was 22.4 (95% CI: 18.3, 27.4) compared to 23.4 (95% CI: 21.5, 25.4) per 100,000 before program introduction. CONCLUSIONS: We have described baseline intussusception hospitalization rates for infants in Canada and have found no evidence of a change in rate after implementation of routine rotavirus immunization programs.

The use of relative incidence ratios in self-controlled case series studies: an overview.

BACKGROUND: The self-controlled case series (SCCS) is a useful design for investigating associations between outcomes and transient exposures. The SCCS design controls for all fixed covariates, but effect modification can still occur. This can be evaluated by including interaction terms in the model which, when exponentiated, can be interpreted as a relative incidence ratio (RIR): the change in relative incidence (RI) for a unit change in an effect modifier. METHODS: We conducted a scoping review to investigate the use of RIRs in published primary SCCS studies, and conducted a case-study in one of our own primary SCCS studies to illustrate the use of RIRs within an SCCS analysis to investigate subgroup effects in the context of comparing whole cell (wcp) and acellular (acp) pertussis vaccines. Using this case study, we also illustrated the potential utility of RIRs in addressing the healthy vaccinee effect (HVE) in vaccine safety surveillance studies. RESULTS: Our scoping review identified 122 primary studies reporting an SCCS analysis. Of these, 24 described the use of interaction terms to test for effect modification. 21 of 24 studies reported stratum specific RIs, 22 of 24 reported the p-value for interaction, and less than half (10 of 24) reported the estimate of the interaction term/RIR, the stratum specific RIs and interaction p-values. Our case-study demonstrated that there was a nearly two-fold greater RI of ER visits and admissions following wcp vaccination relative to acp vaccination (RIR = 1.82, 95 % CI 1.64-2.01), where RI estimates in each subgroup were clearly impacted by a strong healthy vaccinee effect. CONCLUSIONS: We demonstrated in our scoping review that calculating RIRs is not a widely utilized strategy. We showed that calculating RIRs across time periods is useful for the detection of relative changes in adverse event rates that might otherwise be missed due to the HVE. Many published studies of vaccine-associated adverse events could have missed/underestimated important safety signals masked by the HVE. With further development, our application of RIRs could be an important tool to address the HVE, particularly in the context of self-controlled study designs.