scholarly journals Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

BMJ ◽  
2020 ◽  
pp. m3731 ◽  
Author(s):  
Ash K Clift ◽  
Carol A C Coupland ◽  
Ruth H Keogh ◽  
Karla Diaz-Ordaz ◽  
Elizabeth Williamson ◽  
...  
Keyword(s):  
Cohort Study ◽  
Confidence Interval ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Population Based ◽  
Prediction Algorithm ◽  
Derivation Cohort ◽  
Time To Death ◽  
Time Periods

Abstract Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R 2 ); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

scholarly journals Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

BMJ ◽  
2021 ◽  
pp. n2244
Author(s):  
Julia Hippisley-Cox ◽  
Carol AC Coupland ◽  
Nisha Mehta ◽  
Ruth H Keogh ◽  
Karla Diaz-Ordaz ◽  
...  
Keyword(s):  
Cohort Study ◽  
Confidence Interval ◽  
Hospital Admission ◽  
Hospital Admissions ◽  
Validation Cohort ◽  
Vaccine Dose ◽  
Ethnic Origin ◽  
Population Based ◽  
Derivation Cohort ◽  
Risk Algorithm

Abstract Objectives To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination. Design Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries. Settings Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021. Main outcome measures Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices. Results Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down’s syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson’s disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%. Conclusion This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.

scholarly journals An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England

2021 ◽  
Author(s):  
Vahe Nafilyan ◽  
Ben Humberstone ◽  
Nisha Mehta ◽  
Ian Diamond ◽  
Carol Coupland ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Validation Cohort ◽  
Population Based ◽  
Prediction Algorithm ◽  
Risk Prediction Models ◽  
Men And Women ◽  
Time Period ◽  
Time Periods ◽  
First Time

SUMMARYBackgroundTo externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England.MethodsPopulation-based cohort study using the ONS Public Health Linked Data Asset, a cohort based on the 2011 Census linked to Hospital Episode Statistics, the General Practice Extraction Service Data for pandemic planning and research, radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two time periods were used: (a) 24th January to 30th April 2020; and (b) 1st May to 28th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period.FindingsThe study comprises 34,897,648 adults aged 19-100 years resident in England. There were 26,985 COVID-19 deaths during the first time-period and 13,177 during the second. The algorithms had good calibration in the validation cohort in both time periods with close correspondence of observed and predicted risks. They explained 77.1% (95% CI: 76.9% to 77.4%) of the variation in time to death in men in the first time-period (R2); the D statistic was 3.76 (95% CI: 3.73 to 3.79); Harrell’s C was 0.935 (0.933 to 0.937). Similar results were obtained for women, and in the second time-period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first time period was 65.9% for men and 71.7% for women. People in the top 20% of predicted risks of death accounted for 90.8% of all COVID-19 deaths for men and 93.0% for women.InterpretationThe QCovid population-based risk algorithm performed well, showing very high levels of discrimination for COVID-19 deaths in men and women for both time periods. It has the potential to be dynamically updated as the pandemic evolves and therefore, has potential use in guiding national policy.FundingNational Institute of Health ResearchRESEARCH IN CONTEXTEvidence before this studyPublic policy measures and clinical risk assessment relevant to COVID-19 need to be aided by rigorously developed and validated risk prediction models. A recent living systematic review of published risk prediction models for COVID-19 found most models are subject to a high risk of bias with optimistic reported performance, raising concern that these models may be unreliable when applied in practice. A population-based risk prediction model, QCovid risk prediction algorithm, has recently been developed to identify adults at high risk of serious COVID-19 outcomes, which overcome many of the limitations of previous tools.Added value of this studyCommissioned by the Chief Medical Officer for England, we validated the novel clinical risk prediction model (QCovid) to identify risks of short-term severe outcomes due to COVID-19. We used national linked datasets from general practice, death registry and hospital episode data for a population-representative sample of over 34 million adults. The risk models have excellent discrimination in men and women (Harrell’s C statistic>0.9) and are well calibrated. QCovid represents a new, evidence-based opportunity for population risk-stratification.Implications of all the available evidenceQCovid has the potential to support public health policy, from enabling shared decision making between clinicians and patients in relation to health and work risks, to targeted recruitment for clinical trials, and prioritisation of vaccination, for example.

scholarly journals Association between home insulation and hospital admission rates: retrospective cohort study using linked data from a national intervention programme

BMJ ◽  
2020 ◽  
pp. m4571 ◽  
Author(s):  
Caroline Fyfe ◽  
Lucy Telfar ◽  
Barnard ◽  
Philippa Howden-Chapman ◽  
Jeroen Douwes
Keyword(s):  
Cohort Study ◽  
Heart Disease ◽  
Confidence Interval ◽  
Hospital Admission ◽  
Retrospective Cohort ◽  
Rate Ratio ◽  
Hospital Admissions ◽  
Relative Rate ◽  
Intervention Programme ◽  
Admission Rates

Abstract Objectives To investigate whether retrofitting insulation into homes can reduce cold associated hospital admission rates among residents and to identify whether the effect varies between different groups within the population and by type of insulation. Design A quasi-experimental retrospective cohort study using linked datasets to evaluate a national intervention programme. Participants 994 317 residents of 204 405 houses who received an insulation subsidy through the Energy Efficiency and Conservation Authority Warm-up New Zealand: Heat Smart retrofit programme between July 2009 and June 2014. Main outcome measure A difference-in-difference approach was used to compare the change in hospital admissions of the study population post-insulation with the change in hospital admissions of the control population that did not receive the intervention over the same two timeframes. Relative rate ratios were used to compare the two groups. Results 234 873 hospital admissions occurred during the study period. Hospital admission rates after the intervention increased in the intervention and control groups for all population categories and conditions with the exception of acute hospital admissions among Pacific Peoples (rate ratio 0.94, 95% confidence interval 0.90 to 0.98), asthma (0.92, 0.86 to 0.99), cardiovascular disease (0.90, 0.88 to 0.93), and ischaemic heart disease for adults older than 65 years (0.79, 0.74 to 0.84). Post-intervention increases were, however, significantly lower (11%) in the intervention group compared with the control group (relative rate ratio 0.89, 95% confidence interval 0.88 to 0.90), representing 9.26 (95% confidence interval 9.05 to 9.47) fewer hospital admissions per 1000 in the intervention population. Effects were more pronounced for respiratory disease (0.85, 0.81 to 0.90), asthma in all age groups (0.80, 0.70 to 0.90), and ischaemic heart disease in those older than 65 years (0.75, 0.66 to 0.83). Conclusion This study showed that a national home insulation intervention was associated with reduced hospital admissions, supporting previous research, which found an improvement in self-reported health.

scholarly journals Maternal and child gluten intake and risk of type 1 diabetes: The Norwegian Mother and Child Cohort Study

2019 ◽  
Author(s):  
Nicolai A Lund-Blix ◽  
German Tapia ◽  
Karl Mårild ◽  
Anne Lise Brantsaeter ◽  
Pål R Njølstad ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Cohort Study ◽  
Confidence Interval ◽  
Population Based ◽  
Hazard Ratio ◽  
Adjusted Hazard Ratio ◽  
Pregnancy Cohort ◽  
Increased Risk ◽  
Mother And Child

ABSTRACTOBJECTIVETo examine the association between maternal and child gluten intake and risk of type 1 diabetes in children.DESIGNPregnancy cohortSETTINGPopulation-based, nation-wide study in NorwayPARTICIPANTS86,306 children in The Norwegian Mother and Child Cohort Study born from 1999 through 2009, followed to April 15, 2018.MAIN OUTCOME MEASURESClinical type 1 diabetes, ascertained in a nation-wide childhood diabetes registry. Hazard ratios were estimated using Cox regression for the exposures maternal gluten intake up to week 22 of pregnancy and child’s gluten intake when the child was 18 months old.RESULTSDuring a mean follow-up of 12.3 years (range 0.7-16.0), 346 children (0.4%) developed type 1 diabetes (incidence rate 32.6 per 100,000 person-years). The average gluten intake was 13.6 grams/day for mothers during pregnancy, and 8.8 grams/day for the child at 18 months of age. Maternal gluten intake in mid-pregnancy was not associated with the development of type 1 diabetes in the child (adjusted hazard ratio 1.02 (95% confidence interval 0.73 to 1.43) per 10 grams/day increase in gluten intake). However, the child’s gluten intake at 18 months of age was associated with an increased risk of later developing type 1 diabetes (adjusted hazard ratio 1.46 (95% confidence interval 1.06 to 2.01) per 10 grams/day increase in gluten intake).CONCLUSIONSThis study suggests that the child’s gluten intake at 18 months of age, and not the maternal intake during pregnancy, could increase the risk of type 1 diabetes in the child.WHAT IS ALREADY KNOWN ON THIS TOPICA national prospective cohort study from Denmark found that a high maternal gluten intake during pregnancy could increase the risk of type 1 diabetes in the offspring (adjusted hazard ratio 1.31 (95% confidence interval 1.001 to 1.72) per 10 grams/day increase in gluten intake). No studies have investigated the relation between the amount of gluten intake by both the mother during pregnancy and the child in early life and risk of developing type 1 diabetes in childhood.WHAT THIS STUDY ADDSIn this prospective population-based pregnancy cohort with 86,306 children of whom 346 developed type 1 diabetes we found that the child’s gluten intake at 18 months of age was associated with the risk of type 1 diabetes (adjusted hazard ratio 1.46 (95% confidence interval 1.06 to 2.01) per 10 grams/day increase in gluten intake). This study suggests that the child’s gluten intake at 18 months of age, and not the maternal intake during pregnancy, could increase the child’s risk of type 1 diabetes.

scholarly journals Attendance of routine childcare visits in primary care for children of mothers with depression: a nationwide population-based cohort study

2018 ◽  
Vol 68 (667) ◽  
pp. e97-e104 ◽  
Author(s):  
Bente Kjær Lyngsøe ◽  
Claus Høstrup Vestergaard ◽  
Dorte Rytter ◽  
Mogens Vestergaard ◽  
Trine Munk-Olsen ◽  
...  
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Relative Risk ◽  
Confidence Interval ◽  
Maternal Depression ◽  
Vaccination Programme ◽  
Population Based ◽  
Point Of Interest ◽  
Vulnerable Families

BackgroundDepression is a common and potentially debilitating illness worldwide. Attendance to routine childcare appointments is a key point of interest in the effort to improve the health and care for families facing depression.AimTo evaluate the association between maternal depression and offspring non-attendance to the Danish childcare and vaccination programme (CCP) for children from 0–5 years of age. The CCP consists of seven separate visits and several vaccinations. To investigate if exposure to recent and previous depression may affect attendance differently.Design and settingPopulation-based cohort study using Danish nationwide registers.MethodParticipants were all live-born children (n = 853 315) in Denmark in the period from 1 January 2000 until 31 August 2013, and their mothers. The outcome of interest was non-attendance of each one of the seven scheduled childcare visits and two vaccination entities in the CCP. Exposure was maternal (both previous and recent) depression. All information was obtained from Danish national registries.ResultsThe risk of not attending CCP was higher for children of mothers with depression. For children of mothers with previous depression, the relative risk (RR) was 1.01 (95% confidence interval [CI] = 0.98 to 1.03) at the 5-week childcare visit, and 1.12 (95% CI = 1.09 to 1.14) at the 5-year childcare visit. For children of mothers with recent depression, the RR was 1.07 (95% CI = 1.03 to 1.13) at the 5-week visit, and 1.15 (95% CI = 1.13 to 1.17) at the 5-year visit. Furthermore, the risk of missing at least four of the seven childcare visits was higher for children of females with maternal depression (RR = 1.16, 95% CI = 1.13 to 1.19).ConclusionMaternal depression seems to compromise CCP attendance. These findings suggest a need for careful clinical attention to these vulnerable families, even years after a diagnosis of depression.

The occurrence and timing of delirium in acute care hospitalizations in the last year of life: A population-based retrospective cohort study

2020 ◽  
Vol 34 (8) ◽  
pp. 1067-1077
Author(s):  
Colleen Webber ◽  
Christine L Watt ◽  
Shirley H Bush ◽  
Peter G Lawlor ◽  
Robert Talarico ◽  
...  
Keyword(s):  
Cohort Study ◽  
Confidence Interval ◽  
Acute Care ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Prevalence Ratio ◽  
Acute Care Hospitals ◽  
Population Based ◽  
Care Hospital ◽  
Factors Associated

Background: Delirium is a distressing neurocognitive disorder that is common among terminally ill individuals, although few studies have described its occurrence in the acute care setting among this population. Aim: To describe the prevalence of delirium in patients admitted to acute care hospitals in Ontario, Canada, in their last year of life and identify factors associated with delirium. Design: Population-based retrospective cohort study using linked health administrative data. Delirium was identified through diagnosis codes on hospitalization records. Setting/participants: Ontario decedents (1 January 2014 to 31 December 2016) admitted to an acute care hospital in their last year of life, excluding individuals age of <18 years or >105 years at admission, those not eligible for the provincial health insurance plan between their hospitalization and death dates, and non-Ontario residents. Results: Delirium was recorded as a diagnosis in 8.2% of hospitalizations. The frequency of delirium-related hospitalizations increased as death approached. Delirium prevalence was higher in patients with dementia (prevalence ratio: 1.43; 95% confidence interval: 1.36–1.50), frailty (prevalence ratio: 1.67; 95% confidence interval: 1.56–1.80), or organ failure–related cause of death (prevalence ratio: 1.23; 95% confidence interval: 1.16–1.31) and an opioid prescription (prevalence ratio: 1.17; 95% confidence interval: 1.12–1.21). Prevalence also varied by age, sex, chronic conditions, antipsychotic use, receipt of long-term care or home care, and hospitalization characteristics. Conclusion: This study described the occurrence and timing of delirium in acute care hospitals in the last year of life and identified factors associated with delirium. These findings can be used to support delirium prevention and early detection in the hospital setting.

Sign in / Sign up

Export Citation Format

Share Document