Development and Validation of an Obstetric Comorbidity Risk Score for Clinical Use.
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ABSTRACT:
SUBMITTER: Ruppel H
PROVIDER: S-EPMC8617587 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesAn obstetric comorbidity index has been developed recently with superior performance characteristics relative to general comorbidity measures in an obstetric population. This study aimed to externally validate this index and to examine the impact of including hospitalisation/delivery records only when estimating comorbidity prevalence and discriminative performance of the obstetric comorbidity index.DesignValidation study.SettingAlberta, Canada.PopulationPregnant women who delivered a live or stillborn infant in hospital (n = 5995).MethodsAdministrative databases were linked to create a population-based cohort. Comorbid conditions were identified from diagnoses for the delivery hospitalisation, all hospitalisations and all healthcare contacts (i.e. hospitalisations, emergency room visits and physician visits) that occurred during pregnancy and 3 months pre-conception. Logistic regression was used to test the discriminative performance of the comorbidity index.Main outcome measuresMaternal end-organ damage and extended length of stay for delivery.ResultsAlthough prevalence estimates for comorbid conditions were consistently lower in delivery records and hospitalisation data than in data for all healthcare contacts, the discriminative performance of the comorbidity index was constant for maternal end-organ damage [all healthcare contacts area under the receiver operating characteristic curve (AUC) = 0.70; hospitalisation data AUC = 0.67; delivery data AUC = 0.65] and extended length of stay for delivery (all healthcare contacts AUC = 0.60; hospitalisation data AUC = 0.58; delivery data AUC = 0.58).ConclusionsThe obstetric comorbidity index shows similar performance characteristics in an external population and is a valid measure of comorbidity in an obstetric population. Furthermore, the discriminative performance of the comorbidity index was similar for comorbidities ascertained at the time of delivery, in hospitalisation data or through all healthcare contacts.
| S-EPMC5006847 | biostudies-literature
Project description:BackgroundMaking an accurate prognosis for mortality during tuberculosis (TB) treatment in TB-diabetes (TB-DM) comorbid patients remains a challenge for health professionals, especially in low TB prevalent populations, due to the lack of a standardized prognostic model.MethodsUsing de-identified data from TB-DM patients from Texas, who received TB treatment had a treatment outcome of completed treatment or died before completion, reported to the National TB Surveillance System from January 2010-December 2016, we developed and internally validated a mortality scoring system, based on the regression coefficients.ResultsOf 1227 included TB-DM patients, 112 (9.1%) died during treatment. The score used nine characteristics routinely collected by most TB programs. Patients were divided into three groups based on their score: low-risk (< 12 points), medium-risk (12-21 points) and high-risk (≥22 points). The model had good performance (with an area under the receiver operating characteristic (ROC) curve of 0.83 in development and 0.82 in validation), and good calibration. A practical mobile calculator app was also created ( https://oaa.app.link/Isqia5rN6K ).ConclusionUsing demographic and clinical characteristics which are available from most TB programs at the patient's initial visits, our simple scoring system had good performance and may be a practical clinical tool for TB health professionals in identifying TB-DM comorbid patients with a high mortality risk.
| S-EPMC6321653 | biostudies-literature
Project description:INTRODUCTION:The available prognostic scoring systems for severe acute pancreatitis (SAP) have limitations that restrict their clinical value. The aim of this study was to develop a simple model (score) that could rapidly identify those at risk for SAP. METHODS:We derived a risk model using a retrospective cohort of 700 patients by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The classification and regression tree (CART) analysis was used to create risk categories. The model was internally validated by a tenfold cross-validation and externally validated in a separate prospective cohort of 194 patients. RESULTS:The incidence of SAP was 9.7% in the derivation cohort and 9.3% in the validation cohort. A prognostic score (We denoted it as the SABP score), ranging from 0 to 10, consisting of systemic inflammatory response syndrome, serum albumin, blood urea nitrogen and pleural effusion, was developed by logistic regression and bootstrapping analysis. Patients could be divided into three risk categories according to total SABP score based on CART analysis. The mean probability of developing SAP was 1.9%, 12.8% and 41.6% in patients with low (0-3), moderate (4-6) and high (7-10) SABP score, respectively. The AUCs of prognostic score in tenfold cross-validation was 0.873 and 0.872 in the external validation. CONCLUSION:Our risk prediction score may assist physicians in predicting the development of SAP.
| S-EPMC6505180 | biostudies-other
Project description:ObjectiveInflammation-related symptoms such as pain, swelling and tenderness of the affected joint are frequently assessed using 5-point diary rating scales in gout clinical trials. Combining these into a single gout attack symptom intensity score may be a useful summary measure for these data, which is potentially more responsive to change compared with the individual components. The objective of this study was to develop a patient-reported gout flare intensity score, the Gout Attack Intensity Score (GAIS), for use in clinical studies, that includes components for gout-related pain, swelling and tenderness.MethodsData from a randomized controlled trial comparing anakinra to standard of care for the treatment of acute gout attacks were used for this study. A 7-day flare diary was completed by patients, including questions relating to intensity of pain, swelling and tenderness (5-point rating scales). Scalability of these items was assessed using Mokken Scale Analysis, and reliability using greatest lower bound reliability coefficients. Known-groups validity was evaluated, as well as the responsiveness to change and the presence of floor and ceiling effects.ResultsScalability of the single items was supported, and GAIS scores were reliable (greatest lower bound >0.80). GAIS scores demonstrated responsiveness to change with high effect sizes (>0.8), and discriminated better between responders and non-responders compared with its single-item components. No floor and ceiling effects were found.ConclusionThe GAIS seems to be a reliable and responsive instrument for assessing patient-reported gout attack intensity that may be used in gout clinical studies.
| S-EPMC6812713 | biostudies-literature
Project description:BackgroundImproving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data.MethodsRoutinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine-based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration.ResultsThe risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80-0.81] in the development cohort and 0.71 (95% CI 0.70-0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75-0.76) in the Canadian validation cohort.ConclusionWe have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.
| S-EPMC7596889 | biostudies-literature
Project description:ObjectiveDevelop and validate a prognostic model for clinical deterioration or death within days of pulmonary embolism (PE) diagnosis using point-of-care criteria.MethodsWe used prospective registry data from six emergency departments. The primary composite outcome was death or deterioration (respiratory failure, cardiac arrest, new dysrhythmia, sustained hypotension, and rescue reperfusion intervention) within 5 days. Candidate predictors included laboratory and imaging right ventricle (RV) assessments. The prognostic model was developed from 935 PE patients. Univariable analysis of 138 candidate variables was followed by penalized and standard logistic regression on 26 retained variables, and then tested with a validation database (N = 801).ResultsLogistic regression yielded a nine-variable model, then simplified to a nine-point tool (PE-SCORE): one point each for abnormal RV by echocardiography, abnormal RV by computed tomography, systolic blood pressure < 100 mmHg, dysrhythmia, suspected/confirmed systemic infection, syncope, medico-social admission reason, abnormal heart rate, and two points for creatinine greater than 2.0 mg/dL. In the development database, 22.4% had the primary outcome. Prognostic accuracy of logistic regression model versus PE-SCORE model: 0.83 (0.80, 0.86) vs. 0.78 (0.75, 0.82) using area under the curve (AUC) and 0.61 (0.57, 0.64) vs. 0.50 (0.39, 0.60) using precision-recall curve (AUCpr). In the validation database, 26.6% had the primary outcome. PE-SCORE had AUC 0.77 (0.73, 0.81) and AUCpr 0.63 (0.43, 0.81). As points increased, outcome proportions increased: a score of zero had 2% outcome, whereas scores of six and above had ≥ 69.6% outcomes. In the validation dataset, PE-SCORE zero had 8% outcome [no deaths], whereas all patients with PE-SCORE of six and above had the primary outcome.ConclusionsPE-SCORE model identifies PE patients at low- and high-risk for deterioration and may help guide decisions about early outpatient management versus need for hospital-based monitoring.
| S-EPMC8601564 | biostudies-literature
Project description:BackgroundLong-term childhood cancer survivors (CCS) are at increased risk of adverse cardiovascular events; however, there is a paucity of risk-stratification tools to identify those at higher-than-normal risk.Subjects, materials, and methodsThis was a population-based study using data from the Surveillance, Epidemiology, and End Results Program (1973-2013). Long-term CCS (age at diagnosis ≤19 years, survival ≥5 years) were followed up over a median time period of 12.3 (5-40.9) years. Independent predictors of cardiovascular mortality (CVM) were combined into a risk score, which was developed in a derivation set (n = 22,374), and validated in separate patient registries (n = 6,437).ResultsIn the derivation registries, older age at diagnosis (≥10 years vs. reference group of 1-5 years), male sex, non-white race, a history of lymphoma, and a history of radiation were independently associated with an increased risk of CVM among long-term CCS (p < .05). A risk score derived from this model (Childhood and Adolescence Cancer Survivor CardioVascular score [CHACS-CV], range: 0-8) showed good discrimination for CVM (Harrell's C-index [95% confidence interval (CI)]: 0.73 [0.68-0.78], p < .001) and identified a high-risk group (CHACS-CV ≥6), with cumulative CVM incidence over 30 years of 6.0% (95% CI: 4.3%-8.1%) versus 2.6% (95% CI: 1.8%-3.7%), and 0.7% (95% CI: 0.5%-1.0%) in the mid- (CHACS-CV = 4-5) and low-risk groups (CHACS-CV ≤3), respectively (plog-rank < .001). In the validation set, the respective cumulative incidence rates were 4.7%, 3.1%, and 0.8% (plog-rank < .001).ConclusionWe propose a simple risk score that can be applied in everyday clinical practice to identify long-term CCS at increased cardiovascular risk, who may benefit from early cardiovascular screening, and risk-reduction strategies.Implications for practiceChildhood cancer survivors (CCS) are known to be at increased cardiovascular risk. Currently available prognostic tools focus on treatment-related adverse events and late development of congestive heart failure, but there is no prognostic model to date to estimate the risk of cardiovascular mortality among long-term CCS. A simple clinical tool is proposed for cardiovascular risk stratification of long-term CCS based on easily obtainable information from their medical history. This scoring system may be used as a first-line screening tool to assist health care providers in identifying those who may benefit from closer follow-up and enable timely deployment of preventive strategies.
| S-EPMC6156177 | biostudies-literature
Project description:ObjectiveTo develop and validate a claims-based comorbidity score for patients undergoing major surgery, and compare its performance with established comorbidity scores.Data sourceFive percent Medicare data from 2007 to 2014.Study designRetrospective cohort study of patients aged ≥65 years undergoing six major operations (N = 99 250).Data collectionOne-year mortality was the primary outcome. Secondary outcomes were hospital mortality, 30-day mortality, 30-day readmission, and length of stay. The comorbidity score was developed in the derivation cohort (70 percent sample) using logistic regression model. The comorbidity score was calibrated and validated in the validation cohort (30 percent sample), and compared against the Charlson, Elixhauser, and Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) comorbidity scores using c-statistic, net reclassification improvement, and integrated discrimination improvement.Principal findingsIn the validation cohort, the surgery-specific comorbidity score was well calibrated and performed better than the Charlson, Elixhauser, and CMS-HCC comorbidity scores for all outcomes; the performance was comparable to the CMS-HCC for 30-day readmission. For example, the surgery-specific comorbidity score (c-statistic = 0.792; 95% CI, 0.785-0.799) had greater discrimination than the Charlson (c-statistic = 0.747; 95% CI, 0.739-0.755), Elixhauser (c-statistic = 0.747; 95% CI, 0.735-0.755), or CMS-HCC (c-statistic = 0.755; 95% CI, 0.747-0.763) scores in predicting 1-year mortality. The net reclassification improvement and integrated discrimination improvement were greater for surgery-specific comorbidity score compared to the Charlson, Elixhauser, and CMS-HCC scores.ConclusionsCompared to commonly used comorbidity measures, a surgery-specific comorbidity score better predicted outcomes in the surgical population.
| S-EPMC6863229 | biostudies-literature
Project description:BackgroundThe use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a simple prediction model for obstetric morbidity and mortality in resource-limited settings.MethodsWe performed a multivariate logistic regression analysis using a retrospective case-control analysis of secondary data with clinical indices predictive of severe maternal outcome (SMO). Cases for design and validation were randomly selected (n = 500) from 4360 women diagnosed with SMO in 42 Nigerian tertiary-hospitals between June 2012 and mid-August 2013. Controls were 1000 obstetric admissions without SMO diagnosis. We used clinical observations collected within 24 h of SMO occurrence for cases, and normal births for controls. We created a combined dataset with two controls per case, split randomly into development (n = 600) and validation (n = 900) datasets. We assessed the model's validity using sensitivity and specificity measures and its overall performance in predicting SMO using receiver operator characteristic (ROC) curves. We then fitted the final developmental model on the validation dataset and assessed its performance. Using the reference range proposed in the United Kingdom Confidential-Enquiry-into-Maternal-and-Child-Health 2007-report, we converted the model into a simple score-based obstetric EWS algorithm.ResultsThe final developmental model comprised abnormal systolic blood pressure-(SBP > 140 mmHg or < 90 mmHg), high diastolic blood pressure-(DBP > 90 mmHg), respiratory rate-(RR > 40/min), temperature-(> 38 °C), pulse rate-(PR > 120/min), caesarean-birth, and the number of previous caesarean-births. The model was 86% (95% CI 81-90) sensitive and 92%- (95% CI 89-94) specific in predicting SMO with area under ROC of 92% (95% CI 90-95%). All parameters were significant in the validation model except DBP. The model maintained good discriminatory power in the validation (n = 900) dataset (AUC 92, 95% CI 88-94%) and had good screening characteristics. Low urine output (300mls/24 h) and conscious level (prolonged unconsciousness-GCS < 8/15) were strong predictors of SMO in the univariate analysis.ConclusionWe developed and validated statistical models that performed well in predicting SMO using data from a low resource settings. Based on these, we proposed a simple score based obstetric EWS algorithm with RR, temperature, systolic BP, pulse rate, consciousness level, urinary output and mode of birth that has a potential for clinical use in low-resource settings..
| S-EPMC7488502 | biostudies-literature
Project description:IntroductionWe present a new diabetes risk score developed and validated in a multi-ethnic population in Guyana, South America. Measurement of in-country diabetes prevalence is a vital epidemiologic tool to combat the pandemic. It is believed that for every person diagnosed with type 2 diabetes there is another undiagnosed. The International Diabetes Federation (IDF) recommends a two-step detection programme using a risk score questionnaire to identify high-risk individuals followed by glycaemic measure.MethodsData on 798 persons from the 2016 STEPwise Approach to Chronic Disease Risk Factor Surveillance (STEPS) were used to correlate responses to 36 questions with glycated haemoglobin (HbA1C) and fasting plasma glucose (FPG) results. Bootstrapping was used to internally validate the derived seven-variable model. This model with the addition of family history questions was tested in a convenience sample of 659 Guyanese adults and externally validated in a cohort of another 528.ResultsAn 8-item Guyana Diabetes Risk Score (GDRS) was derived. The final model performed with an area under the curve (AUC) of 0.812 CONCLUSIONS: The validated eight-item Guyana Diabetes Risk Score will be extremely useful in identifying individuals at high risk of having diabetes in Caribbean, Black or East Indian populations.
| S-EPMC7136361 | biostudies-literature