Prediction of Mortality in Very Premature Infants: A Systematic Review of Prediction Models

CONTEXT Being born very preterm is associated with elevated risk for neonatal mortality. The aim of this review is to give an overview of prediction models for mortality in very premature infants, assess their quality, identify important predictor variables, and provide recommendations for development of future models. METHODS Studies were included which reported the predictive performance of a model for mortality in a very preterm or very low birth weight population, and classified as development, validation, or impact studies. For each development study, we recorded the population, variables, aim, predictive performance of the model, and the number of times each model had been validated. Reporting quality criteria and minimum methodological criteria were established and assessed for development studies. RESULTS We identified 41 development studies and 18 validation studies. In addition to gestational age and birth weight, eight variables frequently predicted survival: being of average size for gestational age, female gender, non-white ethnicity, absence of serious congenital malformations, use of antenatal steroids, higher 5-minute Apgar score, normal temperature on admission, and better respiratory status. Twelve studies met our methodological criteria, three of which have been externally validated. Low reporting scores were seen in reporting of performance measures, internal and external validation, and handling of missing data. CONCLUSIONS Multivariate models can predict mortality better than birth weight or gestational age alone in very preterm infants. There are validated prediction models for classification and case-mix adjustment. Additional research is needed in validation and impact studies of existing models, and in prediction of mortality in the clinically important subgroup of infants where age and weight alone give only an equivocal prognosis.Stephanie Medlock, Anita C. J. Ravelli, Pieter Tamminga, Ben W. M. Mol, Ameen Abu-Hann

Using HAQ-DI to estimate HUI-3 and EQ-5D utility values for patients with rheumatoid arthritis in Spain

AbstractBackground/ObjectiveUtility values are not usually assessed in clinical trials and do not allow cost-utility analysis to be performed with the data collected. The aim of this study was to derive relation functions so that Health Assessment Questionnaire – Disability Index (HAQ-DI) scores could be used to estimate Health Utilities Index - 3 (HUI-3) and EQ-5D utility values for patients with rheumatoid arthritis (RA).MethodsAn observational, cross-sectional, naturalistic, multicentre study was conducted. A total of 244 patients aged 18 years or older, with RA according to American College of Rheumatology diagnostic criteria, were recruited. Sociodemographic and clinical variables were recorded and patients completed three generic HRQoL questionnaires: the HAQ-DI, the HUI-3, and the EQ-5D. Two linear regression models were used to predict HUI-3 and EQ-5D utility values as functions of HAQ-DI scores, age, and gender.ResultsPatient mean age was 57.8 years old (standard deviation [SD], 13.3 years); 75.8% of the patients were women and 95.9% were white. Mean disease duration was 10.8 years (SD, 9 years). Patient distribution according to HAQ-DI severity was as follows: HAQ-DI < 0.5, 29%; 0.5 ≤ HAQ-DI < 1.1, 28%; 1.1 ≤ HAQ-DI < 1.6, 16%,1.6 ≤ HAQ-DI < 2.1, 15%; and HAQ-DI ≥ 2.1, 12%. HAQ-DI and EQ-5D mean scores were 1.02 (SD, 0.78) and 63.1 (SD, 20.3), respectively. Mean utility values for HUI-3 and time trade-off (TTO) were 0.75 (SD, 0.21) and 0.65 (SD, 0.3), respectively. The equations converting HAQ-DI scores to utilities were HUI-3 = 0.9527 – (0.2018 × HAQ-DI) +ε (R2=0.56), and TTO = 0.9567 – (0.309 × HAQ-DI) + ε (R2=0.54). Error distribution was non-normal. Age and gender were found to have no bearing on the utility functions.ConclusionsHAQ-DI scores can be used to estimate HUI-3 and EQ-5D utility values for patients with RA in data obtained from studies where utility values have not been collected

Increased incidence of hypertensive disorders of pregnancy in women with a history of spontaneous preterm birth:A longitudinal linked national cohort study

Objective: Determine the risk of hypertensive disorders of pregnancy (HD) in women with a history of spontaneous preterm birth (SPTB). Study design: Longitudinal linked national cohort study within the Dutch Perinatal Registry (1999–2009) on linked data among 349,291 women with a first and second singleton pregnancy in the Netherlands. Main outcome measures: The incidence of HD, small for gestational age (SGA) and placental abruption in the second pregnancy. Results: Out of 349,291 women with a singleton first pregnancy, 19,991 (5.7%) had a SPTB. The incidence of HD in the second pregnancy was 8.1% in women with a previous SPTB, as compared to 5.6% in women with a previous term birth (aOR 1.49 (CI 1.41–1.57)). Also after excluding HD, SGA and/or placental abruption in the first pregnancy, women with a history of SPTB had a higher risk of HD in their second pregnancy compared to women with a previous term birth (4.6% versus 2.7%, aOR 1.77 (CI 1.64–191)). Similarly, the incidence of SGA and placental abruption was higher in the second pregnancy in women with a history of SPTB compared to term birth in the first pregnancy. Conclusions: Women with a history of SPTB are at elevated risk of HD in the subsequent pregnancy. These results support shared pathophysiology between SPTB and HD

Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies

BackgroundFederated learning (FL) is a technique for learning prediction models without sharing records between hospitals. Compared to centralized training approaches, the adoption of FL could negatively impact model performance.AimThis study aimed to evaluate four types of multicenter model development strategies for predicting 30-day mortality for patients undergoing transcatheter aortic valve implantation (TAVI): (1) central, learning one model from a centralized dataset of all hospitals; (2) local, learning one model per hospital; (3) federated averaging (FedAvg), averaging of local model coefficients; and (4) ensemble, aggregating local model predictions.MethodsData from all 16 Dutch TAVI hospitals from 2013 to 2021 in the Netherlands Heart Registration (NHR) were used. All approaches were internally validated. For the central and federated approaches, external geographic validation was also performed. Predictive performance in terms of discrimination [the area under the ROC curve (AUC-ROC, hereafter referred to as AUC)] and calibration (intercept and slope, and calibration graph) was measured.ResultsThe dataset comprised 16,661 TAVI records with a 30-day mortality rate of 3.4%. In internal validation the AUCs of central, local, FedAvg, and ensemble models were 0.68, 0.65, 0.67, and 0.67, respectively. The central and local models were miscalibrated by slope, while the FedAvg and ensemble models were miscalibrated by intercept. During external geographic validation, central, FedAvg, and ensemble all achieved a mean AUC of 0.68. Miscalibration was observed for the central, FedAvg, and ensemble models in 44%, 44%, and 38% of the hospitals, respectively.ConclusionCompared to centralized training approaches, FL techniques such as FedAvg and ensemble demonstrated comparable AUC and calibration. The use of FL techniques should be considered a viable option for clinical prediction model development

Ignoring Dependency between Linking Variables and Its Impact on the Outcome of Probabilistic Record Linkage Studies

A b s t r a c t Objectives: This study sought to examine the differences between ignoring (naïve) and incorporating dependency (nonnaïve) among linkage variables on the outcome of a probabilistic record linkage study. Design and Measurements: We used the outcomes of a previously developed probabilistic linkage procedure for different registries in perinatal care assuming independence among linkage variables. We estimated the impact of ignoring dependency by re-estimating the linkage weights after constructing a variable that combines the outcomes of the comparison of 2 correlated linking variables. The results of the original naïve and the new nonnaïve strategy were systematically compared for 3 scenarios: the empirical dataset using 9 variables, the empirical dataset using 5 variables, and a simulated dataset using 5 variables. Results: The linking weight for agreement on 2 correlated variables among nonmatches was estimated considerably higher in the naïve strategy than in the nonnaïve strategy (16.87 vs. 13.55). Therefore, ignoring dependency overestimates the amount of identifying information if both correlated variables agree. The impact on the number of pairs that was classified differently with both approaches was modest in the situation in which there were many different linking variables but grew substantially with fewer variables. The simulation study confirmed the results of the empirical study and suggests that the number of misclassifications can increase substantially by ignoring dependency under less favorable linking conditions. Conclusion: Dependency often exists between linking variables and has the potential to bias the outcome of a linkage study. The nonnaïve approach is a straightforward method for creating linking weights that accommodate dependency. The impact on the number of misclassifications depends on the quality and number of linking variables relative to the number of correlated linking variables. Ⅲ J Am Med Inform Assoc. 2008;15:654 -660