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Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study | BMC Medicine

Examine inhabitants and design

The research inhabitants was drawn from the membership of Kaiser Permanente Northern California (KPNC), an built-in well being care supply system serving 4.5 million members. The KPNC membership accounts for about 30% of the underlying inhabitants and is socio-demographically consultant of the inhabitants residing within the geographic areas served [11, 12]. The built-in info system permits quantifying predictors and outcomes throughout the continuum of being pregnant. People with GDM are recognized by looking the KPNC Being pregnant Glucose Tolerance and GDM Registry, which is an energetic surveillance registry that downloads laboratory information to find out screening and analysis for GDM, the place preexisting sort 1 or 2 diabetes is mechanically excluded. Particularly, pregnant people at KPNC obtain common screening (98%) for GDM with the 50-g, 1-h glucose problem check (GCT) at 24–28 weeks’ gestation [1]. If the screening check is irregular, a diagnostic 100-g, 3-h oral glucose tolerance check (OGTT) is carried out after an 8–12-h quick. GDM is ascertained by assembly any of the next standards: (1) ≥ 2 OGTT plasma glucose values ​​assembly or exceeding the Carpenter-Coustan thresholds: 1-h 180 mg/dL, 2-h 155 mg/dL, and 3-h 140 mg/dL; or (2) 1-h GCT ≥ 180 mg/dL and a fasting glucose ≥ 95 mg/dL carried out alone or throughout the OGTT [13, 14]. Plasma glucose measurements have been carried out utilizing the hexokinase methodology on the KPNC regional laboratory, which participated within the School of American Pathologists’ accreditation and monitoring program [15]. This data-only challenge was authorised by the KPNC Institutional Evaluate Board, which waived the requirement for knowledgeable consent from contributors.

Amongst 405,557 pregnancies with a gestational age at supply < 24 weeks' gestation delivered at 21 KPNC hospitals from January 1, 2007, to December 31, 2017, we excluded 375,041 (92.5%) people with out GDM. Amongst 30,516 GDM pregnancies, we additional excluded people with GDM recognized earlier than the common GDM screening (n= 42), deriving an analytical pattern of 30,474 GDM-complicated pregnancies. We additional derived a discovery set containing 27,240 GDM-complicated pregnancies from 2007 to 2016 and a temporal/future validation set of 3234 GDM-complicated pregnancies in 2017 (Fig. 1).

Fig. 1
figure 1

Flowchart for growing pregnancies cohort with gestational diabetes 2007–2017. GDM: gestational diabetes mellitus

Consequence ascertainment

People recognized with GDM obtain common referral to the KPNC Regional Perinatal Service Heart for the supplemental care program past their normal of prenatal care. MNT was the first-line remedy. If glycemic management targets weren’t achieved with MNT alone, pharmacological remedy was initiated. Based mostly on counseling relating to dangers and advantages of antidiabetic oral brokers versus insulin, pharmacologic remedy was chosen through a patient-physician shared decision-making mannequin: (1) with antidiabetic oral brokers resembling glyburide and metformin being added to MNT and if optimum glycemic management continued to fail, oral treatment was escalated to insulin remedy, and (2) or with insulin remedy initiated immediately past MNT (a further desk reveals this in additional element [see Additional file 1]). We searched the pharmacy info administration database for prescriptions for oral brokers (glyburide 97.9%, metformin or different) and insulin after GDM analysis. Remedy modality was grouped as MNT solely and pharmacologic remedy (oral brokers and/or insulin) past MNT. Notably, regardless of an general giant pattern dimension, we grouped oral brokers (32.6% of all the inhabitants) and insulin (6.2%) into pharmacologic remedy as a consequence of inadequate energy to foretell insulin individually as an end result.

Candidate predictors

Based mostly on threat elements related to GDM remedy modality and enter from clinicians, we chosen 176 (64 steady and 112 categorical) sociodemographic, behavioral, and medical candidate predictors obtained from digital well being data for mannequin improvement. Candidate predictors have been divided into 4 ranges based mostly on availability at diverse phases of being pregnant (a further desk reveals this in additional element [see Additional file 2]): Degree 1 predictors (n= 68) have been obtainable on the initiation of being pregnant and dated again to 1 yr previous to the index being pregnant; stage 2 predictors (n= 26) have been measured from the final menstrual interval to earlier than GDM analysis; stage 3 predictors (n= 12) have been obtainable on the time of GDM analysis; and stage 4 (n= 70) included self-monitoring of blood glucose (SMBG) ranges, as the first measure of glycemic management throughout being pregnant as advisable by the American Diabetes Affiliation [5], measured the primary week after the GDM analysis. All predictors, ranges 1–4, have been measured previous to the end result of curiosity (ie, closing line of GDM remedy). Pregnant people with GDM in our research inhabitants had on common, 11.8 weeks (normal deviation: 6.6 weeks), of SMBG measurements between GDM analysis and supply. We included information 1 week after GDM analysis to permit earlier prediction because it takes on common 5.6 weeks between GDM analysis and the optimum remedy is obtainable. Of observe, people with GDM have been universally supplied enrollment to a supplemental GDM care program managed by nurses and dietitians through telemedicine from the KPNC Regional Perinatal Service Heart [16]. All people with GDM have been instructed to self-monitor and file glucose measurements 4 occasions per day: fasting earlier than breakfast and 1 hour after the beginning of every meal. Measurements of SMBG have been then reported to the nurses or registered dieticians throughout weekly phone counseling calls from enrollment till supply and information have been recorded within the Affected person Reported Capillary Glucose Medical Database.

Statistical evaluation


We imputed lacking values ​​with the random forest algorithm because the algorithm doesn’t require parametric mannequin assumptions, which cut back the effectivity of the predictor (a further desk reveals this in additional element [see Additional file 2]). We evaluated the estimation of true imputation error utilizing normalized root imply squared error and proportion of falsely categorised entries for steady and categorical variables, respectively. Each values ​​have been near 0, indicating good efficiency in imputation (a further desk reveals this in additional element [see Additional file 3]). After preprocessing, we employed t-test and Pearson’s chi-squared check to check participant traits between the invention and temporal/future validation units. We carried out the Mann–Kendall check to look at secular traits for GDM remedy modalities throughout calendar years. The invention set (2007–2016) was stratified by the calendar yr and remedy modality for tenfold cross validation. The temporal/future validation set (2017) was stratified by remedy modality for cross-validated prediction efficiency computation.

Variable choice and full mannequin improvement and comparability

We carried out prediction by classification and regression tree (CART), least absolute shrinkage and choice operator (LASSO) regression, and tremendous learner (SL) predicting with ranges 1, 1–2, 1–3, and 1–4 predictors, respectively. CART and LASSO regression have been chosen as easy prediction strategies in comparison with SL. The SL defines a set of candidate machine studying algorithms, particularly, the library, and combines prediction outcomes by meta-learning through cross-validation [17]. SL has the asymptotic property that it’s not less than nearly as good (in threat, outlined by the adverse log-likelihood) as one of the best becoming algorithm within the library [17]. Though the variables included within the closing ensemble SL can’t be simply interpreted for his or her particular person contributions, SL can be utilized for optimum prediction efficiency and to benchmark less complicated and fewer adaptive approaches [17].

We tuned the prediction strategies as follows. In CART, the Gini index measured the heterogeneity composition of the subset with respect to the end result, and most depth (6) was outlined because the stopping criterion. Accounting for potential errors from the chance curve estimation, the regularization parameter in LASSO regression was chosen from the cross-validated error inside one normal error of its minimal worth [18]. For the SL, we thought-about a easy and a posh library for comparability. The straightforward library included the response-mean, LASSO regression, and CART; the complicated library expanded by moreover together with random forest and excessive gradient boosting (XGBoost). A number of XGBoosts have been thought-about, the place their tuning parameters have been set to 10, 20, 50 timber, 1 to six most depths, and 0.001, 0.01, and 0.1 shrinkage for regularization.

For fashions utilizing predictors at every stage, prediction outcomes have been evaluated utilizing tenfold cross-validated receiver working attribute curves and space underneath the receiver working attribute curve (AUC) statistics within the discovery and temporal/future validation units. We used Delong’s check to check AUCs between totally different prediction algorithms on the identical predictor stage and throughout the identical prediction algorithm throughout ranges, respectively [19]. We used permutation-based variable significance to calculate the AUCs with 5 simulations and obtained the highest 10 necessary options. Permuting one variable at a time, the strategy calculated the AUC distinction earlier than and after permutation to assign an significance measure [20]. The mannequin with the best AUC within the validation set was chosen as the ultimate full mannequin.

Growth of less complicated fashions

To enhance interpretability and potential medical uptake, we used tenfold cross-validated logistic regression to develop less complicated fashions within the discovery set based mostly on a minimal set of an important options at every stage, versus the complete set of options used within the complicated SL. We moreover chosen interplay time period(s) contemplating all cross-products by stepwise ahead and backward choice by the Akaike info criterion. We evaluated the predictive efficiency (ie, simplicity and cross-validated AUCs) of those less complicated fashions on the validation set. Additional, calibration was examined by evaluating the standard of an uncalibrated mannequin through the built-in calibration index, which captured the distribution of predicted possibilities, coupled with a calibration plot. Calibration methodology (ie, isotonic regression) was applied for recalibration within the occasion of noticed over- or under-prediction.

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