Additional file 6 of Threshold heterogeneity of perioperative hemoglobin drop for acute kidney injury after noncardiac surgery: a propensity score weighting analysis

Additional file 6: Fig S5. Subgroup analyses stratified by patient and operative variables in patients with preoperative anemia

Additional file 5 of Threshold heterogeneity of perioperative hemoglobin drop for acute kidney injury after noncardiac surgery: a propensity score weighting analysis

Additional file 5: Fig S4. Subgroup analyses stratified by patient and operative variables in patients without preoperative anemia

Additional file 2 of Threshold heterogeneity of perioperative hemoglobin drop for acute kidney injury after noncardiac surgery: a propensity score weighting analysis

Additional file 2: Fig S1. Restricted cubic spline function curves of the unadjusted and adjusted relationship between Hemoglobin drop and AKI probability. Shaded areas represent 95% confidence intervals

Additional file 1 of Threshold heterogeneity of perioperative hemoglobin drop for acute kidney injury after noncardiac surgery: a propensity score weighting analysis

Additional file 1: Table S1. Definitions of variables. Table S2. Postoperative events. Table S3. Improvement of Hemoglobin drop in full models for classification. Table S4. Sensitivity analyses. Multivariable logistic regression with surgery duration adjustment. Table S5. Sensitivity analyses. Multivariable logistic regression with exclusion of patients with Intraoperative hypotension. Table S6. Sensitivity analyses. Multivariable logistic regression with preoperative hemoglobin mean level within three months instead of the hemoglobin value tested closest to the date of surgery. Table S7. Patient characteristics and operative variables in cohorts by hemoglobin drop more or no more than 43 g/L after propensity score weighting

Additional file 3 of Threshold heterogeneity of perioperative hemoglobin drop for acute kidney injury after noncardiac surgery: a propensity score weighting analysis

Additional file 3: Fig S2. Timeliness between perioperative hemoglobin level and corresponding creatinine. The red line represented minimum hemoglobin level, with its corresponding red axis on the left. In plots A and B, the blue line represented creatinine level, with their corresponding blue axis on the right; in plots C and D, the blue line represented creatinine increment with their corresponding blue axis. Creatinine level changed simultaneously with hemoglobin level within five postoperative days. After day 5, this phenomenon disappeared

Additional file 4 of Threshold heterogeneity of perioperative hemoglobin drop for acute kidney injury after noncardiac surgery: a propensity score weighting analysis

Additional file 4: Fig S3. Timeliness between perioperative hemoglobin drop and corresponding creatinine. The red line represented maximum hemoglobin drop, with its corresponding red axis on the left. In plots A and B, the blue line represented creatinine level, with their corresponding blue axis on the right; in plots C and D, the blue line represented creatinine increment with their corresponding blue axis

A toy example to illustrate the calculation of “dist_DePTH_breadth” and “DePTH_breadth”.

(A) A demonstration for the selection of the TCRs that are associated with at least one of an individual’s HLAs. In the HLA vs. TCR table, a check mark indicates predicted association between an HLA (each row) and a TCR (each column). For each HLA, we collect the set of TCRs that are predicted to be associated with the HLA. The individual-level TCR set is computed by taking the union of these six TCR sets. (B) An illustration for the calculation of between-individual distance (“dist_DePTH_breadth”) and individual-level HLA-I heterogeneity metric (“DePTH_breadth”). |A| and |B| are the sizes of the sets A and B.</p

Summary for data generation and model training.

(A) A toy example to illustrate the selection of positive TCR-HLA pairs by their co-occurrence. A check mark indicates the TCR or HLA is observed in an individual. The p-value for the co-occurrence of a TCR-HLA pair is computed by one-sided Fisher’s exact test. To account for multiple testing across all TCR-HLA pairs, a TCR-HLA pair is considered associated if the corresponding False Discovery Rate (FDR) HLA-B*01:01 or not, based on Emerson data. (C) Illustration of the components in the DePTH model. Each TCR is represented by its amino acid sequences from CDR1, CDR2, CDR2.5 and CDR3 parts, and each HLA is represented by its pseudo sequence (part of its sequence that interact with TCRs or antigens). DePTH learns to predict whether a TCR-HLA pair is associated (positive) or not (negative). For HLA part, the encoded pseudo sequence is flattened and passed to a dense layer. For TCR part, the encoded CDR3 amino acid sequence is passed into a CNN (convolutional neural network). The output of the CNN is concatenated together with CDR3 length and the encoded sequences of CDR1, CDR2, CDR2.5, and CDR3. Then they are passed to a dense layer. Finally, the outputs from TCR and HLA dense layers are concatenated and passed to one or two dense layers to make the final prediction.</p

Association between HLA-based metrics and survival outcomes of 1,443 cancer patients [16].

(A) and (B) summarize the results using between-individual HLA-I distance metrics. (A)-log10(p-value) of kernel regression using five different distance matrices with or without covariates. The covariates include age, log-transformed mutation burden, and drug type (CTLA-4, PD-1, or both). The grey horizontal line corresponds to p-value 0.05. (B) The dendrogram of hierarchical clustering where the distance is defined by “dist_DePTH_breadth”, and the survival curves of the two subgroups identified by cutting the dendrogram tree. The p-value is computed from Cox proportional hazard regression. (C) -log10(p-value) from Cox regression for the associations between individual-level HLA-I heterozygosity metrics and survival outcome, with or without covariates. The covariates include age, log-transformed mutation burden, and drug type (CTLA-4, PD-1, or both). The grey horizontal line corresponds to p-value 0.05.</p

The performance of DePTH models trained on Emerson data.

(A) the ROC curve of DePTH prediction on test data (part of Emerson data) for HLA-I and HLA-II, respectively. (B) Violin plots for the TCR-HLA association scores predicted by DePTH on three sets of TCR-HLA pairs: the positive pairs of Emerson test data, the negative pairs of Emerson test data, and the 54 external positive TCR-HLA pairs from solved TCR-pMHC-I structures provided by Szeto et al. [15]. The scores are given by DePTH model trained on Emerson training data. (C) and (D) compare the performance of DePTH and CLAIRE on McPAS test data while both models are trained on McPAS data. (C) ROC curves of prediction scores across all HLA-I alleles. (D) Comparison of allele-wise AUCs.</p