A Predictive Nursing Tool for Patient Positioning Safety in Hybrid Digital Subtraction Angiography Operating Rooms

Summary

A single-center retrospective study developed and internally validated a staged nursing prediction tool that combines baseline patient surgical risk with modifiable perioperative care processes to support preoperative assessment and post-positioning risk updates for positioning-related complications in a hybrid digital subtraction angiography operating room.

Abstract

The aim of this study was to develop and internally validate a phase-updating nursing predictive assessment tool based on baseline patient-surgical risk and modifiable nursing processes for use before surgery and after positioning in the hybrid digital subtraction angiography operating room. This single-center retrospective cohort included inpatient adults undergoing their first procedure in the hybrid operating room, with surgery as the unit of analysis and follow-up to 72 h postoperatively. The outcome was a composite endpoint of position-related complications.

Missing predictor data were handled using multiple imputation (m = 10), and extraction consistency was assessed with Cohen κ and intraclass correlation coefficients. Candidate variables from univariable screening plus prespecified variables entered least absolute shrinkage and selection operator regression, followed by multivariable logistic modeling and nomogram construction with threshold-based risk stratification. Model performance was evaluated by area under the curve, calibration intercept and slope, Brier score, bootstrap internal validation, and decision curve analysis.

A total of 1,936 cases were analyzed, and the composite outcome occurred in 10.23%. Maximum missingness of key variables was 3.25%, and extraction consistency was good (κ ≥ 0.86, intraclass correlation coefficient ≥ 0.89). Twelve predictors were retained; pressure-point protection and intraoperative position checks were protective (odds ratio 0.68–0.71). The baseline model had an area under the curve of 0.74/0.72 (apparent/corrected), and the full model achieved 0.79/0.77. The optimism-corrected calibration slope was 0.947, the intercept was 0.004, and the Brier score was 0.085.

This tool showed stable discrimination, calibration, and net benefit on internal validation. Nursing process variables added value, and the tool may support preoperative assessment and post-positioning risk updating, pending external validation.

Introduction

The hybrid digital subtraction angiography (DSA) operating room integrates interventional imaging with open surgery and serves as a platform for managing complex vascular disease and critical emergencies¹. The procedural chain is longer, many patients are elderly, and general anesthesia or sedation diminishes protective reflexes and spontaneous adjustment². Constrained by the C-arm, the sterile field, and the layout of access routes, opportunities to turn the body and relieve pressure are reduced once positioning is fixed, while exposure to sustained pressure and stretch/traction increases³. Concurrent hybrid procedures further reduce adjustment windows, and positioning management relies more on team coordination. Position-related complications include pressure injuries, peripheral nerve and musculoskeletal injury, and may also present as positioning-induced respiratory and hemodynamic fluctuations, affecting postoperative recovery^4,5.

The nursing team bears key responsibility for positioning, pressure-point protection, access securement, intraoperative position check, and postoperative observation; early identification of positional risk and precise intervention are core topics in nursing management, with a greater need for standardized, process-based verification and documentation⁶. Existing perioperative risk assessments mostly rely on preoperative generic scales or focus on a single outcome⁷. Much of the evidence comes from the operating room or interventional suite and fails to cover the composite exposures in the hybrid DSA operating room, such as prolonged inability to turn, fluctuations in perfusion and temperature, and sheath indwelling with compression fixation. Tools such as Braden emphasize skin tolerance but are not well suited to characterize specific factors such as the prone position and cumulative exposure to hypotension, making it difficult to support stratified management⁸. The complication spectrum features coexisting skin injury and physiologic instability, and a single score is insufficient to identify process priorities⁹.

Some studies have directly treated nursing measures as preoperative predictors, which readily leads to purpose mismatch and information leakage, making outputs difficult to align with the timing and intensity of interventions and unfavorable quality improvement after risk adjustment¹⁰. There is a lack of an assessment tool that provides a preoperative baseline risk and updates risk after positioning, with corresponding actionable items. In this study, based on a single-center retrospective cohort, a nursing predictive assessment tool was developed; before surgery, baseline risk was estimated using patient and surgical exposure variables, and after positioning, modifiable nursing items were incorporated to update risk. Penalized regression was used for variable selection with internal validation; discrimination, calibration, and net benefit were evaluated; and a nomogram and threshold-based stratification were produced for nursing decision support, providing a basis for risk adjustment and quality improvement.

Protocol

This study involves human subjects’ prior medical records and anesthesia and nursing record data. The study was conducted in compliance with the relevant institutional guidelines, reviewed and approved by the institutional ethics committee (Run Run Shaw Hospital Ethics Review 2025 Research No. 2113), with a waiver of written informed consent; all data were de-identified before export and used only for the purposes of this study.

This study was reported in accordance with the TRIPOD statement for multivariable prediction model development and internal validation; the completed TRIPOD checklist is provided in Supplemental Table S1.

1. Research design and study setting

1. Define the study design as a single-center retrospective observational study using a historical cohort design. Fix the study time frame to January 1, 2021, through December 31, 2024.
2. Set the study objective as developing a nursing predictive assessment tool for position-related complications in the hybrid DSA operating room and performing internal validation and performance evaluation. Define “surgery” as the unit of analysis and assign each surgery a unique identifier (e.g., inpatient number + surgery date + anesthesia start time).
3. When the same patient has multiple eligible surgeries, retain only the first eligible surgery record. Define “first” as the eligible surgery with the earliest anesthesia or sedation start time within the study time window.
4. Define the data extraction start point as the time the patient enters the operating room and anesthesia or sedation begins. Fix the follow-up endpoint at 72 h postoperatively, thereby covering the primary occurrence window of position-related complications.
5. Limit the study setting to the hybrid DSA operating room and document that it is equipped with a flat-panel digital subtraction angiography system and open surgery capability. Record that the commonly used positions are supine, prone, and lateral, and that intraoperative position adjustment is limited by the imaging equipment, the sterile field, and the layout of access routes, resulting in fewer opportunities to turn and a relatively prolonged operative duration.

2. Study population, case screening flow, and sample size strategy

1. Export all “hybrid DSA operating room” surgery entries within the study time window from the consecutively registered operating room register. Retain matching fields such as the procedure code, operating room identifier, surgery date, inpatient number, and anesthesia start time.
2. Use the inpatient number and surgery date for the initial match, and perform a secondary check using the anesthesia start time and operating room identifier. If multiple anesthesia records exist on the same day for the same inpatient number, preferentially match the one with a consistent operating room identifier and an anesthesia start time closest to the surgery registration time.
3. Include inpatient adult patients aged ≥18 years whose surgeries were completed in the hybrid DSA operating room and were neurovascular, cardiovascular, or peripheral vascular interventional or hybrid procedures. Confirm that general anesthesia or sedation was administered during surgery, that the medical, anesthesia, and nursing documentation are complete, and that follow-up to 72 h postoperatively is available.
4. Exclude cases in which the primary outcome event was already present preoperatively to avoid a nonzero baseline for the outcome. Exclude cases in which the procedure was interrupted intraoperatively for reasons unrelated to positioning, such that further evaluation was not possible, as well as cases transferred out of the hospital within 72 h postoperatively, leading to an indeterminate outcome.
5. Determine the threshold for missingness of key predictor variables by calculating the proportion of missingness for each case across prespecified key predictor variables and record the reasons for missingness. If the missingness proportion for a case across these key predictor variables exceeds 30%, exclude the case according to the prespecified rules and document the reason for exclusion in the screening log.
6. Have two researchers independently complete the screening and generate screening logs (including inclusion/exclusion and reasons). For discrepant cases, have a third researcher trace back to the original records for adjudication, and retain the adjudication record for traceability.
7. Do not predefine an upper limit for sample size, adopting a consecutive all-case inclusion strategy. Control model complexity by the number of events, determining the number of parameters ultimately entered into the model according to the principle of at least 15 events per parameter.

3. Study measures and variable definitions

1. Establish a variable dictionary and specify for each variable the source system, field name, value rules, and temporal sequence. For continuous variables, use the most recent preoperative laboratory or assessment value; for surgical and anesthesia variables, use the worst value recorded over the entire procedure or cumulative exposure; for nursing and device variables, use the actual intraoperative implementation. Define prespecified thresholds and category cutoffs a priori according to clinically commonly used criteria, perioperative nursing and anesthesia practice, and published literature. When no single widely accepted cutoff is available, use pragmatic categories to facilitate recording, interpretation, and clinical application.
2. Primary outcome: composite outcome of position-related complications
   1. Define the primary outcome as a composite outcome of position-related complications occurring from intraoperative through 72 h postoperatively. If multiple component events occur in the same case, count the composite outcome once only, and record the time of first occurrence and all component types for secondary analyses.
   2. Define new-onset stage 2 or higher-pressure injury or deep tissue injury within 72 h postoperatively as one of the component events. Use nursing skin assessment form, staging, and nursing records as evidence, and require records that can be localized to specific anatomic sites.
   3. Define peripheral nerve injury as the occurrence of sensory loss, numbness, or decreased muscle strength lasting ≥24 h. Require that the anatomic distribution is consistent with the contact/traction area of the position at the time, and use progress notes or specialist assessment records as the main evidence.
   4. Define joint or musculoskeletal injury as localized pain accompanied by positive imaging findings or specialist physical examination findings suggesting traction/compression injury. Record the imaging modality or examination conclusion, and mark the position-related points of force application.
   5. Define position-related respiratory or hemodynamic instability events as airway compression, restricted respiration, or restricted venous return caused by positioning that requires repositioning, compression, vasopressor escalation, or interruption of the procedure. Use anesthesia system event records, anesthesia record sheets, and nursing/physician records containing explicit “position-related” descriptions and the corresponding management measures as evidence.
   6. Define a hematoma with a diameter ≥5 cm on the puncture side or oozing requiring additional compression fixation, with records indicating aggravation related to positional constraints, as one of the component events. Use nursing records and physician ward-round records within 72 h postoperatively as evidence, and record the additional management measures.
   7. Determine positional relevance according to three criteria: explicit mention of positional relevance in the records, anatomic site consistent with positional exposure points, and lack of a more compelling alternative cause. If there is an attribution conflict between nursing and physician records, prioritize the original record closer to the time of the event, and have two adjudicators review and reach consensus.
3. Definitions of predictor variables
   1. Define patient-level variables as age, sex, body mass index, diabetes mellitus, peripheral neuropathy, peripheral arterial disease, chronic kidney disease stage ≥ 3, congestive heart failure, smoking status, preoperative albumin, preoperative hemoglobin, Charlson comorbidity index, and preoperative Braden score. Define “preoperative” as the most recent valid record before entry into the operating room, within the hospital’s routine perioperative assessment window.
   2. Define surgery- and anesthesia-level variables as procedure category, anesthesia type, position type, head–neck rotation angle categories, upper-limb abduction angle categories, lower-limb abduction or flexion > 45°, operative duration, fluoroscopy duration, intraoperative non-turnable status, estimated blood loss, lowest MAP, cumulative time with MAP< 65 mmHg, lowest core temperature, use of vasoactive agents, and maximum norepinephrine equivalent. Classify position as supine/prone/lateral; classify head–neck rotation as ≤30°, 31–60°, and >60°; classify upper-limb abduction as ≤90° and >90°.
   3. Export the MAP time series with timestamps from the anesthesia information management system. Use the time difference between adjacent records as the interval length, and sum the durations of all intervals with MAP < 65 mmHg to obtain “MAP < 65 cumulative time”; if a single interval is missing or the gap is abnormal (e.g., >5 min without reasonable explanation), mark that interval as missing and do not include it in the cumulative total.
   4. Define nursing- and device-level variables as type of table padding, whether key pressure-point protection was completed, whether the upper limbs were secured to padded arm boards, whether the head was supported in a neutral position, category of puncture site, sheath size, sheath dwell time, puncture-site securement/closure method, and completeness of intraoperative position check documentation. Define “key pressure-point protection completed” as completed when there are protection records at all four sites—occiput, scapulae, sacrococcygeal region, and heels; otherwise, it is considered not fully completed.
   5. Record whether any vasoactive agents were used, and code non-users as “not used”. For users, record the maximum norepinephrine equivalent (µg/kg/min); at each anesthesia-record time point, calculate NE_eq_t = norepinephrine_t + epinephrine_t + 0.01 * dopamine_t + 0.06 * phenylephrine_t + 2.5 * vasopressin_t, where norepinephrine, epinephrine, dopamine, and phenylephrine are expressed in µg/kg/min and vasopressin in U/min; unused agents at a given time point were coded as 0, and maximum_norepinephrine_equivalent = max(NE_eq_t, na.rm = TRUE) over the intraoperative period.
      NOTE: The conversion and aggregation rules were fixed in advance and implemented in the analysis script.

4. Data collection and quality control

1. Provide uniform training to two researchers responsible for data extraction and distribute the standard operating procedures (SOP) and the variable dictionary. After training, complete a small-sample pilot extraction and revise the definitions and extraction pathways of ambiguous fields.
2. Extract demographics and comorbidities from the electronic medical record, preoperative laboratory indicators from the laboratory system, vital signs and medications from the anesthesia system, positioning and nursing measures from the nursing system, and fluoroscopy duration and imaging-related information from PACS. Perform cross-system matching using the inpatient number and surgery date, and retain the matching keys and a matching log.
3. When field conflicts occur, trace back to the original paper records and take the paper records as the standard. Record the conflict type, original value, corrected value, and source of evidence in a conflict handling log.
4. Perform double data entry by two persons for all fields and generate a discrepancy list. Have a third researcher check each item against the original records for adjudication and lock the final database version for analysis.
5. Establish a table of clinically reasonable ranges for continuous variables and perform automatic range checks after import. Trace back out-of-range values for verification; if unverifiable, set them to missing and retain the original value for reference.
6. Perform duplicate extraction for the first 50 cases and calculate consistency, using Cohen κ for categorical variables and ICC (two-way random effects, absolute agreement) for continuous variables. If κ < 0.80 or ICC < 0.85, conduct retraining for the related variables and re-audit until the standard is met11.
7. Calculate the number and proportion of missing cases for each candidate predictor variable and generate a missingness report. For key predictor variables with a missingness proportion not exceeding 30%, initiate multiple imputation; the primary outcome variable will not be imputed.
8. Multiple imputation by chained equations (MICE) implementation details
   1. Fix the number of imputed datasets at m = 10 and set the number of iterations to at least 20 to promote convergence. Set a random seed to ensure reproducibility of imputation and save the mice object for auditability.
   2. Use predictive mean matching (PMM) for continuous variables to preserve distributional form. Use logistic regression imputation for binary variables, multinomial logistic imputation for unordered multicategory variables, and ordinal logistic imputation for ordered categorical variables. In R, unordered multicategory variables were defined as nom_vars <- c("smoking_status","procedure_category","anesthesia_type",
      "position_type","table_padding_type","puncture_site_
      category","puncture_site_closure_method"), and ordered categorical variables were defined as ord_vars <- c("head_neck_rotation_cat","upper_limb_abduction_cat"); dat[nom_vars] <- lapply(dat[nom_vars], factor); dat[ord_vars] <- lapply(dat[ord_vars], ordered); meth <- mice::make.method(dat); meth[sapply(dat, is.numeric)] <- "pmm"; meth[names(dat)[sapply(dat, function(x) is.factor(x) && nlevels(x) == 2)]] <- "logreg"; meth[nom_vars] <- "polyreg"; meth[ord_vars] <- "polr"; and meth[c("inpatient_number","surgery_id","outcome")] <- "".
   3. Include all candidate predictor variables and the outcome variable in the imputation prediction matrix to improve imputation quality, but perform imputation only for predictor variables with missingness. Set identifier fields that should not act as predictors (e.g., inpatient number, unique surgery ID) to not participate in the imputation model to avoid introducing nonclinical information. To follow this protocol, implement the predictor matrix as pred <- mice::make.predictorMatrix(dat); pred[,] <- 1; diag(pred) <- 0; pred[, c("inpatient_number","surgery_id")] <- 0; pred[c("inpatient_number","surgery_id","outcome"), ] <- 0; and pred[setdiff(colnames(pred), c("inpatient_number","surgery_id","outcome")), "outcome"] <- 1, so that the outcome is used only as an auxiliary predictor and not imputed.
   4. Plot iteration trace plots and check whether the means and variances of key continuous variables are stable to assess imputation convergence. Imputation was run as imp <- mice::mice(dat, m = 10, maxit = 20, method = meth, predictorMatrix = pred, seed = 20250101, printFlag = FALSE); convergence was assessed with plot(imp) and by checking stability of the means and variances of key continuous variables across iterations.

5. Development of the nursing predictive assessment tool

1. Preprocessing and coding of candidate variables
   1. Set the reference level for procedure category to "neurovascular", and the reference level for position to "supine". Set the reference level for head–neck rotation to "≤30°", and the reference level for upper-limb abduction to "≤90°". Set the reference level for puncture site category to "radial artery", and the reference level for puncture-site closure method to "vascular closure device".
   2. Scale age per 10 years, operative duration per 60 min, fluoroscopy duration per 10 min, estimated blood loss per 100 mL, sheath dwell time per 60 min, and sheath size per 1 Fr. For variables hypothesized as "the lower the value, the higher the risk" (e.g., lowest core temperature, albumin, hemoglobin, Braden score, lowest MAP), apply reverse coding so that the regression OR represents "risk increase per unit decrease".
      NOTE: Reverse coding can be implemented by taking the negative of the original variable and scaling by the unit, for example, Temp_dec = -(Temp/0.5) or Braden_dec = -(Braden/1), to keep the OR interpretation direction consistent.
2. Univariable analysis and candidate variable screening
   1. Fit a univariable logistic regression model for each candidate predictor and record the OR, 95% CI, and P value. For categorical variables, use dummy coding with the reference level and output effect estimates of each level relative to the reference.
   2. Use restricted cubic splines to test linearity for continuous variables, using four knots placed at the 5%, 35%, 65%, and 95% quantiles. If the test indicates significant nonlinearity, retain the same spline form in the subsequent multivariable model.
   3. Take the union of "clinically prespecified variables" and "variables with univariable P<0.20" as the multivariable candidate set12. Compare the candidate set against the constraint of the number of events, and, when necessary, preferentially retain clinically highly relevant variables to control overfitting.
3. LASSO selection and construction of the main model
   1. Fit a penalized Logistic regression (LASSO, alpha=1) on the candidate set, with family="binomial". Standardize continuous variables (standardize=TRUE) to ensure fair penalization across variables with different scales.
   2. Perform ten-fold cross-validation repeated 1,000x, randomly generating fold partitions each time and recording the cross-validation deviance corresponding to lambda.min. Define the penalty strength as the median of lambda.min across the 1,000 repetitions (or, equivalently, the λ corresponding to the minimum mean deviance), and use this λ for the final LASSO fit to obtain a stable set of variables with nonzero coefficients.
   3. Using the variables with nonzero coefficients selected by LASSO, fit the non-penalized multivariable Logistic regression main model in the completed dataset. If spline terms are retained, fit them in the main model with the same spline form and record the corresponding coefficients or function terms.
   4. Fit the main model separately in the 10 imputed datasets and extract regression coefficients and standard errors. Combine the coefficients, standard errors, and confidence intervals using Rubin’s rules, and report the pooled OR and P value.
4. Risk scoring, nomogram, and risk stratification
   1. Linearly scale the main model regression coefficients to integer points according to the Sullivan method, fixing the base scale and rounding rules. Calculate the total score and map the total score to the predicted probability via the logit function; the mapping formula must be consistent with the intercept of the main model.
   2. Use the rms package to generate a nomogram based on the main model, and export it as a high-resolution image or PDF for publication. Apply versioned file naming to the nomogram outputs to avoid confusion caused by subsequent modifications.
   3. Define predicted probability <5% as low risk, 5% to 14.9% as intermediate risk, and ≥15% as high risk13. Fix the tool outputs to two formats—a one-page paper form and an electronic calculator—and limit the scoring process to no more than three steps to facilitate clinical use.

6. Model validation and performance evaluation

1. Internal validation and optimism correction
   1. Use the bootstrap for internal validation, and set the number of resamples to 1,000 to obtain stable estimates. For each resample, refit the model in the bootstrap sample, and compute performance metrics in both the bootstrap sample and the original sample to estimate optimism.
   2. Define optimism as the average difference between performance in the bootstrap sample and in the original sample. Subtract optimism from the apparent performance to obtain the corrected performance, and report the optimism-corrected AUC, calibration intercept, calibration slope, and Brier score.
2. Discrimination
   1. Plot ROC curves and calculate the AUC as the measure of discrimination. Estimate the AUC and its 95% CI using the DeLong method, and perform the calculations on the same prediction outputs (probabilities) to ensure comparability.
   2. At the predefined thresholds (5% and 15%), calculate sensitivity, specificity, PPV, and NPV. For each threshold, also output the confusion matrix counts to allow verification of the calculation.
3. Calibration and overall error
   1. Fit a calibration model with the model linear predictor (logit) as the independent variable; the intercept indicates systematic bias, and the slope indicates the degree of overfitting. Define ideal calibration as intercept = 0 and slope = 1, and report the metrics before and after correction.
   2. Calculate the Brier score as the measure of overall error, and plot calibration curves using loess smoothing alongside the 45° ideal line. The Brier score was calculated as mean((outcome - predicted_probability)2), and loess smoothing was implemented as stats::loess(outcome ~ predicted_probability, span = 0.75, degree = 2, family = "gaussian", surface = "direct").
4. Clinical net benefit and decision curves
   1. Use decision curve analysis to evaluate net benefit across threshold probabilities of 2% to 25%14, and plot Treat-All and Treat-None strategies as references. Fix the threshold step size at 0.01 and explicitly specify it in the script to ensure reproducibility.
   2. Perform sensitivity analysis of net benefit around the low-risk and high-risk thresholds, and record the direction of the effect of threshold changes on net benefit. Present the sensitivity analysis results alongside the predefined thresholds to verify their rationality.

7. Statistical implementation and reproducible script workflow

1. Software environment and project initialization
   1. Perform analyses in R 4.3.2 using the pROC, rms, glmnet, mice, and rmda packages.
   2. Set a fixed random seed (20250101) at the beginning of the script, and record session information to document the software environment.
2. Data import, type settings, and quality checks
   1. Import the data using read.csv("data/analysis.csv") or readr::read_csv(). Verify whether the row count corresponds one-to-one with the unique surgery identifier, and check for duplicate IDs.
   2. Convert categorical variables to factors and use relevel() to set reference levels to match the preset scheme. Keep continuous variables numeric, and apply unit scaling and reverse coding per the preset before modeling.
   3. For categorical variables, calculate frequencies and percentages; for continuous variables, calculate mean ± standard deviation or median [IQR]. Use graphical methods and shapiro.test() to assess distributional form, and determine the descriptive approach accordingly.
3. Missing data handling and multiple imputation (m = 10)
   1. Calculate the missingness proportion for each variable and export it as output/missing_report.csv. Confirm that the outcome variable has no missingness, and lock the strategy of “impute predictor variables only, do not impute the outcome.”
   2. Use mice() with m = 10 and maxit ≥ 20 to run chained equations imputation, and specify imputation methods according to variable types. Use complete(mids, action=k) to export the kth completed dataset and save it as data/imp_k.csv.
   3. Plot plot(mids) to check the stability of the traces, and compare the pre- and post-imputation distributions for key continuous variables. Save the diagnostic plots to output/imputation_diagnostics.pdf for verification.
4. Univariable regression, spline testing, and generation of the candidate set
   1. Fit univariable Logistic regression in each imputed dataset and use pool() to combine coefficients and standard errors. Export the OR, 95% CI, and P values as output/univariate_pool.csv for candidate screening records.
   2. Use the rms package rcs() to fit spline terms for continuous variables and test the nonlinear terms. Fix the number and locations of knots and record them in script comments to avoid differences across runs.
   3. Take the union of clinically prespecified variables and variables with univariable P<0.20 to generate the candidate list, and save it as output/candidate_list.txt. Check whether the number of candidates and the number of events satisfy the complexity constraint, and record the final variable set used for LASSO.
5. LASSO selection and main model fitting
   1. Use model.matrix() to generate the matrix of independent variables and ensure the reference levels are correct. Use cv.glmnet(x, y, family="binomial", alpha=1, standardize=TRUE) to perform ten-fold cross-validation.
   2. Use a loop to repeat random fold partitions 1000x, record lambda.min and the cross-validation error for each run, and write them to output/lasso_lambda_trace.csv. Take the median of the 1000 lambda.min values as the stable λ, and fit glmnet on the full dataset using this λ to obtain the variables with nonzero coefficients.
   3. In the 10 imputed datasets, fit the main model glm(family=binomial) using the selected variables. Combine β, SE, OR, and 95% CI using Rubin’s rules, and save the results as output/multivariable_pool.csv.
6. Score transformation, nomogram, and risk stratification outputs
   1. Convert β to integer points according to a fixed scale, and map the total score back to the logit and probability using the same scale. Randomly sample several cases to compare the “original model probability” with the “score-converted probability” to ensure the error is within the preset acceptable range.
   2. Use the rms package to construct the nomogram and export it as output/nomogram.pdf. Export the score sheet as output/score_sheet.xlsx or equivalent formats to facilitate production of a one-page paper form.
   3. Perform stratification using the 5% and 15% thresholds and output the number of cases and observed incidence in each stratum. Export Se, Sp, PPV, and NPV at the thresholds as output/threshold_metrics.csv for subsequent presentation.
7. Internal validation, calibration, and decision curves
   1. Set the number of bootstrap resamples to 1,000, and in each resample perform refitting and prediction, calculating AUC, Brier score, calibration intercept, and calibration slope. Write the optimism-corrected results to output/bootstrap_corrected_metrics.csv and retain a summary of intermediate results for each resample for audit.
   2. Use the pROC package roc() and auc() to calculate the AUC, and use ci.auc(method="delong") to output the 95% CI. Save the ROC curve as output/roc_curve.png and fix the resolution and size in the script.
   3. Use the rmda package to compute net benefit and plot DCA curves over the threshold range of 2% to 25%. Export the figure as output/dca_curve.png and fix the threshold step size and curve labels to ensure reproducibility.
8. Sensitivity analyses
   1. Replace the outcome with “pressure injury stage ≥3” and repeat the imputation, LASSO selection, main model fitting, and performance evaluation workflow. Output the performance metrics alongside the main analysis to assess robustness.
   2. Replace the hypotension exposure variable with “cumulative time with MAP<60 mmHg” and repeat the full modeling and validation workflow. Compare coefficient directions and changes in AUC, Brier score, and calibration metrics to test the sensitivity to the threshold setting.

Results

Study population and baseline characteristics
According to the prespecified criteria, 3,274 patient records from the hybrid DSA operating room were identified, and 1,936 (59.1%) met the inclusion criteria and entered model development and validation (Figure 1). The enrolled patients had an age of (66.82 ± 11.94) years; males accounted for 61.16%; BMI was (24.73 ± 3.89) kg/m²; diabetes mellitus accounted for 23.45%. The Charlson comorbidity index was 3 [2,5], t...

Discussion

In the hybrid DSA operating room, position-related complications include coexisting skin and soft-tissue injury and positioning-induced respiratory and hemodynamic instability, indicating that positional safety is not only a matter of local pressure but also reflects whole-body physiologic tolerance. Risk determinants derive from patient vulnerability and surgical exposure. Advanced age and a higher comorbidity burden indicate reduced tissue reserve; a lower Braden score reflects reduced skin tolerance and limited mobili...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Anesthesia information management system	Institutional source	N/A	Source of intraoperative vital signs, vasoactive agents, MAP time series, and anesthesia records.
Electronic medical record system	Institutional source	N/A	Source of demographics, comorbidities, operative records, and follow-up clinical documentation.
glmnet	CRAN	N/A	R package used for penalized logistic regression and LASSO variable selection.
Hybrid digital subtraction angiography operating room	Institutional source	N/A	Study setting for all included procedures; equipped for interventional imaging and open surgery.
Laboratory information system	Institutional source	N/A	Source of preoperative laboratory indicators, including albumin and hemoglobin.
mice	CRAN	N/A	R package used for multiple imputation by chained equations.
Nursing information system	Institutional source	N/A	Source of positioning records, pressure-point protection, intraoperative position checks, and postoperative nursing documentation.
Picture archiving and communication system (PACS)	Institutional source	N/A	Source of fluoroscopy duration and imaging-related information.
pROC	CRAN	N/A	R package used for ROC analysis, AUC estimation, and DeLong confidence intervals.
R	R Foundation for Statistical Computing	N/A	Statistical analysis software environment.
rmda	CRAN	N/A	R package used for decision curve analysis.
rms	CRAN	N/A	R package used for regression modeling strategies, spline functions, and nomogram construction.