A Reproducible Admission Biochemical-Coagulation Scoring Protocol for Multiple Organ Dysfunction Syndrome in Acute Pancreatitis

Summary

Here, we present a protocol for cohort assembly, laboratory harmonization, penalized model development, validation, and bedside score construction for early risk stratification of multiple organ dysfunction syndrome in acute pancreatitis.

Abstract

Early recognition of multiple organ dysfunction syndrome in acute pancreatitis remains difficult because organ deterioration may occur before conventional severity assessments are complete. This article describes a reproducible protocol for developing, validating, and applying an admission biochemical-coagulation composite score based on routine laboratory tests obtained within 24 h of hospital admission. Multiple organ dysfunction syndrome was selected as the primary endpoint because it reflects clinically consequential dysfunction across more than one organ system and directly informs early monitoring intensity, escalation planning, and critical care resource allocation. The protocol covers multicenter cohort assembly, eligibility screening, endpoint adjudication, laboratory unit harmonization, missing-data processing, predictor scaling, penalized regression model development, hold-out validation, score construction, benchmarking against established severity scores, and sensitivity analyses. Continuous laboratory variables are retained during model development to reduce information loss, whereas clinically interpretable categories are used only when translating the final model into a practical bedside score. Procedural checkpoints include confirmation of cohort assembly, missingness thresholds, locked preprocessing rules, model-selection outputs, validation outputs, and final score interpretation. Using this workflow, routinely measured biochemical and coagulation markers can be integrated into a structured admission risk-stratification tool for acute pancreatitis-associated multiple organ dysfunction syndrome. The score is intended to support closer observation, repeated laboratory assessment, and preparation for organ-support pathways, but it should complement rather than replace clinical judgment or established severity frameworks.

Introduction

Acute pancreatitis is a common digestive emergency with a clinical course that ranges from mild, self-limiting inflammation to rapidly progressive disease complicated by organ dysfunction. Current severity assessment is usually guided by the revised Atlanta classification, which defines disease severity according to local complications, systemic complications, and the presence and duration of organ failure¹. Bedside scoring systems are also used during early hospitalization to support risk assessment and escalation decisions. The Bedside Index for Severity in Acute Pancreatitis uses five variables available within the first 24 h and was developed to estimate the risk of in-hospital mortality². Recent clinical guidelines continue to emphasize early recognition of severe disease, repeated reassessment, appropriate fluid resuscitation, organ-support planning, and timely escalation of care³.

Early risk assessment remains difficult in routine practice. The inflammatory response in acute pancreatitis changes rapidly, and some traditional scoring systems require repeated measurements, physiologic variables that evolve after admission, or imaging findings that may not be available at the first clinical encounter⁴. As a result, risk classification may vary with sampling time, local workflow, and treatment pathway. These uncertainties can lead to unnecessary intensive monitoring or intervention in low-risk patients, while patients who deteriorate quickly may experience delayed escalation. A recent review of severity prediction in acute pancreatitis noted that the performance of clinical scores and individual biomarkers varies across patient populations, disease severity distributions, and assessment windows⁵.

Most recent prediction studies have attempted to use variables collected within 24 h of admission, often with persistent organ failure or severe acute pancreatitis as the target outcome. This approach is clinically useful, but many published models remain limited by single-center design, incomplete validation, and insufficient reporting of preprocessing and missing-data methods⁶. Transparent reporting is particularly important for prediction models because eligibility criteria, predictor definitions, sample size, missing-data handling, model-building decisions, and validation procedures directly affect reproducibility and clinical interpretation⁷. For an admission-based method to be usable across institutions, it should rely on routinely available tests, define unit conversion procedures, explain how laboratory values are harmonized, and specify how incomplete predictor values are handled before model fitting.

Biochemical and coagulation abnormalities are both relevant to early deterioration in acute pancreatitis. Inflammatory activation, endothelial injury, hemoconcentration, renal hypoperfusion, and microcirculatory disturbance may occur early and contribute to organ dysfunction. Coagulation activation is closely linked with inflammation, and acute pancreatitis-associated coagulopathy has been described as part of the progression toward persistent organ failure and adverse outcomes⁸. D-dimer has also been reported as an admission marker associated with organ failure, local complications, intensive care admission, and mortality in acute pancreatitis⁹. However, coagulation indicators are often interpreted separately rather than combined with routine biochemical variables in a structured admission workflow.

Multiple organ dysfunction syndrome was selected as the primary endpoint in this protocol because it represents clinically consequential dysfunction across more than one organ system. Persistent organ failure remains central to acute pancreatitis severity classification, but multiple organ dysfunction syndrome is closer to the practical admission question: whether a patient is likely to require closer surveillance, repeated laboratory reassessment, organ-support preparation, or intensive care resources. This endpoint also allows biochemical and coagulation signals to be evaluated in relation to a broader pattern of systemic deterioration rather than a single organ-based outcome.

This method's article describes a reproducible admission biochemical-coagulation scoring protocol for early risk stratification of multiple organ dysfunction syndrome in acute pancreatitis. The protocol uses routine laboratory variables obtained within 24 h of admission, standardizes unit harmonization and missing-data handling, applies penalized model development, and translates the final model into a clinically interpretable composite score. The goal is not to replace clinical judgment or established severity frameworks, but to provide a transparent, replicable workflow that helps institutions organize admission laboratory data into actionable risk information.

Protocol

The study protocol was approved by the Ethics Committee of Biomedical Research Involving Humans of The First People’s Hospital of Jiashan, Jiashan Hospital Affiliated to Jiaxing University (approval no. 2026 Research Approval No. 005; acceptance no. LW2026004; approval date: January 13, 2026). The requirement for individual informed consent was waived because this retrospective study used de-identified clinical data. Before analysis, direct identifiers, including name, hospital identification number, telephone number, address, and national identification number, were removed. Data handling followed institutional data-protection requirements and the Declaration of Helsinki¹⁰.

Study design and multicenter cohort assembly
This retrospective multicenter cohort study used admission biochemical and coagulation data to construct a reproducible scoring workflow for early risk stratification of multiple organ dysfunction syndrome (MODS) in acute pancreatitis (AP). AP was defined by at least two of the following criteria: typical abdominal pain, serum amylase or lipase at least three times the upper limit of normal, or imaging findings consistent with AP. The baseline window was defined as the first 24 h after hospital admission. For each laboratory predictor, the earliest eligible value within this window was used. Values obtained after the first documented onset of MODS were not used for predictor construction.

Consecutive AP hospitalizations from the participating centers from January 1, 2023, through December 20, 2025, were screened using a unified screening log, eligibility checklist, data dictionary, and endpoint adjudication form. The screening log recorded total screened admissions, duplicate admissions, exclusions for unconfirmed AP, age younger than 18 years, chronic pancreatitis, pancreatic malignancy-associated pancreatitis, unreliable admission time, unavailable 24 h laboratory data, incomplete endpoint records, and the final analytic cohort. The locked screening log was used to generate Figure 1. The patient counts in Figure 1 were checked against the final analytic dataset before analysis.

After the analytic cohort was locked, patients were assigned to a development cohort and a hold-out validation cohort at a 7:3 ratio. The split was performed in R version 4.3.2 using the caret package version 6.0-94, with the random seed fixed at 20260420. Stratified sampling was performed by MODS status and center. If the center distribution differed by more than 5 percentage points between cohorts, center-MODS combined strata were used. Cohort allocation was saved before model fitting and was not modified afterward. Preprocessing rules, imputation settings, model tuning, coefficient estimation, score construction, and cutoff selection were derived solely from the development cohort. The hold-out validation cohort was used only for locked-model evaluation.

Eligibility criteria and enrollment procedures
Eligible patients were adults aged 18 years or older with confirmed AP, traceable hospital admission time, at least one biochemical or coagulation laboratory panel within 24 h after admission, and complete in-hospital records for endpoint adjudication. Patients were excluded if AP could not be confirmed, if chronic pancreatitis or pancreatic malignancy-associated pancreatitis was present, or if the time of the first hospital admission could not be reliably reconstructed. Transfer cases were excluded when the first admission time, the first laboratory sampling time, or the early organ dysfunction status were not traceable across institutions. For repeated admissions by the same patient during the study period, only the first eligible hospitalization was retained. One primary exclusion reason was recorded for each excluded case.

Outcomes and endpoint adjudication
The primary outcome was in-hospital MODS, defined as dysfunction of two or more organ systems during hospitalization. Organ dysfunction was assessed from respiratory, cardiovascular, renal, hepatic, coagulation, and neurological records in the electronic medical record. The same prespecified organ-system criteria were applied across centers before model development, and the operational criteria used for endpoint adjudication are summarized in Supplementary Table 1.

Two trained reviewers independently assessed each de-identified record using admission notes, progress notes, intensive care unit records, procedure records, laboratory reports, vital-sign records, and discharge summaries. The adjudication form recorded patient study code, involved organ system, source record, onset date and time, supporting laboratory or physiological value, reviewer decision, and final adjudication status. Agreement between reviewers was accepted as final. Disagreements were resolved by a third senior reviewer. Secondary outcomes included intensive care unit admission, invasive or non-invasive mechanical ventilation, renal replacement therapy, in-hospital mortality, infected necrosis or drainage procedure, and length of hospital stay. Length of stay was calculated as the discharge date minus the admission date plus 1 day.

Candidate predictors at admission
Candidate predictors were restricted to routine variables available within 24 h after admission. For repeated tests within the 24 h window, the earliest eligible value was retained. Biochemical variables included white blood cell count, C-reactive protein, procalcitonin when available, lactate, glucose, total calcium, blood urea nitrogen, creatinine, alanine aminotransferase, total bilirubin, albumin, and lactate dehydrogenase. Coagulation variables included platelet count, prothrombin time, prothrombin time-international normalized ratio, activated partial thromboplastin time, fibrinogen, and D-dimer.

Baseline clinical variables were collected for case-mix description and benchmark-score calculation. These variables included age, sex, body mass index, time from symptom onset to admission, AP etiology, smoking status, previous pancreatitis, comorbidities, admission heart rate, mean arterial pressure, systemic inflammatory response syndrome status, Bedside Index for Severity in Acute Pancreatitis score, Acute Physiology and Chronic Health Evaluation II score, and Sequential Organ Failure Assessment score. When both prothrombin time and prothrombin time-international normalized ratio were available, prothrombin time-international normalized ratio was used for modeling, and prothrombin time was retained for descriptive reporting. The extraction file contained one row per hospitalization and retained center code, patient study code, admission time, sampling time, raw value, raw unit, harmonized value, harmonized unit, source table, and missingness flag.

Laboratory harmonization across centers
All laboratory variables were converted to prespecified target units before analysis: white blood cell count and platelet count, 10⁹/L; C-reactive protein, mg/L; procalcitonin, ng/mL; lactate, glucose, total calcium, and blood urea nitrogen, mmol/L; creatinine and total bilirubin, µmol/L; alanine aminotransferase and lactate dehydrogenase, U/L; albumin and fibrinogen, g/L; prothrombin time and activated partial thromboplastin time, seconds; prothrombin time-international normalized ratio, unitless ratio; and D-dimer, mg/L fibrinogen-equivalent units. D-dimer values reported as µg/mL fibrinogen-equivalent units were converted to mg/L fibrinogen-equivalent units using a 1:1 numeric conversion. D-dimer values reported as D-dimer units were converted only when the local laboratory provided a validated conversion factor. Values without a validated conversion factor were treated as missing for modeling.

Each center’s reporting units, reference ranges, assay records, and analyzer notes were reviewed before pooling. The harmonization file retained the original unit, target unit, conversion rule, and reviewer responsible for checking the conversion. Laboratory values were not normalized by center-specific reference ranges in the primary analysis because the score was intended to use absolute clinical values. Center code was retained during imputation and sensitivity analysis. Negative values for non-negative tests, incompatible units, timestamps outside the 24 h window, and values unsupported by source records were flagged. A value was corrected only when the source record confirmed a transcription or unit-conversion error; otherwise, it was set to missing.

Data preprocessing and missing-data handling
Missingness was summarized by variable, center, and MODS status before model fitting. Candidate predictors were excluded from model development if overall missingness exceeded 30% or if missingness exceeded 50% in any single center. Patients were excluded from the modeling dataset if more than 40% of candidate predictors were missing or if the primary outcome could not be adjudicated.

Continuous predictors were winsorized at the 1st and 99th percentiles estimated in the development cohort. The same thresholds were applied to the hold-out validation cohort. Thresholds were not re-estimated in validation data. Variables with skewness greater than 2 after winsorization were transformed using the natural logarithm; log(x + 0.01) was used for variables that could contain zero. Continuous predictors were standardized using the development-cohort mean and standard deviation, and the same parameters were applied to validation data.

Multiple imputation was performed using chained equations in R with the mice package version 3.16.0. Twenty imputed datasets were generated with 20 iterations per dataset. Predictive mean matching was used for continuous variables, logistic regression for binary variables, and polytomous logistic regression for nominal categorical variables. The imputation model included candidate predictors, center code, cohort indicator, and MODS status for model development. Validation outcomes were not used to modify preprocessing rules, variable eligibility, or thresholds. Regression estimates were pooled using Rubin’s rules when reported. Prediction metrics were calculated within each imputed dataset and summarized as pooled or median estimates with ranges. Two sensitivity analyses were prespecified: complete-case analysis using patients without missing final-model predictors, and baseline-value analysis using the worst eligible laboratory value within 24 h instead of the earliest value.

Statistical analysis and model development
Baseline characteristics were summarized separately in the development and hold-out validation cohorts. Continuous variables were reported as mean ± standard deviation or median with interquartile range, and categorical variables as n (%). Admission biochemical and coagulation variables were compared between patients with and without MODS. Student’s t-test or Wilcoxon rank-sum test was used for continuous variables, and chi-square or Fisher’s exact test was used for categorical variables. These P values were descriptive and were not used alone for predictor selection.

Model development was restricted to the development cohort. Penalized logistic regression was used as the primary model to reduce overfitting in the presence of limited event counts and correlated laboratory predictors. The model was fitted in R version 4.3.2 using the glmnet package version 4.1-8 with a binomial family, standardized predictors, and least absolute shrinkage and selection operator (LASSO) penalization, specified by alpha = 1. Ten-fold cross-validation repeated 10 times was used for penalty tuning, with the random seed fixed at 20260420. The penalty parameter was selected using the one-standard-error rule. The coefficient path, cross-validation error values, selected penalty parameter, and retained predictors were saved. The analysis workflow is shown in Figure 2. The software, statistical packages, database systems, and laboratory platforms used in the protocol are listed in the Table of Materials.

Unpenalized logistic regression was not used as the primary inferential model after penalized selection. If odds ratios were reported for clinical interpretation, they were labeled as descriptive post-selection estimates. Discrimination was estimated using the area under the receiver operating characteristic curve, with 95% confidence intervals calculated using the DeLong method in the pROC package version 1.18.5. Calibration was assessed using calibration intercept, calibration slope, Brier score, and calibration plots. Bootstrap resampling with 1,000 repetitions was used in the development cohort to estimate optimism-corrected performance. Decision-curve analysis was not retained in the revised analysis set; therefore, clinical net-benefit claims were removed from the manuscript.

Score derivation and interpretation
The point-based admission biochemical-coagulation composite score was derived from the locked penalized model coefficients. Continuous variables were used during model fitting. Categorized thresholds were introduced only after model finalization to support bedside calculation. Thresholds were selected according to established clinical reference ranges, commonly used laboratory abnormality thresholds, or coefficient-based cut points defined in the development cohort before validation. Thresholds were not optimized in the validation cohort.

Integer points were assigned according to the direction and relative magnitude of the locked coefficients. Higher points indicated more adverse laboratory profiles. The total score was calculated by summing points across retained components. Prespecified risk categories were used solely for risk stratification; they were not used to define MODS. The score was intended to identify patients who may need closer observation, repeated laboratory assessment, or preparation for organ-support pathways. It was not used as a standalone diagnostic rule, an anticoagulation trigger, or a substitute for clinician reassessment.

Hold-out validation and benchmarking
The locked preprocessing rules, imputation structure, winsorization thresholds, standardization parameters, model coefficients, score rules, and prespecified cutoffs were applied to the hold-out validation cohort. No further predictor screening, model refitting, coefficient recalibration, or threshold optimization was performed. Validation performance was summarized using discrimination, calibration, Brier score, sensitivity, specificity, positive predictive value, and negative predictive value.

Established severity tools were calculated from the same admission window when data were available, including the Bedside Index for Severity in Acute Pancreatitis, Acute Physiology and Chronic Health Evaluation II score, Sequential Organ Failure Assessment score, Ranson score, Harmless Acute Pancreatitis Score, and systemic inflammatory response syndrome criteria. Comparisons used the same endpoint, analysis population, and validation framework. Paired area-under-curve comparisons were performed using DeLong tests.

All validation plots and score-stratified plots were generated from the locked analytic dataset. The sample size in each figure panel was checked against the final analytic cohort, the development cohort, the hold-out validation cohort, or the prespecified score stratum before figure preparation. Visual methods designed for high-dimensional omics, ecological count data, or differential-abundance analysis were not used for routine clinical laboratory data.

Results

Cohort characteristics and baseline comparability
A total of 300 patients with acute pancreatitis were included in the final analytic cohort. According to the prespecified 7:3 stratified hold-out split, 210 patients were allocated to the development cohort and 90 patients to the hold-out validation cohort. All included patients had eligible biochemical and coagulation measurements within 24 h after hospital admission.

Baseline characteristics are shown in Table 1

Discussion

Acute pancreatitis-associated multiple organ dysfunction syndrome (MODS) may evolve within a short period after hospital admission, particularly in patients with early systemic inflammation, endothelial injury, impaired tissue perfusion, and metabolic instability. The present protocol was designed around this admission window because the first 24 h often determine whether a patient remains under routine ward observation or requires closer reassessment, increased monitoring, or preparation for organ-support pathways. The ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Name	Company Name	Catalog Number / URL	Comments
R statistical software	R Foundation for Statistical Computing	Version 4.3.2 https://www.r-project.org/	Statistical computing environment used for data preprocessing, model development, validation, and figure generation.
caret R package	CRAN / Max Kuhn	Version 6.0-94 https://cran.r-project.org/package=caret	Used for the stratified 7:3 development and hold-out validation split with createDataPartition.
glmnet R package	CRAN / glmnet authors	Version 4.1-8 https://cran.r-project.org/package=glmnet	Used for LASSO penalized logistic regression with binomial family and alpha = 1.
mice R package	CRAN / amices project	Version 3.16.0 https://cran.r-project.org/package=mice	Used for multiple imputation by chained equations with 20 imputed datasets and 20 iterations.
pROC R package	CRAN / pROC authors	Version 1.18.5 https://cran.r-project.org/package=pROC	Used for ROC analysis, AUC estimation, confidence intervals, and DeLong comparisons.
cobas 8000 modular analyzer series	Roche Diagnostics	05641446001 / SYS_128 https://diagnostics.roche.com/global/en/products/systems/cobas-8000-analyzer-series-sys-128.html	Biochemical and immunochemical assays. Verify the analyzer model and catalog information against the participating laboratory records before submission.
XN-1000 Automated Hematology Analyzer	Sysmex Corporation	Model XN-1000 https://www.sysmex.com/en-us/lab-solutions/hematology/xn-series/xn-1000	Complete blood count variables, including white blood cell and platelet counts. Verify against the participating laboratory records before submission.
CS-5100 Automated Blood Coagulation Analyzer	Sysmex Corporation	Model CS-5100 https://www.sysmex.com/en-us/lab-solutions/hemostasis/sysmex-cs-5100	Coagulation variables, including PT, APTT, fibrinogen, and D-dimer. Verify against the participating laboratory records before submission.
ABL90 FLEX PLUS blood gas analyzer	Radiometer Medical ApS	Model ABL90 FLEX PLUS https://www.radiometer.com/en/products/blood-gas-testing/abl90-flex-plus-blood-gas-analyzer	Lactate measurement, if this platform was used. Verify against the participating laboratory records before submission.
Electronic medical record system	Participating hospitals	Institution-specific; not applicable	Used to retrieve admission notes, progress notes, ICU records, procedures, vital signs, and discharge records. Replace with the actual vendor and system name when available.
Laboratory information system	Participating hospitals	Institution-specific; not applicable	Used to retrieve laboratory timestamps, raw values, units, and harmonized data. Replace with the actual vendor and system name when available.
Endpoint adjudication form	Study team	Custom study document; not applicable	Standardized form used by two reviewers and a third senior reviewer to adjudicate MODS status and onset.
Data dictionary and unit-conversion table	Study team	Custom study document; not applicable	Defines variable names, target units, conversion rules, missingness flags, center codes, and analysis-ready formats.