A Validated Prognostic Model and Nomogram to Predict Early-Onset Complications Leading to Surgery in Patients With Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND: Predicting aggressive Crohn's disease is crucial for determining therapeutic strategies. OBJECTIVE: We aimed to develop a prognostic model to predict complications leading to surgery within 1 year after diagnosis of Crohn's disease and to create a nomogram to facilitate clinical decision making. DESIGN: This is a retrospective study. SETTING: This study was conducted from January 2012 to December 2016 in a single tertiary IBD center. PATIENTS: Patients diagnosed with Crohn's disease showing B1 behavior according to the Montreal classification were included. MAIN OUTCOME MEASURES: We measured the occurrence of complications that would ultimately lead to surgery, including severe GI bleeding (Glasgow-Blatchford score ≥6), stenosis, and perforations, confirmed by endoscopy, CT scan, and/or interventional radiology. RESULTS: The mean follow-up period was 54 months (SD 13 months). Of the 614 eligible patients, 13.5% developed complications leading to surgery. Multivariable logistic regression revealed the independent predictors of early-onset complications to be age (adjusted odds ratio per 10-year increase in age = 0.4; 95% CI, 0.2-0.8; p = 0.004), disease duration (adjusted odds ratio = 2.7, 95% CI, 1.9-3.8; p < 0.001), perianal disease (adjusted odds ratio = 16.0; 95% CI, 4.3-59.9; p < 0.001), previous surgery (adjusted odds ratio = 3.7; 95% CI, 1.6-8.6; p = 0.003), and extraintestinal manifestations (adjusted odds ratio = 7.6; 95% CI, 2.3-24.9; p = 0.001). The specificity and sensitivity of the prognostic model were 88.3% (95% CI, 84.8%-91.2%) and 96.6% (95% CI, 88.1%-99.6%), and the area under the curve was 0.97 (95% CI, 0.95-0.98). This model was validated with good discrimination and excellent calibration using the Hosmer-Lemeshow goodness-of-fit test. A nomogram was created to facilitate clinical bedside practice. LIMITATIONS: This was a retrospective design and included a small sample size from 1 center. CONCLUSIONS: Our validated prognostic model effectively predicted early-onset complications leading to surgery and screened aggressive Crohn's disease, which will enable physicians to customize therapeutic strategies and monitor disease. See Video Abstract at http://links.lww.com/DCR/B442.Registered at Chinese Clinical Trial Registry (ChiCTR1900025751). UN MODELO DE PRONSTICO VALIDADO Y UN NOMOGRAMA PARA PREDECIR COMPLICACIONES PRECOCES QUE REQUIRAN CIRUGA EN PACIENTES CON ENFERMEDAD DE CROHN: ANTECEDENTES:Predecir una enfermedad de Crohn muy agresiva es fundamental para determinar la estrategia terapéutica.OBJETIVO:Desarrollar un modelo de pronóstico para predecir las complicaciones que requieran cirugía dentro el primer año al diagnóstico de enfermedad de Crohn y crear un nomograma para facilitar la toma de decisiones clínicas.DISEÑO:El presente etudio es retrospectivo.AJUSTE:Estudio realizado entre Enero 2012 y Diciembre 2016, en un único centro terciario de tratamiento de enfermedad inflamatoria intestinal.PACIENTES:Se incluyeron todos aquellos pacientes diagnosticados de enfermedad de Crohn que mostraban manifestaciones tipo B1 según la clasificación de Montreal.PRINCIPALES MEDIDAS DE RESULTADO:Medimos la aparición de complicaciones que finalmente conducirían a una cirugía, incluida la hemorragia digestiva grave (puntuación de Glasgow-Blatchford ≥ 6), estenosis y perforaciones, confirmadas por endoscopía, tomografía computarizada y / o radiología intervencionista.RESULTADOS:El período medio de seguimiento fue de 54 meses (desviación estándar 13 meses). De los 614 pacientes elegibles, el 13,5% desarrolló complicaciones que llevaron a cirugía. La regresión logística multivariable reveló que los predictores independientes de complicaciones de inicio temprano eran la edad (razón de probabilidades ajustada [ORa] por aumento de 10 años en la edad = 0,4; intervalos de confianza del 95% [IC del 95%]: 0,2-0,8, p = 0,004), duración de la enfermedad (ORa = 2,7, IC del 95%: 1,9-3,8, p <0,001), enfermedad perianal (ORa = 16,0, IC del 95%: 4,3-59,9, p <0,001), cirugía previa (ORa = 3,7, 95% IC: 1,6-8,6, p = 0,003) y manifestaciones extraintestinales (ORa = 7,6, IC del 95%: 2,3-24,9, p = 0,001). La especificidad y sensibilidad del modelo pronóstico fueron 88,3% (IC 95%: 84,8% -91,2%) y 96,6% (IC 95%: 88,1% -99,6%), respectivamente, y el área bajo la curva fue 0,97 (95% % CI: 0,95-0,98). Este modelo fue validado con buena discriminación y excelente calibración utilizando la prueba de bondad de ajuste de Hosmer-Lemeshow. Se creó un nomograma para facilitar la práctica clínica al pié de la cama.LIMITACIONES:Diseño retrospectivo que incluyó un tamaño de muestra pequeña en un solo centro.CONCLUSIONES:Nuestro modelo de pronóstico validado predijo eficazmente las complicaciones precoces que conllevaron a cirugía y la detección de enfermedad de Crohn agresiva, lo que permitió a los médicos personalizar las estrategias terapéuticas y controlar la enfermedad. Consulte Video Resumen en http://links.lww.com/DCR/B442.Registrado en el Registro de Ensayos Clínicos de China (ChiCTR1900025751).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".