Evidence map›Paper›PMID 33955409›Full record

ArticleDiseases of the colon and rectum2021

On All Accounts: Cost-Effectiveness Analysis of Limited Preoperative Optimization Efforts Before Colon Cancer Surgery.

Ira L Leeds, Emmanuel F Drabo, Lisa Soleymani Lehmann, Bashar Safar, Fabian M Johnston

Open access · greenAbstract readVideo-Audio Media
In one paragraph

Article in Diseases of the colon and rectum, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 14 citations in OpenAlex.

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  6. For whom the bell tolls: assessing the incremental costs associated with failure to rescue after elective colorectal surgery.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Ira L LeedsDepartment of Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland.
Emmanuel F DraboDepartment of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland.
Lisa Soleymani LehmannVA New England Healthcare System, Harvard Medical School, and Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Bashar SafarDepartment of Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland.
Fabian M JohnstonDepartment of Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland.
Johns Hopkins University · USHarvard University · USJohns Hopkins Medicine · US

Funding

Clinical and Laboratory Research Training for Surgical OncologistsT32CA126607 · NCI · JOHNS HOPKINS UNIVERSITY · PI CAMERON, ANDREW M. · 2008 to 2024
$6.0M
Promoting Early Utilization of Palliative Care for African Americans Using Culturally Informed Patient NavigationK08HS024736 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI JOHNSTON, FABIAN M · 2016 to 2020
$738k
AHRQ HHS K08 HS024736NCI NIH HHS T32 CA126607
6 · The paper itself

Abstract

backgroundReports suggest that preoperative optimization of a patient's serious comorbidities is associated with a reduction in postoperative complications.

objectiveThe purpose of this study was to assess the cost and benefits of preoperative optimization, accounting for total costs associated with postoperative morbidity.

designThis study is a decision tree cost-effectiveness analysis with probabilistic sensitivity analysis (10,000 iterations).

settingThis is a hypothetical scenario of stage II colon cancer surgery. PATIENT: The simulated 65-year-old patient has left-sided, stage II colon cancer.

interventionFocused preoperative optimization targets high-risk comorbidities. OUTCOMES: Total discounted (3%) economic costs (US $2018), effectiveness (quality-adjusted life-years), incremental cost-effectiveness ratio (incremental cost-effectiveness ratio, cost/quality-adjusted life-years gained), and net monetary benefit.

resultsWe calculated the per individual expected health care sector total cost of preoperative optimization and sequelae to be $12,395 versus $15,638 in those not optimized (net monetary benefit: $1.04 million versus $1.05 million). A nonoptimized patient attained an average 0.02 quality-adjusted life-years less than one optimized. Thus, preoperative optimization was the dominant strategy (lower total costs; higher quality-adjusted life-years). Probabilistic sensitivity analysis demonstrated 100% of simulations favoring preoperative optimization. The breakeven cost of optimization to remain cost-effective was $6421 per patient. LIMITATIONS: Generalizability must account for the lack of standardization among existing preoperative optimization efforts, and decision analysis methodology provides guidance for the average patient or general population, and is not patient-specific.

conclusionsAlthough currently not comprehensively reimbursed, focused preoperative optimization may reduce total costs of care while also reducing complications from colon cancer surgery. See Video Abstract at http://links.lww.com/DCR/B494. EN TODO CASO ANLISIS DE RENTABILIDAD DE LOS ESFUERZOS LIMITADOS DE OPTIMIZACIN PREOPERATORIA ANTES DE LA CIRUGA DE CNCER DE COLON: ANTECEDENTES:Los informes sugieren que la optimización preoperatoria de las comorbilidades graves de un paciente se asocia con una reducción de las complicaciones postoperatorias.OBJETIVO:El propósito de este estudio fue evaluar el costo y los beneficios de la optimización preoperatoria, teniendo en cuenta los costos totales asociados con la morbilidad postoperatoria.DISEÑO:Análisis de costo-efectividad de árbol de decisión con análisis de sensibilidad probabilístico (10,000 iteraciones).AJUSTE ENTORNO CLINICO:Escenario hipotético Cirugía de cáncer de colon en estadio II.PACIENTE:Paciente simulado de 65 años con cáncer de colon en estadio II del lado izquierdo.INTERVENCIÓN:Optimización preoperatoria enfocada dirigida a comorbilidades de alto riesgo.RESULTADOS:Costos económicos totales descontados (3%) (US $ 2018), efectividad (años de vida ajustados por calidad [AVAC]), relación costo-efectividad incremental (ICER, costo / AVAC ganado) y beneficio monetario neto (NMB).RESULTADOS:Calculamos que el costo total esperado por sector de atención médica individual de la optimización preoperatoria y las secuelas es de $ 12,395 versus $ 15,638 en aquellos no optimizados (NMB: $ 1.04 millones versus $ 1.05 millones, respectivamente). Un paciente no optimizado alcanzó un promedio de 0.02 AVAC menos que uno optimizado. Por lo tanto, la optimización preoperatoria fue la estrategia dominante (menores costos totales; mayores AVAC). El análisis de sensibilidad probabilístico demostró que el 100% de las simulaciones favorecían la optimización preoperatoria. El costo de equilibrio de la optimización para seguir siendo rentable fue de $ 6,421 por paciente.LIMITACIONES:La generalización debe tener en cuenta la falta de estandarización entre los esfuerzos de optimización preoperatorios existentes y esa metodología de análisis de decisiones proporciona una guía para el paciente promedio o la población general, no específica del paciente.CONCLUSIONES:Si bien actualmente no se reembolsa de manera integral, la optimización preoperatoria enfocada puede reducir los costos totales de la atención y al mismo tiempo reducir las complicaciones de la cirugía de cáncer de colon. Consulte Video Resumen en http://links.lww.com/DCR/B494.

Indexed as

AgedColonic NeoplasmsComorbidityCost-Benefit AnalysisDecision Support TechniquesHealth Care CostsHumansNeoplasm StagingPatient SimulationPostoperative ComplicationsPostoperative PeriodPreoperative CarePreoperative ExerciseQuality-Adjusted Life YearsSensitivity and Specificity

Identifiers

PMID33955409
PMCPMC8835996
OpenAlexW3128664378

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.