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Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): An approach to care stratification.

ABSTRACT: Ascertaining which patients are at highest risk of poor postoperative outcomes could improve care and enhance safety. This study aimed to construct and validate a propensity index for 30-day postoperative mortality. A retrospective cohort study was conducted at Hospital de Clínicas de Porto Alegre, Brazil, over a period of 3 years. A dataset of 13524 patients was used to develop the model and another dataset of 7254 was used to validate it. The primary outcome was 30-day in-hospital mortality. Overall mortality in the development dataset was 2.31% [n = 311; 95% confidence interval: 2.06-2.56%]. Four variables were significantly associated with outcome: age, ASA class, nature of surgery (urgent/emergency vs elective), and surgical severity (major/intermediate/minor). The index with this set of variables to predict mortality in the validation sample (n = 7253) gave an AUROC = 0.9137, 85.2% sensitivity, and 81.7% specificity. This sensitivity cut-off yielded four classes of death probability: class I, <2%; class II, 2-5%; class III, 5-10%; class IV, >10%. Model application showed that, amongst patients in risk class IV, the odds of death were approximately fivefold higher (odds ratio 5.43, 95% confidence interval: 2.82-10.46) in those admitted to intensive care after a period on the regular ward than in those sent to the intensive care unit directly after surgery. The SAMPE (Anaesthesia and Perioperative Medicine Service) model accurately predicted 30-day postoperative mortality. This model allows identification of high-risk patients and could be used as a practical tool for care stratification and rational postoperative allocation of critical care resources.

SUBMITTER: Stefani LC

PROVIDER: S-EPMC5662221 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): An approach to care stratification.

Stefani Luciana Cadore LC Gutierrez Claudia De Souza CS Castro Stela Maris de Jezus SMJ Zimmer Rafael Leal RL Diehl Felipe Polgati FP Meyer Leonardo Elman LE Caumo Wolnei W

PloS one 20171030 10

Ascertaining which patients are at highest risk of poor postoperative outcomes could improve care and enhance safety. This study aimed to construct and validate a propensity index for 30-day postoperative mortality. A retrospective cohort study was conducted at Hospital de Clínicas de Porto Alegre, Brazil, over a period of 3 years. A dataset of 13524 patients was used to develop the model and another dataset of 7254 was used to validate it. The primary outcome was 30-day in-hospital mortality. O ...[more]

PMID: 29084236

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Project description:Patients with critical limb ischemia (CLI) are a heterogeneous population with respect to risk for mortality and limb loss, complicating clinical decision-making. Endovascular options, compared with bypass, offer a tradeoff between reduced procedural risk and inferior durability. Risk stratified data predictive of amputation-free survival (AFS) may improve clinical decision making and allow for better assessment of new technology in the CLI population.This was a retrospective analysis of prospectively collected data from patients who underwent infrainguinal vein bypass surgery for CLI. Two datasets were used: the PREVENT III randomized trial (n = 1404) and a multicenter registry (n = 716) from three distinct vascular centers (two academic, one community-based). The PREVENT III cohort was randomly assigned to a derivation set (n = 953) and to a validation set (n = 451). The primary endpoint was AFS. Predictors of AFS identified on univariate screen (inclusion threshold, P < .20) were included in a stepwise selection Cox model. The resulting five significant predictors were assigned an integer score to stratify patients into three risk groups. The prediction rule was internally validated in the PREVENT III validation set and externally validated in the multicenter cohort.The estimated 1-year AFS in the derivation, internal validation, and external validation sets were 76.3%, 72.5%, and 77.0%, respectively. In the derivation set, dialysis (hazard ratio [HR] 2.81, P < .0001), tissue loss (HR 2.22, P =.0004), age >or=75 (HR 1.64, P = .001), hematocrit <or=30 (HR 1.61, P = .012), and advanced CAD (HR 1.41, P = .021) were significant predictors for AFS in the multivariable model. An integer score, derived from the ss coefficients, was used to generate three risk categories (low <or= 3 [44.4% of cohort], medium 4-7 [46.7% of cohort], high >or=8 [8.8% of cohort]). Stratification of the patients, in each dataset, according to risk category yielded three significantly different Kaplan-Meier estimates for 1-year AFS (86%, 73%, and 45% for low, medium, and high risk groups, respectively). For a given risk category, the AFS estimate was consistent between the derivation and validation sets.Among patients selected to undergo surgical bypass for infrainguinal disease, this parsimonious risk stratification model reliably identified a category of CLI patients with a >50% chance of death or major amputation at 1 year. Calculation of a "PIII risk score" may be useful for surgical decision making and for clinical trial designs in the CLI population.

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Dataset Information

Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): An approach to care stratification.

Publications

Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): An approach to care stratification.

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