Preoperative Score To Predict Postoperative Mortality Research Articles

Validation of the PreOperative Score to predict Post-Operative Mortality (POSPOM) in Dutch non-cardiac surgery patients

BackgroundStandardized risk assessment tools can be used to identify patients at higher risk for postoperative complications and death. In this study, we validate the PreOperative Score to predict Post-Operative Mortality (POSPOM) for in-hospital mortality in a large cohort of non-cardiac surgery patients. In addition, the performance of POSPOM to predict postoperative complications was studied.MethodsData from the control cohort of the TRACE (routine posTsuRgical Anesthesia visit to improve patient outComE) study was analysed. POSPOM scores for each patient were calculated post-hoc. Observed in-hospital mortality was compared with predicted mortality according to POSPOM. Discrimination was assessed by receiver operating characteristic curves with C-statistics for in-hospital mortality and postoperative complications. To describe the performance of POSPOM sensitivity, specificity, negative predictive values, and positive predictive values were calculated. For in-hospital mortality, calibration was assessed by a calibration plot.ResultsIn 2490 patients, the observed in-hospital mortality was 0.5%, compared to 1.3% as predicted by POSPOM. 27.1% of patients had at least one postoperative complication of which 22.4% had a major complication. For in-hospital mortality, POSPOM showed strong discrimination with a C-statistic of 0.86 (95% CI, 0.78–0.93). For the prediction of complications, the discrimination was poor to fair depending on the severity of the complication. The calibration plot showed poor calibration of POSPOM with an overestimation of in-hospital mortality.ConclusionDespite the strong discriminatory performance, POSPOM showed poor calibration with an overestimation of in-hospital mortality. Performance of POSPOM for the prediction of any postoperative complication was poor but improved according to severity.

Comparative evaluation of scales for predicting an unfavorable postoperative outcome: Preliminary results of the multicenter study “The role of concomitant diseases in the stratification of the risk of postoperative complications in abdominal surgery STOPRISK”

INTRODUCTION. The need for accurate risk stratification is obvious. Modern methods are quite cumbersome, which can cause difficulties when applied in routine practice, and therefore relatively simple but accurate forecasting methods have become very popular, which, however, have not been validated in Russia: SORT (Surgical Outcome Risk Tool), SRS (Surgical Risk Scale), POSPOM (Preoperative Score to Predict Postoperative Mortality), NZRISK (New Zealand RISK), SMPM (Surgical Mortality Probability Model). OBJECTIVES. The aim of this work is to determine the prognostic value of risk assessment scales in predicting an unfavorable postoperative outcome based on the analysis of data obtained in the STOPRISK study in patients undergoing open abdominal surgery. MATERIALS AND METHODS. The analysis of data on perioperative parameters of 1,179 patients who underwent open abdominal surgery is presented. RESULTS. The fatal outcome was recorded in 14 patients (1.18 %). A total of 135 complications were registered in 92 patients (7.8 %). All scales demonstrated satisfactory prognostic value in assessing the risk of complications (the area under the operating characteristic curve (AUROC) for the Physical Status Scale of the American Society of Anesthesiologists (ASA) was 0.714 (0.687–0.739), for the Surgical Risk Scale (SRS) — 0.727 (0.701–0.753), for the Surgical Outcome Risk Scale (SORT) — 0.738 (0.712–0.763), for the New Zealand Risk Scale (NZRISK) — 0.763 (0.738–0.787), for the Surgical Mortality Probability Scale (SMPM) — 0.732 (0.706–0.757), for the Preoperative Postoperative mortality Prediction Scale (POSPOM) — 0.764 (0.738–0.788)) and good in assessing the risk of death (AUROC for the ASA scale was 0.82 (0.804–0.843), for the SRS scale — 0.860 (0.838–0.879), for the SORT scale — 0.860 (0.838–0.879), for the NZRISK scale — 0.807 (0.783–0.829), for the SMPM scale — 0.852 (0.831–0.872), for the POSPOM scale — 0.811 (0.788–0.833)). CONCLUSIONS. All the studied scales have good prognostic value in assessing the risk of 30-day mortality after major abdominal surgery. The NZRISK and POSPOM scales demonstrate good prognostic value for cardiovascular complications, POSPOM and SRS scales — for acute renal injury. POSPOM and NZRISK scales showed an excellent prognostic value in relation to the risk of postoperative delirium.
 

The level of postoperative care influences mortality prediction by the POSPOM score: A retrospective cohort analysis.

The Preoperative Score to Predict Postoperative Mortality (POSPOM) assesses the patients' individual risk for postsurgical intrahospital death based on preoperative parameters. We hypothesized that mortality predicted by the POSPOM varies depending on the level of postoperative care. All patients age over 18 years undergoing inpatient surgery or interventions involving anesthesia at a German university hospital between January 2006, and December 2017, were assessed for eligibility for this retrospective study. Endpoint was death in hospital following surgery. Adaptation of the POSPOM to the German coding system was performed as previously described. The whole cohort was divided according to the level of postoperative care (normal ward vs. intensive care unit (ICU) admission within 24 h vs. later than 24 h, respectively). 199,258 patients were finally included. Observed intrahospital mortality was 2.0% (4,053 deaths). 9.6% of patients were transferred to ICU following surgery, and mortality of those patients was increased already at low POSPOM values of 15. 17,165 patients were admitted to ICU within 24 h, and these patients were older, had more comorbidities, or underwent more invasive surgery, reflected by a higher median POSPOM score compared to the normal-ward group (29 vs. 17, p <0.001). Mortality in that cohort was significantly increased to 8.7% (p <0.001). 2,043 patients were admitted to ICU later than 24 h following surgery (therefore denoted unscheduled admission), and the median POSPOM value of that group was 23. Observed mortality in this cohort was highest (13.5%, p <0.001 vs. ICU admission <24 h cohort). Increased mortality in patients transferred to high-care wards reflects the significance of, e.g., intra- or early postoperative events for the patients' outcome. Therefore, scoring systems considering only preoperative variables such as the POSPOM reveal limitations to predict the individual benefit of postoperative ICU admission.

Validation of the Preoperative Score to Predict Postoperative Mortality (POSPOM) in Germany.

BackgroundThe Preoperative Score to Predict Postoperative Mortality (POSPOM) based on preoperatively available data was presented by Le Manach et al. in 2016. This prognostic model considers the kind of surgical procedure, patients' age and 15 defined comorbidities to predict the risk of postoperative in-hospital mortality. Objective of the present study was to validate POSPOM for the German healthcare coding system (G-POSPOM).Methods and findingsAll cases involving anaesthesia performed at the University Hospital Bonn between 2006 and 2017 were analysed retrospectively. Procedures codified according to the French Groupes Homogènes de Malades (GHM) were translated and adapted to the German Operationen- und Prozedurenschlüssel (OPS). Comorbidities were identified by the documented International Statistical Classification of Diseases (ICD-10) coding. POSPOM was calculated for the analysed patient collective using these data according to the method described by Le Manach et al. Performance of thereby adapted POSPOM was tested using c-statistic, Brier score and a calibration plot. Validation was performed using data from 199,780 surgical cases. With a mean age of 56.33 years (SD 18.59) and a proportion of 49.24% females, the overall cohort had a mean POSPOM value of 18.18 (SD 8.11). There were 4,066 in-hospital deaths, corresponding to an in-hospital mortality rate of 2.04% (95% CI 1.97 to 2.09%) in our sample. POSPOM showed a good performance with a c-statistic of 0.771 and a Brier score of 0.021.ConclusionsAfter adapting POSPOM to the German coding system, we were able to validate the score using patient data of a German university hospital. According to previous demonstration for French patient cohorts, we observed a good correlation of POSPOM with in-hospital mortality. Therefore, further adjustments of POSPOM considering also multicentre and transnational validation should be pursued based on this proof of concept.

Evaluation of comorbidity indices in determining the most suitable candidates for uro-oncological surgeries in elderly men.

IntroductionWe aimed to evaluate the superiority of different comorbidity indices in determining the most suitable elderly male candidates for uro-oncological operations. While making this assessment, we also aimed to determine the risk factors that may affect surgery-related major complications and overall survival.Material and methodsData of 543 male patients, 60 years or older, who underwent uro-oncological surgery (radical cystectomy, radical prostatectomy, radical or partial nephrectomy, transurethral resection of bladder tumor) between September 2009 and January 2019 were retrospectively evaluated. Demographic, clinical and pathological characteristics of the patients, preoperative comorbidity indices, postoperative complications, length of hospitalization, re-admission rates within 90 days and postoperative follow-up outcomes were recorded. Patients in similar tumor stages were divided into different subgroups. All subgroups were divided into two main categories: middle age (60–69 years-old) and elderly age (≥70-years-old).ResultsNo significant difference was found for all types of surgery in terms of postoperative outcomes in both age groups (p >0.05). Age-adjusted Charlson Comorbidity Index (ACCI), Preoperative Score to Predict Postoperative Mortality (POSPOM), Rockwood Frailty Index (RFI) and tumor characteristics were found to be more significant predictors for postoperative major complications and overall mortality than Eastern Cooperative Oncology Group (ECOG), American Society of Anesthesiologists (ASA) and New York Heart Association (NYHA) functional classification.ConclusionsOur findings show that patient age alone is not a risk factor for increased postoperative complications and overall mortality. Although many different comorbidity indices have been used in urological practice, ACCI, POSPOM and RFI are more valuable predictors. Uro-oncological surgeries may be performed safely in elderly males after a good clinical decision based on these indices.

Scores for preoperative risk evaluation of postoperative mortality.

Preoperative risk evaluation scores are used prior to surgery to predict perioperative risks. They are also a useful tool to help clinicians communicate the risk-benefit balance of the procedure to patients. This review identifies and assesses the existing preoperative risk evaluation scores (also called prediction scores) of postoperative mortality in all types of surgery (emergency or scheduled) in an adult population. We systematically identified studies using the MEDLINE, Ovid EMBASE and Cochrane databases and published studies reporting the development and validation of preoperative predictive scores of postoperative mortality. We assessed usability, the level of evidence of the studies performed for external validation, and the predictive accuracy of the scores identified. We found 26 scores described within 60 different reports. The most suitable scores with the highest validity identified for anaesthesia practice were the Preoperative Score to Predict Postoperative Mortality (POSPOM), the Universal ACS NSQIP surgical risk calculator (ACS-NSQUIP), the Clinical Frailty Scale (CFS) and the American Society of Anesthesiologists Physical Status (ASA-PS) classification system. While other scores identified in this review could also be endorsed, their level of validity and generalizability to the general surgical population should be carefully considered.

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non-cardiac surgery.

Risk prediction tools can be used in the perioperative setting to identify high-risk patients who may benefit from increased surveillance and monitoring in the postoperative period, to aid shared decision-making, and to benchmark risk-adjusted hospital performance. We evaluated perioperative risk prediction tools relevant to an Australian context. A systematic review of perioperative mortality risk prediction tools used for adults undergoing inpatient noncardiac surgery, published between 2011 and 2019 (following an earlier systematic review). We searched Medline via OVID using medical subject headings consistent with the three main areas of risk, surgery and mortality/morbidity. A similar search was conducted in Embase. Tools predicting morbidity but not mortality were excluded, as were those predicting a composite outcome that did not report predictive performance for mortality separately. Tools were also excluded if they were specifically designed for use in cardiac or other highly specialized surgery, emergency surgery, paediatrics or elderly patients. Literature search identified 2568 studies for screening, of which 19 studies identified 21 risk prediction tools for inclusion. Four tools are candidates for adapting in the Australian context, including the Surgical Mortality Probability Model (SMPM), Preoperative Score to Predict Postoperative Mortality (POSPOM), Surgical Outcome Risk Tool (SORT) and NZRISK. SORT has similar predictive performance to POSPOM, using only six variables instead of 17, contains all variables of the SMPM, and the original model developed in the UK has already been successfully adapted in New Zealand as NZRISK. Collecting the SORT and NZRISK variables in a national surgical outcomes study in Australia would present an opportunity to simultaneously investigate three of our four shortlisted models and to develop a locally valid perioperative mortality risk prediction model with high predictive performance.

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data

BackgroundRapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. MethodsWe report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. ResultsUsing a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910–0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598–0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658–0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829–0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917–0.955). ConclusionsThis automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

Validation of the preoperative score to predict postoperative mortality (POSPOM) in patients undergoing major emergency abdominal surgery.

Patients undergoing major emergency abdominal surgery have a high mortality rate. Preoperative risk prediction tools of in-hospital mortality could assist clinical identification of patients at increased risk and thereby aid clinical decision-making and postoperative pathways. The aim of this study was to validate the preoperative score to predict mortality (POSPOM) in a population of patients undergoing major emergency abdominal surgery. POSPOM was investigated in a retrospectively collected cohort of patients undergoing major emergency abdominal surgery at a Danish University Hospital from 2010 to 2016. Predicted in-hospital mortality by POSPOM was compared to observed in-hospital mortality. Calibration was assessed by Hosmer-Lemeshow goodness-of-fit and calibration plot. Discrimination was assessed by area under the receiver operating characteristic curve and accuracy was assessed with Brier score. The study included 979 patients (513 females) with a median age of 64 (IQR 55-77) years. The majority of patients underwent open surgery (94.5%). The observed in-hospital mortality rate was 10.9%. The estimated mean in-hospital mortality rate by POSPOM was 6.7%. POSPOM showed a good discrimination [AUC 0.82 (95% CI 0.78-0.85)] and an excellent accuracy [Brier score 0.09 (95% CI 0.07-0.10)]. However, a poor calibration was found (p < 0.01) as POSPOM underestimated in-hospital mortality. POSPOM is not an ideal prediction model for in-hospital mortality in patients undergoing major emergency abdominal surgery due a poor calibration.

Predicting mortality in patients admitted to the intensive care unit after open vascular surgery.

Vascular surgery (VS) has a higher perioperative mortality than other types of surgery. We compared different scores for predicting mortality in patients admitted to the intensive care unit (ICU) after open VS. Patients admitted to the ICU after open VS from 2006 to 2013 were included. We calculated the Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Preoperative Score to Predict Postoperative Mortality (POSPOM). We performed multivariate logistic regression to assess independent factors with the calculation of odds ratios (ORs) and 95% confidence intervals (CIs). We tested the predictive ability of the scores using the area under the receiver operating characteristics curve (AUROC). A total of 833 consecutive patients were included. Hospital mortality was 5.1% (1.3% after intermediate-risk and 8.4% after high-risk surgery). In the multivariate analysis, the age (OR 1.04, 95% CI 1.01-1.08, p = 0.013), smoking status (OR 2.46, 95% CI 1.16-5.21, p = 0.019), surgery risk (OR 2.92, 95% CI 1.05-8.08, p = 0.040), serum sodium level (OR 1.17, 95% CI 1.10-1.26, p < 0.001), urea (OR 1.01, 95% CI 1.01-1.02, p = 0.001) and leukocyte count (OR 1.05, 95% CI 1.01-1.10, p = 0.009) at admission were considered independent predictors. Hematocrit (0.86, 95% CI 0.80-0.93, p < 0.001) was considered an independent protective factor. The AUROC of our model was 0.860, compared to SAPS (0.752), APACHE (0.774), POSPOM (0.798) and POSSUM (0.829). The observed mortality was within the predicted range (1-5% after intermediate-risk and > 5% after high-risk surgery). POSSUM and POSPOM had slightly better predictive capacity than SAPS or APACHE.

The perioperative cardiac risk scores for patients with heart diseases undergoing major noncardiac surgery: a review

In order to explore the perioperative management in patients with cardiovascular diseases undergoing major non-cardiac surgery(MNCS), this review summarizes the existing and newly developed evaluation index of perioperative high-risk factors related to non-cardiac surgery in recent years, such the generally Revised Cardiac Risk Index(RCRI), Gupta's Index and other calculator developed by studies and universal perioperative surgical risk calculator. Other studies like Preoperative Score to Predict Postoperative Mortality (POSPOM) and Vascular events In noncardiac Surgery Patients Cohort Evaluation (VISION) are also impressive. There have been considerable advances in the preoperative assessment for patients undergoing MNCS in clinic currently, extensive clinical epidemiological investigation is still needed to improve the evaluation index. Key words: Heart disease; Non-cardiac surgery; Perioperative; Risk scores

Prediction of postoperative mortality in elderly patient with hip fractures: a single-centre, retrospective cohort study

BackgroundElderly patients are at high risk for postoperative complications and increased mortality after hip fracture (HF) surgery due to frailty and co-morbidities. The prediction of postoperative outcome could be used for clinical decision making. A reliable score to predict postoperative mortality after HF surgery in this sub-population remains unavailable.MethodsA single-centre retrospective cohort study was performed in 782 patients who were operated on for HF. Receiver Operating Characteristic (ROC)-curves were used to analyse the performance of gender, age, neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) at admission (D0) as prognostic factors, alone or combined with the PreOperative Score to predict PostOperative Mortality (POSPOM) in univariate and multivariate linear regression models.ResultsNo correlation between gender, age, NLR D0 or CRP D0 and postoperative, intra-hospital mortality was found. The Area Under the ROC-curve (AUC) for age, male gender, NLR and CRP were 0.61 [95% confidence interval (CI) = 0.45–0.61], 0.56 [95% CI = 0.42–0.56], 0.47 [95% CI = 0.29–0.47] and 0.49 [95% CI = 0.31–0.49] respectively. Combination with the POSPOM score did not increase its discriminative capacity as neither age (AUC = 0.69, 95% CI = 0.54–0.69), gender (AUC = 0.72, 95% CI = 0.58–0.72), NLR D0 (AUC = 0.71, 95% CI = 0.56–0.71), nor the CRP D0 (AUC = 0.71, 95% CI = 0.58–0.71) improved the POSPOM performance.ConclusionsNeither age, gender, NLR D0 nor CRP D0 are suitable parameters to predict postoperative, intra-hospital mortality in elderly patients undergoing surgery for HF.

Validation of the Preoperative Score to Predict Postoperative Mortality in Patients Undergoing Radical Cystectomy

Standardized prediction of perioperative mortality risk is of major clinical concern in the radical cystectomy setting. We validated the recently developed Preoperative Score to Predict Postoperative Mortality (POSPOM) in a sample of 1083 consecutive cystectomy patients treated between 1993 and 2014. POSPOM was calculated as originally described based on age and 13 further parameters; three parameters which were not available in our database were ignored. Thirty-day and 90-d mortality were 1.0% and 4.1%, respectively. The areas under the receiver operator characteristic curves were 0.86 for 30-d mortality and 0.78 for 90-d mortality. Below the median of 27 POSPOM risk points, 30-d mortality was 0% and 90-d mortality was 0.5%. Above this level, the corresponding figures were 1.7% and 6.5%, respectively. The 30-d (p<0.0001) and even the 90-d mortality rates (p=0.004) were lower than the POSPOM-predicted in-hospital mortality rate for this sample (5.8%). Nevertheless, with its good discriminative accuracy, POSPOM might standardize the prediction of postoperative mortality after radical cystectomy. The absolute mortality figures in a high volume academic center were, however, lower than predicted based on nationwide collected data. Patient summaryWith a good discriminative accuracy, Preoperative Score to Predict Postoperative Mortality might standardize the prediction of postoperative mortality after radical cystectomy. The absolute mortality figures in a high volume academic center were, however, lower than predicted based on nationwide collected data.

Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation: Erratum

Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation: Erratum

Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation.

An accurate risk score able to predict in-hospital mortality in patients undergoing surgery may improve both risk communication and clinical decision making. The aim of the study was to develop and validate a surgical risk score based solely on preoperative information, for predicting in-hospital mortality. From January 1, 2010, to December 31, 2010, data related to all surgeries requiring anesthesia were collected from all centers (single hospital or hospitals group) in France performing more than 500 operations in the year on patients aged 18 yr or older (n = 5,507,834). International Statistical Classification of Diseases, 10th revision codes were used to summarize the medical history of patients. From these data, the authors developed a risk score by examining 29 preoperative factors (age, comorbidities, and surgery type) in 2,717,902 patients, and then validated the risk score in a separate cohort of 2,789,932 patients. In the derivation cohort, there were 12,786 in-hospital deaths (0.47%; 95% CI, 0.46 to 0.48%), whereas in the validation cohort there were 14,933 in-hospital deaths (0.54%; 95% CI, 0.53 to 0.55%). Seventeen predictors were identified and included in the PreOperative Score to predict PostOperative Mortality (POSPOM). POSPOM showed good calibration and excellent discrimination for in-hospital mortality, with a c-statistic of 0.944 (95% CI, 0.943 to 0.945) in the development cohort and 0.929 (95% CI, 0.928 to 0.931) in the validation cohort. The authors have developed and validated POSPOM, a simple risk score for the prediction of in-hospital mortality in surgical patients.