The DGAV risk calculator: development and validation of statistical models for a web-based instrument predicting complications of colorectal cancer surgery

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review

BACKGROUND

Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice.

OBJECTIVE

This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review.

METHODS

We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review.

RESULTS

We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76.

CONCLUSIONS

There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

Systematic review of preoperative and intraoperative colorectal Anastomotic Leak Prediction Scores (ALPS)

OBJECTIVE

To systematically review preoperative and intraoperative Anastomotic Leak Prediction Scores (ALPS) and validation studies to evaluate performance and utility in surgical decision-making. Anastomotic leak (AL) is the most feared complication of colorectal surgery. Individualised leak risk could guide anastomosis and/or diverting stoma.

METHODS

Systematic search of Ovid MEDLINE and Embase databases, 30 October 2020, identified existing ALPS and validation studies. All records including >1 risk factor, used to develop new, or to validate existing models for preoperative or intraoperative use to predict colorectal AL, were selected. Data extraction followed CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies guidelines. Models were assessed for applicability for surgical decision-making and risk of bias using Prediction model Risk Of Bias ASsessment Tool.

RESULTS

34 studies were identified containing 31 individual ALPS (12 colonic/colorectal, 19 rectal) and 6 papers with validation studies only. Development dataset patient populations were heterogeneous in terms of numbers, indication for surgery, urgency and stoma inclusion. Heterogeneity precluded meta-analysis. Definitions and timeframe for AL were available in only 22 and 11 ALPS, respectively. 26/31 studies used some form of multivariable logistic regression in their modelling. Models included 3-33 individual predictors. 27/31 studies reported model discrimination performance but just 18/31 reported calibration. 15/31 ALPS were reported with external validation, 9/31 with internal validation alone and 4 published without any validation. 27/31 ALPS and every validation study were scored high risk of bias in model analysis.

CONCLUSIONS

Poor reporting practices and methodological shortcomings limit wider adoption of published ALPS. Several models appear to perform well in discriminating patients at highest AL risk but all raise concerns over risk of bias, and nearly all over wider applicability. Large-scale, precisely reported external validation studies are required.

PROSPERO REGISTRATION NUMBER

CRD42020164804.

Benchmarks in colorectal surgery: multinational study to define quality thresholds in high and low anterior resection

BACKGROUND

Benchmark comparisons in surgery allow identification of gaps in the quality of care provided. The aim of this study was to determine quality thresholds for high (HAR) and low (LAR) anterior resections in colorectal cancer surgery by applying the concept of benchmarking.

METHODS

This 5-year multinational retrospective study included patients who underwent anterior resection for cancer in 19 high-volume centres on five continents. Benchmarks were defined for 11 relevant postoperative variables at discharge, 3 months, and 6 months (for LAR). Benchmarks were calculated for two separate cohorts: patients without (ideal) and those with (non-ideal) outcome-relevant co-morbidities. Benchmark cut-offs were defined as the 75th percentile of each centre's median value.

RESULTS

A total of 3903 patients who underwent HAR and 3726 who had LAR for cancer were analysed. After 3 months' follow-up, the mortality benchmark in HAR for ideal and non-ideal patients was 0.0 versus 3.0 per cent, and in LAR it was 0.0 versus 2.2 per cent. Benchmark results for anastomotic leakage were 5.0 versus 6.9 per cent for HAR, and 13.6 versus 11.8 per cent for LAR. The overall morbidity benchmark in HAR was a Comprehensive Complication Index (CCI®) score of 8.6 versus 14.7, and that for LAR was CCI® score 11.9 versus 18.3.

CONCLUSION

Regular comparison of individual-surgeon or -unit outcome data against benchmark thresholds may identify gaps in care quality that can improve patient outcome.

Long-term oncologic outcomes of natural orifice specimen extraction surgery versus conventional laparoscopic-assisted resection in the treatment of rectal cancer: a propensity-score matching study

BACKGROUND

Natural orifice specimen extraction surgery (NOSES) has been increasingly applied in radical surgery of abdominal and pelvic organs, but it is still in the exploratory stage. There is insufficient evidence to prove its efficacy.

METHODS

From January 2013 to June 2017, a total of 351 patients diagnosed with rectal cancer were eventually included in this study. Patients who underwent NOSES were assigned to the NOSES group, while patients undergoing conventional laparoscopic assisted resection were assigned as to the LAP group. Propensity score matching was used to align clinicopathological features between the two groups.

RESULTS

From the perioperative data and postoperative follow-up results of both groups, patients in the NOSES group had less intraoperative bleeding (47.0 ± 60.4 ml vs 87.1 ± 101.2 ml, P = 0.011), shorter postoperative gastrointestinal recovery (50.7 ± 27.3 h vs 58.6 ± 28.5 h, P = 0.040), less postoperative analgesic use (36.8% vs 52.8%, P = 0.019), lower postoperative pain scores (P < 0.001), lower rate of postoperative complications (5.7% vs 15.5%, P = 0.020), more satisfaction with body image (P = 0.001) and cosmesis (P < 0.001) postoperatively. The NOSES group had a higher quality of life. Moreover, there was no significant difference in overall survival (OS) and disease-free survival (DFS) between the two groups.

CONCLUSION

NOSES could be a safe and reliable technique for radical resection of rectal cancer, with better short-term outcomes than conventional laparoscopy, while long-term survival is not significantly different from that of conventional laparoscopic surgery.

Predicting the earliest deviation in weight gain in the course towards manifest overweight in offspring exposed to obesity in pregnancy: a longitudinal cohort study

BACKGROUND

Obesity in pregnancy and related early-life factors place the offspring at the highest risk of being overweight. Despite convincing evidence on these associations, there is an unmet public health need to identify "high-risk" offspring by predicting very early deviations in weight gain patterns as a subclinical stage towards overweight. However, data and methods for individual risk prediction are lacking. We aimed to identify those infants exposed to obesity in pregnancy at ages 3 months, 1 year, and 2 years who likely will follow a higher-than-normal body mass index (BMI) growth trajectory towards manifest overweight by developing an early-risk quantification system.

METHODS

This study uses data from the prospective mother-child cohort study Programming of Enhanced Adiposity Risk in CHildhood-Early Screening (PEACHES) comprising 1671 mothers with pre-conception obesity and without (controls) and their offspring. Exposures were pre- and postnatal risks documented in patient-held maternal and child health records. The main outcome was a "higher-than-normal BMI growth pattern" preceding overweight, defined as BMI z-score >1 SD (i.e., World Health Organization [WHO] cut-off "at risk of overweight") at least twice during consecutive offspring growth periods between age 6 months and 5 years. The independent cohort PErinatal Prevention of Obesity (PEPO) comprising 11,730 mother-child pairs recruited close to school entry (around age 6 years) was available for data validation. Cluster analysis and sequential prediction modelling were performed.

RESULTS

Data of 1557 PEACHES mother-child pairs and the validation cohort were analyzed comprising more than 50,000 offspring BMI measurements. More than 1-in-5 offspring exposed to obesity in pregnancy belonged to an upper BMI z-score cluster as a distinct pattern of BMI development (above the cut-off of 1 SD) from the first months of life onwards resulting in preschool overweight/obesity (age 5 years: odds ratio [OR] 16.13; 95% confidence interval [CI] 9.98-26.05). Contributing early-life factors including excessive weight gain (OR 2.08; 95% CI 1.25-3.45) and smoking (OR 1.94; 95% CI 1.27-2.95) in pregnancy were instrumental in predicting a "higher-than-normal BMI growth pattern" at age 3 months and re-evaluating the risk at ages 1 year and 2 years (area under the receiver operating characteristic [AUROC] 0.69-0.79, sensitivity 70.7-76.0%, specificity 64.7-78.1%). External validation of prediction models demonstrated adequate predictive performances.

CONCLUSIONS

We devised a novel sequential strategy of individual prediction and re-evaluation of a higher-than-normal weight gain in "high-risk" infants well before developing overweight to guide decision-making. The strategy holds promise to elaborate interventions in an early preventive manner for integration in systems of well-child care.

The close interrelation between colorectal cancer, infection and microbiota

PURPOSE OF REVIEW

Evaluate the recent literature about the relation of clinical infection and colorectal cancer in terms of diagnosis of an occult infection and possible impact on oncological outcome and review the possible role of the gut microbiota in the role of colorectal cancer oncogenesis.

RECENT FINDINGS

Data published within the 2 last years have been reviewed and the conclusions, mostly supporting previously published information, have been critically discussed.

SUMMARY

Infection (bacteremia, cellulitis) might be a surrogate of occult colorectal cancer and postoperative infection complications might jeopardize long-term survival after potentially curative surgery. The role of the gut microbiota in the genesis of colorectal cancer remains an exciting though unresolved question.

[Scientific and ethical evaluation of projects in data-driven medicine]

The generation and usage of extensive data from medical care aims at answering crucial medical research questions. Buzzwords in this area are learning health system, data-driven medicine and big data. In addition to classical biostatistical methods, machine learning approaches are frequently applied for analysis.In the evaluation of projects from data-driven medicine by research ethics committees, the question arises of how to assess the benefit-risk ratio and the scientific and social value. Which knowledge is required for that purpose? How can research ethics committees prepare for these challenges? Scientific approaches from the area of observational studies and the consideration of agreed-upon ethical aspects (consent, validity, justice, benefit-risk ratio and transparency) can help to answer the above-mentioned questions. One has to bear in mind that data-driven medicine is no paradigm shift that in principle challenges the established scientific and ethical evaluation procedures. Nevertheless, the evaluation of projects from data-driven medicine requires enhanced specialisation and comprehensive methodical expertise from the areas of machine learning and observational studies.Empirical research of the progression and governance of data-driven medicine will support the development and continual adaptation of effective strategies for evaluation by research ethics committees. Training and networking of experts will enable us to meet the challenges of data-driven medicine.

[German Society for General and Visceral Surgery (DGAV) risk calculator of interventions for colorectal cancer : Presentation of a surgical algorithm on patient participation and quality assurance]

Algorithms are increasingly being developed on the basis of large data sets, also in the field of health, whether for predicting treatment outcomes or life-expectancy. In surgery it is also becoming increasingly more important to analyze complications at an early stage and to subsequently reduce them. The aim is to improve the quality of treatment and quality of life and thus to improve patient well-being. The German Society for General and Visceral Surgery (DGAV) has developed 12 StuDoQ registers in which pseudonymized data from a total of 150,000 patients are recorded. Risk models were developed and validated at the Institute for Medical Information Processing, Biometry and Epidemiology (IBE) of the Ludwig Maximilian University in Munich using the collected data from the StuDoQ|colon cancer and StuDoQ|rectal cancer registers. Based on the collected patient data, the risk calculator determines the statistical probability of the individual complication profile of the patient who is to undergo surgery. The aim is to support surgeons and patients in the decision making process for the individual procedure. The surgeon with his individual experience ultimately remains responsible for the patient.