Sequentially Updated Discharge Model for Optimizing Hospital Resource Use and Surgical Patients’ Satisfaction

Implementing a discharge process for patients undergoing elective surgery: Rapid review

BACKGROUND

Hospital discharge is a 'vulnerable stage' in care. A delayed, inappropriate or poorly planned discharge increases hazards and costs, inhibiting recovery, and often leading to unplanned readmission. New discharge processes could boost practice, reduce the length of stay, and, consequently, reduce costs and improve patients' quality of life.

AIM

To identify technology based interventions that have been implemented to facilitate a safe and timely discharge procedure after elective surgery, and to describe implementation barriers and facilitators and patient satisfaction.

METHOD

This rapid review followed a restricted systematic review framework, searching Medline, EMBASE, CINAHL, PsychINFO, and ClinicalTrials.gov. for relevant studies published from 2015 to 2021 in English.

RESULTS

Eleven studies were included. Most interventions were machine-learning-based, and only one study reported patient involvement. Effective leadership, team work and communication were stated as implementation facilitators. The main barriers to implementation were: lack of support from leaders, poor clinical documentation, resistance to change, and financial and logistical concerns. None of the studies evaluated patient satisfaction.

CONCLUSIONS

Findings highlight factors that support the implementation of technology based interventions aimed at a safe and timely discharge process following elective surgery. Nurses play an important role in the provision of information, and in the development and implementation of discharge processes.

Systematic review of prediction models for postacute care destination decision-making

OBJECTIVE

This article reports a systematic review of studies containing development and validation of models predicting postacute care destination after adult inpatient hospitalization, summarizes clinical populations and variables, evaluates model performance, assesses risk of bias and applicability, and makes recommendations to reduce bias in future models.

MATERIALS AND METHODS

A systematic literature review was conducted following PRISMA guidelines and the Cochrane Prognosis Methods Group criteria. Online databases were searched in June 2020 to identify all published studies in this area. Data were extracted based on the CHARMS checklist, and studies were evaluated based on predictor variables, validation, performance in validation, risk of bias, and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool.

RESULTS

The final sample contained 28 articles with 35 models for evaluation. Models focused on surgical (22), medical (5), or both (8) populations. Eighteen models were internally validated, 10 were externally validated, and 7 models underwent both types. Model performance varied within and across populations. Most models used retrospective data, the median number of predictors was 8.5, and most models demonstrated risk of bias.

DISCUSSION AND CONCLUSION

Prediction modeling studies for postacute care destinations are becoming more prolific in the literature, but model development and validation strategies are inconsistent, and performance is variable. Most models are developed using regression, but machine learning methods are increasing in frequency. Future studies should ensure the rigorous variable selection and follow TRIPOD guidelines. Only 14% of the models have been tested or implemented beyond original studies, so translation into practice requires further investigation.

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

Preoperative Nomogram Predicts Non-home Discharge in Patients Undergoing Pancreatoduodenectomy

BACKGROUND

In patients undergoing pancreatoduodenectomy, non-home discharge is common and often results in an unnecessary delay in hospital discharge. This study aimed to develop and validate a preoperative prediction model to identify patients with a high likelihood of non-home discharge following pancreatoduodenectomy.

METHODS

Patients undergoing pancreatoduodenectomy from 2013 to 2018 were identified using an institutional database. Patients were categorized according to discharge location (home vs. non-home). Preoperative risk factors, including social determinants of health associated with non-home discharge, were identified using Pearson's chi-squared test and then included in a multiple logistic regression model. A training cohort composed of 80% of the sampled patients was used to create the prediction model, and validation carried out using the remaining 20%. Statistical significance was defined as P < 0.05.

RESULTS

Seven hundred sixty-six pancreatoduodenectomy patients met the study criteria for inclusion in the analysis (non-home, 126; home, 640). Independent predictors of non-home discharge on multivariable analysis were age, marital status, mental health diagnosis, functional health status, dyspnea, and chronic obstructive pulmonary disease. The prediction model was then used to generate a nomogram to predict likelihood of non-home discharge. The training and validation cohorts demonstrated comparable performances with an identical area under the curve (0.81) and an accuracy of 84%.

CONCLUSION

A prediction model to reliably assess the likelihood of non-home discharge after pancreatoduodenectomy was developed and validated in the present study.

Predictors of Non-home Discharge after Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy

BACKGROUND

Using a national database of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) recipients, we sought to determine risk factors for nonhome discharge (NHD) in a cohort of patients.

METHODS

Patients undergoing CRS/HIPEC at any one of 12 participating sites between 2000 and 2017 were identified. Univariate analysis was used to compare the characteristics, operative variables, and postoperative complications of patients discharged home and patients with NHD. Multivariate logistic regression was used to identify independent risk factors of NHD.

RESULTS

The cohort included 1593 patients, of which 70 (4.4%) had an NHD. The median [range] peritoneal cancer index in our cohort was 14 [0-39]. Significant predictors of NHD identified in our regression analysis were advanced age (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.05-1.12; P < 0.001), an American Society of Anesthesiologists (ASA) score of 4 (OR, 2.87; 95% CI, 1.21-6.83; P = 0.017), appendiceal histology (OR, 3.14; 95% CI 1.57-6.28; P = 0.001), smoking history (OR, 3.22; 95% CI, 1.70-6.12; P < 0.001), postoperative total parenteral nutrition (OR, 3.14; 95% CI, 1.70-5.81; P < 0.001), respiratory complications (OR, 7.40; 95% CI, 3.36-16.31; P < 0.001), wound site infections (OR, 3.12; 95% CI, 1.58-6.17; P = 0.001), preoperative hemoglobin (OR, 0.81; 95% CI, 0.70-0.94; P = 0.006), and total number of complications (OR, 1.41; 95% CI, 1.16-1.73; P < 0.001).

CONCLUSIONS

Early identification of patients at high risk for NHD after CRS/HIPEC is key for preoperative and postoperative counseling and resource allocation, as well as minimizing hospital-acquired conditions and associated health care costs.

Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis

PURPOSE

We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis.

METHODS

The National Surgical Quality Improvement Program (NSQIP) database was used to select patients that underwent surgical treatment for degenerative spondylolisthesis between 2009 and 2016. Our primary outcome measure was non-home discharge which was defined as any discharge not to home for which we grouped together all non-home discharge destinations including rehabilitation facility, skilled nursing facility, and unskilled nursing facility. We used Akaike information criterion to select the most appropriate model based on the outcomes of the stepwise backward logistic regression. Four machine learning algorithms were developed to predict discharge placement and were assessed by discrimination, calibration, and overall performance.

RESULTS

Nine thousand three hundred and thirty-eight patients were included. Median age was 63 (interquartile range [IQR] 54-71), and 63% (n = 5,887) were female. The non-home discharge rate was 18.6%. Our models included age, sex, diabetes, elective surgery, BMI, procedure, number of levels, ASA class, preoperative white blood cell count, and preoperative creatinine. The Bayes point machine was considered the best model based on discrimination (AUC = 0.753), calibration (slope = 1.111; intercept = - 0.002), and overall model performance (Brier score = 0.132).

CONCLUSION

This study has shown that it is possible to create a predictive machine learning algorithm with both good accuracy and calibration to predict discharge placement. Using our methodology, this type of model can be developed for many other conditions and (elective) treatments. These slides can be retrieved under Electronic Supplementary Material.

The Effect of Preoperative Medications on Length of Stay, Inpatient Pain, and Narcotics Consumption After Minimally Invasive Transforaminal Lumbar Interbody Fusion

STUDY DESIGN

This is a retrospective cohort study.

OBJECTIVE

To determine the association between preoperative medications and length of stay, inpatient pain, and narcotics consumption after a minimally invasive transforaminal lumbar interbody fusion (MIS TLIF).

SUMMARY OF BACKGROUND DATA

Previous studies have identified risk factors for increased length of hospital stay, inpatient pain, and narcotics consumption. However, little is known regarding the effects of preoperative medications on outcomes after spine surgery.

METHODS

A prospectively maintained surgical database of patients undergoing primary, single-level MIS TLIF was retrospectively reviewed. Preoperative medications taken within 30 days before surgery were recorded for each patient and categorized by medication type. Poisson regression with robust error variance was used to determine the association between preoperative medications and length of stay, pain scores, and narcotics consumption. Multivariate analysis was performed using a backwards, stepwise regression to identify independent risk factors.

RESULTS

In total, 138 patients were included in this analysis. On bivariate analysis, benzodiazepines were associated with longer hospital stays [relative risk (RR)=2.03; P=0.031]. Benzodiazepines (RR=3.71; P<0.001) and preoperative narcotics (RR=2.60; P=0.012) were risk factors for pain ≥7 on postoperative day 0. On multivariate analysis, benzodiazepines were an independent risk factor for prolonged stay. Benzodiazepines, narcotics, and nonsteroidal anti-inflammatories were identified as independent risk factors for increased postoperative pain.

CONCLUSIONS

These results suggest that benzodiazepines are a risk factor for increased length of stay and postoperative pain after MIS TLIF. Preoperative narcotics and nonsteroidal anti-inflammatories were also identified as risk factors for postoperative pain though this did not lead to increases in narcotics consumption. Patients taking these medications should undergo more vigilant perioperative monitoring for adequate pain management. More work must be done to further elucidate the association between preoperative medications and postoperative outcomes after MIS TLIF.

Enhanced recovery after surgery (ERAS) pathways in breast reconstruction: systematic review and meta-analysis of the literature

PURPOSE

Enhanced recovery after surgery (ERAS) pathways are increasingly promoted in post-mastectomy reconstruction, with several articles reporting their benefits and safety. This meta-analysis appraises the evidence for ERAS pathways in breast reconstruction.

METHODS

A systematic search of Medline, EMBASE, and Cochrane databases was performed to identify reports of ERAS protocols in post-mastectomy breast reconstruction. Two reviewers screened studies using predetermined inclusion criteria. Studies evaluated at least one of the following end-points of interest: length of stay (LOS), opioid use, or major complications. Risk of bias was assessed for each study. Meta-analysis was performed via a mixed-effects model to compare outcomes for ERAS versus traditional standard of care. Surgical techniques were assessed through subgroup analysis.

RESULTS

A total of 260 articles were identified; 9 (3.46%) met inclusion criteria with a total of 1191 patients. Most studies had "fair" methodological quality and incomplete implementation of ERAS society recommendations was noted. Autologous flaps comprised the majority of cases. In autologous breast reconstruction, ERAS significantly reduces opioid use [Mean difference (MD) = - 183.96, 95% CI - 340.27 to 27.64, p = 0.02) and LOS (MD) = - 1.58, 95% CI - 1.99 to 1.18, p < 0.00001] versus traditional care. There is no significant difference in the incidence of complications (major complications, readmission, hematoma, and infection).

CONCLUSION

ERAS pathways significantly reduce opioid use and length of hospital stay following autologous breast reconstruction without increasing complication rates. This is salient given the current US healthcare climate of rising expenditures and an opioid crisis.

Nomogram to predict non-home discharge following pancreaticoduodenectomy in a national cohort of patients

BACKGROUND

Despite the development of pathways to enhance recovery and discharge to home, a significant proportion of patients are discharged to inpatient facilities after pancreaticoduodenectomy (PD). The aim of this study was to determine the rate of non-home discharge (NHD) following PD in a national cohort of patients and to develop predictive nomograms for NHD.

METHODS

The National Surgical Quality Improvement Program was used to construct and validate pre- and postoperative nomograms for NHD following PD.

RESULTS

A total of 6856 patients who underwent PD were identified, of which 927 (13.5%) had an NHD. The independent preoperative predictors of NHD were being female, older age, higher BMI, low serum albumin, >10% weight loss, ASA class III/IV, and being diagnosed with a bile duct/ampullary neoplasm or neuroendocrine tumor. A preoperative nomogram was constructed with a concordance index of 0.77. When postoperative variables were added to the model, the concordance index increased to 0.82. The postoperative predictors of NHD were return to the operating room, length of stay of ≥14 days, and any inpatient complications.

CONCLUSIONS

These nomograms could be useful risk assessment tools to predict NHD after PD and therefore facilitate patient counseling and improve resource utilization and discharge planning.