Normothermia to Decrease Surgical Site Infection Risk: Silver Bullet or Fool's Gold? A Retrospective Cohort Study.

PURPOSE: Surgical site infection (SSI) is the leading cause of nosocomial infections among surgical patients in the United States. Currently, there is compelling evidence suggesting that temperature dysregulation in surgical patients may be a risk factor for the development of SSI. We examined the relationship between perioperative hypothermia (PH) and SSI in a population of surgical patients with diabetes mellitus (DM). METHODS: This retrospective cohort review was conducted on patients with a history of DM undergoing orthopaedic surgery at our institution between May 1, 2018, and April 1, 2022. Inclusion criteria were age older than 15 years, a history of DM or recent hemoglobin A1c concentration of ≥6.5%, and operation of at least 60 minutes under general anesthesia. Perioperative hypothermia was defined as an intraoperative temperature ≤ 35.5°C. Continuous variables were compared using the t-test and Wilcoxon rank-sum test. Categorical variables were compared using the chi-squared test. We constructed a multivariable logistic regression model to estimate SSI risk while controlling for demographic variables. RESULTS: A total of 236 patients were included in the final analysis. The overall incidence of SSI was 5.93%. 99 patients (42%) experienced PH. No difference was observed in the risk of SSI between the normothermic and hypothermic cohorts. Among the 99 patients who experienced PH, increasing HbA1c was associated with increasing risk of SSI (OR = 2.39, 95% CI = 1.12 to 5.32, P-value = 0.0222). The multivariable logistic regression model had good discriminatory ability (c-statistic 0.74, 95% CI: 0.61 to 0.89) and good predictive accuracy (sensitivity 64%, specificity 73%). DISCUSSION: PH is not an independent risk factor of SSI. However, in the presence of elevated HbA1c, PH may more than double the risk of SSI. Perioperative hypothermia may be an additive risk factor in the setting of poor glycemic control and potentially in the setting of other known risk factors.

What Do We Owe Our Patients? Surgeons' Obligations When Patients are Too Sick for Surgery.

As the principle of respect for patient autonomy has gained salience over the past 75 years, surgeons now struggle to resolve conflicts between autonomy and beneficence in certain clinical scenarios. One such conflict occurs when a patient desires a surgical intervention, but the surgeon concludes that the patient is "too sick for surgery" and thus would not benefit from the operation. We provide historical context for the principle of respect for patient autonomy and review recent qualitative data that demonstrate surgeons experience significant moral distress when asked to perform non-beneficial surgery. Thus, we sought to empower surgeons with the appropriate ethical justifications to decline to perform surgery when they believe it would be nonbeneficial or harmful to patients. We outline four concepts that can help surgeons engage with patients, families, and colleagues in these scenarios. First, we describe the term "futility" and explain the difficulty in precisely defining and employing the term in practice. Second, we contrast patients' positive and negative rights, drawing on historical context to argue that patients have robust negative rights but limited positive rights to request non-beneficial interventions. Third, we use the centuries-old notion of medicine as a profession to show that surgeons have a fiduciary responsibility to act in the best interests of their patients, including and especially when patients request interventions that are not beneficial. Finally, we draw on virtue ethics to give surgeons character-based resources for fulfilling their professional obligations to patients. We contend that surgeons owe their patients the ability to trust that they will always use their knowledge and skills for patients' benefit, even if surgeons must limit patients' autonomy in certain ways to do so.

Erratum: Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health - ERRATUM.

[This corrects the article DOI: 10.1017/cts.2023.670.].

Development and Validation of a Risk Score Predicting Death Without Transplant in Adult Heart Transplant Candidates.

Importance: The US heart allocation system prioritizes medically urgent candidates with a high risk of dying without transplant. The current therapy-based 6-status system is susceptible to manipulation and has limited rank ordering ability. Objective: To develop and validate a candidate risk score that incorporates current clinical, laboratory, and hemodynamic data. Design, Setting, and Participants: A registry-based observational study of adult heart transplant candidates (aged ≥18 years) from the US heart allocation system listed between January 1, 2019, and December 31, 2022, split by center into training (70%) and test (30%) datasets. Adult candidates were listed between January 1, 2019, and December 31, 2022. Main Outcomes and Measures: A US candidate risk score (US-CRS) model was developed by adding a predefined set of predictors to the current French Candidate Risk Score (French-CRS) model. Sensitivity analyses were performed, which included intra-aortic balloon pumps (IABP) and percutaneous ventricular assist devices (VAD) in the definition of short-term mechanical circulatory support (MCS) for the US-CRS. Performance of the US-CRS model, French-CRS model, and 6-status model in the test dataset was evaluated by time-dependent area under the receiver operating characteristic curve (AUC) for death without transplant within 6 weeks and overall survival concordance (c-index) with integrated AUC. Results: A total of 16 905 adult heart transplant candidates were listed (mean [SD] age, 53 [13] years; 73% male; 58% White); 796 patients (4.7%) died without a transplant. The final US-CRS contained time-varying short-term MCS (ventricular assist-extracorporeal membrane oxygenation or temporary surgical VAD), the log of bilirubin, estimated glomerular filtration rate, the log of B-type natriuretic peptide, albumin, sodium, and durable left ventricular assist device. In the test dataset, the AUC for death within 6 weeks of listing for the US-CRS model was 0.79 (95% CI, 0.75-0.83), for the French-CRS model was 0.72 (95% CI, 0.67-0.76), and 6-status model was 0.68 (95% CI, 0.62-0.73). Overall c-index for the US-CRS model was 0.76 (95% CI, 0.73-0.80), for the French-CRS model was 0.69 (95% CI, 0.65-0.73), and 6-status model was 0.67 (95% CI, 0.63-0.71). Classifying IABP and percutaneous VAD as short-term MCS reduced the effect size by 54%. Conclusions and Relevance: In this registry-based study of US heart transplant candidates, a continuous multivariable allocation score outperformed the 6-status system in rank ordering heart transplant candidates by medical urgency and may be useful for the medical urgency component of heart allocation.

Association of high-priority exceptions with waitlist mortality among heart transplant candidates.

BACKGROUND: The US heart allocation system ranks candidates using six categorical status levels. Transplant programs can request exceptions to increase a candidate's status level if they believe their candidate has the same medical urgency as candidates who meet the standard criteria for that level. We aimed to determine if exception candidates have the same medical urgency as standard candidates. METHODS: Using the Scientific Registry of Transplant Recipients, we constructed a longitudinal waitlist history dataset of adult heart-only transplant candidates listed between October 18, 2018 and December 1, 2021. We estimated the association between exceptions and waitlist mortality with a mixed-effects Cox proportional hazards model that treated status and exceptions as time-dependent covariates. RESULTS: Out of 12,458 candidates listed during the study period, 2273 (18.2%) received an exception at listing and 1957 (15.7%) received an exception after listing. After controlling for status, exception candidates had approximately half the risk of waitlist mortality as standard candidates (hazard ratio [HR] 0.55, 95% confidence interval [CI] [0.41, 0.73], p < .001). Exceptions were associated with a 51% lower risk of waitlist mortality among Status 1 candidates (HR 0.49, 95% CI [0.27, 0.91], p = .023) and a 61% lower risk among Status 2 candidates (HR 0.39, 95% CI [0.24, 0.62], p < .001). CONCLUSIONS: Under the new heart allocation policy, exception candidates had significantly lower waitlist mortality than standard candidates, including exceptions for the highest priority statuses. These results suggest that candidates with exceptions, on average, have a lower level of medical urgency than candidates who meet standard criteria.

The Accuracy of Initial U.S. Heart Transplant Candidate Rankings.

BACKGROUND: The U.S. heart allocation system ranks candidates with only 6 treatment-based categorical "statuses" and ignores many objective patient characteristics. OBJECTIVES: This study sought to determine the effectiveness of the standard 6-status ranking system and several novel prediction models in identifying the most urgent heart transplant candidates. METHODS: The primary outcome was death before receipt of a heart transplant. The accuracy of the 6-status system was evaluated using Harrell's C-index and log-rank tests of Kaplan-Meier estimated survival by status for candidates listed postpolicy (November 2018 to March 2020) in the Scientific Registry of Transplant Recipients data set. The authors then developed Cox proportional hazards models and random survival forest models using prepolicy data (2010-2017). The predictor variables included age, diagnosis, laboratory measurements, hemodynamics, and supportive treatment at the time of listing. The performance of these models was compared with the candidate's 6-status ranking in the postpolicy data. RESULTS: Since policy implementation, the 6-status ranking at listing has had moderate ability to rank-order candidates (C-index: 0.67). Statuses 4 and 6 had no significant difference in survival (P = 0.80), and status 5 had lower survival than status 4 (P < 0.001). Novel multivariable prediction models derived with prepolicy data ranked candidates correctly more often than the 6-status rankings (Cox proportional hazards model C-index: 0.76; random survival forest model C-index: 0.74). Objective physiologic measurements, such as glomerular filtration rate, had high variable importance. CONCLUSIONS: The treatment-based 6-status heart allocation system has only moderate ability to rank-order candidates by medical urgency. Predictive models that incorporate physiologic measurements can more effectively rank-order heart transplant candidates by urgency.

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health.

Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on "hyper-local" data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

An updated estimate of posttransplant survival after implementation of the new donor heart allocation policy.

The Organ Procurement and Transplant Network (OPTN) implemented a new heart allocation policy on October 18, 2018. Published estimates of lower posttransplant survival under the new policy in cohorts with limited follow-up may be biased by informative censoring. Using the Scientific Registry of Transplant Recipients, we used the Kaplan-Meier method to estimate 1-year posttransplant survival for pre-policy (November 1, 2016, to October 31, 2017) and post-policy cohorts (November 1, 2018, to October 31, 2019) with follow-up through March 2, 2021. We adjusted for changes in recipient population over time with a multivariable Cox proportional hazards model. To demonstrate the effect of inadequate follow-up on post-policy survival estimates, we repeated the analysis but only included follow-up through October 31, 2019. Transplant programs transplanted 2594 patients in the pre-policy cohort and 2761 patients in the post-policy cohort. With follow-up through March 2, 2021, unadjusted 1-year posttransplant survival was 90.6% (89.5%-91.8%) in the pre-policy cohort and 90.8% (89.7%-91.9%) in the post-policy cohort (adjusted HR = 0.93 [0.77-1.12]). Ignoring follow-up after October 31, 2019, the post-policy estimate was biased downward (1-year: 82.2%). When estimated with adequate follow-up, 1-year posttransplant survival under the new heart allocation policy was not significantly different.