Effects of Intra-operative Cardiopulmonary Variability On Post-operative Pulmonary Complications in Major Non-cardiac Surgery: A Retrospective Cohort Study.

Intraoperative cardiopulmonary variables are well-known predictors of postoperative pulmonary complications (PPC), traditionally quantified by median values over the duration of surgery. However, it is unknown whether cardiopulmonary instability, or wider intra-operative variability of the same metrics, is distinctly associated with PPC risk and severity. We leveraged a retrospective cohort of adults (n = 1202) undergoing major non-cardiothoracic surgery. We used multivariable logistic regression to evaluate the association of two outcomes (1)moderate-or-severe PPC and (2)any PPC with two sets of exposure variables- (a)variability of cardiopulmonary metrics (inter-quartile range, IQR) and (b)median intraoperative cardiopulmonary metrics. We compared predictive ability (receiver operating curve analysis, ROC) and parsimony (information criteria) of three models evaluating different aspects of the intra-operative cardiopulmonary metrics: Median-based: Median cardiopulmonary metrics alone, Variability-based: IQR of cardiopulmonary metrics alone, and Combined: Medians and IQR. Models controlled for peri-operative/surgical factors, demographics, and comorbidities. PPC occurred in 400(33%) of patients, and 91(8%) experienced moderate-or-severe PPC. Variability in multiple intra-operative cardiopulmonary metrics was independently associated with risk of moderate-or-severe, but not any, PPC. For moderate-or-severe PPC, the best-fit predictive model was the Variability-based model by both information criteria and ROC analysis (area under the curve, AUCVariability-based = 0.74 vs AUCMedian-based = 0.65, p = 0.0015; AUCVariability-based = 0.74 vs AUCCombined = 0.68, p = 0.012). For any PPC, the Median-based model yielded the best fit by information criteria. Predictive accuracy was marginally but not significantly higher for the Combined model (AUCCombined = 0.661) than for the Median-based (AUCMedian-based = 0.657, p = 0.60) or Variability-based (AUCVariability-based = 0.649, p = 0.29) models. Variability of cardiopulmonary metrics, distinct from median intra-operative values, is an important predictor of moderate-or-severe PPC.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity.

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69-74) years, with a body mass index of 31 (27-32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102-114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108-118) versus 106 (96-114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden's index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

Morbidity and Mortality After Acute Myocardial Infarction After Elective Major Noncardiac Surgery.

OBJECTIVES: To develop parsimonious models of in-hospital mortality and morbidity risk after perioperative acute myocardial infarction (AMI). DESIGN: Retrospective data analysis. SETTING: National Inpatient Sample (2008-2013), a 20% sample of all non-federal in-patient hospitalizations in the United States. PARTICIPANTS: Patients 45 years or older who experienced perioperative AMI during elective admission for noncardiac surgery. INTERVENTIONS: The study used a mixed principal components analysis and multivariate logistic regression to identify risk factors for in-hospital mortality after perioperative AMI. A model incorporating only preoperative risk factors, defined by the Revised Cardiac Risk Index (RCRI), was compared with a "full risk factor" model, incorporating a large set of preoperative AMI risk factors. The risk of post-AMI disposition to an intermediate care or skilled nursing facility, a marker of functional impairment, then was evaluated. MEASUREMENTS AND MAIN RESULTS: In the present study, 15,574 cases of AMI after elective noncardiac surgery were identified (0.42%, corresponding with 78,122 cases nationally), with a 12.4% in-hospital mortality rate. The "RCRI-only" model was the best-fit model of post-AMI in-hospital mortality risk, without loss of predictive accuracy compared with the "full risk factor" model (area under the receiver operator characteristic curve 0.80, 95% confidence interval [CI] [0.77-0.82] v area under the receiver operator characteristic curve 0.81, 95% CI [0.77-0.83], respectively). Post-AMI mortality risk was the highest for perioperative complications, including sepsis (odds ratio 4.95, 95% CI [4.32-5.67]). Conversely, functional impairment was best predicted by the "full-risk factor" model and depended strongly on chronic preoperative comorbidities. CONCLUSIONS: The RCRI provides a simple but adequate model of preoperative risk factors for in-hospital mortality after perioperative AMI.

Untangling the dynamics of persistence and colonization in microbial communities.

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here, we introduce a dynamic statistical model, based on probit regression, that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We first validate our modeling framework with several simulation studies. Then, we apply the approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

Recurring infection with ecologically distinct HPV types can explain high prevalence and diversity.

The high prevalence of human papillomavirus (HPV), the most common sexually transmitted infection, arises from the coexistence of over 200 genetically distinct types. Accurately predicting the impact of vaccines that target multiple types requires understanding the factors that determine HPV diversity. The diversity of many pathogens is driven by type-specific or "homologous" immunity, which promotes the spread of variants to which hosts have little immunity. To test for homologous immunity and to identify mechanisms determining HPV transmission, we fitted nonlinear mechanistic models to longitudinal data on genital infections in unvaccinated men. Our results provide no evidence for homologous immunity, instead showing that infection with one HPV type strongly increases the risk of infection with that type for years afterward. For HPV16, the type responsible for most HPV-related cancers, an initial infection increases the 1-year probability of reinfection by 20-fold, and the probability of reinfection remains 14-fold higher 2 years later. This increased risk occurs in both sexually active and celibate men, suggesting that it arises from autoinoculation, episodic reactivation of latent virus, or both. Overall, our results suggest that high HPV prevalence and diversity can be explained by a combination of a lack of homologous immunity, frequent reinfections, weak competition between types, and variation in type fitness between host subpopulations. Because of the high risk of reinfection, vaccinating boys who have not yet been exposed may be crucial to reduce prevalence, but our results suggest that there may also be large benefits to vaccinating previously infected individuals.