Faculty Bibliography

OBJECTIVE:To develop a natural language processing (NLP) algorithm that identifies social determinants of health (SDoH), including housing, transportation, food, and medication insecurities, social isolation, abuse, neglect, or exploitation, and financial difficulties for patients with Alzheimer's disease and related dementias (ADRD) from unstructured electronic health records (EHRs). DATA SOURCES AND STUDY SETTING:We leveraged 1000 medical notes randomly selected from 7401 emergency department and inpatient social worker notes generated between 2015 and 2019 for 231 unique patients diagnosed with ADRD at Michigan Medicine. STUDY DESIGN:We developed a rule-based NLP algorithm for the identification of seven domains of SDoH noted above. We also compared the rule-based algorithm with deep learning and regularized logistic regression approaches. These models were compared using accuracy, sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). All notes were split into 700 notes for training NLP algorithms, and 300 notes for validation. DATA COLLECTION/EXTRACTION METHODS:Social worker notes used in this study were extracted from the Michigan Medicine EHR database. PRINCIPAL FINDINGS:Of the 700 notes for training, F1 and AUC for the rule-based algorithm were at least 0.94 and 0.95, respectively, for all SDoH categories. Of the 300 notes for validation, F1 and AUC were at least 0.80 and 0.97, respectively, for all SDoH except housing and medication insecurities. The deep learning and regularized logistic regression algorithms had unsatisfactory performance. CONCLUSIONS:The rule-based algorithm can accurately extract SDoH information in all seven domains of SDoH except housing and medication insecurities. Findings from the algorithm can be used by clinicians and social workers to proactively address social needs of patients with ADRD and other vulnerable patient populations.

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Repeat kidney transplantation (re-KT) is the preferred treatment for patients with graft failure. Changing allocation policies, widening the risk profile of recipients, and improving dialysis care may have altered the survival benefit of a re-KT. We characterized trends in re-KT survival benefit over 3 decades and tested whether it differed by age, race/ethnicity, sex, and panel reactive assay (PRA). By using the Scientific Registry of Transplant Recipient data, we identified 25 419 patients who underwent a re-KT from 1990 to 2019 and 25 419 waitlisted counterfactuals from the same year with the same waitlisted time following graft failure. In the adjusted analysis, a re-KT was associated with a lower risk of death (adjusted hazard ratio [aHR] = 0.63; 95% confidence interval [CI], 0.61-0.65). By using the 1990-1994 era as a reference (aHR = 0.77; 95% CI, 0.69-0.85), incremental improvements in the survival benefit were noted (1995-1999: aHR = 0.72; 95% CI, 0.67-0.78: 2000-2004: aHR = 0.59; 95% CI, 0.55-0.63: 2005-2009: aHR = 0.59; 95% CI, 0.56-0.63: 2010-2014: aHR = 0.57; 95% CI, 0.53-0.62: 2015-2019: aHR = 0.64; 95% CI, 0.57-0.73). The survival benefit of a re-KT was noted in both younger (age = 18-64 years: aHR = 0.63; 95% CI, 0.61-0.65) and older patients (age ≥65 years: aHR = 0.66; 95% CI, 0.58-0.74; P_interaction = .45). Patients of all races/ethnicities demonstrated similar benefits with a re-KT. However, it varied by the sex of the recipient (female patients: aHR = 0.60; 95% CI, 0.56-0.63: male patients: aHR = 0.66; 95% CI, 0.63-0.68; P_interaction = .004) and PRA (0-20: aHR = 0.69; 95% CI, 0.65-0.74: 21-80: aHR = 0.61; 95% CI, 0.57-0.66; P_interaction = .02; >80: aHR = 0.57; 95% CI, 0.53-0.61; P_interaction< .001). Our findings support the continued practice of a re-KT and efforts to overcome the medical, immunologic, and surgical challenges of a re-KT.

RATIONALE & OBJECTIVE/UNASSIGNED:The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on hospitalizations in general and on dialysis patients in particular. This study modeled the impact of COVID-19 on hospitalizations of dialysis patients in 2020. STUDY DESIGN/UNASSIGNED:Retrospective cohort study. SETTING & PARTICIPANTS/UNASSIGNED:Medicare patients on dialysis in calendar year 2020. PREDICTORS/UNASSIGNED:COVID-19 status was divided into 4 stages: COVID1 (first 10 days after initial diagnosis), COVID2 (extends until the Post-COVID stage), Post-COVID (after 21 days with no COVID-19 diagnosis), and Late-COVID (begins after a hospitalization with a COVID-19 diagnosis); demographic and clinical characteristics; and dialysis facilities. OUTCOME/UNASSIGNED:The sequence of hospitalization events. ANALYTICAL APPROACH/UNASSIGNED:A proportional rate model with a nonparametric baseline rate function of calendar time on the study population. RESULTS/UNASSIGNED:A total of 509,609 patients were included in the study, 63,521 were observed to have a SARS-CoV-2 infection, 34,375 became Post-COVID, and 1,900 became Late-COVID. Compared with No-COVID, all 4 stages had significantly greater adjusted risks of hospitalizations with relative rates of 18.50 (95% CI, 18.19-18.81) for COVID1, 2.03 (95% CI, 1.99-2.08) for COVID2, 1.37 (95% CI, 1.35-1.40) for Post-COVID, and 2.00 (95% CI, 1.89-2.11) for Late-COVID. LIMITATIONS/UNASSIGNED:For Medicare Advantage patients, we only had inpatient claim information. The analysis was based on data from the year 2020, and the effects may have changed due to vaccinations, new treatments, and new variants. The COVID-19 effects may be somewhat overestimated due to missing information on patients with few or no symptoms and possible delay in COVID-19 diagnosis. CONCLUSIONS/UNASSIGNED:We discovered a marked time dependence in the effect of COVID-19 on hospitalization of dialysis patients, beginning with an extremely high risk for a relatively short period, with more moderate but continuing elevated risks later, and never returning to the No-COVID level.

INTRODUCTION/BACKGROUND:Extended venous thromboembolism prophylaxis (eVTEp) is recommended for select patients who have undergone major abdominopelvic surgery to prevent postdischarge venous thromboembolism (pdVTE). Criteria for selection of these patients are untested for this purpose and may be ineffective. To address this gap, we investigated the effectiveness of eVTEp on pdVTE rates. METHODS:A retrospective cohort study of patients undergoing abdominopelvic surgery from January 2016 to February 2020 was performed using data from the Michigan Surgical Quality Collaborative. pdVTE was the main outcome. Our exposure variable, eVTEp, was compared dichotomously. Length of stay (LOS) was compared categorically using clinically relevant groups. Age, race, cancer occurrence, inflammatory bowel disease, surgical approach, and surgical time were covariates among other variables. Descriptive statistics, propensity score matching, and multivariable logistic regression were performed to compare pdVTE rates. RESULTS:A total of 45,637 patients underwent abdominopelvic surgery. Of which, 3063 (6.71%) were prescribed eVTEp. Two hundred eighty-five (0.62%) had pdVTE. Of the 285, 59 (21%) patients received eVTEp, while 226 (79%) patients did not. After propensity score matching, multivariable logistic regression analysis showed pdVTE was associated with eVTEp and LOS of 5 d or more (P < 0.001). eVTEp was not associated with LOS. Further analysis showed increased risk of pdVTE with increasing LOS independent of prescription of eVTEp based on known risk factors. CONCLUSIONS:pdVTE was associated with increasing LOS but not with other VTE risk factors after propensity score matching. Current guidelines for eVTEp do not include LOS. Our findings suggest that LOS >5 d should be added to the criteria for eVTEp.