Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.

BACKGROUND: Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states. METHODS: Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression. RESULTS: We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination. CONCLUSIONS: A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

Cost-Effectiveness Analysis and Microsimulation of Serial Multiparametric Magnetic Resonance Imaging in Active Surveillance of Localized Prostate Cancer.

PURPOSE: Many localized prostate cancers will follow an indolent course. Management has shifted toward active surveillance (AS), yet an optimal regimen remains controversial especially regarding expensive multiparametric magnetic resonance imaging (MRI). We aimed to assess cost-effectiveness of MRI in AS protocols. MATERIALS AND METHODS: A probabilistic microsimulation modeled individual patient trajectories for men diagnosed with low-risk cancer. We assessed no surveillance, up-front treatment (surgery or radiation), and scheduled AS protocols incorporating transrectal ultrasound-guided (TRUS) biopsy or MRI based regimens at serial intervals. Lifetime quality-adjusted life-years and costs adjusted to 2020 US$ were used to calculate expected net monetary benefit at $50,000/quality-adjusted life-year and incremental cost-effectiveness ratios. Uncertainty was assessed with probabilistic sensitivity analysis and linear regression metamodeling. RESULTS: Conservative management with AS outperformed up-front definitive treatment in a modeled cohort reflecting characteristics from a multi-institutional trial. Biopsy decision conditional on positive imaging (MRI triage) at 2-year intervals provided the highest expected net monetary benefit (incremental cost-effectiveness ratio $44,576). Biopsy after both positive and negative imaging (MRI pathway) and TRUS biopsy based regimens were not cost-effective. MRI triage resulted in fewer biopsies while reducing metastatic disease or cancer death. Results were sensitive to test performance and cost. MRI triage was the most likely cost-effective strategy on probabilistic sensitivity analysis. CONCLUSIONS: For men with low-risk prostate cancer, our modeling demonstrated that AS with sequential MRI triage is more cost-effective than biopsy regardless of imaging, TRUS biopsy alone or immediate treatment. AS guidelines should specify the role of imaging, and prospective studies should be encouraged.

Analyzing real world data of blood transfusion adverse events: Opportunities and challenges.

BACKGROUND: Blood transfusions are a vital component of modern healthcare, yet adverse reactions to blood product transfusions can cause morbidity, and rarely result in mortality. Therefore, accurate reporting of transfusion related adverse events (TRAEs) is paramount to improved transfusion practice. This study aims to investigate real-world data (RWD) on TRAEs by evaluating differences between ICD 9/10-based electronic health records (EHR) and blood bank-specific reporting. STUDY DESIGN AND METHODS: TRAE data were retrospectively collected from a blood bank-specific database between Jan 2015 and June 2019 as the reference data source and compared it to ICD 9/10 diagnostic codes corresponding to various TRAEs. Seven reactions that have corresponding ICD 9/10 diagnostic codes were evaluated: Transfusion related circulatory overload (TACO), transfusion related acute lung injury (TRALI), febrile non-hemolytic reaction (FNHTR), transfusion-related anaphylactic reaction (TRA), acute hemolytic transfusion reaction (AHTR), delayed hemolytic transfusion reaction (DHTR), and delayed serologic reaction (DSTR). These accounted for 33% of the TRAEs at an academic institution during the study period. RESULTS: Among 18637 adult blood transfusion recipients, there were 229 unique patients with 263 TRAE related ICD codes in the EHR, while there were 191 unique patients with 287 TRAEs identified in the blood bank database. None of the categories of reaction we investigated had perfect alignment between ICD 9/10 codes and blood bank specific diagnoses. DISCUSSION: Multiple systemic challenges were identified that hinder effective reporting of TRAEs. Identifying factors causing inconsistent reporting between blood banks and EHRs is paramount to developing effective workability between these electronic systems, as well as across clinical and laboratory teams.

Clinical Trial Outcomes in Urology: Assessing Early Discontinuation, Results Reporting and Publication in ClinicalTrials.Gov Registrations 2007-2019.

PURPOSE: Clinical trials require significant resources, but benefits are only realized after trial completion and dissemination of results. We comprehensively assessed early discontinuation, registry results reporting, and publication by trial sponsor and subspecialty in urology trials. MATERIALS AND METHODS: We assessed trial registrations from 2007 to 2019 on ClinicalTrials.gov and publication data from PubMed®/MEDLINE®. Associations between sponsor or subspecialty with early discontinuation were assessed using Cox proportional hazards and results reporting or publication with logistic regression at 3 years after completion. RESULTS: Of 8,636 trials 3,541 (41.0%) were completed and 999 (11.6%) were discontinued. Of completed trials 26.9% reported results and 21.6% were published. Sponsors included academic institutions (53.1%), industry (37.1%) and the U.S. government (9.8%). Academic-sponsored (adjusted HR 0.81, 95% CI 0.69-0.96, p=0.012) and government-sponsored trials (adjusted HR 0.62, 95% CI 0.49-0.78, p <0.001) were less likely than industry to discontinue early. Government-sponsored trials were more likely to report (adjusted OR 1.72, 95% CI 1.17-2.54, p=0.006) and publish (adjusted OR 1.89, 95% CI 1.23-2.89, p=0.004). Academic-sponsored trials were less likely to report (adjusted OR 0.65, CI:0.48-0.88, p=0.006) but more likely to publish (adjusted OR 1.72, 95% CI 1.25-2.37, p <0.001). These outcomes were similar across subspecialties. However, endourology was more likely to discontinue early (adjusted HR 2.00, 95% CI 1.53-2.95, p <0.001), general urology was more likely to report results (adjusted OR 1.54, 95% CI 1.13-2.11, p=0.006) and andrology was less likely to publish (adjusted OR 0.53, 95% CI 0.35-0.81, p=0.003). CONCLUSIONS: Sponsor type is significantly associated with trial completion and dissemination. Government-sponsored trials had the best performance, while industry and academic-sponsored trials lagged in completion and results reporting, respectively. Subspecialty played a lesser role. Lack of dissemination remains a problem for urology trials.

Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study.

BACKGROUND: Unlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD). METHODS: This retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare. RESULTS: CLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases. CONCLUSIONS: The performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.

Contemporary Practices and Complications of Surgery for Thoracic Outlet Syndrome in the United States.

BACKGROUND: Thoracic outlet syndrome (TOS) surgery is relatively rare and controversial, given the challenges in diagnosis as well as wide variation in symptomatic and functional recovery. Our aims were to measure trends in utilization of TOS surgery, complications, and mortality rates in a nationally representative cohort and compare higher versus lower volume centers. METHODS: The National Inpatient Sample was queried using International Classification of Diseases, Ninth Revision, codes for rib resection and scalenectomy paired with axillo-subclavian aneurysm (arterial [aTOS]), subclavian deep vein thrombosis (venous [vTOS]), or brachial plexus lesions (neurogenic [nTOS]). Basic descriptive statistics, nonparametric tests for trend, and multivariable hierarchical regression models with random intercept for center were used to compare outcomes for TOS types, trends over time, and higher and lower volume hospitals, respectively. RESULTS: There were 3,547 TOS operations (for an estimated 18,210 TOS operations nationally) performed between 2010 and 2015 (89.2% nTOS, 9.9% vTOS, and 0.9% aTOS) with annual case volume increasing significantly over time (P = 0.03). Higher volume centers (≥10 cases per year) represented 5.2% of hospitals and 37.0% of cases, and these centers achieved significantly lower overall major complication (defined as neurologic injury, arterial or venous injury, vascular graft complication, pneumothorax, hemorrhage/hematoma, or lymphatic leak) rates (adjusted odds ratio [OR] 0.71 [95% confidence interval 0.52-0.98]; P = 0.04], but no difference in neurologic complications such as brachial plexus injury (aOR 0.69 [0.20-2.43]; P = 0.56) or vascular injuries/graft complications (aOR 0.71 [0.0.33-1.54]; P = 0.39). Overall mortality was 0.6%, neurologic injury was rare (0.3%), and the proportion of patients experiencing complications decreased over time (P = 0.03). However, vTOS and aTOS had >2.5 times the odds of major complication compared with nTOS (OR 2.68 [1.88-3.82] and aOR 4.26 [1.78-10.17]; P < 0.001), and ∼10 times the odds of a vascular complication (aOR 10.37 [5.33-20.19] and aOR 12.93 [3.54-47.37]; P < 0.001], respectively. As the number of complications decreased, average hospital charges also significantly decreased over time (P < 0.001). Total hospital charges were on average higher when surgery was performed in lower volume centers (<10 cases per year) compared with higher volume centers (mean $65,634 [standard deviation 98,796] vs. $45,850 [59,285]; P < 0.001). CONCLUSIONS: The annual number of TOS operations has increased in the United States from 2010 to 2015, whereas complications and average hospital charges have decreased. Mortality and neurologic injury remain rare. Higher volume centers delivered higher value care: less or similar operative morbidity with lower total hospital charges.

Major disparities in COVID-19 test positivity for patients with non-English preferred language even after accounting for race and social factors in the United States in 2020.

BACKGROUND: The COVID-19 pandemic has further exposed inequities in our society, demonstrated by disproportionate COVID-19 infection rate and mortality in communities of color and low-income communities. One key area of inequity that has yet to be explored is disparities based on preferred language. METHODS: We conducted a retrospective cohort study of 164,368 adults tested for COVID-19 in a large healthcare system across Washington, Oregon, and California from March - July 2020. Using electronic health records, we constructed multi-level models that estimated the odds of testing positive for COVID-19 by preferred language, adjusting for age, race/ethnicity, and social factors. We further investigated interaction between preferred language and both race/ethnicity and state. Analysis was performed from October-December 2020. RESULTS: Those whose preferred language was not English had higher odds of having a COVID-19 positive test (OR 3.07, p < 0.001); this association remained significant after adjusting for age, race/ethnicity, and social factors. We found significant interaction between language and race/ethnicity and language and state, but the odds of COVID-19 test positivity remained greater for those whose preferred language was not English compared to those whose preferred language was English within each race/ethnicity and state. CONCLUSIONS: People whose preferred language is not English are at greater risk of testing positive for COVID-19 regardless of age, race/ethnicity, geography, or social factors - demonstrating a significant inequity. Research demonstrates that our public health and healthcare systems are centered on English speakers, creating structural and systemic barriers to health. Addressing these barriers are long overdue and urgent for COVID-19 prevention.

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE: This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS: Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS: Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

Type 1 Diabetes Management With Technology: Patterns of Utilization and Effects on Glucose Control Using Real-World Evidence.

This retrospective cohort study evaluated diabetes device utilization and the effectiveness of these devices for newly diagnosed type 1 diabetes. Investigators examined the use of continuous glucose monitoring (CGM) systems, self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), and multiple daily injection (MDI) insulin regimens and their effects on A1C. The researchers identified 6,250 patients with type 1 diabetes, of whom 32% used CGM and 37.1% used CSII. A higher adoption rate of either CGM or CSII in newly diagnosed type 1 diabetes was noted among White patients and those with private health insurance. CGM users had lower A1C levels than nonusers (P = 0.039), whereas no difference was noted between CSII users and nonusers (P = 0.057). Furthermore, CGM use combined with CSII yielded lower A1C than MDI regimens plus SMBG (P <0.001).

Acute pain after breast surgery and reconstruction: A two-institution study of surgical factors influencing short-term pain outcomes.

BACKGROUND AND OBJECTIVES: Acute postoperative pain following surgery is known to be associated with chronic pain development and lower quality of life. We sought to analyze the relationship between differing breast cancer excisional procedures, reconstruction, and short-term pain outcomes. METHODS: Women undergoing breast cancer excisional procedures with or without reconstruction at two systems: an academic hospital (AH) and Veterans Health Administration (VHA) were included. Average pain scores at the time of discharge and at 30-day follow-up were analyzed across demographic and clinical characteristics. Linear mixed effects modeling was used to assess the relationship between patient/clinical characteristics and interval pain scores with a random slope to account for differences in baseline pain. RESULTS: Our study included 1402 patients at AH and 1435 at VHA, of which 426 AH and 165 patients with VHA underwent reconstruction. Pain scores improved over time and were found to be highest at discharge. Time at discharge, 30-day follow-up, and preoperative opioid use were the strongest predictors of high pain scores. Younger age and longer length of stay were independently associated with worse pain scores. CONCLUSIONS: Younger age, preoperative opioid use, and longer length of stay were associated with higher levels of postoperative pain across both sites.

Practice Patterns in Perioperative Nonopioid Analgesic Administration by Anesthesiologists in a Veterans Affairs Hospital.

OBJECTIVE: Although multimodal analgesia (MMA) is recommended for perioperative pain management, previous studies have found substantial variability in its utilization. To better understand the factors that influence anesthesiologists' choices, we assessed the associations between patient or surgical characteristics and number of nonopioid analgesic modes received intraoperatively across a variety of surgeries in a university-affiliated Veteran Affairs hospital. METHODS: We included elective inpatient surgeries (orthopedic, thoracic, spine, abdominal, and pelvic procedures) that used at least one nonopioid analgesic within a one-year period. Multivariable multinomial logistic regression models were used to estimate adjusted odds ratios and 95% confidence intervals (CIs). We also described the combinations of analgesia used in each surgical subtype and conducted exploratory analyses to test the associations between the number of modes used and postoperative outcomes. RESULTS: Of the 1,087 procedures identified, 33%, 53%, and 14% were managed with one, two, and three or more modes, respectively. Older patients had lower odds of receiving three or more modes (adjusted odds ratio [aOR] = 0.28, 95% confidence interval [CI] = 0.15-0.52), as were patients with more comorbidities (two modes: aOR = 0.87, 95% CI = 0.79-0.96; three or more modes: aOR = 0.81, 95% CI = 0.71-0.94). Utilization varied across surgical subtypes P < 0.0001). Increasing the number of modes, particularly use of regional anesthesia, was associated with shorter length of stay. CONCLUSIONS: Our study suggests that age, comorbidities, and surgical type contribute to variability in MMA utilization. Risks and benefits of multiple modes should be carefully considered for older and sicker patients. Future directions include developing patient- and procedure-specific perioperative MMA recommendations.

Integrating Adjuvant Analgesics into Perioperative Pain Practice: Results from an Academic Medical Center.

BACKGROUND: Opioid-sparing postoperative pain management therapies are important considering the opioid epidemic. Total knee arthroplasty (TKA) is a common and painful procedure accounting for a large number of opioid prescriptions. Adjuvant analgesics, nonopioid drugs with primary indications other than pain, have shown beneficial pain management and opioid-sparing effects following TKA in clinical trials. We evaluated the adjuvant analgesic gabapentin for its usage patterns and its effects on opioid use, pain, and readmissions. METHODS: This retrospective, observational study included 4,046 patients who received primary TKA between 2009 and 2017 using electronic health records from an academic tertiary care medical institute. Descriptive statistics and multivariate modeling were used to estimate associations between inpatient gabapentin use and adverse pain outcomes as well as inpatient oral morphine equivalents per day (OME). RESULTS: Overall, there was an 8.72% annual increase in gabapentin use (P < 0.001). Modeled estimates suggest that gabapentin is associated with a significant decrease in opioid consumption (estimate = 0.63, 95% confidence interval = 0.49-0.82, P < 0.001) when controlling for patient characteristics. Patients receiving gabapentin had similar discharge pain scores, follow-up pain scores, and 30-day unplanned readmission rates compared with patients receiving no adjuvant analgesics (P > 0.05). CONCLUSIONS: When assessed in a real-world setting over a large cohort of TKA patients, gabapentin is an effective pain management therapy that is associated with reduced opioid consumption-a national priority in this time of opioid crisis-while maintaining the same quality of pain management.

Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer.

BACKGROUND: The collection of patient-reported outcomes (PROs) is an emerging priority internationally, guiding clinical care, quality improvement projects and research studies. After the deployment of Patient-Reported Outcomes Measurement Information System (PROMIS) surveys in routine outpatient workflows at an academic cancer center, electronic health record data were used to evaluate survey completion rates and self-reported global health measures across 2 tumor types: breast and prostate cancer. METHODS: This study retrospectively analyzed 11,657 PROMIS surveys from patients with breast cancer and 4411 surveys from patients with prostate cancer, and it calculated survey completion rates and global physical health (GPH) and global mental health (GMH) scores between 2013 and 2018. RESULTS: A total of 36.6% of eligible patients with breast cancer and 23.7% of patients with prostate cancer completed at least 1 survey, with completion rates lower among black patients for both tumor types (P < .05). The mean T scores (calibrated to a general population mean of 50) for GPH were 48.4 ± 9 for breast cancer and 50.6 ± 9 for prostate cancer, and the GMH scores were 52.7 ± 8 and 52.1 ± 9, respectively. GPH and GMH were frequently lower among ethnic minorities, patients without private health insurance, and those with advanced disease. CONCLUSIONS: This analysis provides important baseline data on patient-reported global health in breast and prostate cancer. Demonstrating that PROs can be integrated into clinical workflows, this study shows that supportive efforts may be needed to improve PRO collection and global health endpoints in vulnerable populations.

PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes.

BACKGROUND: Most patients with prostate cancer are diagnosed with low-grade, localized disease and may not require definitive treatment. In 2012, the U.S. Preventive Services Task Force (USPSTF) recommended against prostate cancer screening to address overdetection and overtreatment. This study sought to determine the effect of guideline changes on prostate-specific antigen (PSA) screening and initial diagnostic stage for prostate cancer. PATIENTS AND METHODS: A difference-in-differences analysis was conducted to compare changes in PSA screening (exposure) relative to cholesterol testing (control) after the 2012 USPSTF guideline changes, and chi-square test was used to determine whether there was a subsequent decrease in early-stage, low-risk prostate cancer diagnoses. Data were derived from a tertiary academic medical center's electronic health records, a national commercial insurance database (OptumLabs), and the SEER database for men aged ≥35 years before (2008-2011) and after (2013-2016) the guideline changes. RESULTS: In both the academic center and insurance databases, PSA testing significantly decreased for all men compared with the control. The greatest decrease was among men aged 55 to 74 years at the academic center and among those aged ≥75 years in the commercial database. The proportion of early-stage prostate cancer diagnoses (

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.

OBJECTIVE: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches. MATERIALS AND METHODS: Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings. RESULTS: A total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question. CONCLUSION: Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.

Thirty-day unplanned postoperative inpatient and emergency department visits following thoracotomy.

BACKGROUND: Unplanned visits to the emergency department (ED) and inpatient setting are expensive and associated with poor outcomes in thoracic surgery. We assessed 30-d postoperative ED visits and inpatient readmissions following thoracotomy, a high morbidity procedure. MATERIALS AND METHODS: We retrospectively analyzed inpatient and ED administrative data from California, Florida, and New York, 2010-2011. "Return to care" was defined as readmission to inpatient facility or ED within 30 d of discharge. Factors associated with return to care were analyzed via multivariable logistic regressions with a fixed effect for hospital variability. RESULTS: Of 30,154 thoracotomies, 6.3% were admitted to the ED and 10.2% to the inpatient setting within 30 d of discharge. Increased risk of inpatient readmission was associated with Medicare (odds ratio [OR] 1.30; P < 0.001) and Medicaid (OR 1.31; P < 0.0001) insurance status compared to private insurance and black race (OR 1.18; P = 0.02) compared to white race. Lung cancer diagnosis (OR 0.83; P < 0.001) and higher median income (OR 0.89; P = 0.04) were associated with decreased risk of inpatient readmission. Postoperative ED visits were associated with Medicare (OR 1.24; P < 0.001) and Medicaid insurance status (OR 1.59; P < 0.001) compared to private insurance and Hispanic race (OR 1.19; P = 0.04) compared to white race. CONCLUSIONS: Following thoracotomy, postoperative ED visits and inpatient readmissions are common. Patients with public insurance were at high risk for readmission, while patients with underlying lung cancer diagnosis had a lower readmission risk. Emphasizing postoperative management in at-risk populations could improve health outcomes and reduce unplanned returns to care.

Utilization and effectiveness of multimodal discharge analgesia for postoperative pain management.

BACKGROUND: Although evidence-based guidelines recommend a multimodal approach to pain management, limited information exists on adherence to these guidelines and its association with outcomes in a generalized population. We sought to assess the association between discharge multimodal analgesia and postoperative pain outcomes in two diverse health care settings. METHODS: We evaluated patients undergoing four common surgeries associated with high pain in electronic health records from an academic hospital (AH) and Veterans Health Administration (VHA). Multimodal analgesia at discharge was characterized as opioids in combination with acetaminophen (O + A) and nonsteroidal antiinflammatory (O + A + N) drugs. Hierarchical models estimated associations of analgesia with 45-d follow-up pain scores and 30-d readmissions. RESULTS: We identified 7893 patients at AH and 34,581 at VHA. In both settings, most patients were discharged with O + A (60.6% and 54.8%, respectively), yet a significant proportion received opioids alone (AH: 24.3% and VHA: 18.8%). Combining acetaminophen with opioids was associated with decreased follow-up pain in VHA (Odds ratio [OR]: 0.86, 95% confidence interval [CI]: 0.79, 0.93) and readmissions (AH OR: 0.74, CI: 0.60, 0.90; VHA OR: 0.89, CI: 0.82, 0.96). Further addition of nonsteroidal antiinflammatory drugs was associated with further decreased follow-up pain (AH OR: 0.71, CI: 0.53, 0.96; VHA OR: 0.77, CI: 0.69, 0.86) and readmissions (AH OR: 0.46, CI: 0.31, 0.69; VHA OR: 0.84, CI: 0.76, 0.93). In both systems, patients receiving multimodal analgesia received 10%-40% less opioids per day compared to opioids only. CONCLUSIONS: A majority of surgical patients receive a multimodal pain approach at discharge yet many receive only opioids. Multimodal regimen at discharge was associated with better follow-up pain and all-cause readmissions compared to the opioid-only regimen.

Impact of rehabilitation on mortality and readmissions after surgery for hip fracture.

BACKGROUND: Hip fracture in elderly patients is a rising global public health concern because of population ageing, and increasing frailty. Long-term morbidity related to poor management of hip fracture is associated with decreased quality of life, survival, and increase in healthcare costs. Receiving postoperative rehabilitation is associated with better outcomes and a higher likelihood of returning to pre-existing level of functioning. However little is known about which postoperative rehabilitation pathways are more effective to optimize patient outcomes. Few studies have analyzed postoperative rehabilitation pathways in a universal healthcare system. The aim of this study is to analyze the impact of post-acute rehabilitation pathways on mortality and readmission in elderly patients undergoing surgery for hip fracture in a large metropolitan area in Italy. METHODS: In this retrospective cohort study, we analyzed 6-month mortality from admission and 6-month readmission after hospital discharge in patients who underwent surgical repair for hip fracture in the hospitals of the Bologna metropolitan area between 1.1.2013 and 30.6.2014. Data were drawn from the regional hospital discharge records database. Kaplan-Meier estimates and multiple Cox regression were used to analyze mortality as a function of rehabilitation pathways. Multiple logistic regression determined predictors of readmission. RESULTS: The study population includes 2208 patients, mostly women (n = 1677, 76%), with a median age of 83.8 years. Hospital rehabilitation was provided to 519 patients (23.5%), 907 (41.1%) received rehabilitation in private inpatient rehabilitation facilities (IRF) accredited by the National Health System, and 782 (35.4%) received no post-acute rehabilitation. Compared with patient receiving hospital rehabilitation, the other groups showed significantly higher mortality risks (no rehabilitation, Hazard Ratio (HR) = 2.19, 95%CI = 1.54-3.12, p < 0.001; IRF rehabilitation, HR = 1.66, 95%CI = 1.54-1.79, p < 0.001). The risk of readmission did not differ significantly among rehabilitation pathways. CONCLUSIONS: Intensive hospital rehabilitation was significantly associated with a lower risk of mortality compared to IRF rehabilitation and no rehabilitation. Our results may help in the development of evidence-based recommendations aimed to improve resource utilization and quality of care in hip fracture patients. Further research is warranted to investigate the impact of the rehabilitation pathway on other outcomes, such as patients' functional status and quality of life.

Temporal Relationship of Onset of Necrotizing Enterocolitis and Introduction of Enteric Feedings and Powdered Milk Fortifier.

OBJECTIVE: This article evaluates temporal relationships between onset of necrotizing enterocolitis (NEC) in preterm infants and introduction of enteral feedings or powdered human milk fortifier (HMF). STUDY DESIGN: This is a Poisson regression analysis of NEC cases at a single children's hospital between 1999 and 2009, using the self-controlled case series method to estimate adjusted daily event rate ratios (DERR) during postexposure intervals. RESULTS: Of 139 patients with a clinical diagnosis of NEC, 26 had early disease onset prior to initiation of feeding and were considered to be cases of spontaneous intestinal perforation (SIP). For the remaining 113 infants, the DERR for NEC onset were significantly greater on days for which infants were <14 days of age (DERR, 2.15; 95% confidence interval [CI], 1.22-3.79) or ≥31 weeks postmenstrual age (2.94; 95% CI, 1.51-5.83) or which fell within 14 days after initiation of enteric feeding (8.29; 95% CI, 4.73-14.53) or 4 days after introduction of HMF (12.32; 95% CI, 7.13-21.29). CONCLUSION: There are strong temporal associations between onset of NEC and initiation of enteral feeding or powdered HMF in preterm infants.

The Fifth Vital Sign: Postoperative Pain Predicts 30-day Readmissions and Subsequent Emergency Department Visits.

OBJECTIVE: We hypothesized that inpatient postoperative pain trajectories are associated with 30-day inpatient readmission and emergency department (ED) visits. BACKGROUND: Surgical readmissions have few known modifiable predictors. Pain experienced by patients may reflect surgical complications and/or inadequate or difficult symptom management. METHODS: National Veterans Affairs Surgical Quality Improvement data on inpatient general, vascular, and orthopedic surgery from 2008 to 2014 were merged with laboratory, vital sign, health care utilization, and postoperative complications data. Six distinct postoperative inpatient patient-reported pain trajectories were identified: (1) persistently low, (2) mild, (3) moderate or (4) high trajectories, and (5) mild-to-low or (6) moderate-to-low trajectories based on postoperative pain scores. Regression models estimated the association between pain trajectories and postdischarge utilization while controlling for important patient and clinical variables. RESULTS: Our sample included 211,231 surgeries-45.4% orthopedics, 37.0% general, and 17.6% vascular. Overall, the 30-day unplanned readmission rate was 10.8%, and 30-day ED utilization rate was 14.2%. Patients in the high pain trajectories had the highest rates of postdischarge readmissions and ED visits (14.4% and 16.3%, respectively, P < 0.001). In multivariable models, compared with the persistently low pain trajectory, there was a dose-dependent increase in postdischarge ED visits and readmission for pain-related diagnoses, but not postdischarge complications (χ trend P < 0.001). CONCLUSIONS: Postoperative pain trajectories identify populations at risk for 30-day readmissions and ED visits, and do not seem to be mediated by postdischarge complications. Addressing pain control expectations before discharge may help reduce surgical readmissions in high pain categories.