Air pollution and the sequelae of COVID-19 patients: A multistate analysis.

IMPORTANCE: Recent evidence links air pollution to the severity COVID-19 symptoms and to death from the disease. To date, however, few studies have assessed whether air pollution affects the sequelae to more severe states or recovery from COVID-19 in a cohort with individual data. OBJECTIVE: To assess how air pollution affects the transition to more severe COVID-19 states or to recovery from COVID-19 infection in a cohort with detailed patient information. DESIGN AND OUTCOMES: We used a cohort design that followed patients admitted to hospital in the Kaiser Permanente Southern California (KPSC) Health System, which has 4.7 million members with characteristics similar to the general population. Enrollment began on 06/01/2020 and ran until 01/30/2021 for all patients admitted to hospital while ill with COVID-19. All possible states of sequelae were considered, including deterioration to intensive care, to death, discharge to recovery, or discharge to death. Transition risks were estimated with a multistate model. We assessed exposure using chemical transport model that predicted ambient concentrations of nitrogen dioxide, ozone, and fine particulate matter (PM2.5) at a 1 km scale. RESULTS: Each increase in PM2.5 concentration equivalent to the interquartile range was associated with increased risk of deterioration to intensive care (HR of 1.16; 95% CI: 1.12-1.20) and deterioration to death (HR of 1.11; 95% CI: 1.04-1.17). Results for ozone were consistent with PM2.5 effects, but ozone also affected the transition from recovery to death: HR of 1.24 (95% CI: 1.01-1.51). NO2 had weaker effects but displayed some elevated risks. CONCLUSIONS: PM2.5 and ozone were significantly associated with transitions to more severe states while in hospital and to death after discharge from hospital. Reducing air pollution could therefore lead to improved prognosis for COVID-19 patients and a sustainable means of reducing the health impacts of coronaviruses now and in the future.

Machine learning versus regression for prediction of sporadic pancreatic cancer.

BACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed. RESULTS: The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively. CONCLUSIONS: The three models complement each other, but each has unique contributions.

Assessment of Disruptive Life Events for Individuals Diagnosed With Schizophrenia or Bipolar I Disorder Using Data From a Consumer Credit Reporting Agency.

Importance: There is a dearth of population-level data on major disruptive life events (defined here as arrests by a legal authority, address changes, bankruptcy, lien, and judgment filings) for patients with bipolar I disorder (BPI) or schizophrenia, which has limited studies on mental health and treatment outcomes. Objective: To conduct a population-level study on disruptive life events by using publicly available data on disruptive life events, aggregated by a consumer credit reporting agency in conjunction with electronic health record (EHR) data. Design, Setting, and Participants: This study used EHR data from 2 large, integrated health care systems, Kaiser Permanente Southern California and Henry Ford Health. Cohorts of patients diagnosed from 2007 to 2019 with BPI or schizophrenia were matched 1:1 by age at analysis, age at diagnosis (if applicable), sex, race and ethnicity, and Medicaid status to (1) an active comparison group with diagnoses of major depressive disorder (MDD) and (2) a general health (GH) cohort without diagnoses of BPI, schizophrenia, or MDD. Patients with diagnoses of BPI or schizophrenia and their respective comparison cohorts were matched to public records data aggregated by a consumer credit reporting agency (98% match rate). Analysis took place between November 2020 and December 2022. Main Outcomes and Measures: The differences in the occurrence of disruptive life events among patients with BPI or schizophrenia and their comparison groups. Results: Of 46 167 patients, 30 008 (65%) had BPI (mean [SD] age, 42.6 [14.2] years) and 16 159 (35%) had schizophrenia (mean [SD], 41.4 [15.1] years). The majoriy of patients were White (30 167 [65%]). In addition, 18 500 patients with BPI (62%) and 6552 patients with schizophrenia (41%) were female. Patients with BPI were more likely to change addresses than patients in either comparison cohort (with the incidence ratio being as high as 1.25 [95% CI, 1.23-1.28]) when compared with GH cohort. Patients with BPI were also more likely to experience any of the financial disruptive life events with odds ratio ranging from 1.15 [95% CI, 1.07-1.24] to 1.50 [95% CI, 1.42-1.58]). The largest differences in disruptive life events were seen in arrests of patients with either BPI or schizophrenia compared with GH peers (3.27 [95% CI, 2.84-3.78] and 3.04 [95% CI, 2.57-3.59], respectively). Patients with schizophrenia had fewer address changes and were less likely to experience a financial event than their matched comparison cohorts. Conclusions and Relevance: This study demonstrated that data aggregated by a consumer credit reporting agency can support population-level studies on disruptive life events among patients with BPI or schizophrenia.

Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach.

BACKGROUND: New-onset diabetes (NOD) has been suggested as an early indicator of pancreatic cancer. However, the definition of NOD by the American Diabetes Association requires 2 simultaneous or consecutive elevated glycemic measures. We aimed to apply a machine-learning approach using electronic health records to predict the risk in patients with recent-onset hyperglycemia. MATERIALS AND METHODS: In this retrospective cohort study, health plan enrollees 50 to 84 years of age who had an elevated (6.5%+) glycated hemoglobin (HbA1c) tested in January 2010 to September 2018 with recent-onset hyperglycemia were identified. A total of 102 potential predictors were extracted. Ten imputation datasets were generated to handle missing data. The random survival forests approach was used to develop and validate risk models. Performance was evaluated by c -index, calibration plot, sensitivity, specificity, and positive predictive value. RESULTS: The cohort consisted of 109,266 patients (mean age: 63.6 y). The 3-year incidence rate was 1.4 (95% confidence interval: 1.3-1.6)/1000 person-years of follow-up. The 3 models containing age, weight change in 1 year, HbA1c, and 1 of the 3 variables (HbA1c change in 1 y, HbA1c in the prior 6 mo, or HbA1c in the prior 18 mo) appeared most often out of the 50 training samples. The c -indexes were in the range of 0.81 to 0.82. The sensitivity, specificity, and positive predictive value in patients who had the top 20% of the predicted risks were 56% to 60%, 80%, and 2.5% to 2.6%, respectively. CONCLUSION: Targeting evaluation at the point of recent hyperglycemia based on elevated HbA1c could offer an opportunity to identify pancreatic cancer early and possibly impact survival in cancer patients.

Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California.

BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.

INTRODUCTION: There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS: This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS: The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean = 0.77, SD = 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION: Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.

Development and validation of the COVID-19 Hospitalized Patient Deterioration Index.

OBJECTIVES: To develop a COVID-19-specific deterioration index for hospitalized patients: the COVID Hospitalized Patient Deterioration Index (COVID-HDI). This index builds on the proprietary Epic Deterioration Index, which was not developed for predicting respiratory deterioration events among patients with COVID-19. STUDY DESIGN: A retrospective observational cohort was used to develop and validate the COVID-HDI model to predict respiratory deterioration or death among hospitalized patients with COVID-19. Deterioration events were defined as death or requiring high-flow oxygen, bilevel positive airway pressure, mechanical ventilation, or intensive-level care within 72 hours of run time. The sample included hospitalized patients with COVID-19 diagnoses or positive tests at Kaiser Permanente Southern California between May 3, 2020, and October 17, 2020. METHODS: Machine learning models and 118 candidate predictors were used to generate benchmark performance. Logit regression with least absolute shrinkage and selection operator and physician input were used to finalize the model. Split-sample cross-validation was used to train and test the model. RESULTS: The area under the receiver operating curve was 0.83. COVID-HDI identifies patients at low risk (negative predictive value [NPV] > 98.5%) and borderline low risk (NPV > 95%) of an event. Of all patients, 74% were identified as being at low or borderline low risk at some point during their hospitalization and could be considered for discharge with or without home monitoring. A high-risk group with a positive predictive value of 51% included 12% of patients. Model performance remained high in a recent cohort of patients. CONCLUSIONS: COVID-HDI is a parsimonious, well-calibrated, and accurate model that may support clinical decision-making around discharge and escalation of care.

Validation of the Enriching New-Onset Diabetes for Pancreatic Cancer Model in a Diverse and Integrated Healthcare Setting.

BACKGROUND: The risk of pancreatic cancer is elevated among people with new-onset diabetes (NOD). Based on Rochester Epidemiology Project Data, the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) model was developed and validated. AIMS: We validated the END-PAC model in a cohort of patients with NOD using retrospectively collected data from a large integrated health maintenance organization. METHODS: A retrospective cohort of patients between 50 and 84 years of age meeting the criteria for NOD in 2010-2014 was identified. Each patient was assigned a risk score (< 1: low risk; 1-2: intermediate risk; ≥ 3: high risk) based on the values of the predictors specified in the END-PAC model. Patients who developed pancreatic ductal adenocarcinoma (PDAC) within 3 years were identified using the Cancer Registry and California State Death files. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated. RESULTS: Out of the 13,947 NOD patients who were assigned a risk score, 99 developed PDAC in 3 years (0.7%). Of the 3038 patients who had a high risk, 62 (2.0%) developed PDAC in 3 years. The risk increased to 3.0% in white patients with a high risk. The AUC was 0.75. At the 3+ threshold, the sensitivity, specificity, PPV, and NPV were 62.6%, 78.5%, 2.0%, and 99.7%, respectively. CONCLUSIONS: It is critical that prediction models are validated before they are implemented in various populations and clinical settings. More efforts are needed to develop screening strategies most appropriate for patients with NOD in real-world settings.

Association of Glycated Hemoglobin Levels With Risk of Pancreatic Cancer.

Importance: New-onset diabetes after the age of 50 years is a potential indicator of pancreatic cancer. Understanding the associations between hyperglycemia, diabetes, and pancreatic cancer, including pancreatic ductal adenocarcinoma, is key to developing an approach to early detection. Objective: To assess the association of elevation in glycated hemoglobin (HbA1c) with the risk of pancreatic cancer. Design, Setting, and Participants: This cohort study was conducted using data collected from an integrated health care system in California. A total of 851 402 patients aged 50 to 84 years who had HbA1c measurements taken between 2010 and 2014 were identified as the base cohort, with 12 contemporaneous cohorts created based on varying HbA1c thresholds (ie, 6.1%, 6.3%, 6.5%, and 6.7%) and prior diabetes status. Data analysis was conducted from August 2018 to September 2019. Main Outcomes and Measures: New cases of pancreatic cancer identified through cancer registry and California death files during a 3-year period. Three-year risk, incidence rate, sensitivity, number of patients needed to screen to detect 1 case, timing, and stage at diagnosis were determined. Results: Among 851 402 patients in the base cohort, 447 502 (52.5%) were women, 255 441 (30.0%) were Hispanic participants, 383 685 (45.1%) were non-Hispanic white participants, 100 477 (11.8%) were Asian participants, and 88 969 (10.4%) were non-Hispanic black participants, with a median (interquartile range) age of 62 (56-69) years and a median (interquartile range) HbA1c level of 6.0% (5.7%-6.6%). The incidence rate of pancreatic cancer was 0.45 (95% CI, 0.43-0.49) per 1000 person-years. After excluding prior diabetes as well as confirmation of new-onset hyperglycemia based on an HbA1c level of 6.5%, a total of 20 012 patients remained, with 74 of 1041 pancreatic ductal adenocarcinoma cases (7.1%) from the base cohort included. The rate of pancreatic cancer was 0.72 (95% CI, 0.32-1.42) per 1000 person-years among Asian patients, 0.83 (95% CI, 0.35-1.71) per 1000 person-years among non-Hispanic black patients, 0.84 (95% CI, 0.48-1.37) per 1000 person-years among Hispanic patients, and 2.37 (95% CI, 1.75-3.14) per 1000 person-years among non-Hispanic white patients. Overall, 42 of 74 cancers (56.8%) were diagnosed within 1 year of the index laboratory test. Among 1041 total cases, 708 (68.0%) had staging information available, of whom 465 (65.7%) had stage III or IV disease at diagnosis. In the base cohort, the number needed to undergo evaluation to identify a single case of pancreatic ductal adenocarcinoma was 818 (95% CI, 770-869), with estimates ranging from 206 (95% CI, 160-264) to 600 (95% CI, 540-666) in the subcohorts. Conclusions and Relevance: The findings of this study suggest that screening patients for pancreatic cancer based solely on elevation in HbA1c level is unlikely to represent an effective strategy. Future efforts to identify a high-risk population based on changes in glycemic parameters should account for racial/ethnic differences.

Prediction of Pancreatic Cancer Based on Imaging Features in Patients With Duct Abnormalities.

OBJECTIVES: Abnormalities of the main pancreatic duct may be an early indicator of pancreatic ductal adenocarcinoma (PDAC). We develop and validate algorithms that predict the risk of PDAC using features identified on cross-sectional imaging and other clinical characteristics collected through electronic medical records. METHODS: Adult patients with abdominal computed tomography or magnetic resonance imaging in January 2006 to June 2016 demonstrating dilatation of main pancreatic duct were identified. Pancreas-related morphologic features were extracted from radiology reports using natural language processing. The cumulative incidence of PDAC with death as a competing risk was estimated using multistate models. Model discrimination was assessed using c-index. The models were internally validated using bootstrapping. RESULTS: The cohort consisted of 7819 patients (mean age, 71 years; 65% female). A total of 781 patients (10%) developed PDAC within 3 years after the first eligible imaging study. The final models achieved reasonable discrimination (c-index, 0.825-0.833). The 3-year average risk of PDAC in the top 5% of the total eligible patients was 56.0%, more than 20 times of the average risk among the bottom 50% of patients. CONCLUSIONS: Prediction models combining imaging features and clinical measures can be used to further stratify the risk of pancreatic cancer among patients with pancreas ductal dilatation.

Risk Factors and Complications Following Arthroscopic Tenotomy of the Long Head of the Biceps Tendon.

BACKGROUND: Controversy exits regarding performing a tenotomy versus a tenodesis of the long head of the biceps tendon (LHBT). PURPOSE: To evaluate the complications after arthroscopic tenotomy of the LHBT and characterize the incidence of cosmetic deformity, cramping, subjective weakness, and continued anterior shoulder pain (ASP). Additionally, to identify patient-related factors that may predispose a patient to these complications. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Records of patients who underwent an arthroscopic LHBT tenotomy at an integrated health care system under the care of 55 surgeons were retrospectively reviewed. Exclusion criteria included LHBT tenodesis, arthroplasty, neoplastic, or fracture surgery; age younger than 18 years; incomplete documentation of physical examination; or incomplete operative reports. Characteristic data, concomitant procedures, LHBT morphology, and postoperative complications were recorded. Patients with and without postoperative complications-including cosmetic deformity, subjective weakness, continued ASP, and cramping-were analyzed by age, sex, dominant arm, body mass index (BMI), smoking status, workers' compensation status, and intraoperative LHBT morphology to identify risk factors for developing these postoperative complications. RESULTS: A total of 192 patients who underwent LHBT tenotomy were included in the final analysis. Tenotomy was performed with concomitant shoulder procedures in all but 1 individual. The mean ± SD patient age was 60.6 ± 9.5 years, and 55% were male. The overall complication rate was 37%. The most common postoperative complications include cosmetic (Popeye) deformity (14.1%), subjective weakness (10.4%), cramping (10.4%), and continued postoperative ASP over the bicipital groove (7.8%). Every 10-year increase in age was associated with 0.52 (95% CI, 0.28-0.94) times the odds of continued ASP and 0.59 (95% CI, 0.36-0.98) times the odds of cramping pain. Male patients had 3.9 (95% CI, 1.4-10.8) times the odds of cosmetic (Popeye) deformity. Patients who had active workers' compensation claims had 12.5 (95% CI, 2.4-63.4) times the odds of having continued postoperative ASP. Tenotomy on the dominant arm, BMI, and active smoking status demonstrated no statistically significant association with postoperative complications. CONCLUSION: Patients experiencing complications after tenotomy were significantly younger and more likely to be male and to have a workers' compensation injury. LHBT tenotomy may best be indicated for elderly patients, female patients, and those without active workers' compensation claims.

Burden of Specialist-Diagnosed Chronic Cough in Adults.

BACKGROUND: The burden of chronic cough (CC) in patients seeking specialist care is infrequently researched. OBJECTIVE: To describe patient characteristics and disease burden associated with specialist-diagnosed CC. METHODS: Using administrative pharmacy and medical data, we identified patients aged 18 to 85 years with CC diagnosed by pulmonologists, allergists, otolaryngologists, or gastroenterologists. Patients were stratified into 4 subgroups on the basis of the presence or absence of common respiratory diseases or gastroesophageal reflux disease (GERD). Clinical features and health care resource utilization (HCRU) in the baseline and outcome years were compared between the CC subgroups. The baseline factors associated with persistence of CC and a comparison of the CC cohort to a matched noncough cohort were also determined. RESULTS: The 11,290 patients with specialist-diagnosed CC were aged about 61 years and 66.7% were females. The CC cohort experienced frequent GERD (44.1%), asthma (31.2%), obesity (24.3%), upper airway cough syndrome (20.4%), common cough complications (19.4%), and hospitalizations (9.8%). The patients with CC with both respiratory disease and GERD exhibited at baseline and follow-up the most common cough comorbidities, higher HCRU, specialist care, and dispensed respiratory and nonrespiratory medications including proton pump inhibitors, antitussives, psychotherapeutics, oral corticosteroids, and antibiotics compared with the other subgroups. A 40.6% persistence of CC occurred similarly between CC subgroups. In addition, patients with CC in the matched analysis experienced significantly more comorbidities, laboratory evaluations, HCRU, and antitussives than patients without cough. CONCLUSIONS: Specialist-diagnosed CC was associated with considerable disease burden, particularly among those with both respiratory disease and GERD. In addition, CC burden was more pronounced than in matched patients without cough.

Factors Associated With Opioid Use in Patients Hospitalized for Acute Pancreatitis.

Importance: Limited guidance exists regarding the optimal approach to management of pain in acute pancreatitis (AP). Objectives: To investigate sources of variability in opioid use for treatment of acute pain in patients hospitalized for AP and to explore a potential association of opioid prescribing patterns with length of stay. Design, Setting, and Participants: This retrospective cohort study included 4307 patients 18 years and older hospitalized for AP in a community-based integrated health care system, from January 1, 2008, to June 30, 2015. Analysis began in November 2017. Exposures: Opioid use was quantified by morphine equivalent dose (MED). Main Outcomes and Measures: Three analyses were performed: (1) factors associated with increased opioid administration during the initial 12 hours of hospitalization (baseline), (2) association of baseline opioid use with length of stay, and (3) frequency of opioid use 90 days after hospital discharge (persistent use). Results: The cohort included 4307 patients (median [interquartile range] age, 57.4 [44.0-70.2] years; 2241 women [52.0%]) with AP. At baseline, 3443 patients (79.9%) received opioids, and 388 patients (9.6%) had persistent opioid use after discharge. After adjusting for pain and other clinical factors, women received less MED than men (adjusted event ratio, 0.83; 95% CI, 0.79-0.86; P < .001). Hispanic and Asian patients received less MED than non-Hispanic white patients (adjusted event ratio, 0.85; 95% CI, 0.81-0.90; P < .001; and adjusted event ratio, 0.79; 95% CI, 0.72-0.86; P < .001, respectively). Alcohol-related AP etiology was associated with increased MED vs gallstone disorders (adjusted event ratio, 1.11; 95% CI, 1.05-1.18; P < .001). Two of 13 hospitals administered significantly less opioids compared with the others. Median (interquartile range) length of stay was independently associated with MED at baseline, with 3.0 (2.1-4.5) days among patients not receiving opioids vs 5.0 (3.2-8.7) days among patients in the highest quintile of MED (P < .001). Conclusions and Relevance: In addition to pain and disease severity, opioid use varied by etiology of AP, sex, race/ethnicity, and institution of treatment. Increased opioid use at baseline was associated with longer hospitalization. These findings suggest opportunities for improved approaches to pain control for patients with AP.

Enhanced Recovery in Mild Acute Pancreatitis: A Randomized Controlled Trial.

OBJECTIVES: Acute pancreatitis (AP) is a leading cause of hospitalization for a gastrointestinal illness in the United States. We hypothesized that enhanced recovery approaches may lead to earlier time to refeeding in patients with AP. METHODS: We performed a double-blind, randomized controlled trial of patients admitted with mild AP from July 2016 to April 2017 at a tertiary medical center. Participants were randomly assigned to receive either enhanced recovery consisting of nonopioid analgesia, patient-directed oral intake, and early ambulation versus standard treatment with opioid analgesia and physician-directed diet. Primary study end point was time to oral refeeding on an intent-to-treat basis. Secondary end points included differences in pancreatitis activity scores, morphine equivalents, length of stay, and 30-day readmissions. RESULTS: Forty-six participants enrolled. Median age was 53.1 years, and 54.3% were female. There was significant reduction in time to successful oral refeeding in the enhanced recovery versus standard treatment group (median, 13.8 vs 124.8 hours, P < 0.001). Pancreatitis activity scores trended lower at 48 to 96 hours among patients assigned to enhanced recovery (mean, 43.6 vs. 58.9, P = 0.32). No differences found in length of stay or 30-day readmissions. CONCLUSION: In this randomized controlled trial, enhanced recovery was safe and effective in promoting earlier time to refeeding in patients hospitalized with AP.

Serum Lactate and Mortality in Emergency Department Patients with Cancer.

INTRODUCTION: Patients with malignancy represent a particular challenge for the emergency department (ED) given their higher acuity, longer ED length of stay, and higher admission rate. It is unknown if patients with malignancies and hyperlactatemia are at increased risk of mortality. If serum lactic acid could improve detection of at-risk patients with cancer, it would be useful in risk stratification. There is also little evidence that "alarm" values of serum lactate (such as >/=4 mmol/L) are appropriate for the population of patients with cancer. METHODS: This was a continuous retrospective cohort study of approximately two years (2012-2014) at a single, tertiary hospital ED; 5,440 patients had serum lactic acid measurements performed in the ED. Of the 5,440 patients in whom lactate was drawn, 1,837 were cancer patients, and 3,603 were non-cancer patients. Cumulative unadjusted mortality (determined by hospital records and an external death tracking system) was recorded at one day, three days, seven days, and 30 days. We used logistic regression to examine the risk of mortality 30 days after the ED visit after adjusting for confounders. RESULTS: In an unadjusted analysis, we found no statistically significant difference in the mortality of cancer vs. non-cancer patients at one day and three days. Significant differences in mortality were found at seven days (at lactate levels of <2 and 4+) and at 30 days (at all lactate levels) based on cancer status. After adjusting for age, gender, and acuity level, 30-day mortality rates were significantly higher at all levels of lactic acid (<2, 2-4, 4+) for patients with malignancy. CONCLUSION: When compared with non-cancer patients, cancer patients with elevated ED lactic acid levels had an increased risk of mortality at virtually all levels and time intervals we measured, although these differences only reached statistical significance in later time intervals (Day 7 and Day 30). Our results suggest that previous work in which lactate "cutoffs" are used to risk-stratify patients with respect to outcomes may be insufficiently sensitive for patients with cancer. Relatively low serum lactate levels may serve as a marker for serious illness in oncologic patients who present to the ED.

Relationship of Blood Eosinophil Count to Exacerbations in Chronic Obstructive Pulmonary Disease.

BACKGROUND: Eosinophilic airway inflammation characterizes a chronic obstructive pulmonary disease (COPD) phenotype that requires more study. OBJECTIVE: To investigate the relationship of blood eosinophil count to exacerbations in COPD. METHODS: Using administrative pharmacy and health care utilization data from 2009 to 2012, we retrospectively identified patients 40 years or older with a COPD diagnosis, postbronchodilator FEV1/forced vital capacity ratio of less than 0.7, and a blood eosinophil count (N = 7,245). COPD exacerbations were defined as hospitalizations or emergency department visits with a primary diagnosis of COPD, or outpatient visits with systemic corticosteroid dispensing within ±14 days associated with an encounter code consistent with a COPD exacerbation. The relationship between the index blood eosinophil count and the rate of COPD exacerbations in the follow-up year was determined by multivariable analyses. RESULTS: Patients with COPD were predominantly male (57.1%), white (71.8%), often current or past smokers (75.4%), and had frequent comorbidities; 19.0% had eosinophil counts of greater than or equal to 300 cells/mm3, 76.1% were classified as moderate to very severe by lung function, and the COPD exacerbation rate was 0.38 per year (95% CI, 0.37-0.40). After adjustment for potential confounders, COPD exacerbations during 1-year follow-up were significantly greater for patients with blood eosinophil counts of greater than or equal to 300 cells/mm3 (rate ratio [RR], 1.25; 95% CI, 1.10-1.43), greater than or equal to 400 cells/mm3 (RR, 1.48; 95% CI, 1.26-1.75), and greater than or equal to 500 cells/mm3 (RR, 1.76; 95% CI, 1.45-2.14), respectively, compared with patients with eosinophils lower than the cutoffs. CONCLUSIONS: In this study, high blood eosinophil counts were an independent risk factor for future exacerbations in patients with COPD, a phenotype that might benefit from therapy directed at eosinophilic-driven disease and inflammation.

Evaluating changes to sodium content in school meals at a large, urban school district in Los Angeles County, California.

CONTEXT: Children consume more than one-third of their daily food intake in schools, suggesting that these environments are ideal places for intervening on poor dietary behaviors. OBJECTIVE: To assess the impact of strategy-focused menu planning on the sodium content of student meals served in the Los Angeles Unified School District (LAUSD). DESIGN: Pre- and post-LAUSD menu change analyses for school years (SY) 2010-2011 and 2011-2012 were performed using nutritional analysis data and food production records. The analyses assessed changes in sodium content by meal categories. SETTING: 900+ schools, grades K-12, operated by the LAUSD. PARTICIPANTS: The LAUSD Food Services Branch, which serves about 650 000 meals per day. INTERVENTION: A multistage menu planning approach that focused on implementing evidence-based strategies to improve the nutritional content of school breakfast and lunch menus. Engagement and formation of multisectoral partnerships, including public health and parent/student groups, were vital elements of the intervention process. MAIN OUTCOME MEASURE(S): Sodium content changes in the LAUSD menu, SY 2010-2011 versus SY 2011-2012; other measures include documentation of program reach. RESULTS: From SY 2010-2011 to SY 2011-2012, the mean unweighted sodium levels for elementary (K-5) breakfast and for secondary (6-12) breakfast and lunch decreased. These changes met or exceeded the 2014-2015 US Department of Agriculture sodium targets for school meals and for secondary breakfast, the 2022-2023 target(s). These results, however, were not as notable once student food selection patterns (weighted data) and condiments were considered in the analysis. CONCLUSIONS: Use of strategy-focused menu planning as a mechanism to reduce sodium in school meals appeared to be promising, demonstrating favorable declines in mean sodium levels for at least 3 of 4 meal categories in the LAUSD. Student food selection patterns and condiments use, however, can affect the strength of the intervention.