Development and validation of an overdose risk prediction tool using prescription drug monitoring program data.

OBJECTIVES: To develop and validate a machine-learning algorithm to predict fatal overdose using Pennsylvania Prescription Drug Monitoring Program (PDMP) data. METHODS: The training/testing (n = 3020,748) and validation (n = 2237,701) cohorts included Pennsylvania residents with a prescription dispensing from February 2018-September 2021. Potential predictors (n = 222) were measured in the 6 months prior to a random index date. Using a gradient boosting machine, we developed a 20-variable model to predict risk of fatal drug overdose in the 6 months after the index date. RESULTS: Beneficiaries in the training (n = 1,812,448), testing (n = 1,208,300), and validation (n = 2,237,701) samples had similar age, with low rates of fatal overdose during 6-month follow up (0.12%, 0.12%, 0.04%, respectively). The validation c-statistic was 0.86 for predicting fatal overdose using 20 PDMP variables. When ranking individuals based on risk score, the prediction model more accurately identified fatal overdose at 6 months compared to using opioid dosage or opioid/benzodiazepine overlap, although the percentage of individuals in the highest risk percentile who died at 6 months was less than 1%. CONCLUSIONS AND POLICY IMPLICATIONS: A gradient boosting machine algorithm predicting fatal overdose derived from twenty variables performed well in discriminating risk across testing and validation samples, improving on single factor risk measures like opioid dosage.

Cyclic helix B peptide ameliorated the sepsis-induced injury in human HPMEC cells through regulating NF-κB.

BACKGROUND: Sepsis is a life-threatening condition. The incidence of severe sepsis is increasing. Sepsis is often complicated with organ dysfunctions. Cyclic helix B peptide (CHBP) is a peptide derivant of erythropoietin with powerful tissue-protective efficacies. However, the role of CHBP in sepsis-induced injury remains unclear. MATERIAL AND METHODS: Lyso-phosphatidylserine (LPS) was used to induce sepsis in human pulmonary microvascular endothelial cells (HPMECs). Cell growth was detected using Cell Counting Kit-8. Cell permeability was measured using fluorescein isothiocyanate (FITC)-dextran. Cecal ligation and puncture (CLP) method was applied to induce sepsis and CHBP was provided to test its efficacy. Western blot assays were used to evaluate gene expression. RESULTS: Administration of CHBP ameliorated LPS-induced injury in HPMECs dose-dependently. Administration of CHBP decreased the permeability of LPS-treated HPMEC cells in a same way as well. Furthermore, we identified that recombinant CHBP protein (Re-CHBP) ameliorated CLP-induced injury in vivo. Finally, we found that administration of NF-κB activator, TNF-α, abolished the function of Re-CHBP in LPS-treated HPMEC cells. CONCLUSION: CHBP ameliorated sepsis-induced injury dose dependently both in vitro and in vivo through decreasing the permeability of HPMEC cells via suppressing NF-κB signaling and inflammation. Present findings highlight the importance of CHBP/NF-κB signaling in septic injury and provide new insights into therapeutic strategies for sepsis-induced injury.

Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study.

BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose. INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries. FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis.

BACKGROUND AND AIMS: The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions. DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%). MEASUREMENTS: We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time. FINDINGS: Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%). CONCLUSIONS: More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Predicting Mortality Risk After a Hospital or Emergency Department Visit for Nonfatal Opioid Overdose.

BACKGROUND: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose. OBJECTIVE: To predict risk of death after a nonfatal opioid overdose. DESIGN AND PARTICIPANTS: This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period. EXPOSURES, MAIN OUTCOME, AND MEASURES: Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup. KEY RESULTS: Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001). CONCLUSIONS: A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.

Evaluation of a Multicomponent Care Transitions Program for High-Risk Hospitalized Older Adults.

OBJECTIVES: To test the effectiveness of a multicomponent care transition intervention targeted at hospitalized patients, aged 75 years and older, at high risk for hospital readmissions, return emergency department (ED) visits, and related complications. DESIGN: Implementation as a quality improvement program with propensity-matched preintervention and concurrent comparison groups over a 12-month period. SETTING: A 400-bed community teaching hospital. PARTICIPANTS: Patients, aged 75 years and older, admitted to non-intensive care unit beds who met specific high-risk criteria. The intervention group included 202 patients, and the concurrent and preintervention comparison groups included 4142 and 4592 patients, respectively. MEASUREMENTS: Primary outcomes were 30-day hospital readmissions and returns to the ED; 7-day readmissions and ED visits were secondary measures. RESULTS: Among the 202 patients enrolled in the "Safe Transitions for At-Risk Patients" ("STAR") program, 37 (18.3%) were readmitted within 30 days, in contrast to 14.3% and 14.6% in the concurrent and preintervention comparison groups, respectively. Rates for 30-day return ED visits that did not result in hospitalization were 10.9% in the intervention group, and 7.2% and 7.9% in the comparison groups. STAR patients had greater 30-day ED use than patients in the preintervention comparison group (5.0 percentage points; 95% confidence interval = 0.8-9.3 percentage points; P = .020). Implementation challenges included suboptimal involvement of the participating hospital and post-acute care organizations and a relatively high proportion of patients who did not receive the intervention as planned, despite agreeing to participate before leaving the hospital. CONCLUSION: A multicomponent care transitions intervention targeting high-risk patients, aged 75 years and older, admitted to a community teaching hospital was not effective in reducing 30- or 7-day readmissions or return ED visits. Our implementation experience offers many lessons for future programs for similar high-risk geriatric populations. J Am Geriatr Soc 67:2634-2642, 2019.

Clinical, biochemical and molecular analysis of two infants with familial chylomicronemia syndrome.

Familial chylomicronemia syndrome (FCS) is a rare autosomal recessive disease due mainly to inherited deficiencies in the proteins or enzymes involved in the clearance of triglycerides from circulation. It usually happens in late childhood and adolescence, which can have serious consequences if misdiagnosed or untreated. In the present study, we investigated two Chinese male babies (A and B), 30d and 48d in age, respectively, who have milky plasma. Clinical, biochemical, and radiological assessments were performed, while samples from the patients were referred for molecular diagnosis, including genetic testing and subsequent analysis of related genes. The fasting serum lipids of the two patients showed extreme lipid abnormalities. Through a low-lipid formula diet including skimmed milk and dietary advice, their plasma lipid levels were significantly lower and more stable at the time of hospital discharge. The genetic testing revealed compound heterozygote mutations in the lipoprotein lipase (LPL) gene for patient A and two known compound heterozygote LPL gene mutations for the patient B. FCS is the most dramatic example of severe hypertriglyceridemia. Early diagnosis and timely dietary intervention is very important for affected children.

Integrated measures for prevention of invasive Candida infections in preterm infants in a Chinese neonatal intensive care unit.

BACKGROUND: The increasing incidence of invasive Candida infections (ICIs) in preterm infants in the neonatal intensive care unit (NICU) of Xinhua Hospital aroused our concern. We undertook a retrospective study to evaluate the efficacy of different preventive measures for ICI in preterm infants. METHODS: Preterm infants with gestational age (GA) <33 weeks admitted between 2010 and 2013 were divided into 3 groups according to the preventive measures applied in different periods: the control group (CG), fluconazole group (FG), and integrated measures group (IMG). We analyzed the incidence of ICI and distribution of fungal pathogens in these 3 groups, and also evaluated the efficiency of various measures in preventing ICIs in preterm infants. RESULTS: The study sample comprised 261 preterm infants born at <33 weeks GA, including 94 in the CG, 99 in the FG, and 68 in the IMG. The differences among the groups were not significant at baseline. ICI developed in 41 of the 261 infants (15.7%). The incidence of ICI varied significantly among the groups: 22.3% in the CG (21/94), 18.2% in the FG (18/99), and only 2.9% in the IMG (2/68) (P = .003). ICI was less frequent in the IMG compared with the CG (P <.001) and the FG (P = .003). CONCLUSIONS: The integrated measures approach is meaningful for the prevention of ICIs in preterm infants in NICUs with many patients but inadequate medical resources in some developing countries.