Impact of Integrated Care Model (ICM) on Direct Medical Costs in Management of Advanced Chronic Obstructive Pulmonary Disease (COPD)

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction.

Objective

This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.

Methods

The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model’s predictions and suggest tailored interventions.

Results

Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.

Conclusions

Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Prioritization of indicators of the quality of care provided to older adults with frailty by key stakeholders from five canadian provinces

Background

To meet the needs of older adults with frailty better, it is essential to understand which aspects of care are important from their perspective. We therefore sought to assess the importance of a set of quality indicators (QI) for monitoring outcomes in this population.

Methods

In this mixed-method study, key stakeholders completed a survey on the importance of 36 QIs, and then explained their ratings in a semi-structured interview. Stakeholders included older adults with frailty and their caregivers, healthcare providers (HCPs), and healthcare administrators or policy/decision makers (DMs). We conducted descriptive statistical analyses of quantitative variables, and deductive thematic qualitative analyses of interview transcripts.

Results

The 42 participants (8 older adults, 18 HCPs, and 16 DMs) rated six QIs as more important: increasing the patients’ quality of life; increasing healthcare staff skills; decreasing patients’ symptoms; decreasing family caregiver burden; increasing patients’ satisfaction with care; and increasing family doctor continuity of care.

Conclusions

Key stakeholders prioritized QIs that focus on outcomes targeted to patients and caregivers, whereas the current healthcare systems generally focus on processes of care. Quality improvement initiatives should therefore take better account of aspects of care that are important for older adults with frailty, such as having a chance to express their individual goals of care, receiving quality communications from HCPs, or monitoring symptoms that they might not spontaneously describe. Our results point to the need for patient-centred care that is oriented toward quality of life for older adults with frailty.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02843-9.

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

Objective

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

Methods

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

Results

The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Conclusions

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Predicting Severe Chronic Obstructive Pulmonary Disease Exacerbations. Developing a Population Surveillance Approach with Administrative Data

Rationale: Automatic prediction algorithms based on routinely collected health data may be able to identify patients at high risk for hospitalizations related to acute exacerbations of chronic obstructive pulmonary disease (COPD).Objectives: To conduct a proof-of-concept study of a population surveillance approach for identifying individuals at high risk of severe COPD exacerbations.Methods: We used British Columbia's administrative health databases (1997-2016) to identify patients with diagnosed COPD. We used data from the previous 6 months to predict the risk of severe exacerbation in the next 2 months after a randomly selected index date. We applied statistical and machine-learning algorithms for risk prediction (logistic regression, random forest, neural network, and gradient boosting). We used calibration plots and receiver operating characteristic curves to evaluate model performance based on a randomly chosen future date at least 1 year later (temporal validation).Results: There were 108,433 patients in the development dataset and 113,786 in the validation dataset; of these, 1,126 and 1,136, respectively, were hospitalized for COPD within their outcome windows. The best prediction algorithm (gradient boosting) had an area under the receiver operating characteristic curve of 0.82 (95% confidence interval, 0.80-0.83), which was significantly higher than the corresponding value for the model with exacerbation history as the only predictor (current standard of care: 0.68). The predicted risk scores were well calibrated in the validation dataset.Conclusions: Imminent COPD-related hospitalizations can be predicted with good accuracy using administrative health data. This model may be used as a means to target high-risk patients for preventive exacerbation therapies.

Macro and meso level influences on distributed integrated COPD care delivery: a social network perspective

Background

Care guidelines for people with chronic obstructive pulmonary disease (COPD) recommend an integrated approach for holistic, flexible, and tailored interventions. Continuity of care is also emphasised. However, many patients with COPD experience fragmented care. Discontinuities in healthcare and related social services are likely to result in disjointed rather than integrated care which can negatively affect patient health outcomes. The purpose of this qualitative study was to improve our understanding of, and how, contextual features pertaining to structures and processes of COPD integrated care influence delivery of care within patients’ healthcare networks.

Methods

We conducted individual interviews with 28 participants (9 patients, 16 healthcare professionals, and 3 spousal caregivers). Participants were recruited through the lung clinic at a city hospital in western Canada. We employed a social network paradigm to analyse and interpret the data.

Results

The analysis revealed an overarching theme of fragmented COPD care with two sub-themes: (1) Funding shortfalls and availability of resources, and (2) Dis(mis)connected communication pathways. The overarching theme depicts variations, delays, and discontinuities in patient care. The sub-themes describe how macro level influences and meso level shortfalls were perceived to influence the availability of respiratory care resources that contributed to fragmented COPD care.

Conclusions

Employing a social network lens drew particular attention to family physicians’ pivotal role in delivering community-based COPD care. While an integrated approach to care is recommended by care guidelines, institutional and organizational structures and processes, such as financial and communication structures, may inhibit delivery of integrated care. Thus, macro and meso level structures and processes have the potential to shape patient care by constraining family physicians’ purposive and communication actions necessary for facilitating an integrated distributed approach to care. We propose a context of care which fosters a context for family physicians’ delivery of patient-centered care. Integrated care delivery may improve patients’ wellbeing and alleviate financial constraints on the healthcare system.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Cost-Effectiveness Analysis of Integrated Care in Management of Advanced Chronic Obstructive Pulmonary Disease (COPD)

BACKGROUND Chronic obstructive pulmonary disease (COPD) is a common disease that occurs all over the world. Models of care, initially accessed from the clinical point of view, must also be evaluated in terms of their economic effectiveness, as health care systems are limited. The Integrated Care Model (ICM) is a procedure dedicated to patients suffering from advanced COPD that offers home-oriented support from a multidisciplinary team. The main aim of the present study was to evaluate the cost-effectiveness of the ICM. MATERIAL AND METHODS We included 44 patients in the study (31 males, 13 females) with an average age 72 years (Me=71). Costs of care were estimated based on data received from public payer records and included general costs, COPD-related costs, and exacerbation-related costs. To evaluate cost-effectiveness, cost-effectiveness analysis (CEA) was used. The incremental cost-effectiveness ratio (ICER) was calculated based on changes in health care resources utilization and the value of costs observed in 2 consecutive 6-month periods before and after introducing ICM. RESULTS Costs of care of all types decreased after introducing ICM. Demand for ambulatory visits changed significantly (p=0.037) together with a substantial decrease in the number of emergency department appointments and hospitalizations (p=0.033). ICER was more profitable for integrated care than for standard care when assessing costs of avoiding negative parameters such as hospitalizations (-227 EUR), exacerbations-related hospitalizations (-312 EUR), or emergency procedures (-119 EUR). CONCLUSIONS ICM is a procedure that meets the criteria of cost-effectiveness. It allows for avoiding negative parameters such as unplanned hospitalizations with higher economic effectiveness than the standard type of care used in managing COPD.

Assessment of Renal and Hepatic Tissue-Protective Effects of N-Acetylcysteine via Ammonia Metabolism: A Prospective Randomized Study

Background

The present study sought to assess the renal and liver protective effect of N-acetylcysteine through NH3 and urea metabolism in patients with chronic obstructive pulmonary disease who were scheduled for coronary artery bypass grafting surgery.

Material/Methods

Patients with chronic obstructive pulmonary disease (COPD) who were scheduled for coronary artery bypass grafting were divided into 2 groups so as to receive (Group 1, n=35) or not receive (Group 2, n=35) 900 mg/day of n-acetylcysteine for 7 days before the operation starting from their admission to the service by a pulmonologist with the purpose of treating COPD until the day of surgery. Both groups were subjected to the same anesthesia protocol. Blood samples were taken preoperatively, within the first 15^th minute following cessation of the cardiopulmonary bypass, at postoperative 24^th hour, and at postoperative 48^th hour. Blood tests included ammonia (NH3), lactate, blood urea nitrogen, creatinine, aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), troponin I (Tn I), and creatinine kinase-muscle brain (CKMB).

Results

There was a significant difference between the groups’ NH3 and lactate levels after cardiopulmonary bypass, postoperative 24^th hour, and postoperative 48^th hour (respectively, NH3: 39.0±8.8 vs. 55.4±19.6 and 40.1±8.4 vs. 53.2±20.2 mcg/dl, lactate: 1.7±0.9 vs. 2.1±1.2 and 1.2±0.5 vs. 1.8±1.4 mmol/L; p<0.01). Creatinine and BUN levels in Group 2 were found to be significantly higher at the postoperative 48^th hour compared to the levels of Group 1 (P<0.05).

Conclusions

N-acetylcysteine pretreatment appears to improve renal and hepatic functions through regulation of ammonia and nitrogen metabolism and reduction of lactate in patients with chronic obstructive pulmonary disease who undergo coronary artery bypass grafting surgery. We found that N-acetylcysteine improved kidney and/or liver functions.