Accounting for Patient Preferences Regarding Life-Sustaining Treatment in Evaluations of Medical Effectiveness and Quality

Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates

OBJECTIVES

Home health care patients who are at risk for becoming Incapacitated with No Evident Advance Directives or Surrogates (INEADS) may benefit from timely intervention to assist them with advance care planning. This study aimed to develop natural language processing algorithms for identifying home care patients who do not have advance directives, family members, or close social contacts who can serve as surrogate decision-makers in the event that they lose decisional capacity.

DESIGN

Cross-sectional study of electronic health records.

SETTING AND PARTICIPANTS

Patients receiving post-acute care discharge services from a large home health agency in New York City in 2019 (n = 45,390 enrollment episodes).

METHODS

We developed a natural language processing algorithm for identifying information documented in free-text clinical notes (n = 1,429,030 notes) related to 4 categories: evidence of close relationships, evidence of advance directives, evidence suggesting lack of close relationships, and evidence suggesting lack of advance directives. We validated the algorithm against Gold Standard clinician review for 50 patients (n = 314 notes) to calculate precision, recall, and F-score.

RESULTS

Algorithm performance for identifying text related to the 4 categories was excellent (average F-score = 0.91), with the best results for "evidence of close relationships" (F-score = 0.99) and the worst results for "evidence of advance directives" (F-score = 0.86). The algorithm identified 22% of all clinical notes (313,290 of 1,429,030) as having text related to 1 or more categories. More than 98% of enrollment episodes (48,164 of 49,141) included at least 1 clinical note containing text related to 1 or more categories.

CONCLUSIONS AND IMPLICATIONS

This study establishes the feasibility of creating an automated screening algorithm to aid home health care agencies with identifying patients at risk of becoming INEADS. This screening algorithm can be applied as part of a multipronged approach to facilitate clinician support for advance care planning with patients at risk of becoming INEADS.

Potential Conflicts of Interest Arising from Dualism of Loyalty Imposed on Employees of Medical Institutions-Findings and Tools for Ethics Management

Background and Objective: Doctors should have full loyalty to their patients, while patients should be able to trust that physicians will act only in their best interests. However, doctors may be faced with situations where they must choose between the patient's interests and those of a third party. This article presents the results of a study that aimed to identify situations of duality in the decision-making process of medical workers, which can compromise their ethical behavior. Materials and Methods: A cross-sectional study was carried out on a sample of 1070 participants, employed in 120 healthcare facilities in the Republic of Moldova. An online questionnaire was completed anonymously. Descriptive statistics for discrete data were performed by estimating absolute and relative frequencies. To perform the multivariate analysis, the logistic regression was applied. Results: A large number (74.4%) of respondents admitted that they had faced situations of conflicts of interest. Every third respondent (35.3%) had experienced ethical dilemmas when access to expensive treatments should be ensured. Every fourth respondent experienced a conflict between the patient's interests and those of the institution (26.1%) or the insurance company (23.3%). As age increases, the probability of reporting the dilemma decreases. Physicians reported such dilemmas almost 3 times more often than nurses. A low rate of staff sought support when faced with dilemmas. Half of the respondents (50.6%) preferred to discuss the problem only with a colleague, and 40.1% preferred to find solutions without anyone's help. There were significant gaps within organizations in terms of the ethical dimension of the decision-making process. Conclusions: Managers should adopt clear institutional policies and tools to identify and prevent situations of dual loyalty. Ethical support should be offered to employees facing such situations. The need to promote an institutional climate based on trust and openness becomes evident.

Contribution of information about acute and geriatric characteristics to decisions about life-sustaining treatment for old patients in intensive care

BACKGROUND

Life-sustaining treatment (LST) in the intensive care unit (ICU) is withheld or withdrawn when there is no reasonable expectation of beneficial outcome. This is especially relevant in old patients where further functional decline might be detrimental for the self-perceived quality of life. However, there still is substantial uncertainty involved in decisions about LST. We used the framework of information theory to assess that uncertainty by measuring information processed during decision-making.

METHODS

Datasets from two multicentre studies (VIP1, VIP2) with a total of 7488 ICU patients aged 80 years or older were analysed concerning the contribution of information about the acute illness, age, gender, frailty and other geriatric characteristics to decisions about LST. The role of these characteristics in the decision-making process was quantified by the entropy of likelihood distributions and the Kullback-Leibler divergence with regard to withholding or withdrawing decisions.

RESULTS

Decisions to withhold or withdraw LST were made in 2186 and 1110 patients, respectively. Both in VIP1 and VIP2, information about the acute illness had the lowest entropy and largest Kullback-Leibler divergence with respect to decisions about withdrawing LST. Age, gender and geriatric characteristics contributed to that decision only to a smaller degree.

CONCLUSIONS

Information about the severity of the acute illness and, thereby, short-term prognosis dominated decisions about LST in old ICU patients. The smaller contribution of geriatric features suggests persistent uncertainty about the importance of functional outcome. There still remains a gap to fully explain decision-making about LST and further research involving contextual information is required.

TRIAL REGISTRATION

VIP1 study: NCT03134807 (1 May 2017), VIP2 study: NCT03370692 (12 December 2017).

The Future of Cardiothoracic Surgical Critical Care Medicine as a Medical Science: A Call to Action

Cardiothoracic surgical critical care medicine (CT-CCM) is a medical discipline centered on the perioperative care of diverse groups of patients. With an aging demographic and an increase in burden of chronic diseases the utilization of cardiothoracic surgical critical care units is likely to escalate in the coming decades. Given these projections, it is important to assess the state of cardiothoracic surgical intensive care, to develop goals and objectives for the future, and to identify knowledge gaps in need of scientific inquiry. This two-part review concentrates on CT-CCM as its own subspeciality of critical care and cardiothoracic surgery and provides aspirational goals for its practitioners and scientists. In part one, a list of guiding principles and a call-to-action agenda geared towards growth and promotion of CT-CCM are offered. In part two, an evaluation of selected scientific data is performed, identifying gaps in CT-CCM knowledge, and recommending direction to future scientific endeavors.

Advance Care Planning and Treatment Intensity Before Death Among Black, Hispanic, and White Patients Hospitalized with COVID-19

BACKGROUND

Black and Hispanic people are more likely to contract COVID-19, require hospitalization, and die than White people due to differences in exposures, comorbidity risk, and healthcare access.

OBJECTIVE

To examine the association of race and ethnicity with treatment decisions and intensity for patients hospitalized for COVID-19.

DESIGN

Retrospective cohort analysis of manually abstracted electronic medical records.

PATIENTS

7,997 patients (62% non-Hispanic White, 16% non-Black Hispanic, and 23% Black) hospitalized for COVID-19 at 135 community hospitals between March and June 2020 MAIN MEASURES: Advance care planning (ACP), do not resuscitate (DNR) orders, intensive care unit (ICU) admission, mechanical ventilation (MV), and in-hospital mortality. Among decedents, we classified the mode of death based on treatment intensity and code status as treatment limitation (no MV/DNR), treatment withdrawal (MV/DNR), maximal life support (MV/no DNR), or other (no MV/no DNR).

KEY RESULTS

Adjusted in-hospital mortality was similar between White (8%) and Black patients (9%, OR=1.1, 95% CI=0.9-1.4, p=0.254), and lower among Hispanic patients (6%, OR=0.7, 95% CI=0.6-1.0, p=0.032). Black and Hispanic patients were significantly more likely to be treated in the ICU (White 23%, Hispanic 27%, Black 28%) and to receive mechanical ventilation (White 12%, Hispanic 17%, Black 16%). The groups had similar rates of ACP (White 12%, Hispanic 12%, Black 11%), but Black and Hispanic patients were less likely to have a DNR order (White 13%, Hispanic 8%, Black 7%). Among decedents, there were significant differences in mode of death by race/ethnicity (treatment limitation: White 39%, Hispanic 17% (p=0.001), Black 18% (p<0.0001); treatment withdrawal: White 26%, Hispanic 43% (p=0.002), Black 28% (p=0.542); and maximal life support: White 21%, Hispanic 26% (p=0.308), Black 36% (p<0.0001)).

CONCLUSIONS

Hospitalized Black and Hispanic COVID-19 patients received greater treatment intensity than White patients. This may have simultaneously mitigated disparities in in-hospital mortality while increasing burdensome treatment near death.

Overlooked Shortcomings of Observational Studies of Interventions in Coronavirus Disease 2019: An Illustrated Review for the Clinician

The rapid spread of severe acute respiratory syndrome coronavirus 2 infection across the globe triggered an unprecedented increase in research activities that resulted in an astronomical publication output of observational studies. However, most studies failed to apply fully the necessary methodological techniques that systematically deal with different biases and confounding, which not only limits their scientific merit but may result in harm through misleading information. In this article, we address a few important biases that can seriously threaten the validity of observational studies of coronavirus disease 2019 (COVID-19). We focus on treatment selection bias due to patients’ preference on goals of care, medical futility and disability bias, survivor bias, competing risks, and the misuse of propensity score analysis. We attempt to raise awareness and to help readers assess shortcomings of observational studies of interventions in COVID-19.

Deaths following withdrawal of life-sustaining therapy: Opportunities for quality improvement?

BACKGROUND

Mortality is an important trauma center outcome. With many patients initially surviving catastrophic injuries and a growing proportion of geriatric patients, many deaths might occur following withdrawal of life-sustaining therapy (WLST). We utilized the American College of Surgeons Trauma Quality Improvement Program database to explore whether deaths following WLST might be preventable and to evaluate the impact of excluding patients who died following WLST on hospital performance.

METHODS

A retrospective cohort study was conducted using data derived from American College of Surgeons Trauma Quality Improvement Program. Adult trauma patients treated at Levels I and II centers in 2016 were included. Three cohorts of deceased patients were created to assess differences in hospital performance. The first included all deaths, the second included only those who died without WLST, and the third included deaths without WLST and deaths with WLST where death was preceded by a major complication. Hospitals were ranked based on their observed-to-expected mortality ratio calculated using each of the three decedent cohorts. Outcomes included absolute change in hospital ranking and change in performance outlier status between cohorts.

RESULTS

We identified 275,939 patients treated at 447 centers who met inclusion criteria. Overall mortality was 6.9% (n = 19,145). Withdrawal of life-sustaining therapy preceded 43.6% (n = 8,343) of deaths and 23% (n = 1,920) of these patients experienced a major complication before death. The median absolute change in hospital performance rank between the first and second cohort was 58 (p < 0.001), between the first and third cohort was 44 (p < 0.001), and between the second and third cohort was 23 (p < 0.001). Hospital performance outlier status changed significantly between cohorts.

CONCLUSION

The exclusion of patients who die following WLST from benchmarking efforts leads to a major change in hospital ranks. Potentially preventable deaths, such as those following a major complication, should not be excluded.

LEVEL OF EVIDENCE

Epidemiological study, level III.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates

IMPORTANCE

The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.

OBJECTIVE

To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.

EXPOSURES

Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.

MAIN OUTCOMES AND MEASURES

Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).

RESULTS

Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

Health Insurance and Disparities in Mortality among Older Survivors of Critical Illness: A Population Study

OBJECTIVES

The 1.5 million Medicare beneficiaries who survive intensive care each year have a high post-hospitalization mortality rate. We aimed to determine whether mortality after critical illness is higher for Medicare beneficiaries with Medicaid compared with those with commercial insurance.

DESIGN

A retrospective cohort study from 2010 through 2014 with 1 year of follow-up using the New York Statewide Planning and Research Cooperative System database.

SETTING

A New York State population-based study of older (age ≥65 y) survivors of intensive care.

PARTICIPANTS

Adult Medicare beneficiaries age 65 years or older who were hospitalized with intensive care at a New York State hospital and survived to discharge.

INTERVENTION

None.

MEASUREMENT

Mortality in the first year after hospital discharge.

RESULTS

The study included 340 969 Medicare beneficiary survivors of intensive care with a mean (standard deviation) age of 77 (8) years; 20% died within 1 year. There were 152 869 (45%) with commercial insurance, 78 577 (23%) with Medicaid, and 109 523 (32%) with Medicare alone. Compared with those with commercial insurance, those with Medicare alone had a similar 1-year mortality rate (adjusted hazard ratio [aHR] = 1.01; 95% confidence interval [CI] = .99-1.04), and those with Medicaid had a 9% higher 1-year mortality rate (aHR = 1.09; 95% CI = 1.05-1.12). Among those discharged home, the 1-year mortality rate did not vary by insurance coverage, but among those discharged to skilled-care facilities (SCFs), the 1-year mortality rate was 16% higher for Medicaid recipients (aHR = 1.16; 95% CI = 1.12-1.21; P for interaction <.001).

CONCLUSIONS

Older adults with Medicaid insurance have a higher 1-year post-hospitalization mortality compared with those with commercial insurance, especially among those discharged to SCFs. Future studies should investigate care disparities at SCFs that may mediate these higher mortality rates. J Am Geriatr Soc 67:2497-2504, 2019.

An administrative model for benchmarking hospitals on their 30-day sepsis mortality

Background

Given the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.

Methods

We conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.

Results

In 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson’s correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson’s correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.

Conclusions

A novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals’ performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4037-x) contains supplementary material, which is available to authorized users.

More than one in three proxies do not know their loved one's current code status: An observational study in a Maryland ICU

Rationale

The majority of ICU patients lack decision-making capacity at some point during their ICU stay. However the extent to which proxy decision-makers are engaged in decisions about their patient’s care is challenging to quantify.

Objectives

To assess 1)whether proxies know their patient’s actual code status as recorded in the electronic medical record (EMR), and 2)whether code status orders reflect ICU patient preferences as reported by proxy decision-makers.

Methods

We enrolled proxy decision-makers for 96 days starting January 4, 2016. Proxies were asked about the patient’s goals of care, preferred code status, and actual code status. Responses were compared to code status orders in the EMR at the time of interview. Characteristics of patients and proxies who correctly vs incorrectly identified actual code status were compared, as were characteristics of proxies who reported a preferred code status that did vs did not match actual code status.

Measurements and main results

Among 111 proxies, 42 (38%) were incorrect or unsure about the patient’s actual code status and those who were correct vs. incorrect or unsure were similar in age, race, and years of education (P>0.20 for all comparisons). Twenty-nine percent reported a preferred code status that did not match the patient’s code status in the EMR. Matching preferred and actual code status was not associated with a patient’s age, gender, income, admission diagnosis, or subsequent in-hospital mortality or with proxy age, gender, race, education level, or relation to the patient (P>0.20 for all comparisons).

Conclusions

More than 1 in 3 proxies is incorrect or unsure about their patient’s actual code status and more than 1 in 4 proxies reported that a preferred code status that did not match orders in the EMR. Proxy age, race, gender and education level were not associated with correctly identifying code status or code status concordance.

Competing and conflicting interests in the care of critically ill patients

Medical professionals are expected to prioritize patient interests, and most patients trust physicians to act in their best interest. However, a single patient is never a physician's sole concern. The competing interests of other patients, clinicians, family members, hospital administrators, regulators, insurers, and trainees are omnipresent. While prioritizing patient interests is always a struggle, it is especially challenging and important in the ICU setting where most patients lack the ability to advocate for themselves or seek alternative sources of care. This review explores factors that increase the risk, or the perception, that an ICU physician will reason, recommend, or act in a way that is not in their patient's best interest and discusses steps that could help minimize the impact of these factors on patient care.

Outcomes and Mortality Prediction Model of Critically Ill Adults With Acute Respiratory Failure and Interstitial Lung Disease

BACKGROUND

We aimed to examine short- and long-term mortality in a mixed population of patients with interstitial lung disease (ILD) with acute respiratory failure, and to identify those at lower vs higher risk of in-hospital death.

METHODS

We conducted a single-center retrospective cohort study of 126 consecutive adults with ILD admitted to an ICU for respiratory failure at a tertiary care hospital between 2010 and 2014 and who did not undergo lung transplantation during their hospitalization. We examined associations of ICU-day 1 characteristics with in-hospital and 1-year mortality, using Poisson regression, and examined survival using Kaplan-Meier curves. We created a risk score for in-hospital mortality, using a model developed with penalized regression.

RESULTS

In-hospital mortality was 66%, and 1-year mortality was 80%. Those with connective tissue disease-related ILD had better short-term and long-term mortality compared with unclassifiable ILD (adjusted relative risk, 0.6; 95% CI, 0.3-0.9; and relative risk, 0.6; 95% CI, 0.4-0.9, respectively). Our prediction model includes male sex, interstitial pulmonary fibrosis diagnosis, use of invasive mechanical ventilation and/or extracorporeal life support, no ambulation within 24 h of ICU admission, BMI, and Simplified Acute Physiology Score-II. The optimism-corrected C-statistic was 0.73, and model calibration was excellent (P = .99). In-hospital mortality rates for the low-, moderate-, and high-risk groups were 33%, 65%, and 96%, respectively.

CONCLUSIONS

We created a risk score that classifies patients with ILD with acute respiratory failure from low to high risk for in-hospital mortality. The score could aid providers in counseling these patients and their families.

Variation in mortality rates after admission to long-term acute care hospitals for ventilator weaning

PURPOSE

We sought to examine variation in long-term acute care hospital (LTACH) quality based on 90-day in-hospital mortality for patients admitted for weaning from mechanical ventilation.

METHODS

We developed an administrative risk-adjustment model using data from Medicare claims. We validated the administrative model against a clinical model using data from LTACHs participating in a 2002 to 2003 clinical registry. We then used our validated administrative model to assess national variation in 90-day in-hospital mortality rates in LTACHs from 2013.

RESULTS

The administrative risk-adjustment model was derived using data from 9447 patients admitted to 221 LTACHs in 2003. The model had good discrimination (C statistic=0.72) and calibration. Compared to a clinically derived model using data from 1163 patients admitted to 14 LTACHs, the administrative model generated similar performance estimates. National variation in risk-adjusted mortality was assessed using data from 20,453 patients admitted to 380 LTACHs in 2013. LTACH-specific risk-adjusted mortality rates varied from 8.4% to 48.1% (median: 24.2%, interquartile range: 19.7%-30.7%).

CONCLUSIONS

LTACHs vary widely in mortality rates, underscoring the need to better understand the sources of this variation and improve the quality of care for patients requiring long-term ventilator weaning.