Red Blood Cell Distribution Width is a Biomarker of Red Cell Dysfunction Associated with High Systemic Inflammation and a Prognostic Marker in Heart Failure and Cardiovascular Disease: A Potential Predictor of Atrial Fibrillation Recurrence

At the beginning of the 21st century, approximately 2.3 million US adults had atrial fibrillation (AF), and there has been a 60% increase in hospital admissions for AF. Given that the expectancy is a continuous increase in incidence, it portends a severe healthcare problem. Considerable evidence supports the immune system and inflammatory response in cardiac tissue, and circulatory processes are involved in the physiopathology of AF. In this regard, finding novel inflammatory biomarkers that predict AF recurrence after catheter ablation (CA) is a prime importance global healthcare problem. Many inflammatory biomarkers and natriuretic peptides came out and were shown to have predictive capabilities for AF recurrence in patients undergoing CA. In this regard, some studies have shown that red blood cell distribution width (RDW) is associated with the risk of incident AF. This review aimed to provide an update on the evidence of the RDW as a biomarker of red cell dysfunction and its association with high systemic inflammation, and with the risk of incident AF. Through the literature review, we will highlight the most relevant studies of the RDW related to AF recurrence after CA. Many studies demonstrated that RDW is associated with all cause-mortality, heart failure, cardiovascular disease, and AF, probably because RDW is a biomarker of red blood cell dysfunction associated with high systemic inflammation, reflecting an advanced heart disease with prognostic implications in heart failure and cardiovascular disease. Thus, suggesting that could be a potential predictor for AF recurrence after CA. Moreover, the RDW is a parameter included in routine full blood count, which is low-cost, quick, and easy to obtain. We provided an update on the evidence of the most relevant studies of the RDW related to AF recurrence after CA, as well as the mechanism of the high RDW and its association with high systemic inflammation and prognostic marker in cardiovascular disease and heart failure.

Intermittent fasting and changes in clinical risk scores: Secondary analysis of a randomized controlled trial

Background

Intermittent fasting may increase longevity and lower cardiometabolic risk. This study evaluated whether fasting modifies clinical risk scores for mortality [i.e., Intermountain Mortality Risk Score (IMRS)] or chronic diseases [e.g., Pooled Cohort Risk Equations (PCRE), Intermountain Chronic Disease score (ICHRON)].

Methods and results

Subjects (N = 71) completing the WONDERFUL trial were aged 21-70 years, had ≥1 metabolic syndrome criteria, elevated cholesterol, and no anti-diabetes medications, statins, or chronic diseases. The intermittent fasting arm underwent 24-h water-only fasting twice-per-week for 4 weeks and once-per-week for 22 weeks (26 weeks total). Analyses examined the IMRS change score at 26 weeks vs. baseline between intermittent fasting (n = 38) and ad libitum controls (n = 33), and change scores for PCRE, ICHRON, HOMA-IR, and a metabolic syndrome score (MSS). Age averaged 49 years; 65% were female. Intermittent fasting increased IMRS (0.78 ± 2.14 vs. controls: -0.61 ± 2.56; p = 0.010) but interacted with baseline IMRS (p-interaction = 0.010) to reduce HOMA-IR (but not MSS) more in subjects with higher baseline IMRS (median HOMA-IR change: fasters, -0.95; controls, +0.05) vs. lower baseline IMRS (-0.29 vs. -0.32, respectively). Intermittent fasting reduced ICHRON (-0.92 ± 2.96 vs. 0.58 ± 3.07; p = 0.035) and tended to reduce PCRE (-0.20 ± 0.22 vs. -0.14 ± 0.21; p = 0.054).

Conclusions

Intermittent fasting increased 1-year IMRS mortality risk, but decreased 10-year chronic disease risk (PCRE and ICHRON). It also reduced HOMA-IR more in subjects with higher baseline IMRS. Increased IMRS suggests fasting may elevate short-term mortality risk as a central trigger for myriad physiological responses that elicit long-term health improvements. Increased IMRS may also reveal short-term fasting-induced safety concerns.

Comparative Evaluation of Intermountain Risk Score With Mehran Risk Score for Risk Estimation of Contrast-Induced Nephropathy and Short-Term Mortality in ST-Segment Elevation Myocardial Infarction Patients

Contrast-induced nephropathy (CIN) has become one of the most important causes of in-hospital acute renal failure with the increasing use of contrast-mediated imaging tools. This significantly increases the morbidity and mortality of the affected subjects and causes a financial burden on the health system. In this context, prediction of CIN is important and some risk scores have been developed to predict CIN. The most frequently used and popular among these is the Mehran Score (MS), which is based on a number of hemodynamic and metabolic parameters. The Intermountain Risk Score (IMRS) is a recently developed risk score that highly predicts short-term mortality based on common laboratory parameters, and many parameters of this risk score have been found to be closely associated with CIN. In this context, we aimed to compare MS and IMRS in terms of CIN and short-term mortality estimation. The study included 931 patients who underwent percutaneous coronary intervention. CIN developed in 21.5% of patients. Both MS and IMRS independently predicted CIN. In receiver operating characteristic analysis, IMRS was found to be non-inferior to MS in predicting CIN and IMRS was superior to MS in predicting short-term mortality. IMRS and MS were independently associated with short-term mortality.

The influence of modifiable cardiovascular risk factors on cognition, functioning, and inflammatory markers in first-episode psychosis: Results from a 2-year follow-up study

To explore the influence of cardiovascular risk factors (CVRFs) on cognitive symptoms, functional impairment, and systemic inflammatory markers in first-episode psychosis (FEP) patients at baseline and 2-year follow-up. Method: In a sample of 70 FEP patients and 85 age- and sex-matched healthy controls, we assessed nine modifiable CVRFs. All participants were classified into two subgroups according to their CVRF profile: lower (0-1 CVRFs) or higher (≥2 CVRFs). The following outcomes were measured at baseline and 2-year follow-up: cognition; functional outcomes; and white blood cell (WBC) subtype. Adjusted general linear models were conducted to study the effect of diagnosis and CVRF profile on cognition, functioning, WBC, and longitudinal changes in these variables. At baseline, FEP patients with a higher CVRF profile showed a significantly slower performance on the TMT-A test for psychomotor speed and higher lymphocyte levels than patients with a lower CVRF profile. No longitudinal changes were observed in primary outcomes at 2-year follow-up. Among FEP patients with a higher CVRF profile, slower psychomotor speed performance did not correlate with increased lymphocyte levels. Our findings suggest that the cognitive effects of CVRFs manifest early in the course of psychosis, thus highlighting the importance of targeting both CVRFs and cognitive deficits in FEP.

Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors

BACKGROUND

Researchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events.

METHODS

We developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models.

RESULTS

Event rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models.

CONCLUSIONS

We conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.

Association of the Intermountain Risk Score with major adverse health events in patients positive for COVID-19: an observational evaluation of a US cohort

OBJECTIVES

The Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive.

DESIGN

Prospective observational cohort study.

SETTING

Primary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020.

PARTICIPANTS

Patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately.

RESULTS

Among 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged <60 years had HR=3.06 (95% CI 2.01 to 4.65) and HR=7.38 (95% CI 3.14 to 17.34) for moderate and high risks versus low risk, respectively; those ≥60 years had HR=1.95 (95% CI 0.99 to 3.86) and HR=3.40 (95% CI 1.63 to 7.07). In multivariable analyses, IMRS was independently predictive and was shown to capture substantial risk variation of comorbidities.

CONCLUSIONS

IMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.

Behavioral Nudges as Patient Decision Support for Medication Adherence: The ENCOURAGE Randomized Controlled Trial

BACKGROUND

Medication adherence is generally low and challenging to address because patient actions control healthcare delivery outside of medical environments. Behavioral nudging changes clinician behavior, but nudging patient decision-making requires further testing. This trial evaluated whether behavioral nudges can increase statin adherence, measured as the proportion of days covered (PDC).

METHODS

In a 12-month parallel-group, unblinded, randomized controlled trial, adult patients in Intermountain Healthcare cardiology clinics were enrolled. Inclusion required an indication for statins and membership in SelectHealth insurance. Subjects were randomized 1:1 to control or nudges. Nudge content, timing, frequency, and delivery route were personalized by CareCentra using machine learning of subject motivations and abilities from psychographic assessment, demographics, social determinants, and the Intermountain Mortality Risk Score. PDC calculation used SelectHealth claims data.

RESULTS

Among 182 subjects, age averaged 63.2±8.5 years, 25.8% were female, baseline LDL-C was 82.5±32.7 mg/dL, and 93.4% had coronary disease. Characteristics were balanced between nudge (n = 89) and control arms (n = 93). The statin PDC was greater at 12 months in the nudge group (PDC: 0.742±0.318) compared to controls (PDC: 0.639±0.358, P = 0.042). Adherent subjects (PDC ≥80%) were more concentrated in the nudge group (66.3% vs controls: 50.5%, P = 0.036) while a composite of death, myocardial infarction, stroke, and revascularization was non-significant (nudges: 6.7% vs control: 10.8%, P = 0.44).

CONCLUSIONS

Persuasive behavioral nudges driven by artificial intelligence resulted in a clinically important increase in statin adherence in general cardiology patients. This precision patient decision support utilized computerized nudge design and delivery with minimal on-going human input.

Validation of the Intermountain Risk Score and Get with the Guidelines-Heart Failure Score in predicting mortality

Objective

The Intermountain Risk Score (IMRS) was evaluated for validation as a mortality predictor and compared with the American Heart Association’s Get With The Guidelines—Heart Failure (GWTG-HF) risk score in a rural heart failure (HF) population.

Background

IMRS predicts mortality in general populations using common, inexpensive laboratory tests, patient age and sex, but requires validation in patients with HF.

Methods

Individuals were selected from the GWTG-HF registry at Essentia Health. This included consecutive HF inpatients age ≥18 years admitted July 2017–June 2019. IMRS was calculated using sex-specific weightings of the complete blood count, basic metabolic profile, and age.

Results

A total of 703 individuals (mean age: 74.12, 44.38% female) were studied. The 30-day IMRS predicted 30-day mortality for both sexes (females n=312: OR=1.19 (95% CI 1.08 to 1.32) per +1, p<0.001; males n=391: OR=1.23 (CI 1.12 to 1.36) per +1, p<0.001). The GWTG-HF risk score (only available in n=300, 42.7%) was independent of IMRS for 30-day mortality (OR=1.11 (CI 1.06 to 1.16) per +1, p<0.001). Using thresholds in bivariate modelling, IMRS (high vs low risk, OR=8.25 (CI 2.19 to 31.09), p=0.002) and the GWTG-HF score (tertile 3 vs 1: OR=2.18 (CI 0.84 to 5.68), p=0.11) independently predicted mortality. In multivariable analyses including covariables, IMRS (high vs low risk: OR=6.69 (CI 1.75 to 25.60), p=0.005) and the GWTG-HF score (tertile 3 vs 1: OR=2.62 (CI 0.96 to 7.12), p=0.06) remained predictors of mortality. Results were similar for 1-year mortality.

Conclusions

The IMRS and GWTG-HF scores predicted mortality of patients with HF in a large rural healthcare system. Future study of these scores as initial clinical risk estimators for evaluating their utility in improving patient health outcomes and increasing cost effectiveness is warranted.

The Summit Score Stratifies Mortality and Morbidity in Chronic Obstructive Pulmonary Disease

Introduction

Tobacco use and other cardiovascular risk factors often accompany chronic obstructive pulmonary disease (COPD). This study derived and validated the Summit Score to predict mortality in people with COPD and cardiovascular risks.

Methods

SUMMIT trial subjects (N=16,485) ages 40–80 years with COPD were randomly assigned 50%/50% to derivation (N=8181) and internal validation (N=8304). Three external COPD validations from Intermountain Healthcare included outpatients with cardiovascular risks (N=9251), outpatients without cardiovascular risks (N=8551), and inpatients (N=26,170). Cox regression evaluated 40 predictors of all-cause mortality. SUMMIT treatments including combined fluticasone furoate (FF) 100μg/vilanterol 25μg (VI) were not included in the score.

Results

Mortality predictors were FEV₁, heart rate, systolic blood pressure, body mass index, age, smoking pack-years, prior COPD hospitalizations, myocardial infarction, heart failure, diabetes, anti-thrombotics, anti-arrhythmics, and xanthines. Combined in the Summit Score (derivation: c=0.668), quartile 4 vs 1 had HR=4.43 in SUMMIT validation (p<0.001, 95% CI=3.27, 6.01, c=0.662) and HR=8.15 in Intermountain cardiovascular risk COPD outpatients (p<0.001, 95% CI=5.86, 11.34, c=0.736), and strongly predicted mortality in the other Intermountain COPD populations. Among all SUMMIT subjects with scores 14–19, FF 100μg/VI 25μg vs placebo had HR=0.76 (p=0.0158, 95% CI=0.61, 0.95), but FF 100μg/VI 25μg was not different from placebo for scores <14 or >19.

Conclusion

In this post hoc analysis of SUMMIT trial data, the Summit Score was derived and validated in multiple Intermountain COPD populations. The score was used to identify a subpopulation in which mortality risk was lower for FF 100μg/VI 25μg treatment.

Trial Registration

The SUMMIT trial is registered at ClinicalTrials.gov as number NCT01313676.

Comparative evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction

Background

The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown.

Methods

All inpatient admissions except obstetric patients at Intermountain Healthcare’s 21 adult care hospitals from 2010–2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient’s index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality).

Results

Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p<0.001) in univariate analyses. Dual score analysis of Charlson (HR = 1.79, CI = 1.66, 1.92) with IMRS (HR = 13.10, CI = 11.53, 14.87) and of Elixhauser (HR = 3.00, CI = 2.80, 3.21) with IMRS (HR = 11.42, CI = 10.09, 12.92) found significance for both scores in each model. Results were similar for 30-day, 1-year, and 5-year mortality.

Conclusions

IMRS provided the strongest ability to predict mortality, adding to and attenuating the predictive ability of the Charlson and Elixhauser indices whose mortality associations remained statistically significant. IMRS uses common, standardized, objective laboratory data and should be further evaluated for integration into mortality risk evaluations.

Hemoglobin and erythrocyte count are independently and positively associated with arterial stiffness in a community-based study

The association of blood hemoglobin (Hb) concentration and red blood cell (RBC) count with arterial stiffness is not well-defined. Herein, we examined the associations of brachial-ankle pulse wave velocity (baPWV) and augmentation index (AI) with Hb level and RBC count from a population cohort in and around Beijing, China. A total of 3994 participants (57.1 ± 8.8 years old) were included in our analysis. Blood routine examination, baPWV, and possible covariates were examined. The mean Hb, RBC count, AI corrected for a heart rate of 75 bpm (AIP75), and baPWV were 131.4 ± 17.1 g/l, 4.2 ± 0.5 10¹²/l, 80.2 ± 12.0%, and 1665.3 ± 377.1 cm/s, respectively, consistent with previously described cohorts. RBC counts and Hb levels were positively associated with baPWV (β for 10¹²/l RBC: 50.08 cm/s, 95% confidence interval [CI]: 30.54-69.63, p < 0.001; β for 10 g/l Hb: 9.05 cm/s, 95% CI: 3.35-14.76, p = 0.002) and AIP75 (β for 10¹²/l RBC: 1.33%, 95% CI: 0.55-2.12, p < 0.001; β for 10 g/l Hb: 0.34%, 95% CI: 0.12-0.57, p = 0.003), despite adjustment for covariates. The average levels of baPWV in the third-fourth quartile RBC groups were higher than in the first quartile (Q1) group (p < 0.001 for all). The average levels of baPWV in the fourth quartile Hb groups were higher than in the Q1 Hb group (p = 0.038). Mean AIP75 levels in the third-fourth RBC and Hb groups were higher than in the Q1 groups (p < 0.05 for all). In conclusion, circulating blood Hb levels and RBC counts are positively associated with arterial stiffness in our community-based study.

Laboratory-based Intermountain Validated Exacerbation (LIVE) Score stability in patients with chronic obstructive pulmonary disease

BACKGROUND

The Laboratory-based Intermountain Validated Exacerbation (LIVE) Score is associated with mortality and chronic obstructive pulmonary disease (COPD) exacerbation risk across multiple health systems. However, whether the LIVE Score and its associated risk is a stable patient characteristic is unknown.

METHODS

We validated the LIVE Score in a fourth health system. Then we determined the LIVE Score stability in a retrospective cohort of 98 766 patients with COPD in four health systems where it was previously validated. We assessed whether LIVE Scores changed or remained the same over time. Stability was defined as a majority of surviving patients having the same LIVE Score 4 years later.

RESULTS

The LIVE Score separated patients into three LIVE Score risk groups of low, medium, and high mortality and LIVE Score stability. Mortality ranged from 6.2% for low-risk LIVE to 45.8% for high-risk LIVE (p<0.001). We found that low-risk LIVE groups were stable and high-risk LIVE groups were unstable. Low-risk LIVE group patients remained low risk, but few high-risk LIVE group patients remained high risk (79.0% high vs 48.1% medium vs 8.8% low, p<0.001 for all pairwise comparisons).

CONCLUSION

The LIVE Score identifies three major clinically actionable cohorts: a stable low-risk LIVE group, an unstable high-risk LIVE group with high mortality rates, and a medium-risk LIVE group. These observations further our understanding of how existing data used to calculate the LIVE Score may target interventions across risk cohorts of patients with COPD in a health system.

Value of Neutrophil to Lymphocyte Ratio and Its Trajectory in Patients Hospitalized With Acute Heart Failure and Preserved Ejection Fraction

The neutrophil to lymphocyte ratio (NLR) has been proposed as a simple and routinely obtained marker of inflammation. This study sought to determine whether the NLR on admission as well as NLR trajectory would be complementary to the Get with the Guidelines Heart Failure (GWTG-HF) risk score in patients hospitalized with acute heart failure with preserved ejection fraction (HFpEF).Using the Stanford Translational Research Database, we identified 443 patients between January 2002 and December 2013 hospitalized with acute HFpEF and with complete data of NLR both on admission and at discharge. The primary endpoint was all-cause mortality. Mean age was 77 ± 16 years, 58% were female, with a high prevalence of diabetes mellitus (35.4%), coronary artery disease (58.2%), systemic hypertension (96.6%) and history of atrial fibrillation (57.5%). Over a median follow-up of 2.2 years, 121 (27.3%) patients died. The median NLR on admission was 6.5 (IQR 3.6 - 11.1); a majority of patients decreased their NLR during the course of hospitalization. On multivariable Cox modeling, both NLR on admission (HR 1.18 95% CI (1.00 - .38), p = 0.04) and absolute NLR trajectory (HR 1.26 95% CI (1.10 - 1.45), p = 0.001) were shown to be incremental to GWTG-HF risk score (p < 0.05) for outcome prediction. Adding the NLR or absolute NLR trajectory to the GWTG-HF risk score significantly improved the area under the operator-receiver curve and the reclassification up to 3 years after admission.This simple, readily available marker of inflammation may be useful when stratifying the risk of patients hospitalized with HFpEF.

Risk score-guided multidisciplinary team-based Care for Heart Failure Inpatients is associated with lower 30-day readmission and lower 30-day mortality

OBJECTIVE

Using augmented intelligence clinical decision tools and a risk score-guided multidisciplinary team-based care process (MTCP), this study evaluated the MTCP for heart failure (HF) patients' 30-day readmission and 30-day mortality across 20 Intermountain Healthcare hospitals.

BACKGROUND

HF inpatient care and 30-day post-discharge management require quality improvement to impact patient health, optimize utilization, and avoid readmissions.

METHODS

HF inpatients (N = 6182) were studied from January 2013 to November 2016. In February 2014, patients began receiving care via the MTCP based on a phased implementation in which the 8 largest Intermountain hospitals (accounting for 89.8% of HF inpatients) were crossed over sequentially in a stepped manner from control to MTCP over 2.5 years. After implementation, patient risk scores were calculated within 24 hours of admission and delivered electronically to clinicians. High-risk patients received MTCP care (n = 1221), while lower-risk patients received standard HF care (n = 1220). Controls had their readmission and mortality scores calculated retrospectively (high risk: n = 1791; lower risk: n = 1950).

RESULTS

High-risk MTCP recipients had 21% lower 30-day readmission compared to high-risk controls (adjusted P = .013, HR = 0.79, CI = 0.66, 0.95) and 52% lower 30-day mortality (adjusted P < .001, HR = 0.48, CI = 0.33, 0.69). Lower-risk patients did not experience increased readmission (adjusted HR = 0.88, P = .19) or mortality (adjusted HR = 0.88, P = .61). Some utilization was higher, such as prescription of home health, for MTCP recipients, with no changes in length of stay or overall costs.

CONCLUSIONS

A risk score-guided MTCP was associated with lower 30-day readmission and 30-day mortality in high-risk HF inpatients. Further evaluation of this clinical management approach is required.

Red blood cell distribution width and coronary artery disease severity in diabetic patients

Aim: The purpose of the study is to evaluate the relationship between red blood cell distribution width (RDW) and coronary calcium score in diabetic patients. Methods: Hematological parameters of 100 diabetic (Type II) patients were assessed. Computed tomographic angiography was used to asses coronary artery calcium (CAC) score. Results: Mean age of the study cohort was 55 years (males: 60%). Mean RDW was 12.7%. Mean CAC score was 243. There was a significant correlation between RDW and each of: CAC scores (r = 0.53; p < 0.001) and severity of coronary artery disease (CAD; r = 0.25; p = 0.047). A cut-off value >14.2% (receiver operating characteristic curves) predicted CAC score >400. A cut-off value >-14.6% predicted the presence of significant CAD. Conclusion: Diabetic patients with high-CAC scores and significant CAD had higher RDW.

Prediction of Long-Term Incidence of Chronic Cardiovascular and Cardiopulmonary Diseases in Primary Care Patients for Population Health Monitoring: The Intermountain Chronic Disease Model (ICHRON)

OBJECTIVE

To apply the practical parsimonious modeling method of the Intermountain Mortality Risk Score in a primary care environment to predict chronic disease (ChrD) onset.

PATIENTS AND METHODS

Primary care patients free of ChrD (women: n=98,711; men: n=45,543) were evaluated to develop (70% [n=95,882] of patients) and validate (the other 30% [n=48,372]) the sex-specific Intermountain Chronic Disease Risk Score (ICHRON) if seen initially between January 1, 2003, and December 31, 2005. The sex-specific ICHRON was composed of comprehensive metabolic profile and complete blood count components and age. The primary outcome was the first diagnosis of coronary artery disease, myocardial infarction, heart failure, atrial fibrillation, stroke, diabetes, renal failure, chronic obstructive pulmonary disease, peripheral vascular disease, or dementia within 3 years of baseline.

RESULTS

At 3 years, 9.0% of men (mean age, 44±16 years) and 6.6% of women (mean age, 42±16 years) received a diagnosis of ChrD. In the derivation population, C-statistics were 0.783 (95% CI, 0.774-0.791) for men and 0.774 (95% CI, 0.767-0.781) for women. In the validation population, C-statistics were 0.774 (95% CI, 0.762-0.786) for men and 0.762 (95% CI, 0.752-0.772) for women. Evaluation of 10-year outcomes for ICHRON and analysis of its association with each outcome individually at 3 years revealed similar predictive ability.

CONCLUSION

An augmented intelligence clinical decision tool for primary care, ICHRON, is developed using common laboratory parameters, which provides good discrimination of ChrD risk at 3 and 10 years.

Augmented intelligence decision tool for stroke prediction combines factors from CHA2 DS2 -VASc and the intermountain risk score for patients with atrial fibrillation

INTRODUCTION

CHA₂ DS₂ -VASc and CHADS₂ are computationally simple risk prediction tools used to guide anticoagulation decisions for stroke prophylaxis, but they have modest risk discrimination ability and use static dichotomous variables. The Intermountain Mortality Risk Scores (IMRS) are dynamic decision tools using standard clinical laboratory tests. This study derived new stroke prediction scores using variables from both CHA₂ DS₂ -VASc and IMRS.

METHODS AND RESULTS

In outpatients with first atrial fibrillation (AF) diagnosis at the Intermountain Healthcare (females, n = 26 063 males, n = 29 807), sex-specific "IMRS-VASc" scores were derived using variables from CHA₂ DS₂ -VASc, warfarin use, the complete blood count, and the comprehensive metabolic profile. Validation was performed in an independent Intermountain outpatient AF cohort (females, n = 11 021; males, n = 12 641). Stroke occurred among 3.1% and 3.1% of females and 2.3% and 2.5% of males in derivation and validation groups, respectively. IMRS-VASc stratified stroke with similar ability in derivation (c-statistics, females: c = 0.703, males: c = 0.697) and validation groups (females: c = 0.681, males: c = 0.685). CHA₂ DS₂ -VASc (females: c = 0.581 and c = 0.605; males: c = 0.616 and c = 0.613 in derivation and validation, respectively) and CHADS₂ (females: c = 0.581 and c = 0.608; males: c = 0.620 and c = 0.621 in derivation and validation, respectively) were substantially weaker stroke predictors. IMRS was the strongest mortality predictor (females: c = 0.783 and c = 0.782; males: c = 0.796 and c = 0.794 in derivation and validation, respectively) and all scores were poor at predicting bleeding risk.

CONCLUSIONS

A temporally dynamic risk score, IMRS-VASc was derived and validated as a predictor of stroke in outpatients with AF. IMRS-VASc requires further validation and the evaluation of its use in guiding care and treatment decisions for patients with AF.

Improving risk stratification in heart failure with preserved ejection fraction by combining two validated risk scores

Introduction

The Intermountain Risk Score (IMRS) was developed and validated to predict short-term and long-term mortality in hospitalised patients using demographics and commonly available laboratory data. In this study, we sought to determine whether the IMRS also predicts all-cause mortality in patients hospitalised with heart failure with preserved ejection fraction (HFpEF) and whether it is complementary to the Get with the Guidelines Heart Failure (GWTG-HF) risk score or N-terminal pro-B-type natriuretic peptide (NT-proBNP).

Methods and results

We used the Stanford Translational Research Integrated Database Environment to identify 3847 adult patients with a diagnosis of HFpEF between January 1998 and December 2016. Of these, 580 were hospitalised with a primary diagnosis of acute HFpEF. Mean age was 76±16 years, the majority being female (58%), with a high prevalence of diabetes mellitus (36%) and a history of coronary artery disease (60%). Over a median follow-up of 2.0 years, 140 (24%) patients died. On multivariable analysis, the IMRS and GWTG-HF risk score were independently associated with all-cause mortality (standardised HRs IMRS (1.55 (95% CI 1.27 to 1.93)); GWTG-HF (1.60 (95% CI 1.27 to 2.01))). Combining the two scores, improved the net reclassification over GWTG-HF alone by 36.2%. In patients with available NT-proBNP (n=341), NT-proBNP improved the net reclassification of each score by 46.2% (IMRS) and 36.3% (GWTG-HF).

Conclusion

IMRS and GWTG-HF risk scores, along with NT-proBNP, play a complementary role in predicting outcome in patients hospitalised with HFpEF.

Improving CHA2DS2-VASc stratification of non-fatal stroke and mortality risk using the Intermountain Mortality Risk Score among patients with atrial fibrillation

Background

Oral anticoagulation (OAC) therapy guidelines recommend using CHA₂DS₂-VASc to determine OAC need in atrial fibrillation (AF). A usable tool, CHA₂DS₂-VASc is challenged by its predictive ability. Applying components of the complete blood count and basic metabolic profile, the Intermountain Mortality Risk Score (IMRS) has been extensively validated. This study evaluated whether use of IMRS with CHA₂DS₂-VASc in patients with AF improves prediction.

Methods

Patients with AF undergoing cardiac catheterisation (N=10 077) were followed for non-fatal stroke and mortality (mean 5.8±4.1 years, maximum 19 years). CHA₂DS₂-VASc and IMRS were calculated at baseline. IMRS categories were defined based on previously defined criteria. Cox regression was adjusted for demographic, clinical and treatment variables not included in IMRS or CHA₂DS₂-VASc.

Results

In women (n=4122, mean age 71±12 years), the composite of non-fatal stroke/mortality was stratified (all p-trend <0.001) by CHA₂DS₂-VASc (1: 12.6%, 2: 22.8%, >2: 48.1%) and IMRS (low: 17.8%, moderate: 40.9%, high risk: 64.5%), as it was for men (n=5955, mean age 68±12 years) by CHA₂DS₂-VASc (<2: 15.7%, 2: 30.3%, >2: 51.8%) and IMRS (low: 19.0%, moderate: 42.0%, high risk: 65.9%). IMRS stratified stroke/mortality (all p-trend <0.001) in each CHA₂DS₂-VASc category.

Conclusions

Using IMRS jointly with CHA₂DS₂-VASc in patients with AF improved the prediction of stroke and mortality. For example, in patients at the OAC treatment threshold (CHA₂DS₂ -VASc = 2), IMRS provided ≈4-fold separation between low and high risk. IMRS provides an enhancing marker for risk in patients with AF that reflects the underlying systemic nature of this disease that may be considered in combination with the CHA₂DS₂-VASc score.

The Laboratory-Based Intermountain Validated Exacerbation (LIVE) Score Identifies Chronic Obstructive Pulmonary Disease Patients at High Mortality Risk

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR.

Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems.

Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates.

Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.

Preoperative Red Cell Distribution Width and 30-day mortality in older patients undergoing non-cardiac surgery: a retrospective cohort observational study

Increased red cell distribution width (RDW) is associated with poorer outcomes in various patient populations. We investigated the association between preoperative RDW and anaemia on 30-day postoperative mortality among elderly patients undergoing non-cardiac surgery. Medical records of 24,579 patients aged 65 and older who underwent surgery under anaesthesia between 1 January 2012 and 31 October 2016 were retrospectively analysed. Patients who died within 30 days had higher median RDW (15.0%) than those who were alive (13.4%). Based on multivariate logistic regression, in our cohort of elderly patients undergoing non-cardiac surgery, moderate/severe preoperative anaemia (aOR 1.61, p = 0.04) and high preoperative RDW levels in the 3rd quartile (>13.4% and ≤14.3%) and 4th quartile (>14.3%) were significantly associated with increased odds of 30-day mortality - (aOR 2.12, p = 0.02) and (aOR 2.85, p = 0.001) respectively, after adjusting for the effects of transfusion, surgical severity, priority of surgery, and comorbidities. Patients with high RDW, defined as >15.7% (90th centile), and preoperative anaemia have higher odds of 30-day mortality compared to patients with anaemia and normal RDW. Thus, preoperative RDW independently increases risk of 30-day postoperative mortality, and future risk stratification strategies should include RDW as a factor.

Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: a single-center retrospective study

INTRODUCTION

Accurate surgical risk prediction is paramount in clinical shared decision making. Existing risk calculators have limited value in local practice due to lack of validation, complexities and inclusion of non-routine variables.

OBJECTIVE

We aim to develop a simple, locally derived and validated surgical risk calculator predicting 30-day postsurgical mortality and need for intensive care unit (ICU) stay (>24 hours) based on routinely collected preoperative variables. We postulate that accuracy of a clinical history-based scoring tool could be improved by including readily available investigations, such as haemoglobin level and red cell distribution width.

METHODOLOGY

Electronic medical records of 90 785 patients, who underwent non-cardiac and non-neuro surgery between 1 January 2012 and 31 October 2016 in Singapore General Hospital, were retrospectively analysed. Patient demographics, comorbidities, laboratory results, surgical priority and surgical risk were collected. Outcome measures were death within 30 days after surgery and ICU admission. After excluding patients with missing data, the final data set consisted of 79 914 cases, which was divided randomly into derivation (70%) and validation cohort (30%). Multivariable logistic regression analysis was used to construct a single model predicting both outcomes using Odds Ratio (OR) of the risk variables. The ORs were then assigned ranks, which were subsequently used to construct the calculator.

RESULTS

Observed mortality was 0.6%. The Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator, consisting of nine variables, was constructed. The area under the receiver operating curve (AUROC) in the derivation and validation cohorts for mortality were 0.934 (0.917-0.950) and 0.934 (0.912-0.956), respectively, while the AUROC for ICU admission was 0.863 (0.848-0.878) and 0.837 (0.808-0.868), respectively. CARES also performed better than the American Society of Anaesthesiologists-Physical Status classification in terms of AUROC comparison.

CONCLUSION

The development of the CARES surgical risk calculator allows for a simplified yet accurate prediction of both postoperative mortality and need for ICU admission after surgery.

Clinically feasible stratification of 1-year to 3-year post-myocardial infarction risk

Objective

Post-myocardial infarction (MI) care is crucial to preventing recurrent major adverse cardiovascular events (MACE), but can be complicated to personalise. A tool is needed that effectively stratifies risk of cardiovascular (CV) events 1–3 years after MI but is also clinically usable.

Methods

Patients surviving ≥1 year after an index MI with ≥1 risk factor for recurrent MI (ie, age ≥65 years, prior MI, multivessel coronary disease, diabetes, glomerular filtration rate <60 mL/min/1.73 m²) were studied. Cox regression derived sex-specific Intermountain Major Adverse Cardiovascular Events (IMACE) risk scores for the composite of 1-year to 3-year MACE (CV death, MI or stroke). Derivation was performed in 70% of subjects (n=1342 women; 3047 men), with validation in the other 30% (n=576 women; 1290 men). Secondary validations were also performed.

Results

In women, predictors of CV events were glucose, creatinine, haemoglobin, platelet count, red cell distribution width (RDW), age and B-type natriuretic peptide (BNP); among men, they were potassium, glucose, blood urea nitrogen, haematocrit, white blood cell count, RDW, mean platelet volume, age and BNP. In the primary validation, in women, IMACE ranged from 0 to 11 (maximum possible: 12) and had HR=1.44 per +1 score (95% CI 1.29 to 1.61; P<0.001); men had IMACE range 0–14 (maximum: 16) and HR=1.29 per +1 score (95% CI 1.20 to 1.38; P<0.001). IMACE ≥5 in women (≥6 in men) showed strikingly higher MACE risk.

Conclusions

Sex-specific risk scores strongly stratified 1-year to 3-year post-MI MACE risk. IMACE is an inexpensive, dynamic, electronically delivered tool for evaluating and better managing post-MI patient care.

Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A classification and regression tree analysis

To develop a risk stratification model based on complete blood count (CBC) components in patients with acute coronary syndrome (ACS) using a classification and regression tree (CART) method. CBC variables and the Global Registry of Acute Coronary Events (GRACE) scores were determined in 2,693 patients with ACS. The CART analysis was performed to classify patients into different homogeneous risk groups and to determine predictors for major adverse cardiovascular events (MACEs) at 1-year follow-up. The CART algorithm identified the white blood cell count, hemoglobin, and mean platelet volume levels as the best combination to predict MACE risk. Patients were stratified into three categories with MACE rates ranging from 3.0% to 29.8%. Kaplan-Meier analysis demonstrated MACE risk increased with the ascending order of the CART risk categories. Multivariate Cox regression analysis showed that the CART risk categories independently predicted MACE risk. The predictive accuracy of the CART risk categories was tested by measuring discrimination and graphically assessing the calibration. Furthermore, the combined use of the CART risk categories and GRACE scores yielded a more accurate predictive value for MACEs. Patients with ACS can be readily stratified into distinct prognostic categories using the CART risk stratification tool on the basis of CBC components.

Assessment of cumulative health risk in the World Trade Center general responder cohort

BACKGROUND

Multiple comorbidities have been reported among rescue/recovery workers responding to the 9/11/2001 WTC disaster. In this study, we developed an index that quantifies the cumulative physiological burden of comorbidities and predicts life expectancy in this cohort.

METHODS

A machine learning approach (gradient boosting) was used to model the relationship between mortality and several clinical parameters (laboratory test results, blood pressure, pulmonary function measures). This model was used to construct a risk index, which was validated by assessing its association with a number of health outcomes within the WTC general responder cohort.

RESULTS

The risk index showed significant associations with mortality, self-assessed physical health, and onset of multiple chronic conditions, particularly COPD, hypertension, asthma, and sleep apnea.

CONCLUSION

As an aggregate of several clinical parameters, this index serves as a cumulative measure of physiological dysregulation and could be utilized as a prognostic indicator of life expectancy and morbidity risk.

Early inpatient calculation of laboratory-based 30-day readmission risk scores empowers clinical risk modification during index hospitalization

Improving 30-day readmission continues to be problematic for most hospitals. This study reports the creation and validation of sex-specific inpatient (i) heart failure (HF) risk scores using electronic data from the beginning of inpatient care for effective and efficient prediction of 30-day readmission risk.

METHODS

HF patients hospitalized at Intermountain Healthcare from 2005 to 2012 (derivation: n=6079; validation: n=2663) and Baylor Scott & White Health (North Region) from 2005 to 2013 (validation: n=5162) were studied. Sex-specific iHF scores were derived to predict post-hospitalization 30-day readmission using common HF laboratory measures and age. Risk scores adding social, morbidity, and treatment factors were also evaluated.

RESULTS

The iHF model for females utilized potassium, bicarbonate, blood urea nitrogen, red blood cell count, white blood cell count, and mean corpuscular hemoglobin concentration; for males, components were B-type natriuretic peptide, sodium, creatinine, hematocrit, red cell distribution width, and mean platelet volume. Among females, odds ratios (OR) were OR=1.99 for iHF tertile 3 vs. 1 (95% confidence interval [CI]=1.28, 3.08) for Intermountain validation (P-trend across tertiles=0.002) and OR=1.29 (CI=1.01, 1.66) for Baylor patients (P-trend=0.049). Among males, iHF had OR=1.95 (CI=1.33, 2.85) for tertile 3 vs. 1 in Intermountain (P-trend <0.001) and OR=2.03 (CI=1.52, 2.71) in Baylor (P-trend < 0.001). Expanded models using 182-183 variables had predictive abilities similar to iHF.

CONCLUSIONS

Sex-specific laboratory-based electronic health record-delivered iHF risk scores effectively predicted 30-day readmission among HF patients. Efficient to calculate and deliver to clinicians, recent clinical implementation of iHF scores suggest they are useful and useable for more precise clinical HF treatment.

White cell count in the normal range and short-term and long-term mortality: international comparisons of electronic health record cohorts in England and New Zealand

OBJECTIVES

Electronic health records offer the opportunity to discover new clinical implications for established blood tests, but international comparisons have been lacking. We tested the association of total white cell count (WBC) with all-cause mortality in England and New Zealand.

SETTING

Primary care practices in England (ClinicAl research using LInked Bespoke studies and Electronic health Records (CALIBER)) and New Zealand (PREDICT).

DESIGN

Analysis of linked electronic health record data sets: CALIBER (primary care, hospitalisation, mortality and acute coronary syndrome registry) and PREDICT (cardiovascular risk assessments in primary care, hospitalisations, mortality, dispensed medication and laboratory results).

PARTICIPANTS

People aged 30-75 years with no prior cardiovascular disease (CALIBER: N=686 475, 92.0% white; PREDICT: N=194 513, 53.5% European, 14.7% Pacific, 13.4% Maori), followed until death, transfer out of practice (in CALIBER) or study end.

PRIMARY OUTCOME MEASURE

HRs for mortality were estimated using Cox models adjusted for age, sex, smoking, diabetes, systolic blood pressure, ethnicity and total:high-density lipoprotein (HDL) cholesterol ratio.

RESULTS

We found 'J'-shaped associations between WBC and mortality; the second quintile was associated with lowest risk in both cohorts. High WBC within the reference range (8.65-10.05×10⁹/L) was associated with significantly increased mortality compared to the middle quintile (6.25-7.25×10⁹/L); adjusted HR 1.51 (95% CI 1.43 to 1.59) in CALIBER and 1.33 (95% CI 1.06 to 1.65) in PREDICT. WBC outside the reference range was associated with even greater mortality. The association was stronger over the first 6 months of follow-up, but similar across ethnic groups.

CONCLUSIONS

Clinically recorded WBC within the range considered 'normal' is associated with mortality in ethnically different populations from two countries, particularly within the first 6 months. Large-scale international comparisons of electronic health record cohorts might yield new insights from widely performed clinical tests.

TRIAL REGISTRATION NUMBER

NCT02014610.

Red cell distribution width and risk of cardiovascular mortality: Insights from National Health and Nutrition Examination Survey (NHANES)-III

INTRODUCTION

Red cell distribution width (RDW) has been linked to cardiovascular disease. We sought to determine whether addition of RDW improved the Framingham risk score (FRS) model to predict cardiovascular mortality in a healthy US cohort.

METHODS

We performed a post-hoc analysis of the National Health and Nutritional Examination Survey-III (1988-94) cohort, including non-anemic subjects aged 30-79years. Primary endpoint was death from coronary heart disease (CHD). We divided the cohort into three risk categories: <6%, 6-20% and >20%. RDW>14.5 was considered high. Kaplan-Meier survival curves and Cox proportional hazards models were created. Discrimination, calibration and reclassification were used to assess the value of addition of RDW to the FRS model.

RESULTS

We included 7005 subjects with a mean follow up of 14.1years. Overall, there were 233 (3.3%) CHD deaths; 27 (8.2%) in subjects with RDW>14.5 compared to 206 (3.1%) in subjects with RDW≤14.5 (p<0.001). Adjusted hazard ratio of RDW in predicting CHD mortality was 2.02 (1.04-3.94, p=0.039). Addition of RDW to FRS model showed significant improvement in C-statistic (0.8784 vs. 0.8751, p=0.032) and area under curve (0.8565 vs. 0.8544, p=0.05). There was significant reclassification of FRS with a net reclassification index (NRI) of 5.6% (p=0.017), and an intermediate-risk NRI of 9.6% (p=0.011). Absolute integrated discrimination index (IDI) was 0.004 (p=0.02), with relative IDI of 10.4%.

CONCLUSIONS

Our study demonstrates that RDW is a promising biomarker which improves prediction of cardiovascular mortality over and above traditional cardiovascular risk factors.

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation

OBJECTIVE

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients.

METHODS

Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients.

RESULTS

The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility.

CONCLUSIONS

Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care.

Improvement in the predictive ability of the Intermountain Mortality Risk Score by adding routinely collected laboratory tests such as albumin, bilirubin, and white cell differential count

Background:

The Intermountain Mortality Risk Score (IMRS), a sex-specific mortality-prediction metric, has proven to be effective in various populations. IMRS is comprised of the complete blood count (CBC), basic metabolic panel (BMP), and age. Whether the addition of factors from the comprehensive metabolic panel (CMP) and white blood cell (WBC) differential count improves risk stratification is unknown.

Methods:

Patients with baseline complete metabolic panel (CMP) and IMRS measurements were randomly assigned (60%/40%) to independent derivation (n=84,913) and validation (n=56,584) populations. A sex-specific risk score based on IMRS methods was computed in the derivation population using adjusted multivariable regression weights from all significant and noncollinear CMP [expanded IMRS (eIMRS)] and, when available, WBC differential components (eIMRS+diff).

Results:

Age averaged 67±16 years for females and 67±15 years for males. Receiver operator characteristic (ROC) c-statistics for 30-day death showed marked improvement for the eIMRS compared to the IMRS in both females [0.895 (0.882, 0.908) vs. 0.865 (0.850, 0.880)] and males [0.861 (0.847, 0.876) vs. 0.824 (0.807, 0.841)]. These results persisted for 1-year death: females [0.854 (0.847, 0.862) vs. 0.828 (0.819, 0.836)] and males [0.835 (0.826, 0.844) vs. 0.796 (0.789, 0.808)]. In addition, the eIMRS significantly improved risk reclassification. Further precision was seen when WBC differential components were included.

Conclusions:

The addition of the CMP components to the IMRS improved risk prediction. WBC differential also improved risk score predictive ability. These results suggest that the eIMRS may function even better than IMRS as a tool in patient care, risk-adjustment, and clinical research settings for predicting outcomes.

Association of the dispersion in red blood cell volume with mortality

BACKGROUND

The red cell distribution width (RDW) predicts mortality among many populations. RDW is calculated as the standard deviation (SD) of the red blood cell (RBC) volume divided by mean corpuscular volume (MCV). Because higher MCV also predicts mortality, we hypothesized that the RDW numerator (one SD of RBC volume or 1SD-RDW) predicts mortality more strongly than the RDW.

MATERIAL AND METHODS

Adult subjects hospitalized during a contemporary clinical era (10/2005-1/2014, N = 135,963) and a historical era (1/1999-9/2005, N = 119,530) were studied. The RDW was obtained from the complete blood count (CBC), while 1SD-RDW was calculated (RDW multiplied by MCV and divided by 100).

RESULTS

In univariable Cox regression (2005-2014 cohort), 1SD-RDW (quintile 5 vs. 1: hazard ratio [HR] = 8.38, 95% confidence interval [CI] = 7.94, 8.85; P < 0.001) was a superior predictor of mortality compared to RDW (quintile 5 vs. 1: HR = 4.78, CI = 4.57, 5.00; P < 0.001). This superiority remained after adjustment for age, sex, basic metabolic profile components and other CBC factors excluding MCV (1SD-RDW: HR = 2.41, CI = 2.28, 2.55; RDW: HR = 2.01, CI = 1.92, 2.11). Further adjustment for MCV strengthened the RDW association (HR = 2.14, CI = 2.04, 2.24; P < 0.001), becoming indistinct from 1SD-RDW (HR = 2.20, CI = 2.08, 2.33; P < 0.001). Findings were similar for the 1999-2005 cohort.

CONCLUSIONS

The 1SD-RDW predicted mortality more strongly than RDW, suggesting that 1SD-RDW is superior to RDW as an individual risk predictor. Further, these results indicate that the dispersion of RBC volume and its mean are independent risk markers. Further research is required to understand the clinical value and mechanistic basis of these associations.