Comorbidities Can Predict Mortality of Kidney Transplant Recipients: Comparison With the Charlson Comorbidity Index. Transplant Proc. 2018;50:1068-1073. [PMID: 29731067 DOI: 10.1016/j.transproceed.2018.01.044]

Differences in ischemic heart disease between males and females using predictive artificial intelligence models

<b>Background: </b>Cardiovascular health and preventative strategies are influenced by the sex of the individuals. To forecast cardiac events or detect ischemic heart disease (IHD) early, machine-learning algorithms can analyze complex patient data patterns. Early detection allows for lifestyle changes, medication management, or invasive treatments to slow disease progression and improve outcomes.<br /> <b>Aim</b>: To compare and predict the differences in the primary sources of IHD burden between males and females in various age groups, geographical regions, death versus alive, and comorbidity levels.<br /> <b>Methods: </b>A predictive and retrospective design was implemented in this study. Electronic health records were extracted, which were equally distributed among males and females with IHD. The dataset consisted of patients who were admitted between 2015 and 2022. Two of the eight models generated by Modeler software were implemented in this study: the Bayesian network model, which achieved the highest area under curve score (0.600), and the Chi-squared automatic interaction detection (CHAID) model, which achieved the highest overall accuracy score (57.199%).<br /> <b>Results: </b>The study sample included 17,878 men and women, 58% of whom had no comorbidities and 1.7% who died. Age, the Charlson comorbidity index score, and geographical location all predicted IHD, but age was more influential. Bayesian network analysis showed that IHD odds were highest in males 40-59 and females 60-79, with the highest mortality risk in females 80-100. North and south Jordan had higher IHD rates and middle-aged males from north and middle governorates had higher IHD rates according to CHAID.<br /> <b>Conclusion: </b>By using artificial intelligence, clinicians can improve patient outcomes, treatment quality, and save lives in the fight against cardiovascular illnesses. To predict IHD early, machine-learning algorithms can analyze complex patient data patterns to improve outcomes.

Sweet and simple as syrup: A review and guidance for use of novel antihyperglycemic agents for post-transplant diabetes mellitus and type 2 diabetes mellitus after kidney transplantation

Uncontrolled type 2 diabetes mellitus (T2DM) and post-transplant diabetes mellitus (PTDM) increase morbidity and mortality after kidney transplantation. Conventional strategies for diabetes management in this population include metformin, sulfonylureas, meglitinides and insulin. Limitations with these agents, as well as promising new antihyperglycemic agents, create a need and opportunity to explore additional options for transplant diabetes pharmacotherapy. Novel agents including sodium glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP1RA), and dipeptidyl peptidase IV inhibitors (DPP4i) demonstrate great promise for T2DM management in the non-transplant population. Moreover, many of these agents possess renoprotective, cardiovascular, and/or weight loss benefits in addition to improved glucose control while having reduced risk of hypoglycemia compared with certain other conventional agents. This comprehensive review examines available literature evaluating the use of novel antihyperglycemic agents in kidney transplant recipients (KTR) with T2DM or PTDM. Formal grading of recommendations assessment, development, and evaluation (GRADE) system recommendations are provided to guide incorporation of these agents into post-transplant care. Available literature was evaluated to address the clinical questions of which agents provide greatest short- and long-term benefits, timing of novel antihyperglycemic therapy initiation after transplant, monitoring parameters for these antihyperglycemic agents, and concomitant antihyperglycemic agent and immunosuppression regimen management. Current experience with novel antihyperglycemic agents is primarily limited to single-center retrospective studies and case series. With ongoing use and increasing comfort, further and more robust research promises greater understanding of the role of these agents and place in therapy for kidney transplant recipients.

Predictors of inpatient admission likelihood and prolonged length of stay among cerebrovascular disease patients: A nationwide emergency department sample analysis

PURPOSE

To examine the hospital- and patient-related factors associated with increased likelihood of inpatient admission and extended hospitalization.

METHODS

We applied multivariate logistic regression to a subset of ED hospital and patient characteristics linearly extrapolated from the 2019 National Emergency Department Sample database (n=626,508). Patient characteristics with 10 or fewer ED visits after national extrapolation were not reported in the current study to maintain patient confidentiality, in accordance with the HCUP Data Use Agreement. All selected ED visits represented a primary diagnosis of CVD (ICD-10 codes 160-168). All reported hospital and patient characteristics were subject to adjustment for covariates. P-values < 0.05 were considered statistically significant.

MAIN FINDINGS

Medicare beneficiaries report higher inpatient admission rates than uninsured OR 0.81 (0.73-0.91) and privately insured OR 0.86 (0.79-0.94) individuals. Black and Native-American patients were 37% and 55% more likely to be hospitalized long (>75^th percentile) (OR 1.37 [1.25-1.50], OR 1.55 [1.14-2.10]). Northeast emergency departments reported an increased odds of admission compared to the Midwest OR (0.40-0.62), South OR 0.79 (0.63-0.98) and West OR 0.52 (0.39-0.69). Patients with multiple comorbidities (mCCI = 3+) were 226% more likely to have a longer stay OR 3.26 (3.09-3.45) than patients presenting with zero or few comorbidities. Level I, II, and III trauma centers report distinctly high odds of inpatient admission (OR 3.54 [2.84-4.42], OR 2.68 [2.14-3.35], OR 1.51 [1.25-1.84]).

PRINCIPAL CONCLUSIONS

Likelihoods of inpatient admission and long hospital stays were observably stratified through multiple, independently acting hospital and patient characteristics. Significant associations were stratified by race/ethnicity, location, and clinical presentation, among others. Attention to the factors reported here may serve well to mitigate emergency department crowding and its sobering impact on United States healthcare systems and patients.

Recalibration and validation of the Charlson Comorbidity Index in acute kidney injury patients underwent continuous renal replacement therapy

Background

Comorbid conditions impact the survival of patients with severe acute kidney injury (AKI) who require continuous renal replacement therapy (CRRT). The weights assigned to comorbidities in predicting survival vary based on type of index, disease, and advances in management of comorbidities. We developed a modified Charlson Comorbidity Index (CCI) for use in patients with AKI requiring CRRT (mCCI-CRRT) and improved the accuracy of risk stratification for mortality.

Methods

A total of 828 patients who received CRRT between 2008 and 2013, from three university hospital cohorts was included to develop the comorbidity score. The weights of the comorbidities were recalibrated using a Cox proportional hazards model adjusted for demographic and clinical information. The modified index was validated in a university hospital cohort (n = 919) using the data of patients treated from 2009 to 2015.

Results

Weights for dementia, peptic ulcer disease, any tumor, and metastatic solid tumor were used to recalibrate the mCCI-CRRT. Use of these calibrated weights achieved a 35.4% (95% confidence interval [CI], 22.1%–48.1%) higher performance than unadjusted CCI in reclassification based on continuous net reclassification improvement in logistic regression adjusted for age and sex. After additionally adjusting for hemoglobin and albumin, consistent results were found in risk reclassification, which improved by 35.9% (95% CI, 23.3%–48.5%).

Conclusion

The mCCI-CRRT stratifies risk of mortality in AKI patients who require CRRT more accurately than does the original CCI, suggesting that it could serve as a preferred index for use in clinical practice.

Liver transplantation during global COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory disease respiratory syndrome coronavirus-2 has significantly impacted the health care systems globally. Liver transplantation (LT) has faced an unequivocal challenge during this unprecedented time. This targeted review aims to cover most of the clinical issues, challenges and concerns about LT during the COVID-19 pandemic and discuss the most updated literature on this rapidly emerging subject.

Age-adjusted Charlson comorbidity index score is the best predictor for severe clinical outcome in the hospitalized patients with COVID-19 infection

Aged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain.This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit, use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death.Among 5621 patients, the high CCIS (≥ 3) group showed higher proportion of elderly population and lower plasma hemoglobin and lower lymphocyte and platelet counts. The high CCIS group was an independent risk factor for composite outcome (HR 3.63, 95% CI 2.45-5.37, P < .001) and patient mortality (HR 22.96, 95% CI 7.20-73.24, P < .001). The nomogram showed that CCIS was the most important factor contributing to the prognosis followed by the presence of dyspnea (hazard ratio [HR] 2.88, 95% confidence interval [CI] 2.16-3.83), low body mass index < 18.5 kg/m2 (HR 2.36, CI 1.49-3.75), lymphopenia (<0.8 x109/L) (HR 2.15, CI 1.59-2.91), thrombocytopenia (<150.0 x109/L) (HR 1.29, CI 0.94-1.78), anemia (<12.0 g/dL) (HR 1.80, CI 1.33-2.43), and male sex (HR 1.76, CI 1.32-2.34). The nomogram demonstrated that the CCIS was the most potent predictive factor for patient mortality.The predictive nomogram using CCIS for the hospitalized patients with COVID-19 may help clinicians to triage the high-risk population and to concentrate limited resources to manage them.

A simple score to predict early death after kidney transplantation

BACKGROUND

Few studies have focused on risk stratification for premature death after transplantation. However, stratification of individual risk is an essential step in personalized care.

MATERIAL AND METHODS

We have developed a risk score of early post-transplant death (ORLY score) in a prospective multicentre cohort including 942 patients and validated our model in a retrospective independent replication cohort including 874 patients.

RESULTS

60 patients (6.4%) from the prospective cohort died during the first three-year post-transplant. Age, male gender, diabetes, dialysis duration and chronic respiratory failure were associated with early post-transplant death. The multivariable model exhibited good discrimination ability (C-index = 0.78, 95%CI [0.75-0.81]). ORLY score highly predicted early death after transplantation (1.34; 95%CI, 1.22 to 1.48 for each increase of 1 point in score; P < .001). The predictive value of the score in the validation cohort was close to that observed in the experimental cohort (1.41; 95%CI, 1.27 to 1.56 for each increase of 1 point in score; P < .001). Merging the two cohorts, four categories of risk could be individualized: low, 0-5 (n = 522, mean risk, 1%); intermediate, 6-7 (n = 739, mean risk 4.7%); moderate, 8-10 (n = 429, mean risk 10%); and high risk 11-15 (n = 132, mean risk 19%).

CONCLUSIONS

The ORLY score discriminates patients with high risk of early death.

Evaluation of the Effectiveness of Screening for Iliac Arterial Calcification in Kidney Transplant Candidates

Background

Peripheral vascular disease and iliac arterial calcification are prevalent in kidney transplant candidates and jeopardize graft outcomes. We report our experience with computed tomography (CT) screening for iliac arterial calcification.

Material/Methods

We retrospectively reviewed electronic medical records of 493 renal transplant candidates from protocol initiation in 2014. Non-contrast CT was performed or retrospectively reviewed if any of the following criteria were present: diabetes, ESRD >6 years, 25 pack-years of smoking or current smoker, diagnosis of peripheral vascular disease, parathyroidectomy, and coronary artery disease intervention. Differences in evaluation and transplant outcomes between groups were compared with chi-squared analysis. Multivariate logistic regression identified predictive criteria for presence of iliac arterial calcification.

Results

Of 493 candidates evaluated, CTs were reviewed in 346 (70.2%). Iliac arterial calcification was identified in 119 screened candidates (34.4%). Of candidates with iliac arterial calcification identified on CT, 16 (13.4%) were excluded for CT findings, and 9 (7.6%) had their surgical management plan changed. Overall, 91 (76.5%) candidates with iliac arterial calcification on CT were approved, compared to 203 (89.4%) without calcification (P<0.001). The percentage of screened patients with iliac arterial calcification on CT increased with increasing age (P<0.0005). Age and diabetes mellitus were predictive of calcification.

Conclusions

Many kidney transplant candidates are at risk for iliac arterial calcification, although such calcification does not prevent transplantation for most candidates who have it. Algorithmic pre-operative screening has clinical value in determining transplant candidacy and potentially improving postoperative outcomes in patients requiring kidney transplantation.

Solid organ transplantation programs facing lack of empiric evidence in the COVID-19 pandemic: A By-proxy Society Recommendation Consensus approach

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has a drastic impact on national health care systems. Given the overwhelming demand on facility capacity, the impact on all health care sectors has to be addressed. Solid organ transplantation represents a field with a high demand on staff, intensive care units, and follow-up facilities. The great therapeutic value of organ transplantation has to be weighed against mandatory constraints of health care capacities. In addition, the management of immunosuppressed recipients has to be reassessed during the ongoing coronavirus disease 2019 (COVID-19) pandemic. In addressing these crucial questions, transplant physicians are facing a total lack of scientific evidence. Therefore, the aim of this study was to offer an approach of consensus-based guidance, derived from individual information of 22 transplant societies. Key recommendations were extracted and the degree of consensus among different organizations was calculated. A high degree of consensus was found for temporarily suspending nonurgent transplant procedures and living donation programs. Systematic polymerase chain reaction-based testing of donors and recipients was broadly recommended. Additionally, more specific aspects (eg, screening of surgical explant teams and restricted use of marginal donor organs) were included in our analysis. This study offers a novel approach to informed guidance for health care management when a priori no scientific evidence is available.

Abdominal Organ Transplantation: Noteworthy Literature in 2018

A PubMed search revealed 1382 articles on pancreatic transplantation, 781 on intestinal transplantation, more than 7200 on kidney transplantation, and more than 5500 on liver transplantation published between January 1, 2018, and December 31, 2018. After narrowing the list down to human studies, 436 pancreatic, 302 intestinal, 1920 liver, and more than 2000 kidney transplantation studies were screened for inclusion in this review.

Comorbidity assessment for mortality risk stratification in elderly patients with acute coronary syndrome

BACKGROUND

The Charlson's is the most used comorbidity index. It comprises 19 comorbidities, some of which are infrequent in elderly patients with acute coronary syndrome (ACS), while some others are manifestations of cardiac disease rather than comorbidities. Our goal was to simplify comorbidity assessment in elderly non-ST-segment elevation ACS patients.

METHODS

The study group consisted of 1 training (n = 920, 76 ± 7 years) and 1 testing (n = 532; 84 ± 4 years) cohorts. The end-point was all-cause mortality at 1-year follow-up. Comorbidities were assessed selecting those medical disorders other than cardiac disease that were independently associated with mortality by multivariable analysis.

RESULTS

A total of 130 (14%) patients died in the training cohort. Six comorbidities were predictive: renal failure, anemia, diabetes, peripheral artery disease, cerebrovascular disease and chronic lung disease. The increase in the number of comorbidities yielded a gradient of risk on top of well-known clinical predictors: ≥3 comorbidities (27% mortality, HR = 1.90, 95% CI 1.20-3.03, p = .006); 2 comorbidities (16% mortality, HR = 1.29, 95% CI 0.81-2.04, p = .30); and 0-1 comorbidities (7.6% mortality, reference category). The discrimination accuracy (C-statistic = 0.80) and calibration (Hosmer-Lemeshow test, p = .20) of the predictive model using the 6 comorbidities was comparable to the predictive model using the Charlson index (C-statistic = 0.80; Hosmer-Lemeshow test, p = .70). Similar results were reproduced in the testing cohort (≥3 comorbidities: 24% mortality, HR = 2.37, 95% CI 1.25-4.49, p = .008; 2 comorbidities: 14% mortality, HR = 1.59, 95% CI 0.82-3.07, p = .20; 0-1 comorbidities: 7.5% reference category).

CONCLUSION

A simplified comorbidity assessment comprising 6 comorbidities provides useful risk stratification in elderly patients with ACS.