Development of clinical prediction models for renal and cardiovascular outcomes and mortality in patients with type 2 diabetes and chronic kidney disease using time-varying predictors

Modifiable and Non-Modifiable Risk Factors and Vascular Damage Progression in Type 2 Diabetes: A Primary Care Analysis

Background/Objectives: Type 2 diabetes mellitus (DM2) is characterized by the development of micro/macro-vascular complications over time. Factors influencing their course may present specific features in the primary care context. This study aims to identify predictive factors for the evolution of micro/macro-vascular pathology in DM2 patients and evaluate interventions implemented by general practitioners (GPs) in this context. Methods: From the medical records of 1169 DM2 patients from 13 Italian GPs, demographic, socio-environmental, and clinical data were recorded, along with the presence and degree of arterial hypertension and components of diabetic micro/macroangiopathy at the time of study entry and 5 years prior. Laboratory parameters and therapies from the last three years were recorded. Results: Compared to 5 years prior, at the study entry, the number of patients presenting at least one micro- or macro-vascular complication increased from 192 (16.4%) to 344 (29.4%) and from 245 (21.0%) to 350 (29.9%). At the logistic regression, microalbuminuria determination appeared to be the strongest predictor of vascular damage progression, followed by decreasing LDL cholesterol values induced by lipid-lowering therapy. Male gender, age >75 years, and smoking history were associated with greater vascular damage progression in the ANOVA repeated measures test. Conclusions: Advanced age, male gender, and smoking history proved strongly associated with the presence and extent of damage progression. GPs appear to adopt a more aggressive approach in treating risk factors (particularly lipid profile) for damage progression in these patients. Microalbuminuria has proven to be by far the marker most strongly associated with vascular damage progression.

The prognostic reasoning system for chronic kidney disease progression (PROGRES-CKD) may help improve waiting list management for outpatient nephrology services in a second-level public hospital in Italy

BACKGROUND

The management of patients with non-dialysis dependent chronic kidney disease (NDD-CKD) is challenging due to coexisting diseases, competing risks and uncertainties around optimal transition planning. Such clinical challenges are further exacerbated by physician shortage, coupled with rising service demands, which may hinder timely medical access due to long waiting times. Accurate progression risk assessment may help optimize resource allocation and adapting care based on individual patients' needs. This study validated the Prognostic Reasoning System for Chronic Kidney Disease Progression (PROGRES-CKD) in an Italian public hospital and compared its potential impact on waiting list optimization against physician-based protocols.

METHODS

First we first validated PROGRES-CKD by assessing its accuracy in predicting kidney replacement therapy (KRT) initiation within 6 months and 24 months in a historical cohort of patients treated at the San Gerardo Hospital (Italy) between 01-01-2015 and 31-12-2019. In a second study we compared PROGRES-CKD to attending nephrologists' prognostic ratings and simulated their potential impact on a waiting list management protocol.

RESULTS

We included 2005 patients who underwent 11,757 outpatient nephrology visits in 4 years. Most visits occurred for NDD-CKD stage 4 patients; the incidence of KRT onset was 10.8 and 9.32/100 patient-years at the 6 and 24-month prediction horizon cohorts, respectively. PROGRES-CKD demonstrated high accuracy in predicting KRT initiation at 6 and 24 months (AUROC = 0.88 and AUROC = 0.85, respectively). Nephrologists' prognostic performance was highly operator-dependent, albeit always significantly lower than PROGRES-CKD. In the simulation exercise, allocation based on PROGRES-CKD resulted in more follow-up visits for patients progressing to end-stage kidney disease (ESKD) and fewer visits for non-progressing patients, compared to allocation determined by nephrologists' prognosis.

CONCLUSIONS

PROGRES-CKD showed high accuracy in a real-world application. Waiting list simulation suggests that PROGRES-CKD may enable more efficient allocation of resources.

Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

BACKGROUND

Prognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure.

METHODS

We identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library-CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty.

RESULTS

Ninety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death.

CONCLUSIONS

There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty.

SYSTEMATIC REVIEW REGISTRATION

This review has been registered on PROSPERO (CRD42022347295).

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

BACKGROUND

Heart failure (HF), type 2 diabetes (T2D) and chronic kidney disease (CKD) commonly coexist. We studied characteristics, prognosis and healthcare utilisation of individuals with two of these conditions.

METHODS

We performed a retrospective, population-based linked electronic health records study from 1998 to 2020 in England to identify individuals diagnosed with two of: HF, T2D or CKD. We described cohort characteristics at time of second diagnosis and estimated risk of developing the third condition and mortality using Kaplan-Meier and Cox regression models. We also estimated rates of healthcare utilisation in primary care and hospital settings in follow-up.

FINDINGS

We identified cohorts of 64 226 with CKD and HF, 82 431 with CKD and T2D, and 13 872 with HF and T2D. Compared with CKD and T2D, those with CKD and HF and HF and T2D had more severe risk factor profile. At 5 years, incidence of the third condition and all-cause mortality occurred in 37% (95% CI: 35.9%, 38.1%%) and 31.3% (30.4%, 32.3%) in HF+T2D, 8.7% (8.4%, 9.0%) and 51.6% (51.1%, 52.1%) in HF+CKD, and 6.8% (6.6%, 7.0%) and 17.9% (17.6%, 18.2%) in CKD+T2D, respectively. In each of the three multimorbid groups, the order of the first two diagnoses was also associated with prognosis. In multivariable analyses, we identified risk factors for developing the third condition and mortality, such as age, sex, medical history and the order of disease diagnosis. Inpatient and outpatient healthcare utilisation rates were highest in CKD and HF, and lowest in CKD and T2D.

INTERPRETATION

HF, CKD and T2D carry significant mortality and healthcare burden in combination. Compared with other disease pairs, individuals with CKD and HF had the most severe risk factor profile, prognosis and healthcare utilisation. Service planning, policy and prevention must take into account and monitor data across conditions.