Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study

Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization. Methods To reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC’s were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year. Results The ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71. Conclusion The results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.

Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients

Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG® (Adjusted Clinical Groups) System were linked with hospital discharge records in 2013 to identify those who experienced any of a series of hospital admission events and death. Number of comorbidities, comorbidities dyads, and latent classes were used as exposure variable. Regression analyses were applied to examine the associations between dependent and exposure variables. Results: Besides the fact that larger number of chronic conditions is associated with higher odds of hospital admission or death, we showed that certain dyads and classes of diseases have a particularly strong association with these outcomes. Discussion: Unlike morbidity counts, analyzing morbidity clusters and dyads reveals which combinations of morbidities are associated with the highest hospitalization rates or death.

Functional decline and associated factors in patients with multimorbidity at 8 months of follow-up in primary care: the functionality in pluripathological patients (FUNCIPLUR) longitudinal descriptive study

ObjectiveTo analyse short-term functional decline and associated factors in over 65-year-olds with multimorbidity.Design and settingProspective multicentre study conducted in three primary care centres, over an 8-month period. During this period, we also analysed admissions to two referral hospitals.ParticipantsOf the 241 patients ≥65 years included randomly in the study, 155 were already part of a multimorbidity programme (stratified by ‘Adjusted Clinical Groups’) and 86 were newly included (patients who met Ollero’s criteria and with ≥1 hospital admission the previous year). Patients who were institutionalised, unable to complete follow-up or receiving dialysis were excluded.Outcomes and variablesThe primary outcome was the decrease in functional status category (Barthel Index or Lawton Scale). Other variables considered were sociodemographic characteristics, comorbidity, medications, number of admissions and functional status on discharge.ResultsPatients had a median age of 82 years (P7586) and of five selected chronic conditions (IQR 4–6), and took 11 (IQR 9–14) regular medications; 46.9% were women; 38.2% had impaired function at baseline.Overall, 200 persons completed the follow-up; 10.4% (n=25) of the initial sample died within the 8 months. In 20.5% (95% CI 15.5% to 26.6%) of them we recorded a decrease in functionality, associated with older age (OR 1.1, 95% CI 1.0 to 1.2) and with having ≥1 admission during the follow-up (OR 3.6, 95% CI 1.6 to 7.7). There were 133 hospital admissions in total during the follow-up considering all the patients included, and a functional decline was observed in 35.5% (95% CI 25.7% to 46.7%) of the 76 discharges in which functional status was assessed.ConclusionsA fifth of patients showed functional decline or loss of independence in just 8 months. These findings are important as functional decline and the increasing care needs are potentially predictable and modifiable. Age and hospitalisation were closely associated with this decline

Association of the Hospital Volume of Frail Surgical Patients Cared for with Outcomes after Elective, Major Noncardiac Surgery

Background Frailty is a risk factor for adverse postoperative outcomes. Hospitals that perform higher volumes of surgery have better outcomes than low-volume providers. We hypothesized that frail patients undergoing elective surgery at hospitals that cared for a higher volume of similarly frail patients would have improved outcomes. Methods We conducted a retrospective, population-based cohort study using linked administrative data in Ontario, Canada. We identified all adult major, elective noncardiac surgery patients who were frail according to the validated Johns Hopkins Adjusted Clinical Groups (ACG®) frailty-defining diagnoses indicator. Hospitals were categorized into frailty volume quintiles based on volumes of frail surgical patients cared for. Multilevel, multivariable modeling measured the association of frailty volume with 30-day survival (primary outcome), complications, failure to rescue (secondary outcomes), and costs (tertiary outcome). Results Of 63,381 frail patients, 708 (1.1%) died after surgery. The thirty-day mortality rate in the lowest volume quintile was 1.1% compared to 0.9% in the highest. After adjustment for surgical risk, demographic characteristics, comorbidities, and clustering within hospitals, we found a significant association between frailty volume and improved survival (highest volume vs. lowest volume quintile: hazard ratio 0.51; 95% CI, 0.35 to 0.74; P < 0.0001). Although complication rates did not vary significantly between hospitals, failure-to-rescue rates were inversely related to volume. Conclusions Frail patients have reduced survival and increased failure to rescue when they undergo operations at hospitals having a lower volume of frail surgical patients. Concentration of perioperative care in centers that frequently treat high-risk frail patients could improve population outcomes.