A new population-based risk stratification tool was developed and validated for predicting mortality, hospital admissions, and health care costs

Predicting the risk of nursing home placement of elderly persons using a population-based stratification score

OBJECTIVE

To develop and validate a novel score predictive of nursing home placement in elderly.

STUDY DESIGN

Population-based case-control study based on healthcare utilization databases of Lombardy, a region of Northern Italy.

METHODS

The 2.4 million citizens aged ≥65 years who on January 1, 2018 lived outside nursing home formed the target population. Cases were citizens who experienced nursing home admission (the outcome of interest) until December 31, 2019. Cases were matched 1:1 by gender, age, and municipality of residence to one control. Conditional logistic regression was fitted to select candidate predictors (the exposure to 69 clinical conditions and 11 social and healthcare services) independently associated with the outcome. The model was built from the 26,156 cases, and as many controls (training set), and applied to a validation set (15,807 case-control couples). Predictive performance was assessed by discrimination and calibration.

RESULTS

Twenty-one factors were identified as predictive of nursing home admission and were included in the "Elderly Nursing Home Placement" (ENHP) score. Mental health disorders and chronic neurological illnesses contributed most to prediction of nursing home admission. ENHP performance showed an area under the receiver operating characteristic curve of 0.77 and a remarkable calibration of observed and predicted outcome risk.

CONCLUSIONS

A simple score derived from data used for public health management may reliably predict the risk of nursing home placement in elderly. Its use by healthcare decision makers allows to accurately identify high-risk individuals who need home services, thereby avoiding admission to nursing homes.

Promising algorithms to perilous applications: a systematic review of risk stratification tools for predicting healthcare utilisation

OBJECTIVES

Risk stratification tools that predict healthcare utilisation are extensively integrated into primary care systems worldwide, forming a key component of anticipatory care pathways, where high-risk individuals are targeted by preventative interventions. Existing work broadly focuses on comparing model performance in retrospective cohorts with little attention paid to efficacy in reducing morbidity when deployed in different global contexts. We review the evidence supporting the use of such tools in real-world settings, from retrospective dataset performance to pathway evaluation.

METHODS

A systematic search was undertaken to identify studies reporting the development, validation and deployment of models that predict healthcare utilisation in unselected primary care cohorts, comparable to their current real-world application.

RESULTS

Among 3897 articles screened, 51 studies were identified evaluating 28 risk prediction models. Half underwent external validation yet only two were validated internationally. No association between validation context and model discrimination was observed. The majority of real-world evaluation studies reported no change, or indeed significant increases, in healthcare utilisation within targeted groups, with only one-third of reports demonstrating some benefit.

DISCUSSION

While model discrimination appears satisfactorily robust to application context there is little evidence to suggest that accurate identification of high-risk individuals can be reliably translated to improvements in service delivery or morbidity.

CONCLUSIONS

The evidence does not support further integration of care pathways with costly population-level interventions based on risk prediction in unselected primary care cohorts. There is an urgent need to independently appraise the safety, efficacy and cost-effectiveness of risk prediction systems that are already widely deployed within primary care.

Routine perioperative blood tests predict survival of resectable lung cancer

There is growing evidence that inflammatory, immunologic, and metabolic status is associated with cancer patients survival. Here, we built a simple algorithm to predict lung cancer outcome. Perioperative routine blood tests (RBT) of a cohort of patients with resectable primary lung cancer (LC) were analysed. Inflammatory, immunologic, and metabolic profiles were used to create a single algorithm (RBT index) predicting LC survival. A concurrent cohort of patients with resectable lung metastases (LM) was used to validate the RBT index. Charts of 2088 consecutive LC and 1129 LM patients undergoing lung resection were evaluated. Among RBT parameters, C-reactive protein (CRP), lymphocytes, neutrophils, hemoglobin, albumin and glycemia independently correlated with survival, and were used to build the RBT index. Patients with a high RBT index had a higher 5-year mortality than low RBT patients (adjusted HR 1.93, 95% CI 1.62-2.31). High RBT patients also showed a fourfold higher risk of 30-day postoperative mortality (2.3% vs. 0.5%, p 0.0019). The LM analysis validated the results of the LC cohort. We developed a simple and easily available multifunctional tool predicting short-term and long-term survival of curatively resected LC and LM. Prospective external validation of RBT index is warranted.

Chronic anticoagulant treatment and risk of mortality in SARS-Cov2 patients: a large population-based study

BACKGROUND

Hypercoagulability is often seen in COVID-19 patients and thromboembolic events appear frequent; antithrombotic treatment has been proposed therefore as part of standard treatment for COVID-19. Under these premises, prior-to-infection antithrombotic treatment may have a protective effect with respect to COVID-19 related thromboembolic events. Aim of the present work was to evaluate the impact of prior-to-infection anticoagulant or antiplatelet treatment on COVID-19 outcomes.

METHODS

Beneficiaries of the Regional Health Service of the Lombardy region of Italy aged ≥40 years with a COVID-19 diagnosis made between February 21st and July 18th, 2020 were included in the present study. The impact on COVID-19 mortality of pre-existing and chronic therapy with anticoagulant drugs (vitamin-K antagonist or new oral anticoagulants) was evaluated. Analyses were repeated with antiplatelets drugs.

RESULTS

Among 79,934 SARS-CoV-2 patients beneficiaries of the Regional Healthcare System of the Lombardy Region who received a diagnosis between February 21st and July 18th, 2020, chronic pre-existing anticoagulant assumption was present in 6.0% and antiplatelets in 12.7%. The overall unadjusted mortality rate was 20.6%, with male sex, age category and comorbidity burden being significantly associated to increased mortality risk. Anticoagulant chronic treatment was not associated with a reduction in mortality. Similar results were observed when repeating the analyses for pre-existing oral antiplatelet treatment.

CONCLUSIONS

In a large population-based study evaluating more than 79,000 COVID-19 patients, pre-existing antithrombotic therapy was not associated to a benefit in terms of mortality. Further studies are needed to evaluate the role of antithrombotic therapy as standard treatment among COVID-19 patients.

Improved prediction of 5-year mortality by updating the chronic related score for risk profiling in the general population: lessons from the italian region of Lombardy

Objective

The aim of this study was to improve the performance of the Chronic Related Score (CReSc) in predicting mortality and healthcare needs in the general population.

Methods

A population-based study was conducted, including all beneficiaries of the Regional Health Service of Lombardy, Italy, aged 18 years or older in January 2015. Each individual was classified as exposed or unexposed to 69 candidate predictors measured before baseline, updated to include four mental health disorders. Conditions independently associated with 5-year mortality were selected using the Cox regression model on a random sample including 5.4 million citizens. The predictive performance of the obtained CReSc-2.0 was assessed on the remaining 2.7 million citizens through discrimination and calibration.

Results

A total of 35 conditions significantly contributed to the CReSc-2.0, among which Alzheimer's and Parkinson's diseases, dementia, heart failure, active neoplasm, and kidney dialysis contributed the most to the score. Approximately 36% of citizens suffered from at least one condition. CReSc-2.0 discrimination performance was remarkable, with an area under the receiver operating characteristic curve of 0.83. Trends toward increasing short-term (1-year) and long-term (5-year) rates of mortality, hospital admission, hospital stay, and healthcare costs were observed as CReSc-2.0 increased.

Conclusion

CReSC-2.0 represents an improved tool for stratifying populations according to healthcare needs.

Primary Care of the (Near) Future: Exploring the Contribution of Digitalization and Remote Care Technologies through a Case Study

The Italian Government planned to invest €15 billion of European funds on National Health Service digitalization and primary care enhancement. The critical burden brought by the pandemic upon hospital care mean these investments could no longer be delayed, considering the extraordinary backlogs of many treatments and the ordinary gaps of fragmented long-term care, in Italy and abroad. National guidelines have been published to standardize interventions across the Italian regions, and telemedicine is frequently mentioned as a key innovation to achieve both goals. The professional resources needed to run the facilities introduced in primary care are defined with great precision, but no details are given on how digitalization and remote care technologies must be implemented in this context. Building on this policy case, this paper focuses on what contribution digitalization and telemedicine can offer to specific primary care innovations, drawing from implemented technology-driven policies which may support the effective stratification, prevention and management of chronic patient needs, including anticipatory healthcare, population health management, adjusted clinical groups, chronic care management, quality and outcomes frameworks, patient-reported outcomes and patient-reported experience. All these policies can benefit significantly from digitalization and remote care technology, provided that some risks and limitations are considered by design.

Small-area deprivation index does not improve the capability of multisource comorbidity score in mortality prediction

Background

The stratification of the general population according to health needs allows to provide better-tailored services. A simple score called Multisource Comorbidity Score (MCS) has been developed and validated for predicting several outcomes. The aim of this study was to evaluate whether the ability of MCS in predicting 1-year mortality improves by incorporating socioeconomic data (as measured by a deprivation index).

Methods

Beneficiaries of the Italian National Health Service who in the index year (2018) were aged 50-85 years and were resident in the Sicily region for at least 2 years were identified. For each individual, the MCS was calculated according to his/her clinical profile, and the deprivation index of the census unit level of the individual's residence was collected. Frailty models were fitted to assess the relationship between the indexes (MCS and deprivation index) and 1-year mortality. Akaike information criterion and Bayesian information criterion statistics were used to compare the goodness of fit of the model that included only MCS and the model that also contained the deprivation index. The models were further compared by means of the area under the receiver operating characteristic curve (AUC).

Results

The final cohort included 1,062,221 individuals, with a mortality rate of 15.6 deaths per 1,000 person-years. Both MCS and deprivation index were positively associated with mortality.The goodness of fit statistics of the two models were very similar. For MCS only and MCS plus deprivation index models, Akaike information criterion were 17,013 and 17,038, respectively, whereas Bayesian information criterion were 16,997 and 17,000, respectively. The AUC values were 0.78 for both models.

Conclusion

The present study shows that socioeconomic features as measured by the deprivation index did not improve the capability of MCS in predicting 1-year risk of death. Future studies are needed to investigate other sources of data to enhance the risk stratification of populations.

Developing a comorbidity score in cancer patients using healthcare utilization databases during the COVID-19 pandemic: An experience from Italy

BACKGROUND

A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID-19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases.

METHODS

We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO-logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID-19 during the first pandemic wave (March-May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=-1, 0, 1, 2-4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October-December 2020) data.

RESULTS

We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate).

CONCLUSIONS

We developed a high performance comorbidity score for cancer patients and COVID-19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID-19 and cancer impact.

Factors associated with severe or fatal clinical manifestations of SARS-CoV-2 infection after receiving the third dose of vaccine

BACKGROUND

Little is known about vulnerability to severe COVID-19 illness after vaccination completion with three doses of vaccine against COVID-19.

OBJECTIVES

To identify individual features associated with increased risk of severe clinical manifestation of SARS-CoV-2 infections after receiving the third dose of vaccine against COVID-19.

METHODS

We performed a nested case-control study based on 3,360,116 citizens from Lombardy, Italy, aged 12 years or older who received the third dose of vaccine against COVID-19 from 20 September through 31 December 2021. Individuals were followed from 14 days after vaccination completion until the occurrence of severe COVID-19 illness, death unrelated to COVID-19, emigration or 15 March 2022. For each case, controls were randomly selected to be 1:10 matched for the date of vaccination completion and municipality of residence. The association between candidate predictors and outcome was assessed through multivariable conditional logistic regression models.

RESULTS

During 12,538,330 person-months of follow-up, 5171 cases of severe illness occurred. As age increased, a trend towards increasing odds of severe illness was observed. Male gender was a significant risk factor. As the number of contacts with the Regional Health Service increased, a trend towards increasing odds of severe illness was observed. Having had a previous SARS-CoV-2 infection was a significant protective factor. Having received the Moderna vaccine significantly decreased the odds of severe illness. Significant higher odds were associated with 42 diseases/conditions. Odds ratios ranged from 1.23 (diseases of the musculoskeletal system) to 5.00 (autoimmune disease).

CONCLUSIONS

This study provides useful insights for establishing priority in fourth-dose vaccination programs.

Rheumatoid Arthritis and Cardiovascular Risk: Retrospective Matched-Cohort Analysis Based on the RECORD Study of the Italian Society for Rheumatology

Background: Rheumatoid arthritis (RA) is associated with an increase in cardiovascular (CV) risk. This issue maybe not only explained by a genetic component, as well as by the traditional CV risk factors, but also by an underestimation and undertreatment of concomitant CV comorbidities.

Method: This was a retrospective matched-cohort analysis in the Italian RA real-world population based on the healthcare-administrative databases to assess the CV risk factors and incidence of CV events in comparison with the general population. Persistence and adherence to the CV therapy were also evaluated in both groups.

Results: In a RA cohort (N = 21,201), there was a greater prevalence of hypertension and diabetes with respect to the non-RA subjects (N = 249,156) (36.9 vs. 33.4% and 10.2 vs. 9.6%, respectively), while dyslipidemia was more frequent in the non-RA group (15.4 vs. 16.5%). Compared with a non-RA cohort, the patients with RA had a higher incidence of atrial fibrillation (incidence rate ratio, IRR 1.28), heart failure (IRR 1.53), stroke (IRR 1.19), and myocardial infarction (IRR 1.48). The patients with RA presented a significantly lower persistence rate to glucose-lowering and lipid-lowering therapies than the controls (odds ratio, OR 0.73 [95% CI 0.6–0.8] and OR 0.82 [0.8–0.9], respectively). The difference in the adherence to glucose-lowering therapy was significant (OR 0.7 [0.6–0.8]), conversely no statistically significant differences emerged regarding the adherence to lipid-lowering therapy (OR 0.89 [95% CI 0.8–1.0]) and anti-hypertensive therapy (OR 0.96 [95% CI 0.9–1.0]).

Conclusion: The patients with RA have a higher risk of developing CV events compared with the general population, partially explained by the excess and undertreatment of CV risk factors.

Medication adherence in community-dwelling older people exposed to chronic polypharmacy

BACKGROUND

To evaluate medication adherence and associated factors of seven of the most common drug classes prescribed to community-dwelling older people.

METHODS

This is a retrospective cohort study on medication adherence in community-dwelling older people (65-94 years old) on chronic polypharmacy and recorded from 2013 to 2015 in the administrative database of the Lombardy region (Northern Italy). Adherence was assessed for diabetic drugs, antithrombotic agents, drugs acting on the renin-angiotensin system, statins, bisphosphonates, antidepressants and drugs for obstructive airway diseases by calculating the medication possession ratio (MPR). Patients were then divided in fully (MPR ≥80%), partially (40%≤MPR<80%) and poorly adherent (10%<MPR<40%).

RESULTS

Among 140 537 patients included in the study, only 19.3% was fully adherent to all the therapies considered. Almost 40% of them were poorly adherent to at least one drug class, becoming 50% when patients exposed to four or more drug classes were considered. In adjusted regression model, being women (OR=1.14, 95% CI 1.13 to 1.16) and aged ≥80 years old (OR=1.22, 95% CI 1.20 to 1.24) were associated with an overall lower adherence. Instead, the participation to an experimental healthcare programme was associated with higher adherence (OR=0.92, 95% CI 0.87 to 0.96). Furthermore, being coprescribed with ≥10 drugs was associated with lower adherence to all the drug classes, with different effects (ORs from 0.42 to 0.73).

CONCLUSION

This study overall shows a low medication adherence in community-dwelling older people on chronic polypharmacy, especially in women and oldest old. The implementation and promotion of healthcare programmes for these patients could help improve overall adherence to chronic drug therapies.

The role of renin-angiotensin system activated phagocytes in the SARS-CoV-2 coronavirus infection

OBJECTIVE

Management of the pandemic caused by the novel coronavirus SARS-CoV-2 challenges both scientists and physicians to rapidly develop, and urgently assess, effective diagnostic tests and therapeutic interventions. The initial presentation of the disease in symptomatic patients is invariably respiratory, with dry cough being the main symptom, but an increasing number of reports reveal multiple-organ involvement. The aim of this review is to summarize the potential role of the renin-angiotensin system activated phagocytes in the pathogenesis of COVID-19 disease.

METHODS

Data for this review were identified by searches of PubMed and references from relevant articles using the search terms "SARS," "COVID-19," "renin-angiotensin-system," "phagocyte," "reactive free radical," "antioxidant," "ARDS," "thrombosis," "myocardial," "ischaemia," "reperfusion," "microvascular," and "ACE2." Abstracts and reports from meetings were not included in this work. Only articles published in English between 1976 and 2020 were reviewed.

RESULTS

The cellular target of SARS viruses is the angiotensin-converting enzyme 2, a critical regulating protein in the renin-angiotensin system. The elimination of this enzyme by the viral spike protein results in excessive activation of phagocytes, migration into the tissues via the high endothelial venules, and an oxidative burst. In the case of an overstimulated host immune response, not only devastating respiratory symptoms but even systemic or multiorgan involvement may be observed.

CONCLUSIONS

Early-stage medical interventions may assist in returning the exaggerated immune response to a normal range; however, some therapeutic delay might result in excessive tissue damages, occasionally mimicking a systemic disease with a detrimental outcome.

Can the adherence to quality of care indicators for early rheumatoid arthritis in clinical practice reduce risk of hospitalisation? Retrospective cohort study based on the Record Linkage of Rheumatic Disease study of the Italian Society for Rheumatology

OBJECTIVE

To describe the adherence to quality of care indicators in early rheumatoid arthritis (RA) and to evaluate its impact on the risk of hospitalisation in a real-world setting.

DESIGN

Retrospective cohort study.

SETTING

Patients with early-onset RA identified from healthcare regional administrative databases by means of a validated algorithm between 2006 and 2012 in the Lombardy region (Italy).

PARTICIPANTS

The study cohort included 14 203 early-onset RA (71% female, mean age 60 years).

OUTCOME MEASURES

For each patient, a summary adherence score was calculated starting from the compliance to six quality indicators: (1-2) methotrexate or sulfasalazine or leflunomide with/without glucocorticoids, (3-4) other disease-modifying antirheumatic drugs (DMARDs) with/without glucocorticoids, (5) early interruption of glucocorticoids, (6) early clinical assessment.The relationship between low, intermediate and high categories of the summary score and the 12-month risk of hospitalisation for all causes and for RA was assessed.

RESULTS

During a follow-up of 1 year, 2609 hospitalisations occurred, of which 704 were for RA (main or secondary diagnosis) and 252 primarily for RA. In a 7-year period (2006-2012), early DMARDs and timely clinical monitoring treatment increased (from 52% to 62% p trend <0.001 and from 25% to 30% p trend 0.009, respectively).Intermediate and high summary adherence score categories (compared with the low category) were related significantly with a lower risk of hospitalisation (adjusted HR 0.85 (95% CI 0.77 to 0.93), p<0.001 and HR 0.76 (95% CI 0.69 to 0.84), p<0.001, respectively). Among the indicators of the adherence score, early DMARD prescription showed the strongest positive impact, while long-term use of glucocorticoids was the worst negative one.

CONCLUSION

In early RA, adherence to quality standards of care is associated with a lower risk of hospitalisation. Future interventions to improve the adherence to quality standards of care in this setting should decrease the risk of hospitalisation with a significant impact on individual and population health.

Renin-Angiotensin-Aldosterone System Blockers and the Risk of Covid-19

BACKGROUND

A potential association between the use of angiotensin-receptor blockers (ARBs) and angiotensin-converting-enzyme (ACE) inhibitors and the risk of coronavirus disease 2019 (Covid-19) has not been well studied.

METHODS

We carried out a population-based case-control study in the Lombardy region of Italy. A total of 6272 case patients in whom infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was confirmed between February 21 and March 11, 2020, were matched to 30,759 beneficiaries of the Regional Health Service (controls) according to sex, age, and municipality of residence. Information about the use of selected drugs and patients' clinical profiles was obtained from regional databases of health care use. Odds ratios and 95% confidence intervals for associations between drugs and infection, with adjustment for confounders, were estimated by means of logistic regression.

RESULTS

Among both case patients and controls, the mean (±SD) age was 68±13 years, and 37% were women. The use of ACE inhibitors and ARBs was more common among case patients than among controls, as was the use of other antihypertensive and non-antihypertensive drugs, and case patients had a worse clinical profile. Use of ARBs or ACE inhibitors did not show any association with Covid-19 among case patients overall (adjusted odds ratio, 0.95 [95% confidence interval {CI}, 0.86 to 1.05] for ARBs and 0.96 [95% CI, 0.87 to 1.07] for ACE inhibitors) or among patients who had a severe or fatal course of the disease (adjusted odds ratio, 0.83 [95% CI, 0.63 to 1.10] for ARBs and 0.91 [95% CI, 0.69 to 1.21] for ACE inhibitors), and no association between these variables was found according to sex.

CONCLUSIONS

In this large, population-based study, the use of ACE inhibitors and ARBs was more frequent among patients with Covid-19 than among controls because of their higher prevalence of cardiovascular disease. However, there was no evidence that ACE inhibitors or ARBs affected the risk of COVID-19.

Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study

Background

Multimorbidity is a growing concern for healthcare systems, with many countries experiencing demographic transition to older population profiles. A simple multisource comorbidity score (MCS) has been recently developed and validated. A very large real-world investigation was conducted with the aim of measuring inequalities in the MCS distribution across Italy.

Methods

Beneficiaries of the Italian National Health Service aged 50–85 years who in 2018 were resident in one of the 10 participant regions formed the study population (15.7 million of the 24.9 million overall resident in Italy). MCS was assigned to each beneficiary by categorizing the individual sum of the comorbid values (i.e. the weights corresponding to the comorbid conditions of which the individual suffered) into one of the six categories denoting a progressive worsening comorbidity status. MCS distributions in women and men across geographic partitions were compared.

Results

Compared with beneficiaries from northern Italy, those from centre and south showed worse comorbidity profile for both women and men. MCS median age (i.e. the age above which half of the beneficiaries suffered at least one comorbidity) ranged from 60 (centre and south) to 68 years (north) in women and from 63 (centre and south) to 68 years (north) in men. The percentage of comorbid population was lower than 50% for northern population, whereas it was around 60% for central and southern ones.

Conclusion

MCS allowed of capturing geographic variability of multimorbidity prevalence, thus showing up its value for addressing health policy in order to guide national health planning.