Modified-Chronic Disease Score (M-CDS): Predicting the individual risk of death using drug prescriptions.

BACKGROUND: Estimating the morbidity of a population is strategic for health systems to improve healthcare. In recent years administrative databases have been increasingly used to predict health outcomes. In 1992, Von Korff proposed a Chronic Disease Score (CDS) to predict 1-year mortality by only using drug prescription data. Because pharmacotherapy underwent many changes over the last 3 decades, the original version of the CDS has limitations. The aim of this paper is to report on the development of the modified version of the CDS.
METHODS: The modified CDS (M-CDS) was developed using 33 variables (from drug prescriptions within two-year before 01/01/2018) to predict one-year mortality in Bologna residents aged ≥50 years. The population was split into training and testing sets for internal validation. Score weights were estimated in the training set using Cox regression model with LASSO procedure for variables selection. The external validation was carried out on the Imola population. The predictive ability of M-CDS was assessed using ROC analysis and compared with that of the Charlson Comorbidity Index (CCI), that is based on hospital data only, and of the Multisource Comorbidity Score (MCS), which uses hospital and pharmaceutical data.
RESULTS: The predictive ability of M-CDS was similar in the training and testing sets (AUC 95% CI: training [0.760-0.770] vs. testing [0.750-0.772]) and in the external population (Imola AUC 95% CI [0.756-0.781]). M-CDS was significantly better than CCI (M-CDS AUC = 0.761, 95% CI [0.750-0.772] vs. CCI-AUC = 0.696, 95% CI [0.681-0.711]). No significant difference was found between M-CDS and MCS (MCS AUC = 0.762, 95% CI [0.749-0.775]).
CONCLUSIONS: M-CDS, using only drug prescriptions, has a better performance than the CCI score in predicting 1-year mortality, and is not inferior to the multisource comorbidity score. M-CDS can be used for population risk stratification, for risk-adjustment in association studies and to predict the individual risk of death.

Introduction

Estimating the morbidity status of a population is crucial for public health, in order to manage people with multiple chronic diseases efficiently and effectively. Multimorbidity is defined as the presence of multiple (chronic or acute) diseases and medical conditions in one individual [1].

In recent years there has been an increasing use of administrative databases as data sources for conducting clinical and pharmaco-epidemiological studies [2]. The advantages of administrative databases include their immediacy to be analysed, the good reliability, the wide geographical coverage, the long-term follow-up and the good detail of the clinical history of the individual [3].

Administrative databases, however, have some limitations, including the lack of information on the lifestyle, the social and economic characteristics and the presence of bias related to their observational nature.

In the past, the most popular comorbidity indices, i.e. the Charlson Comorbidity Index–CCI [4] and the Elixhauser Index–EI [5] were developed using the diagnoses reported in the hospital discharge records, coded through the International Classification of Diseases (ICD) system.

In 1992, von Korff et al. [6] developed a drug-based index, the Chronic Disease Score (CDS), to predict health outcomes. It originally consisted of 17 diseases with a weighing system assigned a priori and was subsequently updated by Clark et al. [7] to include 28 categories with a weighing system based on regression models. No further updates have been made, inevitably leading to limitations in the use of the score, as new drugs have been introduced in the market to treat chronic diseases (e.g., monoclonal antibodies for autoimmune disorders). Furthermore, in drug prescription databases specific criteria (e.g., amount threshold) to select chronic patients are required.

Several studies have compared the predictive ability of diagnosis-based indexes to drug-based ones, however, the superiority of an approach in predicting health status has not been demonstrated yet [8, 9], and the lack of an updated version of CDS does not allow appropriate performance assessment and comparisons [10].

Corrao et al. [11] proposed the Multisource Comorbidity Score (MCS), that combines information from hospital discharge records and pharmaceutical prescriptions to stratify individuals according to their morbidity profile. The MCS proved to be a better predictor of 1-year mortality compared to the CI, EI and CDS indices.

Merging different databases on an individual level is a very demanding computational work. Using only pharmaceutical database may decrease the computational workload while capturing the complexity of patients’ clinical condition.

The main aim of this study was to implement a new version of the Chronic Disease Score, using detailed information from the pharmaceutical prescription databases that incorporates, in addition to traditional drug treatments, the novel pharmacotherapies introduced over the last 3 decades and the amount of drugs consumed by the individual.

Methods

Data source

The population was extracted from the administrative databases of Emilia-Romagna Regional Health Agency (https://salute.regione.emilia-romagna.it/siseps/sanita), a Northern Italian region with approximately 4.4 million Italian citizens, who have universal access to the Italian National Health System. The retrospective study was carried out in conformity with the regulations on data management with the Italian law on privacy (Legislation Decree 196/2003 amended by Legislation Decree 101/2018). Data were anonymized prior to the analysis at the regional statistical office, where each patient is assigned a unique identifier. This identifier does not allow others to trace the patient’s identity and other sensitive data. Anonymized regional administrative data can be used without a specific written informed consent when patient information is collected for healthcare management and healthcare quality evaluation and improvement (according to art. 110 on medical and biomedical and epidemiological research, Legislation Decree 101/2018). The study was approved by the Independent ethics committee of the Larger Area Emilia Center (CE-AVEC) of the Emilia-Romagna Region, study protocol 398/2019/Oss/AOUBo on 19/06/2019.

Data were retrieved from the Regional Health Authority Outpatient Specialty Database (OSD) of 2016–2017, which includes all specialty visit and laboratory tests. Inclusion criteria were: (1) residence in the catchment area of the Local Healthcare Authorities (LHA) of Bologna or Imola (≈1,013,000 inhabitants) as of 1st January 2018, (2) age ≥50 years, (3) having received at least one outpatient service in the previous 18 months. Patients were followed-up from 01/01/2018 to 31/12/2018 and censored at death or at the end of follow-up. The date of death was retrieved from the Regional Mortality Registry Database (MR) of 2018.

Using a unique pseudonymized patient code, the demographic characteristics of the study cohort identified through the OSD database were linked with the Regional Outpatient Pharmaceutical Database (OPD) of 2016–2017, which includes the drugs reimbursed by the NHS (prescribed by the primary care physician or a specialist, or directly dispensed by the hospital pharmacies) and details on substance name, Anatomical Therapeutic Chemical (ATC) classification system code-V.2013, brand name, date of prescription filling, number of unit doses and number of packages and prescribers.

To compute the Charlson Comorbidity Index and the Multisource Comorbidity Score, the Hospital Discharge Record (HDR) database of 2016–2017 were also linked with the OSD, which contains information on admission and discharge dates, diagnosis and interventions (identified using the International Classification of Diseases, 9th revision, Clinical Modification—ICD-9-CM coding system), and discharge status.

Algorithm and score development

The modified CDS (M-CDS) was developed by updating the Chronic Disease Score proposed by Von Korff et al. [6] with the currently marketed drugs (up to March 2019; https://farmaci.agenziafarmaco.gov.it/bancadatifarmaci/home, of the Italian Medicines Agency) and the list of drug classes refunded by NHS, according to the updated therapeutic guidelines for chronic diseases.

We referred to three key-studies that use pharmacy data to estimate the prevalence of chronic conditions [11-13]. After reviewing these studies, the following decisions were made:

Conditions that could not be discerned on the basis of pharmacotherapy were merged (i.e. Alzheimer disease was merged to dementia, Crohn’s disease to inflammatory bowel diseases, hypertension to Cerebrovascular disease);

Drugs prescribed for conditions different from the ATC target class were removed (i.e. antitussives were removed from cancer) or assigned to other conditions (i.e. benzodiazepines were moved from Parkinson to a condition grouping anxiety, depression and obsessive compulsive disorders, apart from midazolam which was moved to epilepsy; rifabutin was moved from HIV to tuberculosis; antidepressants were moved from psychosis to depression);

Drug not listed in the above-mentioned studies, such as liver therapy in liver diseases, or antivirals for HCV in chronic hepatitis, were added after a thorough in-depth review of drug classes refunded by NHS;

Conditions that could be unambiguously associated with one or more drugs, such as multiple sclerosis, were also added to the list.

Using these criteria, 60 conditions unambiguously associated to pharmaceutical prescriptions were identified; 33 were selected on an epidemiologic rationale based on incidence (e.g. acromegaly was excluded) and on survival expectancy in the older age (e.g. spinal muscular atrophy was excluded). Cystic fibrosis was retained to be consistent with the MCS score (i.e. Corrao et al., 2017), although a low incidence is expected in adults. We decided to distinguish between generic prescriptions for exocrine pancreas failure, that can be related to multiple aetiology, and prescriptions specific to cystic fibrosis.

To limit the inclusion of occasional drug users, a cut-off on the minimum number of prescriptions was set for the years 2016 and 2017, depending on the drug. The list of candidate condition, the corresponding ATC codes and the minimum number of prescriptions are reported in S1 Table. Thresholds of number of prescriptions are defined on the basis of recommended dosing schemes, packages available on the market and relevant prescribing/reimbursing rules.

In order to estimate the weights for the 33 conditions included in the M-CDS, the study population resident in Bologna was randomly split into a training set (80%) and a test set (20%). A Cox regression model to predict one-year mortality was developed in the training set. The LASSO (Least Absolute Shrinkage and Selection Operator) method was applied to select only relevant covariates among gender, age and the 35 conditions recorded in the 2 previous years (2016–2017) [14]. The best algorithm was achieved through 10-fold cross-validation, that chooses the algorithm with the smallest cross-validated mean squared error [15]. To reduce the risk of overfitting, we selected the most parsimonious model, that is within one standard error of the best model [13].

The comorbidity weights were obtained by multiplying the regression coefficients of the Cox model by 10 and rounding them off to the nearest integer number. The M-CDS total score was computed as the sum of the comorbidity weights.

Finally, a classification tree analysis with chi-square automatic interaction detection (CHAID) growing process (maximum tree depth = 3; minimum cases in parent node = 1000, child node = 500; significance level of spitting and merge set to 0.05) was used to identify optimal cut-points of the total M-CDS to stratify the population according to the risk of death [16].

Validation of the M-CDS

The M-CDS was internally validated on the test set sample of Bologna population and then was externally validated in the population of Imola. The log-rank test was used to compare the survival distributions of patients in the M-CDS classes.

The performance of the M-CDS and its discriminant ability were compared between the test set (residents of Bologna) and those of the CCI and MCS using the c-statistic. As a secondary outcome, the ability of M-CDS to predict 1-year hospitalization was evaluated.

The HDR database of 2016 and 2017 was used to reproduce the weighted score of CCI and of MCS (combining HDR with the pharmaceutical databases) to predict mortality in 2018.

The significance level for all the analyses was set at p<0.05. Statistical analyses were performed using R, version 3.6.3 and IBM SPSS version 25.0.

Results

Characteristics of the study population

We identified 436,561 individuals aged ≥ 50 years, resident in the LHAs of Bologna and Imola, alive on January 1st, 2018, with at least 1 outpatient service access in the previous 18 months. The final study population therefore includes 380,849 residents in the LHA of Bologna and 55,712 residents in the LHA of Imola (≈94.4% of Bologna and Imola total residents). Table 1 provides a description of the two LHA study populations.

Table 1

Demographic and characteristics of the Bologna and Imola residents aged ≥50 years.

	LHA of Bologna (n = 380,849)	LHA of Imola (n = 55,712)
Age (mean ± S.D.)	67.5±11.8	67.1±11.7
Females (n, %)	213,572 (56.1%)	30,811 (55.3%)
Condition (n, %)
Cardiovascular and cerebrovascular disease	217,590 (57.1%)	33,372 (59.9%)
Respiratory illness	49,867 (13.1%)	7,982 (14.3%)
Exocrine pancreas failure	1,170 (0.3%)	212 (0.4%)
Cystic fibrosis	1 (0%)	0 (0%)
Tuberculosis	349 (0.1%)	44 (0.1%)
Cancer	13,718 (3.6%)	1,841 (3.3%)
Acid related disorders/peptic ulcer	94,458 (24.8%)	15,940 (28.6%)
Irritable colon	1,515 (0.4%)	234 (0.4%)
Liver diseases	8,227 (2.2%)	718 (1.3%)
Chronic hepatitis	689 (0.2%)	106 (0.2%)
Diabetes	37,256 (9.8%)	5,830 (10.5%)
Glaucoma	25,358 (6.7%)	3,937 (7.1%)
Chronic renal disease	434 (0.1%)	70 (0.1%)
Anaemias	14,926 (3.9%)	2,146 (3.9%)
Bone diseases	26,136 (6.9%)	5,143 (9.2%)
Inflammatory bowel + rheumatologic disease	2,901 (0.8%)	426 (0.8%)
Pain and inflammation	80,737 (21.2%)	13,648 (24.5%)
Hyperuricemia/gout	25,482 (6.7%)	4,595 (8.2%)
Dermatological severe	7,170 (1.9%)	1,159 (2.1%)
Transplantation	473 (0.1%)	65 (0.1%)
Hyperlipidaemia	95,232 (25%)	14,703 (26.4%)
HIV	886 (0.2%)	110 (0.2%)
Hypothyroidism	35,344 (9.3%)	4,997 (9.0%)
Epilepsy	4,888 (1.3%)	694 (1.2%)
Dementia	2,943 (0.8%)	236 (0.4%)
Parkinson’s disease	4,933 (1.3%)	709 (1.3%)
Depression, anxiety, obsessive-compulsive disorder (OCD)	56,376 (14.8%)	7,722 (13.9%)
Bipolar disorders	713 (0.2%)	87 (0.2%)
Psychosis	9,108 (2.4%)	1,443 (2.6%)
Multiple sclerosis	160 (0%)	34 (0.1%)
Haemorrhagic diathesis	1,424 (0.4%)	300 (0.5%)
Allergic disorders	13,839 (3.6%)	2,012 (3.6%)
Addictive disorders	1 (0%)	0 (0%)

Patients had a mean age of 67.5 years in the LHA of Bologna and 67.1 in the LHA of Imola, in both LHA there was a predominance of females (56.1%-55.3%). During 2018 around 2.4% and 2.3% individuals died in the LHA of Bologna and Imola, respectively.

The most frequent conditions were Cerebrovascular disease (57.1%-59.9%), hyperlipidaemia (25–26.4%), acid related disorders/peptic ulcer (24.8%-28.6%) and pain and inflammation (21.2%-24.5%). Cystic fibrosis and additive disorders were excluded from the model as candidate predictors since only one case was found in the LHA of Bologna and zero cases in LHA of Imola. Cases that other scores would have put in cystic fibrosis have been relocated to the more generic label exocrine pancreas failure.

M-CDS total score weights

In Table 2, coefficients and weights of the multiple Cox regression model are presented. The LASSO method selected 18 variables over the 31 conditions (cystic fibrosis and addictive disorders were excluded because they were virtually absent in the population). The variables that mostly contributed to the total score were cancer, chronic renal disease and psychosis. Acid related disorders/peptic ulcer, respiratory illness, exocrine pancreas failure and liver diseases were significant predictors of mortality but with small contributions to the total score. In the training set, the mean M-CDS score was 3.4±4.1 (median = 2; IQR [0-5]; range [0-45]).

Table 2

Coefficients and weights of the multiple Cox regression model to predict one-year mortality in the training population of Bologna.

Selected variables	Coefficient	Weight
Cancer	1.039	10
Chronic renal disease	0.689	7
Psychosis	0.677	7
Haemorrhagic diathesis	0.446	4
Depression, anxiety, OCD	0.440	4
Epilepsy	0.422	4
Anaemias	0.422	4
Parkinson's disease	0.325	3
Diabetes	0.293	3
Gout	0.286	3
Irritable colon	0.282	3
Transplantation	0.256	3
Dementia	0.244	2
Cardiovascular and cerebrovascular disease	0.177	2
Acid related disorders/peptic ulcer	0.139	1
Respiratory illness	0.108	1
Exocrine pancreas failure	0.087	1
Liver disease	0.082	1

The classification tree analysis with CHAID growing process suggested to split the M-CDS score into 6 mutually exclusive classes (≤1, 2, 3–4, 5–6, 7–9 and ≥10). In the training population of Bologna, 9.3% individuals had a M-CDS value ≥10 and 35.6% a value ≤1 (Fig 1).

Fig 1

Tree plot of the classification tree analysis with CHAID growing process.

The distribution of M-CDS was similar between males and females in each age group and became flatter in the older age groups, starting from 70 years (Fig 2).

Fig 2

Distribution of the M-CDS classes by age groups and gender.

Predictive performance

The ability of M-CDS to predict 1-year mortality was similar between the Bologna training set (AUC 0.765; 95% CI [0.760–0.770]) and the test set (AUC 0.761; 95% CI [0.750–0.772]), as well as in the Imola population (AUC 0.768; 95% CI [0.756–0.781]), confirming the stability of the score. Fig 3 shows the ROC curves of M-CDS in the three samples.

Fig 3

ROC curves comparing the ability of M-CDS to predict 1-year mortality in the training and test sets and in Imola population.

The 1-year survival in the test set differed significantly among the M-CDS classes (log-rank test chi-square = 2106.8; p<0.001), and decreased with the increase of M-CDS class from 99.5% in class with score ≤1 to 91.1% in class with score ≥10 (Fig 4).

Fig 4

One-year Kaplan-Meier survival curves by M-CDS classes (test set).

Fig 5 shows the ROC curves for M-CDS, MCS and CCI predicting 1-year mortality, in the test set. The M-CDS (AUC 0.761; 95% CI [0.750–0.772]) was superior to the CCI (AUC 0.696; 95% CI [0.681–0.711]) (χ2 test = 88.14; p<0.001), while it did not differ from the MCS (AUC 0.762; 95% CI [0.750–0.774]) (χ2 test = 0.06; p = 0.8144).

Fig 5

ROC curves comparing M-CDS and MCS to predict 1-year mortality (test set).

Secondary outcome

We also evaluated 1-year hospitalization as secondary outcome. In the Bologna training set the AUC was 0.710 (95% CI [0.660–0.760]), slightly higher than the test set (AUC 0.695; 95% CI [0.594–0.795]) and the Imola population (AUC 0.667; 95% CI [0.570–0.764]).

The M-CDS, evaluated in the test set, did not significantly differ from either the CCI (AUC 0.659; 95% CI [0.517–0.801]; χ2 test = 0.21; p = 0.647) or the MCS (AUC 0.786; 95% CI [0.673–0.899]; χ2 test = 2.14; p = 0.143).

Discussion

We developed an updated version of the CDS that includes up-to-date outpatient drug prescriptions based on recent guidelines for chronic diseases and relevant currently marketed drugs, to predict one-year mortality.

It was derived from the CDS first published in 1992, and its update in 1995. In this study it has been further updated by adding single drug classes (or single agents) to already listed diseases and other chronic diseases, splitting previous categories when appropriate.

The Modified-Chronic Disease Score showed a good performance in predicting individual risk of death in a very large population (individuals aged ≥50 years). Of the 33 variables investigated, corresponding to as many chronic conditions and to 80 drugs (or drug classes), 18 were selected by the analytic procedure as relevant predictors of mortality. Cancer, chronic renal disease and psychosis were the conditions that most contributed to the total M-CDS score (10 to 7 points) while acid related disorders/peptic ulcer, respiratory illness, exocrine pancreas failure and liver diseases contributed less (1 point). These 18 conditions allowed to stratify the population into 6 mutually exclusive classes, with different 1-year survival.

Our results underscore the relevant role of mental disorders as predictors of mortality, consistent with Corrao et al. [11]. A comprehensive meta-analysis of mortality related to mental disorders showed that these patients have a mortality rate 2.22 times higher than the general population [17]. In particular, for all-cause deaths, the population attributable risk was estimated at 1.3% for schizophrenia [18] and 12.7% for depression [19]. Recent studies showed an increased mortality in patients with psychotic disorders after acute coronary syndrome [20] and breast cancer [21] compared with their counterparts without psychosis. Our findings corroborate the need to optimise the coordinated care of general medical conditions in those with mental disorders.

The predictive ability of M-CDS was significantly higher than that of the CCI, which is based on Hospital Discharge Records. A possible reason for the lower performance of the CCI is that Hospital Discharge Records may have high quality of primary diagnosis while the accuracy of secondary could be lower [22]. Moreover, HDR are often subject to restrictions on the number of diagnosis recorded, while our drug-based score does not have this limitation.

Our results are consistent with an Italian study, in which the Drug Derived Complexity Index (DDCI), based on prescription patterns indicative of 19 chronic diseases, proved to be superior to the CCI in predicting 1-year mortality in a large regional population [23].

Furthermore, M-CDS proved to have a discriminatory power similar to that of the MCS, although this latter is based on multiple data sources. Corrao et al. [11] found a difference between MCS and the old CDS of more than ten percentage AUC points, so, even though we have not directly tested the M-CDS against the CDS in our data, we assume that the M-CDS would have a better discriminatory power that the CDS.

The M-CDS weights are based on mortality, therefore when the score is used to predict 1-year hospitalizations it slightly loses its predictive power. Our intent was to develop a score useful for clinicians and researchers [24], while a recalibration of the weights of chronic conditions would be needed to predict healthcare resource utilization.

There are several advantages in using one-source morbidity score: data availability differs radically across countries (both high- or low-income) and it is not always possible to merge individual level data databases [25]; there is a computational simplification and a considerable time saving compared to scores that need multiple databases linkage and the loss in predictive performance is negligible; moreover, performance homogeneity is more easily ensured with the ATC code system, even for comparisons across geographical areas and over time, while diagnostic coding habits may differ.

The M-CDS has different potential applications. It may support policy makers, managers, clinicians in assessing the performance of health systems, population needs and in health policy planning, including resource allocations to local health districts or General Practitioners’ remuneration. We are aware that other multisource and complex risk scores, e.g. Adjusted Clinical Groups (ACG), better capture the patient case-mix and explain variance in individual costs [26], however they are challenging to implement and expensive [27].

M-CDS can also be used for risk adjustment in real-world studies on treatment effectiveness and safety, where treatment arms are usually unbalanced for clinical characteristics.

Several potential limitations of this study should be acknowledged. First, data from dispensing databases do not indicate that the (full) package is consumed. Therefore, the actual exposure to the therapeutic regimen cannot be established. Second, the pharmaceutical prescription database only includes reimbursed dispensations, therefore private supply is not considered. However, this latter bias is partially mitigated by our focus on patients with the main clinically important chronic conditions, to whom drugs are provided free of charge by the National Health Service.

Moreover, the category “cardiovascular and cerebrovascular diseases” is very heterogeneous for possible impact on mortality, since it includes a variety of conditions, the most frequent of which is hypertension. Thus, the weight of each disease included in this category on 1-year mortality is not strictly reflected in the score.

We had no access to the Residents' Registry, so we were forced to use the OSD to select the population at baseline. Still, we were able to cover almost the entire population of interest (94.4%) by extracting all individuals who had at least one contact with outpatient services in the 18 months preceding the index year.

The external validity of score has been tested in a population which is very similar to the original population because it consists of residents in a bordering geographical area located in the same region. Our scoring system might perform differently in countries other than Italy due to possible small differences in both reimbursement policies and drug availability on the market. Its external validity in other health systems needs therefore to be tested. Eventually, periodic updates to the score should be done as new drugs or treatment recommendations are introduced.

Future perspectives include the application of other machine learning algorithms to investigate drug interactions and whether having multiple conditions increases the risk of death in an additive or multiplicative way.

Conclusion

In conclusion, M-CDS index proved to be a good predictor of 1-year mortality. It reflects updated pharmaceutical prescriptions and has advantages compared to other indices because it is based on a single data source not affected by variability in diagnostic coding. Moreover, it encompasses a large number of conditions, therefore allowing in-depth studies on the interplay between mental and physical disorders.

The score is a simple and easy-to-implement instrument to stratify the population, to perform risk adjustment for case-mix in quality of care studies, potentially leading to an improvement in the management of chronic conditions, based on patients’ needs and risks.

List of candidate conditions identified with their specific associated drugs and the minimum number of prescriptions (N).

General criteria: at least 2 prescriptions in the observed year. For drugs which unambiguously identify the specific diagnosis (e.g., drug listed for patients with multiple sclerosis) we considered only 1 prescription. For drugs frequent also in minor and non-chronic conditions (e.g., proton pump inhibitors for peptic ulcer) we considered at least 3 prescriptions. *nonspecific association; #wrong association; §not refunded drug.

(DOCX)

Click here for additional data file.

27 Jul 2020

PONE-D-20-13521

Modified-Chronic Disease Score (M-CDS): predicting the individual risk of death using drug prescriptions

PLOS ONE

Dear Dr. Iommi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 10 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Kevin Lu, PhD

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This interesting study developed and validated the modified CDS score, which can be used to predict the one-year mortality among patients older than 50 years of age. The manuscript is well written and clearly presented. I have a few comments for the authors to consider.

1. The original CDS was developed in the general population. Also, it was developed to predict hospitalization and mortality. Please clarify why this modified-CDS was only developed in patients older than 50 years of age and why only mortality was evaluated.

2. I suggest the authors provide some details such as the penalty parameter of the LASSO procedure for variables selection. Without those details, it is not clear to me why and how the 18 variables were selected. More details of the CHAID analysis are also important for readers to understand how the categories were created.

3. The Imola population used in the external validation was very similar to the population used to develop the modified CDS. I would suggest more discussion about the external validity and generalizability of the modified CDS.

Reviewer #2: The authors proposed the modified Chronic Disease Score (M-CSD) using health administrative database in Northern Italy. The M-CDS was tested and compared on sample sets with different multimorbidity scores. Below are comments for authors to consider.

1. In the abstract section starting line 24, the format should be consistent with the format of abstract in page 1 where abstract section title “background”, “methods”, “results”, and “conclusions” were added.

2. In line 28, the authors mentioned that “the original version of the CDS has limitations”. However, the details of these limitations were not discussed throughout the paper. It would be helpful if the limitations of original CDS were explained in more detail in the introduction section.

3. In line 102, the meaning of abbreviation “OSC” is not clear.

4. In line 135-136, the authors wrote that “a cut-off on the minimum number of prescriptions was set for the years 2016 and 2017, depending on the drug”. What was the exact criteria of the minimum number of prescriptions for each drug?

5. In line 166-167, the authors claimed that “Of these, 1304 were excluded, because of missing data (age or gender)”. For patients who were excluded from 437865 individuals aged older than 50 years due to missing age, how were they included in the first step when one of the inclusion criteria is older than 50 years?

6. Please move all the tables and figure captions to the end of manuscript.

7. In the discussion section, could the authors discuss about the differences between CDS and M-CDS, and what are the advantages of M-CDS over CDS?

8. Another potential limitation is that the scoring system might work differently when it is used in a project with study period that is far away from 2016 and 2017.

9. The conclusion section is too concise. It almost has the same length as the conclusion in abstract. Please elaborate on the conclusion.

10. In line 293, the authors claimed that “The M-CDS proved to be a valid instrument to predict one-year mortality in the population aged 50 years or more”. However, the M-CDS was only observed, not proven, to perform better than CDS and similarly with MCS in test sets.

11. In line 295, the authors claimed that “The score can be easily used for risk adjust in real-world studies”. This claim should be limited to the real-world studies in Italy as the authors has already pointed out in the limitation that scoring system might perform differently in countries other than Italy.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Junjie Ma

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

22 Sep 2020

Journal requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

We revised the style of figures and tables.

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

We clarified the ethics statement in the methods section (line 95-104):

“The retrospective study was carried out in conformity with the regulations on data management with the Italian law on privacy (Legislation Decree 196/2003 amended by Legislation Decree 101/2018). Data were anonymized prior to the analysis at the regional statistical office, where each patient is assigned a unique identifier. This identifier does not allow others to trace the patient’s identity and other sensitive data. Anonymized regional administrative data can be used without a specific written informed consent when patient information is collected for healthcare management and healthcare quality evaluation and improvement (according to art. 110 on medical and biomedical and epidemiological research, Legislation Decree 101/2018). The study was approved by the Independent ethics committee of the Larger Area Emilia Center (CE-AVEC) of the Emilia-Romagna Region, study protocol 398/2019/Oss/AOUBo on 19/06/2019.”

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

The datasets generated and/or analysed during the current study are property of a third party that is Emilia-Romagna Regional Health Agency (https://assr.regione.emilia-romagna.it/) and, although they are anonymized, datasets are not publicly available due to the current regulation on privacy.

The description of the administrative databases is available from the website https://salute.regione.emilia-romagna.it/siseps/sanita/asa/documentazione.

The database includes the following variables: patient ID, age, gender, area of residence, 33 dummy variables for drug prescriptions, death at one year (yes/no) and days of follow-up.

Reviewer #1:

This interesting study developed and validated the modified CDS score, which can be used to predict the one-year mortality among patients older than 50 years of age. The manuscript is well written and clearly presented. I have a few comments for the authors to consider.

1. The original CDS was developed in the general population. Also, it was developed to predict hospitalization and mortality. Please clarify why this modified-CDS was only developed in patients older than 50 years of age and why only mortality was evaluated.

Our purpose was to develop an accurate index to predict one-year mortality among adults aged ≥50 years, who are at risk of having at least one chronic condition, for clinical and research use.

As to the ability of the MCS score to predict hospitalizations, we have now added further analyses in the methods section (line 179-180):

“As a secondary outcome, the ability of M-CDS to predict 1-year hospitalization was further evaluated.”

In the results section (line 251-257):

“Secondary outcome

We also evaluated 1-year hospitalization as secondary outcome. In the Bologna training set the AUC was 0.710 (95% CI [0.660-0.760]), slightly higher than the test set (AUC 0.695; 95% CI [0.594-0.795]) and the Imola population (AUC 0.667; 95% CI [0.570-0.764]).

The M-CDS, evaluated in the test set, did not significantly differ from either the CCI (AUC 0.659; 95% CI [0.517-0.801]; χ2 test=0.21; p=0.647) or the MCS (AUC 0.786; 95% CI [0.673-0.899]; χ2 test=2.14; p=0.143).”

And in the discussion section (line 295-298):

“The M-CDS weights are based on mortality, therefore when the score is used to predict 1-year hospitalizations it slightly loses its predictive power. Our intent was to develop a score useful for clinicians and researchers (23), while a recalibration of the weights of chronic conditions would be needed to predict healthcare resource utilization.”

2. I suggest the authors provide some details such as the penalty parameter of the LASSO procedure for variables selection. Without those details, it is not clear to me why and how the 18 variables were selected. More details of the CHAID analysis are also important for readers to understand how the categories were created.

For the LASSO procedure we chose an α=1 and for variable selection we used a λ value within one standard error from the minimum (in this case λ= 0.0014).

We added to the methods section more details about the LASSO procedure and of the classification tree analysis (line 164-165 and line 170-171).

“To reduce the risk of overfitting, we selected the most parsimonious model, that is within one standard error of the best model (13).”

“Finally, a classification tree analysis with chi-square automatic interaction detection (CHAID) growing process (maximum tree depth=3; minimum cases in parent node=1000, child node=500; significance level of spitting and merge set to 0.05) was used to identify optimal cut-point of the total M-CDS to predict one-year mortality (15). ”

3. The Imola population used in the external validation was very similar to the population used to develop the modified CDS. I would suggest more discussion about the external validity and generalizability of the modified CDS.

We added this point as a potential limitation (line 327-328):

“The external validity of score has been tested in a population which is very similar to the original population, because it consists of residents in a bordering geographical area located in the same region.”

Reviewer #2:

The authors proposed the modified Chronic Disease Score (M-CSD) using health administrative database in Northern Italy. The M-CDS was tested and compared on sample sets with different multimorbidity scores. Below are comments for authors to consider.

1. In the abstract section starting line 24, the format should be consistent with the format of abstract in page 1 where abstract section title “background”, “methods”, “results”, and “conclusions” were added.

Done (line 24, 31, 40 and 47).

2. In line 28, the authors mentioned that “the original version of the CDS has limitations”. However, the details of these limitations were not discussed throughout the paper. It would be helpful if the limitations of original CDS were explained in more detail in the introduction section.

We added some details on limitations of CDS in the Introduction (line 70-77). Maximum number of words in the abstract does not allow to include additional paragraphs.

We add these paragraphs in the introduction section:

“No further updates have been made, inevitably leading to limitations in the use of the score, as new drugs have been introduced in the market to treat chronic diseases (e.g., monoclonal antibodies for autoimmune disorders). Furthermore, in drug prescription databases specific criteria (e.g., amount threshold) to select chronic patients are required.

Several studies have compared the predictive ability of diagnosis-based indexes to drug-based ones, however, the superiority of an approach in predicting health status has not been demonstrated yet (8,9), and the lack of an updated version of CDS does not allow appropriate performance assessment and comparisons (10).”

3. In line 102, the meaning of abbreviation “OSC” is not clear.

We apologize for the typo. It is the abbreviation for Outpatient Speciality Database (OSD) (line 120).

“To compute the Charlson Comorbidity Index and the Multisource Comorbidity Score, the Hospital Discharge Record (HDR) database of 2016-2017 were also linked with the OSD”

4. In line 135-136, the authors wrote that “a cut-off on the minimum number of prescriptions was set for the years 2016 and 2017, depending on the drug”. What was the exact criteria of the minimum number of prescriptions for each drug?

Thresholds are defined based on use recommendation, marketed packages, and prescribing/reimbursing rules. Details have been provided in the text (line 155-157):

“The list of candidate condition, the corresponding ATC codes and the minimum number of prescriptions are reported in Table S1. Thresholds of number of prescriptions are defined on the basis of recommended dosing schemes, packages available on the market and relevant prescribing/reimbursing rules.”

And in the description of the Table S1 in the supporting materials:

“S1 Table. List of candidate conditions identified with their specific associated drugs and the minimum number of prescriptions (N). General criteria: at least 2 prescriptions in the observed year. For drugs which unambiguously identify the specific diagnosis (e.g., drug listed for patients with multiple sclerosis) we considered only 1 prescription. For drugs frequent also in minor and non-chronic conditions (e.g., proton pump inhibitors for peptic ulcer) we considered at least 3 prescriptions.”

5. In line 166-167, the authors claimed that “Of these, 1304 were excluded, because of missing data (age or gender)”. For patients who were excluded from 437865 individuals aged older than 50 years due to missing age, how were they included in the first step when one of the inclusion criteria is older than 50 years?

We extracted all the residents of the LHA of Bologna and Imola, then we excluded individual with missing age or gender and then we selected people aged ≥50 years (line 188-189).

“We identified 436,561 individuals aged ≥ 50 years, resident in the LHAs of Bologna and Imola, alive on January 1st, 2018, with at least 1 outpatient service access in the previous 18 months. Of these, 1,304 were excluded, because of missing data (age or gender).”

6. Please move all the tables and figure captions to the end of manuscript.

We copied the captions of all the tables and figures to the end of manuscript. We also kept each figure caption directly after the paragraph as requested by the journal.

7. In the discussion section, could the authors discuss about the differences between CDS and M-CDS, and what are the advantages of M-CDS over CDS?

Compared to previous versions of CDS, M-CDS represents an unambiguous method of application of the score to prescribing data: we updated each chronic condition and we identified new and relevant ATC codes, establishing the minimum number of prescriptions to define each specific condition, and we selected only conditions actually impacting on mortality.

The performance of the old CDS was not assessed since its application anyway needs some specific subjective steps (ATC code assignment, threshold definition…). As a matter of fact, many previous studies using CDS are lacking specific steps of application... (e.g., Corrao et al. 2017).

We added this sentence to the discussion section (line 291-294):

“Corrao et al. (11) found a difference between MCS and the old CDS of more than ten percentage AUC points , so, even though we have not directly tested the M-CDS against the CDS in our date, we assume that the M-CDS would have a better discriminatory power that the CDS.”

8. Another potential limitation is that the scoring system might work differently when it is used in a project with study period that is far away from 2016 and 2017.

We thank the reviewer for this important comment. The study population consists of people resident in the study areas in 2018, which is not too far away from 2020. We are aware that the scoring system might perform differently if the epidemiology of the population changes, for instance as a result of the recent COVID pandemic. Therefore, the performance of the score should be tested over time.

We have added a sentence to the manuscript (line 330-332):

“Its external validity in other health systems needs therefore to be tested. Eventually, periodic updates to the score should be done as new drugs or treatment recommendations are introduced.”

9. The conclusion section is too concise. It almost has the same length as the conclusion in abstract. Please elaborate on the conclusion.

We have now expanded the conclusion section, as suggested.

10. In line 293, the authors claimed that “The M-CDS proved to be a valid instrument to predict one-year mortality in the population aged 50 years or more”. However, the M-CDS was only observed, not proven, to perform better than CDS and similarly with MCS in test sets.

Please see response to point 7.

11. In line 295, the authors claimed that “The score can be easily used for risk adjust in real-world studies”. This claim should be limited to the real-world studies in Italy as the authors has already pointed out in the limitation that scoring system might perform differently in countries other than Italy.

We revised the paragraph in the discussion section (line 339-346):

“In conclusion, M-CDS index proved to be a good predictor of 1-year mortality. It reflects updated pharmaceutical prescriptions and has advantages compared to other indices because it is based on a single data source not affected by variability in diagnostic coding. Moreover, it encompasses a large number of conditions, therefore allowing in-depth studies on the interplay between mental and physical disorders.

The score is a simple and easy-to-implement instrument to stratify the population, to perform risk adjustment for case-mix in quality of care studies, potentially leading to an improvement in the management of chronic conditions, based on patients’ needs and risks.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

6 Oct 2020

Modified-Chronic Disease Score (M-CDS): predicting the individual risk of death using drug prescriptions

PONE-D-20-13521R1

Dear Dr. Iommi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Kevin Lu, PhD

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Junjie Ma

Reviewer #2: No

8 Oct 2020

PONE-D-20-13521R1

Modified-Chronic Disease Score (M-CDS): predicting the individual risk of death using drug prescriptions

Dear Dr. Iommi:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Kevin Lu

Academic Editor

PLOS ONE