The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards

Rate and risk factors of in-hospital and early post-discharge mortality in patients admitted to an internal medicine ward

BACKGROUND

We sought to quantify in-hospital and early post-discharge mortality rates in hospitalised patients.

METHODS

Consecutive adult patients admitted to an internal medicine ward were prospectively enrolled. The rates of in-hospital and 4-month post-discharge mortality and their possible associated sociodemographic and clinical factors (eg Cumulative Illness Rating Scale [CIRS], body mass index [BMI], polypharmacy, Barthel Index) were assessed.

RESULTS

1,451 patients (median age 80 years, IQR 69-86; 53% female) were included. Of these, 93 (6.4%) died in hospital, while 4-month post-discharge mortality was 15.9% (191/1,200). Age and high dependency were associated (p<0.01) with a higher risk of in-hospital (OR 1.04 and 2.15) and 4-month (HR 1.04 and 1.65) mortality, while malnutrition and length of stay were associated (p<0.01) with a higher risk of 4-month mortality (HR 2.13 and 1.59).

CONCLUSIONS

Several negative prognostic factors for early mortality were found. Interventions addressing dependency and malnutrition could potentially decrease early post-discharge mortality.

Impact of COVID-19 Pandemic on In-Hospital Mortality in Patients Without SARS-CoV-2 Infection in an Internal Medicine Ward of a Tertiary Care Hospital in Portugal

INTRODUCTION

Despite the emergence of a new worldwide cause of death related to COVID-19, several studies have hypothesized that the international mortality rate attributed to non-COVID-19 causes was significantly higher during the COVID pandemic, questioning whether this excess in mortality is related only to COVID-19 or to the difficulties that the healthcare systems faced during the pandemic. Therefore, understanding the impact of the COVID-19 pandemic on the prognosis of patients without severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a major unmet need as this was overshadowed by the overwhelming number of patients with SARS-CoV-2.

METHODS

This is a retrospective, cross-sectional, observational study in the internal medicine non-COVID-19 wards of a tertiary care hospital in Portugal. A total of 2021 patients without SARS-CoV-2 infection admitted between March and May of 2019 and 2020 were included. For each patient, we collected information regarding demographic characteristics, emergency department admission information, hospitalization information, date of discharge or death, health comorbidities, and current medication.

RESULTS

Data from 1013 patients in 2019 and 1008 patients in 2020 was analyzed. The patients' demographic characteristics, health comorbidities, and current medications were distributed in similar patterns in the two studied periods. There was a statistically significant difference in the in-hospital mortality in patients without SARS-CoV-2 infection between 2019 and 2020 (12% vs 17%, p-value < 0.001) and in admission severity in hospitalized patients without SARS-CoV-2 infection between 2019 and 2020 (0.9 vs 0.6, p-value < 0.001).

CONCLUSION

Our work showed a statistically significant increase in in-hospital mortality during the COVID-19 pandemic in patients without SARS-CoV-2 infection, which was not apparently explained by differences in the characteristics of hospitalized patients. As this is one of the first works describing the silent impact of the COVID-19 pandemic in Portugal, we believe it holds an important value in the provision of bases for building up future health policies in case of new COVID-19 outbreaks or other medical emergencies.

The safety and efficacy of systemic versus catheter-based therapies: application of a prognostic model by a pulmonary embolism response team

The decision by pulmonary embolism response teams (PERTs) to utilize anticoagulation (AC) with or without systemic thrombolysis (ST) or catheter-directed therapies (CDT) for pulmonary embolism (PE) is a balance between the desire for a positive outcome and safety. Our primary aim was to develop a predictive model of in-hospital mortality for patients with high- or intermediate-risk PE managed by PERT while externally validating this model. Our secondary aim was to compare the relative safety and efficacy of ST and CDT in this cohort. Consecutive patients hospitalized between June 2014 and January 2020 at the Cleveland Clinic Foundation and The University of Rochester with acute high- or intermediate-risk PE managed by PERT were retrospectively evaluated. Groups were stratified by treatment strategy. The primary outcome was in-hospital mortality, and secondary outcome was major bleeding. A logistic regression model to predict the primary outcome was built using the derivation cohort, with 100-fold bootstrapping for internal validation. External validation was performed and the area under the receiver operating curve (AUC) was calculated. Of 549 included patients, 421 received AC alone, 71 received ST, and 64 received CDT. Predictors of major bleeding include ESC risk category, PESI score, hypoxia, hemodynamic instability, and serum lactate. CDT trended towards lower mortality but with an increased risk of bleeding relative to ST (OR = 0.42; 95% CI [0.15, 1.17] and OR = 2.14; 95% CI [0.9, 5.06] respectively). In the multivariable logistic regression model in the derivation institution cohort, predictors of in-hospital mortality were age, cancer, hemodynamic instability requiring vasopressors, and elevated NT-proBNP (AUC = 0.86). This model was validated using the validation institution cohort (AUC = 0.88). We report an externally-validated model for predicting in-hospital mortality in patients with PE managed by PERT. The decision by PERT to initiate CDT or ST for these patients had no impact on mortality or major bleeding, yet the long-term efficacy of these interventions needs to be elucidated.

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

Padua prediction score and IMPROVE score do predict in-hospital mortality in Internal Medicine patients

Padua prediction score (PPS) and IMPROVE bleeding score are validated tools for venous thromboembolism (VTE) risk assessment recommended by guidelines, albeit not frequently used. Some data suggest that a positive PPS and IMPROVE score may be were associated with early mortality in Internal Medicine patients. Aim of the study was to characterize the predictive ability on mortality of the two scores using two different populations, respectively, as derivation and validation cohort. The derivation cohort consisted of 1956 Internal Medicine patients admitted to La Spezia Hospital in 2013. 399 Internal Medicine patients admitted to Carate Brianza Hospital in 2016 constituted the validation cohort. PPS and IMPROVE scores were applied to each patient using their validated cutoffs. Frequency of positive PPS and mortality were significantly higher in La Spezia patients. In the derivation cohort, the positivity of at least one of the two scores was associated with a significantly higher mortality compared to both negative scores. Similar results were observed in the validation cohort. In the derivation cohort, the sensitivity of a positive PPS score in predicting mortality was 0.97 (0.94, 0.98) but the specificity was 0.21 (0.19, 0.23), the negative likelihood ratio being 0.15. Sensitivity and specificity of a positive IMPROVE gave specular findings but the positive likelihood ratio was 2.19. The accuracy data in the validation cohort were in the same direction. Both PPS and IMPROVE are associated with in-hospital mortality but their additional predictive accuracy is modest. It is unlikely that both scores could be useful in clinical practice to predict death in hospitalized Internal Medicine patients.

Using structured pathology data to predict hospital-wide mortality at admission

Early prediction of in-hospital mortality can improve patient outcome. Current prediction models for in-hospital mortality focus mainly on specific pathologies. Structured pathology data is hospital-wide readily available and is primarily used for e.g. financing purposes. We aim to build a predictive model at admission using the International Classification of Diseases (ICD) codes as predictors and investigate the effect of the self-evident DNR (“Do Not Resuscitate”) diagnosis codes and palliative care codes. We compare the models using ICD-10-CM codes with Risk of Mortality (RoM) and Charlson Comorbidity Index (CCI) as predictors using the Random Forests modeling approach. We use the Present on Admission flag to distinguish which diagnoses are present on admission. The study is performed in a single center (Ghent University Hospital) with the inclusion of 36 368 patients, all discharged in 2017. Our model at admission using ICD-10-CM codes (AUCROC = 0.9477) outperforms the model using RoM (AUCROC = 0.8797 and CCI (AUCROC = 0.7435). We confirmed that DNR and palliative care codes have a strong impact on the model resulting in a decrease of 7% for the ICD model (AUCROC = 0.8791) at admission. We therefore conclude that a model with a sufficient predictive performance can be derived from structured pathology data, and if real-time available, can serve as a prerequisite to develop a practical clinical decision support system for physicians.

Prevalence and Risk Factors Associated With Long-term Opioid Use After Injury Among Previously Opioid-Free Workers

IMPORTANCE

Using opioids for acute pain can lead to long-term use and associated morbidity and mortality. Injury has been documented as a gateway to long-term opioid use in some populations, but data are limited for injured workers.

OBJECTIVE

To evaluate the prevalence and risk factors of long-term opioid use after injury among workers in Tennessee who were opioid free at the time of injury.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study identified injured workers aged 15 to 99 years who reported only 1 injury to the Tennessee Bureau of Workers' Compensation from March 2013 to December 2015 and had no opioid prescription in the 60 days before injury. Participants were matched to their prescription history in Tennessee's prescription drug monitoring program. Analysis was conducted from November 2017 to March 2018. Logistic regression models were used to calculate adjusted odds ratios (ORs) and 95% CIs for associations of demographic, injury, and opioid use variables with long-term use.

MAIN OUTCOMES AND MEASURES

The primary outcome was long-term opioid use, defined as having an opioid supplied for 45 or more days in the 90 days after injury.

RESULTS

Among 58 278 injured workers who received opioids after injury (18 977 [32.5%] aged 15-34 years, 27 514 [47.2%] aged 35-54 years, and 11 787 [20.2%] aged 55-99 years; 32 607 [56.0%] men), 46 399 (79.6%) were opioid free at the time of injury. Among opioid-free injured workers, 1843 (4.0%) began long-term opioid use. After controlling for covariates, long-term use was associated with receiving 20 or more days' supply in the initial opioid prescription compared with receiving less than 5 days' supply (OR, 28.94; 95% CI, 23.44-35.72) and visiting 3 or more prescribers in the 90 days after injury compared with visiting 1 prescriber (OR, 14.91; 95% CI, 12.15-18.29). However, even just 5 days' to 9 days' supply was associated with an increase in the odds of long-term use compared with less than 5 days' supply (OR, 1.83; 95% CI, 1.56-2.14).

CONCLUSIONS AND RELEVANCE

In this study of injured workers, injury was associated with long-term opioid use. The number of days' supply of the initial opioid prescription was the strongest risk factor of developing long-term use, highlighting the importance of careful prescribing for initial opioid prescriptions.

Trends in admission serum albumin and mortality in patients with hospital readmission

AIMS

To determine the relationship between trends in admission serum albumin and long-term mortality in medical patients with hospital readmission.

MATERIALS AND METHODS

We used a cohort of patients admitted to five departments of internal medicine during 3 years. Survival analysis was performed based on mean admission serum albumin levels and trends in albumin values from recurrent admissions.

RESULTS

A total of 5396 patients had 16 640 admissions (readmission cohort), another 9422 patients were admitted only once (single admission cohort). Readmitted patients with low mean albumin were older, predominantly females and had higher comorbidity index than patients with normal mean albumin. The 6-month all-cause mortality rate of the normal and low mean albumin groups was 5.2% and 24.2%, respectively (P < 0.001). Survival analysis showed that patients with persistently normal albumin levels had the highest survival rates at 6 months (97.7%), compared with patients who had hypoalbuminemia at index admission but normalised their albumin levels in subsequent admissions (92%), patients with declining albumin trends (85.6%) and patients with persistently low albumin levels (68.9%) (P < 0.0001).

CONCLUSIONS

Serum albumin is strongly associated with long-term mortality in readmitted medical patients. Persistent hypoalbuminemia during recurrent admissions is associated with increased risk of long-term mortality.

Estimation of the Mismatch between Admission and Discharge Diagnosis for Respiratory Patients, and Implications on the Length of Stay and Hospital Charges

Admission and discharge diagnoses in hospitals are often in discord, and this has significant implications for the cost of care and patient safety. In this paper we used medical claims data to examine these differences for beneficiaries with respiratory conditions and quantified the degree to which specific respiratory conditions are mistaken for other ones, on admission. Since respiratory problems have seasonality, we performed two separate analyses, for summer and for winter admissions. The length of stay and hospital charges were compared between matching and non-matching {admission, discharge Dx} pairs, using independent samples t-test analysis. Results were integrated into a standalone application where physicians can select an admission diagnosis to see (i) the probability for this diagnosis to be correct (matching the discharge Dx), (ii) the probabilities for mismatch and (iii) pair-specific differential diagnosis criteria to consider reassessing the patient before confirming the admission diagnosis.

The new frontiers of rehabilitation medicine in people with chronic disabling illnesses

Because of the demographic shift and the increased proportion of patients surviving acute critical illnesses, the number of people living with severely disabling chronic diseases and, consequently, the demand for rehabilitation are expected to increase sharply over time. As underscored by the World Health Organization, there is substantial evidence that the provision of inpatient rehabilitation in specialized rehabilitation units to people with complex needs is effective in fostering functional recovery, improving health-related quality of life, increasing independence, reducing institutionalization rate, and improving prognosis. Recent studies in the real world setting reinforce the evidence that patients with ischemic heart disease or stroke benefit from rehabilitation in terms of improved prognosis. In addition, there is evidence of the effectiveness of rehabilitation for the prevention of functional deterioration in patients with complex and/or severe chronic diseases. Given this evidence of effectiveness, rehabilitation should be regarded as an essential part of the continuum of care. Nonetheless, rehabilitation still is underdeveloped and underused. Efforts should be devoted to foster healthcare professional awareness of the benefits of rehabilitation and to increase referral and participation.

Infections and internal medicine patients: Could a comorbidity score predict in-hospital mortality?

Infectious diseases (ID) are frequently cause of internal medicine wards (IMW) admission. We aimed to evaluate risk factors for in-hospital mortality (IHM) in IMW patients with ID, and to test the usefulness of a comorbidity score (CS).This study included ID hospital admissions between January 2013, and December 2016, recorded in the database of the local hospital. ICD-9-CM codes were selected to identify infections, development of sepsis, and to calculate a CS.We analyzed 12,173 records, (age 64.8 ± 25.1 years, females 66.2%, sepsis 9.3%). Deceased subjects (1545, 12.7%) were older, had higher percentage of sepsis, pulmonary infections, and endocarditis. Mean value of CS was also significantly higher. At multivariate analysis, the odds ratio (OR) for sepsis (OR 5.961), endocarditis (OR 4.247), pulmonary infections (OR 1.905), other sites of infection (OR 1.671), and urinary tracts infections (OR 0.548), were independently associated with IHM. The CS (OR 1.070 per unit of increasing score), was independently associated with IHM as well. The calculated weighted risk, obtained by multiplying 1.070 for the mean score value in deceased patients, was 19.367. Receiver operating characteristic (ROC) analysis showed that CS and development of sepsis were significant predictors for IHM (area under the curve, AUC: 0.724 and 0.670, respectively).Careful evaluation of comorbidity in internal medicine patients is nowadays matter of extreme importance in IMW patients hospitalized for ID, being IHM related to severity of disease, type and site of infection, and also to concomitant comorbidities. In these patients, a careful evaluation of CS should represent a fundamental step in the disease management.

Predictive modeling of inpatient mortality in departments of internal medicine

Despite overwhelming data on predictors of inpatient mortality, it is unclear which variables are the most instructive in predicting mortality of patients in departments of internal medicine. This study aims to identify the most informative predictors of inpatient mortality, and builds a prediction model on an individual level, given a constellation of patient characteristics. We use a penalized method for developing the prediction model by applying the least-absolute-shrinkage and selection-operator regression. We utilize a cohort of adult patients admitted to any of 5 departments of internal medicine during 3.5 years. We integrated data from electronic health records that included clinical, epidemiological, administrative, and laboratory variables. The prediction model was evaluated using the validation sample. Of 10,788 patients hospitalized during the study period, 874 (8.1%) died during admission. We find that the strongest predictors of inpatient mortality are prior admission within 3 months, malignant morbidity, serum creatinine levels, and hypoalbuminemia at hospital admission, and an admitting diagnosis of sepsis, pneumonia, malignant neoplastic disease, or cerebrovascular disease. The C-statistic of the risk prediction model is 89.4% (95% CI 88.4-90.4%). The predictive performance of this model is better than a multivariate stepwise logistic regression model. By utilizing the prediction model, the AUC for the independent (validation) data set is 85.7% (95% CI 84.1-87.3%). Using penalized regression, this prediction model identifies the most informative predictors of inpatient mortality. The model illustrates the potential value and feasibility of a tool that can aid physicians in decision-making.